Distributed data asset storage method and system

Abstract

The invention discloses a distributed data asset storage method and system. The method comprises the following steps: obtaining a target data asset to be stored and a plurality of available candidate server nodes; determining asset risk parameters of the target data assets according to a risk assessment algorithm; determining a node evaluation parameter of each candidate server node based on a node evaluation algorithm; determining a distributed storage strategy of the target data asset according to a dynamic programming algorithm based on a target function and a limiting condition associated with the asset risk parameter and the node evaluation parameter; the distributed storage policy includes a plurality of storage server nodes and corresponding storage data portions. Therefore, the data assets can be stored more efficiently and safely, the safety accidents of the data assets are reduced, and the storage safety is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a distributed data asset storage method and system. Background Art

[0002] Data assets refer to data owned by an enterprise or individual that can create value after analysis, processing and use. Data assets include customer information, market data, transaction records, sensor data, etc. Since data assets are generally stored in information systems in the form of data, they are different from traditional physical assets. From the collection and processing of data information to the daily maintenance of data assets, and then to asset transactions, the risks they face are complex and diverse. The existing technology generally only uses encrypted storage or access verification for the secure storage of data assets, and does not comprehensively consider the storage method of distributed nodes and the risk assessment of data assets to realize data asset storage. The efficiency and security of data asset storage cannot be guaranteed. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a distributed data asset storage method and system, which can store data assets more efficiently and safely, reduce security incidents of data assets, and improve storage security.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a distributed data asset storage method, the method comprising: Obtain target data assets to be stored and multiple available candidate server nodes; Determining the asset risk parameters of the target data asset according to the risk assessment algorithm; Determining node evaluation parameters of each of the candidate server nodes based on a node evaluation algorithm; Based on the objective function and constraints associated with the asset risk parameters and the node evaluation parameters, a distributed storage strategy for the target data asset is determined according to a dynamic programming algorithm; the distributed storage strategy includes multiple storage server nodes and corresponding storage data parts.

[0005] As an optional implementation, in the first aspect of the present invention, the asset risk parameters include integrity risk parameters, transmission loss risk parameters and leakage risk parameters.

[0006] As an optional implementation, in the first aspect of the present invention, the node evaluation parameters include node transmission efficiency, node security and node computing performance.

[0007] As an optional implementation, in the first aspect of the present invention, determining the asset risk parameter of the target data asset according to the risk assessment algorithm includes: Obtain asset historical access records and asset parameters of the target data asset; the asset parameters include holder information, historical storage location, historical transaction times, and data content type; According to the asset parameters, a target evaluation model is selected from a plurality of candidate data asset evaluation models; The target data asset and the asset historical access record are input into the target assessment model to obtain the asset risk parameter of the target data asset.

[0008] As an optional implementation, in the first aspect of the present invention, the step of selecting a target evaluation model from a plurality of candidate data asset evaluation models according to the asset parameter comprises: For each candidate data asset evaluation model, obtain model training data corresponding to the candidate data asset evaluation model; the model training data includes multiple training data assets and corresponding data asset risk parameter annotations and data asset parameter annotations; the data asset risk parameter annotations include integrity risk parameter annotations, transmission loss risk parameter annotations, and leakage risk parameter annotations; Calculating the similarity between all the data asset parameter annotations and the asset parameters in the model training data; The candidate data asset evaluation model with the highest similarity is determined as the target evaluation model.

[0009] As an optional implementation, in the first aspect of the present invention, determining the node evaluation parameter of each candidate server node based on the node evaluation algorithm includes: For each of the candidate server nodes, obtaining the node communication record, node computing record and node security protection record corresponding to the candidate server node; Calculate the average of the communication time of all communication records in the node communication record to obtain the node transmission efficiency corresponding to the candidate server node; Calculate the average of the calculation times of all calculation records in the node calculation record to obtain the node calculation performance corresponding to the candidate server node; The average communication time of all communication records in the node communication record is calculated to obtain the node security corresponding to the candidate server node.

[0010] As an optional implementation, in the first aspect of the present invention, the distributed storage strategy of the target data asset is determined according to a dynamic programming algorithm based on the objective function and constraints associated with the asset risk parameter and the node evaluation parameter, including: Calculating a weighted average of the integrity risk parameter and the transmission loss risk parameter to obtain a first related parameter; Calculating a weighted average value of the integrity risk parameter and the leakage risk parameter to obtain a second related parameter; For each of the candidate server nodes, calculate the average values ​​of the node transmission efficiency, node security, and node computing performance corresponding to the candidate server node to obtain the node priority parameter corresponding to the candidate server node; Screening out all the candidate server nodes whose node priority parameter is greater than a parameter threshold, and obtaining a plurality of target server nodes; the parameter threshold is proportional to the first related parameter; Based on the objective function and constraints associated with the second related parameter and the node priority parameter, a distributed storage strategy for the target data asset on the target server node is determined according to a dynamic programming algorithm.

[0011] As an optional implementation, in the first aspect of the present invention, the distributed storage strategy of the target data asset on the target server node is determined according to a dynamic programming algorithm based on the objective function and constraint conditions associated with the second related parameter and the node priority parameter, including: The objective function is set to minimize the amount of data in the data asset portion allocated to each target server node in the distributed storage solution; Setting restrictions includes: The amount of data in the data asset portion allocated to each target server node in the distributed storage solution is proportional to the corresponding node priority parameter; The weighted average of the data volume of the data asset portion allocated to all the target server nodes in the distributed storage solution is less than a data volume threshold; the data volume threshold is inversely proportional to the second related parameter; According to the dynamic programming algorithm, based on the objective function and the constraint conditions, iterative calculation is performed until the optimal distributed storage solution is obtained to determine the distributed storage strategy of the target data asset.

[0012] A second aspect of an embodiment of the present invention discloses a distributed data asset storage system, the system comprising: An acquisition module, used to acquire target data assets to be stored and multiple available candidate server nodes; A first determination module, configured to determine the asset risk parameters of the target data asset according to a risk assessment algorithm; A second determination module, configured to determine a node evaluation parameter of each of the candidate server nodes based on a node evaluation algorithm; A planning module is used to determine a distributed storage strategy for the target data asset based on an objective function and constraints associated with the asset risk parameters and the node evaluation parameters according to a dynamic programming algorithm; the distributed storage strategy includes multiple storage server nodes and corresponding storage data parts.

[0013] As an optional implementation, in the second aspect of the present invention, the asset risk parameters include integrity risk parameters, transmission loss risk parameters and leakage risk parameters.

[0014] As an optional implementation, in the second aspect of the present invention, the node evaluation parameters include node transmission efficiency, node security and node computing performance.

[0015] As an optional implementation, in the second aspect of the present invention, the specific manner in which the first determination module determines the asset risk parameter of the target data asset according to the risk assessment algorithm includes: Obtain asset historical access records and asset parameters of the target data asset; the asset parameters include holder information, historical storage location, historical transaction times, and data content type; According to the asset parameters, a target evaluation model is selected from a plurality of candidate data asset evaluation models; The target data asset and the asset historical access record are input into the target assessment model to obtain the asset risk parameter of the target data asset.

[0016] As an optional implementation, in the second aspect of the present invention, the specific manner in which the first determination module selects the target evaluation model from a plurality of candidate data asset evaluation models according to the asset parameter includes: For each candidate data asset evaluation model, obtain model training data corresponding to the candidate data asset evaluation model; the model training data includes multiple training data assets and corresponding data asset risk parameter annotations and data asset parameter annotations; the data asset risk parameter annotations include integrity risk parameter annotations, transmission loss risk parameter annotations, and leakage risk parameter annotations; Calculating the similarity between all the data asset parameter annotations and the asset parameters in the model training data; The candidate data asset evaluation model with the highest similarity is determined as the target evaluation model.

[0017] As an optional implementation, in the second aspect of the present invention, the second determination module determines the specific manner of the node evaluation parameter of each candidate server node based on the node evaluation algorithm, including: For each of the candidate server nodes, obtaining the node communication record, node computing record and node security protection record corresponding to the candidate server node; Calculate the average of the communication time of all communication records in the node communication record to obtain the node transmission efficiency corresponding to the candidate server node; Calculate the average of the calculation times of all calculation records in the node calculation record to obtain the node calculation performance corresponding to the candidate server node; The average communication time of all communication records in the node communication record is calculated to obtain the node security corresponding to the candidate server node.

[0018] As an optional implementation, in the second aspect of the present invention, the planning module determines the specific manner of the distributed storage strategy of the target data asset based on the objective function and constraints associated with the asset risk parameter and the node evaluation parameter according to the dynamic programming algorithm, including: Calculating a weighted average of the integrity risk parameter and the transmission loss risk parameter to obtain a first related parameter; Calculating a weighted average value of the integrity risk parameter and the leakage risk parameter to obtain a second related parameter; For each of the candidate server nodes, calculate the average values ​​of the node transmission efficiency, node security, and node computing performance corresponding to the candidate server node to obtain the node priority parameter corresponding to the candidate server node; Screening out all the candidate server nodes whose node priority parameter is greater than a parameter threshold, and obtaining a plurality of target server nodes; the parameter threshold is proportional to the first related parameter; Based on the objective function and constraints associated with the second related parameter and the node priority parameter, a distributed storage strategy for the target data asset on the target server node is determined according to a dynamic programming algorithm.

[0019] As an optional implementation, in the second aspect of the present invention, the planning module determines the specific manner of the distributed storage strategy of the target data asset on the target server node based on the objective function and constraint conditions associated with the second related parameter and the node priority parameter according to the dynamic programming algorithm, including: The objective function is set to minimize the amount of data in the data asset portion allocated to each of the target server nodes in the distributed storage solution; Setting restrictions includes: The amount of data in the data asset portion allocated to each target server node in the distributed storage solution is proportional to the corresponding node priority parameter; The weighted average of the data volume of the data asset portion allocated to all the target server nodes in the distributed storage solution is less than a data volume threshold; the data volume threshold is inversely proportional to the second related parameter; According to the dynamic programming algorithm, based on the objective function and the constraint conditions, iterative calculation is performed until the optimal distributed storage solution is obtained to determine the distributed storage strategy of the target data asset.

[0020] The third aspect of the present invention discloses another distributed data asset storage system, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the distributed data asset storage method disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the distributed data asset storage method disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can determine the asset risk parameters of data assets and the node evaluation parameters of server nodes based on the risk assessment algorithm and the node evaluation algorithm respectively, so as to determine an accurate and reasonable distributed storage strategy for data assets based on the associated objective function and constraints, thereby being able to store data assets more efficiently and securely, reduce security incidents of data assets, and improve storage security. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a distributed data asset storage method disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural diagram of a distributed data asset storage system disclosed in an embodiment of the present invention.

[0026] Figure 3It is a structural diagram of another distributed data asset storage system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a distributed data asset storage method and system, which can determine the asset risk parameters of data assets and the node evaluation parameters of server nodes based on the risk evaluation algorithm and the node evaluation algorithm, respectively, so as to determine an accurate and reasonable distributed storage strategy for data assets based on the associated objective function and constraint conditions, thereby being able to store data assets more efficiently and safely, reduce security incidents of data assets, and improve storage security. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 This is a flow chart of a distributed data asset storage method disclosed in an embodiment of the present invention. Figure 1 The distributed data asset storage method described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the distributed data asset storage method may include the following operations: 101. Obtain target data assets to be stored and multiple available candidate server nodes.

[0032] 102. Determine the asset risk parameters of the target data assets based on the risk assessment algorithm. 103. Based on the node evaluation algorithm, determine the node evaluation parameters of each candidate server node. 104. Based on the objective function and constraints associated with the asset risk parameters and the node evaluation parameters, a distributed storage strategy for the target data assets is determined according to a dynamic programming algorithm.

[0033] Optionally, the distributed storage strategy includes multiple storage server nodes and corresponding storage data portions.

[0034] It can be seen that the above-mentioned embodiments of the invention can determine the asset risk parameters of data assets and the node evaluation parameters of server nodes based on the risk assessment algorithm and the node evaluation algorithm respectively, so as to determine an accurate and reasonable distributed storage strategy for data assets based on the associated objective functions and constraints, thereby being able to store data assets more efficiently and securely, reduce security incidents of data assets, and improve storage security.

[0035] As an optional embodiment, in the above steps, the asset risk parameters include integrity risk parameters, transmission loss risk parameters and leakage risk parameters.

[0036] It can be seen that through the above optional embodiments, the content of asset risk parameters is limited, which can more comprehensively characterize the relevant characteristics of the target data assets, so as to subsequently calculate an accurate and reasonable distributed storage strategy for data assets, assist in achieving more efficient and secure storage of data assets, reduce safety incidents of data assets, and improve storage security.

[0037] As an optional embodiment, in the above steps, the node evaluation parameters include node transmission efficiency, node security and node computing performance.

[0038] It can be seen that through the above-mentioned optional embodiments, the content of the node evaluation parameters is limited, which can more comprehensively characterize the relevant performance characteristics of the candidate server nodes, so as to subsequently calculate an accurate and reasonable distributed storage strategy for data assets, assist in achieving more efficient and secure storage of data assets, reduce security incidents of data assets, and improve storage security.

[0039] As an optional embodiment, in the above step, determining the asset risk parameters of the target data asset according to the risk assessment algorithm includes: Obtain the asset history access records and asset parameters of the target data asset; optionally, the asset parameters include holder information, historical storage location, historical transaction times, and data content type; According to asset parameters, select a target evaluation model from multiple candidate data asset evaluation models; The target data assets and asset historical access records are input into the target assessment model to obtain the asset risk parameters of the target data assets.

[0040] It can be seen that through the above-mentioned optional embodiments, it is possible to screen out a target assessment model based on asset parameters to achieve a more accurate asset risk parameter assessment, so as to subsequently calculate an accurate and reasonable distributed storage strategy for data assets, thereby assisting in achieving more efficient and secure storage of data assets, reducing safety incidents of data assets, and improving storage security.

[0041] As an optional embodiment, in the above step, selecting a target evaluation model from a plurality of candidate data asset evaluation models according to the asset parameters includes: For each candidate data asset evaluation model, obtain the model training data corresponding to the candidate data asset evaluation model; optionally, the model training data includes multiple training data assets and corresponding data asset risk parameter annotations and data asset parameter annotations; the data asset risk parameter annotations include integrity risk parameter annotations, transmission loss risk parameter annotations, and leakage risk parameter annotations; Calculate the similarity between all data asset parameter annotations and asset parameters in the model training data; The candidate data asset evaluation model with the highest similarity is determined as the target evaluation model.

[0042] It can be seen that through the above-mentioned optional embodiments, the target evaluation model can be determined based on the similarity calculation and screening between the model training data corresponding to the candidate data asset evaluation model and the asset parameters of the current data assets, so as to achieve a more accurate asset risk parameter evaluation, and subsequently, an accurate and reasonable distributed storage strategy for data assets can be calculated to assist in realizing more efficient and secure storage of data assets, reduce safety incidents of data assets, and improve storage security.

[0043] As an optional embodiment, in the above steps, determining the node evaluation parameters of each candidate server node based on the node evaluation algorithm includes: For each candidate server node, obtain the node communication record, node computing record and node security protection record corresponding to the candidate server node; Calculate the average communication time of all communication records in the node communication record to obtain the node transmission efficiency corresponding to the candidate server node; The average calculation time of all calculation records in the calculation record of the calculation node is calculated to obtain the node calculation performance corresponding to the candidate server node; The average communication time of all communication records in the node communication record is calculated to obtain the node security corresponding to the candidate server node.

[0044] It can be seen that through the above-mentioned optional embodiments, the node evaluation parameters of the candidate server nodes can be determined by statistics and calculations of the node communication records, node calculation records and node security protection records corresponding to the candidate server nodes, and subsequently an accurate and reasonable distributed storage strategy for data assets can be calculated to assist in realizing more efficient and secure storage of data assets, reduce security incidents of data assets, and improve storage security.

[0045] As an optional embodiment, in the above steps, based on the objective function and constraints associated with the asset risk parameters and the node evaluation parameters, a distributed storage strategy for the target data asset is determined according to a dynamic programming algorithm, including: Calculating a weighted average of the integrity risk parameter and the transmission loss risk parameter to obtain a first related parameter; Calculate the weighted average of the integrity risk parameter and the leakage risk parameter to obtain a second related parameter; For each candidate server node, calculate the average values ​​of the node transmission efficiency, node security, and node computing performance corresponding to the candidate server node to obtain the node priority parameter corresponding to the candidate server node; Screening out all candidate server nodes whose node priority parameter is greater than a parameter threshold, and obtaining a plurality of target server nodes; optionally, the parameter threshold is proportional to the first related parameter; Based on the objective function and constraints associated with the second related parameter and the node priority parameter, a distributed storage strategy for the target data asset on the target server node is determined according to a dynamic programming algorithm.

[0046] It can be seen that through the above-mentioned optional embodiments, the target server nodes can be screened out by calculating the parameters related to the node evaluation parameters, and dynamic programming can be implemented by calculating the parameters related to the asset risk parameters to calculate an accurate and reasonable distributed storage strategy for data assets, thereby achieving more efficient and secure storage of data assets, reducing security incidents of data assets, and improving storage security.

[0047] As an optional embodiment, in the above steps, based on the objective function and constraint conditions associated with the second related parameter and the node priority parameter, determining the distributed storage strategy of the target data asset on the target server node according to the dynamic programming algorithm includes: The objective function is set to minimize the amount of data assets allocated to each target server node in the distributed storage solution; Setting restrictions includes: The amount of data in the data asset portion allocated to each target server node in the distributed storage solution is proportional to the corresponding node priority parameter; The weighted average of the data volume of the data asset portion allocated to all target server nodes in the distributed storage solution is less than the data volume threshold; optionally, the data volume threshold is inversely proportional to the second related parameter; According to the dynamic programming algorithm, based on the objective function and constraints, iterative calculations are performed until the optimal distributed storage solution is obtained to determine the distributed storage strategy for the target data assets.

[0048] It can be seen that through the above-mentioned optional embodiments, dynamic programming can be implemented through the objective function and constraints associated with the second related parameters and the node priority parameters, so as to calculate an accurate and reasonable distributed storage strategy for data assets, realize more efficient and secure storage of data assets, reduce security incidents of data assets, and improve storage security.

[0049] Embodiment 2 See also Figure 2 , Figure 2 Schematic diagram of a distributed data asset storage system disclosed in an embodiment of the present invention. Figure 2 The distributed data asset storage system described can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the distributed data asset storage system may include: The acquisition module 201 is used to acquire the target data asset to be stored and multiple available candidate server nodes.

[0050] The first determination module 202 is used to determine the asset risk parameters of the target data asset according to the risk assessment algorithm. The second determination module 203 is used to determine the node evaluation parameters of each candidate server node based on the node evaluation algorithm. The planning module 204 is used to determine the distributed storage strategy of the target data asset based on the objective function and constraints associated with the asset risk parameters and the node evaluation parameters according to the dynamic programming algorithm.

[0051] Optionally, the distributed storage strategy includes multiple storage server nodes and corresponding storage data portions.

[0052] It can be seen that the above-mentioned embodiments of the invention can determine the asset risk parameters of data assets and the node evaluation parameters of server nodes based on the risk assessment algorithm and the node evaluation algorithm respectively, so as to determine an accurate and reasonable distributed storage strategy for data assets based on the associated objective functions and constraints, thereby being able to store data assets more efficiently and securely, reduce security incidents of data assets, and improve storage security.

[0053] As an optional embodiment, the asset risk parameters include integrity risk parameters, transmission loss risk parameters and leakage risk parameters.

[0054] It can be seen that through the above optional embodiments, the content of asset risk parameters is limited, which can more comprehensively characterize the relevant characteristics of the target data assets, so as to subsequently calculate an accurate and reasonable distributed storage strategy for data assets, assist in achieving more efficient and secure storage of data assets, reduce safety incidents of data assets, and improve storage security.

[0055] As an optional embodiment, the node evaluation parameters include node transmission efficiency, node security and node computing performance.

[0056] It can be seen that through the above-mentioned optional embodiments, the content of the node evaluation parameters is limited, which can more comprehensively characterize the relevant performance characteristics of the candidate server nodes, so as to subsequently calculate an accurate and reasonable distributed storage strategy for data assets, assist in achieving more efficient and secure storage of data assets, reduce security incidents of data assets, and improve storage security.

[0057] As an optional embodiment, the first determination module determines the specific manner of the asset risk parameter of the target data asset according to the risk assessment algorithm, including: Obtain the asset history access records and asset parameters of the target data asset; optionally, the asset parameters include holder information, historical storage location, historical transaction times, and data content type; According to asset parameters, select a target evaluation model from multiple candidate data asset evaluation models; The target data assets and asset historical access records are input into the target assessment model to obtain the asset risk parameters of the target data assets.

[0058] It can be seen that through the above-mentioned optional embodiments, it is possible to screen out a target assessment model based on asset parameters to achieve a more accurate asset risk parameter assessment, so as to subsequently calculate an accurate and reasonable distributed storage strategy for data assets, thereby assisting in achieving more efficient and secure storage of data assets, reducing safety incidents of data assets, and improving storage security.

[0059] As an optional embodiment, the specific manner in which the first determination module selects the target evaluation model from a plurality of candidate data asset evaluation models according to the asset parameters includes: For each candidate data asset evaluation model, obtain the model training data corresponding to the candidate data asset evaluation model; optionally, the model training data includes multiple training data assets and corresponding data asset risk parameter annotations and data asset parameter annotations; the data asset risk parameter annotations include integrity risk parameter annotations, transmission loss risk parameter annotations, and leakage risk parameter annotations; Calculate the similarity between all data asset parameter annotations and asset parameters in the model training data; The candidate data asset evaluation model with the highest similarity is determined as the target evaluation model.

[0060] It can be seen that through the above-mentioned optional embodiments, the target evaluation model can be determined based on the similarity calculation and screening between the model training data corresponding to the candidate data asset evaluation model and the asset parameters of the current data assets, so as to achieve a more accurate asset risk parameter evaluation, and subsequently, an accurate and reasonable distributed storage strategy for data assets can be calculated to assist in realizing more efficient and secure storage of data assets, reduce safety incidents of data assets, and improve storage security.

[0061] As an optional embodiment, the second determination module determines the specific manner of the node evaluation parameter of each candidate server node based on the node evaluation algorithm, including: For each candidate server node, obtain the node communication record, node computing record and node security protection record corresponding to the candidate server node; Calculate the average communication time of all communication records in the node communication record to obtain the node transmission efficiency corresponding to the candidate server node; The average calculation time of all calculation records in the calculation record of the calculation node is calculated to obtain the node calculation performance corresponding to the candidate server node; The average communication time of all communication records in the node communication record is calculated to obtain the node security corresponding to the candidate server node.

[0062] It can be seen that through the above-mentioned optional embodiments, the node evaluation parameters of the candidate server nodes can be determined by statistics and calculations of the node communication records, node calculation records and node security protection records corresponding to the candidate server nodes, and subsequently an accurate and reasonable distributed storage strategy for data assets can be calculated to assist in realizing more efficient and secure storage of data assets, reduce security incidents of data assets, and improve storage security.

[0063] As an optional embodiment, the planning module determines the specific manner of the distributed storage strategy of the target data asset based on the objective function and constraints associated with the asset risk parameter and the node evaluation parameter according to the dynamic programming algorithm, including: Calculating a weighted average of the integrity risk parameter and the transmission loss risk parameter to obtain a first related parameter; Calculate the weighted average of the integrity risk parameter and the leakage risk parameter to obtain a second related parameter; For each candidate server node, calculate the average values ​​of the node transmission efficiency, node security, and node computing performance corresponding to the candidate server node to obtain the node priority parameter corresponding to the candidate server node; Screening out all candidate server nodes whose node priority parameter is greater than a parameter threshold, and obtaining a plurality of target server nodes; optionally, the parameter threshold is proportional to the first related parameter; Based on the objective function and constraints associated with the second related parameter and the node priority parameter, a distributed storage strategy for the target data asset on the target server node is determined according to a dynamic programming algorithm.

[0064] It can be seen that through the above-mentioned optional embodiments, the target server nodes can be screened out by calculating the parameters related to the node evaluation parameters, and dynamic programming can be implemented by calculating the parameters related to the asset risk parameters to calculate an accurate and reasonable distributed storage strategy for data assets, thereby achieving more efficient and secure storage of data assets, reducing security incidents of data assets, and improving storage security.

[0065] As an optional embodiment, the planning module determines the specific manner of the distributed storage strategy of the target data asset on the target server node based on the objective function and constraint conditions associated with the second related parameter and the node priority parameter according to the dynamic programming algorithm, including: The objective function is set to minimize the amount of data assets allocated to each target server node in the distributed storage solution; Setting restrictions includes: The amount of data in the data asset portion allocated to each target server node in the distributed storage solution is proportional to the corresponding node priority parameter; The weighted average of the data volume of the data asset portion allocated to all target server nodes in the distributed storage solution is less than the data volume threshold; optionally, the data volume threshold is inversely proportional to the second related parameter; According to the dynamic programming algorithm, based on the objective function and constraints, iterative calculations are performed until the optimal distributed storage solution is obtained to determine the distributed storage strategy for the target data assets.

[0066] It can be seen that through the above-mentioned optional embodiments, dynamic programming can be implemented through the objective function and constraints associated with the second related parameters and the node priority parameters, so as to calculate an accurate and reasonable distributed storage strategy for data assets, realize more efficient and secure storage of data assets, reduce security incidents of data assets, and improve storage security.

[0067] Embodiment 3 See also Figure 3 , Figure 3 It is another distributed data asset storage system disclosed in an embodiment of the present invention. Figure 3 The distributed data asset storage system described is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the distributed data asset storage system may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the distributed data asset storage method described in the first embodiment.

[0068] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the distributed data asset storage method described in the first embodiment.

[0069] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the distributed data asset storage method described in the first embodiment.

[0070] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0072] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0073] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0074] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0077] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0078] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0079] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0080] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0081] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0082] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0083] Finally, it should be noted that the distributed data asset storage method and system disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed data asset storage method, characterized in that: The method comprises: Obtain target data assets to be stored and multiple available candidate server nodes; Determining the asset risk parameters of the target data asset according to the risk assessment algorithm; Determining node evaluation parameters of each of the candidate server nodes based on a node evaluation algorithm; Based on the objective function and constraints associated with the asset risk parameters and the node evaluation parameters, a distributed storage strategy for the target data asset is determined according to a dynamic programming algorithm; the distributed storage strategy includes multiple storage server nodes and corresponding storage data parts.

2. The distributed data asset storage method according to claim 1, characterized in that: The asset risk parameters include integrity risk parameters, transmission loss risk parameters and leakage risk parameters.

3. The distributed data asset storage method according to claim 2 is characterized in that: The node evaluation parameters include node transmission efficiency, node security and node computing performance.

4. The distributed data asset storage method according to claim 2 is characterized in that: Determining the asset risk parameters of the target data asset according to the risk assessment algorithm includes: Obtain asset historical access records and asset parameters of the target data asset; the asset parameters include holder information, historical storage location, historical transaction times, and data content type; According to the asset parameters, a target evaluation model is selected from a plurality of candidate data asset evaluation models; The target data asset and the asset historical access record are input into the target assessment model to obtain the asset risk parameter of the target data asset.

5. The distributed data asset storage method according to claim 4 is characterized in that: The step of selecting a target evaluation model from a plurality of candidate data asset evaluation models according to the asset parameters includes: For each candidate data asset evaluation model, obtain model training data corresponding to the candidate data asset evaluation model; the model training data includes multiple training data assets and corresponding data asset risk parameter annotations and data asset parameter annotations; the data asset risk parameter annotations include integrity risk parameter annotations, transmission loss risk parameter annotations, and leakage risk parameter annotations; Calculating the similarity between all the data asset parameter annotations and the asset parameters in the model training data; The candidate data asset evaluation model with the highest similarity is determined as the target evaluation model.

6. The distributed data asset storage method according to claim 3 is characterized in that: The step of determining the node evaluation parameters of each candidate server node based on the node evaluation algorithm includes: For each of the candidate server nodes, obtaining the node communication record, node computing record and node security protection record corresponding to the candidate server node; Calculate the average of the communication time of all communication records in the node communication record to obtain the node transmission efficiency corresponding to the candidate server node; Calculate the average of the calculation times of all calculation records in the node calculation record to obtain the node calculation performance corresponding to the candidate server node; The average communication time of all communication records in the node communication record is calculated to obtain the node security corresponding to the candidate server node.

7. The distributed data asset storage method according to claim 3 is characterized in that: The step of determining the distributed storage strategy of the target data asset based on the objective function and the constraint conditions associated with the asset risk parameter and the node evaluation parameter according to a dynamic programming algorithm includes: Calculating a weighted average of the integrity risk parameter and the transmission loss risk parameter to obtain a first related parameter; Calculating a weighted average value of the integrity risk parameter and the leakage risk parameter to obtain a second related parameter; For each of the candidate server nodes, calculate the average values ​​of the node transmission efficiency, node security, and node computing performance corresponding to the candidate server node to obtain the node priority parameter corresponding to the candidate server node; Screening out all the candidate server nodes whose node priority parameter is greater than a parameter threshold, and obtaining a plurality of target server nodes; the parameter threshold is proportional to the first related parameter; Based on the objective function and constraints associated with the second related parameter and the node priority parameter, a distributed storage strategy for the target data asset on the target server node is determined according to a dynamic programming algorithm.

8. The distributed data asset storage method according to claim 7, characterized in that: The step of determining the distributed storage strategy of the target data asset on the target server node based on the objective function and the constraint conditions associated with the second related parameter and the node priority parameter according to a dynamic programming algorithm includes: The objective function is set to minimize the amount of data in the data asset portion allocated to each of the target server nodes in the distributed storage solution; Setting restrictions includes: The amount of data in the data asset portion allocated to each target server node in the distributed storage solution is proportional to the corresponding node priority parameter; The weighted average of the data volume of the data asset portion allocated to all the target server nodes in the distributed storage solution is less than a data volume threshold; the data volume threshold is inversely proportional to the second related parameter; According to the dynamic programming algorithm, based on the objective function and the constraint conditions, iterative calculation is performed until the optimal distributed storage solution is obtained to determine the distributed storage strategy of the target data asset.

9. A distributed data asset storage system, characterized in that: The system comprises: An acquisition module, used to acquire target data assets to be stored and multiple available candidate server nodes; A first determination module, configured to determine the asset risk parameters of the target data asset according to a risk assessment algorithm; A second determination module, configured to determine a node evaluation parameter of each of the candidate server nodes based on a node evaluation algorithm; A planning module is used to determine a distributed storage strategy for the target data asset based on an objective function and constraints associated with the asset risk parameters and the node evaluation parameters according to a dynamic programming algorithm; the distributed storage strategy includes multiple storage server nodes and corresponding storage data parts.

10. A distributed data asset storage system, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the distributed data asset storage method as described in any one of claims 1-8.

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