Processing method, system and equipment of job application information and medium

Abstract

The invention provides a job application information processing method, system and device and a medium. The method comprises the steps of obtaining an online resume of a recruitment channel through a plug-in; screening the online resumes based on configured post screening information to obtain the online resumes conforming to the post screening information; chatting with job seekers conforming to the post screening information through a large model to obtain chat information; performing intention recognition on the chat information through a large model to obtain a work demand of the job seeker; judging whether the job seeker meets recruitment requirements or not according to the work requirements and the post screening information; and when the job seeker meets the recruitment requirement, obtaining an attachment resume of the job seeker. According to the method, the recruitment efficiency is greatly improved, the chat range is expanded through a large model, the quality and efficiency of communication with candidates are improved, and the workload of recruiters is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent recruitment, and in particular to a job information processing method, system, device and medium. BACKGROUND

[0002] In the field of recruitment, most recruiters currently rely on manual operation when searching for talents on recruitment websites. Alternatively, some simple script programs are used, which can capture basic information, but still require recruiters to manually review resume cards and cannot determine whether the candidate meets the job requirements of the position. The existing recruitment work has the problems of requiring a large amount of manpower to be invested in the greeting workflow, and the manual response is not timely and the response content is highly repetitive. Therefore, there is room for improvement. SUMMARY

[0003] In view of the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide a job information processing method, system, device and medium, which solves the problem of requiring a large amount of manpower cost in the recruitment work in the prior art.

[0004] To achieve the above-mentioned purposes and other related purposes, the present application provides a job information processing method, characterized in that the method comprises:

[0005] obtaining online resumes of recruitment channels through plug-ins;

[0006] screening the online resumes based on configured job screening information to obtain online resumes that meet the job screening information;

[0007] chatting with job seekers who meet the job screening information through a large model to obtain chat information;

[0008] performing intent recognition on the chat information through a large model to obtain the job seekers' work requirements;

[0009] determining whether the job seekers meet the recruitment requirements according to the work requirements and the job screening information;

[0010] When the job seekers meet the recruitment requirements, obtaining the job seekers' attachment resumes. In an embodiment of the present application, the step of screening the online resumes based on the configured job screening information to obtain online resumes that meet the job screening information comprises:

[0011] screening the online resumes based on basic information conditions and region conditions in the configured job screening information to obtain first online resumes that meet the basic information conditions and the region conditions;

[0012] Filter the first online resume based on the job condition in the job screening information, and obtain a second online resume meeting the job condition.

[0013] In an embodiment of the present application, the step of obtaining chat information by chatting with the job seeker meeting the job screening information through the large model comprises:

[0014] sending a preset greeting information to the job seeker meeting the job screening information;

[0015] real-time semantic analysis of the reply information of the job seeker through the large model, generation of response information corresponding to the semantic analysis, and sending of the response information to the job seeker for chatting to obtain chat information.

[0016] In an embodiment of the present application, the step of obtaining the job requirement of the job seeker by intent recognition of the chat information through the large model comprises:

[0017] real-time intent recognition of the chat information through the large model, and judgment of whether the content of the intent recognition corresponds to all screening conditions in the job screening information;

[0018] generating the job requirement of the job seeker based on the content of the intent recognition when the content of the intent recognition corresponds to all screening conditions;

[0019] generating question and answer information based on the screening conditions not corresponding to the content of the intent recognition when the content of the intent recognition does not correspond to all screening conditions, sending the question and answer information to the job seeker for chatting until the content of the intent recognition corresponds to all screening conditions, and generating the job requirement of the job seeker based on the content of the intent recognition.

[0020] In an embodiment of the present application, the step of obtaining the attachment resume of the job seeker when the job seeker meets the recruitment requirement comprises:

[0021] downloading the attachment resume of the job seeker on the recruitment channel when the job requirement of the job seeker meets all screening conditions in the job screening information, or receiving the attachment resume sent by the job seeker.

[0022] In an embodiment of the present application, the step of obtaining the attachment resume of the job seeker when the job seeker meets the recruitment requirement comprises:

[0023] obtaining the matching degree of the job requirement of the job seeker and all screening conditions when the job seeker does not meet the recruitment requirement.

[0024] When the matching degree of the job seeker reaches a preset matching threshold, the job demand of the job seeker is classified according to a preset gear, and the classified job demand is saved to a talent library.

[0025] In an embodiment of the present application, the step of chatting with the job seeker meeting the job screening information by the large model to obtain the chat information comprises:

[0026] Comparing the number of job seekers meeting the job screening information with a preset threshold number;

[0027] Within the threshold number range, chat with the job seeker meeting the job screening information by the large model to obtain the chat information.

[0028] The present application further provides a job information processing system, comprising:

[0029] The first obtaining unit is configured to obtain online resumes of recruitment channels through the plug-in;

[0030] The screening unit is configured to screen the online resumes based on the configured job screening information to obtain online resumes meeting the job screening information;

[0031] The second obtaining unit is configured to chat with the job seeker meeting the job screening information by the large model to obtain chat information;

[0032] The recognition unit is configured to perform intent recognition on the chat information by the large model to obtain job demands of the job seeker;

[0033] The judgment unit is configured to judge whether the job seeker meets the recruitment requirements according to the job demands and the job screening information;

[0034] The third obtaining unit is configured to obtain an attached resume of the job seeker when the job seeker meets the recruitment requirements.

[0035] The present application further provides an electronic device, comprising:

[0036] One or more processors;

[0037] A storage device is configured to store one or more programs, when the one or more programs are executed by the one or more processors, so that the electronic device implements the job information processing method as described in any one of the above.

[0038] The present application further provides a computer readable storage medium having a computer program stored thereon, when the computer program is executed by a processor of a computer, the computer executes the job information processing method as described in any one of the above.

[0039] As described above, the job information processing method, system, device and medium of the present application greatly improve the recruitment efficiency, enabling recruiters to handle more candidates in the same time, expand the chat range through large models, improve the quality and efficiency of communication with candidates, and reduce the workload of recruiters. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The flowchart of the job information processing method provided by an embodiment of the present application is shown.

[0041] Figure 2 The structure block diagram of the job information processing system provided by an embodiment of the present application is shown.

[0042] Figure 3 The structure diagram of the electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0043] The embodiments of the present application will be described in detail with specific examples. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the specification. The present application can also be implemented or applied in different specific embodiments, and the details in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0044] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and the drawings only show the components related to the present application, not the number, shape and size of the components when actually implemented. The actual implementation of each component may be arbitrarily changed in type, number and proportion, and the layout pattern of the components may be more complex.

[0045] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application, however, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details, and in other embodiments, the known structures and devices are shown in the form of block diagrams rather than in the form of details, to avoid making the embodiments of the present application difficult to understand.

[0046] Please refer to Figures 1 to 3 The present application provides a job information processing method, system, device and medium, which can be applied to centralized recruitment of large enterprises, such as campus recruitment season, to quickly process a large amount of candidate information, suitable for headhunting companies, improve the communication efficiency with potential candidates, and quickly obtain the resume of the target candidate. The following will be described in detail through specific embodiments.

[0047] Referring to Figure 1 The application provides a job information processing method, which can include the following steps.

[0048] Step S10, obtaining online resumes of recruitment channels through plug-ins.

[0049] In an embodiment of the application, a front-end plug-in can be developed, and the front-end plug-in is deployed in a cloud desktop environment and connected with recruitment channels. The recruitment channels can be recruitment websites, local talent networks, talent markets, etc., and the front-end plug-in can be connected with Web pages of the recruitment websites, local talent networks, and talent markets.

[0050] Specifically, the employer scans a code to log in on a recruitment platform, a front-end plug-in can be automatically pulled up on the cloud desktop, and the login information of the employer on the recruitment platform is synchronized to the front-end plug-in, and online resumes of recruitment channels are obtained through the front-end plug-in.

[0051] Step S20, screening online resumes based on configured post screening information to obtain online resumes meeting the post screening information.

[0052] In an embodiment of the application, post screening information can be configured in advance, and the post screening information can include post categories, post requirements, screening conditions, etc. Online resumes can be screened through the configured post screening information, that is, the post screening information is compared with online resumes to obtain online resumes meeting the post screening information.

[0053] Step S30, chatting with job seekers meeting the post screening information through a large model to obtain chat information.

[0054] In an embodiment of the application, job seekers meeting the post screening information can be obtained by screening online resumes, and chat information can be obtained by chatting with the job seekers meeting the post screening information through a large model.

[0055] The large model can be ChatGPT, Deepseek, or other network data large models. The large model can generate corresponding response information or question and answer information based on the reply information of the job seekers, so as to continuously communicate with the job seekers and obtain chat information associated with the job seekers and the post screening information.

[0056] Step S40, performing intent recognition on the chat information through the large model to obtain work requirements of the job seekers.

[0057] In an embodiment of the application, the chat records of the job seekers are analyzed in real time through the large model to obtain the work requirements of the job seekers.

[0058] Specifically, when the large model cannot analyze the job seeker's job requirements, that is, cannot determine whether the job seeker meets or does not meet the job screening information, it is necessary to maintain continuous chat with the job seeker until the job seeker's job requirements can be analyzed to determine whether the job seeker meets or does not meet the job screening information.

[0059] Step S50, determining whether the job seeker meets the recruitment requirements according to the job requirements and the job screening information.

[0060] In an embodiment of the present application, the job requirements are compared with the job screening information according to the job requirements of the job seeker and the job screening information of the recruiter, so as to determine whether the job seeker meets the recruitment requirements.

[0061] Step S60, obtaining the job seeker's attached resume when the job seeker meets the recruitment requirements.

[0062] In an embodiment of the present application, when the job seeker meets the recruitment requirements, the job seeker's attached resume is obtained. The attached resume refers to a resume document uploaded or sent to the employer in the form of a file in addition to the online application information when applying for a job. This type of resume is usually in PDF, Word document format, etc. for easy reading and archiving.

[0063] Based on the seamless integration of the front-end plug-in with the recruitment website, it is convenient and efficient, and combines automation and artificial intelligence technology to realize the full-process automation from greeting to chatting to resume acquisition. This greatly improves the recruitment efficiency, enables the recruitment personnel to handle more candidates in the same time, improves the quality of communication with the candidates, expands the chat range through the large model, increases the satisfaction of the candidates, simplifies the resume acquisition process, and reduces the workload of the recruitment personnel.

[0064] Please refer to Figure 1 In an embodiment of the present application, in step S20, the step of screening the online resume based on the configured job screening information to obtain the online resume meeting the job screening information includes steps S210 and S220.

[0065] Step S210, screening the online resume based on the basic information condition and the region condition in the configured job screening information to obtain the first online resume meeting the basic information condition and the region condition.

[0066] Specifically, preliminary screening can be performed first. The screening basis is the basic information condition (such as education, age, work experience, etc.) and the region condition (such as work location, residence, etc.) in the job screening information, and the screening result is to generate a preliminary matched resume set, i.e. the first online resume. Candidates who obviously do not meet the hard conditions can be quickly excluded.

[0067] Step S220, based on the job screening information, the first online resume is screened based on the job conditions, and the second online resume that meets the job conditions is obtained.

[0068] Specifically, job matching screening is performed. The screening basis is based on the job conditions (such as skill requirements, position matching degree, industry experience, etc.) in the job screening information, and the first online resume is screened through the job conditions. The screening result is a further matched resume set, i.e. the second online resume. Fine screening can be performed to ensure that the candidate matches the core needs of the position.

[0069] In this embodiment, through hierarchical screening, the range is quickly narrowed through basic hard conditions first, and then accurately matched through job-related conditions, improving efficiency. The screening conditions can be flexibly configured (such as region, skill combination), and are suitable for different job requirements.

[0070] Please refer to Figure 1 In an embodiment of the present application, in step S30, the step of obtaining chat information by chatting between the large model and the job seeker who meets the job screening information includes steps S310 and S320.

[0071] Step S310, sending a preset greeting information to the job seeker who meets the job screening information.

[0072] Specifically, the preset greeting information can include job title, company profile, core benefits, etc. For example: "Hello, XX company is recruiting Java engineers, salary range 20-30k, are you interested?". The greeting information can be dynamically configured based on the job type, for example, technical positions focus on skill questions, and management positions focus on experience questions.

[0073] Solve the problem of low efficiency of manual sending of greeting information by traditional HR, standardize the content of initial screening communication, and avoid differences in manual expression.

[0074] Step S320, real-time semantic analysis of the reply information of the job seeker through the large model, and generating response information corresponding to the semantic analysis, and sending the response information to the job seeker for chatting to obtain chat information.

[0075] Specifically, the reply information of the job seeker is analyzed semantically, for example: "I am interested in the position, but I need to work remotely".

[0076] The large model extracts key intentions, for example: remote work = demand, interest level = high, and dynamically generates response information based on the analysis result, for example: "Our company supports 3 days of remote work per week. Can you accept it?". The response information is sent to the job seeker to form a continuous dialogue.

[0077] Compared with fixed questionnaires, large models can flexibly cope with the personalized answers of job seekers.

[0078] See Figure 1 In an embodiment of the present application, in step S40, the step of performing intent recognition on the chat information by a large model to obtain the job requirements of the job seeker can include steps S410, S420 and S430.

[0079] Step S410, real-time intent recognition on the chat information by a large model, and determining whether the content of intent recognition corresponds to all screening conditions in the job screening information.

[0080] Specifically, a large model (such as BERT / GPT) is used for intent recognition, and structured labels are extracted, such as {"salary expectation": "25k", "work location": "Beijing", "time to report": "within 1 month"}.

[0081] The intent labels are compared with the job screening information item by item (such as job requirements of salary range 20-30k, location Beijing / Shanghai), and then a missing condition list (such as not mentioning whether to accept business trips) can be generated.

[0082] Step S420, when the content of intent recognition corresponds to all screening conditions, generating the job requirements of the job seeker based on the content of intent recognition.

[0083] Specifically, when intent recognition covers all screening conditions, such as salary, location, skills, and time to report, the intent recognition result is converted into a standardized job requirement object.

[0084] Step S430, when the content of intent recognition does not correspond to all screening conditions, generating question and answer information based on the screening conditions that do not correspond, sending the question and answer information to the job seeker for chat, until the content of intent recognition corresponds to all screening conditions, and generating the job requirements of the job seeker based on the content of intent recognition.

[0085] Specifically, based on the missing screening condition item, such as not mentioning whether to accept overtime work, the large model generates a natural language question based on the missing condition, such as: "The job requires 2 days of overtime work per week. Can you accept it?". The question is sent to the job seeker, and communication with the job seeker continues until all conditions are clear, ensuring that the finally generated job requirements cover 100% of the job screening dimensions, and avoiding misjudgment caused by incomplete information.

[0086] The following can give an example of the scenario:

[0087] Scenario 1: salary is not clear

[0088] Dialogue history: The job seeker only says "interested in the position" and does not mention the salary.

[0089] System action:

[0090] Step S410 detects that the screening condition is missing.

[0091] Step S430 generates the question: "What is your expected salary?"

[0092] The job seeker replies "25k".

[0093] Step S420 outputs the complete requirements.

[0094] Scenario 2: Missing skill details

[0095] Job requirements: Must master Redis.

[0096] Dialogue history: The job seeker does not mention Redis experience.

[0097] System action:

[0098] Step S410 marks must_have_skills as incomplete.

[0099] Step S430 asks: "Do you have experience in Redis cluster optimization?"

[0100] If the answer is "no", step S50 directly determines that it does not match.

[0101] In this embodiment, the high-precision alignment of recruitment requirements is achieved through a closed-loop feedback mechanism, significantly improving the reliability of matching compared to traditional one-way questionnaire screening. If further analysis of the specific model architecture of large models in intent recognition is needed (such as Seq2Seq vs. Transformer), further discussion can be conducted.

[0102] Please refer to Figure 1 In one embodiment of the present application, in step S60, when the job seeker meets the recruitment requirements, the step of obtaining the job seeker's attached resume is:

[0103] When the job seeker's work requirements meet all the screening conditions in the job screening information, download the job seeker's attached resume on the recruitment channel, or receive the attached resume sent by the job seeker.

[0104] Specifically, the resume download interface can be called through the recruitment platform API (such as BOSS direct employment open platform), format processing is required, and PDF / Word format resumes are automatically parsed to extract text content and store in a structured database. For permission requirements, the enterprise account needs to have the resume download permission of the recruitment channel.

[0105] Specifically, it can also be directly requested by a large model dialogue, for example: "Please send your attached resume to xxx mailbox or click the link to upload".

[0106] Please refer to Figure 1 In an embodiment of the present application, after step S60, that is, after the step of obtaining the attached resume of the job seeker when the job seeker meets the recruitment requirements, steps S710 and S720 are included.

[0107] Step S710, when the job seeker does not meet the recruitment requirements, the matching degree of the job seeker's job requirements and all screening conditions is obtained.

[0108] Specifically, each condition item is scored for difference, for example: salary expectation 25k vs. job budget 20-30k, then the matching degree is 100%.

[0109] The total matching degree can also be calculated by configuring weights:

[0110] Total matching degree = (salary weight x salary matching degree + skill weight x skill matching degree) / total weight.

[0111] A structured matching report can be output, for example: {“total matching degree": 75%, “skill matching degree”: 90%, “salary matching degree”: 60%}.

[0112] Step S720, when the matching degree of the job seeker reaches the preset matching threshold, the job requirements of the job seeker are classified according to the preset gear, and the classified job requirements are saved to the talent pool.

[0113] Specifically, the threshold can be configured according to the enterprise demand, for example: total matching degree ≥ 60%, then archive, talent pool storage design can be as follows.

[0114] Candidate ID: 123.

[0115] Classification gear: B gear.

[0116] Job requirements: {salary: 25k, skills: [Java, Redis]}.

[0117] Mismatched items: [not accept overtime].

[0118] Original resume: s3: / / resume / 123.pdf.

[0119] Please refer to Figure 1In an embodiment of the present application, in step S30, the step of chatting with the job seekers meeting the job screening information by the large model to obtain the chat information can further include steps S311 and S321.

[0120] Step S311, compare the number of job seekers meeting the job screening information with a preset threshold number.

[0121] Step S321, within the threshold number range, chat with the job seekers meeting the job screening information by the large model to obtain the chat information.

[0122] Specifically, if the number of job seekers ≤ threshold number, for example, 40 ≤ 50, then all enter the chat process. If the number of job seekers > threshold number, for example, 100 > 50, then the first 50 are selected after sorting by priority. Through fine flow control, the recruitment efficiency and large model service performance are balanced, which is suitable for high concurrency recruitment scenarios.

[0123] If chatting with 1000 people at the same time, it may lead to: API call frequency exceeding the limit (such as OpenAI limiting 60 requests per minute), or response delay rising (affecting the experience of job seekers), resulting in large model overload.

[0124] Please refer to Figure 2 In an embodiment of the present application, the present application provides a job information processing system, which includes a first acquisition unit 110, a screening unit 120, a second acquisition unit 130, an identification unit 140, a judgment unit 150, and a third acquisition unit 160.

[0125] The first acquisition unit 110 is configured to acquire online resumes of recruitment channels through a plug-in.

[0126] The screening unit 120 is configured to screen the online resumes based on the configured job screening information to obtain online resumes meeting the job screening information.

[0127] The second acquisition unit 130 is configured to chat with the job seekers meeting the job screening information by the large model to obtain chat information.

[0128] The identification unit 140 is configured to perform intent recognition on the chat information by the large model to obtain the job seekers' work requirements.

[0129] The judgment unit 150 is configured to judge whether the job seekers meet the recruitment requirements according to the work requirements and the job screening information.

[0130] The third acquisition unit 160 is configured to acquire the job seekers' attachment resumes when the job seekers meet the recruitment requirements.

[0131] Please refer to Figure 3In an embodiment of the present application, the electronic device 11 can include a memory 12, a processor 13 and a bus, and can further include a computer program, such as a job information processing program, stored in the memory 12 and executable on the processor 13.

[0132] The memory 12 includes at least one type of readable storage medium, such as a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 11, such as a mobile hard disk of the electronic device 11. In other embodiments, the memory 12 can also be an external storage device of the electronic device 11, such as a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 12 can include both an internal storage unit and an external storage device of the electronic device 11. The memory 12 can be used to store application software installed in the electronic device 11 and various data, such as codes for processing job information, and can also be used to temporarily store data that has been output or will be output.

[0133] The processor 13 can be composed of an integrated circuit in some embodiments, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more combinations of a central processing unit (CPU), a microprocessor, a digital processing chip, a graphics processor and various control chips, etc. The processor 13 is a control unit of the electronic device 11, and is connected to various components of the electronic device 11 through various interfaces and lines, and executes or runs programs or modules stored in the memory 12 (such as a job information processing program, etc.) and calls data stored in the memory 12 to perform various functions and process data of the electronic device 11.

[0134] The processor 13 executes an operating system and various application programs installed in the electronic device 11. The processor 13 executes the application programs to implement the steps in the above job information processing method.

[0135] The computer program can be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device 11. For example, the computer program can be divided into a first acquisition unit 110, a screening unit 120, a second acquisition unit 130, an identification unit 140, a judgment unit 150, and a third acquisition unit 160.

[0136] The integrated units in the form of software function modules described above can be stored in a computer readable storage medium, which can be non-volatile or volatile. The software function modules described above are stored in a storage medium, including a plurality of instructions for enabling a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to execute part of the functions of the power job information processing method described in various embodiments of the present application.

[0137] In summary, the disclosed job information processing method, system, device and medium greatly improve the recruitment efficiency, enable recruiters to handle more candidates in the same time, improve the quality of communication with candidates, expand the chat range through large models, increase the satisfaction of candidates, simplify the resume acquisition process, and reduce the workload of recruiters. Therefore, the present application effectively overcomes the shortcomings of the prior art and has high industrial utilization value.

[0138] The above embodiments only exemplarily illustrate the principles and effects of the present application, and are not used to limit the present application. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical idea disclosed by the present application should be covered by the claims of the present application.

Claims

1. A method for processing job application information, characterized in that, The method includes: Access online resumes from recruitment channels via plugins; The online resumes are filtered based on the configured job filtering information to obtain online resumes that match the job filtering information. Chat with job seekers who meet the job screening criteria using a large model to obtain chat information; The chat messages are analyzed using a large model to identify the job seeker's job requirements. Based on the job requirements and the job screening information, determine whether the job seeker meets the recruitment requirements; When the job seeker meets the recruitment requirements, obtain the job seeker's attached resume.

2. The method for processing job information according to claim 1, characterized in that, The step of filtering online resumes based on configured job filtering information to obtain online resumes that match the job filtering information includes: Based on the basic information conditions and regional conditions in the configured job screening information, the online resumes are screened to obtain the first online resume that meets the basic information conditions and the regional conditions; Based on the job requirements in the job screening information, the first online resume is screened to obtain a second online resume that meets the job requirements.

3. The method for processing job information according to claim 1, characterized in that, The step of chatting with job seekers who meet the job screening information through a large model to obtain chat information includes: Send a preset greeting message to job seekers who meet the job screening criteria; The large model performs real-time semantic analysis on the job seeker's responses and generates corresponding response information. This response information is then sent to the job seeker to chat and obtain chat information.

4. The method for processing job information according to claim 1, characterized in that, The step of performing intent recognition on the chat information using a large model to obtain the job seeker's job requirements includes: The large model performs real-time intent recognition on the chat information and determines whether the content of the intent recognition corresponds to all the filtering conditions in the job screening information. When the content of the intent recognition corresponds to all the filtering conditions, the job requirements of the job seeker are generated based on the content of the intent recognition. When the content of the intent recognition does not correspond to all the filtering conditions, a question-and-answer message is generated based on the uncorresponding filtering conditions, and the question-and-answer message is sent to the job seeker for a chat until the content of the intent recognition corresponds to all the filtering conditions, and the job requirements of the job seeker are generated based on the content of the intent recognition.

5. The method for processing job information according to claim 4, characterized in that, The step of obtaining the job seeker's attached resume when the job seeker meets the recruitment requirements includes: When the job seeker's job requirements meet all the screening criteria in the job posting information, download the job seeker's attached resume from the recruitment channel, or receive the attached resume sent by the job seeker.

6. The method for processing job information according to claim 5, characterized in that, After the step of obtaining the job seeker's attached resume when the job seeker meets the recruitment requirements, the following steps are included: When a job seeker does not meet the recruitment requirements, obtain the degree of match between the job seeker's job requirements and all screening criteria; When the matching degree of the job seeker reaches the preset matching threshold, the job seeker's job requirements are classified according to the preset level, and the classified job requirements are saved to the talent pool.

7. The method for processing job information according to claim 1, characterized in that, The step of chatting with job seekers who meet the job screening information through a large model to obtain chat information includes: Compare the number of job seekers who match the job screening information with the preset threshold number; Within the specified threshold range, chat with job seekers who meet the job screening information using a large model to obtain chat information.

8. A job information processing system, characterized in that, The system includes: The first acquisition unit is used to acquire online resumes from recruitment channels through a plugin; A filtering unit is used to filter the online resumes based on the configured job filtering information to obtain online resumes that meet the job filtering information; The second acquisition unit is used to chat with job seekers who meet the job screening information through a large model in order to obtain chat information; The recognition unit is used to perform intent recognition on the chat information using a large model in order to obtain the job seeker's job requirements; The judgment unit is used to determine whether the job seeker meets the recruitment requirements based on the job requirements and the job screening information. The third acquisition unit is used to acquire the job seeker's attached resume when the job seeker meets the recruitment requirements.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the job information processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the job information processing method according to any one of claims 1 to 7.

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