A remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine

Abstract

The present invention relates to the technical field of data security, and discloses an off-site disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine. The system includes a data segmentation and compression module, a data backup module, a data migration path establishment module, and a differential data migration module; first, the initial diagnosis and treatment auxiliary analysis data set is segmented and compressed to obtain a processed diagnosis and treatment auxiliary analysis data set; then a data backup tree is established, and the processed diagnosis and treatment auxiliary analysis data set is backed up to obtain a diagnosis and treatment auxiliary analysis data storage tree; the data migration path is optimized using the starfish optimization algorithm to obtain the optimal data migration path, and finally differential data is generated according to data consistency, and the differential data is migrated on the optimal data migration path to complete off-site disaster recovery storage. The present invention achieves the purpose of off-site disaster recovery storage by compressing, backing up, and migrating the diagnosis and treatment auxiliary analysis data, and the method is objective and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of data security, and in particular to a remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine. Background Art

[0002] Chinese patent CN111143114B discloses a virtualization platform disaster recovery method, system, server, and storage medium. The method specifically includes: when receiving a disaster recovery switching request, the off-site disaster recovery server obtains the backup data in the target virtual machine and uses the backup data to establish an off-site virtual machine; creates a preset virtual machine network based on the off-site virtual machine and resumes business processing, and then determines the target disaster recovery server through the disaster recovery switching request; sets disaster recovery control scheduling, performs business-level disaster recovery on the target virtual machine on the target disaster recovery server, obtains the backup data in the target virtual machine, and uses the backup data to establish an off-site virtual machine to achieve virtualization platform disaster recovery. However, this invention does not optimize the storage of backup data and has disadvantages such as slow off-site data calls.

[0003] Traditional off-site disaster recovery storage methods usually migrate local data directly to backup data centers to build multiple backup data centers to prevent disasters, but there are problems such as slow data backup speeds. At the same time, without the use of technologies such as artificial intelligence, the data recovery speed of off-site disaster recovery storage is slow and the data storage occupies a large space, which has shortcomings in data security and continuity. Summary of the Invention

[0004] In response to the problems in the related art, the present invention provides an off-site disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine to overcome the above-mentioned technical problems existing in the existing related art.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a remote disaster recovery storage method for diagnosis and treatment auxiliary analysis data based on an open source engine, comprising the following steps:

[0007] S1. Obtain an initial diagnosis and treatment auxiliary analysis data set, establish a data storage center, use a data segmentation algorithm to segment the initial diagnosis and treatment auxiliary analysis data set, and then perform data compression based on time series to obtain a processed diagnosis and treatment auxiliary analysis data set;

[0008] S2. Calculate the data attributes of the processed diagnosis and treatment auxiliary analysis data set, establish a data backup tree, store the processed diagnosis and treatment auxiliary analysis data set in the data backup tree based on the dynamic backup principle, implement data backup, obtain a diagnosis and treatment auxiliary analysis data storage tree, and establish a data backup center;

[0009] S3. After a disaster occurs in the initial diagnosis and treatment auxiliary analysis data set, the diagnosis and treatment auxiliary analysis data storage tree in the data backup center is migrated to the diagnosis and treatment auxiliary analysis data set in the data storage center, and the starfish optimization algorithm is used to optimize the data migration path to obtain the optimal data migration path;

[0010] S4. Perform data migration according to the optimal data migration path, generate differential data based on the data consistency between the data backup center and the data storage center, migrate the differential data, and complete off-site disaster recovery storage.

[0011] The invention obtains an initial diagnosis and treatment auxiliary analysis data set, performs block processing and data compression on the initial diagnosis and treatment auxiliary analysis data set, obtains a processed diagnosis and treatment auxiliary analysis data set, and establishes a data storage center; the method sets a window to find the cut-off data to ensure that there will be no overly long data blocks after block processing, and then compresses the data according to the time series, which greatly saves data storage space and improves compression efficiency; secondly, a data backup tree is established, and the processed diagnosis and treatment auxiliary analysis data set is stored on the data backup tree based on the dynamic backup principle to obtain a diagnosis and treatment auxiliary analysis data storage tree; the method uses a tree structure to store data, reduces the storage space of backup data, and further reduces the data volume compared to traditional backup methods, preserving Ensure the integrity of data backup; then establish a data migration path from the data backup center to the data storage center, and use the starfish optimization algorithm to optimize the data migration path to obtain the best data migration path; this method balances the transmission time and data loss rate by establishing an objective function to achieve the minimum transmission time and minimum data loss rate. The starfish optimization algorithm simulates the exploration, predation and regeneration behavior of starfish, and has good search capabilities in the process of finding the optimal solution. At the same time, the optimization speed and convergence speed are fast, which saves data transmission time; finally, differential data is generated according to the data consistency of the data backup center and the data storage center, and only the differential data is migrated, which reduces the data migration amount and migration cost, greatly improves the data migration efficiency, and ensures data security.

[0012] Preferably, the S1 comprises the following steps:

[0013] S11. Acquire auxiliary diagnosis and treatment analysis data, wherein the auxiliary diagnosis and treatment analysis data includes patient physiological indicators, patient clinical data, patient diagnosis and treatment resource allocation data, etc., to form an initial auxiliary diagnosis and treatment analysis data set, establish a data storage center based on an open source engine, and store the initial auxiliary diagnosis and treatment analysis data set in the data storage center;

[0014] S12. For the initial diagnosis and treatment auxiliary analysis data set, set a data window and a sliding window, set the data window and the sliding window at the starting position of the initial diagnosis and treatment auxiliary analysis data set, traverse to obtain the maximum diagnosis and treatment auxiliary analysis data in the data window, move the sliding window, and record the maximum diagnosis and treatment auxiliary analysis data in the sliding window. When the maximum diagnosis and treatment auxiliary analysis data in the sliding window is greater than or equal to the maximum diagnosis and treatment auxiliary analysis data in the data window, record the maximum diagnosis and treatment auxiliary analysis data in the data window as the cut-off data, and divide the initial diagnosis and treatment auxiliary analysis data set at the cut-off data to obtain diagnosis and treatment auxiliary analysis data blocks, completing preliminary block processing; set a maximum data block length. When the diagnosis and treatment auxiliary analysis data block is greater than the maximum data block length, record the sliding window length as the cut-off data. Otherwise, move the data window to the maximum diagnosis and treatment auxiliary analysis data in the data window, and continue block processing until the initial diagnosis and treatment auxiliary analysis data set completes block processing to obtain a diagnosis and treatment auxiliary analysis data block set;

[0015] S13, generating a diagnosis and treatment auxiliary analysis data time series according to the generation time of the diagnosis and treatment auxiliary analysis data in the initial diagnosis and treatment auxiliary analysis data set, and converting the diagnosis and treatment auxiliary analysis data time series into a diagnosis and treatment auxiliary analysis data block time series A = {b1, b2, b3, ..., b m}, where b m represents the time point of the mth diagnosis and treatment auxiliary analysis data block, and then the diagnosis and treatment auxiliary analysis data block set is compressed to obtain the processed diagnosis and treatment auxiliary analysis data set. The specific steps are as follows:

[0016] S131. Mark the auxiliary diagnosis and treatment analysis data blocks in the auxiliary diagnosis and treatment analysis data block set to obtain compression tags for the auxiliary diagnosis and treatment analysis data blocks. When the compression tag is 0, it indicates that the auxiliary diagnosis and treatment analysis data block is not compressed. When the compression tag is 1, it indicates that the auxiliary diagnosis and treatment analysis data block is compressed. Compress the auxiliary diagnosis and treatment analysis data blocks corresponding to the compression tag 0 to obtain a compressed auxiliary diagnosis and treatment analysis data block set. The specific steps are as follows:

[0017] S1311. Select the auxiliary treatment analysis data block corresponding to the time point of the first auxiliary treatment analysis data block in the auxiliary treatment analysis data block time series, record it as the first data block, and add it to the compressed auxiliary treatment analysis data block set. Calculate the square difference between the auxiliary treatment analysis data blocks in the auxiliary treatment analysis data block set and the first data block in sequence.

[0018] S1312: Set a difference threshold. When the square difference is less than the difference threshold, add the corresponding diagnosis and treatment auxiliary analysis data block to the compressed diagnosis and treatment auxiliary analysis data block set to obtain a compressed diagnosis and treatment auxiliary analysis data block set. Otherwise, select the diagnosis and treatment auxiliary analysis data block corresponding to the time point of other diagnosis and treatment auxiliary analysis data blocks to complete the preliminary data compression.

[0019] S132. Traverse the time series of the auxiliary diagnosis and treatment analysis data blocks, determine whether the compression tag is 1, until the compression of the auxiliary diagnosis and treatment analysis data blocks is completed, and combine all compressed auxiliary diagnosis and treatment analysis data block sets to obtain a processed auxiliary diagnosis and treatment analysis data set.

[0020] This invention obtains an initial diagnosis and treatment auxiliary analysis data set, performs block processing and data compression on the initial diagnosis and treatment auxiliary analysis data set to obtain a processed diagnosis and treatment auxiliary analysis data set, sets a window to find the cut-off data, ensures that there will be no overly long data blocks after block processing, and then compresses the data according to the time series, which greatly saves data storage space and improves compression efficiency.

[0021] Preferably, said S2 comprises the following steps:

[0022] S21. Set data attribute set A′={b1′, b2′, b3′, ..., b′ m′}, where b′ m Denote the m′th data attribute, for the compressed diagnosis and treatment auxiliary analysis data block set in the processed diagnosis and treatment auxiliary analysis data set, calculate the probability that the compressed diagnosis and treatment auxiliary analysis data block set is assigned to the data attribute set, recorded as the data attribute probability, divide the processed diagnosis and treatment auxiliary analysis data set according to the data attribute probability to obtain the data attributes of the processed diagnosis and treatment auxiliary analysis data set;

[0023] S22. Establish a data backup tree based on data attributes, and store the processed diagnosis and treatment auxiliary analysis data set on the data backup tree based on the dynamic backup principle to obtain a diagnosis and treatment auxiliary analysis data storage tree. The specific steps are as follows:

[0024] S221. Setting an empty data backup tree, starting with the root node of the empty data backup tree, recording the number of data attributes, using the number of data attributes as the first layer of the empty data backup tree, and then using the data attributes of the processed diagnosis and treatment auxiliary analysis data set as the second layer of the empty data backup tree, thereby establishing a data backup tree;

[0025] S222. Based on the dynamic backup principle, according to the data attributes of the processed diagnosis and treatment auxiliary analysis data set, the compressed diagnosis and treatment auxiliary analysis data block set is sequentially divided into corresponding data attribute nodes and stored; the data backup tree is traversed to find the same compressed diagnosis and treatment auxiliary analysis data blocks in the data backup tree nodes, the same compressed diagnosis and treatment auxiliary analysis data blocks are regarded as duplicate data blocks, and the duplicate data blocks are skipped during the dynamic division process until the processed diagnosis and treatment auxiliary analysis data set is completely stored, thereby achieving data backup and obtaining the diagnosis and treatment auxiliary analysis data storage tree;

[0026] S23. Establish a data backup center, and store the diagnosis and treatment auxiliary analysis data storage tree in the data backup center.

[0027] This invention establishes a data backup tree and stores the processed diagnosis and treatment auxiliary analysis data set on the data backup tree based on the dynamic backup principle. It uses a tree structure to store data, reduces the storage space of the backup data, and obtains a diagnosis and treatment auxiliary analysis data storage tree. Compared with traditional backup methods, it further reduces the data volume and ensures the integrity of data backup.

[0028] Preferably, the step S3 includes the following steps:

[0029] S31. After a disaster occurs in the initial diagnosis and treatment auxiliary analysis data set, the diagnosis and treatment auxiliary analysis data storage tree of the data backup center is migrated to the diagnosis and treatment auxiliary analysis data set in the data storage center, and a data migration path is determined. The data migration path includes a number of transmission nodes. The time and data loss rate of the diagnosis and treatment auxiliary analysis data in the diagnosis and treatment auxiliary analysis data storage tree passing through the transmission nodes are calculated. An objective function is established based on the minimum transmission time and the minimum data loss rate to obtain a total transmission time function F1 and a total data loss rate function F2. Weights are assigned to convert the total transmission time function and the total data loss rate function into a data migration objective function F=ω1F1+ω2F2, where ω1 and ω2 represent weights.

[0030] S32: The data migration objective function is regarded as a fitness function, and the data migration path is optimized using the starfish optimization algorithm to obtain the optimal data migration path. The specific steps are as follows:

[0031] S321. The data migration process is regarded as a search space. It is assumed that there is a starfish population in the search space. The number of starfish populations is p, the dimension of the starfish population is q, and the starfish individuals in the starfish population represent the data migration path. The transmission nodes are encoded, the starting transmission node and the target transmission node are determined, the position of the starfish population is initialized, and the fitness function value is evaluated by calculating the data migration objective function value. In the exploration phase of the starfish population, when the starfish population dimension is greater than 5, the starfish individuals use their arms to guide the movement, and the current number of iterations is set to t. The position of the cth starfish individual in the starfish population in the qth dimension is recorded as The arm movement angle of the starfish individual is θ, the movement coefficient is β, and the optimal position of the starfish individual in the qth dimension at the tth iteration is recorded as d1 represents a random number and d1∈[0,1], and the starfish individual motion model is established. Update, when d1≤0.5, When d1>0.5, When the starfish population dimension is less than or equal to 5, the maximum number of iterations is set to T, and the starfish energy d2 and d3 represent random numbers between the interval [0, 1], and the position of a random starfish in the starfish population is recorded as and Update Location completion of the exploration phase;

[0032] S322. In the development stage of the starfish population, the starfish individual corresponding to the current best fitness function value is selected and recorded as the current global best starfish individual. The distance between the current global best starfish individual and the other five random starfish individuals is calculated. The five random starfish individuals move to the position of the current global best starfish individual according to the distance. The starfish individual is set to be preyed on and the growth of the starfish individual is modeled. At this time, the position is continued to be updated. Get the final position of the starfish individual in the t-th iteration; get the upper and lower bounds of the search space. When the final position of the starfish individual in the t-th iteration is greater than or equal to the lower bound of the search space and less than or equal to the upper bound of the search space, the position of the starfish individual in the t+1-th iteration is equal to the final position of the starfish individual in the t-th iteration; otherwise, the position of the starfish individual in the t+1-th iteration is equal to the upper or lower bound of the search space, and the next generation of starfish population is generated. When the current number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the global optimal position of the starfish individual.

[0033] The global optimal fitness function value is obtained at the global optimal starfish individual position, and the starting transmission node, target transmission node and intermediate node are determined according to the global optimal starfish individual position to obtain the optimal data migration path.

[0034] This invention establishes a data migration path and uses a starfish optimization algorithm to optimize the data migration path. By simulating the exploration, predation and regeneration behaviors of starfish, the optimal data migration path is obtained. It has good search capabilities in the process of finding the optimal solution. At the same time, the optimization speed and convergence speed are fast, which saves data transmission time. An objective function is established to balance the transmission time and data loss rate to achieve the minimum transmission time and minimum data loss rate.

[0035] Preferably, the S4 comprises the following steps:

[0036] S41: Perform data migration according to the optimal data migration path, compare the diagnosis and treatment auxiliary analysis data storage tree of the data backup center with the diagnosis and treatment auxiliary analysis data set of the data storage center, and generate difference data based on data consistency. The specific steps are as follows:

[0037] S411: The diagnosis and treatment auxiliary analysis data set of the data storage center is changed to form a post-disaster diagnosis and treatment auxiliary analysis data set, and the diagnosis and treatment auxiliary analysis data storage tree is compared with the post-disaster diagnosis and treatment auxiliary analysis data set; when the post-disaster diagnosis and treatment auxiliary analysis data set contains missing data, missing diagnosis and treatment auxiliary analysis data is obtained; when the post-disaster diagnosis and treatment auxiliary analysis data set contains updated data, updated diagnosis and treatment auxiliary analysis data is obtained;

[0038] S412. Integrate the missing diagnosis and treatment auxiliary analysis data and the updated diagnosis and treatment auxiliary analysis data to obtain difference data;

[0039] S42: Migrate the corresponding difference data in the diagnosis and treatment auxiliary analysis data storage tree to the diagnosis and treatment auxiliary analysis data set after the disaster, generate a new diagnosis and treatment auxiliary analysis data set, and complete the off-site disaster recovery storage.

[0040] The invention generates differential data based on the data consistency between the data backup center and the data storage center, and only migrates the differential data, thereby reducing the amount of data migration and the migration cost, greatly improving the data migration efficiency and ensuring data security.

[0041] This embodiment also discloses a system for off-site disaster recovery storage of diagnosis and treatment auxiliary analysis data based on an open source engine, specifically comprising: a data segmentation and compression module, a data backup module, a data migration path establishment module, and a differential data migration module;

[0042] The data segmentation and compression module is used to segment and compress the initial diagnosis and treatment auxiliary analysis data set to obtain a processed diagnosis and treatment auxiliary analysis data set;

[0043] The data backup module is used to store the processed diagnosis and treatment auxiliary analysis data set in the data backup tree based on the dynamic backup principle, and establish a diagnosis and treatment auxiliary analysis data storage tree;

[0044] The data migration path establishment module is used to establish a data migration path and use the starfish optimization algorithm to optimize the data migration path to obtain the best data migration path;

[0045] The differential data migration module is used to compare the data consistency between the data backup center and the data storage center to generate differential data, and then perform differential data migration.

[0046] The present invention has the following beneficial effects:

[0047] 1. The invention performs block processing and data compression on the initial diagnosis and treatment auxiliary analysis data set, sets a window to find the cut-off data, ensures that there will be no overly long data blocks after block processing, and then compresses the data according to the time series, which greatly saves data storage space and improves compression efficiency.

[0048] 2. This invention establishes a data backup tree, stores the processed diagnosis and treatment auxiliary analysis data set on the data backup tree based on the dynamic backup principle, uses a tree structure to store data, reduces the storage space of the backup data, and obtains a diagnosis and treatment auxiliary analysis data storage tree. Compared with traditional backup methods, it further reduces the data volume and ensures the integrity of data backup.

[0049] 3. The invention optimizes the data migration path by using the starfish optimization algorithm, and obtains the best data migration path by simulating the exploration, predation and regeneration behaviors of the starfish. It has good search capabilities in the process of finding the optimal solution, and at the same time has fast optimization speed and convergence speed, saving data transmission time, and establishing an objective function to balance the transmission time and data loss rate, achieving minimum transmission time and minimum data loss rate.

[0050] 4. This invention generates differential data based on the data consistency between the data backup center and the data storage center, and only migrates the differential data, thereby reducing the amount of data migration and the migration cost, greatly improving the data migration efficiency, and ensuring data security.

[0051] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying any creative work.

[0053] Figure 1The present invention provides a schematic flow chart of off-site disaster recovery storage of off-site disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on open source engine. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0056] Example 1

[0057] See also Figure 1 This implementation discloses a remote disaster recovery storage method for diagnosis and treatment auxiliary analysis data based on an open source engine, specifically including the following:

[0058] S1. Obtain an initial diagnosis and treatment auxiliary analysis data set, establish a data storage center, use a data segmentation algorithm to segment the initial diagnosis and treatment auxiliary analysis data set, and then perform data compression based on time series to obtain a processed diagnosis and treatment auxiliary analysis data set;

[0059] Said S1 comprises the following steps:

[0060] S11. Acquire auxiliary diagnosis and treatment analysis data, wherein the auxiliary diagnosis and treatment analysis data includes patient physiological indicators, patient clinical data, patient diagnosis and treatment resource allocation data, etc., to form an initial auxiliary diagnosis and treatment analysis data set, establish a data storage center based on an open source engine, and store the initial auxiliary diagnosis and treatment analysis data set in the data storage center;

[0061] S12. For the initial diagnosis and treatment auxiliary analysis data set, set a data window and a sliding window, set the data window and the sliding window at the starting position of the initial diagnosis and treatment auxiliary analysis data set, traverse to obtain the maximum diagnosis and treatment auxiliary analysis data in the data window, move the sliding window, and record the maximum diagnosis and treatment auxiliary analysis data in the sliding window. When the maximum diagnosis and treatment auxiliary analysis data in the sliding window is greater than or equal to the maximum diagnosis and treatment auxiliary analysis data in the data window, record the maximum diagnosis and treatment auxiliary analysis data in the data window as the cut-off data, and divide the initial diagnosis and treatment auxiliary analysis data set at the cut-off data to obtain diagnosis and treatment auxiliary analysis data blocks, completing preliminary block processing; set a maximum data block length. When the diagnosis and treatment auxiliary analysis data block is greater than the maximum data block length, record the sliding window length as the cut-off data. Otherwise, move the data window to the maximum diagnosis and treatment auxiliary analysis data in the data window, and continue block processing until the initial diagnosis and treatment auxiliary analysis data set completes block processing to obtain a diagnosis and treatment auxiliary analysis data block set;

[0062] S13, generating a diagnosis and treatment auxiliary analysis data time series according to the generation time of the diagnosis and treatment auxiliary analysis data in the initial diagnosis and treatment auxiliary analysis data set, and converting the diagnosis and treatment auxiliary analysis data time series into a diagnosis and treatment auxiliary analysis data block time series A = {b1, b2, b3, ..., b m}, where b m represents the time point of the mth diagnosis and treatment auxiliary analysis data block, and then the diagnosis and treatment auxiliary analysis data block set is compressed to obtain the processed diagnosis and treatment auxiliary analysis data set. The specific steps are as follows:

[0063] S131. Mark the auxiliary diagnosis and treatment analysis data blocks in the auxiliary diagnosis and treatment analysis data block set to obtain compression tags for the auxiliary diagnosis and treatment analysis data blocks. When the compression tag is 0, it indicates that the auxiliary diagnosis and treatment analysis data block is not compressed. When the compression tag is 1, it indicates that the auxiliary diagnosis and treatment analysis data block is compressed. Compress the auxiliary diagnosis and treatment analysis data blocks corresponding to the compression tag 0 to obtain a compressed auxiliary diagnosis and treatment analysis data block set. The specific steps are as follows:

[0064] S1311. Select the auxiliary treatment analysis data block corresponding to the time point of the first auxiliary treatment analysis data block in the auxiliary treatment analysis data block time series, record it as the first data block, and add it to the compressed auxiliary treatment analysis data block set. Calculate the square difference between the auxiliary treatment analysis data blocks in the auxiliary treatment analysis data block set and the first data block in sequence.

[0065] S1312: Set a difference threshold. When the square difference is less than the difference threshold, add the corresponding diagnosis and treatment auxiliary analysis data block to the compressed diagnosis and treatment auxiliary analysis data block set to obtain a compressed diagnosis and treatment auxiliary analysis data block set. Otherwise, select the diagnosis and treatment auxiliary analysis data block corresponding to the time point of other diagnosis and treatment auxiliary analysis data blocks to complete the preliminary data compression.

[0066] S132: traverse the time series of the auxiliary diagnosis and treatment analysis data blocks, determine whether the compression flag is 1, and wait until the auxiliary diagnosis and treatment analysis data blocks are compressed. Combine all compressed auxiliary diagnosis and treatment analysis data blocks to obtain a processed auxiliary diagnosis and treatment analysis data set.

[0067] S2. Calculate the data attributes of the processed diagnosis and treatment auxiliary analysis data set, establish a data backup tree, store the processed diagnosis and treatment auxiliary analysis data set in the data backup tree based on the dynamic backup principle, implement data backup, obtain a diagnosis and treatment auxiliary analysis data storage tree, and establish a data backup center;

[0068] The S2 comprises the following steps:

[0069] S21. Set data attribute set A′={b1′, b2′, b3′, ..., b′ m′}, where b′ m Denote the m′th data attribute, for the compressed diagnosis and treatment auxiliary analysis data block set in the processed diagnosis and treatment auxiliary analysis data set, calculate the probability that the compressed diagnosis and treatment auxiliary analysis data block set is assigned to the data attribute set, recorded as the data attribute probability, divide the processed diagnosis and treatment auxiliary analysis data set according to the data attribute probability to obtain the data attributes of the processed diagnosis and treatment auxiliary analysis data set;

[0070] S22. Establish a data backup tree based on data attributes, and store the processed diagnosis and treatment auxiliary analysis data set on the data backup tree based on the dynamic backup principle to obtain a diagnosis and treatment auxiliary analysis data storage tree. The specific steps are as follows:

[0071] S221. Setting an empty data backup tree, starting with the root node of the empty data backup tree, recording the number of data attributes, using the number of data attributes as the first layer of the empty data backup tree, and then using the data attributes of the processed diagnosis and treatment auxiliary analysis data set as the second layer of the empty data backup tree, thereby establishing a data backup tree;

[0072] S222. Based on the dynamic backup principle, according to the data attributes of the processed diagnosis and treatment auxiliary analysis data set, the compressed diagnosis and treatment auxiliary analysis data block set is sequentially divided into corresponding data attribute nodes and stored; the data backup tree is traversed to find the same compressed diagnosis and treatment auxiliary analysis data blocks in the data backup tree nodes, the same compressed diagnosis and treatment auxiliary analysis data blocks are regarded as duplicate data blocks, and the duplicate data blocks are skipped during the dynamic division process until the processed diagnosis and treatment auxiliary analysis data set is completely stored, thereby achieving data backup and obtaining the diagnosis and treatment auxiliary analysis data storage tree;

[0073] S23, establishing a data backup center, and storing the diagnosis and treatment auxiliary analysis data storage tree in the data backup center;

[0074] S3. After a disaster occurs in the initial diagnosis and treatment auxiliary analysis data set, the diagnosis and treatment auxiliary analysis data storage tree in the data backup center is migrated to the diagnosis and treatment auxiliary analysis data set in the data storage center, and the starfish optimization algorithm is used to optimize the data migration path to obtain the optimal data migration path;

[0075] The S3 includes the following steps:

[0076] S31. After a disaster occurs in the initial diagnosis and treatment auxiliary analysis data set, the diagnosis and treatment auxiliary analysis data storage tree of the data backup center is migrated to the diagnosis and treatment auxiliary analysis data set in the data storage center, and a data migration path is determined. The data migration path includes a number of transmission nodes. The time and data loss rate of the diagnosis and treatment auxiliary analysis data in the diagnosis and treatment auxiliary analysis data storage tree passing through the transmission nodes are calculated. An objective function is established based on the minimum transmission time and the minimum data loss rate to obtain a total transmission time function F1 and a total data loss rate function F2. Weights are assigned to convert the total transmission time function and the total data loss rate function into a data migration objective function F=ω1F1+ω2F2, where ω1 and ω2 represent weights.

[0077] S32: The data migration objective function is regarded as a fitness function, and the data migration path is optimized using the starfish optimization algorithm to obtain the optimal data migration path. The specific steps are as follows:

[0078] S321. The data migration process is regarded as a search space. It is assumed that there is a starfish population in the search space. The number of starfish populations is p, the dimension of the starfish population is q, and the starfish individuals in the starfish population represent the data migration path. The transmission nodes are encoded, the starting transmission node and the target transmission node are determined, the position of the starfish population is initialized, and the fitness function value is evaluated by calculating the data migration objective function value. In the exploration phase of the starfish population, when the starfish population dimension is greater than 5, the starfish individuals use their arms to guide the movement, and the current number of iterations is set to t. The position of the cth starfish individual in the starfish population in the qth dimension is recorded as The arm movement angle of the starfish individual is θ, the movement coefficient is β, and the optimal position of the starfish individual in the qth dimension at the tth iteration is recorded as d1 represents a random number and d1∈[0,1], and the starfish individual motion model is established. Update, when d1≤0.5, When d1>0.5, When the starfish population dimension is less than or equal to 5, the maximum number of iterations is set to T, and the starfish energy d2 and d3 represent random numbers between the interval [0, 1], and the position of a random starfish in the starfish population is recorded as and Update Location completion of the exploration phase;

[0079] S322. In the development stage of the starfish population, the starfish individual corresponding to the current best fitness function value is selected and recorded as the current global best starfish individual. The distance between the current global best starfish individual and the other five random starfish individuals is calculated. The five random starfish individuals move to the position of the current global best starfish individual according to the distance. The starfish individual is set to be preyed on and the growth of the starfish individual is modeled. At this time, the position is continued to be updated. Get the final position of the starfish individual in the t-th iteration; get the upper and lower bounds of the search space. When the final position of the starfish individual in the t-th iteration is greater than or equal to the lower bound of the search space and less than or equal to the upper bound of the search space, the position of the starfish individual in the t+1-th iteration is equal to the final position of the starfish individual in the t-th iteration; otherwise, the position of the starfish individual in the t+1-th iteration is equal to the upper or lower bound of the search space, and the next generation of starfish population is generated. When the current number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the global optimal position of the starfish individual.

[0080] Obtaining a global optimal fitness function value at the global optimal starfish individual position, determining a starting transmission node, a target transmission node, and an intermediate node based on the global optimal starfish individual position, and obtaining an optimal data migration path;

[0081] S4. Migrate data according to the optimal data migration path, generate differential data based on the data consistency between the data backup center and the data storage center, migrate the differential data, and complete off-site disaster recovery storage;

[0082] The S4 comprises the following steps:

[0083] S41: Perform data migration according to the optimal data migration path, compare the diagnosis and treatment auxiliary analysis data storage tree of the data backup center with the diagnosis and treatment auxiliary analysis data set of the data storage center, and generate difference data based on data consistency. The specific steps are as follows:

[0084] S411: The diagnosis and treatment auxiliary analysis data set of the data storage center is changed to form a post-disaster diagnosis and treatment auxiliary analysis data set, and the diagnosis and treatment auxiliary analysis data storage tree is compared with the post-disaster diagnosis and treatment auxiliary analysis data set; when the post-disaster diagnosis and treatment auxiliary analysis data set contains missing data, missing diagnosis and treatment auxiliary analysis data is obtained; when the post-disaster diagnosis and treatment auxiliary analysis data set contains updated data, updated diagnosis and treatment auxiliary analysis data is obtained;

[0085] S412. Integrate the missing diagnosis and treatment auxiliary analysis data and the updated diagnosis and treatment auxiliary analysis data to obtain difference data;

[0086] S42: Migrate the corresponding difference data in the diagnosis and treatment auxiliary analysis data storage tree to the diagnosis and treatment auxiliary analysis data set after the disaster, generate a new diagnosis and treatment auxiliary analysis data set, and complete the off-site disaster recovery storage.

[0087] Example 2

[0088] This embodiment also discloses a system for off-site disaster recovery storage of diagnosis and treatment auxiliary analysis data based on an open source engine, specifically comprising: a data segmentation and compression module, a data backup module, a data migration path establishment module, and a differential data migration module;

[0089] The data segmentation and compression module is used to segment and compress the initial diagnosis and treatment auxiliary analysis data set to obtain a processed diagnosis and treatment auxiliary analysis data set;

[0090] The data backup module is used to store the processed diagnosis and treatment auxiliary analysis data set in the data backup tree based on the dynamic backup principle, and establish a diagnosis and treatment auxiliary analysis data storage tree;

[0091] The data migration path establishment module is used to establish a data migration path and use the starfish optimization algorithm to optimize the data migration path to obtain the best data migration path;

[0092] The differential data migration module is used to compare the data consistency between the data backup center and the data storage center to generate differential data, and then perform differential data migration.

[0093] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0094] The preferred embodiments of the invention disclosed above are intended only to help illustrate the invention. These preferred embodiments do not exhaust all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification 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.

Claims

1. A remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine, characterized by: include: The data block and compression module is used to block and compress the initial diagnosis and treatment auxiliary analysis data set to obtain a processed diagnosis and treatment auxiliary analysis data set; A data backup module is used to store the processed diagnosis and treatment auxiliary analysis data set in a data backup tree based on the dynamic backup principle, and establish a diagnosis and treatment auxiliary analysis data storage tree; Specifically, it includes: setting a data attribute set, calculating the data attribute probability corresponding to the compressed diagnosis and treatment auxiliary analysis data block set, dividing the processed diagnosis and treatment auxiliary analysis data set according to the data attribute probability, and obtaining the data attributes of the processed diagnosis and treatment auxiliary analysis data set; Setting an empty data backup tree, and establishing a data backup tree according to the number of data attributes and the data attributes of the processed diagnosis and treatment auxiliary analysis data set; Based on the dynamic backup principle, according to the data attributes of the processed diagnosis and treatment auxiliary analysis data set, the compressed diagnosis and treatment auxiliary analysis data block set is divided into corresponding data attribute nodes in sequence and stored to obtain a diagnosis and treatment auxiliary analysis data storage tree, and a data backup center is established to store the diagnosis and treatment auxiliary analysis data storage tree in the data backup center; A data migration path establishment module is used to establish a data migration path and use an optimization algorithm to optimize the data migration path to obtain the best data migration path; The differential data migration module is used to compare the data consistency between the data backup center and the data storage center to generate differential data, and then migrate the differential data.

2. The remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine according to claim 1 is characterized in that: The block processing of the initial diagnosis and treatment auxiliary analysis data set includes: Obtaining auxiliary diagnosis and treatment analysis data to form an initial auxiliary diagnosis and treatment analysis data set, establishing a data storage center based on an open source engine, and storing the initial auxiliary diagnosis and treatment analysis data set in the data storage center; For the initial diagnosis and treatment auxiliary analysis data set, a data window and a sliding window are set. By comparing the maximum diagnosis and treatment auxiliary analysis data in the data window and the sliding window, the cut-off data is determined, and the initial diagnosis and treatment auxiliary analysis data set is segmented until the initial diagnosis and treatment auxiliary analysis data set is completed and the diagnosis and treatment auxiliary analysis data block set is obtained.

3. The remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine according to claim 2 is characterized in that: The data compression includes: According to the generation time of the diagnosis and treatment auxiliary analysis data in the initial diagnosis and treatment auxiliary analysis data set, a diagnosis and treatment auxiliary analysis data block time series is generated; the diagnosis and treatment auxiliary analysis data blocks in the diagnosis and treatment auxiliary analysis data block set are marked to obtain compression tags of the diagnosis and treatment auxiliary analysis data blocks, and the diagnosis and treatment auxiliary analysis data blocks corresponding to the compression tags of 0 are compressed to obtain a compressed diagnosis and treatment auxiliary analysis data block set; until the compression of the diagnosis and treatment auxiliary analysis data blocks is completed, all compressed diagnosis and treatment auxiliary analysis data block sets are combined to obtain a processed diagnosis and treatment auxiliary analysis data set.

4. The remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine according to claim 1 is characterized in that: The establishing of the data migration path includes: After a disaster occurs in the initial diagnosis and treatment auxiliary analysis data set, the diagnosis and treatment auxiliary analysis data storage tree of the data backup center is migrated to the diagnosis and treatment auxiliary analysis data set in the data storage center, and the data migration path is determined. The data migration path includes several transmission nodes. The time and data loss rate of the diagnosis and treatment auxiliary analysis data in the diagnosis and treatment auxiliary analysis data storage tree passing through the transmission nodes are calculated. The objective function is established based on the minimum transmission time and the minimum data loss rate, and the total transmission time function F1 and the total data loss rate function F2 are obtained. The total transmission time function and the total data loss rate function are converted into the data migration objective function F=ω1F1+ω2F2 by assigning weights, where ω1 and ω2 represent weights.

5. The remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine according to claim 4 is characterized in that: Optimizing the data migration path using an optimization algorithm to obtain the best data migration path includes: The data migration objective function is regarded as a fitness function, and the data migration process is regarded as a search space. It is assumed that there is a starfish population in the search space, the number of starfish population is p, the dimension of the starfish population is q, and the starfish individuals in the starfish population represent the data migration path. The transmission nodes are encoded, the starting transmission node and the target transmission node are determined, the position of the starfish population is initialized, and the fitness function value is evaluated by calculating the data migration objective function value; in the exploration stage of the starfish population, when the starfish population dimension is greater than 5, the starfish individual uses the arm to guide the movement, the current number of iterations is set to t, and the position of the cth starfish individual in the starfish population in the qth dimension is recorded as The arm movement angle of the starfish individual is θ, the movement coefficient is β, and the optimal position of the starfish individual in the qth dimension at the tth iteration is recorded as d1 represents a random number and d1∈[0,1], and the starfish individual motion model is established. Update, when d1≤0.5, When d1>0.5, When the starfish population dimension is less than or equal to 5, the maximum number of iterations is set to T, and the starfish energy d2 and d3 represent random numbers between the interval [0, 1], and the position of a random starfish in the starfish population is recorded as and Update Location completion of the exploration phase; During the development phase of the starfish population, the starfish individual corresponding to the current best fitness function value is selected and recorded as the current global best starfish individual. The distance between the current global best starfish individual and the other five random starfish individuals is calculated, and the five random starfish individuals move toward the position of the current global best starfish individual according to the distance. The starfish individual is set to be preyed on, and the growth of the starfish individual is modeled. At this time, the position is continued to be updated. Get the final position of the starfish individual in the t-th iteration; get the upper and lower bounds of the search space. When the final position of the starfish individual in the t-th iteration is greater than or equal to the lower bound of the search space and less than or equal to the upper bound of the search space, the position of the starfish individual in the t+1-th iteration is equal to the final position of the starfish individual in the t-th iteration; otherwise, the position of the starfish individual in the t+1-th iteration is equal to the upper or lower bound of the search space, and the next generation of starfish population is generated. When the current number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the global optimal position of the starfish individual. The global optimal fitness function value is obtained at the global optimal starfish individual position, and the starting transmission node, target transmission node and intermediate node are determined according to the global optimal starfish individual position to obtain the optimal data migration path.

6. The remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine according to claim 5 is characterized in that: Comparing the data consistency between the data backup center and the data storage center to generate difference data includes: The auxiliary diagnosis and treatment analysis data set of the data storage center is changed to form a post-disaster auxiliary diagnosis and treatment analysis data set, and the auxiliary diagnosis and treatment analysis data storage tree is compared with the post-disaster auxiliary diagnosis and treatment analysis data set; when there is missing data and updated data in the post-disaster auxiliary diagnosis and treatment analysis data set, the missing auxiliary diagnosis and treatment analysis data and the updated auxiliary diagnosis and treatment analysis data are obtained respectively, and the missing auxiliary diagnosis and treatment analysis data and the updated auxiliary diagnosis and treatment analysis data are combined to obtain difference data.

7. The remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine according to claim 6 is characterized in that: The differential data migration includes: The corresponding difference data in the diagnosis and treatment auxiliary analysis data storage tree is migrated to the diagnosis and treatment auxiliary analysis data set after the disaster, and a new diagnosis and treatment auxiliary analysis data set is generated to complete the off-site disaster recovery storage.

8. A method for implementing a remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine as described in any one of claims 1 to 7, characterized in that: Specifically include: S1. Obtain an initial diagnosis and treatment auxiliary analysis data set, establish a data storage center, use a data segmentation algorithm to segment the initial diagnosis and treatment auxiliary analysis data set, and then perform data compression based on time series to obtain a processed diagnosis and treatment auxiliary analysis data set; S2. Calculate the data attributes of the processed diagnosis and treatment auxiliary analysis data set, establish a data backup tree, store the processed diagnosis and treatment auxiliary analysis data set in the data backup tree based on the dynamic backup principle, implement data backup, obtain a diagnosis and treatment auxiliary analysis data storage tree, and establish a data backup center; S3. After a disaster occurs in the initial diagnosis and treatment auxiliary analysis data set, the diagnosis and treatment auxiliary analysis data storage tree in the data backup center is migrated to the diagnosis and treatment auxiliary analysis data set in the data storage center, and an optimization algorithm is used to optimize the data migration path to obtain the optimal data migration path; S4. Perform data migration according to the optimal data migration path, generate differential data based on the data consistency between the data backup center and the data storage center, migrate the differential data, and complete off-site disaster recovery storage.

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