Software deployment method and device combining RPA and AI

Abstract

The present disclosure proposes a software deployment method and device combining RPA and AI, which relates to the technical field of RPA and AI. The method is applied to an RPA robot. The specifically disclosed technical solution is: obtaining a deployment request, wherein the deployment request includes: a software identifier, a software version, and a function identifier of at least one business function in the software to be deployed; based on natural language processing (NLP) technology, determining a software deployment configuration for realizing at least one business function according to the software identifier, the software version, and at least one function identifier; performing deployment processing on the software to be deployed according to the software deployment configuration, thereby being able to automatically determine the software deployment configuration for realizing at least one business function by combining RPA and AI, automatically perform deployment processing on the software to be deployed, and being able to perform software deployment processing on some of the business functions therein according to demand, with short deployment time, low cost, and high efficiency.

Description

Technical Field

[0001] The present disclosure relates to the field of robotic process automation and artificial intelligence technology, and in particular to a software deployment method and device combining RPA and AI. Background Art

[0002] Robotic Process Automation (RPA) uses specific "robot software" to simulate human operations on computers and automatically execute process tasks according to rules.

[0003] Artificial Intelligence (AI) is a technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.

[0004] In the related technology, Software-as-a-Service (SaaS) is a completely created software application model that provides software through the Internet. Manufacturers deploy application software on their own servers, and customers can order the required application software services from manufacturers through the Internet according to their actual needs. At present, while providing SaaS services, it also supports the deployment of self-developed products to customer environments, but currently it needs to be deployed manually, which takes a long time, is costly, and inefficient. Summary of the invention

[0005] The embodiments of the present disclosure provide a software deployment method and device combining RPA and AI to solve the technical problems of long deployment time, high cost and poor efficiency when deployed to a customer environment in related technologies. The technical solution is as follows:

[0006] In a first aspect, an embodiment of the present disclosure provides a software deployment method combining RPA and AI, which is applied to an RPA robot, and the method includes: obtaining a deployment request, wherein the deployment request includes: a software identifier, a software version, and a function identifier of at least one business function in the software to be deployed; based on natural language processing (NLP) technology, determining 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 performing deployment processing on the software to be deployed according to the software deployment configuration.

[0007] In one embodiment, the natural language processing (NLP) technology is used to determine the 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, including: based on the natural language processing (NLP) technology, according to the software identifier and at least one of the function identifiers, determining the process identifier of at least one process to be deployed; based on the natural language processing (NLP) technology, according to the process identifier of at least one process to be deployed, the software version and the software identifier, determining at least one process image file, and the configuration image file, service image information and model image file corresponding to the process image file; determining the software deployment configuration according to the at least one process image file, and the configuration image file, service image information and model image file corresponding to the process image file.

[0008] In one embodiment, the method of determining the process identifier of at least one process to be deployed based on the software identifier and at least one function identifier based on natural language processing (NLP) technology includes: for each function identifier, based on the natural language processing (NLP) technology, querying the dependency table corresponding to the software identifier according to the function identifier to determine the dependent function identifier corresponding to the function identifier; determining at least one process included in the business function corresponding to the function identifier and at least one process included in the business function corresponding to the dependent function identifier as the process to be deployed; and determining the process identifier of the process to be deployed.

[0009] In one embodiment, the natural language processing (NLP) technology is used to determine at least one process image file, and the configuration image file, service image information, and model image file corresponding to the process image file according to the process identifier of the at least one process to be deployed, the software version, and the software identifier, including: for each process identifier, based on the natural language processing (NLP) technology, according to the process identifier and the software version, query the process list corresponding to the software identifier, and obtain the process image file identifier, configuration image file identifier, service image information identifier, and model image file identifier corresponding to the process identifier and the software version; according to the image file identifier, configuration image file identifier, service image information identifier, and model image file identifier, obtain the process image file, and the configuration image file, service image information, and model image file corresponding to the process image file.

[0010] In one embodiment, based on natural language processing (NLP) technology, according to the process identifier and the software version, a process list corresponding to the software identifier is queried, and before obtaining the process image file identifier, configuration image file identifier, service image information identifier and model image file identifier corresponding to the process identifier and the software version, the method also includes: determining the published software version of the published software corresponding to the software identifier, each business function in the published software, and the process image file, configuration image file, service image information and model image file of each process in the business function; for each process, 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 published software version, the process list corresponding to the software identifier is updated.

[0011] In one embodiment, determining the published software version of the published software corresponding to the software identifier, each business function in the published software, the process image file of each process in the business function, the configuration image file, service image information and model image file includes: determining the published software version of the published software corresponding to the software identifier, each business function in the published software, the process file, configuration file, service information and model file of each process in the business function; mirroring the process file, configuration file, service information and model file of each process to obtain the process image file, configuration image file, service image information and model image file of each process.

[0012] In a second aspect, an embodiment of the present disclosure provides a software deployment device combining RPA and AI, which is applied to an RPA robot, and the device includes: an acquisition module, used to obtain a deployment request, wherein the deployment request includes: a software identifier, a software version, and a function identifier of at least one business function in the software to be deployed; a first determination module, used to determine a software deployment configuration for implementing at least one of the business functions based on the natural language processing (NLP) technology according to the software identifier, the software version, and at least one of the function identifiers; a deployment module, used to deploy the software to be deployed according to the software deployment configuration.

[0013] In one embodiment, the first determination module includes: a first determination unit, a second determination unit and a third determination unit; the first determination unit is used to determine the process identifier of at least one process to be deployed based on the natural language processing NLP technology, according to the software identifier and at least one function identifier; the second determination unit is used to determine at least one process image file, and the configuration image file, service image information and model image file corresponding to the process image file based on the natural language processing NLP technology, according to the process identifier of the at least one process to be deployed, the software version and the software identifier; the third determination unit is used to determine the software deployment configuration based on the at least one process image file, and the configuration image file, service image information and model image file corresponding to the process image file.

[0014] In one embodiment, the first determination unit is specifically used to, for each function identifier, query the dependency table corresponding to the software identifier based on the function identifier to determine the dependent function identifier corresponding to the function identifier; determine at least one process included in the business function corresponding to the function identifier and at least one process included in the business function corresponding to the dependent function identifier as the process to be deployed; and determine the process identifier of the process to be deployed.

[0015] In one embodiment, the second determination unit is specifically used to, for each process identifier, based on natural language processing (NLP) technology, query the process list corresponding to the software identifier according to the process identifier and the software version, and obtain the process image file identifier, configuration image file identifier, service image information identifier, and model image file identifier corresponding to the process identifier and the software version; and obtain the process image file, and the configuration image file, service image information, and model image file corresponding to the process image file according to the image file identifier, configuration image file identifier, service image information identifier, and model image file identifier.

[0016] In one embodiment, the device also includes: a second determination module and an update module; the second determination module is used to determine the published software version of the published software corresponding to the software identifier, each business function in the published software, the process image file of each process in the business function, the configuration image file, the service image information and the model image file; the update module is used to update the process list corresponding to the software identifier for each process 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 published software version.

[0017] In one embodiment, the second determination module is specifically used to determine the published software version of the published software corresponding to the software identifier, each business function in the published software, the process files, configuration files, service information and model files of each process in the business function; mirroring the process files, configuration files, service information and model files of each process to obtain the process image files, configuration image files, service image information and model image files of each process.

[0018] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising: a memory and a processor, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement a method in any one of the above-mentioned embodiments.

[0019] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method in any one of the above-mentioned embodiments is executed.

[0020] The advantages or beneficial effects of the above technical solution include at least: obtaining a deployment request by adopting an RPA robot, wherein the deployment request includes: the software identifier, software version, and function identifier of at least one business function in the software to be deployed; based on natural language processing (NLP) technology, determining the software deployment configuration for implementing at least one business function according to the software identifier, software version, and at least one function identifier; deploying the software to be deployed according to the software deployment configuration, thereby being able to automatically deploy the software to be deployed, and being able to perform software deployment processing for some of the business functions therein according to demand, with short deployment time, low cost, and high efficiency.

[0021] The above summary is for illustrative purposes 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 accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments according to the present disclosure and should not be regarded as limiting the scope of the present disclosure.

[0023] Figure 1 is a flowchart of a software deployment method combining RPA and AI according to an embodiment of the present disclosure;

[0024] Figure 2 is a flowchart of a software deployment method combining RPA and AI according to another embodiment of the present disclosure;

[0025] Figure 3 It is a framework diagram of software deployment;

[0026] Figure 4 It is a structural diagram of a software deployment device combining RPA and AI according to an embodiment of the present disclosure;

[0027] Figure 5 It is a structural block diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present disclosure, and cannot be understood as limiting the present disclosure.

[0029] In the description of the present disclosure, the term "plurality" refers to two or more.

[0030] It is understandable that in the related art, manually deploying self-developed products into the customer environment takes a long time, is costly, and has low efficiency.

[0031] The present disclosure provides a method of using RPA robots to replace manual work for software deployment through the combination of Robotic Process Automation (RPA) and Artificial Intelligence (AI). Since RPA robots can work 24 / 7 as long as there is data, this can greatly reduce labor costs and improve work efficiency. By combining RPA and AI to deploy software, the deployment time is shortened, the cost is low, the efficiency is high, and the software deployment can be completed in a timely manner.

[0032] In order to clearly illustrate the embodiments of the present invention, the technical terms involved in the embodiments of the present invention are first explained.

[0033] In the description of the present disclosure, the term "OCR" refers to Optical Character Recognition, which specifically refers to the process in which an electronic device examines characters printed on paper, determines their shapes by detecting dark and light patterns, and then translates the shapes into computer text using character recognition methods; that is, for printed characters, an optical method is used to convert the text in a paper document into a black and white dot matrix image file, and recognition software is used to convert the text in the image into a text format for further editing and processing by word processing software.

[0034] In the description of the present disclosure, the term "software version" refers to an identifier for identifying the software updated at each stage during the development and update process of the software, for example, version V1, version V2, and so on.

[0035] In the description of this disclosure, the term "business function" refers to at least one business function obtained by splitting the software according to the function type, wherein the business function is, for example, text recognition, card recognition, verification code recognition, etc.

[0036] In the description of the present disclosure, the term "configuration file" refers to the definition of the values ​​of various variables in the process code, etc., and the definition of the values ​​of various variables combined with the process code can realize the operation of the process code.

[0037] In the description of this disclosure, the term "service information" refers to process routing information, data structures required for initialization during deployment, initialization data, data structure change information, etc.

[0038] In the description of this disclosure, the term "model file" refers to the relevant files of the model needed in the process code execution process. For example, taking the text recognition business function as an example, the process in the model needs to use the image recognition model, then the model file of the process is the relevant file of the image recognition model, which facilitates the process to call the image recognition model and obtain the image recognition result.

[0039] These and other aspects of the embodiments of the present disclosure will be apparent with reference to the following description and drawings. In these descriptions and drawings, some specific implementations of the embodiments of the present disclosure are specifically disclosed to represent some ways of implementing the principles of the embodiments of the present disclosure, but it should be understood that the scope of the embodiments of the present disclosure is not limited thereto. On the contrary, the embodiments of the present disclosure include all changes, modifications and equivalents that fall within the spirit and connotation of the appended claims.

[0040] The following describes a software deployment method and device combining RPA and AI according to an embodiment of the present disclosure in conjunction with the accompanying drawings.

[0041] Figure 1 is a flowchart of a software deployment method combining RPA and AI according to an embodiment of the present disclosure. Figure 1 As shown, the method may include the following steps:

[0042] Step S101, obtaining a deployment request, wherein the deployment request includes: a software identifier of the software to be deployed, a software version, and a function identifier of at least one business function in the software to be deployed.

[0043] It should be noted that the software deployment method combining RPA and AI in the embodiment of the present disclosure is performed by a software deployment device combining RPA and AI. Among them, the software deployment device combining RPA and AI can be implemented by an RPA robot, wherein the RPA robot can be, for example, a service platform that provides SaaS services, or other platforms or servers that communicate with the service platform. In an exemplary embodiment, taking the RPA robot as a service platform as an example, the service platform can be set to execute the method in real time during a specific time period or throughout the day, and the present disclosure does not limit this.

[0044] The deployment request may be a deployment request submitted to the service platform by a customer who needs to deploy software. The customer may select the software to be deployed and the business functions to be implemented in the software according to their actual needs. The customer is generally an enterprise-level customer. The software may be, for example, a blog software, etc., which may be set according to actual needs.

[0045] Among them, customers can register and log in on the service platform. On the software deployment related page after logging in, they can select the software identifier of the software to be deployed, the function identifier of the business function to be implemented in the software, and the software version of the software to be deployed, and then submit. When the service platform detects the customer's submission action, it generates a deployment request based on the customer's operation on the software deployment related page.

[0046] The software identifier of the software to be deployed refers to a unique identifier used to distinguish the software from other software, such as the name and number of the software.

[0047] The software version refers to the identifier of the software updated at each stage during the development and update process of the software, for example, version V1, version V2, etc.

[0048] The business function in the software refers to at least one business function obtained by splitting the software according to the function type, such as text recognition, card recognition, verification code recognition, etc.

[0049] Among them, the functional identifier of a business function refers to a unique identifier used to distinguish a business function from other business functions, such as the name or number of the business function.

[0050] Step S102, based on natural language processing (NLP) technology, according to the software identifier, software version and at least one function identifier, determine a software deployment configuration for implementing at least one business function.

[0051] In an exemplary embodiment, the service platform can determine the software corresponding to the software identifier and the software version based on the software identifier and the software version; determine the deployment configuration of each business function in the software, and select the deployment configuration of the business function corresponding to the function identifier in the deployment request and the deployment configuration of the business function on which the business function corresponding to the function identifier in the deployment request depends based on natural language processing (NLP) technology, and then generate a software deployment configuration for implementing at least one business function.

[0052] In an exemplary embodiment, the deployment configuration of a business function may include: process image files, configuration image files, service image files, and model image files of each process in the business function. Among them, the process image file refers to an image file obtained by mirroring the process code. The configuration image file refers to an image file obtained by mirroring the configuration file of the process code. The service image information refers to the image information obtained by mirroring the service information of the process code. The model image file refers to the image file obtained by mirroring the model file of the process code.

[0053] The configuration file refers to the definition of the values ​​of various variables in the process code. The definition of the values ​​of various variables and the process code can realize the operation of the process code.

[0054] Service information refers to process routing information, data structures required for initialization during deployment, initialization data, data structure change information, etc.

[0055] Model files refer to the files related to the model that are needed during the execution of the process code. For example, taking the text recognition business function as an example, the process in the model needs to use the image recognition model, so the model file of the process is the file related to the image recognition model, which facilitates the process to call the image recognition model and obtain the image recognition result.

[0056] Step S103: deploy the software to be deployed according to the software deployment configuration.

[0057] In an exemplary embodiment, the process of the service platform deploying the software to be deployed according to the software deployment configuration may include, for example, sending the software deployment configuration to the customer's customer platform, and performing software deployment processing based on the software deployment configuration in the environment of the customer platform.

[0058] In the disclosed embodiment, the RPA robot obtains a deployment request, wherein the deployment request includes: a software identifier, a software version, and a function identifier of at least one business function in the software to be deployed; based on natural language processing (NLP) technology, the robot determines 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; and performs deployment processing on the software to be deployed according to the software deployment configuration, thereby being able to automatically deploy the software to be deployed, and being able to perform software deployment processing on some of the business functions therein according to demand, with short deployment time, low cost, and high efficiency.

[0059] Figure 2 is a flowchart of a software deployment method combining RPA and AI according to another embodiment of the present disclosure. Figure 2 As shown, the method may include the following steps:

[0060] Step S201, obtaining a deployment request, wherein the deployment request includes: a software identifier of the software to be deployed, a software version, and a function identifier of at least one business function in the software to be deployed.

[0061] Step S202: Based on natural language processing (NLP) technology, determine the process identifier of at least one process to be deployed according to the software identifier and at least one function identifier.

[0062] In an exemplary embodiment, the service platform can determine the software corresponding to the software identifier based on the software identifier; determine the business function corresponding to at least one function identifier in the software; determine the processes included in the business function, and the processes included in the business function on which the business function depends; and then determine the process identifier. Correspondingly, the process of the service platform executing step S202 can be, for example, for each function identifier, based on the natural language processing (NLP) technology, querying the dependency table corresponding to the software identifier based on the function identifier, and determining the dependent function identifier corresponding to the function identifier; determining at least one process included in the business function corresponding to the function identifier, and at least one process included in the business function corresponding to the dependent function identifier as the process to be deployed; and determining the process identifier of the process to be deployed.

[0063] In an exemplary embodiment, for each function identifier, the service platform can query the dependency table corresponding to the software identifier based on the function identifier based on the natural language processing (NLP) technology to obtain the dependent function identifier corresponding to the function identifier. The dependent function identifier refers to the function identifier of the business function on which the business function corresponding to the function identifier depends. Dependence means that the business function corresponding to the function identifier needs to determine the execution result of the business function corresponding to the business function based on the execution result of the business function on which it depends. The dependency table includes: the dependent function identifier corresponding to each function identifier in the software corresponding to the software identifier.

[0064] Among the business functions that the business function corresponding to at least one function identifier in the software depends on, there may be the same business function. The business functions corresponding to at least one function identifier in the software may also have a mutual dependency relationship, that is, if the business functions corresponding to at least one function identifier in the software are business function A, business function B and business function C, then business function A, business function B and business function C may have a mutual dependency relationship, for example, business function A depends on business function C, and business function B depends on business function C.

[0065] Among them, for each function identifier, based on natural language processing (NLP) technology, the dependency table corresponding to the software identifier is queried according to the function identifier, and the dependent function identifier corresponding to the function identifier can be automatically obtained, and then at least one process included in the business function corresponding to the function identifier, as well as at least one process included in the business function corresponding to the dependent function identifier can be obtained, thereby avoiding missing acquisition of some processes and improving the accuracy of the determined software deployment configuration.

[0066] Step S203, based on natural language processing (NLP) technology, according to the process identifier, software version and software identifier of at least one process to be deployed, determine at least one process image file, and the configuration image file, service image information and model image file corresponding to the process image file.

[0067] In an exemplary embodiment, the process of the service platform executing step S203 can, for example, be: for each process identifier, based on natural language processing (NLP) technology, according to the process identifier and the software version, query the process list corresponding to the software identifier, obtain the process image file identifier, configuration image file identifier, service image information identifier and model image file identifier corresponding to the process identifier and the software version; according to the image file identifier, configuration image file identifier, service image information identifier and model image file identifier, obtain the process image file, and the configuration image file, service image information and model image file corresponding to the process image file.

[0068] The process list corresponding to the software identifier stores information about each process in each software version corresponding to the software identifier. Each piece of process information in the process list includes: process identifier, software version, function identifier of the business function to which the process belongs, process image file identifier, configuration image file identifier, service image information identifier, and model image file identifier.

[0069] Among them, according to the process identifier and the software version, the process list corresponding to the software identifier is automatically queried, and the process image file identifier, configuration image file identifier, service image information identifier and model image file identifier corresponding to the process identifier and the software version are obtained, so that at least one process image file and the configuration image file, service image information and model image file corresponding to the process image file can be obtained in a timely and accurate manner, thereby further improving the accuracy of software deployment configuration.

[0070] In an exemplary embodiment, before step S203, or before querying the process list corresponding to the software identifier based on the process identifier and the software version based on the natural language processing (NLP) technology, the service platform can also perform the following process: determine the published software version of the published software corresponding to the software identifier, each business function in the published software, the process image file of each process in the business function, the configuration image file, the service image information and the model image file; for each process, update the 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 published software version.

[0071] Among them, the update processing of the process list corresponding to the software identifier, that is, for each process, the process identifier of the process, the process image file identifier of the process image file, 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, the released software version and the function identifier of the business function to which the process belongs are added as a piece of process information to the process list to obtain an 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.

[0072] In an exemplary embodiment, the process of the service platform determining the published software version of the published software corresponding to the software identifier, each business function in the published software, the process image file of each process in the business function, the configuration image file, the service image information, and the model image file can be, for example, determining the published software version of the published software corresponding to the software identifier, each business function in the published software, the process file, configuration file, service information, and model file of each process in the business function; performing image processing on the process file, configuration file, service information, and model file of each process to obtain the process image file, configuration image file, service image information, and model image file of each process. Among them, the image processing can be, for example, the Docker image processing of the container cluster management system Kubernetes.

[0073] In an exemplary embodiment, the service information may include: process routing information, data structures required for initialization during deployment, initialization data, data structure change information, etc. The initialization data may be initialization data for at least one specified customer environment. For a specific customer, corresponding initialization data and data structures may be selected according to the customer environment of the customer platform.

[0074] In order to unify the environment of the service platform and the customer platform, the service platform environment and the customer environment of the customer platform can use the container cluster management system Kubernetes to manage containers in a unified manner. That is, Kubernetes plug-ins can be set in the service platform environment and the customer environment, such as Helm plug-ins, Istio plug-ins, StorageClass plug-ins, etc.

[0075] Step S204: determining a 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.

[0076] In an exemplary embodiment, the service platform may package 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 the packaging result as a software deployment configuration.

[0077] Step S205: deploy the software to be deployed according to the software deployment configuration.

[0078] In an exemplary embodiment, the process of the service platform deploying the software to be deployed according to the software deployment configuration may be, for example, sending the software deployment configuration to the client's customer platform, and performing software deployment processing based on the software deployment configuration in the environment of the customer platform. After the software deployment processing is completed, the deployed software may also be internally tested to determine whether the software on the customer platform can realize the business functions required in the deployment request when it is running.

[0079] The specific implementation process and principle of step S201 and step S205 may refer to the description of the above embodiment and will not be repeated here.

[0080] In the embodiment of the present disclosure, the RPA robot obtains a deployment request, wherein the deployment request includes: a software identifier, a software version, and a function identifier of at least one business function in the software to be deployed; based on natural language processing (NLP) technology, determines the process identifier of at least one process to be deployed according to the software identifier and at least one function identifier; based on natural language processing (NLP) technology, determines 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, software version, and software identifier of at least one process to be deployed; determines the software deployment configuration according to at least one process image file, and the configuration image file, service image information, and model image file corresponding to the process image file; and performs deployment processing on the software to be deployed according to the software deployment configuration, thereby being able to automatically deploy the software to be deployed, and being able to perform software deployment processing on some of the business functions therein according to demand, with short deployment time, low cost, and high efficiency.

[0081] In order to explain the above embodiment more clearly, an example is now given for illustration.

[0082] For example, Figure 3 The following is a schematic diagram of the software deployment (release) framework. Figure 3In the process, for a specific software, when a new software version of the software is added (new service is added to the operation and maintenance platform), the software of the software version is put online to the service platform (SaaS environment), that is, released to the service platform; based on the published version of the released software, etc., the process files (service charts), configuration information (various configuration information), model files (models or service additional data), and service information (service images) of each process in the released software are determined, and image processing (i.e. snapshot processing) is performed to obtain the process image files, configuration image files, service image information, and model image files of each process in the released software, and the process list of the software is updated. When a deployment request is obtained, based on the software identifier, software version, and functional identifier of at least one business function in the deployment request, the process identifier of at least one process to be deployed is determined, and then the process list is queried in combination with the software version to obtain the process image files, configuration image files, service image information, and model image files of at least one process to be deployed, and the packaging is processed and deployed and tested.

[0083] In order to implement the above embodiments, the present disclosure also proposes a software deployment device combining RPA and AI. Figure 4 It is a structural diagram of a software deployment device combining RPA and AI according to an embodiment of the present disclosure.

[0084] like Figure 4 As shown, the software deployment device 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.

[0085] The acquisition module 401 is used to acquire a deployment request, wherein the deployment request includes: a software identifier and a software version of the software to be deployed, and a function identifier of at least one business function in the software to be deployed;

[0086] A first determination module 402 is configured to determine, based on 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;

[0087] The deployment module 403 is used to deploy the software to be deployed according to the software deployment configuration.

[0088] In one embodiment of the present disclosure, the first determination module 402 includes: a first determination unit, a second determination unit and a third determination unit; the first determination unit is used to determine the process identifier of at least one process to be deployed based on the natural language processing NLP technology, according to the software identifier and at least one function identifier; the second determination unit is used to determine at least one process image file, and the configuration image file, service image information and model image file corresponding to the process image file based on the natural language processing NLP technology, according to the process identifier of the at least one process to be deployed, the software version and the software identifier; the third determination unit is used to determine the software deployment configuration based on the at least one process image file, and the configuration image file, service image information and model image file corresponding to the process image file.

[0089] In one embodiment of the present disclosure, the first determination unit is specifically used to, for each function identifier, query the dependency table corresponding to the software identifier based on the function identifier to determine the dependent function identifier corresponding to the function identifier; determine at least one process included in the business function corresponding to the function identifier and at least one process included in the business function corresponding to the dependent function identifier as the process to be deployed; and determine the process identifier of the process to be deployed.

[0090] In one embodiment of the present disclosure, the second determination unit is specifically used to, for each process identifier, based on natural language processing (NLP) technology, query the process list corresponding to the software identifier according to the process identifier and the software version, and obtain the process image file identifier, configuration image file identifier, service image information identifier and model image file identifier corresponding to the process identifier and the software version; and obtain the process image file, and the configuration image file, service image information and model image file corresponding to the process image file according to the image file identifier, configuration image file identifier, service image information identifier and model image file identifier.

[0091] In one embodiment of the present disclosure, the device also includes: a second determination module and an update module; the second determination module is used to determine the published software version of the published software corresponding to the software identifier, each business function in the published software, the process image file of each process in the business function, the configuration image file, the service image information and the model image file; the update module is used to update the process list corresponding to the software identifier for each process 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 published software version.

[0092] In one embodiment of the present disclosure, the second determination module is specifically used to determine the published software version of the published software corresponding to the software identifier, each business function in the published software, the process files, configuration files, service information and model files of each process in the business function; mirroring the process files, configuration files, service information and model files of each process to obtain the process image files, configuration image files, service image information and model image files of each process.

[0093] It should be noted that the aforementioned 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. The details not disclosed in the embodiment of the software deployment device combining RPA and AI in this disclosure will not be repeated here.

[0094] In summary, the RPA robot obtains a deployment request, wherein the deployment request includes: the software identifier, software version, and function identifier of at least one business function in the software to be deployed; based on natural language processing (NLP) technology, the software deployment configuration for implementing at least one business function is determined according to the software identifier, software version, and at least one function identifier; the software to be deployed is deployed according to the software deployment configuration, thereby being able to automatically deploy the software to be deployed, and being able to perform software deployment processing for some of the business functions therein according to demand, with short deployment time, low cost, and high efficiency.

[0095] Figure 5 FIG. 1 is a block diagram showing a structure of an electronic device according to an embodiment of the present disclosure. Figure 5As shown, the electronic device includes: a memory 510 and a processor 520, wherein the memory 510 stores a computer program that can be run on the processor 520. When the processor 520 executes the computer program, the software deployment method combining RPA and AI in the above embodiment is implemented. The number of the memory 510 and the processor 520 can be one or more.

[0096] The electronic device also includes:

[0097] The communication interface 530 is used to communicate with external devices and perform data exchange transmission.

[0098] 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 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0099] Optionally, in a specific 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 can communicate with each other through an internal interface.

[0100] An embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, which implements the method provided in the embodiment of the present disclosure when the program is executed by a processor.

[0101] The embodiment of the present disclosure also provides a chip, which includes a processor for calling and executing instructions stored in the memory from the memory, so that a communication device equipped with the chip executes the method provided by the embodiment of the present disclosure.

[0102] The embodiment of the present disclosure also provides a chip, including: 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 via an internal connection path, and the processor is used to execute the code in the memory. When the code is executed, the processor is used to execute the method provided in the disclosed embodiment.

[0103] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor supporting the advanced RISC machines (ARM) architecture.

[0104] Further, optionally, the above-mentioned memory may include a read-only memory and a random access memory, and may also include a non-volatile random access memory. The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct rambus RAM (DR RAM).

[0105] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can 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. When the computer program instructions are loaded and executed on a computer, the process or function according to the present disclosure is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0106] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0107] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood 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 of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0108] Any process or method description described in the flowchart or otherwise herein can be understood to represent a module, segment or portion of code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiments of the present disclosure includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0109] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0110] It should be understood that the various parts of the present disclosure may be implemented with hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods may be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method may be completed by instructing the relevant hardware through a program, which may be stored in a computer-readable storage medium, and when the program is executed, includes one or a combination of the steps of the method embodiment.

[0111] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a disk or an optical disk, etc.

[0112] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A software deployment method combining RPA and AI, characterized in that: Applied to a Robotic Process Automation (RPA) robot, the method includes: Obtaining a deployment request, wherein the deployment request includes: a software identifier and a software version of the software to be deployed, and a function identifier of at least one business function in the software to be deployed; Based on natural language processing (NLP) technology, determining 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; Performing deployment processing on the software to be deployed according to the software deployment configuration; The determining, based on the natural language processing (NLP) technology, of 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, includes: For each function identifier, based on natural language processing (NLP) technology, query the dependency table corresponding to the software identifier according to the function identifier to determine the dependent function identifier corresponding to the function identifier; Determine at least one process included in the business function corresponding to the function identifier and at least one process included in the business function corresponding to the dependent function identifier as processes to be deployed; Determine the process identifier of the process to be deployed; Based on natural language processing (NLP) technology, according to the process identifier of the at least one process to be deployed, the software version and the software identifier, determine 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 deployment configuration is determined according to the at least one process image file, and the configuration image file, service image information, and model image file corresponding to the process image file.

2. The software deployment method combining RPA and AI according to claim 1, characterized in that: The method of 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 based on the natural language processing (NLP) technology according to the process identifier of the at least one process to be deployed, the software version, and the software identifier includes: For each process identifier, based on the natural language processing (NLP) technology, according to the process identifier and the software version, query the process list corresponding to the software identifier, and obtain the process image file identifier, configuration image file identifier, service image information identifier, and model image file identifier corresponding to the process identifier and the software version; According to the image file identifier, the configuration image file identifier, the service image information identifier and the model image file identifier, the process image file and the configuration image file, the service image information and the model image file corresponding to the process image file are obtained.

3. The software deployment method combining RPA and AI according to claim 2, characterized in that: Before querying the process list corresponding to the software identifier based on the process identifier and the software version based on the natural language processing (NLP) technology, and obtaining the process image file identifier, configuration image file identifier, service image information identifier, and model image file identifier corresponding to the process identifier and the software version, the method further includes: Determine the published software version of the published software corresponding to the software identifier, each business function in the published software, the process image file of each process in the business function, the configuration image file, the service image information, and the model image file; For each process, the process list corresponding to the software identifier is updated 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.

4. The software deployment method combining RPA and AI according to claim 3, characterized in that: The determining of the published software version of the published software corresponding to the software identifier, each business function in the published software, a process image file of each process in the business function, a configuration image file, service image information, and a model image file includes: Determine the published software version of the published software corresponding to the software identifier, each business function in the published software, and the process file, configuration file, service information, and model file of each process in the business function; The process files, configuration files, service information and model files of each process are mirrored to obtain the process mirror files, configuration mirror files, service mirror information and model mirror files of each process.

5. A software deployment device combining RPA and AI, characterized in that: Applied to a Robotic Process Automation (RPA) robot, the device comprises: An acquisition module, configured to acquire a deployment request, wherein the deployment request includes: a software identifier and a software version of the software to be deployed, and a function identifier of at least one business function in the software to be deployed; A first determination module is used to determine a software deployment configuration for implementing at least one of the business functions based on the software identifier, the software version and at least one of the function identifiers based on natural language processing (NLP) technology; A deployment module, used to deploy the software to be deployed according to the software deployment configuration; The first determination module includes: a first determination unit, a second determination unit and a third determination unit; The first determining unit is used to determine the process identifier of at least one process to be deployed according to the software identifier and at least one function identifier based on natural language processing (NLP) technology; The second determining unit is used to determine at least one process image file, and a configuration image file, service image information, and model image file corresponding to the process image file based on the process identifier of the at least one process to be deployed, the software version, and the software identifier based on natural language processing (NLP) technology; The third determining unit is used to determine the software deployment configuration according to the at least one process image file, and the configuration image file, service image information and model image file corresponding to the process image file; The first determining unit is specifically configured to: For each function identifier, based on natural language processing (NLP) technology, query the dependency table corresponding to the software identifier according to the function identifier to determine the dependent function identifier corresponding to the function identifier; Determine at least one process included in the business function corresponding to the function identifier and at least one process included in the business function corresponding to the dependent function identifier as the process to be deployed; Determine the process identifier of the process to be deployed.

6. The software deployment device combining RPA and AI according to claim 5, characterized in that: The second determining unit is specifically configured to: For each process identifier, based on the natural language processing (NLP) technology, according to the process identifier and the software version, query the process list corresponding to the software identifier, and obtain the process image file identifier, configuration image file identifier, service image information identifier, and model image file identifier corresponding to the process identifier and the software version; According to the image file identifier, the configuration image file identifier, the service image information identifier and the model image file identifier, the process image file and the configuration image file, the service image information and the model image file corresponding to the process image file are obtained.

7. The software deployment device combining RPA and AI according to claim 6, characterized in that: The device further comprises: a second determining module and an updating module; The second determination module is used to determine the published software version of the published software corresponding to the software identifier, each business function in the published software, the process image file of each process in the business function, the configuration image file, the service image information and the model image file; The update module is used to update the process list corresponding to the software identifier for each process 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.

8. The software deployment device combining RPA and AI according to claim 7, characterized in that: The second determination module is specifically configured to: Determine the published software version of the published software corresponding to the software identifier, each business function in the published software, and the process file, configuration file, service information, and model file of each process in the business function; The process files, configuration files, service information and model files of each process are mirrored to obtain the process mirror files, configuration mirror files, service mirror information and model mirror files of each process.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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