Distributed-based data asset storage method and system

Abstract

The application discloses a kind of based on distributed data asset storage method and system, the method includes: obtaining target data asset to be stored and available multiple candidate server nodes;According to risk assessment algorithm, the asset risk parameter of the target data asset is determined;Based on node evaluation algorithm, the node evaluation parameter of each candidate server node is determined;Based on the objective function and the restriction condition associated with the asset risk parameter and the node evaluation parameter, according to dynamic programming algorithm, the distributed storage strategy of the target data asset is determined;The distributed storage strategy includes multiple storage server nodes and corresponding storage data parts. Visible, the application can more efficiently and securely store data asset, reduce the security incident of data asset, improve storage security.

Description

Technical Field

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

[0002] Data assets refer to data owned by enterprises or individuals that can create value after analysis, processing, and use. Types include customer information, market data, transaction records, and sensor data. Because data assets are generally stored in information systems in data form, unlike traditional physical assets, they face complex and diverse risks from data collection and processing to daily maintenance and asset transactions. Current technologies for secure data asset storage typically only employ encrypted storage or access verification methods, without comprehensively considering distributed node storage methods and risk assessment of data assets. Therefore, the efficiency and security of data asset storage cannot be guaranteed. Clearly, existing technologies have shortcomings that urgently need to be addressed. 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 that can store data assets more efficiently and securely, reduce data asset security incidents, and improve storage security.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a distributed data asset storage method, the method comprising:

[0005] Acquire the target data assets to be stored and multiple available candidate server nodes;

[0006] Based on the risk assessment algorithm, the asset risk parameters of the target data asset are determined;

[0007] Based on the node evaluation algorithm, the node evaluation parameters for each candidate server node are determined;

[0008] 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 using a dynamic programming algorithm; the distributed storage strategy includes multiple storage server nodes and corresponding stored data portions.

[0009] 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.

[0010] 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.

[0011] As an optional implementation, in the first aspect of the present invention, determining the asset risk parameters of the target data asset according to the risk assessment algorithm includes:

[0012] Obtain the asset's historical access records and asset parameters; the asset parameters include holder information, historical storage location, historical transaction count, and data content type;

[0013] Based on the asset parameters, a target valuation model is selected from multiple candidate data asset valuation models;

[0014] The target data asset and its historical access records are input into the target assessment model to obtain the asset risk parameters of the target data asset.

[0015] As an optional implementation, in the first aspect of the present invention, the step of selecting a target valuation model from multiple candidate data asset valuation models based on the asset parameters includes:

[0016] For each candidate data asset evaluation model, obtain the 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;

[0017] Calculate the similarity between all the data asset parameter annotations and the asset parameters in the model training data;

[0018] The candidate data asset evaluation model with the highest similarity is determined as the target evaluation model.

[0019] As an optional implementation, in the first aspect of the invention, determining the node evaluation parameters for each candidate server node based on the node evaluation algorithm includes:

[0020] For each candidate server node, obtain the node communication record, node calculation record, and node security protection record corresponding to that candidate server node;

[0021] 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;

[0022] Calculate the average computation time of all computation records in the node computation record to obtain the node computation performance corresponding to the candidate server node;

[0023] 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.

[0024] As an optional implementation, in the first aspect of the invention, determining the distributed storage strategy for the target data asset based on the objective function and constraints associated with the asset risk parameters and the node evaluation parameters, according to a dynamic programming algorithm, includes:

[0025] The first relevant parameter is obtained by calculating the weighted average of the integrity risk parameter and the transmission loss risk parameter;

[0026] Calculate the weighted average of the integrity risk parameter and the leakage risk parameter to obtain the second relevant parameter;

[0027] For each candidate server node, calculate the average of the node transmission efficiency, node security, and node computing performance corresponding to that candidate server node to obtain the node priority parameters corresponding to that candidate server node.

[0028] All candidate server nodes whose node priority parameter is greater than a parameter threshold are selected to obtain multiple target server nodes; the parameter threshold is proportional to the first relevant parameter.

[0029] Based on the objective function and constraints associated with the second relevant 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.

[0030] As an optional implementation, in a first aspect of the invention, determining the distributed storage strategy for the target data asset on the target server node based on an objective function and constraints associated with the second relevant parameters and the node priority parameters, according to a dynamic programming algorithm, includes:

[0031] The objective function is set to minimize the amount of data assets allocated to each target server node in the distributed storage scheme.

[0032] Setting restrictions includes:

[0033] In the distributed storage scheme, the amount of data assets allocated to each target server node is proportional to the corresponding node priority parameter.

[0034] In the distributed storage scheme, the weighted average of the data asset portion allocated to all the target server nodes is less than the data volume threshold; the data volume threshold is inversely proportional to the second related parameter.

[0035] Based on the dynamic programming algorithm, the objective function and the constraints are used to iteratively calculate until the optimal distributed storage scheme is obtained, thereby determining the distributed storage strategy for the target data asset.

[0036] A second aspect of this invention discloses a distributed data asset storage system, the system comprising:

[0037] The acquisition module is used to acquire the target data assets to be stored and multiple available candidate server nodes;

[0038] The first determining module is used to determine the asset risk parameters of the target data asset according to the risk assessment algorithm;

[0039] The second determining module is used to determine the node evaluation parameters of each candidate server node based on the node evaluation algorithm.

[0040] The planning module 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 a dynamic programming algorithm; the distributed storage strategy includes multiple storage server nodes and corresponding stored data portions.

[0041] As an optional implementation, in a second aspect of the invention, the asset risk parameters include integrity risk parameters, transmission loss risk parameters, and leakage risk parameters.

[0042] As an optional implementation, in a second aspect of the invention, the node evaluation parameters include node transmission efficiency, node security, and node computing performance.

[0043] As an optional implementation, in a second aspect of the invention, the specific method by which the first determining module determines the asset risk parameters of the target data asset according to a risk assessment algorithm includes:

[0044] Obtain the asset's historical access records and asset parameters; the asset parameters include holder information, historical storage location, historical transaction count, and data content type;

[0045] Based on the asset parameters, a target valuation model is selected from multiple candidate data asset valuation models;

[0046] The target data asset and its historical access records are input into the target assessment model to obtain the asset risk parameters of the target data asset.

[0047] As an optional implementation, in a second aspect of the invention, the specific method by which the first determining module selects the target valuation model from multiple candidate data asset valuation models based on the asset parameters includes:

[0048] For each candidate data asset evaluation model, obtain the 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;

[0049] Calculate the similarity between all the data asset parameter annotations and the asset parameters in the model training data;

[0050] The candidate data asset evaluation model with the highest similarity is determined as the target evaluation model.

[0051] As an optional implementation, in a second aspect of the invention, the second determining module determines the specific method by which it determines the node evaluation parameters of each candidate server node based on a node evaluation algorithm, including:

[0052] For each candidate server node, obtain the node communication record, node calculation record, and node security protection record corresponding to that candidate server node;

[0053] 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;

[0054] Calculate the average computation time of all computation records in the node computation record to obtain the node computation performance corresponding to the candidate server node;

[0055] 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.

[0056] As an optional implementation, in a second aspect of the invention, the planning module, based on an objective function and constraints associated with the asset risk parameters and the node evaluation parameters, determines the specific method of the distributed storage strategy for the target data asset according to a dynamic programming algorithm, including:

[0057] The first relevant parameter is obtained by calculating the weighted average of the integrity risk parameter and the transmission loss risk parameter;

[0058] Calculate the weighted average of the integrity risk parameter and the leakage risk parameter to obtain the second relevant parameter;

[0059] For each candidate server node, calculate the average of the node transmission efficiency, node security, and node computing performance corresponding to that candidate server node to obtain the node priority parameters corresponding to that candidate server node.

[0060] All candidate server nodes whose node priority parameter is greater than a parameter threshold are selected to obtain multiple target server nodes; the parameter threshold is proportional to the first relevant parameter.

[0061] Based on the objective function and constraints associated with the second relevant 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.

[0062] As an optional implementation, in a second aspect of the invention, the planning module, based on an objective function and constraints associated with the second relevant parameters and the node priority parameters, determines, according to a dynamic programming algorithm, the specific manner of the distributed storage strategy for the target data asset on the target server node, including:

[0063] The objective function is set to minimize the amount of data assets allocated to each target server node in the distributed storage scheme.

[0064] Setting restrictions includes:

[0065] In the distributed storage scheme, the amount of data assets allocated to each target server node is proportional to the corresponding node priority parameter.

[0066] In the distributed storage scheme, the weighted average of the data asset portion allocated to all the target server nodes is less than the data volume threshold; the data volume threshold is inversely proportional to the second related parameter.

[0067] Based on the dynamic programming algorithm, the objective function and the constraints are used to iteratively calculate until the optimal distributed storage scheme is obtained, thereby determining the distributed storage strategy for the target data asset.

[0068] A third aspect of the present invention discloses another distributed data asset storage system, the system comprising:

[0069] Memory containing executable program code;

[0070] A processor coupled to the memory;

[0071] The processor calls the executable program code stored in the memory to execute some or all of the steps in the distributed data asset storage method disclosed in the first aspect of the present invention.

[0072] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the distributed data asset storage method disclosed in the first aspect of the present invention.

[0073] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0074] This invention can determine the asset risk parameters of data assets and the node evaluation parameters of server nodes based on risk assessment algorithms and node evaluation algorithms, respectively. Based on the associated objective function and constraints, it can determine an accurate and reasonable distributed storage strategy for data assets, thereby enabling more efficient and secure storage of data assets, reducing data asset security incidents, and improving storage security. Attached Figure Description

[0075] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0076] Figure 1 This is a flowchart illustrating a distributed data asset storage method disclosed in an embodiment of the present invention.

[0077] Figure 2 This is a schematic diagram of the structure of a distributed data asset storage system disclosed in an embodiment of the present invention.

[0078] Figure 3 This is a schematic diagram of another distributed data asset storage system disclosed in an embodiment of the present invention. Detailed Implementation

[0079] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0080] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0081] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0082] This invention discloses a distributed data asset storage method and system. It can determine the asset risk parameters of data assets and the node evaluation parameters of server nodes based on risk assessment algorithms and node evaluation algorithms, respectively. Based on associated objective functions and constraints, it determines an accurate and reasonable distributed storage strategy for data assets, thereby enabling more efficient and secure storage of data assets, reducing data asset security incidents, and improving storage security. Detailed descriptions follow.

[0083] Example 1

[0084] Please see Figure 1 , Figure 1 This is a flowchart illustrating a distributed data asset storage method disclosed in an embodiment of the present invention. Wherein, Figure 1 The described distributed data asset storage method can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example... Figure 1 As shown, this distributed data asset storage method may include the following operations:

[0085] 101. Obtain the target data asset to be stored and multiple available candidate server nodes.

[0086] 102. Determine the asset risk parameters of the target data asset based on the risk assessment algorithm.

[0087] 103. Based on the node evaluation algorithm, determine the node evaluation parameters for each candidate server node.

[0088] 104. Based on the objective function and constraints associated with asset risk parameters and node evaluation parameters, a distributed storage strategy for the target data asset is determined using a dynamic programming algorithm.

[0089] Optionally, a distributed storage strategy includes multiple storage server nodes and corresponding storage data portions.

[0090] As can be seen, the above-described embodiments of the invention can determine the asset risk parameters of data assets and the node evaluation parameters of server nodes based on risk assessment algorithms and node evaluation algorithms, respectively. Based on the associated objective function and constraints, an accurate and reasonable distributed storage strategy for data assets can be determined, thereby enabling more efficient and secure storage of data assets, reducing data asset security incidents, and improving storage security.

[0091] As an optional embodiment, the asset risk parameters in the above steps include integrity risk parameters, transmission loss risk parameters, and leakage risk parameters.

[0092] As can be seen, by defining the content of the asset risk parameters through the above optional embodiments, the relevant characteristics of the target data asset can be more comprehensively characterized, so as to calculate an accurate and reasonable distributed storage strategy for the data asset in the subsequent calculation, thereby helping to achieve more efficient and secure storage of data assets, reduce data asset security incidents, and improve storage security.

[0093] As an optional embodiment, the node evaluation parameters in the above steps include node transmission efficiency, node security, and node computing performance.

[0094] As can be seen, by defining the content of the node evaluation parameters through the above optional embodiments, the relevant performance characteristics of candidate server nodes can be more comprehensively characterized, so as to calculate accurate and reasonable distributed storage strategies for data assets in the subsequent calculation, thereby helping to achieve more efficient and secure storage of data assets, reduce data asset security incidents, and improve storage security.

[0095] As an optional embodiment, the step above, determining the asset risk parameters of the target data asset according to the risk assessment algorithm, includes:

[0096] Obtain the target data asset's historical access records and asset parameters; optionally, asset parameters include holder information, historical storage location, historical transaction count, and data content type.

[0097] Based on asset parameters, the target valuation model is selected from multiple candidate data asset valuation models;

[0098] Input the target data asset and its historical access records into the target assessment model to obtain the asset risk parameters of the target data asset.

[0099] As can be seen, through the above optional embodiments, a more accurate asset risk parameter assessment can be achieved by selecting a target assessment model based on asset parameters. This enables the subsequent calculation of an accurate and reasonable distributed storage strategy for data assets, helping to achieve more efficient and secure storage of data assets, reducing data asset security incidents, and improving storage security.

[0100] As an optional embodiment, the step above, selecting the target valuation model from multiple candidate data asset valuation models based on asset parameters, includes:

[0101] For each candidate data asset evaluation model, obtain the model training data corresponding to that 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;

[0102] Calculate the similarity between all data asset parameter annotations and asset parameters in the model training data;

[0103] The candidate data asset evaluation model with the highest similarity is selected as the target evaluation model.

[0104] As can be seen, through the above optional embodiments, the target evaluation model can be determined by calculating and screening the similarity between the model training data corresponding to the candidate data asset evaluation model and the asset parameters of the current data asset, so as to achieve a more accurate asset risk parameter evaluation. Subsequently, an accurate and reasonable distributed storage strategy for data assets can be calculated, which helps to achieve more efficient and secure storage of data assets, reduce data asset security incidents, and improve storage security.

[0105] As an optional embodiment, the step above, determining the node evaluation parameters for each candidate server node based on the node evaluation algorithm, includes:

[0106] For each candidate server node, obtain the node communication record, node computation record, and node security protection record corresponding to that candidate server node;

[0107] The average communication time of all communication records in the node communication record is calculated to obtain the node transmission efficiency corresponding to the candidate server node.

[0108] The average computation time of all computation records in the computation record of the computing node is used to obtain the node computing performance corresponding to the candidate server node.

[0109] The average communication time of all communication records in the node's communication log is used to obtain the node security corresponding to the candidate server node.

[0110] As can be seen, through the above optional embodiments, the node evaluation parameters of the candidate server node can be determined by statistically analyzing and calculating the records in the node communication records, node calculation records, and node security protection records corresponding to the candidate server node. Subsequently, an accurate and reasonable distributed storage strategy for data assets can be calculated, which helps to achieve more efficient and secure storage of data assets, reduce data asset security incidents, and improve storage security.

[0111] As an optional embodiment, the above steps, based on the objective function and constraints associated with asset risk parameters and node evaluation parameters, determine the distributed storage strategy for the target data asset using a dynamic programming algorithm, including:

[0112] The first relevant parameter is obtained by calculating the weighted average of the integrity risk parameter and the transmission loss risk parameter;

[0113] The second relevant parameter is obtained by calculating the weighted average of the integrity risk parameter and the leakage risk parameter;

[0114] For each candidate server node, calculate the average of the node transmission efficiency, node security, and node computing performance corresponding to that candidate server node to obtain the node priority parameters corresponding to that candidate server node.

[0115] Filter out all candidate server nodes whose node priority parameter is greater than the parameter threshold to obtain multiple target server nodes; optionally, the parameter threshold is proportional to the first relevant parameter;

[0116] Based on the objective function and constraints associated with the second relevant parameter and the node priority parameter, a distributed storage strategy for the target data assets on the target server node is determined using a dynamic programming algorithm.

[0117] As can be seen, through the above optional embodiments, target server nodes can be screened by calculating parameters related to node evaluation parameters, and dynamic planning can be achieved by calculating parameters related to 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 data asset security incidents, and improving storage security.

[0118] As an optional embodiment, the above steps, based on the objective function and constraints associated with the second relevant parameters and node priority parameters, determine the distributed storage strategy for the target data assets on the target server node according to the dynamic programming algorithm, including:

[0119] The objective function is set to minimize the amount of data assets allocated to each target server node in the distributed storage scheme.

[0120] Setting restrictions includes:

[0121] In a distributed storage scheme, the amount of data assets allocated to each target server node is proportional to the corresponding node priority parameter.

[0122] In the distributed storage scheme, the weighted average of the data asset portion allocated to all target server nodes is less than the data volume threshold; optionally, the data volume threshold is inversely proportional to the second relevant parameter.

[0123] Based on the dynamic programming algorithm, and taking into account the objective function and constraints, the algorithm iteratively calculates until the optimal distributed storage scheme is obtained, thereby determining the distributed storage strategy for the target data assets.

[0124] As can be seen, through the above optional embodiments, dynamic programming can be achieved by using objective functions and constraints associated with the second relevant parameters and node priority parameters to calculate accurate and reasonable distributed storage strategies for data assets, thereby enabling more efficient and secure storage of data assets, reducing data asset security incidents, and improving storage security.

[0125] Example 2

[0126] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a distributed data asset storage system disclosed in an embodiment of the present invention. Figure 2 The described distributed data asset storage system can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example... Figure 2 As shown, the distributed data asset storage system may include:

[0127] The acquisition module 201 is used to acquire the target data asset to be stored and multiple available candidate server nodes.

[0128] The first determining module 202 is used to determine the asset risk parameters of the target data asset based on the risk assessment algorithm.

[0129] The second determining module 203 is used to determine the node evaluation parameters of each candidate server node based on the node evaluation algorithm.

[0130] 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 asset risk parameters and node evaluation parameters, according to the dynamic programming algorithm.

[0131] Optionally, a distributed storage strategy includes multiple storage server nodes and corresponding storage data portions.

[0132] As can be seen, the above-described embodiments of the invention can determine the asset risk parameters of data assets and the node evaluation parameters of server nodes based on risk assessment algorithms and node evaluation algorithms, respectively. Based on the associated objective function and constraints, an accurate and reasonable distributed storage strategy for data assets can be determined, thereby enabling more efficient and secure storage of data assets, reducing data asset security incidents, and improving storage security.

[0133] As an optional embodiment, asset risk parameters include integrity risk parameters, transmission loss risk parameters, and leakage risk parameters.

[0134] As can be seen, by defining the content of the asset risk parameters through the above optional embodiments, the relevant characteristics of the target data asset can be more comprehensively characterized, so as to calculate an accurate and reasonable distributed storage strategy for the data asset in the subsequent calculation, thereby helping to achieve more efficient and secure storage of data assets, reduce data asset security incidents, and improve storage security.

[0135] As an optional embodiment, node evaluation parameters include node transmission efficiency, node security, and node computing performance.

[0136] As can be seen, by defining the content of the node evaluation parameters through the above optional embodiments, the relevant performance characteristics of candidate server nodes can be more comprehensively characterized, so as to calculate accurate and reasonable distributed storage strategies for data assets in the subsequent calculation, thereby helping to achieve more efficient and secure storage of data assets, reduce data asset security incidents, and improve storage security.

[0137] As an optional embodiment, the first determining module determines the specific method by which it determines the asset risk parameters of the target data asset according to the risk assessment algorithm, including:

[0138] Obtain the target data asset's historical access records and asset parameters; optionally, asset parameters include holder information, historical storage location, historical transaction count, and data content type.

[0139] Based on asset parameters, the target valuation model is selected from multiple candidate data asset valuation models;

[0140] Input the target data asset and its historical access records into the target assessment model to obtain the asset risk parameters of the target data asset.

[0141] As can be seen, through the above optional embodiments, a more accurate asset risk parameter assessment can be achieved by selecting a target assessment model based on asset parameters. This enables the subsequent calculation of an accurate and reasonable distributed storage strategy for data assets, helping to achieve more efficient and secure storage of data assets, reducing data asset security incidents, and improving storage security.

[0142] As an optional embodiment, the specific method by which the first determining module selects the target valuation model from multiple candidate data asset valuation models based on asset parameters includes:

[0143] For each candidate data asset evaluation model, obtain the model training data corresponding to that 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;

[0144] Calculate the similarity between all data asset parameter annotations and asset parameters in the model training data;

[0145] The candidate data asset evaluation model with the highest similarity is selected as the target evaluation model.

[0146] As can be seen, through the above optional embodiments, the target evaluation model can be determined by calculating and screening the similarity between the model training data corresponding to the candidate data asset evaluation model and the asset parameters of the current data asset, so as to achieve a more accurate asset risk parameter evaluation. Subsequently, an accurate and reasonable distributed storage strategy for data assets can be calculated, which helps to achieve more efficient and secure storage of data assets, reduce data asset security incidents, and improve storage security.

[0147] As an optional embodiment, the second determining module determines the specific method of the node evaluation parameters for each candidate server node based on the node evaluation algorithm, including:

[0148] For each candidate server node, obtain the node communication record, node computation record, and node security protection record corresponding to that candidate server node;

[0149] The average communication time of all communication records in the node communication record is calculated to obtain the node transmission efficiency corresponding to the candidate server node.

[0150] The average computation time of all computation records in the computation record of the computing node is used to obtain the node computing performance corresponding to the candidate server node.

[0151] The average communication time of all communication records in the node's communication log is used to obtain the node security corresponding to the candidate server node.

[0152] As can be seen, through the above optional embodiments, the node evaluation parameters of the candidate server node can be determined by statistically analyzing and calculating the records in the node communication records, node calculation records, and node security protection records corresponding to the candidate server node. Subsequently, an accurate and reasonable distributed storage strategy for data assets can be calculated, which helps to achieve more efficient and secure storage of data assets, reduce data asset security incidents, and improve storage security.

[0153] As an optional implementation, the planning module, based on the objective function and constraints associated with asset risk parameters and node evaluation parameters, determines the specific method of the distributed storage strategy for the target data asset according to a dynamic programming algorithm, including:

[0154] The first relevant parameter is obtained by calculating the weighted average of the integrity risk parameter and the transmission loss risk parameter;

[0155] The second relevant parameter is obtained by calculating the weighted average of the integrity risk parameter and the leakage risk parameter;

[0156] For each candidate server node, calculate the average of the node transmission efficiency, node security, and node computing performance corresponding to that candidate server node to obtain the node priority parameters corresponding to that candidate server node.

[0157] Filter out all candidate server nodes whose node priority parameter is greater than the parameter threshold to obtain multiple target server nodes; optionally, the parameter threshold is proportional to the first relevant parameter;

[0158] Based on the objective function and constraints associated with the second relevant parameter and the node priority parameter, a distributed storage strategy for the target data assets on the target server node is determined using a dynamic programming algorithm.

[0159] As can be seen, through the above optional embodiments, target server nodes can be screened by calculating parameters related to node evaluation parameters, and dynamic planning can be achieved by calculating parameters related to 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 data asset security incidents, and improving storage security.

[0160] As an optional embodiment, the planning module, based on the objective function and constraints associated with the second relevant parameter and the node priority parameter, determines the specific method of the distributed storage strategy for the target data asset on the target server node according to the dynamic programming algorithm, including:

[0161] The objective function is set to minimize the amount of data assets allocated to each target server node in the distributed storage scheme.

[0162] Setting restrictions includes:

[0163] In a distributed storage scheme, the amount of data assets allocated to each target server node is proportional to the corresponding node priority parameter.

[0164] In the distributed storage scheme, the weighted average of the data asset portion allocated to all target server nodes is less than the data volume threshold; optionally, the data volume threshold is inversely proportional to the second relevant parameter.

[0165] Based on the dynamic programming algorithm, and taking into account the objective function and constraints, the algorithm iteratively calculates until the optimal distributed storage scheme is obtained, thereby determining the distributed storage strategy for the target data assets.

[0166] As can be seen, through the above optional embodiments, dynamic programming can be achieved by using objective functions and constraints associated with the second relevant parameters and node priority parameters to calculate accurate and reasonable distributed storage strategies for data assets, thereby enabling more efficient and secure storage of data assets, reducing data asset security incidents, and improving storage security.

[0167] Example 3

[0168] Please see Figure 3 , Figure 3 This is another distributed data asset storage system disclosed in the embodiments of the present invention. Figure 3 The described distributed data asset storage system is applied in data processing systems / data processing equipment / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example... Figure 3 As shown, the distributed data asset storage system may include:

[0169] Memory 301 storing executable program code;

[0170] Processor 302 coupled to memory 301;

[0171] 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 Embodiment 1.

[0172] Example 4

[0173] This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps of the distributed data asset storage method described in Embodiment 1.

[0174] Example 5

[0175] This 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 cause a computer to perform the steps of the distributed data asset storage method described in Embodiment 1.

[0176] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0177] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0178] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0179] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0180] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0181] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0182] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0183] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0184] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0185] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0186] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0187] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0188] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0189] Finally, it should be noted that the distributed data asset storage method and system disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate 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 includes: Acquire the target data assets to be stored and multiple available candidate server nodes; Based on the risk assessment algorithm, the asset risk parameters of the target data asset are determined, including integrity risk parameters, transmission loss risk parameters, and leakage risk parameters; Based on the node evaluation algorithm, node evaluation parameters for each candidate server node are determined, including node transmission efficiency, node security, and node computing performance. 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 using a dynamic programming algorithm, including: The first relevant parameter is obtained by calculating the weighted average of the integrity risk parameter and the transmission loss risk parameter; Calculate the weighted average of the integrity risk parameter and the leakage risk parameter to obtain the second relevant parameter; For each candidate server node, calculate the average of the node transmission efficiency, node security, and node computing performance corresponding to that candidate server node to obtain the node priority parameters corresponding to that candidate server node. All candidate server nodes whose node priority parameter is greater than a parameter threshold are selected to obtain multiple target server nodes; the parameter threshold is proportional to the first relevant parameter. Based on the objective function and constraints associated with the second relevant 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; the distributed storage strategy includes multiple storage server nodes and corresponding stored data portions.

2. The distributed data asset storage method according to claim 1, characterized in that, The step of determining the asset risk parameters of the target data asset according to the risk assessment algorithm includes: Obtain the asset's historical access records and asset parameters; the asset parameters include holder information, historical storage location, historical transaction count, and data content type; Based on the asset parameters, a target valuation model is selected from multiple candidate data asset valuation models; The target data asset and its historical access records are input into the target assessment model to obtain the asset risk parameters of the target data asset.

3. The distributed data asset storage method according to claim 2, characterized in that, The step of selecting a target valuation model from multiple candidate data asset valuation models based on the asset parameters includes: For each candidate data asset evaluation model, obtain the 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; Calculate 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.

4. The distributed data asset storage method according to claim 1, characterized in that, The node evaluation algorithm determines the node evaluation parameters for each candidate server node, including: For each candidate server node, obtain the node communication record, node calculation record, and node security protection record corresponding to that 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; Calculate the average computation time of all computation records in the node computation record to obtain the node computation performance corresponding to the candidate server node.

5. The distributed data asset storage method according to claim 1, characterized in that, The step of determining the distributed storage strategy for the target data asset on the target server node based on the objective function and constraints associated with the second relevant parameter and the node priority parameter, according to a 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 scheme. Setting restrictions includes: In the distributed storage scheme, the amount of data assets allocated to each target server node is proportional to the corresponding node priority parameter. In the distributed storage scheme, the weighted average of the data asset portion allocated to all the target server nodes is less than the data volume threshold; the data volume threshold is inversely proportional to the second related parameter. Based on the dynamic programming algorithm, the objective function and the constraints are used to iteratively calculate until the optimal distributed storage scheme is obtained, thereby determining the distributed storage strategy for the target data asset.

6. A distributed data asset storage system, characterized in that, The system includes: The acquisition module is used to acquire the target data assets to be stored and multiple available candidate server nodes; The first determining module is used to determine the asset risk parameters of the target data asset according to the risk assessment algorithm. The asset risk parameters include integrity risk parameters, transmission loss risk parameters, and leakage risk parameters. The second determining module is used to determine the node evaluation parameters of each candidate server node based on the node evaluation algorithm. The node evaluation parameters include node transmission efficiency, node security, and node computing performance. The planning module, based on an objective function and constraints associated with the asset risk parameters and the node evaluation parameters, determines a distributed storage strategy for the target data asset using a dynamic programming algorithm, including: The first relevant parameter is obtained by calculating the weighted average of the integrity risk parameter and the transmission loss risk parameter; Calculate the weighted average of the integrity risk parameter and the leakage risk parameter to obtain the second relevant parameter; For each candidate server node, calculate the average of the node transmission efficiency, node security, and node computing performance corresponding to that candidate server node to obtain the node priority parameters corresponding to that candidate server node. All candidate server nodes whose node priority parameter is greater than a parameter threshold are selected to obtain multiple target server nodes; the parameter threshold is proportional to the first relevant parameter. Based on the objective function and constraints associated with the second relevant 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; the distributed storage strategy includes multiple storage server nodes and corresponding stored data portions.

7. A distributed data asset storage system, characterized in that, The system includes: Memory containing 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 1-5.

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