A resume automatic editing system and method based on artificial intelligence

Abstract

The present invention discloses an automatic resume editing system and method based on artificial intelligence, including a resume data processing system and a user system; wherein the resume data processing system includes a preprocessing module, which preprocesses resume data to obtain standard resume text and non-standard resume text; further includes a model training module, which trains the standard resume text to obtain a data model; a data classification module, which is a data classification module for identifying and classifying non-standard resume text; and an automatic editing module, which automatically edits the classified resume text to form standard resume data. The user system includes a registration and login module for recording user identity and related information, a resume template setting module for setting a resume export file template, a resume content conversion module for converting non-standard resume data input by a user into standard resume data, and a resume export module for exporting the standard resume data confirmed by the user into a resume file according to a preset resume template.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence-based resume automatic editing system and method. Background Art

[0002] Artificial intelligence technology is widely used in text recognition. Artificial intelligence: In this article, it refers to various machine learning, neural network, deep learning algorithms and models. In particular, it is based on algorithms, and models are obtained through data learning, and used for data classification, sorting and prediction.

[0003] Resumes are key data in the recruitment process. In actual business, recruiters often need to deal with resume data in different formats. Resumes with complicated formats have a great negative impact on the efficiency of resume screening, statistics, and analysis during the recruitment process. For example, some resumes put skill information at the front, some at the back, and some lack this information. When the recruitment process involves multiple links, inconsistent resume formats have a greater impact on the efficiency of the entire recruitment process. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides an automatic resume editing system and method based on artificial intelligence technology. The purpose is to provide a method for organizing resumes of different formats into a specified resume format by utilizing artificial intelligence technology, thereby providing a solution to improve efficiency for recruitment-related businesses.

[0005] The technical solution of the present invention is:

[0006] An artificial intelligence-based resume automatic editing system includes a resume data processing system and a user system; wherein:

[0007] The resume data processing system includes: a preprocessing module, a model training module, a data prediction module and an automatic editing module;

[0008] The preprocessing module splits the resume data into several resume texts and removes interference information to obtain the preprocessed resume texts, including standard resume texts and non-standard resume texts;

[0009] The model training module trains the preprocessed standard resume text to obtain a data model;

[0010] A data classification module, based on the data model, identifies and classifies the pre-processed non-standard resume text;

[0011] Automatic editing module, which automatically edits the classified resume text to form standard resume data;

[0012] The user system includes: a registration and login module, a resume template setting module, a resume content conversion module and a resume export module;

[0013] Registration and login module, recording user identity and related information;

[0014] Resume template setting module, to set the resume template used to export resume files;

[0015] The resume content conversion module converts the non-standard resume data input by the user into standard resume data, and feeds back the converted standard resume data to the user for confirmation;

[0016] The resume export module exports the confirmed standard resume data into a resume file according to the preset resume template.

[0017] Preferably, the preprocessing module comprises:

[0018] An identification unit, which obtains the resume data collected in advance and identifies the attributes of the resume data, including standard resume data and non-standard resume data;

[0019] The first preprocessing unit obtains a standard resume text by splitting the standard resume data and marking its classification;

[0020] The second preprocessing unit obtains the non-standard resume text by performing data splitting on the non-standard resume data;

[0021] A filtering unit, deleting interference information in the standard resume text and the non-standard resume text;

[0022] The first definition unit uses the standard resume text after removing interference information as training data for the data model;

[0023] The second definition unit uses the non-standard resume text after removing the interference information as the data to be classified in the data model.

[0024] Furthermore, the resume data is text data corresponding to a complete resume;

[0025] The resume text is text data after the resume data is split and processed according to predefined rules.

[0026] Preferably, the data model training module includes:

[0027] The model building unit trains the standard resume text to obtain the initial data model;

[0028] The adjustment unit, after the user system is applied, it iteratively updates the data model based on the user feedback data.

[0029] Preferably, the data classification module includes:

[0030] The data classification unit identifies and classifies non-standard resume texts according to the data model to obtain resume texts with unified classification identifiers.

[0031] Preferably, the automatic editing module includes:

[0032] An editing unit automatically edits the resume text carrying a unified classification mark based on predefined editing rules to form standard resume data;

[0033] The predefined editing rules include the classification display order and the resume text display order.

[0034] Preferably, the login and registration module includes:

[0035] A recording unit, which records the user's identity and usage records;

[0036] A setting unit is used to set corresponding usage assistance and incentive measures according to the user identity and the user usage record.

[0037] Preferably, the resume template setting module includes:

[0038] Custom unit, select the corresponding format and content within the system preset range, and generate a customized resume template based on the selected format and content;

[0039] The resume content conversion module includes:

[0040] Deletion unit, during the resume data conversion process, deletes the special symbols or non-standard classification information standard resume data standard resume data contained therein;

[0041] A modification unit adjusts the converted standard resume data to obtain resume data confirmed by the user;

[0042] The display unit provides a user application interface, displays the resume content comparison before and after the content conversion, and saves the resume data confirmed by the user.

[0043] An artificial intelligence-based resume automatic editing method, applied to an artificial intelligence-based resume automatic editing system as described in any one of claims 1 to 8, the method comprising:

[0044] Preprocess the resume data;

[0045] Perform data training on the preprocessed standard resume text to obtain a data model;

[0046] Based on the data model, the non-standard resume text obtained after preprocessing is classified and identified, and the classified and identified resume text is automatically edited to form standard resume data;

[0047] After completing the automatic conversion of the current resume data from a non-standard resume to a standard resume through the user system's operating interface, the data model is updated based on actual application feedback.

[0048] Preferably, the preprocessing of resume data includes:

[0049] Acquire the resume data collected in advance, and identify the attributes of the resume data, which include standard resume data and non-standard resume data;

[0050] By splitting the standard resume data and marking its categories, the standard resume text is obtained;

[0051] By splitting the non-standard resume data, the non-standard resume text is obtained;

[0052] Deleting interfering information in the standard resume text and the non-standard resume text;

[0053] The standard resume text after removing interference information is used as training data for the data model;

[0054] The non-standard resume text after removing the interference information is used as the data to be classified in the data model;

[0055] The interference information includes: meaningless special characters, non-standard paragraph symbols, and non-standard classification information.

[0056] Preferably, based on the data model, the non-standard resume text obtained through preprocessing is classified and identified, and the classified and identified resume text is automatically edited to form standard resume data, including:

[0057] Identify and classify non-standard resume texts according to the data model to obtain resume texts with unified classification marks; automatically edit resume texts with unified classification marks based on pre-defined editing rules to form standard resume data;

[0058] The automatic editing method includes: reorganizing the order of the resume text according to the classification display order; and,

[0059] Sort by the position order or time factor of similar resume text in the original resume data.

[0060] The beneficial effects of the present invention are embodied in:

[0061] The present invention proposes an artificial intelligence-based resume automatic editing system and method, which uses artificial intelligence technology to organize resumes of different formats into a specified resume format, providing a solution to improve efficiency for recruitment-related businesses, while also improving the client's user experience for intelligent resume editing. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0063] Figure 1 A schematic diagram of the structure of an automatic resume editing system based on artificial intelligence provided by an embodiment of the present invention;

[0064] Figure 2 A flow chart of a resume data processing method provided by an embodiment of the present invention;

[0065] Figure 3 A flowchart of a method for user-system interaction process provided by an embodiment of the present invention;

[0066] Figure 4 A flow chart of an automatic resume editing method based on artificial intelligence provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0068] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0069] refer to Figure 1 An embodiment of the present invention provides an artificial intelligence-based resume automatic editing system, including: a resume data processing system and a user system. The resume data processing system includes a preprocessing module, a model training module, a data classification module and an automatic editing module; the user system includes a registration and login module, a resume template setting module, a resume content conversion module and a resume export module.

[0070] The preprocessing module splits the resume data into several resume texts and removes interference information; obtains the preprocessed resume text; wherein the preprocessed resume text includes a standard resume text and a non-standard resume text; the resume data is the text data corresponding to a complete resume, and the resume text is the text data after the resume data is split and processed according to certain rules;

[0071] The preprocessing module needs to process resume data from multiple sources, including: for standard resume data, data splitting and classification annotation as data model training data; for non-standard resume data, data splitting is performed without classification annotation as data model classification data.

[0072] Specifically, the preprocessing module includes:

[0073] An identification unit, which obtains the resume data collected in advance and identifies the attributes of the resume data, including standard resume data and non-standard resume data;

[0074] The first preprocessing unit obtains a standard resume text by splitting the standard resume data and marking its classification;

[0075] The second preprocessing unit obtains the non-standard resume text by performing data splitting on the non-standard resume data;

[0076] A filtering unit, deleting interference information in the standard resume text and the non-standard resume text;

[0077] The first definition unit uses the standard resume text after removing interference information as training data for the data model;

[0078] The second definition unit uses the non-standard resume text after removing the interference information as the data to be classified in the data model.

[0079] Among them, the following factors need to be considered comprehensively when splitting data: 1. The order of resume content; 2. Resume title classification; 3. Resume paragraphs and punctuation; 4. Resume text length limit; 5. Removal of invalid and meaningless characters.

[0080] The model training module trains the preprocessed resume text to obtain a data model; the initial data model is derived from a large amount of standard resume data. After the system is applied, the data model is continuously adjusted based on the subsequently supplemented standard resume data and user usage data to improve the model accuracy.

[0081] The data model training module includes:

[0082] The model building unit trains the standard resume text to obtain the initial data model;

[0083] The adjustment unit, after the user system is applied, it iteratively updates the data model based on the user feedback data.

[0084] The data classification module identifies and classifies the preprocessed resume text according to the data model;

[0085] The data classification module includes: a data classification unit, which identifies and classifies non-standard resume text according to a data model. After identification and classification by the data model, a resume text with a unified classification mark can be obtained.

[0086] Among them, the resume text data after classification by the data model needs to be verified, including: date of birth, mobile phone number, ID card number, etc.

[0087] The automatic editing module automatically edits the classified resume text to form standard resume data.

[0088] Specifically, the automatic editing module includes an editing unit, which automatically edits the resume text carrying a unified classification identifier based on predefined editing rules to form standard resume data; wherein, the automatic editing rules need to be set in advance, so the predefined editing rules here are the pre-set automatic editing rules, which include classification display order, resume text display order, etc.

[0089] In the user system: registration and login module, recording user identity and related information; among them, corresponding usage assistance and incentives can be set based on user identity and its usage records.

[0090] The login and registration module includes:

[0091] A recording unit, which records the user's identity and usage records;

[0092] A setting unit is used to set corresponding usage assistance and incentive measures according to the user identity and the user usage record.

[0093] The resume template setting module is used to set the resume export file template; users can use the system preset resume template to export resume data, or they can customize the resume template; when users customize the resume template, they can set the format and content of the resume template by themselves, but the specific format and content need to be selected within the range agreed upon by the system. For example, the system agrees that resume templates are classified into: basic information (name, gender, age, mobile phone number, email address, whether employed), education experience (education background, graduation school, major, start and end time), professional skills, work experience, and project experience. Users can customize templates and choose from these classified information. They can choose all or part of them, and they can adjust the order in which each part of the information appears in the resume.

[0094] The resume template setting module includes:

[0095] Custom unit, select the corresponding format and content within the system preset range, and generate a customized resume template based on the selected format and content;

[0096] The resume content conversion module converts the non-standard resume data input by the user into standard resume data, and feeds back the converted standard resume data to the user for confirmation;

[0097] During the conversion of standard resume data content, the original special symbols or non-standard classification information are removed. The resume conversion function provides two multi-line text input areas: the original resume and the AI ​​resume. By default, both areas are empty. The user copies the resume (non-standard resume) to be converted and pastes it into the "Original Resume" area, confirms the conversion, and after the system processes it, the automatically edited resume is displayed in the "AI Resume" area. The user can directly save the AI ​​resume or modify it based on the AI ​​resume.

[0098] The resume content conversion module includes:

[0099] Deletion unit, during the resume data conversion process, deletes the special symbols or non-standard classification information standard resume data standard resume data contained therein;

[0100] A modification unit adjusts the converted standard resume data to obtain resume data confirmed by the user;

[0101] The display unit provides a user application interface, displays the resume content comparison before and after the content conversion, and saves the resume data confirmed by the user.

[0102] The resume export module exports the confirmed standard resume data into a resume file according to the preset resume template.

[0103] Users can query the saved resume data, select the resume data to be exported and confirm. The system will export the resume data into a resume file based on the resume template set by the user. Users can download the file in the system.

[0104] refer to Figure 2 , the specific execution process is:

[0105] Standard resume data is split into standard resume text data through the preprocessing module; standard resume text data obtains a data model through the data training module; non-standard resume data is split into non-standard resume text through the preprocessing module; non-standard resume text forms classified resume text through the data classification module; classified resume text is converted into resume data edited by AI through the automatic editing module.

[0106] Among them, compared with the standard resume data, the resume data edited by AI may have the following situations: 1. Some agreed content of the standard resume is missing (such as: missing ID number, missing educational information, etc.); 2. Some content is repeated or deviated (such as: educational experience is counted as work experience, etc.).

[0107] refer to Figure 3 , the specific execution process is:

[0108] The system obtains the initial data model by pre-training a certain number of standard resume texts; when the system is applied and implemented, users convert non-standard resume data into standard resume data by using the system (the user system provides front-end and related prompt functions; the resume data processing system provides back-end data processing functions); users confirm the resume data converted by the system, or confirm after modification. The confirmed resume data can be used as training data for subsequent data model updates.

[0109] refer to Figure 4 The embodiment of the present invention also provides a resume automatic editing method based on artificial intelligence, which is applied to the resume automatic editing system based on artificial intelligence described above, and the method includes:

[0110] S101, determining the content of a standard resume, by splitting a certain number of standard resumes to obtain standard resume texts (model training data);

[0111] Accordingly, step S101 corresponds to the step of preprocessing the resume data:

[0112] Acquire the resume data collected in advance and identify the attributes of the resume data; obtain the standard resume text by splitting the standard resume data and marking its classification, and delete the interference information in the standard resume text.

[0113] S102, using the selected artificial intelligence technology to perform data training on the standard resume text to obtain a data model;

[0114] S103, classifying and identifying the non-standard resume text (obtained by splitting the non-standard resume data) through model application; accordingly, step S103 corresponds to the step of preprocessing the resume data: obtaining the resume data collected in advance, identifying the attributes of the resume data, obtaining the non-standard resume text by splitting the non-standard resume data, and deleting the interference information in the non-standard resume text; using the non-standard resume text after deleting the interference information as the data to be classified in the data model;

[0115] S104, automatically editing the recognized resume text to form a standard resume;

[0116] S105, regularly adding the resume data confirmed by the user to the training data, adjusting the training model, and improving the model accuracy.

[0117] Correspondingly, steps S102-S105 are to perform data training on the standard resume text obtained through preprocessing to obtain a data model; based on the data model, classify and identify the non-standard resume text obtained through preprocessing, and automatically edit the classified and identified resume text to form standard resume data;

[0118] After completing the automatic conversion of the current resume data from a non-standard resume to a standard resume through the user system's operating interface, the data model is updated based on actual application feedback.

[0119] Among them, based on the data model, the non-standard resume text obtained after preprocessing is classified and identified, and the classified and identified resume text is automatically edited to form standard resume data, which specifically includes:

[0120] Identify and classify non-standard resume texts according to the data model to obtain resume texts with unified classification identifiers; automatically edit resume texts with unified classification identifiers based on pre-defined editing rules to form standard resume data.

[0121] The standard resume data refers to data that conforms to the preset resume content classification and related requirements; the non-standard resume data refers to other data that is different from the standard resume data format.

[0122] The automatic editing method includes: reorganizing the order of resume texts according to the classification display order; and sorting resume texts of the same type according to the position order or time factor in the original resume data.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An automatic resume editing system based on artificial intelligence, characterized in that: Including resume data processing system and user system; among them, The resume data processing system includes: a preprocessing module, a model training module, a data prediction module and an automatic editing module; The preprocessing module splits the resume data into several resume texts and removes interference information to obtain the preprocessed resume texts, including standard resume texts and non-standard resume texts; The model training module trains the preprocessed standard resume text to obtain a data model; A data classification module, based on the data model, identifies and classifies the pre-processed non-standard resume text; Automatic editing module, which automatically edits the classified resume text to form standard resume data; The user system includes: a registration and login module, a resume template setting module, a resume content conversion module and a resume export module; Registration and login module, recording user identity and related information; Resume template setting module, to set the resume template used to export resume files; The resume content conversion module converts the non-standard resume data input by the user into standard resume data, and feeds back the converted standard resume data to the user for confirmation; The resume export module exports the confirmed standard resume data into a resume file according to the preset resume template.

2. According to claim 1, the resume automatic editing system based on artificial intelligence is characterized in that: The preprocessing module comprises: An identification unit, which obtains the resume data collected in advance and identifies the attributes of the resume data, including standard resume data and non-standard resume data; The first preprocessing unit obtains a standard resume text by splitting the standard resume data and marking its classification; The second preprocessing unit obtains the non-standard resume text by performing data splitting on the non-standard resume data; A filtering unit, deleting interference information in the standard resume text and the non-standard resume text; The first definition unit uses the standard resume text after removing interference information as training data for the data model; The second definition unit uses the non-standard resume text after removing the interference information as the data to be classified in the data model.

3. The resume automatic editing system based on artificial intelligence according to claim 2 is characterized by: The resume data is text data corresponding to a complete resume; The resume text is text data after the resume data is split and processed according to predefined rules.

4. The resume automatic editing system based on artificial intelligence as claimed in claim 1, characterized in that: The model training module includes: The model building unit trains the standard resume text to obtain the initial data model; Adjustment unit: after the user system is applied, the data model is iteratively updated according to the user feedback data; The data classification module comprises: The data classification unit identifies and classifies non-standard resume texts according to the data model to obtain resume texts with unified classification identifiers.

5. The resume automatic editing system based on artificial intelligence as claimed in claim 1, characterized in that: The automatic editing module comprises: An editing unit automatically edits the resume text carrying a unified classification mark based on predefined editing rules to form standard resume data; The predefined editing rules include the classification display order and the resume text display order.

6. The resume automatic editing system based on artificial intelligence as claimed in claim 1, characterized in that: The registration and login module includes: A recording unit, which records the user's identity and usage records; A setting unit is used to set corresponding usage assistance and incentive measures according to the user identity and the user usage record.

7. The resume automatic editing system based on artificial intelligence as claimed in claim 1, characterized in that: The resume template setting module includes: Custom unit, select the corresponding format and content within the system preset range, and generate a customized resume template based on the selected format and content; The resume content conversion module includes: Deleting units, during the resume data conversion process, deletes the special symbols or non-standard classification information it contains; A modification unit adjusts the converted standard resume data to obtain resume data confirmed by the user; The display unit provides a user application interface, displays the resume content comparison before and after the content conversion, and saves the resume data confirmed by the user.

8. An artificial intelligence-based resume automatic editing method, applied to an artificial intelligence-based resume automatic editing system as described in any one of claims 1 to 7, characterized in that: The method comprises: Preprocess the resume data; Perform data training on the preprocessed standard resume text to obtain a data model; Based on the data model, the non-standard resume text obtained after preprocessing is classified and identified, and the classified and identified resume text is automatically edited to form standard resume data; After completing the automatic conversion of the current resume data from a non-standard resume to a standard resume through the user system's operating interface, the data model is updated based on actual application feedback.

9. The method for automatically editing resumes based on artificial intelligence as claimed in claim 8, characterized in that: The preprocessing of resume data includes: Acquire the resume data collected in advance, and identify the attributes of the resume data, which include standard resume data and non-standard resume data; By splitting the standard resume data and marking its categories, the standard resume text is obtained; By splitting the non-standard resume data, the non-standard resume text is obtained; Deleting interfering information in the standard resume text and the non-standard resume text; The standard resume text after removing interference information is used as training data for the data model; The non-standard resume text after removing the interference information is used as the data to be classified in the data model; The interference information includes: meaningless special characters, non-standard paragraph symbols, and non-standard classification information.

10. The method for automatically editing resumes based on artificial intelligence as claimed in claim 8, characterized in that: Based on the data model, the non-standard resume text obtained through preprocessing is classified and identified, and the classified and identified resume text is automatically edited to form standard resume data including: Identify and classify non-standard resume texts according to the data model to obtain resume texts with unified classification marks; automatically edit resume texts with unified classification marks based on pre-defined editing rules to form standard resume data; The automatic editing method includes: reorganizing the order of the resume text according to the classification display order; and, Sort by the position order or time factor of similar resume text in the original resume data.

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