Data consistency guarantee and transaction management method based on event driving

Abstract

The invention relates to the technical field of data processing, and discloses an event-driven data consistency guarantee and transaction management method, which comprises the following steps: acquiring data distributed event information of a data distributed processing system, calculating a constraint coefficient of the data distributed processing system, and generating a data consistency guarantee transaction management group; generating a historical distributed data generation efficiency sequence; calculating a historical distributed data generation efficiency coefficient to obtain a high-efficiency data distributed processing system group and a low-efficiency data distributed processing system group; and calculating a high-efficiency data distributed processing constraint coefficient, constraining a data updating time interval to obtain a constrained data updating time interval, and performing data consistency guarantee transaction management. Through system constraint, preconditions of data consistency processing are effectively ensured, through time constraint, data processing time intervals of different distributed systems are balanced, data processing sequence imbalance is avoided, the problems of data conflicts and inconsistency are eliminated, and dynamic performance, collaboration and real-time performance of data consistency processing are ensured.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an event-driven data consistency assurance and transaction management method. Background Art

[0002] With the rapid development of industrial Internet and enterprise informatization, enterprises are increasingly adopting integrated platforms to connect multiple distributed systems to support complex business needs. These distributed systems cover multiple fields such as production scheduling, inventory management, order processing, and transportation scheduling. Through the unified architecture of the platform, different systems can achieve data exchange and business process collaboration.

[0003] Traditional data consistency assurance and transaction management methods use a two-phase commit protocol to uniformly manage transaction submission and rollback through a coordinator. However, in multi-system high-concurrency scenarios, this method exposes significant limitations: the rigid architecture based on static rules lacks dynamic adaptation capabilities and is difficult to cope with complex business environments. In addition, existing technologies cannot effectively evaluate the strength of associations between distributed systems, and there is a lack of necessary coordination mechanisms between systems, which further exacerbates the difficulty of maintaining data consistency. At the same time, due to the significant differences in data processing rates between different systems, and the inability of existing technologies to effectively constrain high-speed data, the data processing order is unbalanced, which in turn causes data conflicts and inconsistencies. Typical manifestations are: orders are still confirmed when inventory is insufficient, or transportation plans have been generated but the order status is not updated synchronously. In addition, delays in data updates between systems may also lead to decision-making errors and improper resource scheduling, resulting in high event processing delays, and ultimately affecting business response efficiency. These problems highlight the shortcomings of existing data consistency assurance and transaction management methods in terms of dynamism, coordination, and real-time performance. Summary of the invention

[0004] The embodiment of the present invention provides an event-driven data consistency assurance and transaction management method, which intelligently evaluates the correlation of distributed systems, establishes a coordination mechanism, balances the data consistency processing time intervals of different distributed systems, avoids imbalance in data processing order, reduces data conflicts and inconsistency problems, and ensures the dynamic, collaborative and real-time nature of data consistency processing.

[0005] In order to achieve the above object, the present invention provides an event-driven data consistency assurance and transaction management method, comprising: Determine multiple data distributed processing systems, obtain data distributed event information corresponding to each data distributed processing system, calculate the data distributed processing system constraint coefficient of every two data distributed processing systems based on the data distributed event information, and generate a data consistency assurance transaction management group; Collecting multiple historical distributed data generation efficiencies corresponding to each data distributed processing system in the data consistency assurance transaction management group, and generating a historical distributed data generation efficiency sequence based on the collection time sequence; Analyze the historical distributed data generation efficiency sequence, calculate the historical distributed data generation efficiency coefficient of the data distributed processing system based on the analysis result, and group all the data distributed processing systems based on the historical distributed data generation efficiency coefficient to obtain a high-efficiency data distributed processing system group and a low-efficiency data distributed processing system group; Calculating a high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group based on the high-efficiency data distributed processing system group and the low-efficiency data distributed processing system group; Determine the current data update time interval of the data distributed processing system in the high-efficiency data distributed processing system group, constrain the current data update time interval based on the high-efficiency data distributed processing constraint coefficient to obtain the constrained data update time interval, and perform data consistency assurance transaction management according to the constrained data update time interval.

[0006] Further, when obtaining data distributed event information corresponding to each data distributed processing system, calculating the data distributed processing system constraint coefficient of every two data distributed processing systems based on the data distributed event information, and generating a data consistency assurance transaction management group, it includes: Extracting data distributed event information corresponding to each data distributed processing system, wherein the data distributed event information includes a data distributed event process range and a data distributed event flow direction range; Determine the data distributed event process coupling degree based on the data distributed event process range corresponding to each two data distributed processing systems; Determine the data distributed event flow direction coupling degree based on the data distributed event flow direction range corresponding to every two data distributed processing systems; Determine the bidirectional coupling degree of data distributed events based on the data distributed event process range and the data distributed event flow direction range corresponding to every two data distributed processing systems; Calculate the data distributed processing system constraint coefficient of every two data distributed processing systems according to the data distributed event process coupling degree, the distributed event flow direction coupling degree and the data distributed event bidirectional coupling degree; When the constraint coefficient of the data distributed processing system is greater than or equal to the preset constraint coefficient of the data distributed processing system, a data consistency assurance transaction management identifier is generated for the two data distributed processing systems; Randomly selecting two data distributed processing systems for the second time, and calculating the constraint coefficient of the second data distributed processing system, and judging whether a data consistency assurance transaction management identifier can be generated for the two data distributed processing systems randomly selected for the second time based on the constraint coefficient of the second data distributed processing system; When the constraint coefficient of the data distributed processing system is less than the preset constraint coefficient of the data distributed processing system, two data distributed processing systems are randomly selected for the second time; Randomly extract a1 times two data distributed processing systems to obtain multiple data consistency guarantee transaction management identifiers; The number of data consistency assurance transaction management identifiers corresponding to each data distributed processing system is counted and sorted from large to small, and the data distributed processing systems corresponding to the first a2 numbers of data consistency assurance transaction management identifiers are selected to generate the data consistency assurance transaction management group.

[0007] Furthermore, the data distributed event flow coupling degree corresponding to every two data distributed processing systems is calculated according to the following formula: ; Among them, q1 is the data distributed event process coupling degree corresponding to each two data distributed processing systems, is the number of intersections of the data distributed event flow ranges corresponding to every two data distributed processing systems, The number of unions of data distributed event process ranges corresponding to every two data distributed processing systems; The data distributed event flow coupling degree corresponding to each two data distributed processing systems is calculated according to the following formula: ; Among them, q2 is the data distributed event flow coupling degree corresponding to each two data distributed processing systems, is the number of intersections of the data distribution event flow ranges corresponding to every two data distribution processing systems, The number of unions of data distribution event flow ranges corresponding to every two data distribution processing systems; The bidirectional coupling degree of data distributed events corresponding to each two data distributed processing systems is calculated according to the following formula: ; Among them, q3 is the bidirectional coupling degree of data distributed events corresponding to every two data distributed processing systems; The data distributed processing system constraint coefficient of each two data distributed processing systems is calculated according to the following formula: ; Among them, y is the data distributed processing system constraint coefficient of every two data distributed processing systems, t1 is the total number of data distributed event flow ranges and data distributed event flow direction ranges corresponding to one data distributed processing system, t2 is the total number of data distributed event flow ranges and data distributed event flow direction ranges corresponding to another data distributed processing system, max is the maximum value symbol, and min is the minimum value symbol.

[0008] Furthermore, when analyzing the historical distributed data generation efficiency sequence and calculating the historical distributed data generation efficiency coefficient of the data distributed processing system based on the analysis result, it includes: Calculating a distributed data generation efficiency characteristic factor of each historical distributed data generation efficiency in the historical distributed data generation efficiency sequence; Calculate the characteristic factor means and characteristic factor variances corresponding to all distributed data generation efficiency characteristic factors, take the minimum value of the characteristic factor means and the characteristic factor variances as the first partition characteristic factor, and take the maximum value of the characteristic factor means and the characteristic factor variances as the second partition characteristic factor; Sequentially divide all distributed data generation efficiency characteristic factors according to the first division characteristic factor and the second division characteristic factor, and divide all distributed data generation efficiency characteristic factors that are smaller than the first division characteristic factor into a first distributed data generation efficiency characteristic factor sequence; dividing all distributed data generation efficiency characteristic factors greater than or equal to the first division characteristic factor and less than the second division characteristic factor into a second distributed data generation efficiency characteristic factor sequence; dividing all distributed data generation efficiency characteristic factors greater than or equal to the second division characteristic factor into a third distributed data generation efficiency characteristic factor sequence; The historical distributed data generation efficiency coefficient of the data distributed processing system is calculated according to the first distributed data generation efficiency characteristic factor sequence, the second distributed data generation efficiency characteristic factor sequence and the third distributed data generation efficiency characteristic factor sequence.

[0009] Further, when calculating the distributed data generation efficiency characteristic factor of each historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, it includes: For any historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, the historical distributed data generation efficiencies on the left and right sides of the historical distributed data generation efficiency are used as neighborhood historical distributed data generation efficiencies; The distributed data generation efficiency characteristic factor of each historical distributed data generation efficiency in the historical distributed data generation efficiency sequence is calculated according to the following formula: ; Among them, u is the distributed data generation efficiency characteristic factor of the historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, and p i is the change value of the i-th historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, p1 is the mean change value of all change values ​​in the historical distributed data generation efficiency sequence, m is the number of neighborhood historical distributed data generation efficiencies of the historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, p i，s is the change value of the s-th neighborhood historical distributed data generation efficiency of the ith historical distributed data generation efficiency in the historical distributed data generation efficiency sequence.

[0010] Further, when calculating the historical distributed data generation efficiency coefficient of the data distributed processing system according to the first distributed data generation efficiency characteristic factor sequence, the first distributed data generation efficiency characteristic factor sequence and the first distributed data generation efficiency characteristic factor sequence, it includes: Determine a small characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence; ; Among them, d1 is the small characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence, f1 is a preset factor, and its value range is [0.1, 0.2], and g1 is the mean value corresponding to the first distributed data generation efficiency characteristic factor sequence; Determine a large characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence; ; Wherein, d2 is the large characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence; Determine a characteristic factor boundary range according to the small characteristic factor boundary value and the large characteristic factor boundary value; Analyzing the distributed data generation efficiency characteristic factors in the first distributed data generation efficiency characteristic factor sequence, and counting the number of the first distributed data generation efficiency characteristic factors falling within the characteristic factor boundary range; Determine a second minimum characteristic factor boundary value of the second distributed data generation efficiency characteristic factor sequence; Determine a second largest characteristic factor boundary value of the second distributed data generation efficiency characteristic factor sequence; Determine a second characteristic factor boundary range according to the second smallest characteristic factor boundary value and the second largest characteristic factor boundary value; Analyzing the distributed data generation efficiency characteristic factors in the second distributed data generation efficiency characteristic factor sequence, and counting the number of second distributed data generation efficiency characteristic factors falling within a boundary range of the second characteristic factor; Determine a third minimum characteristic factor boundary value of the third distributed data generation efficiency characteristic factor sequence; Determine a third largest characteristic factor boundary value of the third distributed data generation efficiency characteristic factor sequence; Determine a boundary range of a third characteristic factor according to the boundary value of the third smallest characteristic factor and the boundary value of the third largest characteristic factor; Analyzing the distributed data generation efficiency characteristic factors in the third distributed data generation efficiency characteristic factor sequence, and counting the number of the third distributed data generation efficiency characteristic factors falling within the boundary range of the third characteristic factor; A historical distributed data generation efficiency coefficient of the data distributed processing system is calculated based on the first distributed data generation efficiency characteristic factor quantity, the second distributed data generation efficiency characteristic factor quantity and the third distributed data generation efficiency characteristic factor quantity.

[0011] Further, when calculating the historical distributed data generation efficiency coefficient of the data distributed processing system based on the first distributed data generation efficiency characteristic factor quantity, the second distributed data generation efficiency characteristic factor quantity and the third distributed data generation efficiency characteristic factor quantity, it includes: The historical distributed data generation efficiency coefficient of the data distributed processing system is calculated according to the following formula: ; Among them, h is the historical distributed data generation efficiency coefficient of the data distributed processing system, k1 is the number of the first distributed data generation efficiency characteristic factors, k2 is the number of the second distributed data generation efficiency characteristic factors, k3 is the number of the third distributed data generation efficiency characteristic factors, j1 is the first calculation coefficient, j2 is the second calculation coefficient, j3 is the third calculation coefficient, and j1<j2<j3, j1>0, j2>0, j3>0.

[0012] Further, when all the data distributed processing systems are grouped based on the historical distributed data generation efficiency coefficient to obtain a high-efficiency data distributed processing system group and a low-efficiency data distributed processing system group, it includes: Calculate the mean of the distributed data generation efficiency coefficients of all historical distributed data generation efficiency coefficients; Grouping all data distributed processing systems according to the mean value of the distributed data generation efficiency coefficient; When the historical distributed data generation efficiency coefficient is less than the distributed data generation efficiency coefficient mean, the corresponding data distributed processing system is classified into the low-efficiency data distributed processing system group; When the historical distributed data generation efficiency coefficient is greater than or equal to the distributed data generation efficiency coefficient mean, the corresponding data distributed processing system is assigned to the high-efficiency data distributed processing system group.

[0013] Further, when calculating the high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group based on the high-efficiency data distributed processing system group and the low-efficiency data distributed processing system group, it includes: The high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group is calculated according to the following formula; ; Among them, c is the high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group, v1 is the first adjustment coefficient, b1 x Generate the efficiency coefficient for the xth historical distributed data in the high-efficiency data distributed processing system group, b2 z Generate an efficiency coefficient for the zth historical distributed data in the low-efficiency data distributed processing system group, For all The minimum value of For all , v2 is the second adjustment coefficient, n1 is the number of historical distributed data generation efficiency coefficients in the high-efficiency data distributed processing system group, and n2 is the number of historical distributed data generation efficiency coefficients in the low-efficiency data distributed processing system group.

[0014] Further, when constraining the current data update time interval based on the high-efficiency data distributed processing constraint coefficient to obtain the constrained data update time interval, it includes: Preset multiple preset high-efficiency data distributed processing constraint coefficients; Presetting a plurality of preset update time interval constraint factors; According to the relationship between the high-efficiency data distributed processing constraint coefficient and each preset high-efficiency data distributed processing constraint coefficient, selecting a corresponding preset update time interval constraint factor; The product value of the selected preset update time interval constraint factor and the current data update time interval is calculated as the constrained data update time interval.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a data consistency guarantee and transaction management method based on event-driven, which obtains data distributed event information of a data distributed processing system, calculates the constraint coefficient of the data distributed processing system, generates a data consistency guarantee transaction management group; generates a historical distributed data generation efficiency sequence; calculates the historical distributed data generation efficiency coefficient, obtains a high-efficiency and low-efficiency data distributed processing system group; calculates the high-efficiency data distributed processing constraint coefficient, constrains the data update time interval, obtains the constrained data update time interval, and performs data consistency guarantee transaction management. Through system constraints, the prerequisites for data consistency processing are effectively ensured, and through time constraints, the data processing time intervals of different distributed systems are balanced, the imbalance of data processing order is avoided, data conflicts and inconsistency problems are eliminated, and the dynamic, collaborative and real-time nature of data consistency processing is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flow chart of an event-driven data consistency assurance and transaction management method in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0017] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0018] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0019] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0020] like Figure 1 As shown, an embodiment of the present invention discloses a data consistency assurance and transaction management method based on event-driven, comprising: S110: Determine multiple data distributed processing systems, obtain data distributed event information corresponding to each data distributed processing system, calculate the data distributed processing system constraint coefficient of every two data distributed processing systems based on the data distributed event information, and generate a data consistency assurance transaction management group; In some embodiments of the present application, when obtaining data distributed event information corresponding to each data distributed processing system, calculating the data distributed processing system constraint coefficient of every two data distributed processing systems based on the data distributed event information, and generating a data consistency assurance transaction management group, it includes: Extracting data distributed event information corresponding to each data distributed processing system, wherein the data distributed event information includes a data distributed event process range and a data distributed event flow direction range; Determine the data distributed event process coupling degree based on the data distributed event process range corresponding to each two data distributed processing systems; Determine the data distributed event flow direction coupling degree based on the data distributed event flow direction range corresponding to every two data distributed processing systems; Determine the bidirectional coupling degree of data distributed events based on the data distributed event process range and the data distributed event flow direction range corresponding to every two data distributed processing systems; Calculate the data distributed processing system constraint coefficient of every two data distributed processing systems according to the data distributed event process coupling degree, the distributed event flow direction coupling degree and the data distributed event bidirectional coupling degree; When the constraint coefficient of the data distributed processing system is greater than or equal to the preset constraint coefficient of the data distributed processing system, a data consistency assurance transaction management identifier is generated for the two data distributed processing systems; Randomly selecting two data distributed processing systems for the second time, and calculating the constraint coefficient of the second data distributed processing system, and judging whether a data consistency assurance transaction management identifier can be generated for the two data distributed processing systems randomly selected for the second time based on the constraint coefficient of the second data distributed processing system; When the constraint coefficient of the data distributed processing system is less than the preset constraint coefficient of the data distributed processing system, two data distributed processing systems are randomly selected for the second time; Randomly extract a1 times two data distributed processing systems to obtain multiple data consistency guarantee transaction management identifiers; The number of data consistency assurance transaction management identifiers corresponding to each data distributed processing system is counted and sorted from large to small, and the data distributed processing systems corresponding to the first a2 numbers of data consistency assurance transaction management identifiers are selected to generate the data consistency assurance transaction management group.

[0021] In this embodiment, the data distributed event information includes the data distributed event process scope and the data distributed event flow direction scope, wherein the data distributed event process scope refers to the overall scope of the systems, modules and their interactive relationships involved in the data processing and operation related to a certain business event in the data distributed system. It describes the flow and processing process of data between various systems during the entire life cycle from triggering to completion of the event, including order creation, payment processing, logistics distribution, etc. The data distributed event flow direction scope refers to the direction and path of data flow in the distributed system and the interactive relationship between the nodes and systems involved. For example, after the order system generates an order, the data flows to the payment system for payment processing, and then flows to the logistics system for distribution arrangement, such as the warehousing system provides inventory information to the logistics system, and the logistics system feeds back the distribution status to the warehousing system.

[0022] In this embodiment, the preset data distributed processing system constraint coefficient is preferably 6, and can be adjusted according to actual conditions.

[0023] In this embodiment, when the constraint coefficient of the data distributed processing system is greater than or equal to the preset data distributed processing system constraint coefficient, a data consistency assurance transaction management identifier is generated for each data distributed processing system, that is, two data consistency assurance transaction management identifiers are generated.

[0024] In this embodiment, the setting of a1 is consistent with the number of data distributed processing systems. For example, if there are 8 data distributed processing systems, two data distributed processing systems are randomly extracted 8 times.

[0025] In this embodiment, a2 is preferably 5, and can be adjusted according to actual conditions.

[0026] The beneficial effects of the above technical scheme are as follows: the data distributed event flow coupling degree calculated by the present invention can reflect the correlation between the data distributed event flow ranges of the two data distributed processing systems, the data distributed event flow direction coupling degree calculated can reflect the correlation between the data distributed event flow direction ranges of the two data distributed processing systems, and the data distributed event bidirectional coupling degree calculated can reflect the cross-correlation between the data distributed event flow ranges and the data distributed event flow direction ranges of the two data distributed processing systems. The data distributed processing system constraint coefficient of each two data distributed processing systems is calculated based on the data distributed event flow coupling degree, the distributed event flow direction coupling degree and the data distributed event bidirectional coupling degree, which lays the foundation for the division of data consistency guarantee transaction management groups and provides a basis for the division. At the same time, the generation of data consistency guarantee transaction management groups can provide reliable prerequisites for data consistency processing and avoid the intrusion of some inconsistent data.

[0027] In some embodiments of the present application, the data distributed event flow coupling degree corresponding to every two data distributed processing systems is calculated according to the following formula: ; Among them, q1 is the data distributed event process coupling degree corresponding to each two data distributed processing systems, is the number of intersections of the data distributed event flow ranges corresponding to every two data distributed processing systems, The number of unions of data distributed event process ranges corresponding to every two data distributed processing systems; The data distributed event flow coupling degree corresponding to each two data distributed processing systems is calculated according to the following formula: ; Among them, q2 is the data distributed event flow coupling degree corresponding to each two data distributed processing systems, is the number of intersections of the data distribution event flow ranges corresponding to every two data distribution processing systems, The number of unions of data distribution event flow ranges corresponding to every two data distribution processing systems; The bidirectional coupling degree of data distributed events corresponding to each two data distributed processing systems is calculated according to the following formula: ; Among them, q3 is the bidirectional coupling degree of data distributed events corresponding to every two data distributed processing systems; The data distributed processing system constraint coefficient of each two data distributed processing systems is calculated according to the following formula: ; Among them, y is the data distributed processing system constraint coefficient of every two data distributed processing systems, t1 is the total number of data distributed event flow ranges and data distributed event flow direction ranges corresponding to one data distributed processing system, t2 is the total number of data distributed event flow ranges and data distributed event flow direction ranges corresponding to another data distributed processing system, max is the maximum value symbol, and min is the minimum value symbol.

[0028] In this embodiment, the following example illustrates the method of determining the intersection and union: Intersection and union of data distributed event flow ranges: 1. Number of intersections Definition: The number of links that two data distribution systems jointly participate in within the scope of the data distribution event flow.

[0029] Judgment method: List the event processes in which the two data distribution systems participate respectively, and find out the event process links in which the two data distribution systems participate together.

[0030] Example: Event flow in which data distribution system A participates: order creation, payment processing, and logistics delivery.

[0031] Event processes in which data distribution system B participates: payment processing, inventory deduction, and logistics distribution.

[0032] Intersection: payment processing, logistics and distribution.

[0033] Number of intersections: 2.

[0034] 2. Number of unions Definition: The number of all participating links in the event flow of two data distribution systems (after deduplication).

[0035] Judgment method: List the event flows that the two data distribution systems participate in. Merge the event flow links of the two data distribution systems and remove duplicates.

[0036] Example: Event flow in which data distribution system A participates: order creation, payment processing, and logistics delivery.

[0037] Event processes in which data distribution system B participates: payment processing, inventory deduction, and logistics distribution.

[0038] Combined: order creation, payment processing, logistics distribution, inventory deduction.

[0039] Number of unions: 4.

[0040] 2. Intersection and Union of Data Distribution Event Flow Ranges 1. Number of intersections Definition: The number of flows that two data distribution systems jointly participate in in the data flow path.

[0041] Judgment method: List the data flow paths of the two data distribution systems. Find the data flow paths that the two data distribution systems participate in together.

[0042] Example: Data flow path of data distribution system A: order system → payment system, order system → logistics system.

[0043] The data flow path of data distribution system B: payment system → logistics system, payment system → warehousing system.

[0044] Intersection: payment system → logistics system.

[0045] Number of intersections: 1.

[0046] 2. Number of unions Definition: The number of all participating flows in the data flow path between two data distribution systems (after deduplication).

[0047] Judgment method: List the data flow paths of the two data distribution systems. Merge the data flow paths of the two data distribution systems and remove duplicates.

[0048] Example: Data flow path of data distribution system A: order system → payment system, order system → logistics system.

[0049] The data flow path of data distribution system B: payment system → logistics system, payment system → warehousing system.

[0050] Union: order system → payment system, order system → logistics system, payment system → logistics system, payment system → warehousing system.

[0051] Number of unions: 4.

[0052] It should be noted that this is just an example and the specific situation may be determined based on the actual situation.

[0053] S120: collecting multiple historical distributed data generation efficiencies corresponding to each data distributed processing system in the data consistency assurance transaction management group, and generating a historical distributed data generation efficiency sequence based on the collection time sequence; In this embodiment, the historical distributed data generation efficiency refers to the data generation efficiency in the data distributed processing system, such as the generation efficiency of order creation mentioned above.

[0054] In this embodiment, a historical distributed data generation efficiency sequence is generated based on the collection time sequence, that is, all historical distributed data generation efficiencies are sorted according to the collection time to generate a historical distributed data generation efficiency sequence.

[0055] S130: Analyze the historical distributed data generation efficiency sequence, calculate the historical distributed data generation efficiency coefficient of the data distributed processing system based on the analysis result, and group all the data distributed processing systems based on the historical distributed data generation efficiency coefficient to obtain a high-efficiency data distributed processing system group and a low-efficiency data distributed processing system group; In some embodiments of the present application, when analyzing the historical distributed data generation efficiency sequence and calculating the historical distributed data generation efficiency coefficient of the data distributed processing system based on the analysis result, it includes: Calculating a distributed data generation efficiency characteristic factor of each historical distributed data generation efficiency in the historical distributed data generation efficiency sequence; Calculate the characteristic factor means and characteristic factor variances corresponding to all distributed data generation efficiency characteristic factors, take the minimum value of the characteristic factor means and the characteristic factor variances as the first partition characteristic factor, and take the maximum value of the characteristic factor means and the characteristic factor variances as the second partition characteristic factor; Sequentially divide all distributed data generation efficiency characteristic factors according to the first division characteristic factor and the second division characteristic factor, and divide all distributed data generation efficiency characteristic factors that are smaller than the first division characteristic factor into a first distributed data generation efficiency characteristic factor sequence; dividing all distributed data generation efficiency characteristic factors greater than or equal to the first division characteristic factor and less than the second division characteristic factor into a second distributed data generation efficiency characteristic factor sequence; dividing all distributed data generation efficiency characteristic factors greater than or equal to the second division characteristic factor into a third distributed data generation efficiency characteristic factor sequence; The historical distributed data generation efficiency coefficient of the data distributed processing system is calculated according to the first distributed data generation efficiency characteristic factor sequence, the second distributed data generation efficiency characteristic factor sequence and the third distributed data generation efficiency characteristic factor sequence.

[0056] In this embodiment, the distributed data generation efficiency characteristic factor refers to the characteristic degree of each historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, and is calculated based on all historical distributed data generation efficiencies in the historical distributed data generation efficiency sequence.

[0057] In this embodiment, the mean corresponding to all the distributed data generation efficiency characteristic factors is calculated as the characteristic factor mean, and the variance corresponding to all the distributed data generation efficiency characteristic factors is calculated as the characteristic factor variance.

[0058] In this embodiment, the size relationship between the characteristic factor mean and the characteristic factor variance is compared to determine the minimum and maximum values. In special cases, if the characteristic factor mean is equal to the characteristic factor variance, all distributed data generation efficiency characteristic factors that are less than the characteristic factor mean and the characteristic factor variance are divided into the first distributed data generation efficiency characteristic factor sequence, and all distributed data generation efficiency characteristic factors that are greater than or equal to the characteristic factor mean and the characteristic factor variance are divided into the second distributed data generation efficiency characteristic factor sequence. In other words, the third distributed data generation efficiency characteristic factor sequence is not divided, and the calculation method is the same as above, that is, one sequence is missing.

[0059] The beneficial effect of the above technical solution is that the present invention realizes the refined division of the characteristic factors of distributed data generation efficiency according to the characteristic factor mean and the characteristic factor variance, laying a foundation for calculating the historical distributed data generation efficiency coefficient.

[0060] In some embodiments of the present application, when calculating the distributed data generation efficiency characteristic factor of each historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, it includes: For any historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, the historical distributed data generation efficiencies on the left and right sides of the historical distributed data generation efficiency are used as neighborhood historical distributed data generation efficiencies; The distributed data generation efficiency characteristic factor of each historical distributed data generation efficiency in the historical distributed data generation efficiency sequence is calculated according to the following formula: ; Among them, u is the distributed data generation efficiency characteristic factor of the historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, and p i is the change value of the i-th historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, p1 is the mean change value of all change values ​​in the historical distributed data generation efficiency sequence, m is the number of neighborhood historical distributed data generation efficiencies of the historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, p i，s is the change value of the s-th neighborhood historical distributed data generation efficiency of the ith historical distributed data generation efficiency in the historical distributed data generation efficiency sequence.

[0061] In this embodiment, for ease of understanding, an example is given here. For example, if the historical distributed data generation efficiency sequence is {ΔS1, ΔS2, ΔS3, ΔS4, ΔS5, ΔS6}, any one historical distributed data generation efficiency is selected, such as ΔS3, and ΔS1, ΔS2, ΔS4, ΔS5, ΔS6 are used as the neighborhood historical distributed data generation efficiency. The change value of ΔS3 is ΔS2-ΔS3, and then the absolute value is taken. The number of m in the formula is 5, and the change value of the sth neighborhood historical distributed data generation efficiency of the i-th historical distributed data generation efficiency is, for example, ΔS5 is extracted first, and the change value of ΔS5 is ΔS4-ΔS5, that is, p i，s It is ΔS4-ΔS5. It should be noted that the absolute value is not taken here. The absolute value is taken in the final summation. The change value of ΔS1 is itself, that is, ΔS1.

[0062] In some embodiments of the present application, when calculating the historical distributed data generation efficiency coefficient of the data distributed processing system according to the first distributed data generation efficiency characteristic factor sequence, the first distributed data generation efficiency characteristic factor sequence and the first distributed data generation efficiency characteristic factor sequence, it includes: Determine a small characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence; ; Among them, d1 is the small characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence, f1 is a preset factor, and its value range is [0.1, 0.2], and g1 is the mean value corresponding to the first distributed data generation efficiency characteristic factor sequence; Determine a large characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence; ; Wherein, d2 is the large characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence; Determine a characteristic factor boundary range according to the small characteristic factor boundary value and the large characteristic factor boundary value; Analyzing the distributed data generation efficiency characteristic factors in the first distributed data generation efficiency characteristic factor sequence, and counting the number of the first distributed data generation efficiency characteristic factors falling within the characteristic factor boundary range; Determine a second minimum characteristic factor boundary value of the second distributed data generation efficiency characteristic factor sequence; Determine a second largest characteristic factor boundary value of the second distributed data generation efficiency characteristic factor sequence; Determine a second characteristic factor boundary range according to the second smallest characteristic factor boundary value and the second largest characteristic factor boundary value; Analyzing the distributed data generation efficiency characteristic factors in the second distributed data generation efficiency characteristic factor sequence, and counting the number of second distributed data generation efficiency characteristic factors falling within a boundary range of the second characteristic factor; Determine a third minimum characteristic factor boundary value of the third distributed data generation efficiency characteristic factor sequence; Determine a third largest characteristic factor boundary value of the third distributed data generation efficiency characteristic factor sequence; Determine a boundary range of a third characteristic factor according to the boundary value of the third smallest characteristic factor and the boundary value of the third largest characteristic factor; Analyzing the distributed data generation efficiency characteristic factors in the third distributed data generation efficiency characteristic factor sequence, and counting the number of the third distributed data generation efficiency characteristic factors falling within the boundary range of the third characteristic factor; A historical distributed data generation efficiency coefficient of the data distributed processing system is calculated based on the first distributed data generation efficiency characteristic factor quantity, the second distributed data generation efficiency characteristic factor quantity and the third distributed data generation efficiency characteristic factor quantity.

[0063] In this embodiment, when counting the number of first distributed data generation efficiency characteristic factors falling within the characteristic factor boundary range, if the distributed data generation efficiency characteristic factor is equal to the small characteristic factor boundary value or equal to the large characteristic factor boundary value, the number is also counted.

[0064] In this embodiment, the calculation method of the second smallest characteristic factor boundary value and the second largest characteristic factor boundary value is not introduced here. It is consistent with the first smallest characteristic factor boundary value and the first largest characteristic factor boundary value, and adaptive calculation is sufficient.

[0065] In this embodiment, the calculation method of the third smallest characteristic factor boundary value and the third largest characteristic factor boundary value is not introduced here. It is consistent with the first smallest characteristic factor boundary value and the first largest characteristic factor boundary value, and adaptive calculation is sufficient.

[0066] The beneficial effect of the above technical solution is: the present invention calculates the historical distributed data generation efficiency coefficient of the data distributed processing system based on the number of the first distributed data generation efficiency characteristic factors, the number of the second distributed data generation efficiency characteristic factors and the number of the third distributed data generation efficiency characteristic factors, thereby ensuring the calculation accuracy of the historical distributed data generation efficiency coefficient, eliminating the need for manual participation in the calculation, improving the calculation accuracy, and avoiding calculation errors. At the same time, the historical distributed data generation efficiency coefficient can reflect the historical data generation rate of the data distributed processing system, providing a reliable basis for data consistency processing and data update time interval constraints.

[0067] In some embodiments of the present application, when calculating the historical distributed data generation efficiency coefficient of the data distributed processing system based on the first distributed data generation efficiency characteristic factor quantity, the second distributed data generation efficiency characteristic factor quantity, and the third distributed data generation efficiency characteristic factor quantity, it includes: The historical distributed data generation efficiency coefficient of the data distributed processing system is calculated according to the following formula: ; Among them, h is the historical distributed data generation efficiency coefficient of the data distributed processing system, k1 is the number of the first distributed data generation efficiency characteristic factors, k2 is the number of the second distributed data generation efficiency characteristic factors, k3 is the number of the third distributed data generation efficiency characteristic factors, j1 is the first calculation coefficient, j2 is the second calculation coefficient, j3 is the third calculation coefficient, and j1<j2<j3, j1>0, j2>0, j3>0.

[0068] In some embodiments of the present application, when all data distributed processing systems are grouped based on the historical distributed data generation efficiency coefficient to obtain a high-efficiency data distributed processing system group and a low-efficiency data distributed processing system group, it includes: Calculate the mean of the distributed data generation efficiency coefficients of all historical distributed data generation efficiency coefficients; Grouping all data distributed processing systems according to the mean value of the distributed data generation efficiency coefficient; When the historical distributed data generation efficiency coefficient is less than the distributed data generation efficiency coefficient mean, the corresponding data distributed processing system is classified into the low-efficiency data distributed processing system group; When the historical distributed data generation efficiency coefficient is greater than or equal to the distributed data generation efficiency coefficient mean, the corresponding data distributed processing system is assigned to the high-efficiency data distributed processing system group.

[0069] In this embodiment, the average of all historical distributed data generation efficiency coefficients is calculated as the average of the distributed data generation efficiency coefficients.

[0070] In this embodiment, the data distributed processing systems in the low-efficiency data distributed processing system group have a lower data processing efficiency, and the data distributed processing systems in the high-efficiency data distributed processing system group have a higher data processing efficiency.

[0071] The beneficial effect of the above technical solution is: the present invention groups all data distributed processing systems according to the mean value of the distributed data generation efficiency coefficient to obtain a low-efficiency data distributed processing system group and a high-efficiency data distributed processing system group, thereby providing a prerequisite guarantee for the constraints of the high-efficiency data distributed processing system group to avoid errors.

[0072] S140: Calculating a high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group based on the high-efficiency data distributed processing system group and the low-efficiency data distributed processing system group; In some embodiments of the present application, when calculating the high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group based on the high-efficiency data distributed processing system group and the low-efficiency data distributed processing system group, it includes: The high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group is calculated according to the following formula; ; Among them, c is the high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group, v1 is the first adjustment coefficient, b1x Generate the efficiency coefficient for the xth historical distributed data in the high-efficiency data distributed processing system group, b2 z Generate an efficiency coefficient for the zth historical distributed data in the low-efficiency data distributed processing system group, For all The minimum value of For all , v2 is the second adjustment coefficient, n1 is the number of historical distributed data generation efficiency coefficients in the high-efficiency data distributed processing system group, and n2 is the number of historical distributed data generation efficiency coefficients in the low-efficiency data distributed processing system group.

[0073] In this embodiment, v1<v2, and v1+v2=1, wherein the first adjustment coefficient is preferably 0.3, and the second adjustment coefficient is preferably 0.7, which can be adaptively adjusted according to actual conditions.

[0074] The beneficial effect of the above technical solution is: by cross- and combined processing calculations on the high-efficiency data distributed processing system group and the low-efficiency data distributed processing system group, a high-efficiency data distributed processing constraint coefficient is obtained. The high-efficiency data distributed processing constraint coefficient can be used to constrain the data update time interval of the high-efficiency data distributed processing system group to ensure the constraint accuracy.

[0075] S150: Determine the current data update time interval of the data distributed processing system in the high-efficiency data distributed processing system group, constrain the current data update time interval based on the high-efficiency data distributed processing constraint coefficient to obtain the constrained data update time interval, and perform data consistency assurance transaction management according to the constrained data update time interval.

[0076] In this embodiment, as mentioned above, after the order is created, it will be reviewed, revised, etc. The time spent on the review, revision, etc. will be used as the current data update time interval. For example, if the review, revision, etc. time is 10 minutes, then the current data update time interval will be determined to be 10 minutes. The specific time interval can be obtained according to the actual situation.

[0077] In some embodiments of the present application, when constraining the current data update time interval based on the high-efficiency data distributed processing constraint coefficient to obtain the constrained data update time interval, it includes: Preset multiple preset high-efficiency data distributed processing constraint coefficients; Presetting a plurality of preset update time interval constraint factors; According to the relationship between the high-efficiency data distributed processing constraint coefficient and each preset high-efficiency data distributed processing constraint coefficient, selecting a corresponding preset update time interval constraint factor; The product value of the selected preset update time interval constraint factor and the current data update time interval is calculated as the constrained data update time interval.

[0078] In this embodiment, multiple preset high-efficiency data distributed processing constraint coefficients are pre-set, where the number of preset high-efficiency data distributed processing constraint coefficients is preferably 3, including a first preset high-efficiency data distributed processing constraint coefficient, a second preset high-efficiency data distributed processing constraint coefficient and a third preset high-efficiency data distributed processing constraint coefficient, wherein the first preset high-efficiency data distributed processing constraint coefficient is preferably 0.3, the second preset high-efficiency data distributed processing constraint coefficient is preferably 0.6, and the third preset high-efficiency data distributed processing constraint coefficient is preferably 0.9, and the specific value can also be adjusted according to actual conditions.

[0079] In this embodiment, multiple preset update time interval constraint factors are pre-set, where the number of preset update time interval constraint factors is preferably 4, including a first preset update time interval constraint factor, a second preset update time interval constraint factor, a third preset update time interval constraint factor and a fourth preset update time interval constraint factor, wherein the first preset update time interval constraint factor is preferably 1.1, the second preset update time interval constraint factor is preferably 1.15, the third preset update time interval constraint factor is preferably 1.2, and the fourth preset update time interval constraint factor is preferably 1.25, and the specific value can also be adjusted according to actual conditions.

[0080] In this embodiment, when the high-efficiency data distributed processing constraint coefficient is less than the first preset high-efficiency data distributed processing constraint coefficient, the first product value of the first preset update time interval constraint factor and the current data update time interval is calculated as the constrained data update time interval; when the high-efficiency data distributed processing constraint coefficient is greater than or equal to the first preset high-efficiency data distributed processing constraint coefficient and is less than the second preset high-efficiency data distributed processing constraint coefficient, the second product value of the second preset update time interval constraint factor and the current data update time interval is calculated as the constrained data update time interval; when the high-efficiency data distributed processing constraint coefficient is greater than or equal to the second preset high-efficiency data distributed processing constraint coefficient and is less than the third preset high-efficiency data distributed processing constraint coefficient, the third product value of the third preset update time interval constraint factor and the current data update time interval is calculated as the constrained data update time interval; when the high-efficiency data distributed processing constraint coefficient is greater than or equal to the third preset high-efficiency data distributed processing constraint coefficient, the fourth product value of the fourth preset update time interval constraint factor and the current data update time interval is calculated as the constrained data update time interval.

[0081] The beneficial effect of the above technical solution is: the present invention selects the corresponding preset update time interval constraint factor according to the relationship between the high-efficiency data distributed processing constraint coefficient and each preset high-efficiency data distributed processing constraint coefficient, calculates the product value of the selected preset update time interval constraint factor and the current data update time interval as the constrained data update time interval, and the preset update time interval constraint factor can be used to accurately constrain the current data update time interval and balance the data processing time intervals of different distributed systems. For example, if the current data update time interval is 10 minutes, the third preset update time interval constraint factor is selected for constraint, and the constrained data update time interval is 12 minutes, that is, the generated data is updated and released after 12 minutes, which can avoid imbalance in data processing order, eliminate data conflicts and inconsistency problems, and ensure the dynamic, collaborative and real-time nature of data consistency processing.

[0082] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0083] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0084] Those skilled in the art can understand that the above are only 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 aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data consistency assurance and transaction management method based on event-driven, characterized in that: include: Determine multiple data distributed processing systems, obtain data distributed event information corresponding to each data distributed processing system, calculate the data distributed processing system constraint coefficient of every two data distributed processing systems based on the data distributed event information, and generate a data consistency assurance transaction management group; Collecting multiple historical distributed data generation efficiencies corresponding to each data distributed processing system in the data consistency assurance transaction management group, and generating a historical distributed data generation efficiency sequence based on the collection time sequence; Analyze the historical distributed data generation efficiency sequence, calculate the historical distributed data generation efficiency coefficient of the data distributed processing system based on the analysis result, and group all the data distributed processing systems based on the historical distributed data generation efficiency coefficient to obtain a high-efficiency data distributed processing system group and a low-efficiency data distributed processing system group; Calculating a high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group based on the high-efficiency data distributed processing system group and the low-efficiency data distributed processing system group; Determine the current data update time interval of the data distributed processing system in the high-efficiency data distributed processing system group, constrain the current data update time interval based on the high-efficiency data distributed processing constraint coefficient to obtain the constrained data update time interval, and perform data consistency assurance transaction management according to the constrained data update time interval.

2. The event-driven data consistency assurance and transaction management method according to claim 1 is characterized in that: When obtaining data distributed event information corresponding to each data distributed processing system, calculating the data distributed processing system constraint coefficient of every two data distributed processing systems based on the data distributed event information, and generating a data consistency assurance transaction management group, it includes: Extracting data distributed event information corresponding to each data distributed processing system, wherein the data distributed event information includes a data distributed event process range and a data distributed event flow direction range; Determine the data distributed event process coupling degree based on the data distributed event process range corresponding to each two data distributed processing systems; Determine the data distributed event flow direction coupling degree based on the data distributed event flow direction range corresponding to every two data distributed processing systems; Determine the bidirectional coupling degree of data distributed events based on the data distributed event process range and the data distributed event flow direction range corresponding to every two data distributed processing systems; Calculate the data distributed processing system constraint coefficient of every two data distributed processing systems according to the data distributed event process coupling degree, the distributed event flow direction coupling degree and the data distributed event bidirectional coupling degree; When the constraint coefficient of the data distributed processing system is greater than or equal to the preset constraint coefficient of the data distributed processing system, a data consistency assurance transaction management identifier is generated for the two data distributed processing systems; Randomly selecting two data distributed processing systems for the second time, and calculating the constraint coefficient of the second data distributed processing system, and judging whether a data consistency assurance transaction management identifier can be generated for the two data distributed processing systems randomly selected for the second time based on the constraint coefficient of the second data distributed processing system; When the constraint coefficient of the data distributed processing system is less than the preset constraint coefficient of the data distributed processing system, two data distributed processing systems are randomly selected for the second time; Randomly extract a1 times two data distributed processing systems to obtain multiple data consistency guarantee transaction management identifiers; The number of data consistency assurance transaction management identifiers corresponding to each data distributed processing system is counted and sorted from large to small, and the data distributed processing systems corresponding to the first a2 numbers of data consistency assurance transaction management identifiers are selected to generate the data consistency assurance transaction management group.

3. The event-driven data consistency assurance and transaction management method according to claim 2 is characterized in that: The data distributed event flow coupling degree corresponding to each two data distributed processing systems is calculated according to the following formula: ； Among them, q1 is the data distributed event process coupling degree corresponding to each two data distributed processing systems, is the number of intersections of the data distributed event flow ranges corresponding to every two data distributed processing systems, The number of unions of data distributed event process ranges corresponding to every two data distributed processing systems; The data distributed event flow coupling degree corresponding to each two data distributed processing systems is calculated according to the following formula: ； Among them, q2 is the data distributed event flow coupling degree corresponding to each two data distributed processing systems, is the number of intersections of the data distribution event flow ranges corresponding to every two data distribution processing systems, The number of unions of data distribution event flow ranges corresponding to every two data distribution processing systems; The bidirectional coupling degree of data distributed events corresponding to each two data distributed processing systems is calculated according to the following formula: ； Among them, q3 is the bidirectional coupling degree of data distributed events corresponding to every two data distributed processing systems; The data distributed processing system constraint coefficient of each two data distributed processing systems is calculated according to the following formula: ； Among them, y is the data distributed processing system constraint coefficient of every two data distributed processing systems, t1 is the total number of data distributed event flow ranges and data distributed event flow direction ranges corresponding to one data distributed processing system, t2 is the total number of data distributed event flow ranges and data distributed event flow direction ranges corresponding to another data distributed processing system, max is the maximum value symbol, and min is the minimum value symbol.

4. The event-driven data consistency assurance and transaction management method according to claim 1 is characterized in that: When analyzing the historical distributed data generation efficiency sequence and calculating the historical distributed data generation efficiency coefficient of the data distributed processing system based on the analysis result, it includes: Calculating a distributed data generation efficiency characteristic factor of each historical distributed data generation efficiency in the historical distributed data generation efficiency sequence; Calculate the characteristic factor means and characteristic factor variances corresponding to all distributed data generation efficiency characteristic factors, take the minimum value of the characteristic factor means and the characteristic factor variances as the first partition characteristic factor, and take the maximum value of the characteristic factor means and the characteristic factor variances as the second partition characteristic factor; Sequentially divide all distributed data generation efficiency characteristic factors according to the first division characteristic factor and the second division characteristic factor, and divide all distributed data generation efficiency characteristic factors that are smaller than the first division characteristic factor into a first distributed data generation efficiency characteristic factor sequence; dividing all distributed data generation efficiency characteristic factors greater than or equal to the first division characteristic factor and less than the second division characteristic factor into a second distributed data generation efficiency characteristic factor sequence; dividing all distributed data generation efficiency characteristic factors greater than or equal to the second division characteristic factor into a third distributed data generation efficiency characteristic factor sequence; The historical distributed data generation efficiency coefficient of the data distributed processing system is calculated according to the first distributed data generation efficiency characteristic factor sequence, the second distributed data generation efficiency characteristic factor sequence and the third distributed data generation efficiency characteristic factor sequence.

5. The event-driven data consistency assurance and transaction management method according to claim 4 is characterized in that: When calculating the distributed data generation efficiency characteristic factor of each historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, it includes: For any historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, the historical distributed data generation efficiencies on the left and right sides of the historical distributed data generation efficiency are used as neighborhood historical distributed data generation efficiencies; The distributed data generation efficiency characteristic factor of each historical distributed data generation efficiency in the historical distributed data generation efficiency sequence is calculated according to the following formula: ； Among them, u is the distributed data generation efficiency characteristic factor of the historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, and p i is the change value of the i-th historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, p1 is the mean change value of all change values ​​in the historical distributed data generation efficiency sequence, m is the number of neighborhood historical distributed data generation efficiencies of the historical distributed data generation efficiency in the historical distributed data generation efficiency sequence, p i，s is the change value of the s-th neighborhood historical distributed data generation efficiency of the ith historical distributed data generation efficiency in the historical distributed data generation efficiency sequence.

6. The event-driven data consistency assurance and transaction management method according to claim 4 is characterized in that: When calculating the historical distributed data generation efficiency coefficient of the data distributed processing system according to the first distributed data generation efficiency characteristic factor sequence, the first distributed data generation efficiency characteristic factor sequence and the first distributed data generation efficiency characteristic factor sequence, it includes: Determine a small characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence; ； Among them, d1 is the small characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence, f1 is a preset factor, and the value range is [0.1, 0.2], and g1 is the mean value corresponding to the first distributed data generation efficiency characteristic factor sequence; Determine a large characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence; ； Wherein, d2 is the large characteristic factor boundary value of the first distributed data generation efficiency characteristic factor sequence; Determine a characteristic factor boundary range according to the small characteristic factor boundary value and the large characteristic factor boundary value; Analyzing the distributed data generation efficiency characteristic factors in the first distributed data generation efficiency characteristic factor sequence, and counting the number of first distributed data generation efficiency characteristic factors falling within the characteristic factor boundary range; Determine a second minimum characteristic factor boundary value of the second distributed data generation efficiency characteristic factor sequence; Determine a second largest characteristic factor boundary value of the second distributed data generation efficiency characteristic factor sequence; Determine a second characteristic factor boundary range according to the second smallest characteristic factor boundary value and the second largest characteristic factor boundary value; Analyzing the distributed data generation efficiency characteristic factors in the second distributed data generation efficiency characteristic factor sequence, and counting the number of second distributed data generation efficiency characteristic factors falling within a boundary range of the second characteristic factor; Determine a third minimum characteristic factor boundary value of the third distributed data generation efficiency characteristic factor sequence; Determine a third largest characteristic factor boundary value of the third distributed data generation efficiency characteristic factor sequence; Determine a boundary range of a third characteristic factor according to the boundary value of the third smallest characteristic factor and the boundary value of the third largest characteristic factor; Analyzing the distributed data generation efficiency characteristic factors in the third distributed data generation efficiency characteristic factor sequence, and counting the number of the third distributed data generation efficiency characteristic factors falling within the boundary range of the third characteristic factor; A historical distributed data generation efficiency coefficient of the data distributed processing system is calculated based on the first distributed data generation efficiency characteristic factor quantity, the second distributed data generation efficiency characteristic factor quantity and the third distributed data generation efficiency characteristic factor quantity.

7. The event-driven data consistency assurance and transaction management method according to claim 6 is characterized in that: When calculating the historical distributed data generation efficiency coefficient of the data distributed processing system based on the first distributed data generation efficiency characteristic factor quantity, the second distributed data generation efficiency characteristic factor quantity and the third distributed data generation efficiency characteristic factor quantity, it includes: The historical distributed data generation efficiency coefficient of the data distributed processing system is calculated according to the following formula: ； Among them, h is the historical distributed data generation efficiency coefficient of the data distributed processing system, k1 is the number of the first distributed data generation efficiency characteristic factors, k2 is the number of the second distributed data generation efficiency characteristic factors, k3 is the number of the third distributed data generation efficiency characteristic factors, j1 is the first calculation coefficient, j2 is the second calculation coefficient, j3 is the third calculation coefficient, and j1<j2<j3, j1>0, j2>0, j3>0.

8. The event-driven data consistency assurance and transaction management method according to claim 1 is characterized in that: When all the data distributed processing systems are grouped based on the historical distributed data generation efficiency coefficient to obtain a high-efficiency data distributed processing system group and a low-efficiency data distributed processing system group, the method includes: Calculate the mean of the distributed data generation efficiency coefficients of all historical distributed data generation efficiency coefficients; Grouping all data distributed processing systems according to the mean value of the distributed data generation efficiency coefficient; When the historical distributed data generation efficiency coefficient is less than the distributed data generation efficiency coefficient mean, the corresponding data distributed processing system is classified into the low-efficiency data distributed processing system group; When the historical distributed data generation efficiency coefficient is greater than or equal to the distributed data generation efficiency coefficient mean, the corresponding data distributed processing system is assigned to the high-efficiency data distributed processing system group.

9. The event-driven data consistency assurance and transaction management method according to claim 1, characterized in that: When calculating the high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group based on the high-efficiency data distributed processing system group and the low-efficiency data distributed processing system group, it includes: The high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group is calculated according to the following formula; ； Among them, c is the high-efficiency data distributed processing constraint coefficient of the high-efficiency data distributed processing system group, v1 is the first adjustment coefficient, b1 x Generate the efficiency coefficient for the xth historical distributed data in the high-efficiency data distributed processing system group, b2 z Generate an efficiency coefficient for the zth historical distributed data in the low-efficiency data distributed processing system group, For all The minimum value of For all , v2 is the second adjustment coefficient, n1 is the number of historical distributed data generation efficiency coefficients in the high-efficiency data distributed processing system group, and n2 is the number of historical distributed data generation efficiency coefficients in the low-efficiency data distributed processing system group.

10. The event-driven data consistency assurance and transaction management method according to claim 1, characterized in that: When constraining the current data update time interval based on the high-efficiency data distributed processing constraint coefficient to obtain the constrained data update time interval, the method includes: Preset multiple preset high-efficiency data distributed processing constraint coefficients; Presetting a plurality of preset update time interval constraint factors; According to the relationship between the high-efficiency data distributed processing constraint coefficient and each preset high-efficiency data distributed processing constraint coefficient, selecting a corresponding preset update time interval constraint factor; The product value of the selected preset update time interval constraint factor and the current data update time interval is calculated as the constrained data update time interval.

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