A comprehensive data-driven method and system for monitoring students' psychological state

Abstract

This invention discloses a comprehensive data-based method and system for monitoring students' psychological state, relating to the field of electronic digital data processing technology. The comprehensive data-based method for monitoring students' psychological state includes the following steps: data encryption effectiveness assessment; data encryption optimization determination; and psychological monitoring efficiency optimization determination. This invention determines whether to conduct a data encryption effectiveness assessment based on the increase in student psychological state data. If no data encryption effectiveness assessment is conducted, student psychological state monitoring proceeds directly. Otherwise, based on the results of the data encryption effectiveness assessment, it determines whether data encryption optimization should be performed, and student psychological state monitoring proceeds. Finally, it assesses psychological monitoring efficiency based on psychological monitoring efficiency parameters to determine whether psychological monitoring efficiency optimization should be performed. This improves the efficiency of student psychological state monitoring and solves the problem in existing technologies where efficiency assessment during student psychological state monitoring does not consider the impact of data volume differences on encryption operations.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic digital data processing, and particularly relates to a comprehensive data student psychological state monitoring method and system. BACKGROUND

[0002] With the continuous progress of information technology, data collection and use has penetrated into every field of society. Especially in the education industry, student mental health data as a kind of sensitive information involves personal privacy and psychological state, if not properly protected, may bring serious privacy leakage and abuse risks. Therefore, by adding the application of encryption technology in student psychological state monitoring, the student mental health data can be effectively protected in the process of storage and transmission, preventing data leakage, tampering and abuse, not only can guarantee the privacy security of students, enhance the trust of parents and students, but also can provide reliable data support, better carry out student mental health management and intervention.

[0003] The prior art analyzes the behavior, social interaction, academic performance, etc. of students through text analysis technology and machine learning model to predict possible mental health problems of students, realize comprehensive and real-time understanding and evaluation of the mental health status of students.

[0004] For example, the invention patent with publication number CN114780995B discloses an internet-based student mental health file encryption management system and method, which includes: a student mental health data adaptation degree acquisition module, which is used to acquire the adaptation degree between the mental health data of different students; a student mental health data calibration module, which calibrates the collected data in the student mental health data collection module according to the results obtained by the student mental health data adaptation degree acquisition module, and obtains the calibrated student mental health data of students; an encryption and early warning module, which saves the calibrated student mental health data in the database after encryption processing, and analyzes the calibrated student mental health data, and feeds back the unqualified calibrated student mental health data and the corresponding students to the teachers and parents.

[0005] For example, the patent application with publication number CN119848911A discloses a painting psychological evaluation digital service management system, which includes: a painting information acquisition module for transmitting the obtained finished painting to a painting information comprehensive processing module; the painting information comprehensive processing module is used for analyzing the obtained finished painting, acquiring the painting elements on the finished painting through image recognition technology, and classifying the painting elements to obtain painting classification information, and transmitting the painting classification information to a classification information processing module; the classification information processing module is used for processing the obtained painting classification information, dividing the painting classification information into segmented data packets, analyzing the segmented data packets based on the integrity of the data in the segmented data packets to obtain complete data packets, generating random salts of different quantities according to the tail value of the data capacity of the complete data packets, and then transmitting the random salts to an encryption comprehensive processing module; the encryption comprehensive processing module is used for encrypting the complete data packets according to the number of random salts corresponding to the complete data packets, inserting the generated random salts into the complete data packets to generate spliced data, and processing the obtained spliced data to obtain secondary processing data, and encrypting the secondary processing data by using a hash function to generate encryption information, and then transmitting the encryption information to a management information output module; the management information output module is used for encrypting the finished painting according to the obtained encryption information.

[0006] However, in the process of implementing the technical scheme of the embodiments of the present application, the applicant found that the above-mentioned technology at least has the following technical problems:

[0007] In the prior art, the psychological data of students contains personal privacy information such as emotional state, behavior habit, and mental health status. Especially for underage students, their mental health data is particularly vulnerable and needs additional privacy protection. Without encryption, hackers or unauthorized third parties may use the students' mental health data for commercial purposes (such as selling data to advertising companies, health insurance companies, etc.).

[0008] With the continuous improvement of mental health monitoring means, students' psychological data not only includes self-reported emotional state, but also may include physiological data (such as heart rate, sleep quality, etc.) and behavior data (such as social behavior, online behavior, etc.). The diversification and high-frequency collection of data have greatly increased the amount of data, therefore, simple encryption methods may no longer be effective, and stronger encryption methods may be needed to maintain data security. Encrypted data may make the integration and multidimensional analysis of different data sources more difficult, especially when multiple data are processed in real time, encryption may cause the processing time to be prolonged, affecting the real-time and accuracy of multidimensional evaluation, and there is a problem that the efficiency evaluation in the process of monitoring students' psychological state does not consider the influence of data quantity difference on encryption operation. SUMMARY

[0009] The embodiment of the present application provides a comprehensive data student psychological state monitoring method and system, solves the problem that the efficiency evaluation in the student psychological state monitoring process in the prior art does not consider the influence of data quantity difference on encryption operation, and improves the student psychological state monitoring efficiency.

[0010] The embodiment of the present application provides a comprehensive data student psychological state monitoring method, including the following steps: S1, the obtained student psychological state data is encrypted and transmitted and stored, whether the data increase of the stored student psychological state data in a preset time period is greater than a preset data increase is judged, if yes, the data encryption effective evaluation is carried out according to the encryption effective evaluation parameter of the student psychological state data, and S2 is executed, otherwise, the student psychological state monitoring is continued based on the obtained student psychological state data; S2, whether to carry out data encryption optimization is judged based on the result of the data encryption effective evaluation, if not, the student psychological state monitoring is continued based on the obtained student psychological state data, otherwise, the student psychological state monitoring is carried out based on the student psychological state data after data encryption optimization, the psychological monitoring efficiency evaluation is carried out according to the psychological monitoring efficiency parameter in the psychological state monitoring process, and S3 is executed; S3, whether to carry out psychological monitoring efficiency optimization is judged based on the result of the psychological monitoring efficiency evaluation, if the psychological monitoring efficiency optimization is carried out, the student psychological state monitoring is carried out after the psychological monitoring efficiency optimization, otherwise, the student psychological state monitoring is continued.

[0011] The embodiment of the application provides a comprehensive data student psychological state monitoring system, and applies a comprehensive data student psychological state monitoring method, which comprises a data encryption effective evaluation module, a data encryption optimization judgment module and a psychological monitoring efficiency optimization judgment module; wherein the data encryption effective evaluation module is used for performing encrypted transmission and storage on the obtained student psychological state data, judging whether the data increase of the stored student psychological state data in a preset time period is greater than a preset data increase, if yes, performing data encryption effective evaluation according to the encryption effective evaluation parameters of the student psychological state data, and executing the function of the data encryption optimization judgment module, otherwise, continuing to perform student psychological state monitoring based on the obtained student psychological state data; the data encryption optimization judgment module is used for judging whether to perform data encryption optimization based on the result of the data encryption effective evaluation, if not, continuing to perform student psychological state monitoring based on the obtained student psychological state data, otherwise, performing student psychological state monitoring based on the student psychological state data after data encryption optimization, performing psychological monitoring efficiency evaluation according to the psychological monitoring efficiency parameters in the psychological state monitoring process, and executing the function of the psychological monitoring efficiency optimization judgment module; the psychological monitoring efficiency optimization judgment module is used for judging whether to perform psychological monitoring efficiency optimization based on the result of the psychological monitoring efficiency evaluation, if yes, performing student psychological state monitoring after psychological monitoring efficiency optimization, otherwise, continuing to perform student psychological state monitoring.

[0012] The one or more technical solutions provided in the embodiment of the application have at least the following technical effects or advantages:

[0013] 1. The data increase of the student psychological state data is used to judge whether to perform data encryption effective evaluation, then the result of the data encryption effective evaluation is used to judge whether to perform data encryption optimization, and student psychological state monitoring is performed, then the psychological monitoring efficiency parameters are used to perform psychological monitoring efficiency evaluation, and it is judged whether to perform psychological monitoring efficiency optimization, so that the security of data encryption is improved, and the student psychological state monitoring efficiency is improved, and the problem that the efficiency evaluation in the student psychological state monitoring process in the prior art does not consider the influence of data quantity difference on encryption operation is effectively solved.

[0014] 2. The data leakage change coefficient is obtained by processing the data leakage in the preset time period and the historical time period, then the cracking time change coefficient is obtained according to the average key cracking time in the preset time period and the historical time period, then the key rotation frequency comparison coefficient is obtained according to the key rotation frequency and the preset key rotation frequency, and finally the data encryption effective evaluation index is obtained according to the data leakage change inverse coefficient, the cracking time change coefficient and the key rotation frequency comparison coefficient, so that the influence degree of the data increase on the data encryption effectiveness is quantitatively evaluated, and the improvement of the data encryption effectiveness is realized.

[0015] 3、obtaining a data available coefficient deviation coefficient by processing the data available coefficient, then obtaining an encryption-decryption time comparison coefficient according to the encryption-decryption time and the preset encryption-decryption time, then processing the data available coefficient deviation coefficient and the encryption-decryption time comparison coefficient to obtain a psychological monitoring efficiency evaluation coefficient, and finally obtaining a psychological monitoring efficiency evaluation index according to the data encryption effective influence factor and the psychological monitoring efficiency evaluation coefficient, so as to quantitatively evaluate the monitoring efficiency in the student psychological state monitoring process after data encryption optimization, and further improve the monitoring efficiency in the student psychological state monitoring process. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A flowchart of a comprehensive data student psychological state monitoring method provided by the embodiment of the application is shown in the figure.

[0017] Figure 2 A flowchart of data encryption optimization provided by the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0018] The embodiment of the application provides a comprehensive data student psychological state monitoring method and system, solves the problem that the efficiency evaluation in the student psychological state monitoring process in the prior art does not consider the influence of data quantity difference on encryption operation, determines whether to perform data encryption effective evaluation according to the data increase of student psychological state data, if not, directly performs student psychological state monitoring, otherwise, judges whether to perform data encryption optimization based on the result of data encryption effective evaluation, and performs student psychological state monitoring, then performs psychological monitoring efficiency evaluation according to the psychological monitoring efficiency parameter, judges whether to perform psychological monitoring efficiency optimization, and improves the student psychological state monitoring efficiency.

[0019] The technical solution in the embodiment of the application is to solve the problem that the efficiency evaluation in the student psychological state monitoring process in the prior art does not consider the influence of data quantity difference on encryption operation, and the general idea is as follows:

[0020] Determine whether to perform data encryption effective evaluation according to the data increase of student psychological state data, then optimize data encryption based on the result of data encryption effective evaluation, then perform psychological monitoring efficiency evaluation according to the psychological monitoring efficiency parameter to optimize the psychological monitoring efficiency, and improve the student psychological state monitoring efficiency.

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail in combination with the drawings in the specification and specific embodiments.

[0022] The application provides a comprehensive data student psychological state monitoring method, including the following steps:

[0023] S1, data encryption effective evaluation: the obtained student psychological state data is encrypted and transmitted and stored, it is judged whether the data increase of the student psychological state data stored in the preset time period is greater than the preset data increase, if yes, data encryption effective evaluation is carried out according to the encryption effective evaluation parameter of the student psychological state data, and S2 is executed, otherwise, the student psychological state monitoring is continued based on the obtained student psychological state data; the student psychological state data includes but is not limited to user basic information (such as gender, age, name, etc.), social interaction data (such as student message interaction, tree hole information), small intelligence test data (such as anxiety, academic pressure test) and examination score data; the data increase is obtained by difference operation of the average data amount of the student psychological state data in the preset time period and the average data amount of the student psychological state data in the historical time period, and then the result of the difference operation is ratio operated with the average data amount of the student psychological state data in the historical time period; the preset data increase is set according to the preset personnel; the preset time period is set according to the preset personnel, for example, it can be set to 1 month; the student psychological state data in the historical time period usually represents the student psychological state data in the last preset time period.

[0024] S2, data encryption optimization determination: whether to perform data encryption optimization is determined based on the result of data encryption effective evaluation, if not, the student psychological state monitoring is continued based on the obtained student psychological state data, otherwise, the student psychological state monitoring is carried out based on the student psychological state data after data encryption optimization, the psychological monitoring efficiency is evaluated according to the psychological monitoring efficiency parameter in the psychological state monitoring process, and S3 is executed.

[0025] S3, psychological monitoring efficiency optimization determination: whether to perform psychological monitoring efficiency optimization is determined based on the result of psychological monitoring efficiency evaluation, if yes, the student psychological state monitoring is carried out after the psychological monitoring efficiency optimization, otherwise, the student psychological state monitoring is continued based on the student psychological state data after data encryption optimization.

[0026] Before designing a comprehensive data student psychological state monitoring method, a database for storing various types of setting data is established, the database includes but is not limited to the average key cracking time of the student psychological state data in the historical time period, the preset encryption-decryption time length and the data encryption effective compensation value, the salt value length mapping set, various types of values in the database are directly set by technicians, among them, the setting of the preset encryption-decryption time length can be set according to the preset personnel, for example, the preset encryption-decryption time length is represented by the average value of the historical encryption-decryption time length in the historical time period in the database, in addition, various types of values in the database can be set and fine-tuned by technicians according to actual debugging.

[0027] In this embodiment, as shown in Figure 1 The flowchart of the comprehensive data student psychological state monitoring method provided by the embodiment of the application is shown in FIG. 6. The specific logic is as follows: the obtained student psychological state data is encrypted and stored, it is judged whether the data increase of the stored student psychological state data is greater than a preset data increase, if not, the student psychological state monitoring is continued based on the obtained student psychological state data, if yes, the data encryption effective evaluation is performed according to the encryption effective evaluation parameter of the student psychological state data, it is judged whether the data encryption effective evaluation index is greater than a preset encryption effective threshold, if yes, the student psychological state monitoring is continued based on the obtained student psychological state data, if not, the data encryption optimization is performed, and the student psychological state monitoring is performed based on the student psychological state data after the data encryption optimization, the psychological monitoring efficiency evaluation is performed according to the psychological monitoring efficiency parameter in the psychological state monitoring process, it is judged whether the psychological monitoring efficiency evaluation index is greater than a preset monitoring efficiency threshold, if not, the psychological monitoring efficiency optimization is performed, and the student psychological state monitoring is performed after the psychological monitoring efficiency optimization, if yes, the student psychological state monitoring is performed.

[0028] The student psychological state data is personal privacy, and the encryption of these data can ensure that their personal information is not viewed, used or disclosed by others without permission. In particular, for underage students, their mental health data is particularly vulnerable and requires additional privacy protection.

[0029] Without encryption, hackers or unauthorized third parties may use student mental health data for commercial purposes (such as selling data to advertising companies, health insurance companies, etc.).

[0030] With the continuous improvement of mental health monitoring means, student psychological data not only includes self-reported emotional state, but also may include physiological data (such as heart rate, sleep quality, etc.) and behavioral data (such as social behavior, online behavior, etc.). The diversification and high-frequency collection of data have greatly increased the amount of data, so with the increase of data, simple encryption methods may no longer be effective enough, and stronger encryption methods may be needed to maintain data security, which may also increase the time for data encryption and decryption, and thus increase the delay of student psychological state monitoring. Complex encryption processing may occupy more CPU resources and GPU resources, resulting in reduced student psychological state monitoring efficiency. In order to accurately assess the psychological state of students, the monitoring system needs to comprehensively analyze multi-dimensional data (such as text, voice, behavior, etc.). However, encrypted data may make the integration and multi-dimensional analysis of different data sources more difficult, especially when real-time processing of multiple data is required, encryption may cause processing time to be prolonged, affecting the real-time and accuracy of multi-dimensional evaluation.

[0031] In the present application, the data encryption scheme and the dynamic adaptation of data volume growth are realized through the data increase amount determination triggered data encryption effective evaluation, and the data encryption security is improved, and the data volume explosion of student psychological state data becoming an attack target and the improvement of student psychological state monitoring delay are avoided; after the data encryption optimization, the efficiency of student psychological state monitoring and psychological monitoring efficiency optimization determination are realized, the efficiency of student psychological state monitoring is improved, the dynamic balance of data encryption security and student psychological state monitoring efficiency is ensured, the student psychological state monitoring efficiency is reduced due to the improvement of data encryption complexity, and the accuracy of student psychological state monitoring is ensured.

[0032] Further, the specific process of encrypting and storing the obtained student psychological state data is as follows:

[0033] First, the obtained student psychological state data is transmitted by using the Transport Layer Security / Secure Sockets Layer (TLS / SSL) protocol.

[0034] Second, the student psychological state data is hashed by using a hash function to obtain the hash value of the student psychological state data; the hash processing means that the student psychological state data is converted into a hash value; the hash function can be Secure Hash Algorithm 3 (SHA-3).

[0035] Third, the hash value of the student psychological state data is signed by using the private key in the asymmetric encryption algorithm (such as Rivest-Shamir-Adleman, RSA), and the signature is verified by the public key at the receiving end of the student psychological state data, to determine whether the signature verification success rate is greater than the preset signature verification success rate obtained from the preset database, if greater, directly execute the fourth step, otherwise feedback (prompt the preset personnel that the signature verification fails), to ensure that the student psychological state data is not leaked in the transmission process; the signature verification success rate is obtained by ratio operation of the number of signature verification successes and the total number of signature verifications in a preset time period, if the hash value decrypted by the public key at the receiving end is the same as the hash value generated when the sending end signs, it means that the signature verification is successful, otherwise it means that the signature verification fails; the preset signature verification success rate is set according to the preset personnel, for example, it can be set to 0.99.

[0036] Fourth step, the account password in the mental state monitoring platform is encrypted by Blowfish-based Cryptographic Hashing Function (Bcrypt algorithm), and a salt value with an initial salt value length is generated for each account password using a cryptographically secure random number generator (such as / dev / urandom or Secrets module) to improve the security of the account password.

[0037] Fifth step, the student mental state data is symmetrically encrypted and stored by Advanced Encryption Standard-256-Galois / Counter Mode (AES-256-GCM).

[0038] In this embodiment, the encrypted communication channel in TLS / SSL ensures that the data is not stolen during transmission, and certificates can also be used for identity verification to ensure that both parties of the communication are trustworthy; SHA-3 has anti-collision ability, avoiding the situation where the input hash value is the same, and compared with other hash algorithms, it has higher performance and faster processing speed, suitable for larger data volume hash processing; through the asymmetric encryption algorithm (such as RSA), the confidentiality of the data during transmission can be ensured, avoiding man-in-the-middle attacks, which is the most widely used public key encryption standard, suitable for digital signature and encrypted data transmission; through data encryption transmission, the student mental state data can be effectively prevented from being stolen during transmission; through hash processing and signature verification, it is ensured that the student mental state data is not leaked during transmission; through the use of Bcrypt algorithm and salt value, the difficulty of cracking the password is increased; through AES-256-GCM, the confidentiality and integrity of the student mental state data in storage are guaranteed; the larger the data volume of the student mental state data, the more likely it is to affect the performance of transmission encryption and storage encryption, which requires more computing resources for encryption and decryption operations, which may cause network delay and longer response time, so as to lay the foundation for subsequent data encryption effective evaluation and optimization, mental monitoring efficiency evaluation and optimization.

[0039] Further, according to the encrypted effective evaluation parameter of the student mental state data, the data encryption effective evaluation is carried out, and the specific method is as follows:

[0040] When the data leakage amount of the student psychological state data in the preset time period is not less than the data leakage amount of the student psychological state data in the historical time period, deviation comparison processing is performed according to the data leakage amount of the student psychological state data in the preset time period and the data leakage amount of the student psychological state data in the historical time period to obtain a data leakage change coefficient, otherwise the data leakage change coefficient is recorded as 0; the specific limit expression of the data leakage change coefficient is:

[0041] ;

[0042] In the formula, XIB represents the data leakage change coefficient, represents the data leakage amount of the student psychological state data in the preset time period, which is represented by the data amount accessed in the data leakage time period recorded by the network security log in the preset time period.

[0043] represents the data leakage amount of the student psychological state data in the historical time period, which is obtained from the preset database and represented by the data amount accessed in the data leakage time period recorded by the network security log in the historical time period.

[0044] When the average key cracking time of the student psychological state data in the preset time period is not less than the average key cracking time of the student psychological state data in the historical time period, deviation comparison processing is performed according to the average key cracking time of the student psychological state data in the preset time period and the average key cracking time of the student psychological state data in the historical time period to obtain a cracking time change coefficient, otherwise the cracking time change coefficient is recorded as 0; the specific limit expression of the cracking time change coefficient is:

[0045] ;

[0046] In the formula, POB represents the cracking time change coefficient, represents the average key cracking time of the student psychological state data in the preset time period, which is represented by the average value of all key cracking times recorded by the network security log in the preset time period.

[0047] represents the average key cracking time of the student psychological state data in the historical time period, which is obtained from the preset database and represented by the average value of all key cracking times recorded by the network security log in the historical time period.

[0048] According to the key rotation frequency and the preset key rotation frequency obtained from the preset database, a key rotation frequency comparison coefficient is obtained, that is, , LUN represents the key rotation frequency, and represents the number of times of changing the encryption key in a unit time period, which is obtained by configuring the encryption system. represents a preset key rotation frequency, represents a maximum value that the key switching frequency can reach, and is set according to a preset person, for example, can be set to once per minute.

[0049] The data leakage change coefficient is inversely proportional to the data leakage change coefficient, that is, .

[0050] The data encryption effective compensation value is introduced to assign and couple the data leakage change coefficient, the cracking time change coefficient, and the key rotation frequency comparison coefficient, to obtain a data encryption effective evaluation index, which is used to quantitatively evaluate the influence degree of the data increase of the student psychological state data on the data encryption effectiveness.

[0051] The specific limit expression of the data encryption effective evaluation index is:

[0052] ;

[0053] In the formula, MI represents the data encryption effective evaluation index in the student psychological state data encryption process, represents the first data encryption effective compensation value, represents the second data encryption effective compensation value, represents the third data encryption effective compensation value.

[0054] The data encryption effective compensation value is obtained from a preset database, the first data encryption effective compensation value represents the influence degree of the data leakage of the student psychological state data in a preset time period on the data encryption effective evaluation index, the second data encryption effective compensation value represents the influence degree of the average key cracking time of the student psychological state data in a preset time period on the data encryption effective evaluation index, and the third data encryption effective compensation value represents the influence degree of the key rotation frequency on the data encryption effective evaluation index; the sum of the three is 1, for example, the data leakage of the student psychological state data in a preset time period and the preset first data encryption effective compensation value form a data leakage mapping set, the real-time data leakage of the student psychological state data in a preset time period is input into the data leakage mapping set to obtain the corresponding first data encryption effective compensation value; the average key cracking time of the student psychological state data in a preset time period and the preset second data encryption effective compensation value form a key cracking time mapping set, the real-time average key cracking time of the student psychological state data in a preset time period is input into the key cracking time mapping set to obtain the corresponding second data encryption effective compensation value; the key rotation frequency and the preset third data encryption effective compensation value form a key rotation frequency mapping set, the real-time key rotation frequency is input into the key rotation frequency mapping set to obtain the corresponding third data encryption effective compensation value; the mapping relationship therein can be one-to-one or many-to-one.

[0055] In the embodiment, increasing the key rotation frequency generally leads to a decrease in the data leakage change coefficient, while increasing the key cracking time; the longer the key cracking time, the less likely it is that the student psychological state data is leaked, thereby reducing the data leakage change coefficient; the increase in the key rotation frequency means that even if the attacker successfully cracks the key in a certain period of time, it will not be effective for a long time, thus indirectly increasing the difficulty of key cracking, so that the cracking time change coefficient may increase; the key rotation can effectively reduce the attack window period (i.e., the event in which the attacker can obtain the student psychological state data after obtaining the key) when the student psychological state data leakage event occurs, thereby effectively reducing the data leakage change coefficient.

[0056] The data leakage change coefficient measures the change in data leakage in a preset time period, effectively determining whether there is a vulnerability in the data encryption scheme; the greater the data leakage change coefficient, the worse the data encryption effectiveness; the cracking time change coefficient compares the change in cracking time, effectively evaluating the strength of the data encryption algorithm; the greater the cracking time change coefficient, the better the data encryption effectiveness; the key rotation frequency comparison coefficient ensures that the encryption key will not be cracked due to long use time; the greater the key rotation frequency comparison coefficient, the better the data encryption effectiveness; by comprehensively considering the data leakage change coefficient, the cracking time change coefficient, and the key rotation frequency comparison coefficient, the influence of the increase in the student psychological state data on the data encryption effectiveness is quantitatively evaluated, which is beneficial to timely discovering potential security threats in the student psychological state data and providing a basis for subsequent data encryption optimization, thereby improving the protection effect of the student psychological state data in student psychological state monitoring.

[0057] Further, based on the result of the data encryption effectiveness evaluation, it is determined whether to perform data encryption optimization, and the specific process is as follows:

[0058] It is determined whether the data encryption effectiveness evaluation index is greater than a preset encryption effectiveness threshold value obtained from a preset database; if the data encryption effectiveness evaluation index is not greater than the preset encryption effectiveness threshold value obtained from the preset database, the salt value length is optimized, and the data encryption effectiveness evaluation index is reacquired, otherwise, based on the acquired student psychological state data, the student psychological state monitoring is continued; the preset encryption effectiveness threshold value is represented by the average value of the data encryption effectiveness evaluation index in a historical time period.

[0059] determining whether the data encryption effective evaluation index after the salt value length optimization is greater than the preset encryption effective threshold value obtained from the preset database, if the data encryption effective evaluation index after the salt value length optimization is not greater than the preset encryption effective threshold value obtained from the preset database, data noise is added, and the data encryption effective evaluation index is reacquired, otherwise, the data encryption optimization is ended, and the student psychological state monitoring is continued based on the student psychological state data after the salt value length optimization.

[0060] determining whether the data encryption effective evaluation index after the data noise addition is greater than the preset encryption effective threshold value obtained from the preset database, if the data encryption effective evaluation index after the data noise addition is not greater than the preset encryption effective threshold value obtained from the preset database, the key rotation frequency optimization is performed, and the data encryption effective evaluation index is reacquired, otherwise, the data encryption optimization is ended, and the student psychological state monitoring is continued based on the student psychological state data after the data noise addition.

[0061] The data encryption optimization includes salt value length optimization, data noise addition and key rotation frequency optimization.

[0062] Specifically, the salt value length optimization means that the encryption process of the account password in the psychological state monitoring platform is optimized by correcting the salt value length, so as to improve the security of the student psychological state data encryption.

[0063] The correction of the salt value length is obtained by inputting the data encryption effective evaluation index and the initial salt value length into the salt value length mapping set, the salt value length mapping set is obtained from the preset database, and represents the mapping relationship between the data encryption effective evaluation index, the initial salt value length and the corrected salt value length; the initial salt value length is set according to the preset personnel, for example, it can be set to 16 bytes.

[0064] The data noise addition means that the Laplace noise is added in the student psychological state data through the differential privacy parameter in the differential privacy mechanism, so as to improve the protection strength of the student privacy, the differential privacy parameter includes the privacy budget and the privacy failure probability; the specific calculation formula of the Laplace noise scale parameter is: , wherein b is the Laplace noise scale parameter, is the global sensitivity, which represents the maximum influence of the change of a single record on the query result, for example, the average value of the psychological score (0-100) of 1000 students needs to be queried, and the global sensitivity is . is the privacy budget.

[0065] The specific process of the data noise addition is that the Laplace noise is generated for the student psychological state data of each record The Laplace noise is added to the student psychological state data to obtain the student psychological state data after adding data noise, for example, the average value of the psychological score of 1000 students in the query is 80, and the Laplace noise scale parameter is 0.2, and the student psychological state data after adding data noise is .

[0066] The privacy budget is obtained by inputting the data encryption effective evaluation index into the privacy budget mapping set, and the privacy budget mapping set is obtained from the preset database and represents the mapping relationship between the data encryption effective evaluation index and the privacy budget.

[0067] The privacy failure probability is obtained by inputting the data encryption effective evaluation index and the data volume of the student psychological state data into the privacy failure probability mapping set, and the privacy failure probability mapping set is obtained from the preset database and represents the mapping relationship between the data encryption effective evaluation index, the data volume of the student psychological state data and the privacy failure probability.

[0068] The key rotation frequency optimization means adjusting the current key rotation frequency to the first corrected key rotation frequency; the first corrected key rotation frequency is obtained by inputting the data encryption effective evaluation index and the current key rotation frequency into the key rotation frequency mapping set, and the key rotation frequency mapping set is obtained from the preset database and represents the mapping relationship between the data encryption effective evaluation index, the current key rotation frequency and the first corrected key rotation frequency.

[0069] It should be understood that the key rotation frequency optimization also includes the specific process of key rotation frequency optimization and determination as follows:

[0070] If there is a preset data security abnormal event (including but not limited to student psychological state data leakage, abnormal access) in the corrected key rotation period corresponding to the first corrected key rotation frequency, the key rotation frequency is increased according to a preset proportion to obtain a second corrected key rotation frequency, and data encryption pre-monitoring is performed according to the second corrected key rotation frequency, otherwise the first corrected key rotation frequency is maintained; the preset proportion is set according to the preset personnel, for example, it can be set to 10%.

[0071] It is determined whether there is a preset data security abnormal event in the corrected key rotation period corresponding to the second corrected key rotation frequency, if there is a preset data security abnormal event, the key rotation frequency is continuously increased according to the preset proportion until the termination condition of the key rotation frequency increase is reached, otherwise the second corrected key rotation frequency is maintained.

[0072] The termination condition of the key rotation frequency increase is:

[0073] If no preset data security anomaly event occurs within the modified key rotation cycle, the increase in the key rotation frequency is terminated; otherwise, it is determined whether the optimized key rotation frequency is less than the preset key rotation frequency. If the optimized key rotation frequency is less than the preset key rotation frequency, the key rotation frequency is increased further; otherwise, the increase in the key rotation frequency is terminated, and the preset personnel are prompted that the data encryption optimization is incorrect.

[0074] In this embodiment, as Figure 2 The flowchart shown is a data encryption optimization process provided in this application embodiment. The specific logic is as follows: When performing data encryption optimization, first, the salt value length is optimized, and it is determined whether the data encryption effectiveness evaluation index after salt value length optimization is greater than a preset encryption effectiveness threshold. If so, student psychological state monitoring continues based on the student psychological state data after salt value length optimization; otherwise, data noise is added, and it is determined whether the data encryption effectiveness evaluation index after data noise addition is greater than a preset encryption effectiveness threshold. If so, student psychological state monitoring continues based on the student psychological state data after data noise addition; otherwise, the key rotation frequency is optimized to obtain a first modified key rotation frequency. The process is then determined based on the first modified key rotation frequency. If no preset data security anomaly event exists within the modified key rotation cycle corresponding to the key rotation frequency, the first modified key rotation frequency is maintained while psychological state monitoring is performed. Otherwise, the key rotation frequency is increased according to a preset ratio to obtain a second modified key rotation frequency. If no preset data security anomaly event exists within the modified key rotation cycle corresponding to the second modified key rotation frequency, the second modified key rotation frequency is maintained while psychological state monitoring is performed. Otherwise, it is determined whether the optimized key rotation frequency is less than the preset key rotation frequency. If so, the key rotation frequency is further increased. Otherwise, the key rotation frequency optimization is terminated while psychological state monitoring is performed.

[0075] Bcrypt generates hashes with salt values, ensuring that the same password produces different hashes, thus preventing rainbow table attacks. Optimizing the salt length increases the salt length in the Bcrypt algorithm, reducing the probability of hash collisions and enhancing hash uniqueness, further resisting rainbow table attacks. Differential privacy mechanisms add noise to the data, protecting student privacy and ensuring that attackers cannot infer student information from their psychological state data. A smaller privacy budget results in more noise in the student psychological state data, reducing its usability. Optimizing the key rotation frequency reduces the risk of key leakage for long-term used keys. Termination conditions for increasing the key rotation frequency limit the process, preventing inefficient student psychological state monitoring caused by blindly increasing the frequency. These measures ultimately improve the security of student psychological state data, ensuring it is not leaked.

[0076] Due to the increase of the salt value length, the hash calculation time may increase; the smaller the privacy budget and the privacy failure probability, the greater the calculation overhead of noise addition; the greater the key rotation frequency, the greater the network load; and the student psychological state monitoring efficiency may be reduced, so by dynamically optimizing data encryption and subsequent psychological monitoring efficiency evaluation and optimization, the student psychological state monitoring efficiency is improved, and the dynamic balance of data encryption security and student psychological state monitoring efficiency is ensured.

[0077] Further, the psychological monitoring efficiency is evaluated according to the psychological monitoring efficiency parameter in the psychological state monitoring process, and the specific method is as follows:

[0078] The data availability coefficient is processed by relative difference to obtain the data availability coefficient deviation coefficient, the data availability coefficient is obtained by calculating the cosine similarity of the student psychological state data before encryption and the student psychological state data after decryption, and is used to quantitatively evaluate the consistency of the student psychological state data before encryption and the student psychological state data after decryption, that is ; In the formula, KYO represents the data availability coefficient in the student psychological state monitoring process, the student psychological state data before encryption and the student psychological state data after decryption are converted into vector form (such as text data into word vector, behavior data into feature vector), and then the cosine similarity of the two vectors is calculated to obtain the data availability coefficient.

[0079] According to the encryption-decryption time length and the preset encryption-decryption time length obtained from the preset database, the encryption-decryption time length comparison coefficient is obtained, that is ; In the formula, MIT represents the encryption-decryption time length in the student psychological state monitoring process, the encryption time length is obtained by the time difference between the start time and the end time of the recorded encryption operation, then the decryption time length is obtained by the time difference between the start time and the end time of the recorded decryption operation, and finally the encryption time length and the decryption time length are summed to obtain the encryption-decryption time length; represents the preset encryption-decryption time length, which is represented by the average value of the encryption-decryption time length in the historical time period; the comparison processing here means ratio operation.

[0080] When the data encryption effective evaluation index is greater than the preset encryption effective threshold value, the data encryption effective influence factor is 0, otherwise the data encryption effective evaluation index and the preset encryption effective threshold value are processed by deviation comparison to obtain the data encryption effective influence factor; the deviation comparison processing here means that after the difference operation of the data encryption effective evaluation index and the preset encryption effective threshold value, the result of the difference operation and the preset encryption effective threshold value are ratio operated; the specific limit expression of the data encryption effective influence factor is:

[0081] ;

[0082] In the formula, MIX represents the data encryption effective influence factor, MI represents the data encryption effective evaluation index in the student psychological state data encryption process, represents the preset encryption effective threshold value.

[0083] After introducing the psychological monitoring efficiency evaluation compensation value to the data available coefficient deviation coefficient and the encryption-decryption time length comparison coefficient, the psychological monitoring efficiency evaluation coefficient is obtained by inverse proportional operation. The psychological monitoring efficiency evaluation compensation value includes the first psychological monitoring efficiency evaluation compensation value and the second psychological monitoring efficiency evaluation compensation value.

[0084] After introducing the third psychological monitoring efficiency evaluation compensation value to the data encryption effective influence factor, the psychological monitoring efficiency evaluation index is obtained by coupling processing with the psychological monitoring efficiency evaluation coefficient. The psychological monitoring efficiency evaluation index is used to quantitatively evaluate the monitoring efficiency in the student psychological state monitoring process after data encryption optimization.

[0085] The specific limit expression of the psychological monitoring efficiency evaluation index is:

[0086] ;

[0087] In the formula, LV represents the psychological monitoring efficiency evaluation index in the student psychological state monitoring process, MIX represents the data encryption effective influence factor, represents the first psychological monitoring efficiency evaluation compensation value, represents the second psychological monitoring efficiency evaluation compensation value, represents the third psychological monitoring efficiency evaluation compensation value.

[0088] The psychological monitoring efficiency evaluation compensation values are obtained from a preset database, the first psychological monitoring efficiency evaluation compensation value represents the influence degree of the data availability coefficient on the psychological monitoring efficiency evaluation index, the second psychological monitoring efficiency evaluation compensation value represents the influence degree of the encryption-decryption time length on the psychological monitoring efficiency evaluation index, and the third psychological monitoring efficiency evaluation compensation value represents the influence degree of the data encryption effective evaluation index on the psychological monitoring efficiency evaluation index; the sum of the three is 1. For example, the data availability coefficient and the preset first psychological monitoring efficiency evaluation compensation value form a data availability coefficient mapping set, the real-time data availability coefficient is input into the data availability coefficient mapping set to obtain the corresponding first psychological monitoring efficiency evaluation compensation value; the encryption-decryption time length and the preset second psychological monitoring efficiency evaluation compensation value form an encryption-decryption time length mapping set, the real-time encryption-decryption time length is input into the encryption-decryption time length mapping set to obtain the corresponding second psychological monitoring efficiency evaluation compensation value; the data encryption effective evaluation index and the preset third psychological monitoring efficiency evaluation compensation value form a data encryption effective evaluation index mapping set, the real-time data encryption effective evaluation index is input into the data encryption effective evaluation index mapping set to obtain the corresponding third psychological monitoring efficiency evaluation compensation value; the mapping relationship can be one-to-one or many-to-one.

[0089] In the embodiment, the greater the data encryption effective evaluation index, the higher the security of the data encryption scheme, which may result in a decrease in the data availability coefficient; an increase in the encryption-decryption time length may result in an increase in the data encryption effective evaluation index; the longer the encryption-decryption time, the more complex the data encryption algorithm, which may result in a decrease in the data availability coefficient.

[0090] The data consistency before and after encryption is quantitatively evaluated by the data availability coefficient deviation coefficient, and the loss degree of the encryption technology on the student psychological state data is measured; the greater the data availability coefficient deviation coefficient, the lower the efficiency of the student psychological state monitoring; the efficiency of the data encryption process is evaluated by the encryption-decryption time length comparison coefficient; the greater the encryption-decryption time length comparison coefficient, the lower the efficiency of the student psychological state monitoring; by comprehensively considering the data encryption effective influence factor, the data availability coefficient deviation coefficient and the encryption-decryption time length comparison coefficient, the monitoring efficiency in the student psychological state monitoring process after the optimization of the data encryption is quantitatively evaluated, which provides a basis for the subsequent psychological monitoring efficiency optimization and solves the problem of low student psychological state monitoring efficiency caused by the optimization of the data encryption.

[0091] Further, whether to perform psychological monitoring efficiency optimization is determined based on the result of the psychological monitoring efficiency evaluation, and the specific process is as follows:

[0092] determining whether the psychological monitoring efficiency evaluation index is greater than a preset monitoring efficiency threshold value obtained from the preset database, if the psychological monitoring efficiency evaluation index is not greater than the preset monitoring efficiency threshold value obtained from the preset database, performing cost factor optimization of Bcrypt, otherwise continuing student state monitoring; the preset monitoring efficiency threshold value is represented by an average value of the psychological monitoring efficiency evaluation index in a historical time period.

[0093] The cost factor optimization represents adjusting the current cost factor in Bcrypt to a corrected cost factor; the corrected cost factor is obtained by inputting the psychological monitoring efficiency evaluation index and the current cost factor into a cost factor mapping set, and the cost factor mapping set is a set obtained from the preset database and representing the mapping relationship between the psychological monitoring efficiency evaluation index, the current cost factor and the corrected cost factor; the hash process of Bcrypt needs to set the cost factor, which determines the amount of calculation needed when calculating the hash; the cost factor determines the calculation complexity of the hash process, and the Bcrypt cost factor is an exponential value of 2, for example, the cost factor of 10 represents that the hash calculation needs to perform 2 to the power of 10 (1024) times of calculation iteration.

[0094] determining whether the psychological monitoring efficiency evaluation index after the cost factor optimization is greater than the preset monitoring efficiency threshold value obtained from the preset database, if the psychological monitoring efficiency evaluation index after the cost factor optimization is not greater than the preset monitoring efficiency threshold value obtained from the preset database, performing communication optimization, otherwise continuing student state monitoring.

[0095] The communication optimization includes encoding optimization and task parallel processing.

[0096] The encoding optimization represents encoding the student psychological state data using variable-length integer encoding according to the encoding length, so as to reduce the storage space occupied by the student psychological state data; the encoding length is obtained by inputting the data amount of the student psychological state data and the psychological monitoring efficiency evaluation index into an encoding length mapping set, and the encoding length mapping set is a set obtained from the preset database and representing the mapping relationship between the data amount of the student psychological state data, the psychological monitoring efficiency evaluation index and the encoding length.

[0097] The task parallel processing represents grouping tasks according to operation types (such as summation and classification), and performing batch calculation on a preset number of tasks in each group; the preset number of tasks is obtained by inputting the psychological monitoring efficiency evaluation index and the total number of tasks in each group into a preset number of tasks mapping set, and the preset number of tasks mapping set is a set obtained from the preset database and representing the mapping relationship between the psychological monitoring efficiency evaluation index, the total number of tasks in each group and the preset number of tasks.

[0098] In the embodiment, by adjusting the cost factor of Bcrypt, the data encryption performance in the student psychological state monitoring process can be optimized on the basis of ensuring data encryption security, the encryption operation is more efficient, and thus the efficiency of the student psychological state monitoring process is improved; through coding optimization, the storage space occupied by the student psychological state data is reduced, the burden of communication and data processing is reduced, the overall efficiency of the student psychological state monitoring is improved, the bandwidth requirement of student psychological state data transmission is reduced, and more efficient data transmission is realized; through task parallel processing, multiple tasks are processed at the same time, the processing time of the student psychological state data is reduced, the utilization rate of the computing resources in the student psychological state monitoring process is optimized, and efficient operation of the student psychological state monitoring is ensured; through the above steps, the efficiency of the student psychological state monitoring process is maximally improved on the premise of ensuring the security of the student psychological state data.

[0099] The student psychological state monitoring system provided by the embodiment of the application applies a student psychological state monitoring method, which comprises a data encryption effective evaluation module, a data encryption optimization determination module, and a psychological monitoring efficiency optimization determination module.

[0100] The data encryption effective evaluation module is configured to perform encrypted transmission and storage of the obtained student psychological state data, determine whether the data increase of the student psychological state data stored in a preset time period is greater than a preset data increase, perform data encryption effective evaluation according to the encryption effective evaluation parameter of the student psychological state data and execute the function of the data encryption optimization determination module if the data increase is greater than the preset data increase, or continue to perform student psychological state monitoring based on the obtained student psychological state data.

[0101] The data encryption optimization determination module is configured to determine whether to perform data encryption optimization based on the result of the data encryption effective evaluation, continue to perform student psychological state monitoring based on the obtained student psychological state data if data encryption optimization is not performed, or perform student psychological state monitoring based on the student psychological state data after data encryption optimization, perform psychological monitoring efficiency evaluation according to the psychological monitoring efficiency parameter in the psychological state monitoring process, and execute the function of the psychological monitoring efficiency optimization determination module.

[0102] The psychological monitoring efficiency optimization determination module is configured to determine whether to perform psychological monitoring efficiency optimization based on the result of the psychological monitoring efficiency evaluation, perform student psychological state monitoring after psychological monitoring efficiency optimization if psychological monitoring efficiency optimization is performed, or continue to perform student psychological state monitoring based on the student psychological state data after data encryption optimization.

[0103] In the embodiment, the data encryption effective evaluation module ensures the security of the student psychological state data in the transmission and storage process, protects the student psychological state data from unauthorized access, and ensures that the effectiveness of data encryption can still be maintained as the data volume of the student psychological state data increases; the data encryption optimization determination module adjusts the encryption strategy to improve the security of data encryption, solving the problem of reduced data encryption security caused by the increase in the data volume of the student psychological state data; the psychological monitoring efficiency optimization determination module optimizes various operations in the student psychological state monitoring process, reduces the delay and computational burden, thereby improving the real-time monitoring capability of the student psychological state monitoring, ensuring the real-time and accuracy of the student psychological state data, and avoiding the reduction in the student psychological state monitoring efficiency caused by the large data volume of the student psychological state data and the high data encryption complexity.

[0104] In summary, the embodiments of the present application determine whether to perform data encryption effective evaluation according to the data increase of the student psychological state data, then determine whether to perform data encryption optimization based on the result of the data encryption effective evaluation, and perform student psychological state monitoring, and then perform psychological monitoring efficiency evaluation according to the psychological monitoring efficiency parameter to determine whether to perform psychological monitoring efficiency optimization, thereby improving the security of data encryption, and further improving the student psychological state monitoring efficiency, effectively solving the problem that the efficiency evaluation in the student psychological state monitoring process in the prior art does not consider the influence of data volume difference on encryption operation.

[0105] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0106] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks

[0107] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0109] Although preferred embodiments of the application have been described herein, substitutions and alterations are possible in view of the teachings of this application. Accordingly, the appended claims are intended to encompass all such substitutions and alterations. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

[0110] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A comprehensive data student psychological state monitoring method, characterized in that, The method comprises the following steps: S1, the acquired student psychological state data is encrypted and stored, and it is judged whether the data increase of the stored student psychological state data in a preset time period is greater than a preset data increase, if yes, data encryption effective evaluation is carried out according to the encrypted effective evaluation parameter of the student psychological state data, and S2 is executed, otherwise, the student psychological state monitoring is continued based on the acquired student psychological state data; S2, whether data encryption optimization is carried out is judged based on the result of data encryption effective evaluation, if not, the student psychological state monitoring is continued based on the acquired student psychological state data, otherwise, the student psychological state monitoring is carried out based on the student psychological state data after data encryption optimization, the psychological monitoring efficiency is evaluated according to the psychological monitoring efficiency parameter in the psychological state monitoring process, and S3 is executed; The psychological monitoring efficiency is evaluated according to the psychological monitoring efficiency parameter in the psychological state monitoring process, and the specific method is as follows: The data available coefficient is processed by relative difference to obtain the data available coefficient deviation coefficient, the data available coefficient is obtained by calculating the cosine similarity of the student psychological state data before encryption and the student psychological state data after decryption, and is used for quantitative evaluation of the consistency of the student psychological state data before encryption and the student psychological state data after decryption; The encryption-decryption time length is compared with the preset encryption-decryption time length obtained from the preset database to obtain the encryption-decryption time length comparison coefficient; After the data available coefficient deviation coefficient and the encryption-decryption time length comparison coefficient are coupled by introducing the psychological monitoring efficiency evaluation compensation value, the psychological monitoring efficiency evaluation coefficient is obtained by inverse proportional operation, and the psychological monitoring efficiency evaluation compensation value includes the first psychological monitoring efficiency evaluation compensation value and the second psychological monitoring efficiency evaluation compensation value; After the data encryption effective influence factor is valued by introducing the third psychological monitoring efficiency evaluation compensation value, the psychological monitoring efficiency evaluation index is obtained by coupling with the psychological monitoring efficiency evaluation coefficient, and the psychological monitoring efficiency evaluation index is used for quantitative evaluation of the monitoring efficiency in the student psychological state monitoring process after data encryption optimization; S3, whether psychological monitoring efficiency optimization is carried out is judged based on the result of psychological monitoring efficiency evaluation, if psychological monitoring efficiency optimization is carried out, the student psychological state monitoring is carried out after psychological monitoring efficiency optimization, otherwise, the student psychological state monitoring is continued.

2. The method of claim 1, wherein: The specific process of encrypting and storing the acquired student psychological state data is as follows: Firstly, the acquired student psychological state data is encrypted and transmitted; Secondly, the student psychological state data is hashed to obtain the hash value of the student psychological state data; Thirdly, the hash value of the student psychological state data is signed by the private key, and the signature is verified by the public key, it is judged whether the signature verification success rate is greater than the preset signature verification success rate obtained from the preset database, if yes, the fourth step is directly executed, otherwise, feedback is carried out; Fourthly, the account password in the psychological state monitoring platform is encrypted, and a salt value with an initial salt value length is generated for each account password; The fifth step is to symmetrically encrypt and store the student psychological state data.

3. The method of claim 1, wherein: The data encryption effective evaluation is performed according to the encrypted effective evaluation parameter of the student psychological state data, and the specific method is as follows: When the data leakage amount of the student psychological state data in the preset time period is not less than the data leakage amount of the student psychological state data in the historical time period, the deviation comparison processing is performed according to the data leakage amount of the student psychological state data in the preset time period and the data leakage amount of the student psychological state data in the historical time period, and the data leakage change coefficient is obtained, otherwise the data leakage change coefficient is recorded as 0; When the average key cracking time of the student psychological state data in the preset time period is not less than the average key cracking time of the student psychological state data in the historical time period, the deviation comparison processing is performed according to the average key cracking time of the student psychological state data in the preset time period and the average key cracking time of the student psychological state data in the historical time period, and the cracking time change coefficient is obtained, otherwise the cracking time change coefficient is recorded as 0; The key rotation frequency is compared with the preset key rotation frequency obtained from the preset database, and the key rotation frequency comparison coefficient is obtained; The data leakage change coefficient is inversely proportional to the data leakage change inverse coefficient; The data encryption effective evaluation index is obtained by introducing the data encryption effective compensation value to assign and couple the data leakage change inverse coefficient, the cracking time change coefficient and the key rotation frequency comparison coefficient, and the data encryption effective evaluation index is used to quantitatively evaluate the influence degree of the data increase amount of the student psychological state data on the data encryption effectiveness.

4. The method of claim 3, wherein: The result of the data encryption effective evaluation is used to judge whether to perform data encryption optimization, and the specific process is as follows: If the data encryption effective evaluation index is not greater than the preset encryption effective threshold value obtained from the preset database, the salt value length optimization is performed, and the data encryption effective evaluation index is reacquired, otherwise the student psychological state monitoring is continued based on the acquired student psychological state data; If the data encryption effective evaluation index after the salt value length optimization is not greater than the preset encryption effective threshold value obtained from the preset database, the data noise addition is performed, and the data encryption effective evaluation index is reacquired, otherwise the data encryption optimization is ended, and the student psychological state monitoring is continued based on the student psychological state data after the salt value length optimization; If the data encryption effective evaluation index after the data noise addition is not greater than the preset encryption effective threshold value obtained from the preset database, the key rotation frequency optimization is performed, and the data encryption effective evaluation index is reacquired, otherwise the data encryption optimization is ended, and the student psychological state monitoring is continued based on the student psychological state data after the data noise addition; The data encryption optimization includes salt value length optimization, data noise addition and key rotation frequency optimization.

5. The method of claim 4, wherein: The salt value length optimization means that the encryption process of the account password in the psychological state monitoring platform is optimized by modifying the salt value length; The modified salt value length is obtained by inputting the data encryption effective evaluation index and the initial salt value length into a salt value length mapping set, and the salt value length mapping set is obtained from a preset database and represents a mapping relationship among the data encryption effective evaluation index, the initial salt value length, and the modified salt value length; The data noise addition indicates that noise is added to the student psychological state data through a differential privacy parameter in a differential privacy mechanism, and the differential privacy parameter includes a privacy budget and a privacy failure probability; The privacy budget is obtained by inputting the data encryption effective evaluation index into a privacy budget mapping set, and the privacy budget mapping set is obtained from a preset database and represents a mapping relationship between the data encryption effective evaluation index and the privacy budget; The privacy failure probability is obtained by inputting the data encryption effective evaluation index and the data amount of the student psychological state data into a privacy failure probability mapping set, and the privacy failure probability mapping set is obtained from a preset database and represents a mapping relationship among the data encryption effective evaluation index, the data amount of the student psychological state data, and the privacy failure probability; The key rotation frequency optimization indicates that the current key rotation frequency is adjusted to a first modified key rotation frequency; The first modified key rotation frequency is obtained by inputting the data encryption effective evaluation index and the current key rotation frequency into a key rotation frequency mapping set, and the key rotation frequency mapping set is obtained from a preset database and represents a mapping relationship among the data encryption effective evaluation index, the current key rotation frequency, and the first modified key rotation frequency.

6. The method of claim 5, wherein: The key rotation frequency optimization further includes the specific process of key rotation frequency optimization and determination as follows: If there is a preset data security abnormal event in the modified key rotation period corresponding to the first modified key rotation frequency, the key rotation frequency is increased by a preset proportion to obtain a second modified key rotation frequency, and data encryption pre-monitoring is performed according to the second modified key rotation frequency, otherwise the first modified key rotation frequency is maintained; It is determined whether there is a preset data security abnormal event in the modified key rotation period corresponding to the second modified key rotation frequency, if there is a preset data security abnormal event, the key rotation frequency is continuously increased by a preset proportion until a termination condition of key rotation frequency increase is reached, otherwise the second modified key rotation frequency is maintained; The termination condition of the key rotation frequency increase is that: If there is no preset data security abnormal event in the modified key rotation period, the increase of the key rotation frequency is terminated, otherwise it is determined whether the optimized key rotation frequency is less than a preset key rotation frequency; If the optimized key rotation frequency is less than the preset key rotation frequency, the key rotation frequency is continuously increased, otherwise the increase of the key rotation frequency is terminated.

7. The method of claim 1, wherein: The result of the psychological monitoring efficiency evaluation is used to determine whether to perform psychological monitoring efficiency optimization, and the specific process is as follows: ​ If the psychological monitoring efficiency evaluation index is not greater than a preset monitoring efficiency threshold value obtained from a preset database, the cost factor optimization is performed, otherwise the student state monitoring is continued; The cost factor optimization indicates that the current cost factor is adjusted to a modified cost factor; The modified cost factor is obtained by inputting the mental monitoring efficiency evaluation index and the current cost factor into a cost factor mapping set, and the cost factor mapping set is obtained from a preset database and represents a mapping relationship among the mental monitoring efficiency evaluation index, the current cost factor and the modified cost factor; It is judged whether the mental monitoring efficiency evaluation index after optimization of the cost factor is greater than a preset monitoring efficiency threshold obtained from the preset database. If the mental monitoring efficiency evaluation index after optimization of the cost factor is not greater than the preset monitoring efficiency threshold obtained from the preset database, communication optimization is performed, otherwise student state monitoring is continued.

8. The method of claim 7, wherein the comprehensive data student psychological state monitoring method is characterized by: The communication optimization includes coding optimization and task parallel processing; The coding optimization means that the student mental state data is coded according to the coding length; The coding length is obtained by inputting the data amount of the student mental state data and the mental monitoring efficiency evaluation index into a coding length mapping set, and the coding length mapping set is obtained from a preset database and represents a mapping relationship among the data amount of the student mental state data, the mental monitoring efficiency evaluation index and the coding length; The task parallel processing means that tasks are grouped according to operation types, and batch calculation is performed on a preset number of tasks in each group; The preset number of tasks is obtained by inputting the mental monitoring efficiency evaluation index and the total number of tasks in each group into a preset number of tasks mapping set, and the preset number of tasks mapping set is obtained from a preset database and represents a mapping relationship among the mental monitoring efficiency evaluation index, the total number of tasks in each group and the preset number of tasks.

9. A comprehensive data student psychological state monitoring system, applying the comprehensive data student psychological state monitoring method as claimed in any one of claims 1 to 8, characterized in that, Comprise: a data encryption effective evaluation module, a data encryption optimization determination module and a mental monitoring efficiency optimization determination module; The data encryption effective evaluation module is used for encrypted transmission and storage of the obtained student mental state data, and it is judged whether the data increase of the stored student mental state data in a preset time period is greater than a preset data increase. If yes, data encryption effective evaluation is performed according to the encryption effective evaluation parameter of the student mental state data, and the function of the data encryption optimization determination module is executed, otherwise student mental state monitoring is continued based on the obtained student mental state data; The data encryption optimization determination module is used for judging whether to perform data encryption optimization based on the result of data encryption effective evaluation. If not, student mental state monitoring is continued based on the obtained student mental state data, otherwise student mental state monitoring is performed based on the student mental state data after data encryption optimization, mental monitoring efficiency evaluation is performed according to the mental monitoring efficiency parameter in the mental state monitoring process, and the function of the mental monitoring efficiency optimization determination module is executed; The mental monitoring efficiency optimization determination module is used for judging whether to perform mental monitoring efficiency optimization based on the result of mental monitoring efficiency evaluation. If mental monitoring efficiency optimization is performed, student mental state monitoring is performed after mental monitoring efficiency optimization, otherwise student mental state monitoring is continued.

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