Cloud mobile phone multi-terminal data synchronization system and method

Abstract

The invention relates to the technical field of cloud mobile phone multi-terminal data synchronization, in particular to a cloud mobile phone multi-terminal data synchronization system and method. According to the technical scheme, the system comprises a cloud server, a plurality of terminal devices and a data synchronization module; the cloud server combines a distributed architecture and an edge computing technology and is composed of a plurality of data centers distributed at different geographic positions, and each data center is provided with a high-performance server cluster and a large-capacity storage device. According to the cloud mobile phone multi-terminal data synchronization method, a distributed architecture is combined with edge computing, advanced technologies such as artificial intelligence, quantum encryption and deep learning are applied, efficient data synchronization, high security, adaptive interface adjustment and personalized synchronization strategies are achieved, the method has high reliability and stability, the real-time synchronization requirement and security guarantee of a user are met, and the method is suitable for large-scale popularization and application. And user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud phone multi-terminal data synchronization, and in particular to a cloud phone multi-terminal data synchronization system and method. Background Art

[0002] With the rapid development of information technology, cloud phones, as an emerging technology application, have gradually attracted people's attention. Cloud phones run the operating system and applications of mobile phones on cloud servers. Users can access and use cloud phones through various terminal devices, realizing cross-device mobile office and entertainment. However, there are some problems with the current cloud phone multi-terminal data synchronization system.

[0003] On the one hand, traditional data synchronization methods are inefficient and cannot meet users' needs for real-time data synchronization. In the process of multi-end data synchronization, data transmission is slow, and data loss and inconsistency are prone to occur. In addition, the existing data synchronization system also has deficiencies in security. Data can be easily stolen or tampered with during transmission, which brings security risks to users.

[0004] On the other hand, the screen size, resolution and performance of different terminal devices vary greatly. How to implement adaptive interface adjustment and data synchronization strategy to meet the needs of different users is also a challenge faced by the current cloud phone multi-terminal data synchronization system. At the same time, with the continuous increase in the number of cloud phone users, the reliability and stability of the system has also become an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to propose a cloud phone multi-terminal data synchronization system and method to address the problem of low efficiency of traditional data synchronization methods in the background technology.

[0006] In a first aspect, the present application provides a cloud phone multi-terminal data synchronization system, including a cloud server, multiple terminal devices, and a data synchronization module;

[0007] The cloud server adopts a combination of distributed architecture and edge computing technology, and is composed of multiple data centers distributed in different geographical locations. Each data center is equipped with a high-performance server cluster and large-capacity storage devices;

[0008] The multiple terminal devices include but are not limited to smart phones, tablet computers, laptop computers, and smart TVs, and each terminal device is connected to the cloud server via a high-speed network;

[0009] The data synchronization module includes a data detection unit, a data transmission unit and a data integration unit;

[0010] The data detection unit uses artificial intelligence algorithms to monitor the cloud phone data on the terminal device in real time. The algorithm can accurately identify subtle changes in data and potential synchronization requirements. The formula is: C(t) = f AI (D(t), D(t-Δt), E(t)), where C(t) represents the data change state at time t, D(t) represents the cloud phone data at time t, D(t-Δt) represents the cloud phone data at time t minus a time interval Δt, E(t) represents the environmental parameters of the terminal device, and f AI Analyze functions for artificial intelligence;

[0011] The data transmission unit uses quantum encryption technology and multiplexing transmission strategy to ensure the security and efficiency of data during transmission. Quantum encryption technology uses the principles of quantum mechanics to encrypt data. The key generation formula is: K(t) = g Q (t, UID, RAND, Q param ), where K(t) represents the key at time t, g Q is the quantum key generation function, t is the current time, UID is the unique identifier of the terminal device, RAND is the random value generated by the random number generator, Q param For quantum encryption parameters, the multiplexing transmission strategy dynamically allocates transmission channels according to network conditions and data priority to improve data transmission efficiency;

[0012] The data integration unit uses deep learning algorithms to integrate data on cloud servers. The deep learning algorithm automatically adjusts the data synchronization strategy according to the characteristics of different terminal devices and user needs. The formula is: S(D, E) = h DL (P(D), T(E), U(E)), where S(D, E) indicates whether to synchronize data D to terminal device E, P(D) indicates data priority, T(E) indicates terminal device characteristics, U(E) indicates user usage habits of terminal devices, and h DL is the deep learning decision function;

[0013] It also includes a fault detection and recovery module, which monitors the operating status of cloud servers and terminal devices in real time. When a fault is found, it immediately starts the automatic recovery mechanism to ensure the continuity and stability of data synchronization. The fault detection and recovery module uses a fault diagnosis algorithm based on the Bayesian network, and its formula is expressed as: F(t) = b BN (S(t), E(t), H(t)), where F(t) represents the fault state at time t, S(t) represents the system state parameters, E(t) represents the environmental parameters, H(t) represents the historical fault data, and b BN It is the Bayesian network diagnostic function.

[0014] Optionally, the data centers of the cloud server are connected via a high-speed optical fiber network to achieve fast data synchronization and backup. Each data center is equipped with an independent energy supply system to ensure continuous operation in the event of an external power failure. The reliability calculation formula of the energy supply system is: Where R energy Represents the reliability of the energy supply system, T uptime represents the system uptime, T total Indicates the total time.

[0015] Optionally, a cloud phone client is installed on the terminal device, and the cloud phone client has an adaptive interface adjustment function, which automatically adjusts the operation interface of the cloud phone according to the screen size and resolution of different terminal devices. The adaptive interface adjustment function also includes the user's visual preferences and operating habits, and continuously optimizes the interface layout through a machine learning algorithm. Suppose the user visual preference parameter is P vision , the operating habit parameter is P habit , then the interface optimization function is: F UI_opt =h ML (R res , P vision , P habit ), where F UI_opt represents the optimized interface fitness, h ML For machine learning functions, R res Indicates the interface resolution adaptation degree.

[0016] Optionally, the adaptive interface adjustment function of the cloud phone client adopts an optimization strategy based on a genetic algorithm, and its fitness function is: F UI =w1*R res +w2*U exp , where F UI represents fitness, R res Indicates the interface resolution adaptation, U exp represents the user experience index, w1 and w2 are weight coefficients, and in the evolution process of the genetic algorithm, the mutation probability parameter P is introduced mutation , in order to increase the algorithm's search space and avoid local optimal solutions, the mutation probability calculation formula is: P mutation =f var (G generation , P initial ), where P mutation represents the mutation probability, G generation represents the current evolutionary generation, P initial represents the initial mutation probability, f var Adjustment function for mutation probability.

[0017] Optionally, the data synchronization module further includes a data compression unit, which uses a lossless compression algorithm to compress the data before transmission to improve data transmission efficiency. The data compression unit dynamically adjusts the compression parameters according to the type and content of the data. Suppose the data type parameter is T data , the data content complexity parameter is C content , then the compression parameter adjustment function is: P compression =g comp (T data , C content ), where P compression Indicates the compression parameter, g comp Adjust the function for compression parameters.

[0018] Optionally, the compression ratio calculation formula of the data compression unit is: Where CR represents the compression ratio, O size Indicates the original data size, C size Indicates the size of the compressed data. At the same time, in order to ensure the accuracy of the data during compression and decompression, the compression error index E is introduced compression , the calculation formula is: Where E compression represents the compression error, D original represents the original data, D decompressed Represents the decompressed data.

[0019] Optionally, the fault detection and recovery module uses data redundancy technology during the fault recovery process to ensure data integrity and availability. Data redundancy uses a combination of multi-copy storage and erasure coding technology. Suppose the number of data copies is N. replicas , the erasure code parameter is E code , then the data redundancy calculation formula is:

[0020] Where RD represents data redundancy, R size Indicates the redundant data size, T size Indicates the total data size, f RD It is the data redundancy calculation function.

[0021] Optionally, the fault detection and recovery module also includes a fault prediction function, which uses a time series prediction algorithm to predict potential faults through real-time monitoring of the system operation status and historical data analysis. Suppose the system operation status parameter is S status , the historical fault data is H fault , then the fault prediction function is: F prediction =h TS (S status , H fault ), where F predictionrepresents the fault prediction result, h TS For time series prediction function

[0022] Optionally, the system sets up a data security audit module to monitor and audit the entire process of data synchronization. The data security audit module uses blockchain technology to ensure that the audit records cannot be tampered with and are traceable. The audit event is E audit , then the audit record generation function is: R audit =g blockchain (E audit ), where R audit Indicates audit records, g blockchain Generate functions for blockchain-based records.

[0023] In a second aspect, the present application provides a cloud phone multi-terminal data synchronization method, which is applied to the cloud phone multi-terminal data synchronization system as described in the first aspect, and includes the following steps:

[0024] Step S1: The cloud phone client on the terminal device uses the artificial intelligence algorithm in the data detection unit to detect changes in the cloud phone data. The algorithm formula is C(t)=f AI (D(t), D(t-Δt), E(t)), accurately identifying subtle changes in data and potential synchronization requirements. At the same time, the environmental parameters of the terminal device are considered to ensure the accuracy of detection.

[0025] Step S2: Transmit the changed data to the cloud server through the data transmission unit using quantum encryption technology and multiplexing transmission strategy. Quantum encryption technology uses the key generation formula K(t)=g Q (t, UID, RAND, Q param ) encrypts data to ensure the security of data transmission. The multiplexing transmission strategy dynamically allocates transmission channels according to network conditions and data priority to improve data transmission efficiency.

[0026] Step S3: The cloud server receives and processes the data from the terminal device and stores the data in the storage unit. The cloud server adopts a distributed architecture combined with edge computing technology. It consists of multiple data centers distributed in different geographical locations. The data centers are connected by high-speed fiber optic networks to achieve fast data synchronization and backup. Each data center is equipped with a high-performance server cluster and large-capacity storage devices, and has an independent energy supply system. The reliability calculation formula of the energy supply system is Ensures continued operation in the event of external power failure.

[0027] Step S4: The data integration unit uses a deep learning algorithm to integrate the data on the cloud server and synchronize it to other terminal devices. The deep learning algorithm automatically adjusts the data synchronization strategy according to the characteristics of different terminal devices and user needs. The formula is S(D, E) = h DL (P(D), T(E), U(E)). At the same time, during the data synchronization process, the data compression unit uses a lossless compression algorithm to compress the data before transmission to improve data transmission efficiency. The data compression unit dynamically adjusts the compression parameters according to the type and content of the data. The compression ratio calculation formula is: And introduce the compression error index Ensure data accuracy. The fault detection and recovery module monitors the operating status of cloud servers and terminal devices in real time, and adopts a fault diagnosis algorithm based on Bayesian networks, with the formula F(t)=b BN (S(t), E(t), H(t)). When a fault is found, the automatic recovery mechanism is immediately started to ensure the continuity and stability of data synchronization. The fault detection and recovery module also uses data redundancy technology. Data redundancy uses a combination of multi-copy storage and erasure coding technology. The data redundancy calculation formula is: Ensure data integrity and availability. In addition, the fault detection and recovery module also has a fault prediction function. Through real-time monitoring of the system operation status and historical data analysis, a time series prediction algorithm is used to predict potential faults. The fault prediction function is F prediction =h TS (S status , H fault The data security audit module set up by the system monitors and audits the entire process of data synchronization, and uses blockchain technology to ensure that the audit records cannot be tampered with and are traceable. The audit record generation function is R audit =g blockchain (E audit ).

[0028] Compared with the prior art, the present invention has at least one of the following beneficial technical effects:

[0029] The cloud server that uses a distributed architecture combined with edge computing technology, as well as a multiplexed transmission strategy, greatly improves data transmission efficiency. The high-speed fiber optic network connection between data centers ensures fast data synchronization and backup, meeting users' needs for real-time data synchronization. The data detection unit uses artificial intelligence algorithms to accurately identify subtle changes in data and potential synchronization needs, reducing unnecessary data transmission and further improving synchronization efficiency.

[0030] The data transmission unit uses quantum encryption technology and encrypts data using the principles of quantum mechanics, ensuring the security of data during transmission. The dynamic and complex nature of the key generation formula makes it difficult for data to be stolen or cracked. The data security audit module uses blockchain technology to monitor and audit the entire process of data synchronization, ensuring that audit records cannot be tampered with and are traceable, thus enhancing the security of the system.

[0031] The cloud phone client has an adaptive interface adjustment function, which can automatically adjust the cloud phone's operating interface according to the screen size, resolution, user visual preferences and operating habits of different terminal devices, improving the user experience. The data integration unit uses deep learning algorithms to automatically adjust the data synchronization strategy according to the characteristics of different terminal devices and user needs, realizing personalized data synchronization services.

[0032] The data center of the cloud server is equipped with an independent energy supply system to ensure continuous operation in the event of an external power failure, thus improving the reliability of the system. The fault detection and recovery module uses a Bayesian network-based fault diagnosis algorithm and data redundancy technology to monitor the system operation status in real time. Once a fault is found, the automatic recovery mechanism is immediately activated to ensure the continuity and stability of data synchronization. At the same time, the fault prediction function can predict potential faults in advance and take corresponding preventive measures.

[0033] The cloud phone multi-terminal data synchronization of the present invention adopts a combination of distributed architecture and edge computing, and uses advanced technologies such as artificial intelligence, quantum encryption, deep learning, etc. to achieve efficient data synchronization, strong security, adaptive interface adjustment and personalized synchronization strategy, with high reliability and stability, meeting users' real-time synchronization needs and security guarantees, and improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a block diagram of a cloud phone multi-terminal data synchronization system. DETAILED DESCRIPTION

[0035] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0036] Example 1

[0037] like Figure 1 As shown, a cloud phone multi-terminal data synchronization system proposed in the present invention includes a cloud server, multiple terminal devices and a data synchronization module, which is explained in detail below.

[0038] The cloud server adopts a combination of distributed architecture and edge computing technology. It consists of multiple data centers distributed in different geographical locations. Each data center is equipped with a high-performance server cluster and large-capacity storage devices. The data centers of the cloud server are connected by a high-speed optical fiber network to achieve fast data synchronization and backup. Each data center is equipped with an independent energy supply system to ensure continuous operation in the event of an external power failure. The reliability calculation formula of the energy supply system is: Where R energy Represents the reliability of the energy supply system, T uptime represents the system uptime, T total Indicates the total time.

[0039] The multiple terminal devices include but are not limited to smart phones, tablet computers, laptops, and smart TVs. Each terminal device is connected to the cloud server through a high-speed network. A cloud phone client is installed on the terminal device. The cloud phone client has an adaptive interface adjustment function, which automatically adjusts the operation interface of the cloud phone according to the screen size and resolution of different terminal devices. The adaptive interface adjustment function also includes the user's visual preferences and operating habits. The interface layout is continuously optimized through a machine learning algorithm. Suppose the user's visual preference parameter is P vision , the operating habit parameter is P habit , then the interface optimization function is: F UI_opt =h ML (R res , P vision , P habit ), where F UI_opt represents the optimized interface fitness, h ML For machine learning functions, R res Indicates the adaptability of the interface resolution. The cloud phone client has an adaptive interface adjustment function, which can automatically adjust the cloud phone's operating interface according to the screen size, resolution, user visual preferences and operating habits of different terminal devices, thereby improving the user experience.

[0040] In addition, the adaptive interface adjustment function of the cloud phone client adopts an optimization strategy based on genetic algorithm, and its fitness function is: F UI =w1*R res +w2*U exp , where F UI represents fitness, R res Indicates the interface resolution adaptation, U exp represents the user experience index, w1 and w2 are weight coefficients, and in the evolution process of the genetic algorithm, the mutation probability parameter P is introduced mutation , in order to increase the algorithm's search space and avoid local optimal solutions, the mutation probability calculation formula is: P mutation =f var(G generation , P initial ), where P mutation represents the mutation probability, G generation represents the current evolutionary generation, P initial represents the initial mutation probability, f var The cloud server that uses a distributed architecture combined with edge computing technology and a multiplexing transmission strategy greatly improves data transmission efficiency. The high-speed fiber optic network connection between data centers ensures fast data synchronization and backup, meeting users' needs for real-time data synchronization.

[0041] The data synchronization module includes a data detection unit, a data transmission unit and a data integration unit;

[0042] The data detection unit uses artificial intelligence algorithms to monitor the cloud phone data on the terminal device in real time. The algorithm can accurately identify subtle changes in data and potential synchronization requirements. The formula is: C(t) = f AI (D(t), D(t-Δt), E(t)), where C(t) represents the data change state at time t, D(t) represents the cloud phone data at time t, D(t-Δt) represents the cloud phone data at time t minus a time interval Δt, E(t) represents the environmental parameters of the terminal device, and f AI It is an artificial intelligence analysis function; the data detection unit uses artificial intelligence algorithms to accurately identify subtle changes in data and potential synchronization requirements, reducing unnecessary data transmission and further improving synchronization efficiency.

[0043] The data transmission unit uses quantum encryption technology and multiplexing transmission strategy to ensure the security and efficiency of data during transmission. Quantum encryption technology uses the principles of quantum mechanics to encrypt data. The key generation formula is: K(t) = g Q (t, UID, RAND, Q param ), where K(t) represents the key at time t, g Q is the quantum key generation function, t is the current time, UID is the unique identifier of the terminal device, RAND is the random value generated by the random number generator, Q param The multiplexing transmission strategy dynamically allocates transmission channels according to network conditions and data priority to improve data transmission efficiency. The data transmission unit uses quantum encryption technology to encrypt data using the principles of quantum mechanics, ensuring the security of data during transmission. The dynamic and complex key generation formula makes it difficult to steal or crack data.

[0044] The system sets up a data security audit module to monitor and audit the entire process of data synchronization. The data security audit module uses blockchain technology to ensure that the audit records cannot be tampered with and are traceable. The audit event is E audit , then the audit record generation function is: R audit =g blockchain (E audit ), where R audit Indicates audit records, g blockchain Generates functions for blockchain-based records. The data security audit module uses blockchain technology to monitor and audit the entire process of data synchronization, ensuring that audit records cannot be tampered with and are traceable, thus enhancing the security of the system.

[0045] The data integration unit uses deep learning algorithms to integrate data on cloud servers. The deep learning algorithm automatically adjusts the data synchronization strategy according to the characteristics of different terminal devices and user needs. The formula is: S(D, E) = h DL (P(D), T(E), U(E)), where S(D, E) indicates whether to synchronize data D to terminal device E, P(D) indicates data priority, T(E) indicates terminal device characteristics, U(E) indicates user usage habits of terminal devices, and h DL It is a deep learning decision function. The data integration unit uses deep learning algorithms to automatically adjust data synchronization strategies according to the characteristics of different terminal devices and user needs, thereby realizing personalized data synchronization services.

[0046] It is worth noting that the data synchronization module also includes a data compression unit. The data compression unit uses a lossless compression algorithm to compress the data before transmission to improve data transmission efficiency. The data compression unit dynamically adjusts the compression parameters according to the type and content of the data. Suppose the data type parameter is T data , the data content complexity parameter is C content , then the compression parameter adjustment function is: P compression =g comp (T data , C content ), where P compression Indicates the compression parameter, g comp Adjust the function for compression parameters.

[0047] Furthermore, the compression ratio calculation formula of the data compression unit is: Where CR represents the compression ratio, O size Indicates the original data size, C size Indicates the size of the compressed data. At the same time, in order to ensure the accuracy of the data during compression and decompression, the compression error index E is introduced compression , the calculation formula is: Where Ecompression represents the compression error, D original represents the original data, D decompressed Represents the decompressed data.

[0048] In this embodiment, the data synchronization module also includes a fault detection and recovery module, which monitors the operating status of the cloud server and the terminal device in real time. When a fault is found, the automatic recovery mechanism is immediately started to ensure the continuity and stability of data synchronization. The fault detection and recovery module adopts a fault diagnosis algorithm based on the Bayesian network, and its formula is expressed as: F(t) = b BN (S(t), E(t), H(t)), where F(t) represents the fault state at time t, S(t) represents the system state parameters, E(t) represents the environmental parameters, H(t) represents the historical fault data, and b BN It is the Bayesian network diagnostic function.

[0049] The fault detection and recovery module uses data redundancy technology to ensure data integrity and availability during the fault recovery process. Data redundancy uses a combination of multi-copy storage and erasure coding technology. Suppose the number of data copies is N. replicas , the erasure code parameter is E code , then the data redundancy calculation formula is:

[0050] Where RD represents data redundancy, R size Indicates the redundant data size, T size Indicates the total data size, f RD It is the data redundancy calculation function.

[0051] The fault detection and recovery module also includes a fault prediction function. Through real-time monitoring of the system operation status and historical data analysis, a time series prediction algorithm is used to predict potential faults. Suppose the system operation status parameter is S status , the historical fault data is H fault , then the fault prediction function is: F prediction =h TS (S status , H fault ), where F prediction represents the fault prediction result, h TS is a time series prediction function. The fault detection and recovery module uses a Bayesian network-based fault diagnosis algorithm and data redundancy technology to monitor the system operation status in real time. Once a fault is found, the automatic recovery mechanism is immediately activated to ensure the continuity and stability of data synchronization. At the same time, the fault prediction function can predict potential faults in advance and take corresponding preventive measures.

[0052] Example 2

[0053] This embodiment provides a cloud phone multi-terminal data synchronization method, which is applied to the cloud phone multi-terminal data synchronization system described in Embodiment 1, and includes the following steps:

[0054] Step S1: The cloud phone client on the terminal device uses the artificial intelligence algorithm in the data detection unit to detect changes in the cloud phone data. The algorithm formula is C(t)=f AI (D(t), D(t-Δt), E(t)), accurately identifying subtle changes in data and potential synchronization requirements. At the same time, the environmental parameters of the terminal device are considered to ensure the accuracy of detection.

[0055] Step S2: Transmit the changed data to the cloud server through the data transmission unit using quantum encryption technology and multiplexing transmission strategy. Quantum encryption technology uses the key generation formula K(t)=g Q (t, UID, RAND, Q param ) encrypts data to ensure the security of data transmission. The multiplexing transmission strategy dynamically allocates transmission channels according to network conditions and data priority to improve data transmission efficiency.

[0056] Step S3: The cloud server receives and processes the data from the terminal device and stores the data in the storage unit. The cloud server adopts a distributed architecture combined with edge computing technology. It consists of multiple data centers distributed in different geographical locations. The data centers are connected by high-speed fiber optic networks to achieve fast data synchronization and backup. Each data center is equipped with a high-performance server cluster and large-capacity storage devices, and has an independent energy supply system. The reliability calculation formula of the energy supply system is Ensures continued operation in the event of external power failure.

[0057] Step S4: The data integration unit uses a deep learning algorithm to integrate the data on the cloud server and synchronize it to other terminal devices. The deep learning algorithm automatically adjusts the data synchronization strategy according to the characteristics of different terminal devices and user needs. The formula is S(D, E) = h DL (P(D), T(E), U(E)). At the same time, during the data synchronization process, the data compression unit uses a lossless compression algorithm to compress the data before transmission to improve data transmission efficiency. The data compression unit dynamically adjusts the compression parameters according to the type and content of the data. The compression ratio calculation formula is: And introduce the compression error index Ensure data accuracy. The fault detection and recovery module monitors the operating status of cloud servers and terminal devices in real time, and adopts a fault diagnosis algorithm based on Bayesian networks, with the formula F(t)=b BN(S(t), E(t), H(t)). When a fault is found, the automatic recovery mechanism is immediately started to ensure the continuity and stability of data synchronization. The fault detection and recovery module also uses data redundancy technology. Data redundancy uses a combination of multi-copy storage and erasure coding technology. The data redundancy calculation formula is: Ensure data integrity and availability. In addition, the fault detection and recovery module also has a fault prediction function. Through real-time monitoring of the system operation status and historical data analysis, a time series prediction algorithm is used to predict potential faults. The fault prediction function is F prediction =h TS (S status , H fault The data security audit module set up by the system monitors and audits the entire process of data synchronization, and uses blockchain technology to ensure that the audit records cannot be tampered with and are traceable. The audit record generation function is R audit =g blockchain (E audit ).

[0058] The above specific embodiments are only several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A cloud phone multi-terminal data synchronization system, characterized in that: Includes cloud server, multiple terminal devices and data synchronization module; The cloud server adopts a combination of distributed architecture and edge computing technology, and is composed of multiple data centers distributed in different geographical locations. Each data center is equipped with a high-performance server cluster and large-capacity storage devices; The multiple terminal devices include but are not limited to smart phones, tablet computers, laptop computers, and smart TVs, and each terminal device is connected to the cloud server via a high-speed network; The data synchronization module includes a data detection unit, a data transmission unit and a data integration unit; The data detection unit uses artificial intelligence algorithms to monitor the cloud phone data on the terminal device in real time. The algorithm can accurately identify subtle changes in data and potential synchronization requirements. The formula is: C(t) = f AI (D(t), D(t-Δt), E(t)), where C(t) represents the data change state at time t, D(t) represents the cloud phone data at time t, D(t-Δt) represents the cloud phone data at time t minus a time interval Δt, E(t) represents the environmental parameters of the terminal device, and f AI Analyze functions for artificial intelligence; The data transmission unit adopts quantum encryption technology and multiplexing transmission strategy. Quantum encryption technology uses the principles of quantum mechanics to encrypt data. The key generation formula is: K(t) = g Q (t, UID, RAND, Q param ), where K(t) represents the key at time t, g Q is the quantum key generation function, t is the current time, UID is the unique identifier of the terminal device, RAND is the random value generated by the random number generator, Q param For quantum encryption parameters, the multiplexing transmission strategy dynamically allocates transmission channels according to network conditions and data priority; The data integration unit uses deep learning algorithms to integrate data on cloud servers. The deep learning algorithm automatically adjusts the data synchronization strategy according to the characteristics of different terminal devices and user needs. The formula is: S(D, E) = h DL (P(D), T(E), U(E)), where S(D, E) indicates whether to synchronize data D to terminal device E, P(D) indicates data priority, T(E) indicates terminal device characteristics, U(E) indicates user usage habits of terminal devices, and h DL is the deep learning decision function; It also includes a fault detection and recovery module, which monitors the operating status of the cloud server and terminal equipment in real time. When a fault is found, the automatic recovery mechanism is immediately started. The fault detection and recovery module adopts a fault diagnosis algorithm based on the Bayesian network, and its formula is expressed as: F(t) = b BN (S(t), E(t), H(t)), where F(t) represents the fault state at time t, S(t) represents the system state parameters, E(t) represents the environmental parameters, H(t) represents the historical fault data, and b BN It is the Bayesian network diagnostic function.

2. A cloud phone multi-terminal data synchronization system according to claim 1, characterized in that: The data centers of the cloud servers are connected via a high-speed optical fiber network. Each data center is equipped with an independent energy supply system. The reliability calculation formula of the energy supply system is: Where R energy Represents the reliability of the energy supply system, T uptime represents the system uptime, T total Indicates the total time.

3. A cloud phone multi-terminal data synchronization system according to claim 1, characterized in that: The terminal device is installed with a cloud phone client, which has an adaptive interface adjustment function, and automatically adjusts the operation interface of the cloud phone according to the screen size and resolution of different terminal devices. The adaptive interface adjustment function also includes the user's visual preference and operation habits, and continuously optimizes the interface layout through a machine learning algorithm. Suppose the user's visual preference parameter is P vision , the operating habit parameter is P habit , then the interface optimization function is: F UI_opt =h ML (R res , P vision , P habit ), where F UI_opt represents the optimized interface fitness, h ML For machine learning functions, R res Indicates the interface resolution adaptation degree.

4. A cloud phone multi-terminal data synchronization system according to claim 3, characterized in that: The adaptive interface adjustment function of the cloud phone client adopts an optimization strategy based on genetic algorithm, and its fitness function is: F UI =w1*R res +w2*U exp , where F UI represents fitness, R res Indicates the interface resolution adaptation, U exp represents the user experience index, w1 and w2 are weight coefficients, and in the evolution process of the genetic algorithm, the mutation probability parameter P is introduced mutation , in order to increase the algorithm's search space and avoid local optimal solutions, the mutation probability calculation formula is: P mutation =f var (G generation , P initial ), where P mutation represents the mutation probability, G generation represents the current evolutionary generation, P ini ti al represents the initial mutation probability, f var Adjustment function for mutation probability.

5. The cloud phone multi-terminal data synchronization system according to claim 1, characterized in that: The data synchronization module also includes a data compression unit. The data compression unit uses a lossless compression algorithm to compress the data before transmission. The data compression unit dynamically adjusts the compression parameters according to the type and content of the data. Suppose the data type parameter is T data , the data content complexity parameter is C content , then the compression parameter adjustment function is: P compression =g comp (T data , C content ), where P compression Indicates the compression parameter, g comp Adjust the function for compression parameters.

6. A cloud phone multi-terminal data synchronization system according to claim 5, characterized in that: The compression ratio calculation formula of the data compression unit is: Where CR represents the compression ratio, O size Indicates the original data size, C size Indicates the size of the compressed data and introduces the compression error index E compression , the calculation formula is: Where E compression represents the compression error, D original represents the original data, D decompressed Represents the decompressed data.

7. A cloud phone multi-terminal data synchronization system according to claim 1, characterized in that: The fault detection and recovery module uses data redundancy technology in the fault recovery process. Data redundancy uses a combination of multi-copy storage and erasure coding technology. Suppose the number of data copies is N. replicas , the erasure code parameter is E code , then the data redundancy calculation formula is: Where RD represents data redundancy, R size Indicates the redundant data size, T size Indicates the total data size, f RD It is the data redundancy calculation function.

8. A cloud phone multi-terminal data synchronization system according to claim 7, characterized in that: The fault detection and recovery module also includes a fault prediction function. Through real-time monitoring of the system operation status and historical data analysis, a time series prediction algorithm is used to predict potential faults. Suppose the system operation status parameter is S status , the historical fault data is H fault , then the fault prediction function is: F prediction =h TS (S status , H fault ), where F prediction represents the fault prediction result, h TS is the time series prediction function.

9. A cloud phone multi-terminal data synchronization system according to claim 1, characterized in that: The system sets up a data security audit module to monitor and audit the entire process of data synchronization. The data security audit module uses blockchain technology and sets the audit event as E audit , then the audit record generation function is: R audit =g blockchain (E audit ), where R audit Indicates audit records, g blockchain Generate functions for blockchain-based records.

10. A cloud phone multi-terminal data synchronization method, applied to the cloud phone multi-terminal data synchronization system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step S1: The cloud phone client on the terminal device uses the artificial intelligence algorithm in the data detection unit to detect changes in the cloud phone data. The algorithm formula is C(t)=f AI (D(t), D(t-Δt), E(t)), accurately identifying subtle changes in data and potential synchronization needs; Step S2: The changed data is transmitted to the cloud server through the data transmission unit using quantum encryption technology and multiplexing transmission strategy. The quantum encryption technology uses the key generation formula K(t)=g Q (t, UID, RAND, Q param ) Encrypt the data; Step S3: The cloud server receives and processes data from the terminal device and stores the data in the storage unit. The cloud server adopts a distributed architecture combined with edge computing technology. It consists of multiple data centers distributed in different geographical locations. The data centers are connected by high-speed optical fiber networks. Each data center is equipped with a high-performance server cluster and large-capacity storage devices, and has an independent energy supply system. The reliability calculation formula of the energy supply system is: Step S4: The data integration unit uses a deep learning algorithm to integrate the data on the cloud server and synchronize it to other terminal devices. The deep learning algorithm automatically adjusts the data synchronization strategy according to the characteristics of different terminal devices and user needs. The formula is S(D, E) = h DL (P(D), T(E), U(E)). At the same time, during the data synchronization process, the data compression unit uses a lossless compression algorithm to compress the data before transmission to improve data transmission efficiency. The data compression unit dynamically adjusts the compression parameters according to the type and content of the data. The compression ratio calculation formula is: And introduce the compression error index The fault detection and recovery module monitors the operating status of cloud servers and terminal devices in real time, and adopts a fault diagnosis algorithm based on Bayesian networks, with the formula F(t)=b BN (S(t), E(t), H(t)), when a fault is found, the automatic recovery mechanism is immediately started to ensure the continuity and stability of data synchronization.

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