Open source engine-based remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data

Abstract

The invention relates to the technical field of data security, and discloses a remote disaster recovery storage system for diagnosis and treatment auxiliary analysis data based on an open source engine. The system comprises a data partitioning and compressing module, a data backup module, a data migration path establishing module and a difference data migration module. Firstly, an initial diagnosis and treatment auxiliary analysis data set is subjected to block processing and data compression, and a processed diagnosis and treatment auxiliary analysis data set is obtained; establishing a data backup tree, and performing data backup on the processed diagnosis and treatment auxiliary analysis data set to obtain a diagnosis and treatment auxiliary analysis data storage tree; and optimizing the data migration path by using a starfish optimization algorithm to obtain an optimal data migration path, finally generating differential data according to data consistency, and migrating the differential data on the optimal data migration path to complete remote disaster recovery storage. The diagnosis and treatment auxiliary analysis data is compressed, backed up and migrated, the purpose of remote disaster recovery storage is achieved, and the method is objective and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of data security, and specifically to a remote disaster recovery storage system for diagnosis and treatment assistance 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 that when a remote disaster recovery server receives a disaster recovery switch request, it obtains backup data in a target virtual machine, and uses the backup data to establish a remote virtual machine; creates a preset virtual machine network according to the remote virtual machine and resumes business processing, and then determines a target disaster recovery server through the disaster recovery switch request; sets disaster recovery control scheduling, performs business-level disaster recovery on the target virtual machine on the target disaster recovery server to obtain backup data in the target virtual machine, and uses the backup data to establish a remote 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 remote data call.

[0003] Traditional remote disaster recovery storage methods usually directly migrate local data to a standby data center to build multiple standby data centers to prevent disasters, but there are problems such as slow data backup speed; at the same time, since technologies such as artificial intelligence are not used, the data recovery speed of remote disaster recovery storage is slow and the data storage occupies a large space, and there are deficiencies in data security and continuity. Summary of the Invention

[0004] In view of the problems in the related art, the present invention provides a remote disaster recovery storage system for diagnosis and treatment assistance analysis data based on an open-source engine to overcome the above-mentioned technical problems existing in the existing related technologies.

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

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

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

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

[0009] S3. After a disaster occurs to the initial diagnosis and treatment assistance analysis data set, migrate the diagnosis and treatment assistance analysis data storage tree in the data backup center to the diagnosis and treatment assistance analysis data set in the data storage center, and use the starfish optimization algorithm 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, and migrate the differential data to complete off-site disaster tolerance storage.

[0011] The invention obtains the initial diagnosis and treatment assistance analysis data set, performs block processing and data compression on the initial diagnosis and treatment assistance analysis data set to obtain the processed diagnosis and treatment assistance analysis data set, and establishes a data storage center; the method finds the tangent point data by setting a window to ensure that there will be no overly long data blocks after block processing, and then performs data compression according to the time series, greatly saving the data storage space and improving the compression efficiency; secondly, establish a data backup tree, and store the processed diagnosis and treatment assistance analysis data set on the data backup tree based on the dynamic backup principle to obtain the diagnosis and treatment assistance analysis data storage tree; the method uses a tree structure to store data, reducing the storage space of the backup data, further reducing the data volume compared with the traditional backup method, and ensuring the integrity of data backup; then establish the 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 optimal data migration path; the method balances the transmission time and the data loss rate by establishing an objective function to achieve the minimum transmission time and the minimum data loss rate. The starfish optimization algorithm has good search ability in the process of finding the optimal solution by simulating the exploration, predation and regeneration behaviors of starfish, and at the same time has a fast optimization speed and convergence speed, saving the data transmission time; finally, generate differential data based on the data consistency between the data backup center and the data storage center, and only migrate the differential data, reducing the data migration volume and migration cost, greatly improving the data migration efficiency, and ensuring data security.

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

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

[0014] S12. Set a data window and a sliding window for the initial set of auxiliary diagnosis and treatment analysis data. Place the data window and the sliding window at the starting position of the initial set of auxiliary diagnosis and treatment analysis data. Traverse to obtain the maximum auxiliary diagnosis and treatment analysis data in the data window. Move the sliding window and record the maximum auxiliary diagnosis and treatment analysis data in the sliding window. When the maximum auxiliary diagnosis and treatment analysis data in the sliding window is greater than or equal to the maximum auxiliary diagnosis and treatment analysis data in the data window, record the maximum auxiliary diagnosis and treatment analysis data in the data window as the tangent point data. Split the initial set of auxiliary diagnosis and treatment analysis data at the tangent point data to obtain auxiliary diagnosis and treatment analysis data blocks, thus completing the preliminary block splitting process. Set a maximum data block length. When the auxiliary diagnosis and treatment analysis data block is greater than the maximum data block length, at this time, record the sliding window length as the tangent point data. Otherwise, move the data window to the position of the maximum auxiliary diagnosis and treatment analysis data in the data window and continue the block splitting process until the initial set of auxiliary diagnosis and treatment analysis data is completely block-split to obtain a set of auxiliary diagnosis and treatment analysis data blocks;

[0015] S13. Generate an auxiliary diagnosis and treatment analysis data time series based on the generation time of the auxiliary diagnosis and treatment analysis data in the initial set of auxiliary diagnosis and treatment analysis data. Convert the auxiliary diagnosis and treatment analysis data time series into an auxiliary diagnosis and treatment analysis data block time series A={b1, b2, b3,..., b m} according to the block splitting process, where b m represents the m-th auxiliary diagnosis and treatment analysis data block time point. Then, perform data compression on the set of auxiliary diagnosis and treatment analysis data blocks to obtain a processed set of auxiliary diagnosis and treatment analysis data. The specific steps are as follows:

[0016] S131. Mark the auxiliary diagnosis and treatment analysis data blocks in the set of auxiliary diagnosis and treatment analysis data blocks to obtain compression labels for the auxiliary diagnosis and treatment analysis data blocks. When the compression label is 0, it means the auxiliary diagnosis and treatment analysis data block has not been compressed. When the compression label is 1, it means the auxiliary diagnosis and treatment analysis data block has been compressed. Compress the auxiliary diagnosis and treatment analysis data blocks corresponding to the compression label of 0 to obtain a set of compressed auxiliary diagnosis and treatment analysis data blocks. The specific steps are as follows:

[0017] S1311. Select the auxiliary diagnosis and treatment analysis data block corresponding to the first auxiliary diagnosis and treatment analysis data block time point in the auxiliary diagnosis and treatment analysis data block time series, denote it as the first data block, and add it to the set of compressed auxiliary diagnosis and treatment analysis data blocks. Calculate the squared difference between the auxiliary diagnosis and treatment analysis data blocks in the set of auxiliary diagnosis and treatment analysis data blocks and the first data block in turn;

[0018] S1312. Set the differential threshold. When the squared difference is less than the differential threshold, add the corresponding medical diagnosis and treatment assistance analysis data block to the set of compressed medical diagnosis and treatment assistance analysis data blocks to obtain the set of compressed medical diagnosis and treatment assistance analysis data blocks; otherwise, select the medical diagnosis and treatment assistance analysis data block corresponding to other time points of the medical diagnosis and treatment assistance analysis data blocks to complete the preliminary data compression.

[0019] S132. Traverse the time series of the medical diagnosis and treatment assistance analysis data blocks, and judge whether the compression label is 1 until the compression of the medical diagnosis and treatment assistance analysis data blocks is completed, and combine all the sets of compressed medical diagnosis and treatment assistance analysis data blocks to obtain the processed medical diagnosis and treatment assistance analysis data set.

[0020] The present invention obtains the initial medical diagnosis and treatment assistance analysis data set, performs block processing and data compression on the initial medical diagnosis and treatment assistance analysis data set to obtain the processed medical diagnosis and treatment assistance analysis data set, finds the tangent point data by setting a window to ensure that there will be no overly long data blocks after block processing, and then performs data compression according to the time series, greatly saving the data storage space and improving the compression efficiency.

[0021] Preferably, the S2 includes the following steps:

[0022] S21. Set the data attribute set A′={b1′, b2′, b3′,..., b′ m′}, where b′ m represents the m′-th data attribute. For the set of compressed medical diagnosis and treatment assistance analysis data blocks in the processed medical diagnosis and treatment assistance analysis data set, calculate the probability that the set of compressed medical diagnosis and treatment assistance analysis data blocks is assigned to the data attribute set, denoted as the data attribute probability, and divide the processed medical diagnosis and treatment assistance analysis data set according to the data attribute probability to obtain the data attributes of the processed medical diagnosis and treatment assistance analysis data set.

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

[0024] S221. Set an empty data backup tree. Starting from the root node of the empty data backup tree, record the number of data attributes, and use the number of data attributes as the first layer of the empty data backup tree. Then, use the data attributes of the processed medical diagnosis and treatment assistance analysis data set as the second layer of the empty data backup tree to establish the data backup tree.

[0025] S222. Based on the principle of dynamic backup, according to the data attributes of the processed collection of diagnostic assistance analysis data, the compressed collection of diagnostic assistance analysis data blocks is sequentially divided under the corresponding data attribute nodes and stored; traverse the data backup tree, find the same compressed diagnostic assistance analysis data blocks in the data backup tree nodes, regard the same compressed diagnostic assistance analysis data blocks as duplicate data blocks, and skip the duplicate data blocks during the dynamic division until the processed collection of diagnostic assistance analysis data is stored completely, to achieve data backup and obtain the diagnostic assistance analysis data storage tree;

[0026] S23. Establish a data backup center and store the diagnostic assistance analysis data storage tree in the data backup center.

[0027] The invention stores the processed collection of diagnostic assistance analysis data on the data backup tree based on the principle of dynamic backup by establishing a data backup tree, uses a tree structure to store data, reduces the storage space of the backup data, obtains the diagnostic assistance analysis data storage tree, and further reduces the data volume compared with the traditional backup method, ensuring the integrity of data backup.

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

[0029] S31. After a disaster occurs to the initial collection of diagnostic assistance analysis data, migrate the diagnostic assistance analysis data storage tree in the data backup center to the collection of diagnostic assistance analysis data in the data storage center, determine the data migration path, the data migration path includes several transmission nodes, calculate the time and data loss rate of the diagnostic assistance analysis data in the diagnostic assistance analysis data storage tree passing through the transmission nodes, establish an objective function based on the minimum transmission time and minimum data loss rate, obtain the total transmission time function F1 and the total data loss rate function F2, and assign weights 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. Regard the data migration objective function as a fitness function and use the starfish optimization algorithm to optimize the data migration path to obtain the best data migration path. The specific steps are as follows:

[0031] S321. Regard the data migration process as a search space. Assume that there is a starfish population in the search space, where the number of starfish in the population is p, the dimension of the starfish population is q, and the starfish individuals in the starfish population represent data migration paths. Encode the transmission nodes to determine the starting transmission node and the target transmission node. Initialize the positions of the starfish population, and evaluate the fitness function value by calculating the data migration objective function value. During the exploration phase of the starfish population, when the dimension of the starfish population is greater than 5, the starfish individuals use their arms to guide movement. Set the current iteration number as t, and denote the position of the c-th starfish individual in the q-th dimension of the starfish population as The movement angle of the starfish individual's arm is θ, the movement coefficient is β, and the best position of the starfish individual in the q-th dimension at the t-th iteration is denoted as d1 represents a random number and d1 ∈ [0, 1]. Establish a starfish individual movement model to update the position When d1 ≤ 0.5, When d1 > 0.5, When the dimension of the starfish population is less than or equal to 5, set the maximum number of iterations as T, and the starfish energy d2 and d3 represent random numbers in the interval [0, 1]. Denote the positions of two random starfish individuals in the starfish population as and Update the position Complete the exploration phase;

[0032] S322. During the exploitation phase of the starfish population, select the starfish individual corresponding to the current best fitness function value, denoted as the current global best starfish individual. Calculate the distances between the current global best starfish individual and five other random starfish individuals, and the five random starfish individuals move towards the position of the current global best starfish individual according to the distances. Assume that the starfish individual is preyed upon, and model the growth of the starfish individual. At this time, continue to update the position to obtain the final position of the starfish individual at the t-th iteration. Obtain the upper and lower bounds of the search space. When the final position of the starfish individual at 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 at the (t + 1)-th iteration is equal to the final position of the starfish individual at the t-th iteration; otherwise, the position of the starfish individual at the (t + 1)-th iteration is equal to the upper bound or the lower bound of the search space. Generate the next generation of starfish population. When the current iteration number reaches the maximum number of iterations, stop the iteration to obtain the position of the global best starfish individual;

[0033] Obtain the global best fitness function value at the position of the global best starfish individual, and determine the starting transmission node, the target transmission node, and the intermediate nodes according to the position of the global best starfish individual to obtain the best data migration path.

[0034] The invention optimizes the data migration path by establishing a data migration path and using a starfish optimization algorithm. By simulating the exploration, predation, and regeneration behaviors of starfish, the optimal data migration path is obtained. It has good search ability during the process of finding the optimal solution, and at the same time, the optimization speed and convergence speed are fast, saving data transmission time. A target function is established to balance the transmission time and data loss rate, achieving the minimum transmission time and the minimum data loss rate.

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

[0036] S41. Perform data migration according to the optimal data migration path, compare the medical treatment assistance analysis data storage tree of the data backup center and the medical treatment assistance analysis data set of the data storage center, and generate differential data according to data consistency. The specific steps are as follows:

[0037] S411. The medical treatment assistance analysis data set of the data storage center changes to form the medical treatment assistance analysis data set after a disaster. Compare the medical treatment assistance analysis data storage tree with the medical treatment assistance analysis data set after the disaster; when there is missing data in the medical treatment assistance analysis data set after the disaster, obtain the missing medical treatment assistance analysis data, and when there is updated data in the medical treatment assistance analysis data set after the disaster, obtain the updated medical treatment assistance analysis data;

[0038] S412. Integrate the missing medical treatment assistance analysis data and the updated medical treatment assistance analysis data to obtain differential data;

[0039] S42. Migrate the corresponding differential data in the medical treatment assistance analysis data storage tree, and after migrating it to the medical treatment assistance analysis data set after the disaster, generate a new medical treatment assistance analysis data set to complete off-site disaster recovery storage.

[0040] The invention generates differential data according to the data consistency between the data backup center and the data storage center, and only migrates the differential data, reducing the data migration volume and 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 method of medical treatment assistance analysis data based on an open-source engine, specifically including: a data block division and compression module, a data backup module, a data migration path establishment module, and a differential data migration module;

[0042] The data block division and compression module is used to perform block processing and data compression on the initial medical treatment assistance analysis data set to obtain a processed medical treatment assistance analysis data set;

[0043] The data backup module is used to store the processed medical treatment assistance analysis data set in the data backup tree based on the principle of dynamic backup to establish a medical treatment assistance analysis data storage tree;

[0044] The data migration path establishment module is used to establish a data migration path and optimize the data migration path using the starfish optimization algorithm to obtain the optimal 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 present invention performs block processing and data compression on the initial diagnosis and treatment assistance analysis data set, searches for tangent point data by setting a window, ensures that there will be no overly long data blocks after block processing, and then performs data compression according to the time series, greatly saving data storage space and improving compression efficiency.

[0048] 2. The present invention establishes a data backup tree, stores the processed diagnosis and treatment assistance analysis data set on the data backup tree based on the principle of dynamic backup, uses a tree structure to store data, reduces the storage space of backup data, obtains a diagnosis and treatment assistance analysis data storage tree, and further reduces the data volume compared with traditional backup methods, ensuring the integrity of data backup.

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

[0050] 4. The present 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, reducing the data migration volume and migration cost, greatly improving the data migration efficiency, and ensuring data security.

[0051] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the invention, and those of ordinary skill in the art can also obtain the drawings based on these drawings without creative efforts.

[0053] Figure 1This is a schematic diagram of the off-site disaster recovery storage process of the medical treatment assistance analysis data based on an open-source engine provided by the present invention. Detailed implementation manners

[0054] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0055] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "top", "middle", "inner", etc. indicating the orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation of the present invention.

[0056] Embodiment 1

[0057] Please refer to Figure 1 , this embodiment discloses an off-site disaster recovery storage method for medical treatment assistance analysis data based on an open-source engine, which specifically includes the following contents:

[0058] S1. Obtain an initial medical treatment assistance analysis data set, establish a data storage center, perform block processing on the initial medical treatment assistance analysis data set using a data block algorithm, and then perform data compression based on the time series to obtain a processed medical treatment assistance analysis data set;

[0059] The S1 includes the following steps:

[0060] S11. Obtain medical treatment assistance analysis data, where the medical treatment assistance analysis data includes patient physiological indicators, patient clinical data, patient medical resource allocation data, etc., form an initial medical treatment assistance analysis data set, establish a data storage center based on the open-source engine, and store the initial medical treatment assistance analysis data set in the data storage center;

[0061] S12. Set a data window and a sliding window for the initial diagnosis and treatment assistance analysis data set. Place the data window and the sliding window at the starting position of the initial diagnosis and treatment assistance analysis data set. Traverse to obtain the maximum diagnosis and treatment assistance analysis data in the data window. Move the sliding window and record the maximum diagnosis and treatment assistance analysis data in the sliding window. When the maximum diagnosis and treatment assistance analysis data in the sliding window is greater than or equal to the maximum diagnosis and treatment assistance analysis data in the data window, record the maximum diagnosis and treatment assistance analysis data in the data window as the tangent point data. Split the initial diagnosis and treatment assistance analysis data set at the tangent point data to obtain diagnosis and treatment assistance analysis data blocks, thus completing the preliminary block processing. Set a maximum data block length. When the diagnosis and treatment assistance analysis data block is greater than the maximum data block length, record the sliding window length as the tangent point data at this time. Otherwise, move the data window to the position of the maximum diagnosis and treatment assistance analysis data in the data window and continue the block processing until the initial diagnosis and treatment assistance analysis data set is completely block-processed to obtain a diagnosis and treatment assistance analysis data block set;

[0062] S13. Generate a diagnosis and treatment assistance analysis data time series according to the generation time of the diagnosis and treatment assistance analysis data in the initial diagnosis and treatment assistance analysis data set. Convert the diagnosis and treatment assistance analysis data time series into a diagnosis and treatment assistance analysis data block time series A={b1, b2, b3,..., b m} according to the block processing, where b m represents the m-th diagnosis and treatment assistance analysis data block time point. Then, perform data compression on the diagnosis and treatment assistance analysis data block set to obtain a processed diagnosis and treatment assistance analysis data set. The specific steps are as follows:

[0063] S131. Mark the diagnosis and treatment assistance analysis data blocks in the diagnosis and treatment assistance analysis data block set to obtain compression labels for the diagnosis and treatment assistance analysis data blocks. When the compression label is 0, it means that the diagnosis and treatment assistance analysis data block has not been compressed. When the compression label is 1, it means that the diagnosis and treatment assistance analysis data block has been compressed. Compress the diagnosis and treatment assistance analysis data blocks corresponding to the compression label of 0 to obtain a compressed diagnosis and treatment assistance analysis data block set. The specific steps are as follows:

[0064] S1311. Select the diagnosis and treatment assistance analysis data block corresponding to the first diagnosis and treatment assistance analysis data block time point in the diagnosis and treatment assistance analysis data block time series, record it as the first data block, and add it to the compressed diagnosis and treatment assistance analysis data block set. Calculate the squared difference between the diagnosis and treatment assistance analysis data blocks in the diagnosis and treatment assistance analysis data block set and the first data block in turn;

[0065] S1312. Set the differential threshold. When the squared difference is less than the differential threshold, add the corresponding medical diagnosis and treatment assistance analysis data block to the set of compressed medical diagnosis and treatment assistance analysis data blocks to obtain the set of compressed medical diagnosis and treatment assistance analysis data blocks; otherwise, select the medical diagnosis and treatment assistance analysis data block corresponding to other medical diagnosis and treatment assistance analysis data block time points to complete the preliminary data compression.

[0066] S132. Traverse the time series of medical diagnosis and treatment assistance analysis data blocks, and determine whether the compression label is 1 until the compression of the medical diagnosis and treatment assistance analysis data blocks is completed. Combine all the sets of compressed medical diagnosis and treatment assistance analysis data blocks to obtain the processed medical diagnosis and treatment assistance analysis data set.

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

[0068] The S2 includes the following steps:

[0069] S21. Set the data attribute set A′ = {b1′, b2′, b3′,..., b′ m′}, where b′ m represents the m′th data attribute. Calculate the probability that the set of compressed medical diagnosis and treatment assistance analysis data blocks in the processed medical diagnosis and treatment assistance analysis data set is assigned to the data attribute set, which is recorded as the data attribute probability. Divide the processed medical diagnosis and treatment assistance analysis data set according to the data attribute probability to obtain the data attributes of the processed medical diagnosis and treatment assistance analysis data set.

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

[0071] S221. Set an empty data backup tree. Starting from the root node of the empty data backup tree, record the number of data attributes, and use the number of data attributes as the first layer of the empty data backup tree. Then use the data attributes of the processed medical diagnosis and treatment assistance analysis data set as the second layer of the empty data backup tree to establish the data backup tree.

[0072] S222. Based on the principle of dynamic backup, according to the data attributes of the processed diagnosis and treatment assistance analysis data set, the compressed diagnosis and treatment assistance analysis data block set is sequentially divided under the corresponding data attribute nodes and stored; traverse the data backup tree, find the same compressed diagnosis and treatment assistance analysis data blocks in the data backup tree nodes, regard the same compressed diagnosis and treatment assistance analysis data blocks as duplicate data blocks, and skip the duplicate data blocks during the dynamic division until the processed diagnosis and treatment assistance analysis data set is stored completely, realizing data backup and obtaining the diagnosis and treatment assistance analysis data storage tree;

[0073] S23. Establish a data backup center and store the diagnosis and treatment assistance analysis data storage tree in the data backup center;

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

[0075] S3 includes the following steps:

[0076] S31. After a disaster occurs to the initial diagnosis and treatment assistance analysis data set, migrate the diagnosis and treatment assistance analysis data storage tree in the data backup center to the diagnosis and treatment assistance analysis data set in the data storage center, determine the data migration path, the data migration path contains several transmission nodes, calculate the time and data loss rate of the diagnosis and treatment assistance analysis data in the diagnosis and treatment assistance analysis data storage tree passing through the transmission nodes, establish an objective function based on the minimum transmission time and minimum data loss rate, obtain the total transmission time function F1 and the total data loss rate function F2, and assign weights to convert the total transmission time function and the total data loss rate function into the data migration objective function F = ω1F1 + ω2F2, where ω1 and ω2 represent weights;

[0077] S32. Regard the data migration objective function as the fitness function and use the starfish optimization algorithm to optimize the data migration path to obtain the optimal data migration path. The specific steps are as follows:

[0078] S321. Regard the data migration process as the search space, assume that there is a starfish population in the search space, the number of the starfish population is p, the dimension of the starfish population is q, the starfish individuals in the starfish population represent the data migration path, perform encoding processing on the transmission nodes, determine the starting transmission node and the target transmission node, initialize the positions of the starfish population, and evaluate the fitness function value by calculating the data migration objective function value; in the exploration stage of the starfish population, when the dimension of the starfish population is greater than 5, the starfish individuals use the arm to guide the movement, assume that the current iteration number is t, and record the position of the c-th starfish individual in the q-th dimension of the starfish population as The movement angle of the starfish individual's arm is θ, the movement coefficient is β, and the best position of the starfish individual in the q-th dimension at the t-th iteration is denoted as d1 represents a random number and d1 ∈ [0, 1]. A starfish individual movement model is established to update the position When d1 ≤ 0.5, When d1 > 0.5, When the dimension of the starfish population 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 in the interval [0, 1]. The positions of random starfish individuals in the starfish population are denoted as and Update the position Complete the exploration stage;

[0079] S322. In the exploitation stage of the starfish population, select the starfish individual corresponding to the current best fitness function value, denoted as the current global best starfish individual. Calculate the distances between the current global best starfish individual and five other random starfish individuals. The five random starfish individuals move towards the position of the current global best starfish individual according to the distances. Assume that a starfish individual is preyed upon and model the growth of the starfish individual. At this time, continue to update the position Obtain the final position of the starfish individual at the t-th iteration; obtain the upper and lower bounds of the search space. When the final position of the starfish individual at 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 at the (t + 1)-th iteration is equal to the final position of the starfish individual at the t-th iteration; otherwise, the position of the starfish individual at the (t + 1)-th iteration is equal to the upper bound or the lower bound of the search space. Generate the next generation of the starfish population. When the current number of iterations reaches the maximum number of iterations, stop the iteration and obtain the position of the global best starfish individual;

[0080] Obtain the global best fitness function value at the position of the global best starfish individual. Determine the source transfer node, target transfer node, and intermediate node according to the position of the global best starfish individual to obtain the optimal data migration path;

[0081] 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, and migrate the differential data to complete off-site disaster recovery storage;

[0082] S4 includes the following steps:

[0083] S41. Perform data migration according to the optimal data migration path, compare the medical treatment assistance analysis data storage tree in the data backup center and the medical treatment assistance analysis data set in the data storage center, and generate differential data according to data consistency. The specific steps are as follows:

[0084] S411. When the diagnostic assistance analysis data set of the data storage center changes to form the diagnostic assistance analysis data set after the disaster, compare the diagnostic assistance analysis data storage tree with the diagnostic assistance analysis data set after the disaster; when there is missing data in the diagnostic assistance analysis data set after the disaster, obtain the missing diagnostic assistance analysis data, and when there is updated data in the diagnostic assistance analysis data set after the disaster, obtain the updated diagnostic assistance analysis data;

[0085] S412. Combine the missing diagnostic assistance analysis data and the updated diagnostic assistance analysis data to obtain the differential data;

[0086] S42. Migrate the corresponding differential data in the diagnostic assistance analysis data storage tree to the diagnostic assistance analysis data set after the disaster, and generate a new diagnostic assistance analysis data set to complete off-site disaster recovery storage.

[0087] Embodiment 2

[0088] This embodiment also discloses a system for off-site disaster recovery storage method of diagnostic assistance analysis data based on an open-source engine, specifically including: a data chunking and compression module, a data backup module, a data migration path establishment module, and a differential data migration module;

[0089] The data chunking and compression module is used to perform chunking processing and data compression on the initial diagnostic assistance analysis data set to obtain the processed diagnostic assistance analysis data set;

[0090] The data backup module is used to store the processed diagnostic assistance analysis data set in the data backup tree based on the dynamic backup principle to establish a diagnostic assistance analysis data storage tree;

[0091] The data migration path establishment module is used to establish a data migration path and optimize the data migration path using the starfish optimization algorithm to obtain the optimal 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] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0094] The preferred embodiments of the invention disclosed above are only used to help illustrate the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the invention, so that those skilled in the art can well understand and utilize the invention.

Claims

1. A remote disaster recovery storage system for diagnosis and treatment assistance analysis data based on an open-source engine, characterized in that Including: A data chunking and compression module, which is used to perform chunking processing and data compression on the initial medical diagnosis and treatment assistance analysis data set to obtain a processed medical diagnosis and treatment assistance analysis data set; A data backup module, which is used to store the processed medical diagnosis and treatment assistance analysis data set in a data backup tree based on the dynamic backup principle to establish a medical diagnosis and treatment assistance analysis data storage tree; A data migration path establishment module, which is used to establish a data migration path and optimize the data migration path using an optimization algorithm to obtain an optimal data migration path; A differential data migration module, which 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.

2. The off-site disaster tolerance storage system for diagnosis and treatment assistance analysis data based on an open-source engine according to claim 1, wherein The chunking processing of the initial medical diagnosis and treatment assistance analysis data set includes: Taking medical diagnosis and treatment assistance analysis data to form an initial medical diagnosis and treatment assistance analysis data set, establishing a data storage center based on an open-source engine, and storing the initial medical diagnosis and treatment assistance analysis data set in the data storage center; For the initial medical diagnosis and treatment assistance analysis data set, setting a data window and a sliding window, determining the tangent point data by comparing the maximum medical diagnosis and treatment assistance analysis data in the data window and the sliding window, and splitting the initial medical diagnosis and treatment assistance analysis data set until the initial medical diagnosis and treatment assistance analysis data set is completely chunked to obtain a medical diagnosis and treatment assistance analysis data block set.

3. The disaster recovery storage system for off-site storage of diagnosis and treatment assistance analysis data based on an open-source engine according to claim 2, wherein The data compression includes: Generating a time series of medical diagnosis and treatment assistance analysis data blocks according to the generation time of the medical diagnosis and treatment assistance analysis data in the initial medical diagnosis and treatment assistance analysis data set; marking the medical diagnosis and treatment assistance analysis data blocks in the medical diagnosis and treatment assistance analysis data block set to obtain compression labels of the medical diagnosis and treatment assistance analysis data blocks, and compressing the medical diagnosis and treatment assistance analysis data blocks corresponding to the compression label of 0 to obtain a compressed medical diagnosis and treatment assistance analysis data block set; until the compression of the medical diagnosis and treatment assistance analysis data blocks is completed, combining all the compressed medical diagnosis and treatment assistance analysis data block sets to obtain a processed medical diagnosis and treatment assistance analysis data set.

4. The off-site disaster recovery storage system for diagnosis and treatment assistance analysis data based on an open-source engine according to claim 3, characterized in that, The storing of the processed medical diagnosis and treatment assistance analysis data set in a data backup tree based on the dynamic backup principle includes: Setting a data attribute set, calculating the data attribute probabilities corresponding to the compressed medical diagnosis and treatment assistance analysis data block set, and dividing the processed medical diagnosis and treatment assistance analysis data set according to the data attribute probabilities to obtain the data attributes of the processed medical diagnosis and treatment assistance 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 medical diagnosis and treatment assistance analysis data set; Based on the dynamic backup principle, dividing the compressed medical diagnosis and treatment assistance analysis data block set into corresponding data attribute nodes in sequence according to the data attributes of the processed medical diagnosis and treatment assistance analysis data set, and storing them to obtain a medical diagnosis and treatment assistance analysis data storage tree, and establishing a data backup center to store the medical diagnosis and treatment assistance analysis data storage tree in the data backup center.

5. The disaster recovery storage system for off-site auxiliary analysis data of diagnosis and treatment based on an open-source engine according to claim 4, wherein The establishment of the data migration path includes: After a disaster occurs to the initial diagnosis and treatment assistance analysis data set, migrate the diagnosis and treatment assistance analysis data storage tree in the data backup center to the diagnosis and treatment assistance analysis data set in the data storage center, determine the data migration path, where the data migration path includes several transmission nodes, calculate the time and data loss rate of the diagnosis and treatment assistance analysis data in the diagnosis and treatment assistance analysis data storage tree passing through the transmission nodes, establish an objective function based on the minimum transmission time and the minimum data loss rate, obtain the total transmission time function F1 and the total data loss rate function F2, and assign weights to convert the total transmission time function and the total data loss rate function into the data migration objective function F = ω1F1 + ω2F2, where ω1 and ω2 represent weights.

6. The off-site disaster tolerance storage system for diagnosing and treating auxiliary analysis data based on an open-source engine according to claim 5, wherein The use of an optimization algorithm to optimize the data migration path to obtain the best data migration path includes: Regarding the data migration objective function as the fitness function and the data migration process as the search space, it is assumed that there is a starfish population in the search space, where the number of starfish in the population is p, the dimension of the starfish population is q, and the starfish individuals in the starfish population represent the data migration paths. The transmission nodes are encoded, the starting transmission node and the target transmission node are determined, the positions of the starfish population are initialized, and the fitness function value is evaluated by calculating the data migration objective function value. During the exploration phase of the starfish population, when the dimension of the starfish population is greater than 5, the starfish individuals use their arms to guide movement. Let the current iteration number be t, and denote the position of the c-th starfish individual in the q-th dimension of the starfish population as The movement angle of the starfish individual's arm is θ, the movement coefficient is β, and the best position of the starfish individual in the q-th dimension at the t-th iteration is denoted as d1 represents a random number and d1 ∈ [0, 1]. A starfish individual movement model is established to update the position When d1 ≤ 0.5, When d1 > 0.5, When the dimension of the starfish population is less than or equal to 5, let the maximum iteration number be T, and the starfish energy d2 and d3 represent random numbers in the interval [0, 1]. Denote the positions of two random starfish individuals in the starfish population as and Update the position Complete the exploration phase; In the development stage of the starfish population, select the starfish individual corresponding to the current best fitness function value, denoted as the current global best starfish individual. Calculate the distances between the current global best starfish individual and five other randomly selected starfish individuals. The five randomly selected starfish individuals move towards the position of the current global best starfish individual according to the distances. Assume that a starfish individual is preyed upon and model the growth of the starfish individual. At this time, continue to update the position. Obtain the final position of the starfish individuals in the t-th iteration. Obtain the upper and lower bounds of the search space. When the final position of the starfish individuals 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 individuals in the (t + 1)-th iteration is equal to the final position of the starfish individuals in the t-th iteration; otherwise, the position of the starfish individuals in the (t + 1)-th iteration is equal to the upper or lower bound of the search space. Generate the next generation of starfish population. Stop the iteration until the current iteration number reaches the maximum iteration number, and obtain the position of the global best starfish individual. Obtain the global best fitness function value at the position of the global best starfish individual, and determine the starting transmission node, the target transmission node, and the intermediate nodes according to the position of the global best starfish individual to obtain the best data migration path.

7. The off-site disaster tolerance storage system for diagnosis and treatment assistance analysis data based on an open-source engine according to claim 6, wherein The comparison of the data consistency between the data backup center and the data storage center to generate differential data includes: The diagnosis and treatment assistance analysis data set in the data storage center changes to form the diagnosis and treatment assistance analysis data set after the disaster, and compare the diagnosis and treatment assistance analysis data storage tree with the diagnosis and treatment assistance analysis data set after the disaster; when there are missing data and updated data in the diagnosis and treatment assistance analysis data set after the disaster, obtain the missing diagnosis and treatment assistance analysis data and the updated diagnosis and treatment assistance analysis data respectively, and synthesize the missing diagnosis and treatment assistance analysis data and the updated diagnosis and treatment assistance analysis data to obtain the differential data.

8. The off-site disaster tolerance storage system for diagnosing and treating auxiliary analysis data based on an open-source engine according to claim 7, characterized in that The implementation of differential data migration includes: Migrate the corresponding differential data in the diagnosis and treatment assistance analysis data storage tree. After migrating to the diagnosis and treatment assistance analysis data set after the disaster, generate a new diagnosis and treatment assistance analysis data set to complete off-site disaster tolerance storage.

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

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