RPA and ai-based recruitment information publishing method and device

Abstract

The disclosure provides a recruitment information publishing method and device based on RPA and AI, and relates to the technical fields of RPA and AI. The method comprises the following steps: receiving a recruitment information publishing instruction, wherein the recruitment information publishing instruction comprises the identification of the to-be-recruited post and the identification of each recruitment website in a first group of recruitment websites; obtaining the first recruitment information corresponding to each to-be-recruited post from a recruitment information data set according to the identification of each to-be-recruited post; obtaining the RPA publishing process corresponding to each recruitment website from a publishing information data set according to the identification of each recruitment website in the first group of recruitment websites; and sequentially executing the RPA publishing process corresponding to each recruitment website to publish the first recruitment information to each recruitment website. Thus, the RPA robot can sequentially publish the recruitment information to be published in the corresponding recruitment website according to the RPA publishing process corresponding to each recruitment website, thereby saving human resources and improving the publishing efficiency of the recruitment information.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, specifically to the fields of Robotic Process Automation (RPA) and Artificial Intelligence (AI), and particularly to a method and apparatus for publishing recruitment information based on RPA and AI. Background Technology

[0002] Robotic Process Automation (RPA) uses specific "robot software" to simulate human operations on a computer 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] With the development of computer technology, the internet has become increasingly widespread. In the recruitment process, companies can post job openings on various recruitment websites. However, this often requires HR personnel to post each position's information on different recruitment websites, which not only wastes a significant amount of their time but also results in low efficiency. Therefore, improving the efficiency of job posting has become a key research direction. Summary of the Invention

[0005] This disclosure provides a method and apparatus for publishing recruitment information based on RPA and AI to solve the problems existing in related technologies. The technical solution is as follows:

[0006] In a first aspect, embodiments of this disclosure provide a method for publishing recruitment information based on RPA and AI, including:

[0007] Receive a recruitment information posting instruction, wherein the recruitment information posting instruction includes the identifier of the job position to be recruited and the identifier of each recruitment website in the first group of recruitment websites, the first group of recruitment websites including at least one recruitment website;

[0008] Based on the identifier of the job position to be recruited, obtain the first recruitment information corresponding to the job position from the recruitment information dataset;

[0009] Based on the identifier of each recruitment website in the first group of recruitment websites, obtain the RPA deployment process corresponding to each recruitment website in the first group of recruitment websites from the release information dataset;

[0010] The RPA publishing process corresponding to each recruitment website in the first group of recruitment websites is executed sequentially to publish the first recruitment information corresponding to the job to be recruited to each recruitment website in the first group of recruitment websites.

[0011] In one implementation, the RPA publishing process corresponding to each recruitment website in the first group of recruitment websites is executed sequentially to publish the first recruitment information corresponding to the job opening to each recruitment website in the first group of recruitment websites, further comprising:

[0012] Take a screenshot of the current page of each of the first group of recruitment websites and save it;

[0013] Optical Character Recognition (OCR) is used to parse the screenshots corresponding to each of the first group of recruitment websites to determine the posting results of the first recruitment information on the first group of recruitment websites.

[0014] The published result is returned to the client.

[0015] In one implementation, before obtaining the RPA deployment process corresponding to each recruitment website in the first group of recruitment websites from the posting information dataset based on the identifier of each recruitment website in the first group of recruitment websites, the method further includes:

[0016] Receive a recording instruction, wherein the recording instruction includes the identifier of each recruitment website in the first group of recruitment websites;

[0017] Start recording to obtain operation data on posting recruitment information on the first set of recruitment websites;

[0018] The operation data is processed to generate an RPA deployment process for each recruitment website in the first group of recruitment websites;

[0019] The identifier of each recruitment website in the first group of recruitment websites and the corresponding RPA publishing process are associated and stored in the publishing information dataset.

[0020] In one implementation, receiving the recruitment information posting instruction includes:

[0021] Receive the recruitment information posting instruction sent by the client;

[0022] or,

[0023] Receive the recruitment information posting instruction sent by the chatbot.

[0024] In one implementation, it further includes:

[0025] In response to receiving a job posting information update instruction, the Natural Language Processing (NLP) service is invoked to process the update instruction in order to determine the identifier of the job posting to be updated and the identifier of each job posting in the second group of job posting websites, wherein the second group of job posting websites includes at least one job posting website.

[0026] Based on the identifier of the job posting to be updated, obtain the second job posting information corresponding to the job posting to be updated from the job posting information dataset;

[0027] Based on the identifier of the job posting to be updated and the identifier of each job posting in the second group of job posting websites, retrieve the published third job posting information corresponding to the identifier of the job posting to be updated from the second group of job posting websites;

[0028] The third recruitment information is updated based on the second recruitment information.

[0029] Secondly, embodiments of this disclosure provide a recruitment information publishing device based on RPA and AI, comprising:

[0030] The first receiving module is used to receive a recruitment information release instruction, wherein the recruitment information release instruction includes the identifier of the job to be recruited and the identifier of each recruitment website in the first group of recruitment websites, and the first group of recruitment websites includes at least one recruitment website.

[0031] The first acquisition module is used to acquire the first recruitment information corresponding to each of the recruitment positions from the recruitment information dataset based on the identifier of each recruitment position.

[0032] The second acquisition module is used to acquire the RPA release process corresponding to each recruitment website in the first group of recruitment websites from the release information dataset based on the identifier of each recruitment website in the first group of recruitment websites;

[0033] The publishing module is used to sequentially execute the RPA publishing process corresponding to each of the first group of recruitment websites, so as to publish the first recruitment information corresponding to the job to be recruited to each of the first group of recruitment websites.

[0034] In one implementation, a first determining module is further included, specifically used for:

[0035] Take a screenshot of the current page of each of the first group of recruitment websites and save it;

[0036] Optical Character Recognition (OCR) is used to parse the screenshots corresponding to each of the first group of recruitment websites to determine the posting results of the first recruitment information on the first group of recruitment websites.

[0037] The published result is returned to the client.

[0038] In one implementation, it further includes:

[0039] The second receiving module is used to receive a recording instruction, wherein the recording instruction includes the identifier of each recruitment website in the first group of recruitment websites;

[0040] The recording module is used to start recording in order to obtain operation data of posting recruitment information on the first group of recruitment websites;

[0041] The generation module is used to process the operation data to generate the RPA release process for each recruitment website in the first group of recruitment websites;

[0042] The storage module is used to associate and store the identifier of each recruitment website in the first group of recruitment websites with the corresponding RPA publishing process in the publishing information dataset.

[0043] In one embodiment, the first receiving module is specifically used for:

[0044] Receive the recruitment information posting instruction sent by the client;

[0045] or,

[0046] Receive the recruitment information posting instruction sent by the chatbot.

[0047] In one implementation, it further includes:

[0048] The second determining module is used to respond to receiving a job information update instruction by calling a natural language processing service (NLP) to process the update instruction in order to determine the identifier of the job posting to be updated and the identifier of each job posting in the second group of job posting websites, wherein the second group of job posting websites includes at least one job posting website.

[0049] The third acquisition module is used to acquire the second recruitment information corresponding to the recruitment position to be updated from the recruitment information dataset based on the identifier of the recruitment position to be updated.

[0050] The fourth acquisition module is used to acquire, based on the identifier of the job posting to be updated and the identifier of each job posting in the second group of job posting websites, the published third job posting information corresponding to the identifier of the job posting to be updated.

[0051] The update module is used to update the third recruitment information based on the second recruitment information.

[0052] Thirdly, this disclosure provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the program, it implements a recruitment information publishing method based on RPA and AI as provided in the first aspect embodiment.

[0053] Fourthly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a recruitment information publishing method based on RPA and AI as provided in the first aspect of the embodiments.

[0054] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements a recruitment information publishing method based on RPA and AI as provided in the first aspect embodiment.

[0055] The advantages or beneficial effects of the above technical solutions include at least the following:

[0056] In this embodiment, a recruitment information posting instruction is first received. Then, based on the identifier of each job opening, the first recruitment information corresponding to each job opening is obtained from the recruitment information dataset. Based on the identifier of each recruitment website in the first group of recruitment websites, the RPA posting process corresponding to each recruitment website in the first group of recruitment websites is obtained from the posting information dataset. Finally, the RPA posting process corresponding to each recruitment website in the first group of recruitment websites is executed sequentially to post the first recruitment information corresponding to the job opening to each recruitment website in the first group of recruitment websites. Therefore, the RPA robot can sequentially post the recruitment information of the job openings to the corresponding recruitment websites according to the RPA posting process corresponding to each recruitment website, thereby not only saving human resources but also improving the efficiency of recruitment information posting.

[0057] The above overview is for illustrative purposes only and is not intended to be limiting in any way. Further aspects, embodiments, and features of this disclosure will become readily apparent from the accompanying drawings and the following detailed description, in addition to the illustrative aspects, embodiments, and features described above. Attached Figure Description

[0058] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this disclosure and should not be construed as limiting the scope of this disclosure.

[0059] Figure 1This is a flowchart illustrating a recruitment information publishing method based on RPA and AI, provided in one embodiment of this disclosure.

[0060] Figure 2 A flowchart illustrating a recruitment information publishing method based on RPA and AI, provided as another embodiment of this disclosure;

[0061] Figure 2a This is a schematic diagram of a client display interface provided in an embodiment of the present disclosure;

[0062] Figure 3 A flowchart illustrating a recruitment information publishing method based on RPA and AI, provided as another embodiment of this disclosure;

[0063] Figure 4 This is a schematic diagram of the structure of a data analysis device combining RPA and AI according to an embodiment of this disclosure;

[0064] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0065] The embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this disclosure, and should not be construed as limiting this disclosure.

[0066] In the description of this disclosure, the term "multiple" means two or more.

[0067] In this disclosure, Artificial Intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, as well as machine learning, deep learning, big data processing, and knowledge graph technologies.

[0068] In the description of this disclosure, Robotic Process Automation (RPA) provides an alternative way to automate end-user manual processes by mimicking the manual operation of an end-user on a computer.

[0069] In this disclosure, Natural Language Processing (NPL) refers to the technology of using natural language, the language humans use for communication, to interact and communicate with machines. Through human processing of natural language, computers can read and understand it. Research into Natural Language Processing began with human exploration of machine translation.

[0070] In the description of this disclosure, Optical Character Recognition (OCR) specifically refers to the process by which electronic devices examine characters printed on paper, determine their shape by detecting dark and light patterns, and then translate the shape into computer text using character recognition methods; that is, for printed characters, optical methods are used to convert the text in paper documents into black and white dot matrix image files, and recognition software is used to convert the text in the image into text format for further editing and processing by word processing software.

[0071] In the description of this disclosure, the term "job posting instruction" can refer to an instruction that instructs a PRA robot to post job postings. It can be any type of instruction, and this disclosure does not limit it.

[0072] In the description of this disclosure, the term "job posting update instruction" can be an instruction that instructs an RPA robot to update published job postings. It can be any type of instruction, and this disclosure does not limit it.

[0073] These and other aspects of the embodiments of this disclosure will become clear from the following description and accompanying drawings. In these descriptions and drawings, specific embodiments of the present disclosure are specifically disclosed to illustrate some ways of implementing the principles of the embodiments of the present disclosure; however, it should be understood that the scope of the embodiments of the present disclosure is not limited thereto. Rather, the embodiments of the present disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0074] Figure 1 This is a flowchart illustrating a recruitment information publishing method based on RPA and AI, provided in an embodiment of this disclosure.

[0075] It should be noted that RPA technology can intelligently understand the existing applications of electronic devices through the user interface, and automate repetitive, rule-based, and large-scale routine operations, such as automatically reading emails, reading Office components, operating databases and web pages, and client software. It collects data and performs tedious calculations to generate a large number of files and reports. Thus, RPA technology can significantly reduce the input of human resources and effectively improve office efficiency.

[0076] The execution entity of the RPA and AI-based recruitment information publishing method of this disclosure can be an RPA robot or an RPA and AI-based recruitment information publishing device. The RPA robot and / or the RPA and AI-based recruitment information publishing device can be configured in any electronic device to execute the RPA and AI-based recruitment information publishing method of this disclosure. Optionally, the RPA robot may include an RPA robot.

[0077] like Figure 1 As shown, the method may include the following steps:

[0078] Step 101: Receive the recruitment information posting instruction, which includes the identifier of the job opening and the identifier of each recruitment website in the first group of recruitment websites.

[0079] The first group of recruitment websites includes at least one recruitment website.

[0080] The style or presentation of the logo for the positions to be recruited can be predetermined, such as "Position 1", "Position A2", etc., and this disclosure does not limit this.

[0081] The style or presentation of the logo for each recruitment website in the first group of recruitment websites can be pre-defined, such as "Website 1", "Website B2", etc. This disclosure does not limit this.

[0082] Optionally, the job posting instruction may include identifiers for one or more job openings, which is not limited in this disclosure.

[0083] For example, the job posting instructions might identify the positions to be advertised as "Position 1" and "Position 2," and each job website in the first group of recruitment websites could be identified as "Website A" and "Website B." This means the RPA robot needs to post the job information corresponding to "Position 1" and the job information corresponding to "Position 2" to "Website A" and "Website B," respectively.

[0084] Step 102: Based on the identifier of each job opening, retrieve the first job posting corresponding to the job opening from the job posting dataset.

[0085] The recruitment information dataset stores recruitment information and an identifier for each job posting. Therefore, the RPA robot can retrieve the first recruitment information for a job posting from the dataset based on its identifier.

[0086] Optionally, the company's human resources management staff can store the recruitment information for each job opening in an Excel or Word file according to a specified format. This disclosure does not impose any restrictions on this.

[0087] For example, the specified format could be: the first line is "Hiring Company"; the second line is "Job Position"; the third line is "Educational Requirements", etc. This publication does not impose any restrictions on this.

[0088] Step 103: Based on the identifier of each recruitment website in the first group of recruitment websites, obtain the RPA deployment process corresponding to each recruitment website in the first group of recruitment websites from the published information dataset.

[0089] The job posting dataset centrally stores the identifier of each recruitment website and the detailed process for posting job information for each website. Therefore, the RPA robot can retrieve the corresponding RPA posting process for each recruitment website in the first group of recruitment websites from the job posting dataset based on the identifier of each website in the first group.

[0090] Among them, the RPA publishing process can be the operation process for RPA robots to publish recruitment information on recruitment websites.

[0091] The RPA publishing process may include logging into a recruitment website, opening the recruitment information input interface, entering the initial recruitment information in the corresponding position on the input interface, and clicking the publish button, etc. This disclosure does not limit this process.

[0092] Understandably, since RPA robots need to log in to recruitment websites before posting job information, the posted information dataset can also include login information for each recruitment website. This login information may include the website address, username, and password. This disclosure does not limit this.

[0093] Step 104: Execute the RPA publishing process for each recruitment website in the first group of recruitment websites in sequence to publish the first recruitment information for the job opening to each recruitment website in the first group of recruitment websites.

[0094] Understandably, after the RPA robot obtains the first recruitment information for each job posting and the RPA publishing process for each of the first group of recruitment websites, it can sequentially execute the RPA publishing process for each of the first group of recruitment websites, and publish the first recruitment information for the first job posting to each of the first group of recruitment websites.

[0095] Optionally, since the job posting instruction can include identifiers for multiple open positions and multiple job websites, the RPA robot can first log in to one job website, then post the job information for multiple open positions sequentially on the currently logged-in job website, then log in to the next job website and post the job information for multiple open positions sequentially; and so on, until the job information for all open positions is posted on multiple job websites.

[0096] Alternatively, the RPA robot can first publish the job posting for one open position sequentially on each of the first set of job posting websites, and then publish the job posting for the next open position sequentially on each of the first set of job posting websites; and so on, until all job postings for all open positions are published on each of the first set of job posting websites.

[0097] In this embodiment, a recruitment information posting instruction is first received. Then, based on the identifier of each job opening, the first recruitment information corresponding to each job opening is obtained from the recruitment information dataset. Based on the identifier of each recruitment website in the first group of recruitment websites, the RPA posting process corresponding to each recruitment website in the first group of recruitment websites is obtained from the posting information dataset. Finally, the RPA posting process corresponding to each recruitment website in the first group of recruitment websites is executed sequentially to post the first recruitment information corresponding to the job opening to each recruitment website in the first group of recruitment websites. Therefore, the RPA robot can sequentially post the recruitment information of the job openings to the corresponding recruitment websites according to the RPA posting process corresponding to each recruitment website, thereby not only saving human resources but also improving the efficiency of recruitment information posting.

[0098] Figure 2 A flowchart illustrating a recruitment information publishing method based on RPA and AI, provided as another embodiment of this disclosure;

[0099] like Figure 2 As shown, this RPA and AI-based method for publishing job postings includes the following steps:

[0100] Step 201: Receive the recruitment information posting instruction, which includes the identifier of the job opening and the identifier of each recruitment website in the first group of recruitment websites.

[0101] The first group of recruitment websites includes at least one recruitment website.

[0102] Optionally, the RPA robot can receive job posting instructions sent by the client.

[0103] The client can be a web browser or an application, etc., and this disclosure does not limit it.

[0104] Figure 2a This is a schematic diagram of a client display interface provided in an embodiment of the present disclosure, as shown below. Figure 2 As shown, human resource management staff can select the identifiers of the positions to be recruited and the identifiers of each recruitment website in the first group of recruitment websites on the client display interface. Then, they can click the start button to start the RPA robot to publish the recruitment information of the positions to be recruited selected on the display interface to each recruitment website in the first group of recruitment websites.

[0105] Alternatively, RPA robots can also receive job posting instructions from chatbots.

[0106] Optionally, the chatbot can be a chatbot or any other chatbot capable of dialogue. This disclosure does not limit it.

[0107] Understandably, the chatbot can parse the voice or text data input by human resources management staff to determine whether the voice or text data contains instructions to publish recruitment information. If it does, it sends a recruitment information publishing instruction to the RPA robot, so that the RPA robot can publish the recruitment information corresponding to the job opening on the relevant recruitment website.

[0108] Step 202: Based on the identifier of each job opening, retrieve the first job posting corresponding to each job opening from the job posting dataset.

[0109] The specific implementation of step 202 can be found in the detailed descriptions of other embodiments in this disclosure, and will not be repeated here.

[0110] Step 203: Based on the identifier of each recruitment website in the first group of recruitment websites, obtain the RPA deployment process corresponding to each recruitment website in the first group of recruitment websites from the published information dataset.

[0111] It should be noted that before obtaining the RPA deployment process for each recruitment website in the first set of recruitment websites from the deployment information dataset, it is also necessary to record the operation process of posting recruitment information on each recruitment website and store it in the deployment information dataset. In this way, if recruitment information needs to be posted on the same recruitment website again, the corresponding RPA deployment process can be directly called from the deployment information dataset, thereby saving resources and improving the efficiency of resume posting.

[0112] Optionally, the specific steps for recording and storing the process of posting job information on a recruitment website into a posting information dataset may include:

[0113] (1) Receive recording instructions, wherein the recording instructions include the identifier of each recruitment website in the first group of recruitment websites.

[0114] The recording command can be any form of command, such as voice or triggered by a control, etc. This disclosure does not limit it.

[0115] Optionally, by parsing the recording instructions, the identifiers of the recruitment websites contained within can be determined. For example, by parsing the recording instructions and determining that they contain "website 1", the corresponding recruitment website can be identified as "website 1", and so on. This disclosure does not limit this aspect.

[0116] (2) Start recording to obtain operational data on posting recruitment information on the first set of recruitment websites.

[0117] Understandably, once the identifier of each recruitment website in the first group of recruitment websites is determined, the process of posting recruitment information on the recruitment websites can be recorded to obtain the operation data of posting recruitment information on each recruitment website in the first group of recruitment websites.

[0118] Optionally, the operation data may include: the operation object, the operation type, the operation location, etc. This disclosure does not limit this.

[0119] The object of operation can be any object being operated on, such as any control, button, text, etc. in the process of posting recruitment information. This disclosure does not limit this.

[0120] The operation types are varied, such as mouse clicks or keyboard key input, and this disclosure does not limit them.

[0121] The operation position can be the location where the operation or processing action occurs, such as being represented in coordinate form or in other forms, etc., and this disclosure does not limit it.

[0122] (3) Process the operation data to generate the RPA deployment process for each recruitment website in the first group of recruitment websites.

[0123] Among them, the RPA deployment process is a deployment process that can be recognized by the RPA robot and can be run directly.

[0124] Understandably, once an RPA robot generates an RPA deployment process, it can then post job openings on the corresponding recruitment websites according to that process.

[0125] (4) Associate and store the identifier of each recruitment website in the first group of recruitment websites with the corresponding RPA publishing process in the publishing information dataset.

[0126] In this process, the identifiers of each recruitment website in the first group of recruitment websites and their corresponding RPA deployment processes can be stored in a table format, or in a document format, etc. This disclosure does not limit the scope of the disclosure.

[0127] Step 204: Execute the RPA publishing process for each recruitment website in the first group of recruitment websites in sequence to publish the first recruitment information for the job opening to each recruitment website in the first group of recruitment websites.

[0128] The specific implementation of step 204 can be found in the detailed descriptions of other embodiments in this disclosure, and will not be repeated here.

[0129] Step 205: Take a screenshot of the current page of each recruitment website in the first group and save it.

[0130] In this embodiment of the disclosure, after the RPA robot publishes the first recruitment information corresponding to the job to be recruited to the first group of recruitment websites, in order to determine whether the first recruitment information corresponding to the job to be recruited has been successfully published to the first group of recruitment websites, it can further take screenshots of the recruitment information publishing interface of each recruitment website in the first group of recruitment websites and save them, and then judge the publishing result of the first recruitment information based on the screenshots.

[0131] Step 206: Use Optical Character Recognition (OCR) to parse the screenshots corresponding to each recruitment website in the first group of recruitment websites to determine the posting results of the first recruitment information in the first group of recruitment websites.

[0132] Understandably, after obtaining screenshots of the current page of each recruitment website in the first group of recruitment websites, OCR technology can be used to recognize the screenshots to obtain the characters contained in the screenshots. Then, based on the characters in the screenshots, it can be determined whether the first recruitment information has been successfully published to each recruitment website in the first group of recruitment websites.

[0133] The publishing result can include successful publishing and publishing failure.

[0134] Step 207: Return the published results to the client.

[0135] Understandably, after determining the result of posting recruitment information, the RPA robot can return the result to the client, so that human resources management staff can confirm the result of posting recruitment information through the client.

[0136] Optionally, the RPA robot can also return the published results to a specified email address. This specified email address can be the email address of a human resources management staff member; this disclosure does not impose any restrictions on this.

[0137] In this embodiment, a recruitment information posting instruction is first received. Then, based on the identifier of each job opening, the first recruitment information corresponding to each job opening is obtained from the recruitment information dataset. Based on the identifier of each recruitment website in the first group of recruitment websites, the RPA posting process corresponding to each recruitment website in the first group of recruitment websites is obtained from the posting information dataset. Then, the RPA posting process corresponding to each recruitment website in the first group of recruitment websites is executed sequentially to post the first recruitment information corresponding to the job opening to each recruitment website in the first group of recruitment websites. Finally, the current page of each recruitment website in the first group of recruitment websites is screenshotted and saved. Optical Character Recognition (OCR) is used to parse the screenshot to determine the posting result of the recruitment information corresponding to the job opening, and the posting result is returned to the client. Therefore, the RPA robot can sequentially post the recruitment information of the job opening to be posted on the corresponding recruitment websites according to the posting process corresponding to each recruitment website, and can also return the posting result to the client. This not only saves human resources and improves the efficiency of recruitment information posting, but also allows human resource management staff to promptly determine the posting result.

[0138] Figure 3 This is a flowchart illustrating a recruitment information publishing method based on RPA and AI, provided as another embodiment of this disclosure.

[0139] like Figure 3 As shown, this RPA and AI-based method for publishing job postings includes the following steps:

[0140] Step 301: In response to receiving the job information update instruction, call the Natural Language Processing (NLP) service to process the update instruction to determine the identifier of the job posting to be updated and the identifier of each recruitment website in the second group of recruitment websites.

[0141] The second group of recruitment websites includes at least one recruitment website. For example, the second group of recruitment websites may include recruitment website A; or it may include recruitment website A, recruitment website B, and recruitment website C.

[0142] It should be noted that the recruitment websites included in the second group of recruitment websites may be the same as or different from those included in the first group of recruitment websites. This disclosure does not impose any restrictions on this.

[0143] For example, the first group of recruitment websites includes recruitment website A and recruitment website B; the second group of recruitment websites may include recruitment website A and recruitment website B; or the second group of recruitment websites may include recruitment website A, recruitment website B, and recruitment website C.

[0144] Understandably, after posting the first job posting for a position to be filled to the first set of recruitment websites, the company's human resources managers may update the job postings in the job posting dataset. For example, they may change the "educational requirements" for a "sales position." Then, they may send a job posting update instruction to the RPA robot through a client or chatbot, so that the RPA robot can update the already posted job postings based on the updated job postings.

[0145] The job information update instruction received by the RPA robot contains the identifier of the job to be updated and the identifier of each job in the second group of recruitment websites. Therefore, after receiving the job information update instruction, the RPA robot can call NLP to process the job information update instruction to determine the identifier of the job to be updated and the identifier of each recruitment website in the second group of recruitment websites.

[0146] The style or presentation of the identifier for the job posting to be updated can be predetermined, such as "Job A", "Job B", etc., and this disclosure does not impose any restrictions on this.

[0147] The second group of recruitment websites consists of those that have already posted job information for the positions that are yet to be updated. For example, if the job posting is for "Software Testing Engineer," then every website in the second group of recruitment websites has already posted job information for "Software Testing Engineer."

[0148] The logo style or presentation of each recruitment website in the second group can be pre-defined, such as "Website 1", "Website 2", etc. This disclosure does not limit this.

[0149] Optionally, the job information update instruction may include identifiers of one or more job positions to be updated, which is not limited in this disclosure.

[0150] For example, the job posting update instruction might identify the job openings as "Job A" and "Job B," while each job website in the second group of recruitment websites might be identified as "Website 1" and "Website 2." This means the RPA robot needs to update the job postings for "Job A" and "Job B" already published on "Website A" and "Website B" based on the updated job postings for "Job A" and "Job B" contained in the job posting dataset.

[0151] Step 302: Based on the identifier of the job posting to be updated, retrieve the second job posting information corresponding to the job posting to be updated from the job posting information dataset.

[0152] Among these features, corporate human resource managers can update the recruitment information contained in the recruitment information dataset, thereby adjusting the recruitment needs for recruitment positions in a timely manner.

[0153] Understandably, after the RPA robot obtains the identifier of the job posting to be updated from the received job information update instruction, it can retrieve the updated second job posting information corresponding to the job posting to be updated from the updated job information dataset based on the identifier of the job posting to be updated.

[0154] Step 303: Based on the identifier of the job posting to be updated and the identifier of each job posting in the second group of job posting websites, retrieve the published third job posting information corresponding to the identifier of the job posting to be updated from the second group of job posting websites.

[0155] Understandably, since each recruitment website in the second group of recruitment websites can contain a wide variety of recruitment information, after the RPA obtains the identifier of each recruitment website in the second group of recruitment websites from the received job information update instruction, it can obtain the published third recruitment information corresponding to the updated job from the large amount of recruitment information contained in each recruitment website in the second group of recruitment websites based on the identifier of the job to be updated.

[0156] Step 304: Update the third recruitment information based on the second recruitment information.

[0157] It should be noted that after obtaining the second and third job postings, the RPA robot can update the published third job posting based on the second job posting.

[0158] Optionally, the RPA robot can directly replace the third job posting that has already been published on the second job posting website with the second job posting.

[0159] Alternatively, the RPA robot can determine the content that needs to be updated in the third job posting based on the second job posting, and then only update the content that needs to be updated.

[0160] For example, if the third job posting requires a bachelor's degree while the second job posting requires a master's degree, then the educational requirement in the second job posting can be updated to "master's degree".

[0161] In this embodiment, upon receiving a job information update instruction, NLP is invoked to process the update instruction to determine the identifier of the job posting to be updated and the identifier of each recruitment website in the second group of recruitment websites. Then, based on the identifier of the job posting to be updated, second recruitment information corresponding to the job posting to be updated is obtained from the recruitment information dataset. Based on the identifier of the job posting to be updated and the identifier of each recruitment website in the second group of recruitment websites, published third recruitment information corresponding to the identifier of the job posting to be updated is obtained from the second group of recruitment websites. Finally, the third recruitment information is updated based on the second recruitment information. Therefore, the published third recruitment information for the job posting to be updated can be updated based on the updated second recruitment information, thus not only enabling timely updates of recruitment information but also saving human resources and improving the efficiency of recruitment information updates.

[0162] To implement the above embodiments, this disclosure also proposes a recruitment information publishing device based on RPA and AI.

[0163] Figure 4 This is a schematic diagram of a recruitment information publishing device based on RPA and AI, provided in an embodiment of this disclosure.

[0164] like Figure 4 As shown, the recruitment information publishing device 400 based on RPA and AI includes: a first receiving module 410, a first acquiring module 420, a second acquiring module 430, and a publishing module 440.

[0165] The first receiving module 410 is used to receive a recruitment information release instruction, wherein the recruitment information release instruction includes the identifier of the job to be recruited and the identifier of each recruitment website in the first group of recruitment websites, and the first group of recruitment websites includes at least one recruitment website.

[0166] The first acquisition module 420 is used to acquire the first recruitment information corresponding to each job position from the recruitment information dataset based on the identifier of each job position.

[0167] The second acquisition module 430 is used to acquire the RPA release process corresponding to each recruitment website in the first group of recruitment websites from the release information dataset based on the identifier of each recruitment website in the first group of recruitment websites;

[0168] The publishing module 440 is used to sequentially execute the RPA publishing process corresponding to each of the first group of recruitment websites, so as to publish the first recruitment information corresponding to the job to be recruited to each of the first group of recruitment websites.

[0169] In one implementation, a first determining module is further included, specifically used for:

[0170] Take a screenshot of the current page of each of the first group of recruitment websites and save it;

[0171] Optical character recognition (OCR) is used to parse the screenshots corresponding to each recruitment website in the first group of recruitment websites to determine the posting results of the first recruitment information in the first group of recruitment websites;

[0172] The results will be returned to the client.

[0173] In one implementation, it further includes:

[0174] The second receiving module is used to receive recording instructions, wherein the recording instructions include the identifier of each recruitment website in the first group of recruitment websites;

[0175] The recording module is used to initiate recording in order to obtain operational data on posting job information on the first set of recruitment websites;

[0176] The generation module is used to process the operational data to generate the RPA deployment process for each recruitment website in the first set of recruitment websites;

[0177] The storage module is used to associate and store the identifier of each recruitment website in the first group of recruitment websites with the corresponding RPA publishing process in the publishing information dataset.

[0178] In one embodiment, the first receiving module 410 is specifically used for:

[0179] Receive job posting instructions from the client;

[0180] or,

[0181] Receive job posting instructions from the chatbot.

[0182] In one implementation, it further includes:

[0183] The second determination module is used to respond to receiving a job information update instruction by calling the Natural Language Processing (NLP) service to process the update instruction in order to determine the identifier of the job posting to be updated and the identifier of each job posting in the second group of job posting websites, wherein the second group of job posting websites includes at least one job posting website.

[0184] The third acquisition module is used to obtain the second recruitment information corresponding to the recruitment position to be updated from the recruitment information dataset based on the identifier of the recruitment position to be updated.

[0185] The fourth acquisition module is used to retrieve the published third recruitment information corresponding to the identifier of the job posting to be updated from the second group of recruitment websites, based on the identifier of the job posting to be updated and the identifier of each recruitment website in the second group of recruitment websites.

[0186] The update module is used to update the third recruitment information based on the second recruitment information.

[0187] It should be noted that the functions and specific implementation principles of the modules described above in this disclosure embodiment can be referred to the above method embodiments, and will not be repeated here.

[0188] The RPA and AI-based business processing device provided in this embodiment first receives a recruitment information posting instruction. Then, based on the identifier of each job opening, it retrieves the first recruitment information corresponding to each job opening from the recruitment information dataset. Next, based on the identifier of each recruitment website in the first group of recruitment websites, it retrieves the RPA posting process corresponding to each recruitment website in the first group of recruitment websites from the posting information dataset. Finally, it executes the RPA posting process corresponding to each recruitment website in the first group of recruitment websites sequentially to post the first recruitment information corresponding to the job opening to each recruitment website in the first group of recruitment websites. Therefore, the RPA robot can sequentially post the recruitment information of the job openings to the corresponding recruitment websites according to the RPA posting process corresponding to each recruitment website, thereby not only saving human resources but also improving the efficiency of recruitment information posting.

[0189] To implement the above embodiments, this disclosure also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the recruitment information publishing method based on RPA and AI as proposed in the foregoing embodiments of this disclosure.

[0190] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided according to an embodiment of this disclosure. Figure 5As shown, the electronic device 500 includes a memory 510 and a processor 520. The memory 510 stores a computer program that can run on the processor 520. When the processor 520 executes the computer program, it implements the RPA and AI-based job posting method described in the above embodiments. The number of memories 510 and processors 520 can be one or more.

[0191] The electronic device also includes:

[0192] The communication interface 530 is used to communicate with external devices and exchange and transmit data.

[0193] If the memory 510, processor 520, and communication interface 530 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0194] Optionally, in a specific implementation, if the memory 510, processor 520, and communication interface 530 are integrated on a single chip, then the memory 510, processor 520, and communication interface 530 can communicate with each other through an internal interface.

[0195] This disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the recruitment information publishing method based on RPA and AI as proposed in the foregoing embodiments of this disclosure.

[0196] This disclosure also provides a computer program product that, when executed by an instruction processor, implements the recruitment information publishing method based on RPA and AI as proposed in the foregoing embodiments of this disclosure.

[0197] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting the Advanced Reduced Instruction Set Computing (RISC) machine (ARM) architecture.

[0198] Further, optionally, the aforementioned memory may include read-only memory and random access memory, and may also include non-volatile random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0199] In the above embodiments, implementation can be achieved, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to this disclosure is generated. 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 transferred from one computer-readable storage medium to another.

[0200] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0201] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0202] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0203] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0204] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments.

[0205] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0206] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this disclosure, and these should all be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for publishing recruitment information based on Robotic Process Automation (RPA) and Artificial Intelligence (AI), applied to RPA robots, characterized in that, include: Receive a recruitment information posting instruction, wherein the recruitment information posting instruction includes the identifier of the job position to be recruited and the identifier of each recruitment website in the first group of recruitment websites, the first group of recruitment websites including at least one recruitment website; Based on the identifier of the job position to be recruited, obtain the first recruitment information corresponding to the job position from the recruitment information dataset; Receive a recording instruction, wherein the recording instruction includes the identifier of each recruitment website in the first group of recruitment websites; Start recording to record the process of posting job information on recruitment websites; to obtain operation data of posting job information on the first group of recruitment websites; The operation data is processed to generate an RPA deployment process for each recruitment website in the first group of recruitment websites; The identifier of each recruitment website in the first group of recruitment websites and the corresponding RPA publishing process are associated and stored in the publishing information dataset; Based on the identifier of each recruitment website in the first group of recruitment websites, obtain the RPA deployment process corresponding to each recruitment website in the first group of recruitment websites from the release information dataset; The RPA publishing process corresponding to each recruitment website in the first group of recruitment websites is executed sequentially to publish the first recruitment information corresponding to the job to be recruited to each recruitment website in the first group of recruitment websites; Also includes: In response to receiving a job posting information update instruction, the Natural Language Processing (NLP) service is invoked to process the update instruction in order to determine the identifier of the job posting to be updated and the identifier of each job posting in the second group of job posting websites, wherein the second group of job posting websites includes at least one job posting website. Based on the identifier of the job posting to be updated, obtain the second job posting information corresponding to the job posting to be updated from the job posting information dataset; Based on the identifier of the job posting to be updated and the identifier of each job posting in the second group of job posting websites, retrieve the published third job posting information corresponding to the identifier of the job posting to be updated from the second group of job posting websites; The third recruitment information is updated based on the second recruitment information.

2. The method as described in claim 1, characterized in that, After publishing the first recruitment information corresponding to the job opening to the first group of recruitment websites, the process also includes: Take a screenshot of the current page of each of the first group of recruitment websites and save it; Optical Character Recognition (OCR) is used to parse the screenshots corresponding to each of the first group of recruitment websites to determine the posting results of the first recruitment information on the first group of recruitment websites. The published results are returned to the client.

3. The method as described in claim 1, characterized in that, Receive instructions to post job openings, including: Receive the recruitment information posting instruction sent by the client; or, Receive the recruitment information posting instruction sent by the chatbot.

4. A recruitment information publishing device based on RPA and AI, characterized in that, include: The first receiving module is used to receive a recruitment information release instruction, wherein the recruitment information release instruction includes the identifier of the job to be recruited and the identifier of each recruitment website in the first group of recruitment websites, and the first group of recruitment websites includes at least one recruitment website. The first acquisition module is used to acquire the first recruitment information corresponding to each of the recruitment positions from the recruitment information dataset based on the identifier of each recruitment position. The second acquisition module is used to acquire the RPA release process corresponding to each recruitment website in the first group of recruitment websites from the release information dataset based on the identifier of each recruitment website in the first group of recruitment websites; The publishing module is used to sequentially execute the RPA publishing process corresponding to each of the first group of recruitment websites, so as to publish the first recruitment information corresponding to the job to be recruited to each of the first group of recruitment websites. The second receiving module is used to receive a recording instruction, wherein the recording instruction includes the identifier of each recruitment website in the first group of recruitment websites; The recording module is used to start recording and record the process of posting recruitment information on recruitment websites; to obtain the operation data of posting recruitment information on the first group of recruitment websites. The generation module is used to process the operation data to generate the RPA release process for each recruitment website in the first group of recruitment websites; The storage module is used to associate and store the identifier of each recruitment website in the first group of recruitment websites with the corresponding RPA publishing process in the publishing information dataset; Also includes: The second determining module is used to respond to receiving a job information update instruction by calling a natural language processing service (NLP) to process the update instruction in order to determine the identifier of the job posting to be updated and the identifier of each job posting in the second group of job posting websites, wherein the second group of job posting websites includes at least one job posting website. The third acquisition module is used to acquire the second recruitment information corresponding to the recruitment position to be updated from the recruitment information dataset based on the identifier of the recruitment position to be updated. The fourth acquisition module is used to acquire, based on the identifier of the job posting to be updated and the identifier of each job posting in the second group of job posting websites, the published third job posting information corresponding to the identifier of the job posting to be updated. The update module is used to update the third recruitment information based on the second recruitment information.

5. The apparatus as described in claim 4, characterized in that, It also includes a first determining module, specifically used for: Take a screenshot of the current page of each of the first group of recruitment websites and save it; Optical Character Recognition (OCR) is used to parse the screenshots corresponding to each of the first group of recruitment websites to determine the posting results of the first recruitment information on the first group of recruitment websites. The published results are returned to the client.

6. The apparatus as claimed in claim 4, characterized in that, The first receiving module is specifically used for: Receive the recruitment information posting instruction sent by the client; or, Receive the recruitment information posting instruction sent by the chatbot.

7. An electronic device, characterized in that, include: The system includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements a recruitment information posting method based on Robotic Process Automation (RPA) and Artificial Intelligence (AI) as described in any one of claims 1-3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a recruitment information publishing method based on Robotic Process Automation (RPA) and Artificial Intelligence (AI) as described in any one of claims 1-3.

9. A computer program product, comprising a computer program, which, when executed by a processor, implements a recruitment information posting method based on Robotic Process Automation (RPA) and Artificial Intelligence (AI) according to any one of claims 1-3.

Images