CN114385196A

CN114385196A - Software deployment method and device combining RPA and AI

Info

Publication number: CN114385196A
Application number: CN202111531506.1A
Authority: CN
Inventors: 李烨; 汪冠春; 胡一川; 褚瑞; 李玮
Original assignee: Beijing Laiye Network Technology Co Ltd; Laiye Technology Beijing Co Ltd
Current assignee: Beijing Laiye Network Technology Co Ltd; Laiye Technology Beijing Co Ltd
Priority date: 2021-12-14
Filing date: 2021-12-14
Publication date: 2022-04-22

Abstract

The invention provides a software deployment method and a device combining RPA and AI, relating to the technical field of RPA and AI, wherein the method is applied to an RPA robot, and the specific disclosed technical scheme is as follows: acquiring a deployment request, wherein the deployment request comprises: the software identification and the software version of the software to be deployed and the function identification of at least one service function in the software to be deployed; based on the natural language processing NLP technology, determining software deployment configuration for realizing at least one service function according to the software identification, the software version and at least one function identification; the software to be deployed is deployed according to the software deployment configuration, so that the software deployment configuration for realizing at least one service function can be automatically determined in a mode of combining RPA and AI, the software to be deployed is automatically deployed, the software deployment can be performed on part of the service functions according to requirements, the deployment time is short, the cost is low, and the efficiency is high.

Description

Software deployment method and device combining RPA and AI

Technical Field

The disclosure relates to the technical field of robot process automation and artificial intelligence, in particular to a software deployment method and device combining RPA and AI.

Background

Robot Process Automation (RPA) is a Process task automatically executed according to rules by simulating human operations on a computer through specific robot software.

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 art, Software-as-a-Service (SaaS) is a completely created Software application mode, and provides a Software mode through the Internet, and a manufacturer uniformly deploys application Software on its own servers, so that a customer can order a required application Software Service to the manufacturer through the Internet according to its actual needs. At present, while SaaS service is provided, self-developed products are also supported to be deployed in a customer environment, but at present, manual deployment is needed, deployment time is long, cost is high, and efficiency is poor.

Disclosure of Invention

The embodiment of the disclosure provides a software deployment method and device combining RPA and AI, in order to solve the technical problems of long deployment time, high cost and poor efficiency when deployed to a client environment in the related art, the technical scheme is as follows:

in a first aspect, an embodiment of the present disclosure provides a software deployment method combining an RPA and an AI, applied to an RPA robot, where the method includes: obtaining a deployment request, wherein the deployment request comprises: the method comprises the steps of identifying software identification and software version of software to be deployed and identifying at least one service function in the software to be deployed; based on a Natural Language Processing (NLP) technology, determining software deployment configuration for realizing at least one business function according to the software identification, the software version and at least one function identification; and deploying the software to be deployed according to the software deployment configuration.

In one embodiment, the determining, based on the NLP technology, a software deployment configuration for implementing at least one business function according to the software identifier, the software version, and at least one function identifier includes: based on a Natural Language Processing (NLP) technology, determining a process identifier of at least one process to be deployed according to the software identifier and at least one function identifier; based on a Natural Language Processing (NLP) technology, determining at least one process image file, and a configuration image file, service image information and a model image file corresponding to the process image file according to the process identifier, the software version and the software identifier of the at least one process to be deployed; and determining the software deployment configuration according to the at least one process image file, and the configuration image file, the service image information and the model image file corresponding to the process image file.

In one embodiment, the determining, by the NLP based technology, a process identifier of at least one process to be deployed according to the software identifier and at least one function identifier includes: for each function identifier, based on a Natural Language Processing (NLP) technology, inquiring a dependency relation table corresponding to the software identifier according to the function identifier, and determining a dependent function identifier corresponding to the function identifier; determining at least one process included in the service function corresponding to the function identifier and at least one process included in the service function corresponding to the dependent function identifier as the process to be deployed; and determining the process identification of the process to be deployed.

In an embodiment, the determining, by the NLP technology, at least one process image file, and a configuration image file, service image information, and a model image file corresponding to the process image file according to the process identifier, the software version, and the software identifier of the at least one process to be deployed includes: for each process identification, based on a Natural Language Processing (NLP) technology, inquiring a process list corresponding to the software identification according to the process identification and the software version, and acquiring the process identification, a process image file identification, a configuration image file identification, a service image information identification and a model image file identification corresponding to the process identification and the software version; and acquiring the process image file, and the configuration image file, the service image information and the model image file corresponding to the process image file according to the image file identification, the configuration image file identification, the service image information identification and the model image file identification.

In an embodiment, before querying a process list corresponding to the software identifier according to the process identifier and the software version based on a natural language processing NLP technology, and acquiring a process image file identifier, a configuration image file identifier, a service image information identifier, and a model image file identifier corresponding to the process identifier and the software version, the method further includes: determining a published software version of published software corresponding to the software identifier, each service function in the published software, a process image file, a configuration image file, service image information and a model image file of each process in the service function; and for each process, updating a process list corresponding to the software identification according to the process identification of the process, the process image file identification of the process image file of the process, the configuration image file identification of the configuration image file, the service image information identification of the service image information, the model image file identification of the model image file and the released software version.

In one embodiment, the determining a published software version of published software corresponding to the software identifier, each service function in the published software, a process image file, a configuration image file, service image information, and a model image file of each process in the service function includes: determining a published software version of published software corresponding to the software identifier, each service function in the published software, and a process file, a configuration file, service information and a model file of each process in the service function; and carrying out mirror image processing on the process file, the configuration file, the service information and the model file of each process to obtain the process mirror image file, the configuration mirror image file, the service mirror image information and the model mirror image file of each process.

In a second aspect, an embodiment of the present disclosure provides a software deploying apparatus combining RPA and AI, applied to an RPA robot, including: an obtaining module, configured to obtain a deployment request, where the deployment request includes: the method comprises the steps of identifying software identification and software version of software to be deployed and identifying at least one service function in the software to be deployed; a first determining module, configured to determine, based on a natural language processing NLP technique, a software deployment configuration for implementing at least one of the business functions according to the software identifier, the software version, and at least one of the function identifiers; and the deployment module is used for deploying the software to be deployed according to the software deployment configuration.

In one embodiment, the first determining module comprises: a first determination unit, a second determination unit, and a third determination unit; the first determining unit is configured to determine, based on a natural language processing NLP technique, a process identifier of at least one process to be deployed according to the software identifier and the at least one function identifier; the second determining unit is configured to determine, based on a natural language processing NLP technology, at least one process image file, and a configuration image file, service image information, and a model image file corresponding to the process image file, according to the process identifier, the software version, and the software identifier of the at least one process to be deployed; the third determining unit is configured to determine the software deployment configuration according to the at least one process image file, and the configuration image file, the service image information, and the model image file corresponding to the process image file.

In an embodiment, the first determining unit is specifically configured to, for each function identifier, query, based on a natural language processing NLP technique, a dependency relationship table corresponding to the software identifier according to the function identifier, and determine a dependent function identifier corresponding to the function identifier; determining at least one process included in the service function corresponding to the function identifier and at least one process included in the service function corresponding to the dependent function identifier as the process to be deployed; and determining the process identification of the process to be deployed.

In an embodiment, the second determining unit is specifically configured to, for each process identifier, query, based on a natural language processing NLP technique, a process list corresponding to the software identifier according to the process identifier and the software version, and obtain the process identifier and a process image identifier, a configuration image identifier, a service image information identifier, and a model image identifier corresponding to the software version; and acquiring the process image file, and the configuration image file, the service image information and the model image file corresponding to the process image file according to the image file identification, the configuration image file identification, the service image information identification and the model image file identification.

In one embodiment, the apparatus further comprises: a second determining module and an updating module; the second determining module is configured to determine a published software version of published software corresponding to the software identifier, each service function in the published software, a process image file, a configuration image file, service image information, and a model image file of each process in the service function; and the updating module is used for updating a process list corresponding to the software identifier according to the process identifier of the process, the process image file identifier of the process image file of the process, the configuration image file identifier of the configuration image file, the service image information identifier of the service image information, the model image file identifier of the model image file and the released software version aiming at each process.

In an embodiment, the second determining module is specifically configured to determine a released software version of the released software corresponding to the software identifier, each business function in the released software, a process file, a configuration file, service information, and a model file of each process in the business functions; and carrying out mirror image processing on the process file, the configuration file, the service information and the model file of each process to obtain the process mirror image file, the configuration mirror image file, the service mirror image information and the model mirror image file of each process.

In a third aspect, an embodiment of the present disclosure provides an electronic device, including: a memory and a processor, the memory having stored therein instructions that are loaded and executed by the processor to implement the method of any of the above aspects.

In a fourth aspect, the disclosed embodiments provide a computer-readable storage medium storing a computer program, which when executed by a processor implements the method in any one of the above aspects.

The advantages or beneficial effects in the above technical solution at least include: the method comprises the steps of obtaining a deployment request by adopting an RPA robot, wherein the deployment request comprises: the software identification and the software version of the software to be deployed and the function identification of at least one service function in the software to be deployed; based on the natural language processing NLP technology, determining software deployment configuration for realizing at least one service function according to the software identification, the software version and at least one function identification; the software to be deployed is deployed according to the software deployment configuration, so that the software to be deployed can be automatically deployed, and the software can be deployed according to part of service functions in the software to be deployed, and the method is short in deployment time, low in cost and high in efficiency.

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 disclosure will be readily apparent by reference to the drawings and following detailed description.

Drawings

In the drawings, like reference numerals refer to the same 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 not to be considered limiting of its scope.

FIG. 1 is a flow diagram of a software deployment method incorporating RPA and AI according to one embodiment of the present disclosure;

FIG. 2 is a flow chart of a software deployment method incorporating RPA and AI according to another embodiment of the present disclosure;

FIG. 3 is a framework diagram of a software deployment;

FIG. 4 is a schematic structural diagram of a software deployment apparatus incorporating RPA and AI according to an embodiment of the present disclosure;

fig. 5 is a block diagram of an electronic device according to an embodiment of the present disclosure.

Detailed Description

Reference will now be made in detail to the embodiments of the present disclosure, 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 functions throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of illustrating the present disclosure and should not be construed as limiting the same.

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

It can be appreciated that the related art manually deploys the self-developed product into the customer environment, which is long in deployment time, high in cost and poor in efficiency.

The present disclosure provides an idea of performing software deployment using an RPA robot instead of a human by a combination of Robot Process Automation (RPA) and Artificial Intelligence (AI). The RPA robot can work continuously for 7-24 hours as long as data exists, so that the labor cost can be greatly reduced, the working efficiency is improved, the software deployment is carried out by adopting a mode of combining the RPA and the AI, the deployment time is shortened, the cost is low, the efficiency is high, and the software deployment can be completed in time.

For the purpose of clearly explaining the embodiments of the present invention, terms related to the embodiments of the present invention will be explained first.

In the description of the present disclosure, the term "OCR" refers to Optical Character Recognition (Optical Character Recognition), and specifically refers to a process in which an electronic device examines a Character printed on paper, determines its shape by detecting dark and light patterns, and then translates the shape into a computer text by a Character Recognition method; the method is characterized in that characters in a paper document are converted into an image file with a black-white dot matrix in an optical mode aiming at print characters, and the characters in the image are converted into a text format through recognition software for further editing and processing by word processing software.

In the description of the present disclosure, the term "software version" refers to an identifier that identifies software updated at each stage during the development and updating process of the software, for example, V1 version, V2 version, etc.

In the description of the present disclosure, the term "business function" refers to at least one business function obtained by splitting software according to function types. The service function includes, for example, character recognition, card recognition, verification code recognition, and the like.

In the description of the present disclosure, the term "configuration file" refers to definitions of values of variables in process code, and the like, and in combination with the definitions of the values of the variables and the process code, the running of the process code can be realized.

In the description of the present disclosure, the term "service information" refers to process routing information, data structures required for initialization at deployment time, initialization data, data structure change information, and the like.

In the description of the present disclosure, the term "model file" refers to a file related to a model that needs to be used in the process of executing process code. For example, taking a text recognition service function as an example, if an image recognition model is needed for a process in the model, a model file of the process is a related file of the image recognition model, so that the process can call the image recognition model conveniently to obtain an image recognition result.

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

A software deployment method and apparatus combining RPA and AI according to an embodiment of the present disclosure is described below with reference to the accompanying drawings.

Fig. 1 is a flowchart of a software deployment method incorporating RPA and AI according to an embodiment of the present disclosure, and as shown in fig. 1, the method may include the following steps:

step S101, acquiring a deployment request, wherein the deployment request comprises: the software identification, the software version and the function identification of at least one service function in the software to be deployed.

It should be noted that the software deployment method combining the RPA and the AI according to the embodiment of the present disclosure is executed by a software deployment device combining the RPA and the AI. The software deploying device combining RPA and AI may be implemented by an RPA robot, where the RPA robot may be, for example, a service platform providing SaaS service, or another platform or server communicating with the service platform. In an exemplary embodiment, taking the RPA robot as an example of the implementation of the service platform, the service platform may be set to execute the method in real time for a specific time period or all day, which is not limited by the present disclosure.

The deployment request can be a deployment request submitted to the service platform by a client needing software deployment. The client can select the software to be deployed and the service function to be realized in the software according to the actual needs of the client. Wherein the client is generally an enterprise-level client. The software may be blog software, for example, and may be set according to actual needs.

The client can register and log in the service platform, the software identifier of the software to be deployed, the function identifier of the business function to be realized in the software, the software version of the software to be deployed and the like are selected on the software deployment related page after logging in and then submitted, and when the service platform detects the submitting action of the client, the service platform generates a deployment request according to the operation of the client on the software deployment related page.

The software identifier of the software to be deployed refers to a unique identifier used for distinguishing the software from other software, for example, a name, a number, and the like of the software.

The software version refers to an identifier for identifying software updated at each stage during the development and updating processes of the software, for example, a V1 version, a V2 version, and the like.

The service function in the software refers to at least one service function obtained by splitting the software according to the function type. The service function includes, for example, character recognition, card recognition, verification code recognition, and the like.

The function identifier of the service function refers to a unique identifier for distinguishing the service function from other service functions, such as a name and a number of the service function.

Step S102, based on the natural language processing NLP technology, determining software deployment configuration for realizing at least one service function according to the software identification, the software version and at least one function identification.

In an exemplary embodiment, the service platform may determine, according to the software identifier and the software version, software that corresponds to the software identifier and is of the software version; determining the deployment configuration of each service function in the software, selecting the deployment configuration of the service function corresponding to the function identifier in the deployment request and the deployment configuration of the service function depended by the service function corresponding to the function identifier in the deployment request based on a Natural Language Processing (NLP) technology, and further generating the software deployment configuration for realizing at least one service function.

In an exemplary embodiment, the deployment configuration of the service function may include: the process image files, the configuration image files, the service image files, the model image files and the like of each process in the service function. The process image file refers to an image file obtained by performing image processing on the process code. And configuring the mirror image file, namely performing mirror image processing on the configuration file of the process code to obtain the mirror image file. The service mirror image information refers to mirror image information obtained by performing mirror image processing on the service information of the process code. And the model mirror image file refers to a mirror image file obtained by carrying out mirror image processing on the model file of the process code.

The configuration file refers to definitions of values of variables in the process code, and the like, and the process code can be run by combining the definitions of the values of the variables and the process code.

The service information refers to process routing information, a data structure required for initialization at deployment time, initialization data, data structure change information, and the like.

The model file refers to a relevant file of a model required to be used in the process of executing the process code. For example, taking a text recognition service function as an example, if an image recognition model is needed for a process in the model, a model file of the process is a related file of the image recognition model, so that the process can call the image recognition model conveniently to obtain an image recognition result.

And S103, deploying the software to be deployed according to the software deployment configuration.

In an exemplary embodiment, the process of the service platform performing the deployment processing on the software to be deployed according to the software deployment configuration may be, for example, sending the software deployment configuration to a client platform of a client, and performing the software deployment processing based on the software deployment configuration in an environment of the client platform.

In the embodiment of the present disclosure, the RPA robot obtains a deployment request, where the deployment request includes: the software identification and the software version of the software to be deployed and the function identification of at least one service function in the software to be deployed; based on the natural language processing NLP technology, determining software deployment configuration for realizing at least one service function according to the software identification, the software version and at least one function identification; the software to be deployed is deployed according to the software deployment configuration, so that the software to be deployed can be automatically deployed, and the software can be deployed according to part of service functions in the software to be deployed, and the method is short in deployment time, low in cost and high in efficiency.

Fig. 2 is a flowchart of a software deployment method incorporating RPA and AI according to another embodiment of the present disclosure, which may include the following steps, as shown in fig. 2:

step S201, obtaining a deployment request, where the deployment request includes: the software identification, the software version and the function identification of at least one service function in the software to be deployed.

Step S202, based on the natural language processing NLP technology, determining a process identifier of at least one process to be deployed according to the software identifier and at least one function identifier.

In an exemplary embodiment, the service platform may determine, according to the software identifier, software corresponding to the software identifier; determining a service function corresponding to at least one function identifier in the software; determining the process included in the business function and the process included in the business function which is depended by the business function; and further determines a process identification. Correspondingly, the process of the service platform executing step S202 may be, for example, for each function identifier, based on a natural language processing NLP technique, querying a dependency relationship table corresponding to the software identifier according to the function identifier, and determining a dependent function identifier corresponding to the function identifier; determining at least one process included by the service function corresponding to the function identifier and at least one process included by the service function corresponding to the dependent function identifier as a process to be deployed; and determining the process identification of the process to be deployed.

In an exemplary embodiment, for each function identifier, the service platform may query, based on a natural language processing NLP technique, a dependency relationship table corresponding to the software identifier according to the function identifier, and obtain a dependent function identifier corresponding to the function identifier. The dependent function identifier refers to a function identifier of a service function, on which a service function corresponding to the function identifier depends. The dependency means that the service function corresponding to the function identifier needs to determine an execution result of the service function corresponding to the service function based on an execution result of the dependent service function. Wherein, the dependency relationship table comprises: and the software identifier corresponds to the dependent function identifier corresponding to each function identifier in the software.

At least one function identifier in the software may have the same service function in the service functions that the service function corresponding to the function identifier depends on. The service functions corresponding to at least one function identifier in the software may also have a mutual dependency relationship, that is, the service functions corresponding to at least one function identifier in the software are respectively a service function a, a service function B, and a service function C, and then there may be a mutual dependency relationship between the service function a, the service function B, and the service function C, for example, the service function a depends on the service function C, and the service function B depends on the service function C.

According to each function identifier, based on a Natural Language Processing (NLP) technology, a dependency relation table corresponding to the software identifier is inquired according to the function identifier, the dependency function identifier corresponding to the function identifier can be automatically acquired, at least one process included in the service function corresponding to the function identifier and at least one process included in the service function corresponding to the dependency function identifier are further acquired, missing acquisition of part of the processes is avoided, and accuracy of the determined software deployment configuration is improved.

Step S203, based on the natural language processing NLP technology, determining at least one process image file, and a configuration image file, service image information and model image file corresponding to the process image file according to the process identifier, the software version and the software identifier of at least one process to be deployed.

In an exemplary embodiment, the process of the service platform executing step S203 may be, for example, to query, based on a natural language processing NLP technology, a process list corresponding to the software identifier according to the process identifier and the software version, and obtain the process identifier and a process image file identifier, a configuration image file identifier, a service image information identifier, and a model image file identifier corresponding to the software version; and acquiring the process image file, and the configuration image file, the service image information and the model image file corresponding to the process image file according to the image file identifier, the configuration image file identifier, the service image information identifier and the model image file identifier.

And the process list corresponding to the software identifier stores each process information in the software of each software version corresponding to the software identifier. Wherein, each piece of process information in the process list comprises: the system comprises a process identifier, a software version, a function identifier of a business function to which the process belongs, a process image file identifier, a configuration image file identifier, a service image information identifier, a model image file identifier and the like.

According to the process identification and the software version, a process list corresponding to the software identification is automatically inquired, and the process identification, the process image file identification, the configuration image file identification, the service image information identification and the model image file identification corresponding to the software version are obtained, so that at least one process image file, the configuration image file, the service image information and the model image file corresponding to the process image file can be timely and accurately obtained, and the accuracy of software deployment configuration is further improved.

In an exemplary embodiment, before step S203, or before querying the process list corresponding to the software identifier according to the process identifier and the software version based on the natural language processing NLP technology, the service platform may further perform the following processes: determining a published software version of published software corresponding to a software identifier, each service function in the published software, a process image file, a configuration image file, service image information and a model image file of each process in the service function; and aiming at each process, updating the process list corresponding to the software identification according to the process identification of the process, the process image file identification of the process image file of the process, the configuration image file identification of the configuration image file, the service image information identification of the service image information, the model image file identification of the model image file and the released software version.

The method comprises the steps of updating a process list corresponding to a software identifier, namely adding a process identifier of a process, a process image file identifier of a process image file of the process, a configuration image file identifier of the configuration image file, a service image information identifier of the service image information, a model image file identifier of a model image file, a published software version and a function identifier of a business function to which the process belongs to the process as process information into the process list to obtain the updated process list, so that the process list can be updated in time, the accuracy of the process list is improved, and the accuracy of the determined software deployment configuration is further improved.

In an exemplary embodiment, the process of the service platform determining the published software version of the published software, each business function in the published software, a process image file, a configuration image file, service image information and a model image file of each process in the business functions corresponding to the software identifier may be, for example, determining the published software version of the published software, each business function in the published software, a process file, a configuration file, service information and a model file of each process in the business functions corresponding to the software identifier; and carrying out mirror image processing on the process file, the configuration file, the service information and the model file of each process to obtain the process mirror image file, the configuration mirror image file, the service mirror image information and the model mirror image file of each process. The mirroring may be, for example, a Docker mirroring of the container cluster management system Kubernetes.

In an exemplary embodiment, the service information may include: process routing information, data structures required for initialization at deployment, initialization data, data structure change information, and the like. Wherein the initialization data may be initialization data for a specified at least one guest environment. Wherein, for a specific client, the corresponding initialization data and data structure can be selected according to the client environment of the client platform.

In order to facilitate the unification of the environments of the service platform and the client platform, the environments of the service platform and the client platform can adopt a container cluster management system Kubernetes to perform unified management on the containers. The environments of the service platform and the client environment can be provided with the plug-ins of Kubernets, such as Helm plug-ins, Istio plug-ins, StorageClass plug-ins and the like.

Step S204, determining the software deployment configuration according to at least one process image file, and the configuration image file, the service image information and the model image file corresponding to the process image file.

In an exemplary embodiment, the service platform may perform packaging processing on at least one process image file, and a configuration image file, service image information, and a model image file corresponding to the process image file, and use a packaging processing result as software deployment configuration.

And S205, deploying the software to be deployed according to the software deployment configuration.

In an exemplary embodiment, the process of the service platform performing the deployment processing on the software to be deployed according to the software deployment configuration may be, for example, sending the software deployment configuration to a client platform of a client, and performing the software deployment processing based on the software deployment configuration in an environment of the client platform. After the software deployment process is completed, the deployed software can be subjected to internal test to determine that the service function required to be realized in the deployment request can be realized when the software on the client platform runs.

The specific implementation process and principle of step S201 and step S205 may refer to the description of the above embodiments, and are not described herein again.

In the embodiment of the present disclosure, the RPA robot obtains a deployment request, where the deployment request includes: the software identification and the software version of the software to be deployed and the function identification of at least one service function in the software to be deployed; based on a Natural Language Processing (NLP) technology, determining a process identifier of at least one process to be deployed according to a software identifier and at least one function identifier; based on a Natural Language Processing (NLP) technology, determining at least one process image file, and a configuration image file, service image information and a model image file corresponding to the process image file according to a process identifier, a software version and a software identifier of at least one process to be deployed; determining software deployment configuration according to at least one process image file, and a configuration image file, service image information and a model image file corresponding to the process image file; the software to be deployed is deployed according to the software deployment configuration, so that the software to be deployed can be automatically deployed, and the software can be deployed according to part of service functions in the software to be deployed, and the method is short in deployment time, low in cost and high in efficiency.

In order to more clearly illustrate the above embodiments, the description will now be made by way of example.

For example, as shown in fig. 3, it is a framework diagram of software deployment (release). In fig. 3, for specific software, when a software version of the software is newly added (a new service is added to an operation and maintenance platform), the software of the software version is online to a service platform (SaaS environment), that is, is released to the service platform; determining a process file (service chart), configuration information (various types of configuration information), a model file (model or service extra data) and service information (service mirror image) of each process in the published software based on the published version of the published software and the like, carrying out mirror image processing (namely snapshot processing) to obtain the process mirror image file, the configuration mirror image file, the service mirror image information and the model mirror image file of each process in the published software, and updating a process list of the software. When a deployment request is obtained, determining a process identifier of at least one process to be deployed based on a software identifier, a software version and a function identifier of at least one service function in the deployment request, further querying a process list in combination with the software version, obtaining a process image file, a configuration image file, service image information and a model image file of the at least one process to be deployed, packaging, deploying and testing.

In order to implement the above embodiments, the present disclosure further provides a software deployment device combining RPA and AI. Fig. 4 is a schematic structural diagram of a software deployment apparatus that combines RPA and AI according to an embodiment of the present disclosure.

As shown in fig. 4, the software deploying apparatus 400 combining RPA and AI is applied to an RPA robot, and includes: an acquisition module 401, a first determination module 402 and a deployment module 403.

The obtaining module 401 is configured to obtain a deployment request, where the deployment request includes: the method comprises the steps of identifying software identification and software version of software to be deployed and identifying at least one service function in the software to be deployed;

a first determining module 402, configured to determine, based on a natural language processing NLP technology, a software deployment configuration for implementing at least one of the business functions according to the software identifier, the software version, and at least one of the function identifiers;

a deployment module 403, configured to perform deployment processing on the software to be deployed according to the software deployment configuration.

In one embodiment of the present disclosure, the first determining module 402 includes: a first determination unit, a second determination unit, and a third determination unit; the first determining unit is configured to determine, based on a natural language processing NLP technique, a process identifier of at least one process to be deployed according to the software identifier and the at least one function identifier; the second determining unit is configured to determine, based on a natural language processing NLP technology, at least one process image file, and a configuration image file, service image information, and a model image file corresponding to the process image file, according to the process identifier, the software version, and the software identifier of the at least one process to be deployed; the third determining unit is configured to determine the software deployment configuration according to the at least one process image file, and the configuration image file, the service image information, and the model image file corresponding to the process image file.

In an embodiment of the present disclosure, the first determining unit is specifically configured to, for each function identifier, query, based on a natural language processing NLP technique, a dependency relationship table corresponding to the software identifier according to the function identifier, and determine a dependent function identifier corresponding to the function identifier; determining at least one process included in the service function corresponding to the function identifier and at least one process included in the service function corresponding to the dependent function identifier as the process to be deployed; and determining the process identification of the process to be deployed.

In an embodiment of the present disclosure, the second determining unit is specifically configured to, for each process identifier, query, based on a natural language processing NLP technique, a process list corresponding to the software identifier according to the process identifier and the software version, and obtain the process identifier and a process image identifier, a configuration image identifier, a service image information identifier, and a model image identifier corresponding to the software version; and acquiring the process image file, and the configuration image file, the service image information and the model image file corresponding to the process image file according to the image file identification, the configuration image file identification, the service image information identification and the model image file identification.

In one embodiment of the present disclosure, the apparatus further comprises: a second determining module and an updating module; the second determining module is configured to determine a published software version of published software corresponding to the software identifier, each service function in the published software, a process image file, a configuration image file, service image information, and a model image file of each process in the service function; and the updating module is used for updating a process list corresponding to the software identifier according to the process identifier of the process, the process image file identifier of the process image file of the process, the configuration image file identifier of the configuration image file, the service image information identifier of the service image information, the model image file identifier of the model image file and the released software version aiming at each process.

In an embodiment of the present disclosure, the second determining module is specifically configured to determine a released software version of the released software corresponding to the software identifier, each business function in the released software, a process file, a configuration file, service information, and a model file of each process in the business functions; and carrying out mirror image processing on the process file, the configuration file, the service information and the model file of each process to obtain the process mirror image file, the configuration mirror image file, the service mirror image information and the model mirror image file of each process.

It should be noted that the foregoing explanation of the embodiment of the software deployment method combining RPA and AI is also applicable to the software deployment device combining RPA and AI in this embodiment, and details that are not disclosed in the embodiment of the software deployment device combining RPA and AI in the present disclosure are not described here again.

In summary, the RPA robot obtains a deployment request, wherein the deployment request includes: the software identification and the software version of the software to be deployed and the function identification of at least one service function in the software to be deployed; based on the natural language processing NLP technology, determining software deployment configuration for realizing at least one service function according to the software identification, the software version and at least one function identification; the software to be deployed is deployed according to the software deployment configuration, so that the software to be deployed can be automatically deployed, and the software can be deployed according to part of service functions in the software to be deployed, and the method is short in deployment time, low in cost and high in efficiency.

Fig. 5 shows a block diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 5, the electronic apparatus includes: a memory 510 and a processor 520, the memory 510 having stored therein computer programs that are executable on the processor 520. The processor 520, when executing the computer program, implements the software deployment method in conjunction with RPA and AI in the above embodiments. The number of the memory 510 and the processor 520 may be one or more.

The electronic device further includes:

the communication interface 530 is used for communicating with an external device to perform data interactive transmission.

If the memory 510, the processor 520, and the communication interface 530 are implemented independently, the memory 510, the processor 520, and the communication interface 530 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. 5, but this is not intended to represent only one bus or type of bus.

Optionally, in an implementation, if the memory 510, the processor 520, and the communication interface 530 are integrated on a chip, the memory 510, the processor 520, and the communication interface 530 may complete communication with each other through an internal interface.

Embodiments of the present disclosure provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements a method provided in embodiments of the present disclosure.

The disclosed embodiment also provides a chip, which comprises a processor and is used for calling and executing the instructions stored in the memory from the memory, so that the communication device provided with the chip executes the method provided by the disclosed embodiment.

The embodiment of the present disclosure further provides a chip, including: the system comprises an input interface, an output interface, a processor and a memory, wherein the input interface, the output interface, the processor and the memory are connected through an internal connection path, the processor is used for executing codes in the memory, and when the codes are executed, the processor is used for executing the method provided by the disclosed embodiment.

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 disclosure may be fully or partially generated upon loading and execution of the computer program instructions 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 present disclosure. 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 disclosure, "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 disclosure includes additional implementations in which functions may be executed out of order from that 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 disclosure 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 disclosure 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 disclosure, but the scope of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of various changes or substitutions within the technical scope of the present disclosure, which should be covered by the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A software deployment method combining RPA and AI, which is applied to a robot process automation RPA robot, the method comprises:

obtaining a deployment request, wherein the deployment request comprises: the method comprises the steps of identifying software identification and software version of software to be deployed and identifying at least one service function in the software to be deployed;

based on a Natural Language Processing (NLP) technology, determining software deployment configuration for realizing at least one business function according to the software identification, the software version and at least one function identification;

and deploying the software to be deployed according to the software deployment configuration.

2. The RPA and AI combined software deployment method of claim 1, wherein the determining a software deployment configuration for implementing at least one of the business functions based on the natural language processing NLP technique according to the software identifier, the software version, and at least one of the function identifiers comprises:

based on a Natural Language Processing (NLP) technology, determining a process identifier of at least one process to be deployed according to the software identifier and at least one function identifier;

based on a Natural Language Processing (NLP) technology, determining at least one process image file, and a configuration image file, service image information and a model image file corresponding to the process image file according to the process identifier, the software version and the software identifier of the at least one process to be deployed;

and determining the software deployment configuration according to the at least one process image file, and the configuration image file, the service image information and the model image file corresponding to the process image file.

3. The RPA and AI combined software deployment method of claim 2, wherein the determining a process identifier of at least one process to be deployed according to the software identifier and at least one function identifier based on the NLP technology comprises:

for each function identifier, based on a Natural Language Processing (NLP) technology, inquiring a dependency relation table corresponding to the software identifier according to the function identifier, and determining a dependent function identifier corresponding to the function identifier;

determining at least one process included in the service function corresponding to the function identifier and at least one process included in the service function corresponding to the dependent function identifier as the process to be deployed;

and determining the process identification of the process to be deployed.

4. The software deploying method combining RPA and AI according to claim 2, wherein the determining at least one process image file, and a configuration image file, service image information, and model image file corresponding to the process image file according to the process identifier, the software version, and the software identifier of the at least one process to be deployed based on the NLP technology comprises:

for each process identification, based on a Natural Language Processing (NLP) technology, inquiring a process list corresponding to the software identification according to the process identification and the software version, and acquiring the process identification, a process image file identification, a configuration image file identification, a service image information identification and a model image file identification corresponding to the process identification and the software version;

and acquiring the process image file, and the configuration image file, the service image information and the model image file corresponding to the process image file according to the image file identification, the configuration image file identification, the service image information identification and the model image file identification.

5. The RPA and AI combined software deployment method of claim 4, wherein before querying the process list corresponding to the software identifier based on a natural language processing NLP technique according to the process identifier and the software version to obtain the process identifier and the process image identifier, the configuration image identifier, the service image information identifier, and the model image identifier corresponding to the software version, the method further comprises:

determining a published software version of published software corresponding to the software identifier, each service function in the published software, a process image file, a configuration image file, service image information and a model image file of each process in the service function;

and for each process, updating a process list corresponding to the software identification according to the process identification of the process, the process image file identification of the process image file of the process, the configuration image file identification of the configuration image file, the service image information identification of the service image information, the model image file identification of the model image file and the released software version.

6. The RPA and AI-combined software deployment method of claim 5, wherein said determining a published software version of published software corresponding to said software identifier, each business function in said published software, a process image file, a configuration image file, service image information, and a model image file for each process in said business functions comprises:

determining a published software version of published software corresponding to the software identifier, each service function in the published software, and a process file, a configuration file, service information and a model file of each process in the service function;

and carrying out mirror image processing on the process file, the configuration file, the service information and the model file of each process to obtain the process mirror image file, the configuration mirror image file, the service mirror image information and the model mirror image file of each process.

7. A software deploying apparatus combining RPA and AI, applied to a robot process automation RPA robot, the apparatus comprising:

an obtaining module, configured to obtain a deployment request, where the deployment request includes: the method comprises the steps of identifying software identification and software version of software to be deployed and identifying at least one service function in the software to be deployed;

a first determining module, configured to determine, based on a natural language processing NLP technique, a software deployment configuration for implementing at least one of the business functions according to the software identifier, the software version, and at least one of the function identifiers;

and the deployment module is used for deploying the software to be deployed according to the software deployment configuration.

8. The RPA and AI-integrated software deployment device of claim 7, wherein the first determining module comprises: a first determination unit, a second determination unit, and a third determination unit;

the first determining unit is configured to determine, based on a natural language processing NLP technique, a process identifier of at least one process to be deployed according to the software identifier and the at least one function identifier;

the second determining unit is configured to determine, based on a natural language processing NLP technology, at least one process image file, and a configuration image file, service image information, and a model image file corresponding to the process image file, according to the process identifier, the software version, and the software identifier of the at least one process to be deployed;

the third determining unit is configured to determine the software deployment configuration according to the at least one process image file, and the configuration image file, the service image information, and the model image file corresponding to the process image file.

9. The RPA and AI-integrated software deployment device of claim 8, wherein said first determining unit is specifically configured to,

and determining the process identification of the process to be deployed.

10. The RPA and AI-integrated software deployment device of claim 8, wherein said second determining unit is specifically configured to,

11. The RPA and AI integrated software deployment device of claim 10, wherein the device further comprises: a second determining module and an updating module;

the second determining module is configured to determine a published software version of published software corresponding to the software identifier, each service function in the published software, a process image file, a configuration image file, service image information, and a model image file of each process in the service function;

and the updating module is used for updating a process list corresponding to the software identifier according to the process identifier of the process, the process image file identifier of the process image file of the process, the configuration image file identifier of the configuration image file, the service image information identifier of the service image information, the model image file identifier of the model image file and the released software version aiming at each process.

12. The RPA and AI-combining software deployment device of claim 11 wherein said second 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 to 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 to 6.