A server content updating and rollback method based on static technology

Abstract

The application relates to the technical field of server content management, in particular to a server content updating and rollback method based on static technology; the application detects storage hot area changes through an entropy value difference matrix, combines a time stamp marking feature moment, realizes accurate division and unique identification of a historical version, avoids redundant operations of full scanning, only carries out version processing on a change hot area, significantly optimizes storage space utilization, and improves the efficiency and accuracy of version tracing; the application generates an incremental patch through a binary difference algorithm accelerated by a suffix array, combines network topology optimization to build an optimal transmission path, adds error correction code check information to data blocks, reduces data transmission volume, enhances transmission fault tolerance through the error correction code, and ensures data integrity and transmission efficiency when network fluctuation or node failure occurs.

Description

Technical Field

[0001] This invention relates to the field of server content management technology, specifically to a method for updating and rolling back server content based on static technology. Background Technology

[0002] Server content management technology encompasses multiple aspects, including content storage, distribution, version control, security, and intelligent scheduling, aiming to improve content service efficiency, storage utilization, and user experience. Server rollback is a core operation in server content management, referring to the process of restoring the server state (including files, databases, configurations, etc.) to a historical version when the system experiences failure, data errors, or configuration anomalies. Its core objective is to quickly restore service availability and minimize business downtime.

[0003] First, the version control of existing server rollback systems relies on full file scanning, which cannot accurately identify changed areas, resulting in wasted storage space.

[0004] Secondly, existing server rollback systems rely on timestamp-based or manually marked version management (such as Git), lacking dynamic hotspot detection, making it difficult to accurately classify historical versions and resulting in low tracing efficiency. Furthermore, the lack of network topology optimization leads to suboptimal transmission paths and high latency.

[0005] To address the aforementioned issues, it is necessary to propose a server content update and rollback method based on static technology. Summary of the Invention

[0006] The purpose of this invention is to solve the problems existing in the background technology and to propose a server content update and rollback method based on static technology.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A method for updating and rolling back server content based on static technology includes the following steps:

[0009] Step 1: Versioned metadata collection and entropy detection;

[0010] The system accesses the server's storage module, performs file unit segmentation, and divides static content into fixed-size data blocks. It calculates the byte-level information and entropy distribution data for each data block, and detects change hotspots based on the entropy difference matrix. Through cascaded hashing, it fuses the byte-level information and entropy distribution data of all data blocks to generate a unique version identifier and version fingerprint for the server's static content.

[0011] At preset time intervals, the server's storage module is accessed to scan the static file collection and divide it into blocks, resulting in several data blocks with a fixed size of 4MB.

[0012] Calculate the information entropy for all data blocks, and statistically analyze the frequency Pk of each byte value k within each data block using a preset formula. Calculate the information entropy E(Bi) of each data block Bi, where i is the data block number, i=1,2,...,n; and n is the total number of data blocks.

[0013] In a preferred embodiment of the present invention, a version fingerprint of the server is generated using a fingerprint generator. The specific process is as follows:

[0014] The information entropy of all data blocks is concatenated to obtain the combined entropy value {B1|B2|...|Bn}; the combined entropy value is then subjected to SHA3-512 operation to obtain the server's version fingerprint Vf(t) at the current moment, where t is the timestamp, i.e., the generation time of the version fingerprint Vf(t).

[0015] In a preferred embodiment of the present invention, an entropy difference matrix is ​​constructed based on information entropy. The information entropy E(Bi) of each data block i is arranged on the diagonal, and the difference between the information entropy E(Bi) is arranged in other positions to obtain the entropy difference matrix at the current time t:

[0016] ;

[0017] in, , ..., The entropy difference represents the difference in information entropy.

[0018] The difference between the information entropy E(B1) of data block 1 and the information entropy E(B2) of data block 2 is represented.

[0019] Calculate the standard deviation σ(E,t) of all entropy differences to identify variations in hot zones of storage space within the server.

[0020] When the absolute value of the difference between the standard deviation σ(E,t) of the entropy difference at the current moment and the standard deviation σ(E,t-1) of the entropy difference at the previous moment is identified... If the value exceeds the preset threshold, it is determined that a change in the server storage space hot zone has occurred between time t-1 and time t, and time t is marked as the characteristic time.

[0021] Based on the characteristic time, the historical versions of the server storage module are divided, and all data stored in the server storage module between two adjacent characteristic times are recorded as one historical version.

[0022] All historical versions are numbered with the identifier j, where j = 1, 2, ..., m; m is the total number of historical versions.

[0023] Step 2: Incremental coding and topology optimization;

[0024] Incremental data packets and distribution paths are constructed, and binary patches are generated based on version fingerprint differences. The optimal transmission path is calculated according to the node topology, and erasure coding verification information is added to the data blocks. All incremental data packets are encapsulated into atomic transaction data units, which serve as the smallest operation unit for subsequent version rollback and version switching, completing network-aware minimal transmission coding.

[0025] Obtain the version fingerprint Vf(t) at each time t, the version fingerprint Vf(t-1) at the previous time t-1, and the entropy difference matrix E(t).

[0026] Difference encoding is performed based on the entropy difference matrix, and a binary difference algorithm accelerated by suffix arrays is used to calculate the entropy difference matrix encoded eigenvalues. The longest common subsequence of the old and new files is found by sorting by suffix. Unmodified data blocks are marked, resulting in a sequence of data blocks that have not been modified compared to the previous time step.

[0027] In a preferred embodiment of the present invention, the server node number e of each data block i is obtained, along with its real-time network latency and available bandwidth. An optimal distribution tree decision model is then constructed.

[0028]

[0029] Solve for the condition Given the minimum set of nodes T, where e is the server node number. , and These represent the network latency, available bandwidth, and maximum hop count for node e, respectively. λ is a preset weighting factor with a default value of 0.7. Adjusting λ to 1 reduces intermediate nodes and lowers the failure rate; adjusting λ to 0 prioritizes short-hop, high-speed links.

[0030] Assign a public and private key to the server's version fingerprint Vf(t). Encode the eigenvalues ​​using the version fingerprint Vf(t), the public and private keys of the version fingerprint, and the entropy difference matrix. The node set T is encapsulated as a transaction message and directly entered into the version arbitrator memory pool via RDMA, serving as the basis for hardware arbitration and version switching decisions.

[0031] Step 3: Hardware arbitration and version switching decisions;

[0032] Anomaly detection is performed based on incremental data packets, the cryptographic signature of each data packet is verified, the stability index of historical versions is calculated, the optimal target version for rollback operation is determined based on the system state transition probability, and a rollback trigger signal is output.

[0033] The eigenvalues ​​of the entropy difference matrix are encoded using a version arbitrator. Data analysis is performed on the node set T to verify the version fingerprint and calculate the historical version stability score. The specific process is as follows:

[0034] The fingerprint generator is invoked again to calculate the server's real-time version fingerprint Vf(t). The encapsulated transaction message is retrieved from the version arbitrator's memory pool, and the public key of the server's current version fingerprint Vf(t) is obtained. The real-time version fingerprint is verified using the public key of the server's current version fingerprint. If the verification fails, a server information tampering signal is output.

[0035] Obtain the continuous runtime Aj, the number of failures Fj, and the number of data blocks Nj contained in the data block column that has not undergone data modification in each historical version. The stability score Sj of each historical version j is obtained by weighted summing of Aj, Fj, and Nj.

[0036] In a preferred embodiment of the present invention, dynamic switching decision analysis is performed through a Markov decision process. Upon detecting a server information tampering signal, three preset server maintenance operations are obtained:

[0037] Operation 1: Keep the current version;

[0038] Operation 2: Switch to the version with the highest historical stability score;

[0039] Operation 3: Switch to the previous version;

[0040] By using a preset formula:

[0041]

[0042] Calculate the expected values ​​of actions 1, 2, and 3. , and , where a=1, 2, 3; correspond to three server maintenance operations, namely, maintaining the current version, switching to the version with the highest historical stability score, and switching to the previous version.

[0043] Where s is the encapsulated transaction message, namely the version fingerprint Vf(t), the public and private keys of the version fingerprint, and the entropy difference matrix encoded eigenvalues. The set of nodes T represents the current state of the server.

[0044] Among them For immediate rewards, i.e., the previously encapsulated transaction message was... When, the increase in stability score after taking operation a is taken, where γ is a preset weighting factor. For historical transaction messages similar to the current transaction message s, i.e. and The difference in information entropy is less than a preset threshold.

[0045] Among them The state transition probability represents historical transaction messages. In the context, the probability of taking action a = 1, 2, or 3.

[0046] Among them State value represents historical transaction messages. The reference value of historical information The similarity to the current transaction message s, specifically the numerical value is: The difference between the information entropy of s and s.

[0047] Obtain the expected values ​​of actions 1, 2, and 3. , and The maximum value in.

[0048] If the maximum value is Output sustain signal;

[0049] If the maximum value is It outputs a rollback trigger signal and identifies the target version for the rollback operation as the historical version with the highest stability score.

[0050] If the maximum value is It outputs a rollback trigger signal and locates the target version of the rollback operation as the previous historical version.

[0051] Step 4: Atomic version switching and rollback execution;

[0052] Upon receiving a rollback trigger, the optimal target version for the rollback operation is located, and the rollback operation is executed to complete the final execution of the version switch operation. This includes updating the storage mapping through comparison and exchange instructions, redirecting the service access logic address, and rolling back the logs. Upon receiving an atomic switch instruction, data restoration is performed on a block-by-block basis.

[0053] No further action is taken after receiving the sustain signal;

[0054] Upon receiving a rollback trigger signal, locate the target version of the rollback operation and obtain the information contained in all data blocks in the target version and their storage server node numbers e. Establish a storage mapping {i→e} from data blocks to server node number e, and perform location migration and content restoration on existing data blocks. Record the operation type and trigger time of the rollback operation in the rollback log.

[0055] As a preferred embodiment of the present invention, if the target version of the rollback operation is the previous version, the calculation result of the binary difference algorithm accelerated by the suffix array is retrieved to obtain the data restoration patch from the current version to the previous version.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] 1. This invention detects changes in storage hotspots using an entropy difference matrix and combines this with timestamps to mark key moments, achieving precise segmentation and unique identification of historical versions. This technology avoids the redundant operation of full-scale scanning, performing versioning only on change hotspot areas, significantly optimizing storage space utilization and improving the efficiency and accuracy of version tracing;

[0058] 2. This invention employs a binary differential algorithm accelerated by suffix arrays to generate incremental patches, combines network topology optimization to construct the optimal transmission path, and adds erasure coding verification information to data blocks. This design significantly reduces data transmission volume, while enhancing transmission fault tolerance through erasure coding, ensuring data integrity and transmission efficiency during network fluctuations or node failures.

[0059] 3. This invention dynamically evaluates the stability of historical versions and combines cryptographic signature verification to ensure version authenticity. This mechanism can automatically select the optimal rollback target and ensure the atomicity and data consistency of the rollback process through atomic operations, significantly improving the system's resistance to tampering and fault recovery efficiency. Attached Figure Description

[0060] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings:

[0061] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0062] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0063] Please see Figure 1As shown, a method for updating and rolling back server content based on static technology includes the following steps:

[0064] Step 1: Versioned metadata collection and entropy detection;

[0065] The system accesses the server's storage module, performs file unit segmentation, and divides static content into fixed-size data blocks. It calculates the byte-level information and entropy distribution data for each data block, and detects change hotspots based on the entropy difference matrix. Through cascaded hashing, it fuses the byte-level information and entropy distribution data of all data blocks to generate a unique version identifier and version fingerprint for the server's static content.

[0066] At preset time intervals, the server's storage module is accessed to scan the static file collection and divide it into blocks, resulting in several data blocks with a fixed size of 4MB.

[0067] Calculate the information entropy for all data blocks, and statistically analyze the frequency Pk of each byte value k within each data block using a preset formula. Calculate the information entropy E(Bi) of each data block Bi, where i is the data block number, i=1,2,...,n; and n is the total number of data blocks.

[0068] Furthermore, a version fingerprint for the server is generated using a fingerprint generator. The specific process is as follows:

[0069] The information entropy of all data blocks is concatenated to obtain the combined entropy value {B1|B2|...|Bn}; the combined entropy value is then subjected to SHA3-512 operation to obtain the server's version fingerprint Vf(t) at the current moment, where t is the timestamp, i.e., the generation time of the version fingerprint Vf(t).

[0070] Furthermore, based on the information entropy, an entropy difference matrix is ​​constructed. The information entropy E(Bi) of each data block i is arranged on the diagonal, and the difference between the information entropy E(Bi) is arranged in other positions, thus obtaining the entropy difference matrix at the current time t:

[0071] ;

[0072] in, , ..., The entropy difference represents the difference in information entropy.

[0073] The difference between the information entropy E(B1) of data block 1 and the information entropy E(B2) of data block 2 is represented.

[0074] Calculate the standard deviation σ(E,t) of all entropy differences to identify variations in hot zones of storage space within the server.

[0075] When the absolute value of the difference between the standard deviation σ(E,t) of the entropy difference at the current moment and the standard deviation σ(E,t-1) of the entropy difference at the previous moment is identified... If the value exceeds the preset threshold, it is determined that a change in the server storage space hot zone has occurred between time t-1 and time t, and time t is marked as the characteristic time.

[0076] Based on the characteristic time, the historical versions of the server storage module are divided, and all data stored in the server storage module between two adjacent characteristic times are recorded as one historical version.

[0077] All historical versions are numbered with the identifier j, where j = 1, 2, ..., m; m is the total number of historical versions.

[0078] Step 2: Incremental coding and topology optimization;

[0079] Incremental data packets and distribution paths are constructed, and binary patches are generated based on version fingerprint differences. The optimal transmission path is calculated according to the node topology, and erasure coding verification information is added to the data blocks. All incremental data packets are encapsulated into atomic transaction data units, which serve as the smallest operation unit for subsequent version rollback and version switching, completing network-aware minimal transmission coding.

[0080] Obtain the version fingerprint Vf(t) at each time t, the version fingerprint Vf(t-1) at the previous time t-1, and the entropy difference matrix E(t).

[0081] Difference encoding is performed based on the entropy difference matrix, and a binary difference algorithm accelerated by suffix arrays is used to calculate the entropy difference matrix encoded eigenvalues. The longest common subsequence of the old and new files is found by sorting by suffix. Unmodified data blocks are marked, resulting in a sequence of data blocks that have not been modified compared to the previous time step.

[0082] It should be noted that bsdiff is an incremental update algorithm for binary files, specifically designed for efficiently generating file difference patches.

[0083] Furthermore, obtain the server node number e where each data block i is located, and obtain its real-time network latency and available bandwidth. Construct the optimal distribution tree decision model:

[0084]

[0085] Solve for the condition Given the minimum set of nodes T, where e is the server node number. , and These represent the network latency, available bandwidth, and maximum hop count for node e, respectively. λ is a preset weighting factor with a default value of 0.7. Adjusting λ to 1 reduces intermediate nodes and lowers the failure rate; adjusting λ to 0 prioritizes short-hop, high-speed links.

[0086] Assign a public and private key to the server's version fingerprint Vf(t). Encode the eigenvalues ​​using the version fingerprint Vf(t), the public and private keys of the version fingerprint, and the entropy difference matrix. The node set T is encapsulated as a transaction message and directly entered into the version arbitrator memory pool via RDMA, serving as the basis for hardware arbitration and version switching decisions.

[0087] Step 3: Hardware arbitration and version switching decisions;

[0088] Anomaly detection is performed based on incremental data packets, the cryptographic signature of each data packet is verified, the stability index of historical versions is calculated, the optimal target version for rollback operation is determined based on the system state transition probability, and a rollback trigger signal is output.

[0089] The eigenvalues ​​of the entropy difference matrix are encoded using a version arbitrator. Data analysis is performed on the node set T to verify the version fingerprint and calculate the historical version stability score. The specific process is as follows:

[0090] The fingerprint generator is invoked again to calculate the server's real-time version fingerprint Vf(t). The encapsulated transaction message is retrieved from the version arbitrator's memory pool, and the public key of the server's current version fingerprint Vf(t) is obtained. The real-time version fingerprint is verified using the public key of the server's current version fingerprint. If the verification fails, a server information tampering signal is output.

[0091] Obtain the continuous runtime Aj, the number of failures Fj, and the number of data blocks Nj contained in the data block column that has not undergone data modification in each historical version. The stability score Sj of each historical version j is obtained by weighted summing of Aj, Fj, and Nj.

[0092] Furthermore, dynamic switching decision analysis is performed through Markov decision processes. Upon detecting server information tampering signals, three preset server maintenance operations are obtained:

[0093] Operation 1: Keep the current version;

[0094] Operation 2: Switch to the version with the highest historical stability score;

[0095] Operation 3: Switch to the previous version;

[0096] By using a preset formula:

[0097]

[0098] Calculate the expected values ​​of actions 1, 2, and 3. , and , where a=1, 2, 3; correspond to three server maintenance operations, namely, maintaining the current version, switching to the version with the highest historical stability score, and switching to the previous version.

[0099] Where s is the encapsulated transaction message, namely the version fingerprint Vf(t), the public and private keys of the version fingerprint, and the entropy difference matrix encoded eigenvalues. The set of nodes T represents the current state of the server.

[0100] Among them For immediate rewards, i.e., the previously encapsulated transaction message was... When, the increase in stability score after taking operation a is taken, where γ is a preset weighting factor. For historical transaction messages similar to the current transaction message s, i.e. and The difference in information entropy is less than a preset threshold.

[0101] Among them The state transition probability represents historical transaction messages. In the context, the probability of taking action a = 1, 2, or 3.

[0102] Among them State value represents historical transaction messages. The reference value of historical information The similarity to the current transaction message s, specifically the numerical value is: The difference between the information entropy of s and s.

[0103] Obtain the expected values ​​of actions 1, 2, and 3. , and The maximum value in.

[0104] If the maximum value is Output sustain signal;

[0105] If the maximum value is It outputs a rollback trigger signal and identifies the target version for the rollback operation as the historical version with the highest stability score.

[0106] If the maximum value is It outputs a rollback trigger signal and locates the target version of the rollback operation as the previous historical version.

[0107] Step 4: Atomic version switching and rollback execution;

[0108] Upon receiving a rollback trigger, the optimal target version for the rollback operation is located, and the rollback operation is executed to complete the final execution of the version switch operation. This includes updating the storage mapping through comparison and exchange instructions, redirecting the service access logic address, and rolling back the logs. Upon receiving an atomic switch instruction, data restoration is performed on a block-by-block basis.

[0109] No further action is taken after receiving the sustain signal;

[0110] Upon receiving a rollback trigger signal, locate the target version of the rollback operation and obtain the information contained in all data blocks in the target version and their storage server node numbers e. Establish a storage mapping {i→e} from data blocks to server node number e, and perform location migration and content restoration on existing data blocks. Record the operation type and trigger time of the rollback operation in the rollback log.

[0111] If the target version of the rollback operation is the previous version, retrieve the calculation result of the binary difference algorithm accelerated by the suffix array to obtain the data restoration patch from the current version to the previous version.

[0112] It should be understood that the terms “comprising” and “including” used in this disclosure and claims indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0113] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the disclosure. As used in this disclosure and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this disclosure and claims means any combination and all possible combinations of one or more of the associated listed items, and includes such combinations;

[0114] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for updating and rolling back server content based on static technology, characterized in that, Includes the following steps: Step 1: Versioned metadata collection and entropy detection; Access the server's storage module to perform file unit chunking, dividing static content into fixed-size data blocks; Calculate the byte-level information and entropy distribution data of each data block, and detect change hotspots based on the entropy difference matrix; perform data fusion on the byte-level information and entropy distribution data of all data blocks through cascaded hash operation to generate a unique version identifier and version fingerprint of the server's static content; Step 2: Incremental coding and topology optimization; Construct incremental data packets and distribution paths, generate binary patches based on version fingerprint differences; calculate the optimal transmission path according to node topology, and add erasure coding verification information to data blocks; encapsulate all incremental data packets into atomic transaction data units, which serve as the smallest operation unit for subsequent version rollback and version switching, and complete network-aware minimal transmission coding. Step 3: Hardware arbitration and version switching decisions; Anomaly detection is performed based on incremental data packets, the cryptographic signature of each data packet is verified, the stability index of historical versions is calculated, the version fingerprint is verified, and the stability score of historical versions is calculated. The optimal target version for the rollback operation is determined based on the system state transition probability, and a rollback trigger signal is output. Step 4: Atomic version switching and rollback execution; Upon receiving a rollback trigger, locate the optimal target version for the rollback operation, execute the rollback operation, and complete the final execution of the version switching operation, including updating the storage mapping through comparison exchange instructions, redirecting the service access logical address, and rolling back the logs; upon receiving an atomic switch instruction, perform data restoration on a block-by-block basis.

2. The server content update and rollback method based on static technology according to claim 1, characterized in that, The specific process of dividing static content into fixed-size data blocks and calculating entropy distribution data is as follows: At preset time intervals, the server's storage module is accessed to scan the static file collection and divide it into blocks to obtain several data blocks with a fixed size of 4MB. Calculate the information entropy for all data blocks, and statistically analyze the frequency Pk of each byte value k within each data block using a preset formula. Calculate the information entropy E(Bi) of each data block Bi, where i is the data block number, i=1,2,...,n; and n is the total number of data blocks. Generate the server's version fingerprint using a fingerprint generator.

3. The server content update and rollback method based on static technology according to claim 2, characterized in that, The specific process of generating a server version fingerprint using a fingerprint generator is as follows: The information entropy of all data blocks is concatenated to obtain the combined entropy value {B1|B2|...|Bn}; the combined entropy value is subjected to SHA3-512 operation to obtain the server version fingerprint Vf(t) at the current time, where t is the timestamp, that is, the generation time of the version fingerprint Vf(t); Based on the information entropy, an entropy difference matrix is ​​constructed. The information entropy E(Bi) of each data block i is arranged on the diagonal, and the difference between the information entropy E(Bi) is arranged in other positions to obtain the entropy difference matrix at the current time t: ； in, , ..., The entropy difference represents the difference in information entropy. The difference between the information entropy E(B1) of data block 1 and the information entropy E(B2) of data block 2; Calculate the standard deviation σ(E,t) of all entropy differences to identify changes in hot zones of storage space in the server and to classify historical versions.

4. The server content update and rollback method based on static technology according to claim 3, characterized in that, The specific process for dividing historical versions is as follows: When the absolute value of the difference between the standard deviation σ(E,t) of the entropy difference at the current moment and the standard deviation σ(E,t-1) of the entropy difference at the previous moment is identified... If the value exceeds the preset threshold, it is determined that a change in the server storage space hot zone has occurred between time t-1 and time t, and time t is marked as the characteristic time. Based on the characteristic time, the historical versions of the server storage module are divided, and all the data stored in the server storage module between two adjacent characteristic times are recorded as one historical version. All historical versions are numbered with the identifier j, where j = 1, 2, ..., m; m is the total number of historical versions.

5. The method for updating and rolling back server content based on static technology according to claim 1, characterized in that, The specific process for generating binary patches based on version fingerprint differences is as follows: Obtain the version fingerprint Vf(t) at each time t, the version fingerprint Vf(t-1) at the previous time t-1, and the entropy difference matrix E(t). Difference encoding is performed based on the entropy difference matrix, and a binary difference algorithm accelerated by suffix arrays is used to calculate the entropy difference matrix encoded eigenvalues. Use suffix sorting to find the longest common subsequence of the old and new files, mark the unmodified data blocks, and obtain the sequence of data blocks that have not been modified compared with the previous time step; The optimal transmission path is calculated by combining the entropy difference matrix encoding eigenvalues.

6. A method for updating and rolling back server content based on static technology according to claim 5, characterized in that, The specific process for calculating the optimal transmission path is as follows: Obtain the server node number e where each data block i is located, and obtain its real-time network latency and available bandwidth; construct the optimal distribution tree decision model: ； Solve for the satisfaction Given the minimum set of nodes T, where e is the server node number. , and These represent the network latency, available bandwidth, and maximum hop count for distribution of node e, respectively. λ is a preset weighting factor with a default value of 0.

7. Adjusting λ to 1 reduces intermediate nodes and lowers the failure rate; adjusting λ to 0 prioritizes short-hop, high-speed links. Assign a public and private key to the server's version fingerprint Vf(t); encode the eigenvalues ​​of the version fingerprint Vf(t), the public and private keys of the version fingerprint, and the entropy difference matrix. The node set T is encapsulated as a transaction message and directly entered into the version arbitrator memory pool via RDMA, serving as the basis for hardware arbitration and version switching decisions.

7. A method for updating and rolling back server content based on static technology according to claim 1, characterized in that, The specific process for verifying version fingerprints and calculating historical version stability scores is as follows: Recall the fingerprint generator, calculate the server's real-time version fingerprint Vf(t), retrieve the encapsulated transaction message from the version arbitrator memory pool, and obtain the public key of the server's current version fingerprint Vf(t). Verify the real-time version fingerprint using the public key of the server's current version fingerprint. If the verification fails, output a server information tampering signal. Obtain the continuous runtime Aj, the number of failures Fj, and the number of data blocks Nj contained in the data block column that has not undergone data modification in each historical version. The stability score Sj of each historical version j is obtained by weighted summing of Aj, Fj, and Nj. Dynamic switching decision analysis is performed using Markov decision processes.

8. A method for updating and rolling back server content based on static technology according to claim 7, characterized in that, The specific process of dynamic switching decision analysis using Markov decision processes is as follows: Get three preset server maintenance operations: Operation 1: Keep the current version; Operation 2: Switch to the version with the highest historical stability score; Operation 3: Switch to the previous version; By using a preset formula: ； Calculate the expected values ​​of actions 1, 2, and 3. , and , where a=1,2,3; correspond to three server maintenance operations, namely, maintaining the current version, switching to the version with the highest historical stability score, and switching to the previous version; Where s is the encapsulated transaction message, namely the version fingerprint Vf(t), the public and private keys of the version fingerprint, and the entropy difference matrix encoded eigenvalues. The set of nodes T represents the current state of the server; Among them For immediate rewards, i.e., the previously encapsulated transaction message was... When, the increase in stability score after taking operation a, where γ is a preset weighting factor; where For historical transaction messages similar to the current transaction message s, i.e. and The difference in information entropy is less than a preset threshold; Among them The state transition probability represents historical transaction messages. In the context, the probability of taking action a = 1, 2, 3; Among them State value represents historical transaction messages. The reference value of historical information The similarity to the current transaction message s, specifically the numerical value is: The difference in information entropy between and s; Obtain the expected values ​​of actions 1, 2, and 3. , and The maximum value in.

9. A method for updating and rolling back server content based on static technology according to claim 1, characterized in that, The final execution process for completing the version switch operation is as follows: No further action is taken after receiving the sustain signal; Upon receiving a rollback trigger signal, locate the target version of the rollback operation, obtain the information contained in all data blocks in the target version and their stored server node number e; establish a storage mapping {i→e} from data blocks to server node number e, and perform location migration and storage content restoration on the existing data blocks; Record the operation type and start time of any rollback operation, and record them in the rollback log; If the target version of the rollback operation is the previous version, retrieve the calculation result of the binary difference algorithm accelerated by the suffix array to obtain the data restoration patch from the current version to the previous version.

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