Personnel management system convenient for information query and method thereof

Abstract

The invention discloses a personnel management system convenient for information query and a method thereof, and relates to the technical field of personnel information management. After an analysis module records student audio, emotion colors in the student audio are analyzed based on an emotion analysis algorithm, and after motion posture data of a student are obtained, the motion posture data are stored; the physical ability level and coordination of the student are analyzed based on a posture estimation algorithm, after text data of the student are obtained, professional knowledge of the student is analyzed based on a text analysis algorithm, and the comprehensive evaluation module comprehensively analyzes audio analysis data, image analysis data and text analysis data and then evaluates the comprehensive ability of the student. And the screening module screens the students which do not meet the requirements and sends enrollment notifications to the students which meet the requirements. The management system encrypts data in the student information transmission process, effectively avoids student information leakage, objectively evaluates the comprehensive ability of all students, effectively avoids human errors, reduces the labor burden, and improves the evaluation fairness.

Description

Technical Field

[0001] The present invention relates to the technical field of personnel information management, and particularly relates to a personnel management system and method for facilitating information query. Background Art

[0002] The enrollment of sports special students refers to schools or educational institutions providing special enrollment channels and selection mechanisms for selecting students with outstanding sports talents. The selection of sports special students usually requires multiple rounds of evaluation, including interviews, physical tests, skill tests, etc. To better coordinate these activities, the system needs to provide a centralized management platform so that evaluators and decision-makers in each link can conveniently access and update relevant information. The management system is a software system designed to effectively manage the enrollment process of sports special students. Such systems are usually adopted by schools, educational institutions or sports training institutions to more effectively manage and track the enrollment process of sports special students.

[0003] The prior art has the following deficiencies:

[0004] 1. Existing management systems usually only provide functions for personnel information entry, storage and query. In actual operation, the analysis and evaluation of students' performance are all carried out manually. After the enrollment personnel score and screen the students' performance, the qualified students' information is entered into the management system. However, due to frequent human intervention, there is not only a risk of human error, and the subjective nature of human evaluation is strong, which is likely to cause problems of apparent fairness. Moreover, manual evaluation also increases the labor burden and cost;

[0005] 2. The data security of existing management systems is poor, and the privacy protection of students' information is poor, which is likely to lead to the leakage of students' information and cause unnecessary losses;

[0006] Based on this, the present invention provides a personnel management system and method for facilitating information query, which can objectively analyze and evaluate students and has data security and privacy protection measures to solve the problems raised in the background art. Summary of the Invention

[0007] The purpose of the present invention is to provide a personnel management system and method for facilitating information query to solve the deficiencies in the background art.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A personnel management system for facilitating information query, including an enrollment application module, a preliminary review module, a data storage module, an analysis module, a comprehensive evaluation module, a screening module, a sorting module, and a query module:

[0009] The enrollment application module: used for students to submit enrollment applications and provide online application forms;

[0010] Preliminary review module: Check the enrollment application information based on the review mechanism, and send interview information to the trainee after the trainee passes the preliminary review;

[0011] Data storage module: Encrypt the trainee information through the Secure Sockets Layer protocol, and store all the obtained trainee information in the database. When the trainee coming for an interview matches any trainee information in the database, wake up the analysis module;

[0012] Analysis module: After inputting the trainee's audio, analyze the emotional color in the trainee's audio based on the sentiment analysis algorithm. After obtaining the trainee's motion posture data, analyze the trainee's physical fitness level and coordination based on the pose estimation algorithm. After obtaining the trainee's text data, analyze the trainee's professional knowledge based on the text analysis algorithm;

[0013] Comprehensive evaluation module: Comprehensively analyze the audio analysis data, image analysis data, and text analysis data to evaluate the comprehensive ability of the trainee;

[0014] Screening module: According to the trainee evaluation results, screen out the trainees who do not meet the requirements and send admission notices to the trainees who meet the requirements;

[0015] Sorting module: Sort all the trainees who have successfully completed the enrollment according to the comprehensive ability evaluation results to generate a trainee ranking list;

[0016] Query module: After verifying the user's identity based on the authentication mechanism, the user queries the trainee ranking list and trainee information.

[0017] In a preferred embodiment, the analysis module analyzes the emotional color in the trainee's audio based on the sentiment analysis algorithm, including the following steps:

[0018] Receive the trainee's audio file or real-time audio stream, and preprocess the audio;

[0019] Use Mel Frequency Cepstral Coefficients to extract the features required for sentiment analysis from the audio. The features include the pitch, speech rate, tone, and energy of the audio;

[0020] Identify the emotional labels in the audio features through the Convolutional Neural Network algorithm, and output the predicted confidence values of different emotional labels;

[0021] Perform a weighted average calculation on the predicted confidence levels of different emotional labels to obtain the trainee's emotional index X yp 。

[0022] In a preferred embodiment, the analysis module analyzes the trainee's physical fitness level and coordination based on the pose estimation algorithm, including the following steps:

[0023] Obtain the motion posture data of the trainee through sensors and camera devices. The motion posture data includes joint angles, body positions, and motion trajectories.

[0024] Preprocess the collected posture data. Convert the collected posture information into specific posture data through a posture estimation algorithm. The posture data includes calculating joint flexibility, joint relative speed, motion trajectory length, and joint acceleration.

[0025] After weighted calculation of joint flexibility, joint relative speed, motion trajectory length, and joint acceleration, obtain the physical fitness index X of the trainee tn 。

[0026] In a preferred embodiment, the analysis module analyzes the professional knowledge of the trainee based on a text analysis algorithm, including the following steps:

[0027] Clean the text data, including removing stop words, handling spelling mistakes, and performing stemming or lemmatization operations.

[0028] Divide the text into sentences or paragraphs and mark the vocabulary. Use entity recognition algorithms to identify named entities in the text.

[0029] Construct the relationships between the concepts and entities involved in the text, create a knowledge graph, and use semantic similarity algorithms to compare the sentences or paragraphs in the text.

[0030] Determine the important vocabulary in the text through a keyword extraction algorithm, calculate the text similarity index, and the calculation expression is:

[0031]

[0032] In the formula, X wb is the text similarity index, j = 1, 2, 3,..., k, k represents the number of elements of the text topic distribution vector F and the domain topic distribution vector G, and k is a positive integer. F j represents the j-th element of the text topic distribution vector F, and G j represents the j-th element of the domain topic distribution vector G.

[0033] In a preferred embodiment, the comprehensive evaluation module evaluates the comprehensive ability of the trainee, including the following steps:

[0034] The audio analysis data is the emotion index, the image analysis data is the physical fitness index, and the text analysis data is the text similarity index.

[0035] After normalizing the emotion index, physical fitness index, and text similarity index, comprehensively calculate to obtain the ability coefficient nl of the trainee x ,and the calculation expression is:

[0036]

[0037] Wherein, X tn is the physical fitness index, X yp is the emotional index, X wb is the text similarity index, and α, β, and γ are the proportionality coefficients of the physical fitness index, emotional index, and text similarity index respectively, and α > β > γ > 0;

[0038] The obtained ability coefficient nl x The larger the value, the stronger the comprehensive ability of the student. After obtaining the ability coefficient nl x value, compare the ability coefficient nl x value with the preset ability threshold;

[0039] If the ability coefficient nl x value is greater than or equal to the ability threshold, it is evaluated that the comprehensive ability of the student is strong. If the ability coefficient nl x value is less than the ability threshold, it is evaluated that the comprehensive ability of the student is weak.

[0040] In a preferred embodiment, the screening module classifies students with weak comprehensive ability as students who do not meet the requirements, classifies students with strong comprehensive ability as students who meet the requirements, and separately marks students who meet the requirements and students who do not meet the requirements, and generates an admission notice for students who meet the requirements;

[0041] The sorting module obtains the information of all students who have completed the admission procedures and the corresponding ability coefficient nl x for each student, sorts all students in descending order according to the ability coefficient nl x to generate a student ranking list.

[0042] In a preferred embodiment, the data storage module encrypts the student information through the Secure Sockets Layer (SSL) protocol, including the following steps:

[0043] Apply for and obtain a trusted SSL certificate, install the SSL certificate on the server, configure the server to enable the SSL / TLS protocol, and configure the SSL certificate in the server configuration;

[0044] The client sends a connection request to the server by initiating a connection request, and the server and the client start the SSL handshake protocol to negotiate keys during the handshake process;

[0045] After the handshake is successful, a secure channel is established, and the student information is symmetrically encrypted using the negotiated shared key.

[0046] In a preferred embodiment, the client sends a connection request to the server by initiating a connection request, and the server and the client start the SSL handshake protocol. During the handshake process, key negotiation is performed, including the following steps:

[0047] The client sends a ClientHello message to the server. The ClientHello message includes the SSL / TLS version, a list of cipher suites, and random number information;

[0048] After receiving the ClientHello, the server sends a ServerHello message to the client, selects the SSL / TLS version, a cipher suite, generates a random number for the server, and attaches an SSL certificate;

[0049] After receiving the server's SSL certificate and verifying its validity, the client decrypts the digital signature in the certificate using the server's public key;

[0050] The client generates a random number PreMasterSecret and encrypts this random number using the server's public key, then sends it to the server;

[0051] The server decrypts the PreMasterSecret sent by the client using its own private key to obtain the shared key;

[0052] The client and the server generate a master key based on the negotiated SSL / TLS version, the random numbers in ClientHello and ServerHello, and the PreMasterSecret. The master key is used as the session key for symmetric encryption communication;

[0053] The client sends a Finished message. The Finished message includes the hash value of the handshake messages. After the handshake process is completed, the server also sends a Finished message, which includes the hash value of the handshake messages.

[0054] In a preferred embodiment, after the handshake is successful, the data storage module establishes a secure channel and symmetrically encrypts the trainee information using the negotiated shared key, including the following steps:

[0055] The client and the server select the AES symmetric encryption algorithm according to the information negotiated in the handshake protocol to generate a random initialization vector;

[0056] The trainee information to be encrypted is block-processed according to the requirements of the symmetric encryption algorithm. If the information length is not an integer multiple of the block size, the information length is padded;

[0057] Using the negotiated shared key and initialization vector, each data block is symmetrically encrypted, and an authentication tag is generated for each encrypted data block using the HMAC algorithm. The authentication tag is used to verify whether the data has been tampered with during transmission;

[0058] The encrypted ciphertext and the generated authentication tag are transmitted to the receiver through a secure channel. The receiver decrypts the received ciphertext using the negotiated shared key and initialization vector, and restores the original student information after merging the decrypted data blocks.

[0059] The present invention also provides a personnel management method for facilitating information query. The management method includes the following steps:

[0060] S1: The student submits a sports specialty student enrollment application through the client. The review end checks the enrollment application information based on the review mechanism. After the student passes the preliminary review, the review end sends interview information to the student;

[0061] S2: After encrypting the student information through the Secure Sockets Layer protocol, all the obtained student information is stored in the database;

[0062] S3: When the student coming for the interview matches any student information in the database, the processing end records the student's audio and analyzes the emotional color in the student's audio based on the emotion analysis algorithm, obtains the student's motion posture data and analyzes the student's physical fitness level and coordination based on the posture estimation algorithm, and obtains the student's text data and analyzes the student's professional knowledge based on the text analysis algorithm;

[0063] S4: After comprehensively analyzing the audio analysis data, image analysis data, and text analysis data, the comprehensive ability of the student is evaluated;

[0064] S5: According to the student evaluation results, the students who do not meet the requirements are screened out and admission notices are sent to the students who meet the requirements;

[0065] S6: All the students who have successfully completed enrollment are sorted according to the comprehensive ability evaluation results to generate a student ranking list. After verifying the user's identity through the identity verification mechanism, the user queries the student ranking list and the student information.

[0066] In the above technical solution, the technical effects and advantages provided by the present invention:

[0067] 1. After encrypting the information of students through the data storage module using the Secure Sockets Layer protocol, all the obtained student information is stored in the database. After the analysis module inputs the student audio, the emotional color in the student audio is analyzed based on the sentiment analysis algorithm. After obtaining the motion posture data of the students, the physical fitness level and coordination of the students are analyzed based on the pose estimation algorithm. After obtaining the text data of the students, the professional knowledge of the students is analyzed based on the text analysis algorithm. The comprehensive evaluation module comprehensively analyzes the audio analysis data, image analysis data, and text analysis data to evaluate the comprehensive ability of the students. The screening module screens out the students who do not meet the requirements according to the evaluation results of the students and sends admission notices to the students who meet the requirements. This management system encrypts the data during the transmission of student information, effectively avoiding the leakage of student information. Moreover, the management system objectively evaluates the comprehensive ability of all students, effectively avoiding human errors, reducing the manual burden, and improving the fairness of the evaluation;

[0068] 2. The present invention sorts all the students who have successfully completed the admission procedures according to the comprehensive ability evaluation results through the sorting module to generate a student ranking list. After the query module verifies the user's identity based on the identity authentication mechanism and passes, the user can query the student ranking list and student information, which not only has higher security protection for the data but also facilitates the user to query the student information;

[0069] 3. After the comprehensive evaluation module normalizes the emotional index, physical fitness index, and text similarity index, the ability coefficient nl of the students is obtained through comprehensive calculation x , which not only effectively improves the data processing efficiency but also comprehensively analyzes multiple data to evaluate the comprehensive ability of the students, and the analysis is more comprehensive. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0071] Figure 1 It is the system module diagram of the present invention.

[0072] Figure 2 It is the method flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without any creative effort belong to the scope of protection of the present invention.

[0074] Embodiment 1: Please refer to Figure 1 As shown, a personnel management system for facilitating information query in this embodiment includes an enrollment application module, a preliminary review module, a data storage module, an analysis module, a comprehensive evaluation module, a screening module, a sorting module, and a query module:

[0075] Enrollment application module: Students submit applications for sports specialty enrollment through this module. The enrollment application module provides an online application form and can upload relevant supporting documents, such as sports transcripts, honor certificates, etc. The enrollment application information is sent to the preliminary review module. Specifically:

[0076] Students first need to log in or register in the system to ensure that each applicant has a unique identity identifier, which helps to track and manage the application process. Provide an intuitive and easy-to-fill online application form, including students' basic information, such as name, age, contact information, etc., and detailed information on special issues related to sports specialties, ensure that the form design is user-friendly and easy to understand, allow students to upload relevant supporting documents, such as sports transcripts, honor certificates, screenshots of competition results, etc., ensure that the system supports multiple file formats, and provide sufficient storage capacity;

[0077] Conduct a preliminary verification of the information submitted by students to ensure that all required fields are filled and check the format to reduce errors and delays in subsequent reviews. Provide students with the opportunity to confirm the application information, which may include an electronic signature or a confirmation button to ensure that students understand and agree to the submitted information. The system can automatically send a confirmation email or text message to students, notifying them that their application has been successfully submitted and providing further information, such as review progress and contact information. After students successfully submit their applications, the system automatically transfers the application information to the preliminary review module for the next step of review and processing.

[0078] Preliminary review module: Check the enrollment application information based on the review mechanism. After the preliminary review of the student is passed, send interview information to the student and send the student information to the data storage module. After receiving the interview information, the student can choose to go to the school for an interview. Specifically:

[0079] The system checks the enrollment application information submitted by students to ensure that all required fields are filled, the file format is correct, and the data is complete and error-free. Based on the enrollment criteria, a preliminary screening is conducted to exclude ineligible applications to improve the efficiency of subsequent processing. For students who pass the preliminary review, a notice is sent to inform them that they have successfully passed the preliminary review and provide detailed information about the interview. The notice can be sent via email, text message, or in-system message;

[0080] After receiving the notice, students can view the specific interview arrangements in the system, including the interview time, location, relevant requirements, etc. The system should ensure that the interview time matches the students' available time. After students confirm receiving the interview notice, they can confirm in the system whether they are willing to participate in the interview. The system may provide a selection function where students can choose whether to go to the school for the interview. Provide the interview preparation materials required by students, such as interview guides, school introductions, etc., to help students prepare for the interview. For students who choose to go to the school for the interview, the system can provide an interview check-in function to record the arrival time of students and improve the management efficiency of the interview site.

[0081] Data storage module: After encrypting the students' information through the Secure Sockets Layer (SSL) protocol, all the obtained students' information is stored in the database. When a student coming for an interview matches any student information in the database, the analysis module is awakened, including the following steps:

[0082] Encrypt the data in transit through the Secure Sockets Layer (SSL) protocol to ensure that the data is not easily obtained maliciously during transmission. Select a suitable database to store students' information. Common database systems include MySQL, PostgreSQL, MongoDB, etc. Choose the database type that suits the requirements, design the database tables to ensure that various types of students' information can be stored, including personal basic information, application materials, interview results, etc. Adopt appropriate data types and relationship models to ensure data consistency and integrity. Encrypt the students' information stored in the database to increase data security. Sensitive information such as personal ID numbers and contact information can be protected using field-level encryption techniques. Implement a strict access control mechanism to ensure that only authorized personnel can access, modify, and delete students' information in the database. Use the access permission management function of the database to restrict the permissions of different users. Develop a regular database backup strategy to ensure timely data recovery in case of accidental data loss. The backup data should be stored in a secure place and be subject to corresponding access control to ensure that the way the database stores and processes students' information complies with relevant regulations and privacy standards, such as the General Data Protection Regulation (GDPR), etc.;

[0083] Encrypting the data in transit through the Secure Sockets Layer (SSL) protocol includes the following steps:

[0084] To apply for and obtain a trusted SSL certificate, it is usually necessary to purchase it through a Certificate Authority (CA), install the SSL certificate on the server to ensure that the server can use the certificate for secure communication, configure the server to enable the SSL / TLS protocol to ensure that the server supports secure communication, specify the SSL certificate to be used in the server configuration to ensure a secure connection with the client.

[0085] SSL usually uses port 443. By default, the server listens on port 443 to receive secure connection requests. As needed, other ports can be configured, but generally, port 443 is the most commonly used port for SSL encrypted communication. The client sends a connection request to the server by initiating a connection request, indicating its intention to establish a secure connection. The server and the client start the SSL handshake protocol to establish a secure connection, select the SSL / TLS protocol version used in the communication to ensure that both parties support the same secure protocol version, conduct key negotiation during the handshake to ensure the generation of a shared key for encrypted communication. After the handshake is successful, a secure channel is established to ensure that data is encrypted and protected during transmission. Symmetric encryption is used with the negotiated shared key to ensure the confidentiality of the data.

[0086] The client sends a connection request to the server by initiating a connection request. The server and the client start the SSL handshake protocol, and key negotiation is carried out during the handshake, including the following steps:

[0087] The client sends a ClientHello message to the server, which contains information such as the supported SSL / TLS versions, a list of cipher suites, and a random number. After receiving the ClientHello, the server sends a ServerHello message to the client, selects the SSL / TLS version, cipher suite, generates its own random number, and attaches the SSL certificate. After receiving the server's SSL certificate, the client verifies the validity of the certificate, including whether the issuer of the certificate is trustworthy and whether it has expired. The client uses the server's public key to decrypt the digital signature in the certificate to verify the integrity of the certificate. The client generates a random number PreMasterSecret and encrypts this random number using the server's public key and sends it to the server. The server uses its private key to decrypt the PreMasterSecret sent by the client to obtain the shared key;

[0088] The client and the server generate the master key (MasterSecret) based on the negotiated SSL / TLS version, the random numbers in ClientHello and ServerHello, and the PreMasterSecret. The master key is the basis for generating the session key in the symmetric encryption process. The client and the server use the master key to generate the session key for symmetric encryption communication. The client sends a Finished message containing the hash value of the handshake messages to prove that the handshake process is completed. The server also sends a Finished message containing the hash value of the handshake messages.

[0089] After the handshake is successful, a secure channel is established, and the student information is symmetrically encrypted using the negotiated shared key.

[0090] The client and the server select the AES (Advanced-Encryption-Standard) symmetric encryption algorithm according to the information negotiated in the handshake protocol. The symmetric encryption algorithm usually requires an initialization vector to increase the encryption strength. A random initialization vector is generated to ensure that the encryption result is different each time. The student information to be encrypted is block-processed according to the requirements of the symmetric encryption algorithm. If the information length is not an integer multiple of the block size, padding may be required.

[0091] Using the negotiated shared key and the initialization vector, each data block is symmetrically encrypted. The result after encryption is the ciphertext. An authentication tag is generated for each encrypted data block, usually using algorithms such as HMAC (Hash-based-Message-Authentication-Code). The authentication tag is used to verify whether the data has been tampered with during transmission. The encrypted ciphertext and the generated authentication tag are transmitted to the receiving party, which can be directly transmitted through the secure channel to ensure that it will not be eavesdropped or tampered with during transmission.

[0092] The receiving party decrypts the received ciphertext using the negotiated shared key and the initialization vector. At the same time, the authentication tag is verified to ensure data integrity. The receiving party merges the decrypted data blocks and may need to remove the padding part to restore the original student information.

[0093] The client and the server select the AES symmetric encryption algorithm to generate a random initialization vector according to the information negotiated in the handshake protocol. Specifically:

[0094] In the ClientHello and ServerHello messages, the client and the server determine the symmetric encryption algorithm to be used, which may include AES. The choice of algorithm is usually determined based on the optimal algorithm in the list of algorithms supported by both parties. After selecting the AES algorithm, both the client and the server need to generate a random initialization vector (IV). This vector is an important element for increasing the encryption strength. A binary data that meets the length requirements of AES is randomly generated as the initialization vector. The length of the IV is usually the same as the block size of the AES algorithm. The ClientHello and ServerHello messages contain the negotiated information, including the selected symmetric encryption algorithm and the generated random initialization vector. Before the encrypted communication starts, the client and the server can use the generated IV to encrypt the sensitive data in the handshake phase to ensure the confidentiality of this data during transmission.

[0095] Use the HMAC algorithm to generate an authentication tag for each encrypted data block. The authentication tag is used to verify whether the data has been tampered with during transmission. Specifically:

[0096] In the SSL / TLS handshake protocol, the client and the server negotiate the HMAC algorithm to be used. Common choices include HMAC-SHA256, HMAC-SHA384, etc., depending on the security and performance requirements. The HMAC algorithm requires a key to generate the authentication tag. This key is usually derived from the master secret negotiated in the handshake protocol. Calculate the authentication tag for each encrypted data block using the HMAC algorithm. The calculation of the HMAC algorithm usually involves performing an exclusive OR (XOR) operation on the data block and the key, and inputting the result into a hash function;

[0097] After the calculation is completed, the generated hash value is the authentication tag. This tag will be used to verify the integrity of the data block. Transmit the generated authentication tag together with the corresponding encrypted data block to the receiving party. The receiving party uses the same HMAC algorithm and the same key to calculate the authentication tag for the received encrypted data block. The receiving party compares the calculated authentication tag with the authentication tag transmitted by the sending party. If the two match, it means the data has not been tampered with during transmission; otherwise, the authentication fails, indicating that the data may have been tampered with or damaged.

[0098] Analysis module: After inputting the trainee's audio, analyze the emotional color in the trainee's audio based on the emotion analysis algorithm. After obtaining the trainee's motion posture data, analyze the trainee's physical fitness level and coordination based on the posture estimation algorithm. After obtaining the trainee's text data, analyze the trainee's professional knowledge based on the text analysis algorithm. Send the audio analysis data, image analysis data, and text analysis data to the comprehensive evaluation module;

[0099] Comprehensive Evaluation Module: After comprehensively analyzing the audio analysis data, image analysis data, and text analysis data, it evaluates the comprehensive ability of the students, and sends the evaluation results to the Screening Module and the Ranking Module;

[0100] Screening Module: Based on the evaluation results of the students, it screens out the students who do not meet the requirements, and sends admission notices to the students who meet the requirements. The screening results are sent to the Ranking Module;

[0101] Ranking Module: After the admission processing deadline has passed, it ranks all the students who have successfully completed the admission process according to the comprehensive ability evaluation results, generates a student ranking list, and sends the student ranking list and student information to the Query Module;

[0102] Query Module: After verifying the user's identity through the identity verification mechanism, it ensures that only authorized personnel can access the management system. The user can query the student ranking list and student information. This module facilitates the school users to query the information related to the students, thus facilitating the school's management of the students. Specifically:

[0103] Before a user attempts to access the Query Module, they must pass the identity verification, which can include the verification of the username and password, or the use of more powerful identity verification methods, such as multi-factor authentication (MFA). Once the user's identity verification is passed, the system needs to check the user's permissions to ensure that only authorized personnel can access the Query Module. This can be achieved through a role and permission management system to ensure that each user can only access the information within their authorized scope;

[0104] Authorized users can access the student ranking list through the Query Module. The student ranking list may include the basic information, application status, interview scores, etc. of the students. Users can use query conditions (such as name, application status, etc.) to retrieve and sort the student information. Authorized users can view the detailed information of specific students, which may include personal information, application materials, interview scores, admission status, etc. Users can find specific students through the unique identifier of the students (such as student number). The Query Module should display the query results in an easy-to-read manner, which may be a table or a list, including the key information of the selected students. Users can customize the displayed fields and sorting methods according to their needs;

[0105] The Query Module can record the user's query history so that users can view the previous query results. This helps users track the changes and developments of student information. It can provide an export function that allows users to export the query results to common document formats, such as Excel or PDF. This is convenient for users to view and share student information in an offline environment. Record the usage of the Query Module, including who accessed which information and when the query was made. This helps with security audits and tracking potential unauthorized access.

[0106] After encrypting the trainee information through the data storage module using the Secure Sockets Layer protocol, all the obtained trainee information is stored in the database. After the analysis module inputs the trainee audio, it analyzes the emotional color in the trainee audio based on the sentiment analysis algorithm. After obtaining the trainee's motion posture data, it analyzes the trainee's physical fitness level and coordination based on the pose estimation algorithm. After obtaining the trainee's text data, it analyzes the trainee's professional knowledge based on the text analysis algorithm. The comprehensive evaluation module comprehensively analyzes the audio analysis data, image analysis data, and text analysis data to evaluate the comprehensive ability of the trainee. The screening module screens out the trainees who do not meet the requirements based on the trainee evaluation results and sends admission notices to the trainees who meet the requirements. This management system encrypts the data during the transmission of trainee information, effectively avoiding the leakage of trainee information. Moreover, the management system objectively evaluates the comprehensive ability of all trainees, effectively avoiding human errors, reducing the manual burden, and improving the fairness of the evaluation.

[0107] In this application, the sorting module sorts all the trainees who have successfully completed the admission procedures according to the comprehensive ability evaluation results to generate a trainee ranking list. After the query module verifies the user's identity through the identity authentication mechanism, the user can query the trainee ranking list and trainee information, which not only provides higher security protection for the data but also facilitates the user to query the trainee information.

[0108] Example 2: A. After the analysis module inputs the trainee audio, it analyzes the emotional color in the trainee audio based on the sentiment analysis algorithm, including the following steps:

[0109] Receive the trainee's audio file or real-time audio stream, ensure the quality and integrity of the audio, and preprocess the audio, including noise reduction, removal of silent segments, audio normalization, etc., to improve the accuracy of sentiment analysis. Use Mel-Frequency-Cepstral-Coefficients (MFCC) to extract the features required for sentiment analysis from the audio, including features such as the tone, speech rate, pitch, and energy of the audio. Identify the emotional labels in the audio features through the convolutional neural network algorithm, output the predicted confidence values of different emotional labels, and then perform a weighted average calculation on the predicted confidence values of different emotional labels to obtain the trainee's emotional index. The expression is: In the formula, X yp is the emotional index, i = 1, 2, 3,..., n, n is the number of emotional labels of the trainee, and n is a positive integer, qg i represents the predicted confidence value of the i-th type of emotional label, ω i represents the weight of the i-th type of emotional label, and ω i is greater than 0. The emotional index is used to reflect the emotional intensity in the trainee's audio. The larger the emotional index, the stronger the emotion in the trainee's audio and the higher the score;

[0110] To better illustrate the above solution, we give the following example:

[0111] Suppose that the emotion labels identified from the audio features by the convolutional neural network algorithm include Positive, Neutral, and Negative, and the predicted confidence values of the three emotion labels are qg1 = 0.8, qg1 = 0.5, and qg1 = 0.3 respectively, and the preset emotion weights are ω1 = 2, ω1 = 1, and ω1 = 0.5 respectively. Substitute them into the emotion index calculation expression:

[0112]

[0113] In the formula, the emotion index X yp is approximately 0.6429, indicating that the emotion intensity of the trainee is average.

[0114] The steps to extract the features required for emotion analysis from the audio using Mel-Frequency-Cepstral-Coefficients (MFCC) are as follows:

[0115] First, segment the speech signal into short-time windows. Usually, a Hamming window or other window functions are used to ensure small waveform attenuation at both ends of the window. This process can be regarded as performing a local short-time Fourier transform (STFT) on the speech signal, converting the time-domain signal into a frequency-domain signal, performing a Fourier transform on the signal within each window to obtain the spectrum, calculating the power spectrum of the spectrum to represent the signal intensity at different frequencies, and mapping the continuous spectrum to the Mel frequency scale to simulate the human auditory perception of sound frequencies. Usually, a set of Mel filters are used. These filters have higher resolution in the low-frequency band and lower resolution in the high-frequency band, simulating the perception characteristics of the human ear for sound frequencies;

[0116] Take the logarithm of the energy of each Mel filter to increase the sensitivity to the lower-energy part, and apply the discrete cosine transform to the logarithmic energy coefficients to obtain the MFCC coefficients. The DCT is used to convert the information in the frequency domain into the cepstral domain and retain the most important information. Usually, only the first few coefficients of the DCT output are retained. These coefficients are called MFCC coefficients. The first few MFCC coefficients usually contain the key information of the speech signal, while the subsequent coefficients may contain noise or redundant information.

[0117] After the analysis module obtains the motion posture data of the trainee, it analyzes the physical fitness level and coordination of the trainee based on the pose estimation algorithm, including the following steps:

[0118] Obtain the motion posture data of the trainee through devices such as sensors and cameras. This can include information such as joint angles, body positions, and motion trajectories. The accuracy and integrity of the data are crucial for subsequent analysis. Preprocess the collected posture data, including operations such as denoising, filtering, and data alignment, to ensure data quality and improve the accuracy of subsequent analysis. Apply a posture estimation algorithm to convert the collected posture information into specific posture data. The posture data includes calculating joint flexibility, joint relative speed, motion trajectory length, and joint acceleration. After weighted calculation of joint flexibility, joint relative speed, motion trajectory length, and joint acceleration, obtain the physical fitness index of the trainee. The expression is: X tn = a1 * LH - a2 * XS + a3 * YC + a4 * JD, where X tn is the physical fitness index, LH, XS, YC, and JD are joint flexibility, joint relative speed, motion trajectory length, and joint acceleration respectively, and a1, a2, a3, and a4 are the weights of joint flexibility, joint relative speed, motion trajectory length, and joint acceleration respectively, and a1, a2, a3, and a4 are all greater than 0. The larger the physical fitness index, the better the physical fitness level and coordination of the trainee.

[0119] Among them, joint flexibility is used to reflect the joint flexibility of the trainee, and joint flexibility = range of joint angle change / maximum joint angle range. Joint relative speed XS = |GS1 - GS2|, where GS1 is the evaluated joint and GS2 is the symmetric joint of the evaluated joint. The smaller the joint relative speed, the better the joint coordination of the trainee. Motion trajectory length is used to reflect the heart tolerance of the trainee. Joint acceleration JD = (CW - ZW) / Δt, where CW is the initial speed of the joint, ZW is the final speed of the joint, and Δt is the time interval. Joint acceleration is used to reflect the explosive power of the trainee. It should be noted that joint relative speed is obtained by calculating the relative speed of two symmetric joints (such as two calves), and joint acceleration refers to the position change speed of a joint within a specified time.

[0120] C. After the analysis module obtains the text data of the trainee, analyze the trainee's professional knowledge based on the text analysis algorithm, including the following steps:

[0121] Clean the text data, including removing stop words (such as common words like "de", "shi", etc.), handling spelling mistakes, performing stemming or lemmatization operations, etc. This helps improve the accuracy and efficiency of text analysis. Divide the text into sentences or paragraphs and mark the words, which helps the algorithm better understand the structure of the text. Use entity recognition algorithms to identify named entities in the text, such as person names, place names, organizations, etc. This helps extract key information. Determine the important words in the text through keyword extraction algorithms, which helps understand the theme and key concepts of the text. Create a knowledge graph, which can help deeply understand the student's professional knowledge system. Detect domain-specific terms used in the text through professional term recognition algorithms, which helps determine whether the student has mastered the professional terms and concepts in the relevant field. Use semantic similarity algorithms to compare sentences or paragraphs in the text to determine their similarity, which helps discover the connections between relevant concepts in the text. Calculate the text similarity index, and the calculation formula is:

[0122]

[0123] In the formula, X wb is the text similarity index, j = 1, 2, 3,..., k, where k represents the number of elements in the text topic distribution vector F and the domain topic distribution vector G, and k is a positive integer. F j represents the j-th element of the text topic distribution vector F, and G j represents the j-th element of the domain topic distribution vector G. The larger the text similarity index, the more similar the student's text topic distribution is to the domain topic distribution, that is, the better the student's professional knowledge;

[0124] Clean the text data, including removing stop words, handling spelling mistakes, and performing stemming or lemmatization operations. Specifically:

[0125] Stop words are words that frequently appear in text but usually do not carry useful information, such as "the", "and", "is", etc. Removing these stop words can reduce the data dimension and improve the analysis efficiency. Use a spelling check tool or algorithm to detect and correct spelling mistakes in the text. Spelling mistakes may affect the correct extraction and understanding of keywords. Stemming is the process of converting words to their stems or base forms. For example, "running" can be stemmed to "run". This helps map related words to the same base form and reduces the variant forms of the vocabulary. Lemmatization is the process of reducing words to their original lexical forms. Different from stemming, lemmatization takes into account the context of the word and usually outputs a valid base form of the vocabulary. For example, "better" can be lemmatized to "good". Remove special characters and punctuation marks in the text to prevent them from interfering with the analysis. This includes commas, periods, question marks, etc. Convert all letters in the text to lowercase to ensure case consistency. This helps avoid different case forms of the same vocabulary being regarded as different words;

[0126] Divide the text into sentences or paragraphs and tag the vocabulary. Use entity recognition algorithms to identify named entities in the text. Specifically:

[0127] Use natural language processing (NLP) tools or techniques, such as a tokenizer or a sentence splitter, to divide the original text into sentences or paragraphs. This helps break down the text into smaller language units for subsequent processing. Tag each vocabulary in the text, that is, map each vocabulary to its category or part of speech in the language. This usually involves using a part-of-speech tagger that can identify nouns, verbs, adjectives, etc. Tagging the vocabulary helps in-depth understanding of the grammar structure and meaning of the text. Use entity recognition algorithms to identify named entities in the text, such as person names, place names, organization names, etc. Entity recognition usually uses pre-trained models that can identify vocabulary related to specific categories in the text. Common entity recognition techniques include rule-based methods, statistical learning-based methods, and deep learning-based methods. Once named entities are identified, they are usually tagged to indicate their types. For example, "John" may be tagged as a person name (PERSON), and "New-York" may be tagged as a place name (LOCATION). Process multi-word entities, such as "New-York" or "Artificial-Intelligence", to ensure they are recognized as a whole named entity rather than individual words, and perform subsequent processing, such as removing possible errors, verifying the accuracy of entity recognition, and making manual corrections as needed;

[0128] Construct the relationships between the concepts and entities involved in the text, create a knowledge graph, and use semantic similarity algorithms to compare sentences or paragraphs in the text. Specifically:

[0129] Use entity recognition algorithms to identify the concepts and entities in the text, including names of people, places, organizations, etc., mark and classify them, construct a list of entities, analyze the text, identify the sentences or phrases that describe the relationships between entities. For example, the sentence "Steve Jobs is the founder of Apple Inc." describes the relationship between "Steve Jobs" and "Apple Inc.". Extracting these relationships helps to establish connections between entities. Represent the entities and relationships in the form of a graph to construct a knowledge graph. The nodes of the graph represent entities, and the edges represent the relationships between entities. Each node and edge may have attributes, such as the type of entity, the strength of the relationship, etc.

[0130] Use semantic similarity algorithms to compare sentences or paragraphs in the text to measure the degree of semantic similarity between them. Common semantic similarity algorithms include word vector-based methods (such as Word-Embeddings) and graph-based methods. Use pre-trained word vector models (such as Word2Vec, GloVe, Fast-Text) to convert the words in a sentence or paragraph into vector representations, and then calculate the similarity between the vectors. Cosine similarity is a commonly used metric. Utilize the structure of the knowledge graph to measure semantic similarity by comparing the positions of two sentences or paragraphs in the graph, the entities and relationships they share, etc. Graph traversal and similarity algorithms can be used for this purpose.

[0131] Apply semantic similarity to compare texts. For example, use the similarity score to evaluate the degree of association between texts in tasks such as information retrieval, question answering systems, or text summarization. Conduct experiments and adjust the model parameters as needed to optimize the semantic similarity algorithm to obtain more accurate text comparison and association degree metrics;

[0132] Determine the important words in the text through keyword extraction algorithms. Specifically:

[0133] Segment the text into words or phrases. This step usually involves using a tokenizer or other NLP tools to divide the text into basic language units. For each word, calculate its term frequency in the text, that is, the number of times the word appears in the text. This is a local weight indicating the importance of the word in the text. The expression is: In the formula, TF is the term frequency, CXS is the number of times the word appears in the text, and ZCS is the total number of words in the text. For each word, calculate its inverse document frequency, which is the reciprocal of the number of documents containing the word in the entire corpus. This is a global weight indicating the universality of the word in the entire corpus. The expression is: Wherein, IDF is the inverse document frequency of the vocabulary, ZWD is the total number of documents in the corpus, TWS is the number of documents containing the vocabulary. Multiply the TF and IDF of the vocabulary to obtain the TF-IDF score. The vocabulary with a higher TF-IDF score has higher importance in the text. The expression is: TF-IDF = TF * IDF. Sort the vocabulary according to the calculated TF-IDF score, and select the vocabulary with a higher ranking as the keyword. The number of keywords can be adjusted according to requirements.

[0134] D. The comprehensive evaluation module comprehensively analyzes the audio analysis data, image analysis data, and text analysis data, and then evaluates the comprehensive ability of the trainee, including the following steps:

[0135] The audio analysis data is the emotion index, the image analysis data is the physical fitness index, and the text analysis data is the text similarity index;

[0136] After normalizing the emotion index, physical fitness index, and text similarity index, comprehensively calculate to obtain the ability coefficient nl of the trainee x , and the calculation expression is:

[0137]

[0138] Wherein, X tn is the physical fitness index, X yp is the emotion index, X wb is the text similarity index, α, β, γ are the proportionality coefficients of the physical fitness index, emotion index, and text similarity index respectively, and α > β > γ > 0;

[0139] From the calculation expression of the ability coefficient nl x , it can be seen that the larger the obtained ability coefficient nl x value, the stronger the comprehensive ability of the trainee. Therefore, after obtaining the ability coefficient nl x value, compare the ability coefficient nl x value with the preset ability threshold. The ability threshold is used to distinguish whether the trainee's comprehensive ability is strong / weak. Specifically:

[0140] If the ability coefficient nl x value is greater than or equal to the ability threshold, evaluate that the trainee's comprehensive ability is strong. If the ability coefficient nl x value is less than the ability threshold, evaluate that the trainee's comprehensive ability is weak.

[0141] In this application, after the comprehensive evaluation module normalizes the emotion index, physical fitness index, and text similarity index, it comprehensively calculates to obtain the ability coefficient nl of the trainee x, not only effectively improves the data processing efficiency, but also comprehensively analyzes multiple data to evaluate the comprehensive ability of students, with a more comprehensive analysis.

[0142] E. The screening module, based on the student evaluation results, screens out the students who do not meet the requirements and sends admission notices to the students who meet the requirements. Specifically:

[0143] Classify the students with weak comprehensive ability as students who do not meet the requirements, and classify the students with strong comprehensive ability as students who meet the requirements. Mark or distinguish the students who meet the requirements and those who do not meet the requirements. For the students who meet the requirements, the system generates an admission notice. The content of the notice may include information such as the school name, enrollment time, registration process, and enrollment instructions. The generation of the admission notice can include template design to ensure the consistency and integrity of the notice. Send the generated admission notice to the students who meet the requirements. The notice can be sent via email, text message, online platform message, etc. Record the information of the students to whom the notice has been sent for subsequent tracking and management. In case of need, the system can receive the feedback or confirmation from the students to ensure that the students have received and understood the admission notice. Generate a notice result report to summarize statistical information such as the number of students to whom the notice has been sent, the number of successfully sent notices, and the number of failed notices.

[0144] F. After the enrollment processing time limit has passed, the sorting module sorts all the students who have successfully completed the enrollment according to the comprehensive ability evaluation results to generate a student sorting list. Specifically:

[0145] Obtain the information of all the students who have completed the enrollment and the corresponding ability coefficient nl of the student x , sort all the students according to the ability coefficient nl x in descending order to generate a student sorting list. The higher the ranking of the student in the student sorting list, the stronger the comprehensive ability of the student.

[0146] Embodiment 3: Please refer to Figure 2 shown. The personnel management method for facilitating information query described in this embodiment includes the following steps:

[0147] Students submit applications for sports specialty student enrollment through the client. The client provides an online application form and can upload relevant supporting documents, such as sports transcripts, honor certificates, etc. The review end checks the enrollment application information based on the review mechanism. After the initial review of the student is passed, interview information is sent to the student. After encrypting the student information through the Secure Sockets Layer protocol, all the obtained student information is stored in the database. When the student coming for the interview matches any student information in the database, after the processing end records the student's audio, it analyzes the emotional color in the student's audio based on the sentiment analysis algorithm. After obtaining the student's motion posture data, it analyzes the student's physical fitness level and coordination based on the pose estimation algorithm. After obtaining the student's text data, it analyzes the student's professional knowledge based on the text analysis algorithm. And after comprehensively analyzing the audio analysis data, image analysis data, and text analysis data, it evaluates the comprehensive ability of the student. According to the evaluation results of the student, the students who do not meet the requirements are screened out, and admission notices are sent to the students who meet the requirements. After the enrollment processing time limit has passed, all the students who have successfully completed enrollment are sorted according to the comprehensive ability evaluation results to generate a student ranking list. After verifying the user's identity through the identity verification mechanism, it is ensured that only authorized personnel can access the management system. The user can query the student ranking list and student information.

[0148] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0149] It should be understood that the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Among them, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the front and back associated objects, but it may also represent an "and / or" relationship. The specific meaning can be understood by referring to the context.

[0150] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0151] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0152] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application and should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A personnel management system facilitating information query, characterized in that: It includes an enrollment application module, a preliminary review module, a data storage module, an analysis module, a comprehensive evaluation module, a screening module, a sorting module, and a query module: Enrollment application module: Used for students to submit enrollment applications and provide online application forms; Preliminary review module: Checks the enrollment application information based on the review mechanism. After the student passes the preliminary review, sends interview information to the student; Data storage module: Encrypts the student information through the Secure Sockets Layer protocol and stores all the obtained student information in the database. When a student coming for an interview matches any student information in the database, wakes up the analysis module; Analysis module: After inputting the student's audio, analyzes the emotional color in the student's audio based on the sentiment analysis algorithm. After obtaining the student's motion posture data, analyzes the student's physical fitness level and coordination based on the pose estimation algorithm. After obtaining the student's text data, analyzes the student's professional knowledge based on the text analysis algorithm; Comprehensive evaluation module: Comprehensively analyzes the audio analysis data, image analysis data, and text analysis data to evaluate the comprehensive ability of the student; Screening module: Based on the student evaluation results, screens out students who do not meet the requirements and sends admission notices to students who meet the requirements; Sorting module: Sorts all students who have successfully completed enrollment according to the comprehensive ability evaluation results and generates a student ranking list; Query module: After verifying the user's identity based on the authentication mechanism, the user queries the student ranking list and student information.

2. The personnel management system for facilitating information query according to claim 1, wherein: The steps for the analysis module to analyze the emotional color in the student's audio based on the sentiment analysis algorithm are as follows: Receives the student's audio file or real-time audio stream and preprocesses the audio; Uses Mel Frequency Cepstral Coefficients to extract the features required for sentiment analysis from the audio. The features include the tone, speech rate, pitch, and energy of the audio; Identifies the emotional labels in the audio features through the Convolutional Neural Network algorithm and outputs the predicted confidence values of different emotional labels; Calculate the weighted average of the prediction confidence levels for different sentiment tags to obtain the sentiment index X of the student yp 。 3. The personnel management system for facilitating information query according to claim 2, characterized in that: The steps for the analysis module to analyze the student's physical fitness level and coordination based on the pose estimation algorithm are as follows: Obtains the student's motion posture data through sensors and camera devices. The motion posture data includes joint angles, body positions, and motion trajectories; Preprocesses the collected posture data and converts the collected posture information into specific posture data through the pose estimation algorithm. The posture data includes calculating joint flexibility, joint relative speed, motion trajectory length, and joint acceleration; After weighted calculation of joint flexibility, joint relative speed, movement trajectory length, and joint acceleration, the physical fitness index X of the trainee is obtained. tn .

4. The personnel management system for facilitating information query according to claim 3, wherein: The steps for the analysis module to analyze the student's professional knowledge based on the text analysis algorithm are as follows: Cleans the text data, including removing stop words, handling spelling mistakes, and performing stemming or lemmatization operations; Divides the text into sentences or paragraphs and tags the vocabulary, and uses the entity recognition algorithm to identify the named entities in the text; Constructs the relationships between the concepts and entities involved in the text, creates a knowledge graph, and uses the semantic similarity algorithm to compare the sentences or paragraphs in the text; Determines the important vocabulary in the text through the keyword extraction algorithm, calculates the text similarity index, and the calculation expression is: Where X wb is the text similarity index, j = 1, 2, 3, ..., k, where k represents the number of elements in the text topic distribution vector F and the domain topic distribution vector G, and k is a positive integer, F j represents the j-th element of the text topic distribution vector F, and G j represents the j-th element of the domain topic distribution vector G.

5. The personnel management system for facilitating information query according to claim 4, wherein: The steps for the comprehensive evaluation module to evaluate the comprehensive ability of the student are as follows: The audio analysis data is the emotion index, the image analysis data is the physical fitness index, and the text analysis data is the text similarity index; After normalizing the sentiment index, physical fitness index, and text similarity index, the ability coefficient nl of the student is comprehensively calculated x , and the calculation formula is: where X tn is the physical fitness index, X yp is the emotional index, X wb is the text similarity index, and α, β, and γ are the proportionality coefficients of the physical fitness index, emotional index, and text similarity index, respectively, and α > β > γ > 0; The obtained ability coefficient nl x The larger the value is, the stronger the comprehensive ability of the trainee is. After obtaining the ability coefficient nl x value, the ability coefficient nl x value is compared with the preset ability threshold; If the ability coefficient nl x value is greater than or equal to the ability threshold, it is evaluated that the comprehensive ability of the trainee is strong. If the ability coefficient nl x value is less than the ability threshold, it is evaluated that the comprehensive ability of the trainee is weak.

6. The personnel management system for facilitating information query according to claim 5, wherein: The screening module classifies students with weak comprehensive abilities as students who do not meet the requirements, and classifies students with strong comprehensive abilities as students who meet the requirements, and marks students who meet the requirements and students who do not meet the requirements respectively. For students who meet the requirements, an admission notice is generated; The sorting module obtains the information of all students who have completed the enrollment process and the corresponding ability coefficient nl for each student x , and sorts all students according to the ability coefficient nl x in descending order to generate a student sorting table.

7. A personnel management system for facilitating information query according to claim 1, characterized in that: The data storage module encrypts the student information through the Secure Sockets Layer protocol, including the following steps: After applying for and obtaining a trusted SSL certificate, install the SSL certificate on the server, configure the server to enable the SSL / TLS protocol, and configure the SSL certificate in the server configuration; The client sends a connection request to the server by initiating a connection request, and the server and the client start the SSL handshake protocol, and key negotiation is performed during the handshake; After the handshake is successful, a secure channel is established, and the student information is symmetrically encrypted using the shared key negotiated; 8. The personnel management system for facilitating information query according to claim 7, wherein: The client sends a connection request to the server by initiating a connection request, and the server and the client start the SSL handshake protocol, and key negotiation is performed during the handshake, including the following steps: The client sends a ClientHello message to the server. The ClientHello message includes the SSL / TLS version, the list of cipher suites, and random number information; After the server receives the ClientHello, it sends a ServerHello message to the client, selects the SSL / TLS version, the cipher suite, generates a random number for the server, and attaches the SSL certificate; After the client receives the SSL certificate of the server, after verifying the validity of the certificate, it decrypts the digital signature in the certificate using the public key of the server; The client generates a random number PreMasterSecret and encrypts this random number using the public key of the server and sends it to the server; The server uses its own private key to decrypt the PreMasterSecret sent by the client to obtain the shared key; The client and the server generate the master key according to the negotiated SSL / TLS version, the random numbers of ClientHello and ServerHello, and the PreMasterSecret. The master key is used for the session key of symmetric encryption communication; The client sends a Finished message. The Finished message includes the hash value of the handshake message. After the handshake process is completed, the server also sends a Finished message. The Finished message includes the hash value of the handshake message.

9. The personnel management system for facilitating information query according to claim 8, wherein: After the handshake is successful, the data storage module establishes a secure channel and symmetrically encrypts the student information using the shared key negotiated, including the following steps: The client and the server select the AES symmetric encryption algorithm according to the information negotiated in the handshake protocol to generate a random initialization vector; The student information to be encrypted is block-processed according to the requirements of the symmetric encryption algorithm. If the information length is not an integer multiple of the block size, the information length is padded; Use the negotiated shared key and initialization vector to perform symmetric encryption on each data block, and use the HMAC algorithm to generate an authentication tag for each encrypted data block. The authentication tag is used to verify whether the data has been tampered with during transmission; Transmit the encrypted ciphertext and the generated authentication tag to the receiving party through a secure channel. The receiving party uses the negotiated shared key and initialization vector to decrypt the received ciphertext, and merges the decrypted data blocks to restore the original student information.

10. A personnel management method for facilitating information query, implemented based on the management system described in any one of claims 1-9, characterized in that: The management method includes the following steps: S1: The student submits a sports specialty student enrollment application through the client. The review end checks the enrollment application information based on the review mechanism. After the student passes the preliminary review, the review end sends interview information to the student; S2: After encrypting the student information through the Secure Sockets Layer protocol, store all the obtained student information in the database; S3: When the student coming for an interview matches any student information in the database, the processing end records the student's audio and analyzes the emotional color in the student's audio based on the sentiment analysis algorithm, obtains the student's motion posture data and analyzes the student's physical fitness level and coordination based on the pose estimation algorithm, and obtains the student's text data and analyzes the student's professional knowledge based on the text analysis algorithm; S4: Evaluate the comprehensive ability of the student after comprehensively analyzing the audio analysis data, image analysis data, and text analysis data; S5: According to the student evaluation results, screen out the students who do not meet the requirements and send admission notices to the students who meet the requirements; S6: Sort all the students who have successfully completed enrollment according to the comprehensive ability evaluation results to generate a student ranking list. After verifying the user identity through the identity verification mechanism, the user queries the student ranking list and student information.

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