Screen display encryption method and encryption system for LED display screen

Abstract

The invention discloses an LED display screen display encryption method and encryption system, and relates to the technical field of screen display encryption. Comprising a secret key management module which is responsible for generating, distributing and updating secret keys and ensuring safe storage and transmission of the secret keys; the encryption and decryption module is used for realizing encryption and decryption algorithms and supporting multiple encryption strength selections; the data transmission module is responsible for transmitting and receiving encrypted data and comprises an error detection and correction mechanism; and the user interface module provides a user operation interface and has encryption parameter setting and key input functions. According to the invention, through the intelligent key management module, a key generation strategy is dynamically adjusted according to historical data and real-time analysis and prediction of a possible attack mode, so that the security of the key is improved; in combination with an artificial intelligence algorithm, the encryption strength is automatically adjusted according to the environment where the LED display screen is located and the sensitivity of the display content, and the safety and the system resource consumption are balanced.

Description

Technical Field

[0001] The present invention relates to the field of screen display encryption technology, and in particular to a LED display screen display encryption method and encryption system. Background Art

[0002] With the widespread use of LED displays in public places and commercial fields, the security of their display content has become increasingly prominent. Traditional encryption methods often use fixed keys and encryption algorithms, which are difficult to cope with complex and changing security threats. At the same time, encryption operations consume a lot of system resources, which may affect the display effect and performance of LED displays. Therefore, how to ensure the data security of LED displays while balancing system resource consumption has become an urgent problem to be solved.

[0003] After searching, the application scheme of Chinese patent application No. 202310239588.5 discloses an LED display screen encryption method and system, wherein the method includes: when a user inputs a content playback request to the LED display screen, obtaining the viewer who is about to view the LED display screen; responding to the content playback request, obtaining the content to be played input by the user; determining the encrypted content that the user is not convenient to show to the viewer from the content to be played; encrypting the content to be encrypted; and controlling the LED display screen to play the current content to be played. The encryption method in the above patent has the following shortcomings: it cannot update the secret key well, and the abnormality detection capability is insufficient, resulting in poor security. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an LED display screen encryption method and encryption system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An LED display screen encryption system, comprising:

[0007] Key management module: responsible for the generation, distribution and update of keys, ensuring the secure storage and transmission of keys;

[0008] Encryption and decryption module: implements encryption and decryption algorithms and supports multiple encryption strength options;

[0009] Data transmission module: responsible for the transmission and reception of encrypted data, including error detection and correction mechanisms;

[0010] User interface module: provides a user operation interface, including encryption parameter settings and key input functions;

[0011] Security monitoring module: monitors system operation status, records all operation logs, and prevents unauthorized access;

[0012] Intelligent key management module: uses machine learning algorithms to predict possible attack patterns based on historical data and real-time analysis, and dynamically adjusts key generation strategies;

[0013] Adaptive encryption strength adjustment module: Combined with artificial intelligence algorithms, it automatically adjusts the encryption strength according to the environment in which the LED display is located and the sensitivity of the displayed content; through environmental perception technology and content analysis, the intelligent algorithm can determine the appropriate encryption level and balance security and system resource consumption;

[0014] Abnormal behavior detection and response module: integrated with abnormal detection algorithm to monitor the operation behavior of LED display screen; when abnormal behavior is detected, the system automatically takes measures;

[0015] User behavior analysis and authentication module: Combine user behavior analysis and biometric recognition technology to authenticate operators and conduct behavior analysis; by analyzing user behavior, unauthorized operations can be discovered in a timely manner, thereby improving the overall security of the system.

[0016] Preferably, the encryption method of the encryption system comprises:

[0017] S1: Data preprocessing: Convert the original data into a numerical sequence using ASCII code;

[0018] S2: Key generation: Use a pseudo-random number generator to generate a key sequence K of the same length as the original data, where K = {k1, k2, ..., kn};

[0019] S3: Data encryption: Use a symmetric encryption algorithm to encrypt the preprocessed data to obtain a ciphertext C, where C = E(P);

[0020] S4: Confusion transformation: introduce nonlinear function f to perform confusion transformation on ciphertext C to increase the difficulty of cracking;

[0021] S5: Redundancy addition: In order to resist noise interference and data loss, redundant information R is added to the ciphertext to generate the final transmission sequence T = {C', R};

[0022] S6: The decryption process is the inverse process of encryption, including redundancy removal, inverse obfuscation transformation, application of decryption algorithm and data decoding.

[0023] Preferably: the key management module comprises:

[0024] Key generation unit: uses a cryptographic strong random number generator to generate the initial key to ensure the randomness and unpredictability of the key;

[0025] Key distribution unit: uses a secure key exchange protocol to distribute keys to authorized users;

[0026] Key update and recovery unit: set the key validity period according to the security policy, automatically update the key regularly, and safely recover the invalidated keys;

[0027] The encryption and decryption module comprises:

[0028] Data encryption unit: implements the symmetric encryption algorithm AES, which is used to encrypt the original data;

[0029] Data decryption unit: implements the AES decryption algorithm to ensure that authorized users can accurately restore data;

[0030] Encryption strength selection unit: Provides different levels of encryption strength selection, and selects the appropriate encryption level based on security needs and system performance requirements.

[0031] Preferably: the encryption and decryption module applies the AES encryption algorithm, including key expansion, initial round, several intermediate rounds and the final round; in each round, the data block will undergo byte substitution, row shift, column mixing and round key addition operations; the specific formula is as follows:

[0032]

[0033] Among them, S i,j is the byte in the state matrix, x i,j is the input, representing finite field multiplication, g(x i,j ) is a specific polynomial function;

[0034] In the encryption and decryption module, the AES decryption process includes inverse byte substitution, reverse row shift, reverse column mixing and round key encryption; decryption formula:

[0035]

[0036] Among them, h and g -1 It is a function specific to the AES decryption process.

[0037] Preferably: the data transmission module comprises:

[0038] Encrypted data transmission unit: Use the secure transport layer protocol to encrypt all transmitted data to ensure the security of data during transmission;

[0039] Data integrity check unit: uses checksum or digital signature technology to ensure data integrity and prevent data from being tampered with during transmission;

[0040] Error detection and correction unit: implements error detection and correction mechanism, and uses forward error correction coding for error recovery;

[0041] The user interface module comprises:

[0042] Parameter setting unit: provides an intuitive user interface for users to select encryption parameters;

[0043] Key input unit: Design a secure user key input interface to ensure that the key is not leaked during the input process;

[0044] Operation feedback unit: implements the operation feedback mechanism to display system status, operation results and safety warning information to users.

[0045] Preferably: the security monitoring module comprises:

[0046] System status monitoring unit: real-time monitoring of the system's operating status, including CPU usage, memory usage, and network status;

[0047] Operation log recording unit: records the operation logs of all users, including login, key change, encryption and decryption operations, for easy auditing and tracking;

[0048] Unauthorized access prevention unit: implements access control mechanism to prevent unauthorized access through user authentication and permission management;

[0049] The intelligent key management module comprises:

[0050] Attack pattern prediction unit: uses machine learning algorithms to analyze historical data, predict possible attack patterns, and adjust key policies accordingly;

[0051] Key usage analysis unit: Analyzes key usage through deep learning and optimizes the key generation, distribution and update process;

[0052] Key Security Assessment Unit: Use artificial intelligence to conduct security assessments on existing key systems and provide suggestions for improvements.

[0053] Preferably: the intelligent key management module uses a machine learning algorithm to analyze historical data, predict possible attack patterns, and adjust key strategies accordingly, and predicts future attack patterns based on the characteristics of historical attack data through a decision tree classification algorithm; the specific formula is:

[0054]

[0055] Wherein, D(x) is the decision function, c m is the category, p(y=c m |x) is the category c under feature x m The conditional probability of

[0056] The intelligent key management module uses a recurrent neural network to analyze the mode and frequency of key usage to optimize the key management strategy, specifically:

[0057] h t =σ(W hh h t-1 +W xh x t +b h )

[0058] y t =W hy h t +b y

[0059] Among them, h t is the hidden state at time step t, σ is the activation function, and W and b are the weight and bias respectively.

[0060] Preferably, the intelligent key management module uses an AI system to evaluate the security of the key system through entropy calculation and support vector machine algorithm, and proposes improvement suggestions based on the evaluation results. The specific formula is:

[0061]

[0062] Where H(X) is the entropy of the random variable X, P(x i ) is the event x i The probability of

[0063]

[0064] in:

[0065] x is the input data;

[0066] x i represents the support vector;

[0067] y i ∈{-1,1} is a classification label, indicating whether the data instance belongs to the vulnerable category;

[0068] K(x,x i ) is the kernel function, which is used to calculate the similarity between the input data and the support vector;

[0069] a i is the Lagrange multiplier;

[0070] b is the bias term.

[0071] Preferably: the adaptive encryption strength adjustment module comprises:

[0072] Environmental perception unit: obtains the environmental information of the LED display through integrated sensors;

[0073] Content sensitivity analysis unit: analyzes the sensitivity of the content to be displayed and determines whether high-level encryption protection is required;

[0074] Dynamic encryption strength adjustment unit: intelligently adjusts encryption strength based on environment and content analysis results to balance security requirements and system performance;

[0075] The abnormal behavior detection and response module includes:

[0076] Anomaly Detection Unit: uses clustering and neural network techniques to monitor abnormal patterns in system and user behavior;

[0077] Security response decision unit: When an anomaly is detected, the intelligent decision system selects the most appropriate security response measure;

[0078] Automatic Isolation and Reporting Unit: performs security response and automatically reports to administrators;

[0079] The intelligent redundant data processing module comprises:

[0080] Redundancy analysis unit: predicts the error rate in data transmission through regression analysis and determines the amount of redundant data required;

[0081] Redundancy optimization computing unit: uses machine learning to optimize redundant data strategies to improve the efficiency and reliability of data transmission;

[0082] Redundant data integration unit: adds optimized redundant information to the transmitted data to enhance the data's anti-interference capability;

[0083] The user behavior analysis and authentication module includes:

[0084] User behavior monitoring unit: tracks user operation habits, including login time, operation frequency, and commonly used functions;

[0085] Biometric Authentication Unit: Combines fingerprint recognition and facial recognition biometric technologies to provide multi-factor authentication;

[0086] User behavior analysis and anomaly detection unit: Use data mining technology to analyze user behavior data, detect abnormal behavior in a timely manner, and enhance security.

[0087] Preferably, it also includes:

[0088] Intelligent redundant data processing module:

[0089] Utilize regression analysis and prediction models in machine learning to intelligently select and calculate the amount of redundant data that needs to be added; the system can dynamically adjust the strategy of redundant data according to the error rate and noise level during transmission to optimize the efficiency and accuracy of data transmission;

[0090] Encryption algorithm optimization module:

[0091] Apply genetic algorithms and simulated annealing optimization technology to optimize existing encryption algorithms and find more efficient and secure encryption parameters and processes; through continuous learning and adjustment, the algorithm can reduce the computational cost of the encryption and decryption process while ensuring security;

[0092] The intelligent redundant data processing module comprises:

[0093] Redundancy analysis unit: predicts the error rate in data transmission through regression analysis and determines the amount of redundant data required;

[0094] Redundancy optimization computing unit: uses machine learning to optimize redundant data strategies to improve the efficiency and reliability of data transmission;

[0095] Redundant data integration unit: adds optimized redundant information to the transmitted data to enhance the data's anti-interference capability;

[0096] The encryption algorithm optimization module includes:

[0097] Algorithm parameter optimization unit: Use genetic algorithms and simulated annealing optimization techniques to find the best parameter configuration for encryption algorithms;

[0098] Performance evaluation unit: real-time evaluation of the performance of encryption algorithms, including encryption and decryption speed and resource consumption;

[0099] Automatic algorithm update unit: Automatically updates and optimizes encryption algorithms based on performance evaluation results to ensure system security and efficient operation.

[0100] The beneficial effects of the present invention are:

[0101] 1. The present invention uses an intelligent key management module to predict possible attack modes based on historical data and real-time analysis, dynamically adjust the key generation strategy, and improve the security of the key; combined with an artificial intelligence algorithm, the encryption strength is automatically adjusted according to the environment in which the LED display is located and the sensitivity of the displayed content, thereby balancing security and system resource consumption.

[0102] 2. The present invention integrates an abnormality detection algorithm to monitor the operating behavior of the LED display screen. When abnormal behavior is detected, the system automatically takes measures to improve the security protection capability of the system; combined with user behavior analysis and biometric recognition technology, the operator's identity authentication and behavior analysis are performed to improve the overall security of the system.

[0103] 3. The present invention applies genetic algorithms and simulated annealing optimization technology to optimize existing encryption algorithms, find more efficient and secure encryption parameters and processes, and reduce the computational cost of the encryption and decryption process; it uses regression analysis and prediction models in machine learning to intelligently select and calculate the amount of redundant data that needs to be added, thereby improving the efficiency and accuracy of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] Figure 1 The present invention provides a flow chart of a method for encrypting the display of an LED display screen. DETAILED DESCRIPTION

[0105] The technical solution of the present invention is further described in detail below in conjunction with specific implementation methods.

[0106] Embodiment 1:

[0107] An LED display screen encryption system, comprising:

[0108] Key management module: responsible for the generation, distribution and update of keys, ensuring the secure storage and transmission of keys;

[0109] Encryption and decryption module: implements encryption and decryption algorithms and supports multiple encryption strength options;

[0110] Data transmission module: responsible for the transmission and reception of encrypted data, including error detection and correction mechanisms;

[0111] User interface module: provides a user operation interface, including encryption parameter settings, key input and other functions;

[0112] Security monitoring module: monitors system operation status, records all operation logs, and prevents unauthorized access.

[0113] Additionally, it includes:

[0114] Intelligent key management module: Using machine learning algorithms, predicting possible attack patterns based on historical data and real-time analysis, and dynamically adjusting key generation strategies; for example, using deep learning networks to analyze past key usage and security incidents, predicting possible future security threats, and adjusting key complexity and update frequency accordingly;

[0115] Adaptive encryption strength adjustment module: Combined with artificial intelligence algorithms, it automatically adjusts the encryption strength according to the environment of the LED display and the sensitivity of the displayed content; through environmental perception technology (such as location information, surrounding light conditions, etc.) and content analysis (whether sensitive information is contained), the intelligent algorithm can determine the appropriate encryption level and balance security and system resource consumption;

[0116] Abnormal behavior detection and response module: Integrate abnormal detection algorithms, such as cluster-based anomaly detection or neural network, to monitor the operation behavior of the LED display; when abnormal behavior is detected, the system can automatically take measures, such as temporarily enhancing encryption, locking interfaces, notifying administrators, etc.;

[0117] User behavior analysis and authentication module: Combines user behavior analysis and biometric recognition technology to authenticate operators and conduct behavior analysis; by analyzing the user's login time, operating habits, and even keyboard typing rhythm, the intelligent algorithm can detect unauthorized operations in a timely manner and improve the overall security of the system.

[0118] The encryption method of the encryption system comprises:

[0119] S1: Data preprocessing: Convert the original data into a numerical sequence using ASCII code;

[0120] S2: Key generation: Use a pseudo-random number generator to generate a key sequence K of the same length as the original data, where K = {k1, k2, ..., kn};

[0121] S3: Data encryption: Use a symmetric encryption algorithm (such as AES) to encrypt the preprocessed data to obtain a ciphertext C, where C = E(P);

[0122] S4: Confusion transformation: introduce nonlinear function f to perform confusion transformation on ciphertext C to increase the difficulty of cracking;

[0123] S5: Redundancy addition: In order to resist noise interference and data loss, redundant information R is added to the ciphertext to generate the final transmission sequence T = {C', R};

[0124] S6: The decryption process is the inverse process of encryption, including redundancy removal, inverse obfuscation transformation, application of decryption algorithm and data decoding.

[0125] Wherein, the key management module includes:

[0126] Key generation unit: uses a cryptographic strong random number generator to generate the initial key to ensure the randomness and unpredictability of the key;

[0127] Key distribution unit: uses secure key exchange protocols, such as the Diffie-Hellman protocol, to securely distribute keys to authorized users;

[0128] Key update and recovery unit: Set the key validity period according to the security policy, automatically update the key regularly, and safely recover the invalidated keys.

[0129] Wherein, the encryption and decryption module includes:

[0130] Data encryption unit: implements the symmetric encryption algorithm AES, which is used to encrypt the original data;

[0131] Data decryption unit: implements the AES decryption algorithm to ensure that authorized users can accurately restore data;

[0132] Encryption strength selection unit: Provides different levels of encryption strength selection, and selects the appropriate encryption level based on security needs and system performance requirements.

[0133] The encryption and decryption module applies the AES encryption algorithm, including key expansion, initial round, several intermediate rounds and the final round. In each round, the data block will undergo byte substitution, row shift, column mixing and round key addition operations. The specific formula is as follows:

[0134]

[0135] Among them, S i,j is the byte in the state matrix, x i,j is the input, representing finite field multiplication, g(x i,j ) is a specific polynomial function.

[0136] Among them, in the encryption and decryption module, the AES decryption process includes inverse byte substitution, inverse row shift, inverse column mixing and round key encryption; decryption formula:

[0137]

[0138] Among them, h and g -1 It is a function specific to the AES decryption process.

[0139] Wherein, the data transmission module includes:

[0140] Encrypted data transmission unit: Use the secure transport layer protocol to encrypt all transmitted data to ensure the security of data during transmission;

[0141] Data integrity check unit: uses technologies such as checksum or digital signature to ensure data integrity and prevent data from being tampered with during transmission;

[0142] Error detection and correction unit: implements error detection and correction mechanisms, such as using cyclic redundancy check to detect data transmission errors and forward error correction coding for error recovery.

[0143] Wherein, the user interface module includes:

[0144] Parameter setting unit: provides an intuitive user interface for users to select encryption parameters, such as encryption algorithm, key length, etc.

[0145] Key input unit: Design a secure user key input interface to ensure that the key is not leaked during the input process;

[0146] Operation feedback unit: implements the operation feedback mechanism to display information such as system status, operation results and security warnings to users.

[0147] Wherein, the security monitoring module includes:

[0148] System status monitoring unit: real-time monitoring of the system's operating status, including CPU usage, memory usage, network status, etc.;

[0149] Operation log recording unit: records the operation logs of all users, including login, key change, encryption and decryption operations, etc., for easy auditing and tracking;

[0150] Unauthorized access prevention unit: implements access control mechanism to prevent unauthorized access through user authentication and permission management.

[0151] Wherein, the intelligent key management module includes:

[0152] Attack pattern prediction unit: uses machine learning algorithms to analyze historical data, predict possible attack patterns, and adjust key policies accordingly;

[0153] Key usage analysis unit: Analyzes key usage through deep learning and optimizes the key generation, distribution and update process;

[0154] Key Security Assessment Unit: Use artificial intelligence to conduct security assessments on existing key systems and provide suggestions for improvements.

[0155] The intelligent key management module uses a machine learning algorithm to analyze historical data, predict possible attack patterns, and adjust key strategies accordingly. It uses a decision tree classification algorithm to predict future attack patterns based on the characteristics of historical attack data. The specific formula is:

[0156]

[0157] Wherein, D(x) is the decision function, c m is the category, p(y=cm |x) is the category c under feature x m The conditional probability of

[0158] The intelligent key management module uses a recurrent neural network to analyze the mode and frequency of key usage to optimize the key management strategy, specifically:

[0159] h t =σ(W hh h t-1 +W xh x t +b h )

[0160] y t =W hy h t +b y

[0161] Among them, h t is the hidden state at time step t, σ is the activation function, and W and b are the weight and bias respectively.

[0162] The intelligent key management module uses the AI ​​system to evaluate the security of the key system through entropy calculation and support vector machine algorithm, and puts forward improvement suggestions based on the evaluation results. The specific formula is:

[0163]

[0164] Where H(X) is the entropy of the random variable X, P(x i ) is the event x i The probability of

[0165]

[0166] in:

[0167] x is the input data (e.g., key usage frequency, breach event records);

[0168] x i represents the support vector;

[0169] y i ∈{-1,1} is a classification label, indicating whether the data instance belongs to the vulnerable category;

[0170] K(x,x i ) is the kernel function, which is used to calculate the similarity between the input data and the support vector;

[0171] a i is the Lagrange multiplier;

[0172] b is the bias term.

[0173] Wherein, the adaptive encryption strength adjustment module includes:

[0174] Environmental sensing unit: obtains environmental information of the LED display screen, such as location, temperature, humidity, etc., through integrated sensors;

[0175] Content sensitivity analysis unit: analyzes the sensitivity of the content to be displayed and determines whether high-level encryption protection is required;

[0176] Dynamic encryption strength adjustment unit: Intelligently adjusts encryption strength based on environment and content analysis results to balance security requirements and system performance.

[0177] Wherein, the abnormal behavior detection and response module includes:

[0178] Anomaly Detection Unit: uses clustering and neural network techniques to monitor abnormal patterns in system and user behavior;

[0179] Security response decision unit: When an anomaly is detected, the intelligent decision system selects the most appropriate security response measure;

[0180] Automatic Isolation and Reporting Unit: Performs security responses such as isolating systems, prohibiting specific operations, and automatically reports to administrators.

[0181] Wherein, the intelligent redundant data processing module includes:

[0182] Redundancy analysis unit: predicts the error rate in data transmission through regression analysis and determines the amount of redundant data required;

[0183] Redundancy optimization computing unit: uses machine learning to optimize redundant data strategies to improve the efficiency and reliability of data transmission;

[0184] Redundant data integration unit: adds optimized redundant information to the transmitted data to enhance the data's anti-interference capability.

[0185] Wherein, the user behavior analysis and authentication module includes:

[0186] User behavior monitoring unit: tracks user operation habits, including login time, operation frequency, common functions, etc.;

[0187] Biometric authentication unit: combines biometric technologies such as fingerprint recognition and facial recognition to provide multi-factor authentication;

[0188] User behavior analysis and anomaly detection unit: Use data mining technology to analyze user behavior data, detect abnormal behavior in a timely manner, and enhance security.

[0189] Embodiment 2:

[0190] A LED display screen display encryption method and encryption system, based on embodiment 1, this embodiment also includes:

[0191] Intelligent redundant data processing module:

[0192] Utilize regression analysis and prediction models in machine learning to intelligently select and calculate the amount of redundant data that needs to be added. The system can dynamically adjust the strategy of redundant data according to the error rate and noise level during transmission to optimize the efficiency and accuracy of data transmission;

[0193] Encryption algorithm optimization module:

[0194] Apply optimization techniques such as genetic algorithms and simulated annealing to optimize existing encryption algorithms and find more efficient and secure encryption parameters and processes. Through continuous learning and adjustment, the algorithm can reduce the computational cost of the encryption and decryption process while ensuring security.

[0195] Wherein, the intelligent redundant data processing module includes:

[0196] Redundancy analysis unit: predicts the error rate in data transmission through regression analysis and determines the amount of redundant data required;

[0197] Redundancy optimization computing unit: uses machine learning to optimize redundant data strategies to improve the efficiency and reliability of data transmission;

[0198] Redundant data integration unit: adds optimized redundant information to the transmitted data to enhance the data's anti-interference capability.

[0199] Wherein, the encryption algorithm optimization module includes:

[0200] Algorithm parameter optimization unit: Use optimization techniques such as genetic algorithms and simulated annealing to find the best parameter configuration for encryption algorithms;

[0201] Performance evaluation unit: real-time evaluation of the performance of encryption algorithms, including encryption and decryption speed, resource consumption, etc.

[0202] Automatic algorithm update unit: Automatically updates and optimizes encryption algorithms based on performance evaluation results to ensure system security and efficient operation.

[0203] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A LED display screen encryption system, characterized in that: include: Key management module: responsible for the generation, distribution and update of keys, ensuring the secure storage and transmission of keys; Encryption and decryption module: implements encryption and decryption algorithms and supports multiple encryption strength options; Data transmission module: responsible for the transmission and reception of encrypted data, including error detection and correction mechanisms; User interface module: provides a user operation interface, including encryption parameter settings and key input functions; Security monitoring module: monitors system operation status, records all operation logs, and prevents unauthorized access; Intelligent key management module: uses machine learning algorithms to predict possible attack patterns based on historical data and real-time analysis, and dynamically adjusts key generation strategies; Adaptive encryption strength adjustment module: Combined with artificial intelligence algorithms, it automatically adjusts the encryption strength according to the environment in which the LED display is located and the sensitivity of the displayed content; through environmental perception technology and content analysis, the intelligent algorithm can determine the appropriate encryption level and balance security and system resource consumption; Abnormal behavior detection and response module: integrated with abnormal detection algorithm to monitor the operation behavior of LED display screen; when abnormal behavior is detected, the system automatically takes measures; User behavior analysis and authentication module: Combine user behavior analysis and biometric recognition technology to authenticate operators and conduct behavior analysis; by analyzing user behavior, unauthorized operations can be discovered in a timely manner, thereby improving the overall security of the system.

2. The LED display screen encryption system according to claim 1, characterized in that: The encryption method of the encryption system comprises: S1: Data preprocessing: Convert the original data into a numerical sequence using ASCII code; S2: Key generation: Use a pseudo-random number generator to generate a key sequence K of the same length as the original data, where K = {k1, k2, ..., kn}; S3: Data encryption: Use a symmetric encryption algorithm to encrypt the preprocessed data to obtain a ciphertext C, where C = E(P); S4: Confusion transformation: introduce nonlinear function f to perform confusion transformation on ciphertext C to increase the difficulty of cracking; S5: Redundancy addition: In order to resist noise interference and data loss, redundant information R is added to the ciphertext to generate the final transmission sequence T = {C', R}; S6: The decryption process is the inverse process of encryption, including redundancy removal, inverse obfuscation transformation, application of decryption algorithm and data decoding.

3. The LED display screen encryption system according to claim 1, characterized in that: The key management module comprises: Key generation unit: uses a cryptographic strong random number generator to generate the initial key to ensure the randomness and unpredictability of the key; Key distribution unit: uses a secure key exchange protocol to distribute keys to authorized users; Key update and recovery unit: set the key validity period according to the security policy, automatically update the key regularly, and safely recover the invalidated keys; The encryption and decryption module comprises: Data encryption unit: implements the symmetric encryption algorithm AES, which is used to encrypt the original data; Data decryption unit: implements the AES decryption algorithm to ensure that authorized users can accurately restore data; Encryption strength selection unit: Provides different levels of encryption strength selection, and selects the appropriate encryption level based on security needs and system performance requirements.

4. The LED display screen encryption system according to claim 3, characterized in that: The encryption and decryption module applies the AES encryption algorithm, including key expansion, initial round, several intermediate rounds and the final round; in each round, the data block will undergo byte substitution, row shift, column mixing and round key addition operations; the specific formula is as follows: Among them, S i,j is the byte in the state matrix, x i,j is the input, representing finite field multiplication, g(x i,j ) is a specific polynomial function; In the encryption and decryption module, the AES decryption process includes inverse byte substitution, reverse row shift, reverse column mixing and round key encryption; decryption formula: Among them, h and g -1 It is a function specific to the AES decryption process.

5. The LED display screen encryption system according to claim 1, characterized in that: The data transmission module comprises: Encrypted data transmission unit: Use the secure transport layer protocol to encrypt all transmitted data to ensure the security of data during transmission; Data integrity check unit: uses checksum or digital signature technology to ensure data integrity and prevent data from being tampered with during transmission; Error detection and correction unit: implements error detection and correction mechanism, and uses forward error correction coding for error recovery; The user interface module comprises: Parameter setting unit: provides an intuitive user interface for users to select encryption parameters; Key input unit: Design a secure user key input interface to ensure that the key is not leaked during the input process; Operation feedback unit: implements the operation feedback mechanism to display system status, operation results and safety warning information to users.

6. The LED display screen encryption system according to claim 1, characterized in that: The security monitoring module comprises: System status monitoring unit: real-time monitoring of the system's operating status, including CPU usage, memory usage, and network status; Operation log recording unit: records the operation logs of all users, including login, key change, encryption and decryption operations, for easy auditing and tracking; Unauthorized access prevention unit: implements access control mechanism to prevent unauthorized access through user authentication and permission management; The intelligent key management module comprises: Attack pattern prediction unit: uses machine learning algorithms to analyze historical data, predict possible attack patterns, and adjust key policies accordingly; Key usage analysis unit: Analyzes key usage through deep learning and optimizes the key generation, distribution and update process; Key Security Assessment Unit: Use artificial intelligence to conduct security assessments on existing key systems and provide suggestions for improvements.

7. The LED display screen encryption system according to claim 1, characterized in that: The intelligent key management module uses a machine learning algorithm to analyze historical data, predict possible attack patterns, and adjust key strategies accordingly. It uses a decision tree classification algorithm to predict future attack patterns based on the characteristics of historical attack data. The specific formula is: Wherein, D(x) is the decision function, c m is the category, p(y=c m |x) is the category c under feature x m The conditional probability of The intelligent key management module uses a recurrent neural network to analyze the mode and frequency of key usage to optimize the key management strategy, specifically: h t =σ(W hh h t-1 +W xh x t +b h ) y t =W hy h t +b y Among them, h t is the hidden state at time step t, σ is the activation function, and W and b are the weight and bias respectively.

8. The LED display screen encryption system according to claim 1, characterized in that: The intelligent key management module uses the AI ​​system to evaluate the security of the key system through entropy calculation and support vector machine algorithm, and puts forward improvement suggestions based on the evaluation results. The specific formula is: Where H(X) is the entropy of the random variable X, P(x i ) is the event x i probability; in: x is the input data; x i represents the support vector; y i ∈{-1,1} is a classification label, indicating whether the data instance belongs to the vulnerable category; K(x,x i ) is the kernel function, which is used to calculate the similarity between the input data and the support vector; a i is the Lagrange multiplier; b is the bias term.

9. The LED display screen encryption system according to claim 1, characterized in that: The adaptive encryption strength adjustment module comprises: Environmental perception unit: obtains the environmental information of the LED display through integrated sensors; Content sensitivity analysis unit: analyzes the sensitivity of the content to be displayed and determines whether high-level encryption protection is required; Dynamic encryption strength adjustment unit: intelligently adjusts encryption strength based on environment and content analysis results to balance security requirements and system performance; The abnormal behavior detection and response module includes: Anomaly Detection Unit: uses clustering and neural network techniques to monitor abnormal patterns in system and user behavior; Security response decision unit: When an anomaly is detected, the intelligent decision system selects the most appropriate security response measure; Automatic Isolation and Reporting Unit: performs security response and automatically reports to administrators; The intelligent redundant data processing module comprises: Redundancy analysis unit: predicts the error rate in data transmission through regression analysis and determines the amount of redundant data required; Redundancy optimization computing unit: uses machine learning to optimize redundant data strategies to improve the efficiency and reliability of data transmission; Redundant data integration unit: adds optimized redundant information to the transmitted data to enhance the data's anti-interference capability; The user behavior analysis and authentication module includes: User behavior monitoring unit: tracks user operation habits, including login time, operation frequency, and commonly used functions; Biometric Authentication Unit: Combines fingerprint recognition and facial recognition biometric technologies to provide multi-factor authentication; User behavior analysis and anomaly detection unit: Use data mining technology to analyze user behavior data, detect abnormal behavior in a timely manner, and enhance security.

10. The LED display screen encryption system according to claim 1, characterized in that: Also includes: Intelligent redundant data processing module: Utilize regression analysis and prediction models in machine learning to intelligently select and calculate the amount of redundant data that needs to be added; the system can dynamically adjust the strategy of redundant data according to the error rate and noise level during transmission to optimize the efficiency and accuracy of data transmission; Encryption algorithm optimization module: Apply genetic algorithms and simulated annealing optimization technology to optimize existing encryption algorithms and find more efficient and secure encryption parameters and processes; through continuous learning and adjustment, the algorithm can reduce the computational cost of the encryption and decryption process while ensuring security; The intelligent redundant data processing module comprises: Redundancy analysis unit: predicts the error rate in data transmission through regression analysis and determines the amount of redundant data required; Redundancy optimization computing unit: uses machine learning to optimize redundant data strategies to improve the efficiency and reliability of data transmission; Redundant data integration unit: adds optimized redundant information to the transmitted data to enhance the data's anti-interference capability; The encryption algorithm optimization module includes: Algorithm parameter optimization unit: Use genetic algorithms and simulated annealing optimization techniques to find the best parameter configuration for encryption algorithms; Performance evaluation unit: real-time evaluation of the performance of encryption algorithms, including encryption and decryption speed and resource consumption; Automatic algorithm update unit: Automatically updates and optimizes encryption algorithms based on performance evaluation results to ensure system security and efficient operation.

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