Teaching cloud disk data processing method based on AI computing power architecture

Abstract

The invention discloses a teaching cloud disk data processing method based on an AI computing power architecture, and relates to the technical field of teaching cloud disk data protection, and the method comprises the following steps: obtaining data needing security protection in a teaching cloud disk, and naming the data as private data; performing textualization processing on the image data, and naming the image data after textualization processing as textualized data; calculating the text data and the textualized data through a privacy security protection algorithm to obtain privacy security data; performing secure storage on the privacy security data; when the user accesses the privacy security data, verifying the identity information of the user and the user permission; the method is used for solving the problems that the consumption of computing resources is increased and the data is easy to steal and crack due to insufficient universality of protection of data in different formats and insufficient protection means of the data in the existing teaching cloud disk data protection technology.

Description

Technical Field

[0001] The present invention relates to the technical field of teaching cloud disk data protection, and specifically to a method for processing teaching cloud disk data based on an AI computing power architecture. Background Art

[0002] Teaching cloud disk data protection technology refers to a specialized technical system that, through comprehensive security strategies and technical means, ensures the confidentiality, integrity, availability, and compliance of educational data stored, processed, and transmitted in teaching cloud disks during their life cycle. Its core goal is to resist risks such as data leakage, tampering, and loss, while meeting the special requirements of the education industry for privacy protection and intellectual property rights.

[0003] The teaching cloud disk belongs to a type of intelligent education platform. The users of the teaching cloud disk are usually schools, and schools manage researchers, teachers, and students. Everyone's personal information is stored in the teaching cloud disk to facilitate the school's management of individuals and classes. Therefore, there is personal information involving privacy in the teaching cloud disk that needs to be securely protected. In addition, researchers usually use the teaching cloud disk to share research materials and research progress. When the research is not yet public, the research data at the confidential level of the research materials also needs to be securely protected. Among these data, there are usually data in text format and image format. Existing teaching cloud disk data protection technologies usually use different encryption models to encrypt data of different formats, which undoubtedly increases the consumption of computing resources. Moreover, the encrypted ciphertext is usually long, consuming more storage space when stored, and the longer the encrypted content, the greater the storage space consumed. At the same time, existing teaching cloud disk data protection technologies focus more on the verification of user identities and lack means of protecting against malicious data theft. For example, in the patent application with the publication number CN112801834A, "A security management system and security management method applied to an intelligent education platform" is disclosed. This solution focuses on the verification of user identities and lacks sufficient means of protecting the data itself. Existing teaching cloud disk data protection technologies also have insufficient versatility in protecting data of different formats and insufficient means of protecting the data itself, resulting in increased consumption of computing resources and the problem that data is easily stolen and cracked. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to some extent. By obtaining the data in the teaching cloud disk that needs to be protected, named privacy data, the privacy data includes text data and image data. Then, the RGB attributes of the pixel points in the image data are extracted, and the image data is converted into text-based data based on the RGB attributes. Then, the protection key set by the administrator is obtained, and the text data, text-based data, and protection key are converted into the encoded state used by the privacy security protection algorithm. Then, the privacy security protection algorithm is used to perform operations on the encoded text data, text-based data, and protection key to obtain privacy security data. Then, the privacy security data is securely stored. Finally, when the user accesses the privacy security data, the user's identity information and user permissions are verified, so as to solve the problems that the existing teaching cloud disk data protection technology still has insufficient versatility in protecting data in different formats and insufficient protection means for the data itself, resulting in increased consumption of computing resources and the data being easily stolen and cracked.

[0005] To achieve the above object, in a first aspect, the present application provides a method for processing teaching cloud disk data based on an AI computing power architecture, including the following steps:

[0006] Obtain the data in the teaching cloud disk that needs to be protected, named privacy data, and the privacy data includes text data and image data;

[0007] Perform text-based processing on the image data, and name the text-based processed image data as text-based data;

[0008] Calculate the text data and text-based data through the privacy security protection algorithm to obtain privacy security data;

[0009] Securely store the privacy security data;

[0010] When the user accesses the privacy security data, verify the user's identity information and user permissions.

[0011] Further, obtaining the data in the teaching cloud disk that needs to be protected, named privacy data, includes the following sub-steps:

[0012] Summarize the file information of students and teachers in the teaching cloud disk as privacy data;

[0013] Summarize the research materials among researchers in the teaching cloud disk as privacy data;

[0014] Both the file information and the research materials contain text data and image data.

[0015] Further, performing text-based processing on the image data, and naming the text-based processed image data as text-based data includes the following sub-steps:

[0016] Extract the RGB attributes of the pixel points in the image data;

[0017] Convert the image data into text data based on the RGB attributes.

[0018] Furthermore, extracting the RGB attributes of the pixel points in the image data includes the following sub-steps:

[0019] Number the pixel points in the image data, and mark the pixel point at the nth row and the mth column as P nm , where both n and m are non-zero natural numbers and both n and m are the serial numbers of P;

[0020] Obtain the RGB attributes of P nm , marked as C nm , and the RGB attributes include R value, G value and B value, which respectively represent the values of P nm in the red, green and blue color channels. Mark the R value, G value and B value of C nm as R nm , G nm and B nm ;

[0021] Obtain the number of digits of the numbers in R nm , G nm and B nm , and mark them as RD nm , GD nm and BD nm .

[0022] Furthermore, converting the image data into text data based on the RGB attributes includes the following sub-steps:

[0023] Add RD nm before R nm to form a new number, marked as RN nm ; add GD nm before G nm to form a new number, marked as GN nm ; add BD nm before B nm to form a new number, marked as BN nm ;

[0024] Starting with n = 1 and m = 1, place RN nm+1 at the end of RN nm , increment m by 1 and repeat. When m reaches the maximum, reset m to 1 and increment n by 1, and repeat the placement of RN nm+1 until both n and m reach the maximum. Name the final string composed of all RN nm as the R-encoding;

[0025] Start with n = 1 and m = 1, place GN m+1 at the end of GN nm and m + 1, then repeat. When m reaches the maximum, reset m to 1 and n + 1, and repeat the placement of GN nm+1 until both n and m reach the maximum. Name the string finally composed of all GN nm as the G - pass code;

[0026] Start with n = 1 and m = 1, place BN nm+1 at the end of B nm and m + 1, then repeat. When m reaches the maximum, reset m to 1 and n + 1, and repeat the placement of BN nm+1 until both n and m reach the maximum. Name the string finally composed of all BN nm as the B - pass code;

[0027] The R - pass code, G - pass code, and B - pass code are the texturized data.

[0028] Furthermore, calculating the privacy - secure data from the text data and the texturized data through a privacy - secure protection algorithm includes the following sub - steps:

[0029] Obtain the protection key set by the administrator, and convert the text data, texturized data, and protection key into the encoding state used by the privacy - secure protection algorithm;

[0030] Perform operations on the text data, texturized data, and protection key in the encoding state through the privacy - secure protection algorithm to obtain the privacy - secure data.

[0031] Furthermore, obtaining the protection key set by the administrator and converting the text data, texturized data, and protection key into the encoding state used by the privacy - secure protection algorithm includes the following sub - steps:

[0032] Obtain the protection key set by the administrator, convert the protection key into the decimal encoding in ASCII encoding with each character as a unit, and name it the key encoding;

[0033] Obtain the text data, and convert the text data into the decimal encoding based on Unicode encoding, and name it the text encoding;

[0034] Obtain the texturized data. The R - pass code, G - pass code, and B - pass code are the encoding states of the texturized data, and name it the texturized encoding;

[0035] Collectively name the text encoding and the texturized encoding as the encoding to be protected.

[0036] Further, the privacy - security data is obtained by performing operations on the encoded text data, text - based data, and protection keys through a privacy - security protection algorithm, including the following sub - steps:

[0037] When performing privacy - security protection calculations for any encoding to be protected, obtain the encoding to be protected and the key encoding. Mark the encoding to be protected as W and the key encoding as M;

[0038] Calculate W / M, obtain the integer of the calculation result, mark it as Q, and obtain the number of digits of Q, marked as t;

[0039] Calculate W%M, and mark the remainder as R, where % is the modulo operator;

[0040] Place Q at the first position of R, mark the recombined string as G, and obtain the number of digits of G;

[0041] Obtain the number of digits of M, marked as a. Compare t with a. If t is greater than or equal to a, output an iterative calculation signal; if t is less than a, output a stop - iteration signal;

[0042] If an iterative calculation signal is output, mark G as W and recalculate G until a stop - iteration signal is output;

[0043] Number t, and mark it as T in the order of its appearance, i where i is a positive integer and i is the serial number of T;

[0044] Place T i Combine them into a string in ascending order of i, and add the symbol "." (excluding quotes) before each T i and mark the final obtained string as H;

[0045] Count the number of iterations, marked as F. Place F at the first position of G, add the symbol "." (excluding quotes) between F and G, and place H at the end of G. Name the recombined string as privacy - security data.

[0046] Further, the secure storage of the privacy - security data includes the following sub - steps:

[0047] If the plaintext of the privacy - security data is text data, store the text data independently through a blockchain storage platform;

[0048] If the plaintext of the privacy and security data is text data, place the privacy and security data corresponding to the B-channel encoding at the end of the privacy and security data corresponding to the G-channel encoding. At the same time, place the privacy and security data corresponding to the G-channel encoding at the end of the privacy and security data corresponding to the R-channel encoding. Also, add a line break between the privacy and security data corresponding to the R-channel encoding, G-channel encoding, and B-channel encoding, and store it through the blockchain storage platform.

[0049] Further, when a user accesses the privacy and security data, verifying the user's identity information and user permissions includes the following sub-steps:

[0050] When a user accesses the privacy and security data, verify the user's identity information through identity verification technology. The identity verification technology includes account login verification, mobile phone verification code verification, face recognition verification, and fingerprint recognition verification;

[0051] After successful verification, query the user's permissions in the teaching cloud disk. If the user's permission is that of a student, the user can only view their own file information; if the user's permission is that of a teacher, the user can view the file information of all students in the class where the user is located and their own file information; if the user's permission is that of a researcher, the user can view the research materials of the research group where the user is located; if the user's permission is that of an administrator, the user can view all file information and research materials, but when viewing file information and research materials, authorization from the teacher of the class to which the file information belongs or the researcher of the research group to which the research materials belong is required;

[0052] The user's permissions can be any two or one of a student, a teacher, and a researcher at the same time.

[0053] Advantages of the present invention: By obtaining the data that needs to be protected in the teaching cloud disk and naming it privacy data, the privacy data includes text data and image data. Then, extract the RGB attributes of the pixel points in the image data and convert the image data into text data based on the RGB attributes. The advantage is that the data formats are usually divided into text and image categories. By converting the image data into text data, all the data that needs to be protected can be encrypted and calculated through the same encryption model, and the ciphertext formats after encryption are the same. Others cannot confirm whether the original data is text or image, saving computing resources while improving the security of the teaching cloud disk data;

[0054] The present invention obtains a protection key set by an administrator, converts text data, textified data, and the protection key into a coded state used by a privacy security protection algorithm, then performs operations on the coded text data, textified data, and protection key through the privacy security protection algorithm to obtain privacy security data, and then securely stores the privacy security data. Finally, when a user accesses the privacy security data, the user's identity information and user permissions are verified. The advantage is that the privacy security data finally calculated by the encryption algorithm adopted in the present invention is very concise, which can save a large amount of storage space, reduce the waste of storage space, and at the same time improve the conciseness of the ciphertext in the encryption algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flowchart of the steps of the method of the present invention;

[0056] Figure 2 is an example diagram of the image data of the present invention;

[0057] Figure 3 is a flowchart of the steps of calculating the privacy security data of the present invention;

[0058] Figure 4 is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0060] Example 1, please refer to Figure 1 As shown, the present application provides a method for processing teaching cloud disk data based on an AI computing power architecture, including the following steps:

[0061] Step S1, obtain the data that needs to be securely protected in the teaching cloud disk, named privacy data, and the privacy data includes text data and image data; Step S1 includes the following sub-steps:

[0062] Step S101, summarize the file information of students and teachers in the teaching cloud disk as privacy data;

[0063] Step S102, summarize the research materials among researchers in the teaching cloud disk as privacy data;

[0064] Both the file information and the research materials include text data and image data;

[0065] In specific implementation, there are usually course videos, course test questions, and user personal information in the teaching cloud disk. At the same time, researchers in the school will also use the teaching cloud disk to share research materials. Data such as course videos and course test questions are usually public resources and do not require security protection. However, personal information and research materials involve personal privacy and research secrets, so security protection is required.

[0066] Step S2: Perform text processing on the image data, and name the image data after text processing as texturized data; Step S2 includes the following sub-steps:

[0067] Step S201: Extract the RGB attributes of the pixel points in the image data;

[0068] Step S201 includes the following sub-steps:

[0069] Please refer to Figure 2 As shown, in Step S201.1, number the pixel points in the image data, and mark the pixel point at the nth row and mth column as P nm , where both n and m are non-zero natural numbers and both n and m are the serial numbers of P;

[0070] Step S201.2: Obtain the RGB attribute of P nm , and mark it as C nm . The RGB attribute includes R value, G value, and B value, which respectively represent the values of P nm in the red, green, and blue color channels. Mark the R value, G value, and B value of C nm as R nm , G nm , and B nm ;

[0071] Step S201.3: Obtain the number of digits of the numbers in R nm , G nm , and B nm , and mark them as RD nm , GD nm , and BD nm ;

[0072] In specific implementation, since the image data usually has a high resolution, the converted texturized data is very long and inconvenient to display in this embodiment. Therefore, this embodiment uses Figure 2 4 pixel points shown to illustrate the specific implementation process of converting image data into texturized data; The image data is as Figure 2 shown, which includes P 11 , P 12 , P 21 , and P 22 , and the corresponding C 11is (62, 71, 41), C 12 is (122, 123, 86), C 21 is (124, 119, 80), C 22 is (148, 134, 85), the first number is R nm , the second number is G nm , the third number is B nm ; taking C 11 as an example, where R 11 is 62, G 11 is 71, B 11 is 41, and the number of digits of the numbers is all 2. Therefore, RD 11 , GD 11 and BD 11 are all 2;

[0073] Step S202, convert the image data into text data based on the RGB attributes;

[0074] Step S202 includes the following sub-steps:

[0075] Step S202, add RD nm before R nm to form a new number, marked as RN nm ; add GD nm before G nm to form a new number, marked as GN nm ; add BD nm before B nm to form a new number, marked as BN nm ;

[0076] In a specific implementation, R 11 is 62, RD 11 is 2. Therefore, RN 11 is 262. Similarly, GN 11 is 271, BN 11 is 241. RN 12 , GN 12 and BN 12 are 3122, 3123, and 286 in sequence. RN 21 , GN 21 and BN 21 are 3124, 3119, and 280 in sequence. RN 22 , GN 22 and BN 22 are 3148, 3134, and 285 in sequence;

[0077] Step S202.1, start with n = 1 and m = 1, and use RN nm+1Place it at the end of RN nm At the end, increment m by 1 and repeat. When m reaches its maximum value, reset m to 1 and increment n by 1, then repeat the placement of RN nm+1 The placement is carried out until both n and m reach their maximum values. Name the string finally composed of all RN nm as the R general code;

[0078] Step S202.2, starting with n = 1 and m = 1, place GN m+1 at the end of GN nm At the end, increment m by 1 and repeat. When m reaches its maximum value, reset m to 1 and increment n by 1, then repeat the placement of GN nm+1 The placement is carried out until both n and m reach their maximum values. Name the string finally composed of all GN nm as the G general code;

[0079] Step S202.3, starting with n = 1 and m = 1, place BN nm+1 at the end of B nm At the end, increment m by 1 and repeat. When m reaches its maximum value, reset m to 1 and increment n by 1, then repeat the placement of BN nm+1 The placement is carried out until both n and m reach their maximum values. Name the string finally composed of all BN nm as the B general code;

[0080] Step S202.4, the R general code, G general code, and B general code are the texturized data;

[0081] In a specific implementation, starting with n = 1 and m = 1, at this time RN nm+1 is RN 12 Place RN 12 at the end of RN 11 to get 2623122. Increment m by 1. At this time, m = 2 and m reaches its maximum value. Before resetting m to 1, it is also necessary to place RN n+11 at the end of RN nm that is, place RN 21 at the end of RN 12 and then reset m to 1 and increment n by 1 to get 26231223124. Repeat the operation. Finally, the R general code is obtained as 262312231243148. The conversion processes of the G general code and B general code are exactly the same as that of the R general code and will not be specifically described in this embodiment. Similarly, the G general code is obtained as 271312331193134, and the R general code is 241286280285.

[0082] Step S3, calculate the text data and the textified data through the privacy and security protection algorithm to obtain the privacy and security data; Step S3 includes the following sub-steps:

[0083] Step S301, obtain the protection key set by the administrator, and convert the text data, the textified data, and the protection key into the encoding state used by the privacy and security protection algorithm;

[0084] Step S301 includes the following sub-steps:

[0085] Step S301.1, obtain the protection key set by the administrator, convert the protection key into a decimal encoding in ASCII encoding with each character as a unit, and name it the key encoding;

[0086] Step S301.2, obtain the text data, and convert the text data into a decimal encoding based on the Unicode encoding, and name it the text encoding;

[0087] Step S301.3, obtain the textified data, and the R-channel encoding, G-channel encoding, and B-channel encoding are the encoding states of the textified data, and name it the textified encoding;

[0088] Step S301.4, collectively refer to the text encoding and the textified encoding as the encoding to be protected;

[0089] In a specific implementation, the protection key set by the administrator is obtained as pwd159, and the converted key encoding is 707764313539. Among them, p is 70, w is 77, d is 64, 1 is 31, 5 is 35, and 9 is 39. Since the subsequent analysis processes for the text encoding and the textified encoding are the same, this embodiment only takes the above R-channel encoding as an example for illustration, and marks the R-channel encoding 262312231243148 as the encoding to be protected;

[0090] Step S302, calculate the encoded text data, the textified data, and the protection key through the privacy and security protection algorithm to obtain the privacy and security data;

[0091] Step S302 includes the following sub-steps:

[0092] Step S302.1, when performing privacy and security protection calculations for any encoding to be protected, obtain the encoding to be protected and the key encoding, mark the encoding to be protected as W, and mark the key encoding as M;

[0093] Step S302.2, calculate W / M, obtain the integer of the calculation result, mark it as Q, and obtain the number of digits of Q, mark it as t;

[0094] Step S302.3, calculate W % M, and mark the remainder as R, where % is the modulo operator;

[0095] Step S302.4: Place Q at the beginning of R, and mark the recombined string as G;

[0096] Step S302.5: Obtain the number of digits of M, marked as a, compare t with a. If t is greater than or equal to a, output an iterative calculation signal; if t is less than a, output a stop iteration signal;

[0097] Step S302.6: If the iterative calculation signal is output, mark G as W and recalculate G until the stop iteration signal is output;

[0098] Step S302.7: Number t, and mark it as T in the order of appearance of t i , where i is a positive integer and i is the serial number of T;

[0099] Step S302.8: Combine T i into a string in ascending order of i, and add the symbol "." in front of each T i , excluding the quotation marks, and mark the finally obtained string as H;

[0100] Step S302.9: Count the number of iterations, marked as F, place F at the beginning of G, and add the symbol "." between F and G, excluding the quotation marks, and place H at the end of G. Name the recombined string as privacy security data;

[0101] In a specific implementation, mark the to-be-protected code 262312231243148 as W, the key code 707764313539 as M, calculate W / M, and get Q as 370. Q only obtains the integer part without rounding the decimal. Obtain the number of digits t of Q as 3, calculate W % M, and get R as 439435233718. After recombination, get G as 370439435233718. Obtain the number of digits a of M as 12. By comparison, get that t is less than a, and output a stop iteration signal. At this time, there is only one t, that is, numbering to get T1, and processing to get H as ".3", excluding the quotation marks. Count the number of iterations F as 1. Finally, get the privacy security data as 1.370439435233718.3. Since this embodiment only uses 4 pixel points as an example to illustrate the calculation process of the privacy security data, the calculation amount is small. Usually, the number of digits of the to-be-protected code may reach millions, and large integer calculations are required. This embodiment accelerates the calculation process through the AI calculation example architecture. For a to-be-protected code with millions of digits, it can finally be reduced to a very short privacy security data in the same format as 1.370439435233718.3 through several iterations. When restoring, the number of iterations and T can be directly extracted i , and then according to Ti Just extract the factors and remainders and then perform the calculations.

[0102] Step S4: Securely store the privacy and security data; Step S4 includes the following sub-steps:

[0103] Step S401: If the plaintext of the privacy and security data is text data, independently store the text data through the blockchain storage platform.

[0104] Step S402: If the plaintext of the privacy and security data is texturized data, place the privacy and security data corresponding to the B - pass encoding at the end of the privacy and security data corresponding to the G - pass encoding, and at the same time place the privacy and security data corresponding to the G - pass encoding at the end of the privacy and security data corresponding to the R - pass encoding. At the same time, add line breaks between the privacy and security data corresponding to the R - pass encoding, G - pass encoding, and B - pass encoding, and store it through the blockchain storage platform.

[0105] In specific implementation, when performing security protection on image data, calculations should be performed on the R - pass encoding, G - pass encoding, and B - pass encoding simultaneously, and they should be saved in the same file. At the same time, they are distinguished by line breaks, and the security of the storage process of privacy and security data is ensured through the existing blockchain storage platform.

[0106] Step S5: When a user accesses the privacy and security data, verify the user's identity information and user permissions; Step S5 includes the following sub - steps:

[0107] Step S501: When a user accesses the privacy and security data, verify the user's identity information through identity verification technologies, which include account login verification, mobile phone verification code verification, face recognition verification, and fingerprint recognition verification.

[0108] Step S502: After successful verification, query the user's permissions in the teaching cloud disk. If the user's permission is that of a student, the user can only view their own file information. If the user's permission is that of a teacher, the user can view the file information of all students in the class where the user is located and their own file information. If the user's permission is that of a researcher, the user can view the research materials of the research group where the user is located. If the user's permission is that of an administrator, the user can view all file information and research materials, but when viewing file information and research materials, authorization from the teacher of the class to which the file information belongs or the researcher of the research group to which the research materials belong is required.

[0109] Step S503: The user's permissions can be any two or one of student, teacher, and researcher at the same time.

[0110] In specific implementation, the identity of the user is verified through existing identity verification technologies. When verifying, it is only necessary to ensure that at least two identity verification technologies are used. The user permissions can be "student", "teacher", "researcher", "student, researcher", and "teacher, researcher", but cannot be "student, teacher". The specific permission content for different user permissions has been described in detail in step S502, and will not be further explained in this embodiment.

[0111] Embodiment 2, please refer to Figure 4 as shown in Figure 4 illustrates a schematic structural diagram of an electronic device. The electronic device may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a teaching cloud disk data processing method based on an AI computing power architecture are run to implement the following functions: obtaining the data that needs to be securely protected in the teaching cloud disk, named privacy data; performing text processing on the image data, and naming the text-processed image data as text data; calculating the text data and the text data through a privacy security protection algorithm to obtain privacy security data; securely storing the privacy security data; when a user accesses the privacy security data, verifying the user's identity information and user permissions.

[0112] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software function units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0113] Embodiment 3. The present application further provides a computer program product. The computer program product includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a method for processing teaching cloud disk data based on an AI computing power architecture provided by each of the above methods. The method includes: obtaining data that needs to be protected by security in the teaching cloud disk, named privacy data; performing text processing on the image data, and naming the image data after text processing as text data; calculating the text data and the text data after text processing through a privacy security protection algorithm to obtain privacy security data; securely storing the privacy security data; and verifying the user's identity information and user permissions when the user accesses the privacy security data.

[0114] Embodiment 4. The present application further provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in a method for processing teaching cloud disk data based on an AI computing power architecture as described above are run to achieve the following functions: obtaining data that needs to be protected by security in the teaching cloud disk, named privacy data; performing text processing on the image data, and naming the image data after text processing as text data; calculating the text data and the text data after text processing through a privacy security protection algorithm to obtain privacy security data; securely storing the privacy security data; and verifying the user's identity information and user permissions when the user accesses the privacy security data.

[0115] Through the description of the above embodiments, the embodiments of the present invention can be provided as a method, a system or a computer program product. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0116] In the embodiments provided by the present application, it should be understood that the disclosed system or method can be implemented in other ways. The above-described embodiments are merely illustrative. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of systems, modules and units can be in an electrical, mechanical or other form.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for processing teaching cloud disk data based on an AI computing power architecture, characterized in that, It includes the following steps: Obtain the data that needs to be protected in the teaching cloud disk, named private data, and the private data includes text data and image data; Perform textification processing on the image data, and name the textified image data as textified data; Calculate the text data and the textified data through a privacy security protection algorithm to obtain privacy security data; Perform secure storage on the privacy security data; When a user accesses the privacy security data, verify the user's identity information and user permissions.

2. The data processing method of the teaching cloud disk based on the AI computing power architecture according to claim 1, wherein, Obtain the data that needs to be protected in the teaching cloud disk, named private data, including the following sub-steps: Summarize the file information of students and teachers in the teaching cloud disk as private data; Summarize the research materials among researchers in the teaching cloud disk as private data; Both the file information and the research materials contain text data and image data.

3. The data processing method of the teaching cloud disk based on the AI computing power architecture according to claim 2, wherein, Perform textification processing on the image data, and name the textified image data as textified data, including the following sub-steps: Extract the RGB attributes of the pixel points in the image data; Convert the image data into textified data based on the RGB attributes.

4. A method for processing teaching cloud disk data based on an AI computing power architecture according to claim 3, characterized in that, Extract the RGB attributes of the pixel points in the image data, including the following sub-steps: Number the pixel points in the image data, and mark the pixel point at the nth row and the mth column as P nm , where both n and m are non-zero natural numbers and both n and m are the serial numbers of P; Obtain P nm 's RGB attributes, marked as C nm , where the RGB attributes include R value, G value and B value, respectively representing P nm 's values in the three color channels of red, green and blue, and mark the R value, G value and B value of C nm as R nm , G nm and B nm ; Obtain R nm 、G nm and B nm in the number of digits, and mark them as RD nm 、GD nm and BD nm .

5. A method for processing teaching cloud disk data based on an AI computing power architecture according to claim 4, characterized in that, Convert the image data into textified data based on the RGB attributes, including the following sub-steps: Before R nm add RD nm to form a new number, labeled as RN nm ; Before G nm add GD nm to form a new number, labeled as GN nm ; Before B nm add BD nm to form a new number, labeled as BN nm ; Starting with n = 1 and m = 1, place RN nm+1 at the end of RN nm m + 1 and repeat. When m reaches the maximum, reset m to 1 and increment n by 1, then repeat the placement of RN nm+1 until both n and m reach the maximum. Name the string finally composed of all RN nm as the R - pass code; Starting with n = 1 and m = 1, place GN m+1 at the end of GN nm m + 1 and repeat. When m reaches the maximum, reset m to 1 and increment n by 1, and repeat the placement of GN nm+1 until both n and m reach the maximum. Name the string finally composed of all GN nm as the G - pass code; Starting with n = 1 and m = 1, place BN nm+1 at the end of B nm , increment m by 1 and repeat. When m reaches its maximum, reset m to 1 and increment n by 1, then repeat the placement of BN nm+1 until both n and m reach their maximums. Name the resulting string composed of all BN nm as the B-encoding; The R-channel code, G-channel code, and B-channel code are the encoding states of the textified data.

6. The data processing method of a teaching cloud disk based on an AI computing power architecture according to claim 5, characterized in that, Calculate the text data and the textified data through a privacy security protection algorithm to obtain privacy security data, including the following sub-steps: Obtain the protection key set by the administrator, and convert the text data, textified data, and protection key into the encoding state used by the privacy security protection algorithm; Perform operations on the encoded text data, textified data, and protection key through the privacy security protection algorithm to obtain privacy security data.

7. A method for processing teaching cloud disk data based on an AI computing power architecture according to claim 6, characterized in that, Obtain the protection key set by the administrator, and convert the text data, textified data, and protection key into the encoding state used by the privacy security protection algorithm, including the following sub-steps: Obtain the protection key set by the administrator, convert the protection key into a decimal code in ASCII encoding with each character as a unit, and name it the key code; Obtain the text data, and convert the text data into a decimal code based on Unicode encoding, named the text code; Obtain the textified data, and the R-channel code, G-channel code, and B-channel code are the encoding states of the textified data, named the textified code; Collectively refer to the text code and the textified code as the codes to be protected.

8. A data processing method for a teaching cloud disk based on an AI computing power architecture according to claim 7, characterized in that, Perform operations on the encoded text data, textified data, and protection key through the privacy security protection algorithm to obtain privacy security data, including the following sub-steps: When performing privacy security protection calculations on any code to be protected, obtain the code to be protected and the key code, mark the code to be protected as W, and mark the key code as M; Calculate W / M, obtain the integer of the calculation result, mark it as Q, and obtain the number of digits of Q, marked as t; Calculate W % M, and mark the remainder as R, where % is the modulo operator; Place Q at the first place of R, mark the recombined string as G, and obtain the number of digits of G; Obtain the number of digits of M, marked as a, and compare t with a. If t is greater than or equal to a, output an iterative calculation signal; if t is less than a, output a stop iteration signal; If an iterative calculation signal is output, mark G as W and recalculate G until a stop iteration signal is output; Number t and label it as T in the order of its appearance, where i is a positive integer and the serial number of T; i , where i is a positive integer and the serial number of T; Combine T i in ascending order of i into a string, and add the symbol "." before each T i without quotes, and mark the resulting string as H; Count the number of iterations, marked as F, place F at the beginning of G, and add the symbol "." between F and G, excluding the quotation marks, and place H at the end of G. Name the recombined string as privacy security data.

9. A method for processing teaching cloud disk data based on an AI computing power architecture according to claim 8, characterized in that, The secure storage of privacy security data includes the following sub-steps: If the plaintext of the privacy security data is text data, store the text data independently through the blockchain storage platform; If the plaintext of the privacy security data is textified data, place the privacy security data corresponding to the B pass encoding at the end of the privacy security data corresponding to the G pass encoding, and at the same time place the privacy security data corresponding to the G pass encoding at the end of the privacy security data corresponding to the R pass encoding. At the same time, add a line break between the privacy security data corresponding to the R pass encoding, G pass encoding, and B pass encoding, and store it through the blockchain storage platform.

10. A method for processing teaching cloud disk data based on an AI computing power architecture according to claim 9, characterized in that, When a user accesses privacy security data, verifying the user's identity information and user permissions includes the following sub-steps: When a user accesses privacy security data, verify the user's identity information through identity verification technology, and the identity verification technology includes account login verification, mobile phone verification code verification, face recognition verification, and fingerprint recognition verification; After verification, query the user's permissions in the teaching cloud disk. If the user's permission is a student, the user can only view the user's own file information; If the user's permission is a teacher, the user can view the file information of all students in the user's class and the user's own file information; if the user's permission is a researcher, the user can view the research materials of the research group where the user is located; if the user's permission is an administrator, the user can view all file information and research materials, but when viewing file information and research materials, authorization from the teacher of the class to which the file information belongs or the researcher of the research group to which the research materials belong is required; The user's permissions can be any two or one of a student, a teacher, and a researcher at the same time.

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