Dynamic update database encryption method based on AI

Abstract

The invention relates to the technical field of vehicle annual inspection data management systems and database encryption, discloses an AI-based dynamic update database encryption method, and is mainly suitable for an encryption scene of a dynamic update database in a vehicle annual inspection data management system. According to the method, through cooperative work of a data acquisition module, an AI dynamic encryption module, a key management module and a database storage module, the problems that an existing static encryption method cannot adapt to data dynamic updating, key management is complex, and encryption efficiency and safety are difficult to balance are solved. The AI dynamic encryption module dynamically adjusts an encryption strategy based on a machine learning algorithm, and the key management module adopts a distributed technology to simplify key management, so that efficient and safe dynamic encryption is realized, the high-frequency data updating requirement is met, the encryption flexibility is improved, and the key leakage risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of vehicle annual inspection data management system and database encryption technology, specifically to an AI-based dynamic database encryption method, applicable to encryption scenarios for dynamically updated databases in vehicle annual inspection data management systems. Background Technology

[0002] With the continuous growth of car ownership, the application of vehicle annual inspection data management systems is becoming increasingly widespread. The vehicle annual inspection data stored in these systems contains a large amount of sensitive information, making data security and privacy protection core issues of concern in the industry. Currently, vehicle annual inspection data management systems generally use static encryption methods to protect data. However, with the surge in data volume and the increasing demand for dynamic updates, static encryption methods are gradually revealing significant shortcomings.

[0003] In existing technologies, the commonly used encryption methods in vehicle annual inspection data management systems include symmetric encryption, asymmetric encryption, and hybrid encryption. Symmetric encryption, represented by the AES algorithm, has high encryption efficiency, but the key management process is complex and prone to key leakage risks. Asymmetric encryption, such as the RSA algorithm, has strong security, but its computational overhead is high, making it difficult to adapt to high-frequency data update scenarios. Hybrid encryption methods, on the other hand, fail to fundamentally solve the problem of dynamic update adaptation.

[0004] Existing encryption technologies suffer from three major flaws: First, static encryption mechanisms cannot flexibly cope with dynamic data updates, leading to decreased encryption efficiency or reduced security; second, the key management system is imperfect, and centralized key management is prone to becoming a security vulnerability; third, encryption efficiency and security are difficult to balance, and this contradiction is particularly prominent in scenarios with high-frequency data updates. Therefore, there is an urgent need for a database encryption solution that can adapt to dynamic update requirements, simplify key management, and balance encryption efficiency and security. Summary of the Invention

[0005] The purpose of this invention is to provide an AI-based dynamic database encryption method to solve the problems of static encryption being unable to adapt to dynamic updates, complex key management, and an imbalance between efficiency and security in existing technologies, thereby achieving efficient and secure encryption for the vehicle annual inspection data management system.

[0006] Therefore, the technical solution proposed in this invention is an AI-based dynamic database encryption method, comprising the following steps: S1: collecting vehicle annual inspection data through a data acquisition module; S2: using a deep learning model to analyze data characteristics and update frequency, dynamically adjusting the encryption strategy, which includes encryption strength and key update frequency; S3: using distributed key management technology to generate and distribute encryption keys, which are matched with the dynamically adjusted encryption strategy; S4: receiving the encrypted vehicle annual inspection data and storing it securely, with each module connected through a data transmission interface to form a closed-loop system.

[0007] Furthermore, the AI ​​dynamic encryption module uses an LSTM deep learning model and the AES-256 algorithm as the basic encryption algorithm, combined with dynamically generated keys for encryption processing.

[0008] Furthermore, the key management module uses blockchain technology to achieve distributed key management, including the full lifecycle management of key generation, distribution, updating and destruction.

[0009] Furthermore, the vehicle inspection data collected by the data acquisition module includes basic vehicle information, inspection results, inspection time, and inspection personnel information. The data transmission process uses an encrypted channel to ensure security.

[0010] Furthermore, the AI ​​dynamic encryption module adjusts the encryption strength based on the data sensitivity level, which is divided into three levels: high, medium, and low, each corresponding to different encryption strength parameters.

[0011] Furthermore, the key update frequency is determined by the data update frequency. When the data update frequency is greater than a preset threshold, the key update frequency is increased accordingly. The preset threshold is set to 10 times per hour.

[0012] Furthermore, the deep learning model in the AI ​​dynamic encryption module can be replaced with the GRU time-series prediction model, while still maintaining the accuracy and real-time performance of the dynamic adjustment of the encryption strategy.

[0013] Furthermore, the distributed key management technology in the key management module can be replaced with a distributed key generation and distribution protocol to ensure the security and efficiency of key management.

[0014] The present invention also proposes an AI-based dynamic database encryption system, including a data acquisition module, an AI dynamic encryption module, a key management module, and a database storage module. Each module is connected through a data transmission interface, and the system is used to implement the encryption method mentioned above.

[0015] Furthermore, the AI ​​dynamic encryption module is combined with edge computing to deploy a lightweight AI model at the data acquisition end, enabling real-time data encryption and reducing data transmission latency.

[0016] Therefore, the beneficial effects of the present invention are as follows:

[0017] (1) It realizes efficient dynamic encryption of the vehicle annual inspection data management system and adapts to the high-frequency data update requirements; (2) It improves the security and flexibility of encryption by dynamically adjusting the encryption strategy through AI technology; (3) It simplifies the key management process and reduces the risk of key leakage; (4) It improves the efficiency of data encryption and reduces system resource consumption. Attached Figure Description

[0018] Figure 1 This is a diagram illustrating the data flow sequence of a specific embodiment of the present invention.

[0019] Figure 2 This is a data flow sequence description for Embodiment 2 of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1

[0022] Please refer to Figure 1 The AI-based dynamic database encryption system in this embodiment includes a data acquisition module, an AI dynamic encryption module, a key management module, and a database storage module. Each module is connected through an encrypted data transmission interface to form a closed-loop working system.

[0023] Data acquisition module: It adopts a multi-interface data acquisition device, which supports data docking with vehicle annual inspection equipment and manual data entry terminals to collect data such as basic vehicle information, inspection results, inspection time and inspection personnel information.

[0024] AI dynamic encryption module: Equipped with an LSTM deep learning model and using AES-256 as the basic encryption algorithm, it dynamically adjusts the encryption strength and key update frequency by analyzing data characteristics and update frequency.

[0025] Key Management Module: Based on blockchain technology, a distributed key management node is built to realize the full lifecycle management of key generation, distribution, update and destruction.

[0026] Database storage module: It adopts an encrypted database storage architecture, which only allows legitimate access verified by key, ensuring the security of encrypted data storage.

[0027] 1. Data Acquisition Phase (S1): The data acquisition module establishes a connection with the various data source terminals of the vehicle annual inspection, collects vehicle annual inspection data in real time, and transmits it to the AI ​​dynamic encryption module through an encrypted channel.

[0028] 2. Encryption Strategy Adjustment Phase (S2): After receiving data, the AI ​​dynamic encryption module uses the LSTM model to determine the data sensitivity level and simultaneously counts the data update frequency. Based on the determination results, it dynamically adjusts the encryption strength and key update frequency.

[0029] 3. Key generation and distribution stage (S3): The key management module generates the corresponding encryption key according to the encryption strategy output by the AI ​​dynamic encryption module, and distributes it to the AI ​​dynamic encryption module through the blockchain distributed nodes.

[0030] 4. Data Encryption and Storage Stage (S4): The AI ​​dynamic encryption module uses the AES-256 algorithm combined with the distributed key to encrypt the data. The encrypted data is then transmitted to the database storage module for secure storage.

[0031] This embodiment selects a vehicle annual inspection data management system in a certain region as the experimental object, and compares the performance indicators of the method of the present invention with existing static encryption methods (AES static encryption, RSA encryption). The experimental data are shown in the table below:

[0032]

[0033] Experimental data show that the encryption latency of the method of the present invention is significantly lower than that of existing methods under different data update frequencies, the key leakage risk rate is controlled below 1%, and the system resource consumption is lower. It achieves a balance between efficient encryption and high security, and can be well adapted to the needs of high-frequency data updates.

[0034] Example 2

[0035] Please refer to Figure 2 This embodiment, based on Embodiment 1, combines the AI ​​dynamic encryption module with edge computing to optimize system response speed. The system still includes a data acquisition module, an AI dynamic encryption module, a key management module, and a database storage module. The core improvements are as follows:

[0036] AI dynamic encryption module: Deploy a lightweight GRU time series prediction model at the data acquisition end to replace the centralized LSTM model and realize real-time encryption processing after data acquisition.

[0037] Key Management Module: Adopts a distributed key generation and distribution protocol to replace blockchain technology, simplifying the key management process and improving key distribution efficiency.

[0038] Data transmission optimization: Encrypted data from edge nodes is transmitted to the database storage module via a dedicated channel, reducing latency and security risks during transmission.

[0039] Method steps and flowchart

[0040] 1. Edge node deployment phase (S0): Deploy lightweight GRU models and edge encryption processing units on each data acquisition terminal and establish distributed communication connections with the key management module.

[0041] 2. Data Acquisition and Real-time Encryption Stage (S1-S2): After the data acquisition module collects the vehicle annual inspection data, it directly transmits it to the local edge encryption unit. The GRU model analyzes the data characteristics and adjusts the encryption strategy to complete the data encryption in real time.

[0042] 3. Key Synchronization and Update Phase (S3): The key management module synchronizes the key to each edge node through a distributed protocol and dynamically updates the key according to the data update frequency to ensure key timeliness.

[0043] 4. Encrypted data upload and storage stage (S4): The edge node uploads the encrypted data to the database storage module through a dedicated channel to complete data storage and backup.

[0044] Using the same experimental subject, the performance of the method in this embodiment was compared with that of Embodiment 1 and existing encryption methods. The experimental data are shown in the table below:

[0045]

[0046] Experimental results show that this embodiment, by combining edge computing with a lightweight model, further reduces encryption latency and data transmission latency, improves key distribution efficiency by about 50%, and maintains efficient and stable encryption performance even in ultra-high frequency data update scenarios, meeting the real-time encryption requirements of the vehicle annual inspection data management system.

[0047] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An AI-based method for dynamically updating database encryption, characterized in that, Includes the following steps: S1: Collect vehicle annual inspection data through the data acquisition module; S2: The AI ​​dynamic encryption module uses a deep learning model to analyze data characteristics and update frequency, and dynamically adjusts the encryption strategy, which includes encryption strength and key update frequency. S3: The key management module uses distributed key management technology to generate and distribute encryption keys, which are matched with dynamically adjusted encryption strategies. S4: The database storage module receives and securely stores the encrypted vehicle annual inspection data. All modules are connected through the data transmission interface to form a closed-loop system.

2. The AI-based dynamic database encryption method according to claim 1, characterized in that, The AI ​​dynamic encryption module uses an LSTM deep learning model and the AES-256 algorithm as the basic encryption algorithm, combined with dynamically generated keys for encryption processing.

3. The AI-based dynamic database encryption method according to claim 1, characterized in that, The key management module uses blockchain technology to achieve distributed key management, including full lifecycle management of key generation, distribution, updating and destruction.

4. The AI-based dynamic database encryption method according to claim 1, characterized in that, The data acquisition module collects vehicle annual inspection data including basic vehicle information, inspection results, inspection time, and inspection personnel information. The data transmission process uses an encrypted channel to ensure security.

5. The AI-based dynamic database encryption method according to claim 1, characterized in that, The AI ​​dynamic encryption module adjusts the encryption strength based on the data sensitivity level, which is divided into three levels: high, medium, and low, each corresponding to a different encryption strength parameter.

6. The AI-based dynamic database encryption method according to claim 1, characterized in that, The key update frequency is determined by the data update frequency. When the data update frequency is greater than a preset threshold, the key update frequency is increased synchronously. The preset threshold is set to 10 times per hour.

7. The AI-based dynamic database encryption method according to claim 1, characterized in that, The deep learning model in the AI ​​dynamic encryption module can be replaced with the GRU time-series prediction model, while maintaining the accuracy and real-time performance of the dynamic adjustment of the encryption strategy.

8. The AI-based dynamic database encryption method according to claim 1, characterized in that, The distributed key management technology in the key management module can be replaced with a distributed key generation and distribution protocol to ensure the security and efficiency of key management.

9. An AI-based dynamically updated database encryption system, characterized in that, The system includes a data acquisition module, an AI dynamic encryption module, a key management module, and a database storage module. Each module is connected through a data transmission interface. The system is used to implement the encryption method described in any one of claims 1-8.

10. The AI-based dynamically updated database encryption system according to claim 9, characterized in that, The AI ​​dynamic encryption module is combined with edge computing to deploy a lightweight AI model at the data acquisition end, enabling real-time data encryption and reducing data transmission latency.

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