An intelligent management system for AI big data job screening

Abstract

The present invention relates to the technical field of data processing, and in particular to an intelligent management system for screening AI big data positions. The present invention is provided with a historical determination module, an event analysis module, a preliminary analysis module, and a retrieval module. By obtaining the resume information uploaded by each user terminal and the job application information uploaded by the enterprise terminal, key events are extracted according to the event response standard, and the event information corresponding to the key events is recorded; according to the conditional association probability of the corresponding keywords in the resume information and the job application information in the event information, the association relationship of each keyword is determined, and an association relationship database is constructed; the resume information uploaded by the user terminal is parsed to identify the distribution characteristics of each associated keyword, and the association density characterization parameter is calculated according to the distribution characteristics, and a distribution label is set for the resume information; the job application information is retrieved adaptively according to the distribution label. Furthermore, on the premise of ensuring data reliability, the present invention reduces the amount of data to be screened and matched, and improves the efficiency and accuracy of screening and matching.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an intelligent management system for screening AI big data job positions. Background Art

[0002] In the labor market, the number of job seekers is huge. Especially in popular industries and fields, the job competition is fierce. Job seekers hope to find jobs that match their abilities and career plans more efficiently. Therefore, an intelligent and comprehensive screening system is needed to assist in decision-making.

[0003] Chinese Patent Application Publication No.: CN118798836A, discloses a precise matching recruitment system based on big data analysis, including: loading the description documents of all recruitment positions of an enterprise, and using the TF-IDF method to screen out the common word set and the unique word set of each recruitment position from the description documents; screening out the target position set from all recruitment positions according to the common word set; retrieving the resume to be matched of a job seeker and the keyword set in the resume to be matched, and extracting the common word set and the unique word set of the target position, and inputting the keyword set, the common word set and the unique word set into a pre-trained convolutional neural network for judging whether the person-job match is consistent to obtain a judgment result; summarizing all the position sets suitable for the job seeker from the target position set according to the judgment result; the present invention is beneficial to avoiding repeated resume screening and interviews, and improving the recruitment accuracy and efficiency of enterprises.

[0004] However, the following problems still exist in the prior art.

[0005] It is easy to ignore the different contributions of the corresponding keyword pairs of resume information and job application information to reaching an interview agreement, making the screening and matching of job application information unclear, the screening and matching target relatively vague, and reducing the accuracy of screening and matching. Summary of the Invention

[0006] For this reason, the present invention provides an intelligent management system for screening AI big data job positions to overcome the problem in the prior art that it is easy to ignore the different contributions of the corresponding keyword pairs of resume information and job application information to reaching an interview agreement, making the screening and matching of job application information unclear, the screening and matching target relatively vague, and reducing the accuracy of screening and matching.

[0007] To achieve the above object, the present invention provides an intelligent management system for screening AI big data job positions, which includes:

[0008] A historical determination module, which is used to obtain the resume information uploaded by each user terminal and the job application information uploaded by the enterprise terminal, extract key events according to the event response standard, and record the event information corresponding to the key events;

[0009] An event analysis module, which is connected to the historical determination module and is used to call the event information of key events, determine the correlation relationship of each keyword according to the conditional correlation probability of the corresponding keywords of the resume information and job hunting information in the event information, and construct a correlation relationship database;

[0010] A preliminary analysis module, which is connected to the event analysis module and, in response to the resume information uploaded by the user terminal, is used to parse the resume information, identify the distribution characteristics of each associated keyword, calculate the correlation density characterization parameter according to the distribution characteristics, and set a distribution label for the resume information;

[0011] A retrieval module, which is connected to the preliminary analysis module and includes a deep matching unit and a conventional matching unit;

[0012] The deep matching unit is used to perform a deletion operation on the keywords of the resume information to obtain reserved keywords, determine the push sequence for job hunting information based on the conditional correlation probability of the reserved keywords and the corresponding keywords of each job hunting information, match the job hunting information based on each keyword corresponding to the push sequence segment, and determine the push priority of each job hunting information based on the sequence segment to perform job hunting information push;

[0013] The conventional matching unit is used to determine the semantic correlation degree of the corresponding keywords of the resume information and job hunting information, generate a job hunting information push list according to the semantic correlation degree, and perform job hunting information push;

[0014] Among them, the distribution characteristics include the number of associated keywords and the average value of the conditional correlation probability corresponding to each associated keyword.

[0015] Further, the historical determination module is used to extract key events according to the event response standard, including

[0016] Determining the event that meets the event response standard as the key event;

[0017] Among them, the event response standard includes that the user terminal and the enterprise terminal reach an interview agreement; the event includes an interaction operation between the user terminal and the enterprise terminal.

[0018] Further, the event analysis module is used to determine the correlation relationship of each keyword, including

[0019] If the conditional correlation probability of the corresponding keywords of the resume information and job hunting information is greater than or equal to the conditional correlation probability threshold, it is determined that there is a correlation relationship between each keyword;

[0020] Among them, the conditional correlation probability is the probability that the keyword appears in both the resume information and job hunting information.

[0021] Further, the preliminary analysis module is used to calculate the associated dense representation parameter according to the distribution characteristics, including

[0022] using the ratio of the number of associated keywords to the number threshold as the first associated dense feature;

[0023] using the ratio of the average conditional association probability of each associated keyword to the conditional association probability average threshold as the second associated dense feature;

[0024] using the weighted sum of the first associated dense feature and the second associated dense feature as the associated dense representation parameter;

[0025] wherein, the associated keywords are keywords with an association relationship.

[0026] Further, the preliminary analysis module is used to set distribution labels for the resume information, including

[0027] if the associated dense representation parameter is greater than or equal to the associated dense representation parameter threshold, setting the label of the resume information as dense distribution;

[0028] if the associated dense representation parameter is less than the associated dense representation parameter threshold, setting the label of the resume information as scattered distribution.

[0029] Further, the retrieval module is used to call the deep matching unit or the conventional matching unit according to the distribution label, including

[0030] if the label set for the resume information is dense distribution, calling the deep matching unit;

[0031] if the label set for the resume information is scattered distribution, calling the conventional matching unit.

[0032] Further, the deep matching unit is used to eliminate keywords, including

[0033] determining each keyword in the resume information;

[0034] eliminating keywords without an association relationship among each keyword.

[0035] Further, the deep matching unit is used to determine the push sequence for the job application information based on the conditional association probability between the remaining keywords and the corresponding keywords of each job application information, including

[0036] respectively determining the conditional association probability between the remaining keywords and the corresponding keywords of each job application information;

[0037] sorting the job application information in descending order according to the conditional association probability to obtain the push sequence.

[0038] Further, the deep matching module is used to match job seeking information based on each keyword corresponding to the push sequence segment. Determining the push priority of each job seeking information based on the sequence segment includes:

[0039] being used to divide the push sequence into several push sequence segments;

[0040] being used to call each keyword in the sequence segment to match with each keyword in the job seeking information to determine the job seeking information that meets the matching conditions;

[0041] being used to preferentially push the job seeking information corresponding to the sequence segment with a higher ranking;

[0042] Wherein, the matching condition is that the semantic association degree between each keyword and the keyword in the job seeking information is greater than a predetermined semantic association degree threshold.

[0043] Further, the conventional matching unit is used to generate a job seeking information push list according to the semantic association degree, including:

[0044] If the semantic association degree is greater than or equal to the semantic association degree threshold, the corresponding job seeking information is placed in the job seeking information push list.

[0045] Compared with the prior art, the present invention sets a historical determination module, an event analysis module, a preliminary analysis module and a retrieval module. By obtaining the resume information uploaded by each user terminal and the job seeking information uploaded by the enterprise terminal, key events are extracted according to the event response standard, and the event information corresponding to the key events is recorded; the association relationship between each keyword is determined according to the conditional association probability of the corresponding keywords of the resume information and the job seeking information in the event information, and an association relationship database is constructed; the resume information uploaded by the user terminal is analyzed to identify the distribution characteristics of each associated keyword, and the association density characterization parameter is calculated according to the distribution characteristics, and a distribution label is set for the resume information; the job seeking information is retrieved adaptively according to the distribution label. Therefore, on the premise of ensuring data reliability, the present invention reduces the amount of data screened and matched, and improves the efficiency and accuracy of screening and matching.

[0046] In particular, the present invention sets up an event analysis module, which determines the correlation between resume information and job application information corresponding to the interview agreement by analyzing pre-recorded key events. In actual situations, job seekers and employers will upload the corresponding resume information and job application information to the recruitment platform, and interact on the platform to facilitate the interview agreement. Based on this, the present invention analyzes a large number of events before the interview agreement is reached to identify the similarities in such events. For example, the correlation between the resume information and the job application information is analyzed by the degree of matching of the corresponding keywords of the resume information and the job application information. The correlation relationship is used to represent the high contribution of the corresponding keywords contained in the resume information to the interview agreement reached by both parties, and at the same time reflects that the keyword has high availability and strong data representation when matching the corresponding position.

[0047] In particular, the present invention sets up a preliminary analysis module, which preliminarily analyzes the distribution of keywords associated with the job search information in the resume information, based on the basic conditions between the pre-analyzed resume information and the corresponding keywords of the job search information that can facilitate the job seeker and the employer to reach an interview agreement. That is, the probability of occurrence of such keywords in the resume information is analyzed. In actual situations, the proportion and accuracy of the keywords contained in the resume information will affect the accuracy of the screening and matching of the job search information. Therefore, the present invention calculates the association density characterization parameter by associating the keywords and the mean of the conditional association probabilities of each associated keyword and the corresponding keywords of the job search information, so as to characterize the distribution density of the keywords corresponding to the resume information uploaded by the user and the corresponding job search information, and then characterizes the matching degree of the resume information and the job search information. Under the premise of ensuring data reliability, the present invention reduces the amount of data for screening and matching, and improves the efficiency and accuracy of screening and matching.

[0048] In particular, the present invention sets up a deep matching unit. Since the resume information in which the labels are set is densely distributed and contains more keywords that facilitate the two parties to reach an interview agreement, the present invention takes these keywords as the key consideration basis, pre-eliminates keywords that have no correlation with the corresponding job search information, and reduces the amount of data for screening and matching and related interference words that affect the screening and matching while ensuring the accuracy of screening. A deep analysis is performed based on the retained keywords after the elimination operation combined with the conditional association probability corresponding to the corresponding retained keywords to determine the push sequence of the job search information, determine the push priority, and push the job search information in the obtained sequence segment, thereby improving the accuracy of screening and matching. Therefore, the present invention reduces the amount of data for screening and matching while ensuring data reliability, and improves the efficiency and accuracy of screening and matching. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a functional module diagram of an AI big data job screening intelligent management system based on an embodiment of the invention;

[0050] Figure 2 Logic decision diagram for setting distribution labels for resume information in the invention embodiment;

[0051] Figure 3 Logic decision diagram for calling the deep matching unit or the conventional matching unit according to the distribution label in the invention embodiment;

[0052] Figure 4 Logic decision diagram for generating a job information push list according to the semantic relevance degree in the invention embodiment. Detailed implementation manners

[0053] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0055] In addition, it should be noted that in the description of the present invention, unless otherwise clearly defined and limited, the term "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0056] Please refer to Figure 1 as shown Figure 1 which is a functional module diagram of the AI big data job screening intelligent management system according to an embodiment of the present invention. The AI big data job screening intelligent management system according to an embodiment of the present invention includes:

[0057] A historical determination module, which is used to obtain the resume information uploaded by each user terminal and the job hunting information uploaded by the enterprise terminal, extract key events according to the event response standard, and record the event information corresponding to the key events;

[0058] An event analysis module, which is connected to the historical determination module, is used to call the event information of the key events, determine the association relationship of each keyword according to the conditional association probability of the corresponding keywords of the resume information and the job hunting information in the event information, and construct an association relationship database;

[0059] A preliminary analysis module, which is connected to the event analysis module, in response to the resume information uploaded by the user terminal, is used to parse the resume information, identify the distribution characteristics of each associated keyword, calculate the association density characterization parameter according to the distribution characteristics, and set a distribution label for the resume information;

[0060] A retrieval module, which is connected to the preliminary analysis module and includes a deep matching unit and a conventional matching unit;

[0061] The deep matching unit is used to remove keywords from the resume information to obtain reserved keywords, determine a push sequence for job search information based on a conditional association probability between the reserved keywords and keywords corresponding to each job search information, match job search information based on push sequence segments corresponding to each keyword, and determine a push priority for each job search information based on the sequence segments to push job search information;

[0062] The conventional matching unit is used to determine the semantic relevance between the resume information and the keywords corresponding to the job search information, and generate a job search information push list according to the semantic relevance to push the job search information;

[0063] The distribution characteristics include the number of associated keywords and the mean value of the conditional association probability corresponding to each of the associated keywords.

[0064] Specifically, the user end refers to job seekers, and the enterprise end refers to employers. Both parties can facilitate the conclusion of an interview agreement through relevant interactions on the recruitment platform. At the same time, based on the relevant rules of the recruitment platform, the resume information uploaded by job seekers on the recruitment platform and the job search information uploaded by employers will be stored in the background of the recruitment platform. Therefore, the present invention gradually analyzes the information recorded in the background and adaptively pushes the job search information to the user end.

[0065] Specifically, before obtaining resume information and job application information, you need to obtain authorization from the corresponding terminal, which will not be repeated here.

[0066] Specifically, there is no limitation on the specific structures of the history determination module, event analysis module, preliminary analysis module and retrieval module, and they themselves or each unit therein can be composed of logical components or a combination of logical components, and the logical components include field programmable processors, computers or microprocessors in computers.

[0067] Specifically, the history determination module is used to extract key events according to the event response standard, including:

[0068] To identify the events that meet the event response criteria as the critical events;

[0069] Among them, the event response standard includes that the user end and the enterprise end reach an interview agreement; the event includes interactive operations between the user end and the enterprise end.

[0070] It can be understood that the interactive operation refers to the relevant operations performed by the user side and the enterprise side to facilitate the interview agreement, including the interview invitation pop-up window sent by the enterprise side to the user side and the user side clicking the button in response to the interview invitation pop-up window and the interaction between the user side and the enterprise side in the interactive interface, etc., which will not be elaborated here.

[0071] Specifically, the event analysis module is used to determine the association relationship of each keyword, including:

[0072] If the conditional association probability of the keywords corresponding to the resume information and the job application information is greater than or equal to the conditional association probability threshold, it is determined that there is an association relationship between the keywords;

[0073] The conditional association probability is the probability that the keyword appears in both the resume information and the job search information.

[0074] It is understandable that the degree of matching between the resume information obtained to reach an interview agreement and the keywords corresponding to the job application information can reflect the degree to which both parties are inclined to reach an interview agreement. Therefore, in this embodiment, keywords with an associated relationship are determined as associated keywords, which will not be repeated here.

[0075] In implementation, the conditional association probability is determined by calculating the co-occurrence frequency of keywords corresponding to the resume information and the job application information. The co-occurrence frequency can be determined by constructing a bag-of-words model. Of course, other methods can also be used, which will not be repeated here.

[0076] Specifically, event information refers to the resume information uploaded by the corresponding user side and the job search information uploaded by the enterprise side after the key event is achieved.

[0077] Specifically, when calculating the conditional association probability, the recorded time information can be stored in a database, and the probability of the keywords appearing in both the resume information and the job search information can be calculated based on the information in the database to obtain the conditional association probability, which will not be repeated here.

[0078] In implementation, the purpose of setting the conditional association probability threshold of the keywords corresponding to the resume information and the job application information is to characterize the situation where the co-occurrence frequency and matching degree of the keywords in the resume information and the job application information are high;

[0079] Among them, the conditional association probability threshold is determined based on the mean value of the conditional association probabilities of the keywords corresponding to the resume information and the job application information. By obtaining the event information corresponding to several key events recorded in the background for reaching an interview agreement, the conditional association probabilities of the keywords corresponding to the resume information and the job application information are called, and the mean value of the conditional association probability is solved. For the purpose of setting the conditional association probability threshold, the conditional association probability threshold is determined between 1.15 times and 1.35 times the mean value of the conditional association probability.

[0080] Specifically, the present invention sets up an event analysis module, which determines the correlation between resume information and job application information corresponding to the interview agreement by analyzing pre-recorded key events. In actual situations, job seekers and employers will upload the corresponding resume information and job application information to the recruitment platform, and interact on the platform to facilitate the interview agreement. Based on this, the present invention analyzes a large number of events before the interview agreement is reached to identify the similarities in such events. For example, the correlation between the resume information and the job application information is analyzed by the degree of matching of the corresponding keywords. The correlation relationship is used to represent the high contribution of the corresponding keywords contained in the resume information to the interview agreement between the two parties, and at the same time reflects that the keyword has high availability and strong data representation when matching the corresponding position.

[0081] Specifically, the preliminary analysis module is used to calculate the associated dense representation parameters according to the distribution characteristics, including:

[0082] The ratio of the number of related keywords to the number threshold is used as the first related dense feature;

[0083] The ratio of the mean value of the conditional association probability corresponding to each of the associated keywords to the mean value of the conditional association probability threshold is used as a second association intensive feature;

[0084] Using the first association intensive feature and the second association intensive feature to perform a weighted sum as the association intensive representation parameter;

[0085] The associated keywords are keywords with an associated relationship.

[0086] In this implementation, when performing weighted summation, the weight of the first associated dense feature is set to 0.45, and the weight of the second associated dense feature is set to 0.55;

[0087] Regarding the determination of the number threshold of associated keywords and the mean threshold of the conditional association probability between each associated keyword and the keywords corresponding to the job search information, during implementation, the event information corresponding to several key events for reaching interview agreement recorded in the background is obtained, the number data of associated keywords and the mean data of the conditional association probability between each associated keyword and the keywords corresponding to the job search information are called, and the average value of the number of associated keywords and the average value of the conditional association probability mean are solved. Since the purpose of setting the number threshold of associated keywords and the mean threshold of the conditional association probability between each associated keyword and the keywords corresponding to the job search information is to represent the high degree of match between the keywords in the resume information and the job search information and the dense distribution of the associated keywords, the number threshold of associated keywords is determined between 1.1 times and 1.02 times the average value of the number of associated keywords, and the conditional association probability mean threshold is determined between 1.15 times and 1.2 times the average value of the conditional association probability mean.

[0088] Specifically, see Figure 2 As shown, it is a logical decision diagram for setting distribution labels for resume information in an embodiment of the present invention. The preliminary analysis module is used to set distribution labels for the resume information, including:

[0089] If the associated dense representation parameter is greater than or equal to the associated dense representation parameter threshold, setting the label of the resume information to dense distribution;

[0090] If the association intensive representation parameter is less than the association intensive representation parameter threshold, the label of the resume information is set to dispersed distribution.

[0091] The threshold of the association dense representation parameter is selected in the interval [1.25, 1.34].

[0092] Specifically, the present invention sets up a preliminary analysis module, which preliminarily analyzes the distribution of keywords associated with the job search information in the resume information, based on the basic conditions between the pre-analyzed resume information and the corresponding keywords of the job search information that can facilitate the job seeker and the employer to reach an interview agreement. That is, the probability of occurrence of such keywords in the resume information is analyzed. In actual situations, the proportion and accuracy of the keywords contained in the resume information will affect the accuracy of the screening and matching of the job search information. Therefore, the present invention calculates the association density characterization parameter by associating the keywords and the mean of the conditional association probabilities of each associated keyword and the corresponding keywords of the job search information, so as to characterize the distribution density of the keywords corresponding to the resume information uploaded by the user and the corresponding job search information, and then characterizes the matching degree of the resume information and the job search information. Under the premise of ensuring data reliability, the present invention reduces the amount of data for screening and matching, and improves the efficiency and accuracy of screening and matching.

[0093] Specifically, see Figure 3 As shown, it is a logical decision diagram for calling a deep matching unit or a conventional matching unit according to a distribution tag in an embodiment of the present invention. The retrieval module is used to call a deep matching unit or a conventional matching unit according to the distribution tag, and includes:

[0094] If the labels set in the resume information are densely distributed, the deep matching unit is called;

[0095] If the label set for the resume information is dispersed distribution, the regular matching unit is called.

[0096] Specifically, the deep matching unit is used to remove keywords including:

[0097] Used to identify keywords in resume information;

[0098] It is used to eliminate keywords that have no correlation among the keywords.

[0099] If the conditional association probability between the resume information and the keywords corresponding to the job application information is less than the conditional association probability threshold, it is determined that there is no association relationship between the keywords.

[0100] Specifically, the deep matching unit is used to determine the push sequence for the job application information based on the conditional association probability between the reserved keywords and the keywords corresponding to each job application information, including

[0101] To respectively determine the conditional association probability between the reserved keywords and the keywords corresponding to each job application information;

[0102] To sort the job application information in descending order according to the conditional association probability to obtain the push sequence.

[0103] It can be understood that the reserved keywords are all keywords with an association relationship. When determining the corresponding keywords, the conditional association probability is used, so the conditional association probability of the reserved keywords can be determined.

[0104] Specifically, the deep matching module is used to match the job application information based on each keyword corresponding to the push sequence segment, and determine the push priority of each job application information based on the sequence segment, including

[0105] To divide the push sequence into several push sequence segments;

[0106] To call each keyword in the sequence segment to match with each keyword in the job application information to determine the job application information that meets the matching conditions;

[0107] To preferentially push the job application information corresponding to the sequence segment with a higher ranking. It can be understood that the greater the average value of the conditional association probability corresponding to each keyword in the sequence segment, the higher the corresponding ranking.

[0108] Wherein, the matching condition is that the semantic association degree between each keyword and the keyword in the job application information is greater than a predetermined semantic association degree threshold.

[0109] Specifically, there is no limitation on the division method of the push sequence segment, which can be determined according to the proportion of the total number of keywords, and the proportion of keywords corresponding to each sequence segment is the same.

[0110] Specifically, there is no limitation on the method for determining the semantic association degree between the resume information and the keywords corresponding to the job application information. In some possible implementations, a word vector model, such as Word2Vec or FastText, is used to map relevant words to a vector space, and the cosine similarity is used to represent the semantic association degree.

[0111] The semantic relevance threshold is determined in advance. By analyzing the pre-recorded key events, event information is determined, and the average value of the semantic relevance between each keyword in the job application information and the resume information is solved. The semantic relevance threshold is set to be between 0.75 times and 0.85 times of the average value of the semantic relevance.

[0112] Specifically, the present invention sets a deep matching unit. Since the set tags are for resume information with dense distribution and the resume contains more keywords that contribute to both parties reaching an interview agreement, the present invention takes these keywords as the key consideration basis, and pre-removes the keywords that have no association relationship with the corresponding job application information. On the premise of ensuring the screening accuracy, the amount of data for screening and matching and the interference words that affect the screening and matching are reduced. Based on the remaining keywords after the removal operation and the conditional association probability corresponding to the corresponding remaining keywords, a deep analysis is performed to determine the push sequence of the job application information, and the push priority is determined to push the job application information in the obtained sequence segment, improving the accuracy of screening and matching. Furthermore, on the premise of ensuring the data reliability, the present invention reduces the amount of data for screening and matching, and improves the efficiency and accuracy of screening and matching.

[0113] Specifically, please refer to Figure 4 As shown, it is a logical decision diagram for the present invention's embodiment to generate a job application information push list according to the semantic relevance. The conventional matching unit for generating a job application information push list according to the semantic relevance includes

[0114] If the semantic relevance is greater than or equal to the semantic relevance threshold, the corresponding job application information is placed into the job application information push list.

[0115] It can be understood that after being placed into the job application information push list, it is pushed according to the list order, which will not be elaborated here.

[0116] So far, the technical solution of the present invention has been described in combination with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. An intelligent management system for AI big data job screening, characterized in that, Including: A historical determination module, which is used to obtain the resume information uploaded by each client and the job application information uploaded by the enterprise side, extract key events according to the event response standard, and record the event information corresponding to the key events; An event analysis module, which is connected to the historical determination module, and is used to call the event information of the key events, determine the association relationship of each keyword according to the conditional association probability of the corresponding keywords in the resume information and the job application information in the event information, and construct an association relationship database; A preliminary analysis module, which is connected to the event analysis module, and in response to the resume information uploaded by the client, is used to parse the resume information, identify the distribution characteristics of each associated keyword, calculate the association density characterization parameter according to the distribution characteristics, and set a distribution label for the resume information; A retrieval module, which is connected to the preliminary analysis module, and includes a deep matching unit and a conventional matching unit; The deep matching unit is used to perform a deletion operation on the keywords of the resume information to obtain reserved keywords, determine the push sequence for the job application information based on the conditional association probability of the reserved keywords and the corresponding keywords of each job application information, match the job application information based on each keyword in the push sequence segment, and determine the push priority of each job application information based on the sequence segment to perform job application information push; The conventional matching unit is used to determine the semantic association degree of the corresponding keywords in the resume information and the job application information, and generate a job application information push list according to the semantic association degree to perform job application information push; Among them, the distribution characteristics include the number of associated keywords and the average value of the conditional association probabilities corresponding to each of the associated keywords.

2. The intelligent management system for AI big data job screening according to claim 1, wherein The historical determination module is used to extract key events according to the event response standard, including Determining the event that meets the event response standard as the key event; Among them, the event response standard includes that the client and the enterprise side reach an interview agreement; the event includes an interaction operation between the client and the enterprise side.

3. The intelligent management system for AI big data job screening according to claim 1, characterized in that, The event analysis module is used to determine the association relationship of each keyword, including If the conditional association probability of the corresponding keywords in the resume information and the job application information is greater than or equal to the conditional association probability threshold, it is determined that there is an association relationship between each keyword; Among them, the conditional association probability is the probability that the keyword appears in both the resume information and the job application information.

4. The intelligent management system for AI big data job screening according to claim 1, characterized in that, The preliminary analysis module is used to calculate the association density characterization parameter according to the distribution characteristics, including Using the ratio of the number of associated keywords to the number threshold as the first association density feature; Using the ratio of the average value of the conditional association probabilities corresponding to each of the associated keywords to the conditional association probability average value threshold as the second association density feature; Using the weighted sum of the first association density feature and the second association density feature as the association density characterization parameter; Among them, the associated keyword is a keyword with an association relationship.

5. The AI big data-based intelligent management system for job screening according to claim 1, characterized in that, The preliminary analysis module is used to set a distribution label for the resume information, including If the association density characterization parameter is greater than or equal to the association density characterization parameter threshold, the label of the resume information is set to dense distribution; If the associated dense representation parameter is less than the associated dense representation parameter threshold, the label of the resume information is set to a scattered distribution.

6. The intelligent management system for AI big data job screening according to claim 1, characterized in that, The retrieval module is used to call the deep matching unit or the conventional matching unit according to the distribution label, including If the label set for the resume information is a dense distribution, the deep matching unit is called; If the label set for the resume information is a scattered distribution, the conventional matching unit is called.

7. The intelligent management system for AI big data job screening according to claim 1, characterized in that, The deep matching unit is used to eliminate keywords, including Determining each keyword in the resume information; Eliminating the keywords that have no association relationship among the keywords.

8. The intelligent management system for AI big data job screening according to claim 1, characterized in that, The deep matching unit is used to determine the push sequence for the job application information based on the conditional association probability between the remaining keywords and the corresponding keywords of each job application information, including Determining the conditional association probability between the remaining keywords and the corresponding keywords of each job application information respectively; Sorting the job application information in descending order according to the conditional association probability to obtain the push sequence.

9. The intelligent management system for AI big data job screening according to claim 8, characterized in that, The deep matching module is used to match the job application information based on each keyword corresponding to the push sequence segment, and determine the push priority of each job application information based on the sequence segment, including Dividing the push sequence into several push sequence segments; Calling each keyword in the sequence segment to match with each keyword in the job application information to determine the job application information that meets the matching conditions; Giving priority to pushing the job application information corresponding to the sequence segment with a higher ranking; Among them, the matching condition is that the semantic association degree between each keyword and the keyword in the job application information is greater than a predetermined semantic association degree threshold.

10. The intelligent management system for AI big data job screening according to claim 1, characterized in that, The conventional matching unit is used to generate a job application information push list according to the semantic association degree, including If the semantic association degree is greater than or equal to the semantic association degree threshold, the corresponding job application information is placed in the job application information push list.

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