Intelligent collaboration method and system for distributed data storage

Abstract

The invention discloses an intelligent collaboration method and system for distributed data storage, and belongs to the technical field of data collaboration. Through a global data collaborative topological graph based on a distributed data storage architecture, data transmission risk nodes and data collaborative nodes are identified, a data docking relation pair is constructed, a storage time period is initialized, the interaction rate of the docking relation pair in the storage time period is calculated, and an interaction characteristic curve is constructed. Mapping all characteristic curves corresponding to the same risk node to the same coordinate, taking an interaction rate average value of curve intersection points to generate a standard reference straight line, dividing high-efficiency and low-efficiency interaction states, counting a high-efficiency and low-efficiency state duration set, calculating the intelligent collaboration degree of the risk node, and comparing a collaboration degree average value to execute authorization or pause authority of the risk node. According to the method, accurate identification of risk nodes and dynamic description of interaction states are realized, intelligent authority control is realized by quantifying the collaboration degree, and the efficiency and stability of a distributed storage system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data collaboration, in particular to an intelligent collaboration method and system for distributed data storage. BACKGROUND

[0002] With the rapid development of big data technology, distributed data storage has been widely used in service transaction platforms, government data management, financial data backup and other scenarios due to its strong scalability and large storage capacity. In such scenarios, the distributed storage architecture usually contains dozens or even hundreds of data nodes, which are distributed in different physical locations and interact and collaborate through the network to form a complex global data collaboration topology. However, the existing distributed data storage collaboration technology still has many key problems, which are difficult to meet the needs of efficient, secure and dynamic collaboration in actual applications, specifically in the following aspects:

[0003] Current technologies mostly judge risk nodes by the number of node connections or a single load indicator, without considering the core attribute that "risk nodes are hub nodes connected to multiple collaboration nodes" in the identification logic, nor building a dedicated docking relationship pair between risk nodes and corresponding collaboration nodes. For example, some solutions only mark nodes with load exceeding the threshold as risk nodes, ignoring whether the node is connected to multiple collaboration nodes and whether it affects the overall data transmission link, leading to misjudgment or missed judgment of risk nodes, and further causing local data interaction congestion or link interruption.

[0004] Current mainstream solutions mostly use fixed interaction rate thresholds to divide high and low efficiency states, such as setting "interaction rate > 50GB / h as high efficiency", but do not consider the interaction characteristics differences of different time periods and node pairs. The interaction needs of the same node pair are different at different time periods, and the normal interaction rate benchmarks of different node pairs also differ. Fixed thresholds cannot adapt to complex scenarios. For example, the "risk node-service case node" pair in a service transaction platform can have a normal interaction rate of 80GB / h during peak hours, while a fixed threshold of 50GB / h will misjudge 60GB / h during off-peak hours as high efficiency, leading to resource mismatch.

[0005] Current technologies mostly only monitor whether nodes are running normally, without considering "the proportion of the duration of high and low efficiency interaction states" as a core indicator, which cannot quantify the actual contribution of nodes in the overall collaboration link. For example, some solutions only judge whether the node is online, ignoring that it is in a low efficiency interaction state most of the time, and still authorizing its collaboration rights, leading to waste of data transmission resources and reducing the overall system interaction efficiency.

[0006] The prior art is static configuration for the management and control of data collaboration permissions, and cannot dynamically adjust according to node collaboration performance. The current scheme usually presets node permissions when the system is deployed, and is not updated according to the change of node collaboration degree subsequently. If the collaboration degree of a node is continuously lower than the average level, the original permission of the node is maintained, which causes the node to become a data transmission bottleneck. If the collaboration degree of a node is improved, but the permission is not optimized in time, the collaboration ability of the node cannot be fully utilized.

[0007] The above problems commonly cause the existing distributed data storage system to have defects such as low interaction efficiency, poor resource utilization, and insufficient running stability, and an urgent technical scheme is needed that can accurately identify risk nodes, dynamically divide interaction states, quantify collaboration degrees, and dynamically manage permissions. SUMMARY

[0008] The purpose of the present application is to provide an intelligent collaboration method and system for distributed data storage to solve the problems raised in the background art.

[0009] In order to solve the above technical problems, the present application provides the following technical scheme:

[0010] An intelligent collaboration system for distributed data storage, the system comprising: a node relationship construction module, an interaction feature analysis module, an interaction state division module, and a collaboration permission management module;

[0011] The node relationship construction module identifies data transmission risk nodes and data collaboration nodes based on a global data collaboration topology graph, and constructs data docking relationship pairs.

[0012] The interaction feature analysis module is used to calculate the interaction rate of data docking relationship pairs in different storage time periods, and to construct an interaction feature curve.

[0013] The interaction state division module is used to establish an interaction feature standard reference straight line, and to divide the high-efficiency and low-efficiency interaction states of data docking relationship pairs.

[0014] The collaboration permission management module is used to statistically analyze the high and low efficiency interaction state time length, to evaluate the intelligent collaboration degree, and to dynamically manage the data collaboration permissions.

[0015] As a preferred scheme of the present application, the node relationship construction module comprises a topology graph analysis unit, a node marking unit, and a relationship pair construction unit. The topology graph analysis unit is used to read the global data collaboration topology graph in the distributed data storage architecture, and to extract node association information. The node marking unit is used to mark data transmission risk nodes and data collaboration nodes from the global data collaboration topology graph. The relationship pair construction unit is used to plan the collaboration nodes associated with each data transmission risk node, to form a data collaboration node set, and to construct the docking relationship pairs of data transmission risk nodes and collaboration nodes.

[0016] In a preferred embodiment of the present invention, the interaction feature analysis module includes a timestamp initialization unit, an interaction rate calculation unit, and a feature curve construction unit. The timestamp initialization unit is used to set the storage time nodes of the global data collaboration topology graph and divide it into consecutive storage time periods. The interaction rate calculation unit is used to count the data interaction volume of data docking pairs within each storage time period and calculate the interaction rate in combination with the duration of the time period. The feature curve construction unit is used to establish two-dimensional coordinates of interaction features, map the interaction rate data of each storage time period, and smoothly connect them to form an interaction feature curve.

[0017] In a preferred embodiment of the present invention, the interaction state division module includes a curve mapping unit, a standard straight line construction unit, and a state determination unit; the curve mapping unit is used to map all interaction feature curves corresponding to the same data transmission risk node to the same two-dimensional coordinate system; the standard straight line construction unit is used to identify the intersection points of all interaction feature curves, calculate the average value of the interaction rate corresponding to the intersection points, and generate an interaction feature standard reference straight line; the state determination unit is used to compare the positional relationship between the interaction feature curves and the standard reference straight line, and divide the interaction state into high-efficiency interaction state and low-efficiency interaction state.

[0018] As a preferred embodiment of the present invention, the collaborative permission management module includes a duration statistics unit, a collaboration degree evaluation unit, and a permission control unit; the duration statistics unit is used to separately count the duration of curve segments corresponding to high-efficiency interaction states and low-efficiency interaction states to form corresponding duration sets; the collaboration degree evaluation unit is used to calculate the intelligent collaboration degree of each data transmission risk node based on the duration set and determine the average collaboration degree of all risk nodes; the permission control unit is used to compare the collaboration degree of a single risk node with the average collaboration degree, perform permission authorization or suspension operation, and provide feedback on the control results.

[0019] An intelligent collaborative method for distributed data storage, comprising the following steps:

[0020] Step S1: Based on the global data collaboration topology diagram of the distributed data storage architecture, identify data transmission risk nodes and data collaboration nodes, and construct data docking relationship pairs;

[0021] Step S2: Evaluate the data interaction rate of the data docking relationship within different storage time periods and construct the corresponding interaction characteristic curves;

[0022] Step S3: Establish a standard reference line for interaction features, and divide the data docking relationship pairs into high-efficiency interaction states and low-efficiency interaction states based on this line;

[0023] Step S4: Evaluate the intelligent collaboration degree of data transmission risk nodes based on the duration of high and low efficiency interaction states, and authorize or suspend data collaboration permissions for data transmission risk nodes based on the collaboration degree results.

[0024] As a preferred embodiment of the present invention, the specific implementation process of step S1 includes:

[0025] Based on the global data collaboration topology graph stored in the distributed data storage architecture, data transmission risk nodes and data collaboration nodes are marked. The data transmission risk node is a data collaboration node that connects several data collaboration nodes in the global data collaboration topology graph.

[0026] Several data collaboration nodes connected to the data transmission risk node are coordinated to form a data collaboration node set, and the data transmission risk node and its corresponding generated data collaboration node set form a data docking relationship pair.

[0027] Let the i-th data transmission risk node be denoted as Transmitting data at risk nodes The corresponding generated set of data collaboration nodes is denoted as This constitutes a data docking relationship. ,in, Represents a set of data collaboration nodes The j-th data collaboration node in the process.

[0028] As a preferred embodiment of the present invention, the specific implementation process of step S2 includes:

[0029] Initialize the storage timestamps of the global data collaboration topology graph, and form a storage time segment between two adjacent storage timestamps. , where x is the index of the storage time period, retrieves the data within the storage time period. Internal data transmission risk nodes Data collaboration nodes The amount of data interaction between the two sides is determined by the data interaction volume and the storage time period. The ratio of duration is obtained in the storage time period Internal data transmission risk nodes Data collaboration nodes Data interaction rate ;

[0030] Based on the data interaction rate, a two-dimensional coordinate system for interaction characteristics is established, with the storage time period sequence number as the horizontal axis index and the data connection relationship as the basis. Corresponding data interaction rate Using the y-axis as the index, we obtain the data docking relationship pairs. During the storage period coordinates of the point The data docking relationships are connected smoothly in sequence. The coordinates of points at different storage time periods constitute the data docking relationship pairs. The interaction feature curve, denoted as .

[0031] As a preferred embodiment of the present invention, the specific implementation process of step S3 includes:

[0032] Data collaboration node set Each data collaboration node and data transmission risk node in the middle Interaction feature curves are constructed, mapped onto the same two-dimensional coordinate system, and all intersection points of the interaction feature curves are marked. The average data interaction rate corresponding to all intersection points is then obtained to form a standard reference line for the interaction feature. The x-coordinate value of the interaction feature standard reference line is The vertical axis value is the average data interaction rate;

[0033] The standard reference line of the interaction feature will be located on The curve segment of the interaction feature curve above is denoted as the high-efficiency interaction state, and will be located on the standard reference line of the interaction feature. The curve segment of the interaction feature curve below is denoted as the inefficient interaction state.

[0034] As a preferred embodiment of the present invention, the specific implementation process of step S4 includes:

[0035] Obtain the time span of each curve segment corresponding to a high-efficiency interaction state, and construct a high-efficiency interaction state duration set. ,in, denoted by , where represents the time span of the time storage of the a-th curve segment corresponding to the high-efficiency interaction state, and A represents the total number of curve segments corresponding to the high-efficiency interaction state.

[0036] Obtain the time span of each curve segment corresponding to the inefficient interaction state, and construct a duration set of inefficient interaction states. ,in, The time span of the b-th curve segment corresponding to the inefficient interaction state is represented by B, where B represents the total number of curve segments corresponding to the inefficient interaction state.

[0037] Assess data transmission risk nodes based on the duration sets of high-efficiency and low-efficiency interaction states. intelligent collaboration ;

[0038] If intelligent collaboration If the intelligent coordination degree is greater than or equal to the average value of the data transmission risk nodes, then the data transmission risk nodes are authorized. Data collaboration permissions, if intelligent collaboration level If the intelligent coordination level of each data transmission risk node is less than the average level, then the data transmission risk node is suspended. Data collaboration permissions.

[0039] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0040] This invention, through its technical feature of "defining hub nodes connecting multiple collaborative nodes as data transmission risk nodes based on a global data collaboration topology graph, and constructing exclusive docking relationship pairs of 'risk node-collaboration node set'", differs from existing solutions that only vaguely identify risk nodes based on load or connection count. This design can accurately pinpoint the core nodes affecting the overall collaborative link, avoiding resource waste caused by misjudging non-hub nodes as risk nodes, or link risks caused by missing hub nodes, thus achieving "targeted" risk node identification. This is an improvement in accuracy brought about by the combination of "hub attributes + exclusive docking relationship" that existing technologies have not considered.

[0041] This invention differs from existing schemes that only count the interaction rate at a single point in time by using the technical feature of "initializing timestamps to divide storage time periods, calculating the interaction rate of pairs within each storage time period, and constructing a smooth interaction feature curve with the time period number as the horizontal axis and the interaction rate as the vertical axis." This design can dynamically reflect the interaction patterns of pairs in different time periods (such as the rate changes during peak / valley periods), providing a temporal dimension basis for subsequent state division. It avoids the defect that existing fixed time point data cannot reflect the interaction trend, and realizes the "temporal" characterization of interaction features. This is a dynamic advantage that existing technologies do not combine with "time segmentation + curve mapping."

[0042] This invention differs from existing schemes that use fixed thresholds to classify high and low efficiency states by mapping all interaction feature curves corresponding to the same risk node to the same coordinate system, extracting the average interaction rate of all curve intersections, and generating a dedicated standard reference line adapted to the risk node. This design can generate a "personalized" reference benchmark based on the actual interaction level of different collaborative node pairs under the same risk node, avoiding the shortcomings of fixed thresholds that cannot adapt to different node pairs and different time periods (such as avoiding misjudging the normal low rate of a node pair as inefficiency). This achieves "objectivity" in the classification of interaction states, which is an adaptive improvement brought about by "multi-curve intersection statistics + node-specific benchmark" that existing technologies have not considered.

[0043] This invention differs from existing statically preset permission schemes by using the technical feature of "statistically calculating the intelligent collaboration degree by dividing the sum of high-efficiency and low-efficiency state durations by the total interaction time, and then authorizing or suspending permissions by comparing the average collaboration degree of all risk nodes." This design can transform node collaboration capabilities into quantifiable values ​​(collaboration degree) and dynamically adjust permissions by "comparing with the average value." If the collaboration degree meets the standard, permissions are granted to enable the node to utilize its capabilities; if it does not meet the standard, permissions are suspended to avoid bottlenecks. This avoids the "capability and permission mismatch" problem caused by existing static permissions and achieves "intelligent" permission control. This is a resource optimization effect that existing technologies have not combined with "quantification of time proportion + comparison with the average value." Attached Figure Description

[0044] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0045] Figure 1 This is a schematic diagram illustrating the steps of an intelligent collaborative method for distributed data storage according to the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0047] In this first embodiment: an intelligent collaborative system for distributed data storage is provided, which includes: a node relationship construction module, an interaction feature analysis module, an interaction state division module, and a collaborative permission management module;

[0048] The node relationship construction module identifies data transmission risk nodes and data collaboration nodes based on the global data collaboration topology graph, and constructs data docking relationship pairs.

[0049] The node relationship construction module includes a topology graph parsing unit, a node marking unit, and a relationship pair construction unit. The topology graph parsing unit is used to read the global data collaboration topology graph in the distributed data storage architecture and extract node association information. The node marking unit is used to mark data transmission risk nodes and data collaboration nodes from the global data collaboration topology graph. The relationship pair construction unit is used to coordinate the collaboration nodes associated with each data transmission risk node to form a data collaboration node set and construct the docking relationship pairs between data transmission risk nodes and collaboration nodes.

[0050] The interaction feature analysis module is used to calculate the interaction rate of data docking pairs in different storage time periods and construct interaction feature curves.

[0051] The interaction feature analysis module includes a timestamp initialization unit, an interaction rate calculation unit, and a feature curve construction unit. The timestamp initialization unit is used to set the storage time nodes of the global data collaboration topology graph and divide it into consecutive storage time periods. The interaction rate calculation unit is used to count the data interaction volume of data docking pairs within each storage time period and calculate the interaction rate in combination with the duration of the time period. The feature curve construction unit is used to establish two-dimensional coordinates of interaction features, map the interaction rate data of each storage time period, and smoothly connect them to form an interaction feature curve.

[0052] The interaction state division module is used to establish a standard reference line for interaction characteristics and to divide the data docking relationship pairs into high-efficiency and low-efficiency interaction states.

[0053] The interaction state division module includes a curve mapping unit, a standard line construction unit, and a state determination unit. The curve mapping unit is used to map all interaction feature curves corresponding to the same data transmission risk node to the same two-dimensional coordinate system. The standard line construction unit is used to identify the intersection points of all interaction feature curves, calculate the average value of the interaction rate corresponding to the intersection points, and generate a standard reference line for interaction features. The state determination unit is used to compare the positional relationship between the interaction feature curves and the standard reference line to classify high-efficiency interaction states and low-efficiency interaction states.

[0054] The collaborative permission management module is used to count the duration of high and low efficiency interaction states, evaluate the degree of intelligent collaboration, and dynamically manage data collaboration permissions.

[0055] The collaborative permission management module includes a duration statistics unit, a collaboration degree evaluation unit, and a permission control unit. The duration statistics unit is used to separately count the duration of curve segments corresponding to high-efficiency and low-efficiency interaction states, forming corresponding duration sets. The collaboration degree evaluation unit is used to calculate the intelligent collaboration degree of each data transmission risk node based on the duration set, and determine the average collaboration degree of all risk nodes. The permission control unit is used to compare the collaboration degree of a single risk node with the average collaboration degree, perform permission authorization or suspension operations, and provide feedback on the control results.

[0056] Please see Figure 1In this second embodiment, an intelligent collaborative method for distributed data storage is provided, applicable to the first embodiment described above. This embodiment uses the distributed data storage collaborative management of a technology trading platform as an application scenario. The platform adopts a distributed storage architecture, storing 20 data nodes, including service institution case storage nodes, user demand storage nodes, and transaction interaction data nodes. These nodes are distributed across three physical data centers and interconnected via a fiber optic network, forming a global data collaborative topology.

[0057] The method includes the following steps:

[0058] Step S1: Based on the global data collaboration topology diagram of the distributed data storage architecture, identify data transmission risk nodes and data collaboration nodes, and construct data docking relationship pairs;

[0059] For example, based on the global data collaboration topology graph stored in the distributed data storage architecture, data transmission risk nodes and data collaboration nodes are marked. The data transmission risk node is a data collaboration node that connects several data collaboration nodes in the global data collaboration topology graph.

[0060] Several data collaboration nodes connected to the data transmission risk node are coordinated to form a data collaboration node set, and the data transmission risk node and its corresponding generated data collaboration node set form a data docking relationship pair.

[0061] Let the i-th data transmission risk node be denoted as Transmitting data at risk nodes The corresponding generated set of data collaboration nodes is denoted as This constitutes a data docking relationship. ,in, Represents a set of data collaboration nodes The j-th data collaboration node in;

[0062] For example, the topology graph parsing unit reads the global data collaboration topology graph and extracts node connection information; the node marking unit marks three data transmission risk nodes (denoted as K1, K2, and K3) based on the hub attribute standard of "connected nodes ≥ 10". Among them, K1 connects 12 collaboration nodes (service institution case nodes), K2 connects 10 collaboration nodes (user demand nodes), and K3 connects 8 collaboration nodes (transaction interaction nodes); the relationship pair construction unit coordinates the collaboration nodes of each risk node to form three data collaboration node sets, E1, E2, and E3, and constructs three sets of data docking relationship pairs (K1, e1j), (K2, e2j), and (K3, e3j).

[0063] Step S2: Evaluate the data interaction rate of the data docking relationship within different storage time periods and construct the corresponding interaction characteristic curves;

[0064] For example, the storage timestamps of the global data collaboration topology graph are initialized, forming a storage time period between two adjacent storage timestamps. , where x is the index of the storage time period, retrieves the data within the storage time period. Internal data transmission risk nodes Data collaboration nodes The amount of data interaction between the two sides is determined by the data interaction volume and the storage time period. The ratio of duration is obtained in the storage time period Internal data transmission risk nodes Data collaboration nodes Data interaction rate ;

[0065] Based on the data interaction rate, a two-dimensional coordinate system for interaction characteristics is established, with the storage time period sequence number as the horizontal axis index and the data connection relationship as the basis. Corresponding data interaction rate Using the y-axis as the index, we obtain the data docking relationship pairs. During the storage period coordinates of the point The data docking relationships are connected smoothly in sequence. The coordinates of points at different storage time periods constitute the data docking relationship pairs. The interaction feature curve, denoted as ;

[0066] The timestamp initialization unit sets 00:00 as the initial time node and divides each hour into a storage time period (T1-T12). The interaction rate calculation unit counts the data interaction volume of each docking relationship pair within each time period. For example, the interaction volume of (K1, e11) in T1-T12 is 70GB, 68GB, 85GB, 75GB, 90GB, 82GB, 88GB, 95GB, 80GB, 78GB, 92GB, and 86GB, respectively. Combined with the 1-hour duration, the interaction rates are calculated to be 70GB / h, 68GB / h, 85GB / h, 75GB / h, 90GB / h, 82GB / h, 88GB / h, 95GB / h, 80GB / h, 78GB / h, 92GB / h, and 86GB / h, respectively. The feature curve construction unit uses the time period number as the horizontal axis and the interaction rate as the vertical axis to smoothly connect each data point, forming the interaction feature curve of (K1, e11).

[0067] Step S3: Establish a standard reference line for interaction features, and divide the data docking relationship pairs into high-efficiency interaction states and low-efficiency interaction states based on this line;

[0068] For example, a data collaboration node set Each data collaboration node and data transmission risk node in the middle Interaction feature curves are constructed, mapped onto the same two-dimensional coordinate system, and all intersection points of the interaction feature curves are marked. The average data interaction rate corresponding to all intersection points is then obtained to form a standard reference line for the interaction feature. The x-coordinate of the standard reference line for interactive features is... The vertical axis value is the average data interaction rate;

[0069] The standard reference line of the interaction feature will be located on The curve segment of the interaction feature curve above is denoted as the high-efficiency interaction state, and will be located on the standard reference line of the interaction feature. The curve segment of the interaction feature curve below is denoted as the inefficient interaction state;

[0070] For example, the curve mapping unit maps the 12 interactive feature curves corresponding to K1 to the same two-dimensional coordinates; the standard straight line construction unit identifies a total of 18 intersection points of all curves, calculates the average interaction rate corresponding to the intersection points as 83GB / h, and generates a standard reference straight line for interactive features; the state determination unit compares the position of each curve with the reference straight line, and determines the segment of the curve above the reference straight line as a high-efficiency interaction state, and the segment below as a low-efficiency interaction state. For example, (K1, e11) is in a high-efficiency state during the time periods T3, T5, T7-T8, and T11, and in a low-efficiency state during the time periods T2, T4, and T10.

[0071] Step S4: Evaluate the intelligent collaboration degree of data transmission risk nodes based on the duration of high and low efficiency interaction states, and authorize or suspend data collaboration permissions of data transmission risk nodes according to the collaboration degree results;

[0072] For example, the time span of each curve segment corresponding to a high-efficiency interaction state is obtained to form a high-efficiency interaction state duration set. ,in, denoted by , where represents the time span of the time storage of the a-th curve segment corresponding to the high-efficiency interaction state, and A represents the total number of curve segments corresponding to the high-efficiency interaction state.

[0073] Obtain the time span of each curve segment corresponding to the inefficient interaction state, and construct a duration set of inefficient interaction states. ,in, The time span of the b-th curve segment corresponding to the inefficient interaction state is represented by B, where B represents the total number of curve segments corresponding to the inefficient interaction state.

[0074] Assess data transmission risk nodes based on the duration sets of high-efficiency and low-efficiency interaction states. intelligent collaboration ;

[0075] If intelligent collaboration If the intelligent coordination degree is greater than or equal to the average value of the data transmission risk nodes, then the data transmission risk nodes are authorized. Data collaboration permissions, if intelligent collaboration level If the intelligent coordination level of each data transmission risk node is less than the average level, then the data transmission risk node is suspended. Data collaboration permissions;

[0076] For example, the duration statistics unit counts the high and low efficiency durations of each risk node. K1 has a high efficiency duration of 5 hours and a low efficiency duration of 3 hours, K2 has a high efficiency duration of 3 hours and a low efficiency duration of 4 hours, and K3 has a high efficiency duration of 6 hours and a low efficiency duration of 2 hours. The collaboration evaluation unit calculates the intelligent collaboration degree. K1 is 5 / (5+3)=62.5%, K2 is 3 / (3+4)≈42.9%, and K3 is 6 / (6+2)=75%. The average collaboration degree of all risk nodes is (62.5%+42.9%+75%) / 3≈60.1%. The permission control unit compares and judges, and K1 (62.5%≥60.1%) and K3 (75%≥60.1%) are authorized to have data collaboration permissions, while K2 (42.9%<60.1%) has its permissions suspended.

[0077] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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 process, method, article, or apparatus.

[0078] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent collaboration for distributed data storage, characterized in that, The method comprises the following steps: Step S1: Based on the global data collaboration topology graph of the distributed data storage architecture, identify data transmission risk nodes and data collaboration nodes, and construct data docking relationship pairs; Step S2: Evaluate the data interaction rate of the data docking relationship pair in different storage time periods, and construct the corresponding interaction characteristic curve; Step S3: Establish an interaction characteristic standard reference straight line, and divide the high-efficiency interaction state and the low-efficiency interaction state of the data docking relationship pair according to the straight line; Step S4: Based on the time length of high and low efficiency interaction state, evaluate the intelligent collaboration degree of data transmission risk node, and authorize or suspend the data collaboration permission of data transmission risk node according to the collaboration degree result.

2. The intelligent collaboration method for distributed data storage of claim 1, wherein, The specific implementation process of step S1 comprises: Based on the global data collaboration topology graph stored in the distributed data storage architecture, mark the data transmission risk nodes and the data collaboration nodes, the data transmission risk nodes are a data collaboration node connected to several data collaboration nodes in the global data collaboration topology graph; Organize several data collaboration nodes connected to the data transmission risk node to form a data collaboration node set, and the data transmission risk node and the corresponding generated data collaboration node set form a data docking relationship pair; Let the ith data transmission risk node be denoted as Let the data transmission risk node Corresponding to the generated data coordination node set be denoted as Then a data docking relationship pair is constituted Wherein, Indicates the jth data coordination node in the data coordination node set .

3. The intelligent collaboration method for distributed data storage of claim 2, wherein, The specific implementation process of step S2 comprises: Initialize the storage timestamps of the global data collaboration topology graph, and form a storage time segment between two adjacent storage timestamps. , where x is the index of the storage time period, retrieves the data within the storage time period. Internal data transmission risk nodes Data collaboration nodes The amount of data interaction between the two sides is determined by the data interaction volume and the storage time period. The ratio of duration is obtained in the storage time period Internal data transmission risk nodes Data collaboration nodes Data interaction rate ; Based on the data interaction rate, a two-dimensional coordinate of interaction features is established, taking the storage time period sequence number as the horizontal coordinate index, and taking the data docking relationship pair The corresponding data interaction rate As the vertical coordinate index, the data docking relationship pair At the storage time period The point coordinates The data docking relationship pairs are sequentially and smoothly connected The point coordinates at different storage time periods constitute the interaction feature curve of the data docking relationship pair , denoted as .

4. The intelligent collaboration method for distributed data storage of claim 3, wherein, The specific implementation process of step S3 comprises: Set of data coordination nodes Each data coordination node and data transmission risk node An interactive feature curve is formed, mapped in the same interactive feature two-dimensional coordinate, all intersection points in the interactive feature curve are marked, and the average value of the data interaction rate corresponding to all intersection points is obtained to form an interactive feature standard reference straight line The horizontal coordinate value of the interactive feature standard reference straight line is The vertical coordinate value is the average value of the data interaction rate The standard reference line of the interaction feature will be located on The curve segment of the interaction feature curve above is denoted as the high-efficiency interaction state, and will be located on the standard reference line of the interaction feature. The curve segment of the interaction feature curve below is denoted as the inefficient interaction state.

5. The intelligent collaboration method for distributed data storage of claim 4, wherein, The specific implementation process of step S4 comprises: The time storage time span of each curve segment corresponding to the high-efficiency interaction state is acquired, and a high-efficiency interaction state duration set is constituted wherein, denotes the time storage time span of the a-th curve segment corresponding to the high-efficiency interaction state, and A denotes the total number of the curve segments corresponding to the high-efficiency interaction state; The time span of each curve segment corresponding to the low-efficiency interaction state is stored, and a low-efficiency interaction state duration set is formed wherein, The time span of each curve segment corresponding to the low-efficiency interaction state is stored, and a low-efficiency interaction state duration set is formed Assessing data transmission risk nodes based on a high-efficiency interaction state time length set and a low-efficiency interaction state time length set of intelligent collaboration degree ; If intelligent collaboration If the intelligent coordination degree is greater than or equal to the average value of the data transmission risk nodes, then the data transmission risk nodes are authorized. Data collaboration permissions, if intelligent collaboration level If the intelligent coordination level of each data transmission risk node is less than the average level, then the data transmission risk node is suspended. Data collaboration permissions.

6. An intelligent collaboration system for distributed data storage, carrying out an intelligent collaboration method for distributed data storage according to any one of claims 1 to 5, characterized in that The system comprises: node relationship construction module, interaction characteristic analysis module, interaction state division module and collaboration permission management module; The node relationship construction module identifies data transmission risk nodes and data collaboration nodes based on the global data collaboration topology graph, and constructs data docking relationship pairs; The interaction characteristic analysis module is used for calculating the interaction rate of the data docking relationship pair in different storage time periods, and constructing the interaction characteristic curve; The interaction state division module is used for establishing an interaction characteristic standard reference straight line, and dividing the high-efficiency and low-efficiency interaction state of the data docking relationship pair; The collaboration permission management module is used for statistical analysis of the time length of high and low efficiency interaction state, evaluation of intelligent collaboration degree and dynamic management of data collaboration permission.

7. The intelligent collaborative system for distributed data storage of claim 6, wherein, The node relationship construction module comprises a topology graph analysis unit, a node marking unit and a relationship pair construction unit; the topology graph analysis unit is used for reading the global data collaboration topology graph in the distributed data storage architecture, and extracting node association information; the node marking unit is used for marking data transmission risk nodes and data collaboration nodes from the global data collaboration topology graph; The relationship pair construction unit is used for organizing the collaboration nodes associated with each data transmission risk node to form a data collaboration node set, and constructing the docking relationship pair of the data transmission risk node and the collaboration node.

8. The intelligent collaborative system for distributed data storage of claim 6, wherein, The interaction characteristic analysis module comprises a timestamp initialization unit, an interaction rate calculation unit and a characteristic curve construction unit; the timestamp initialization unit is used for setting the storage time node of the global data collaboration topology graph, and dividing the continuous storage time period; the interaction rate calculation unit is used for calculating the data interaction amount of the data docking relationship pair in each storage time period, and calculating the interaction rate combined with the time period length; The feature curve construction unit is configured to establish an interactive feature two-dimensional coordinate, map the interactive rate data of each storage time period, and smoothly connect to form an interactive feature curve.

9. The intelligent collaborative system for distributed data storage of claim 6, wherein, The interaction state division module comprises a curve mapping unit, a standard straight line construction unit, and a state determination unit. The curve mapping unit is configured to map all the interactive feature curves corresponding to the same data transmission risk node to the same two-dimensional coordinate system. The standard straight line construction unit is configured to identify the intersection points of all the interactive feature curves, calculate the average value of the interactive rate corresponding to the intersection points, and generate an interactive feature standard reference straight line. The state determination unit is configured to compare the positional relationship between the interactive feature curve and the standard reference straight line, and divide the high-efficiency interaction state and the low-efficiency interaction state.

10. The intelligent collaborative system for distributed data storage of claim 6, wherein, The collaborative authority management module comprises a time length statistical unit, a collaboration degree evaluation unit, and an authority control unit. The time length statistical unit is configured to respectively count the curve segment time lengths corresponding to the high-efficiency interaction state and the low-efficiency interaction state, and form corresponding time length sets. The collaboration degree evaluation unit is configured to calculate the intelligent collaboration degrees of the data transmission risk nodes according to the time length sets, and determine the average value of the collaboration degrees of all the risk nodes. The authority control unit is configured to compare the collaboration degree of a single risk node with the average value of the collaboration degrees, execute an authority authorization or suspension operation, and feed back the control result.

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