Event target analysis method and system based on block chain data analysis

Abstract

The invention discloses an event target analysis method and system based on block chain data analysis, and relates to the technical field of block chain data analysis, and the method comprises the steps: constructing a clear event expression model through fine event data extraction and timestamp sorting; an event-target mapping template is established, event factors are closely connected with a specific implementation target, and a visual event factor mapping tree is formed; according to the technical scheme, deep analysis of the block chain data is realized, and the internal relation between the event and the target is accurately revealed; by constructing the event-based target analysis database, real-time target determination is provided for the block chain system, and the accuracy and application value of data analysis are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of blockchain data analysis, and in particular to an event-based target analysis method and system based on blockchain data analysis. Background Art

[0002] In the current era of accelerated digital transformation, data analysis has become a key force in driving business decisions, optimizing resource allocation, and improving operational efficiency. However, with the explosive growth of data volume and the diversification of data sources, traditional data analysis methods have gradually exposed many limitations. Especially in the field of blockchain data analysis, due to the decentralized and distributed characteristics of blockchain technology, although the data has extremely high security and transparency, it also brings new challenges such as difficulty in data integration and weak correlation between events and targets.

[0003] On the one hand, the timeliness and integrity of blockchain data are its core value, but traditional data analysis methods often have difficulty effectively processing the massive, heterogeneous, and time-sensitive data in blockchain, resulting in chaotic data timeliness and difficulty in accurately reflecting the true order and correlation of events. On the other hand, although blockchain data is transparent and traceable, how to extract valuable information from this data and map it with specific business goals has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide an event-based target analysis method and system that can clarify the mapping relationship between events and business goals.

[0005] The present invention discloses an event-based target analysis method based on blockchain data analysis, comprising: Randomly select several monomer blockchains, analyze each monomer blockchain, determine the event data contained in different blocks, determine the timestamp corresponding to each event data, and sort the event data according to the timestamp to form an event data sequence, and build an event expression model according to the event data sequence, and associate the event expression model with the event data sequence; Constructing an event-goal mapping template, wherein the event-goal mapping template includes a number of implementation goals, and each implementation goal corresponds to an event factor mapping tree, wherein each branch of the event factor mapping tree includes a number of event factors, and if all event factors of the same branch are completely mapped, it is determined that the implementation goal corresponding to the event factor mapping tree is achieved; Randomly select a number of single blockchains, and reorganize the corresponding event data sequences according to the order of timestamps to obtain the overall event data sequence, and select an event factor mapping tree that matches the overall event data sequence from a number of event factor mapping trees, and record the selected event factor mapping tree as a reference event factor mapping tree, and determine the reference event factor mapping tree to be retained based on the relationship between the actual recorded realization goals and the realization goals of the reference event factor mapping tree, and record it as the comparison event factor mapping tree; Based on the overall event data sequence corresponding to the comparison event factor mapping tree, several event expression models corresponding to the comparison event factor mapping tree are determined, recorded as comparison event expression models, and the combination of the actual implementation goal, the comparison event factor mapping tree and several event expression models is recorded as the comparison blockchain data group; Several comparison blockchain data sets are constructed into an event-based target analysis database, and the event-based target database is used to determine the current implementation goals of the blockchain system.

[0006] In some embodiments disclosed in the present invention, a method for constructing an event expression model according to an event data sequence includes: Determine each event data in the event data sequence, construct an event node for each event data, and determine the time interval between different event nodes based on the timestamp corresponding to each event data; Based on the sequence between event nodes and the time interval between different event nodes, the event nodes are connected in sequence, and the length of the line segments between the event nodes is determined according to the time interval to obtain a time expression model.

[0007] In some embodiments disclosed in the present invention, a method for selecting an event factor mapping tree that matches the overall event data sequence from among a plurality of event factor mapping trees includes: Analyze the event data in the overall event data sequence, determine the event factor corresponding to each event data, and determine the order of the event factors based on the time relationship of the event factors, record the combination of event factors corresponding to each event data as an event factor group monomer, and sort the event factor group monomers based on the order of the event data in the overall event data sequence to obtain an event factor group sequence; Performing several random event factor extraction operations on the event factor group sequence, each random event factor extraction operation includes randomly selecting several event factors from the event factor group sequence, and combining them according to the original order of the event factors to obtain a random event factor group; The random event factor group is compared with each branch of different event factor mapping trees respectively. If the mapping ratio of the mapped event factors in the branch is greater than or equal to the preset value, the branch is identified as the mapped branch, and the matching sub-parameter of the branch is calculated. Based on the matching sub-parameters corresponding to each branch in each event factor mapping tree, the matching parameters between the event factor mapping tree and the overall event data sequence are determined. If the matching parameters are greater than or equal to the preset value, the two are considered to match.

[0008] In some embodiments disclosed in the present invention, the expression for calculating the matching parameter between the event factor mapping tree and the overall event data sequence is: ; Among them, W is the anastomosis parameter, L is the branch anastomosis influence adjustment coefficient, is the mapping ratio of the event factors mapped in the i-th branch of the event factor mapping tree, is the mapping probability weight adjustment coefficient of the i-th branch of the event factor mapping tree, c is the branch matching influence adjustment constant, and n is the total number of branches in the event factor mapping tree; The determination method includes calculating the ratio of the number of times the i-th branch is mapped to the total number of times the branches in the event factor mapping tree are mapped, and determining based on the ratio of the number of times.

[0009] In some embodiments disclosed in the present invention, a method for constructing a plurality of comparison blockchain data sets into an event-based target analysis database includes: Analyze each comparison blockchain data group, determine the feature labels of the comparison factor mapping tree and the feature labels of the event expression model, combine the feature labels of the comparison factor mapping tree and the feature labels of the event expression model into a retrieval label group, and establish an association relationship between the retrieval label group and the comparison blockchain data group; A plurality of search tag groups are constructed as a search tag set.

[0010] In some embodiments disclosed in the present invention, the feature tags of the event expression model include the time length of the event expression model and the number of event nodes in the time expression model; The feature labels of the factor mapping tree for comparison include the number of branches and the number of event factors.

[0011] In some embodiments disclosed in the present invention, the method of using the evented target database to determine the current implementation goal of the blockchain system includes: Randomly intercept the current monomer blockchain of the blockchain system, analyze each current monomer blockchain, and build the current event expression model and current event data sequence based on the analysis results; Reorganize a number of current event data sequences as a whole to obtain a current overall data sequence, and construct a current event factor group sequence based on the current overall data sequence, and determine a corresponding current comparison event factor mapping tree based on the current event factor group sequence; The current comparison event factor mapping tree is compared with the comparison event factor mapping tree in the event-based target database to obtain a first matching parameter, and the current event expression model is compared with the comparison event expression model in the event-based target database to obtain a second matching parameter. Based on the first matching parameter and the second matching parameter, a comprehensive matching parameter is determined. If the comprehensive matching parameter is greater than or equal to a preset value, the implementation target in the corresponding comparison blockchain data group is determined to be the current implementation target of the blockchain system.

[0012] In some embodiments disclosed in the present invention, the method for determining the comprehensive matching parameter includes: Compare the current comparison event factor mapping tree with the comparison event factor mapping tree in the evented target database to determine the branch occupancy ratio of all branches occupied by mutually equivalent mapped branches, and determine the first matching parameter based on the branch occupancy ratio; Compare the current event expression model with the event expression model in the event target database, determine the time intervals between event nodes, and compare them with each other to determine the time interval ratio between each pair of time intervals. If the time interval ratio is within a preset range, the time interval is considered to be an equivalent time interval. Based on the equivalent time interval ratio relative to all time intervals, determine the second matching parameter; Among them, the expression for calculating the comprehensive matching parameter is: ; Among them, F is the comprehensive matching parameter, is the weight adjustment coefficient of the first corresponding sub-parameter, is the weight adjustment coefficient of the second corresponding sub-parameter, The branch occupancy ratio of all branches occupied by the mapped branches that are equivalent to each other, The number of branches in the tree mapped with the event factor for the current alignment, is the number of branches of the event expression model in the evented target database, is the branch occupancy ratio impact adjustment coefficient, The branch occupancy ratio affects the adjustment constant, is the t-th pair of time interval equivalence judgment function. If the time interval ratio is within the preset range, then Output 1, otherwise output 0, T is the total logarithm used to judge the time intervals that are equal to each other, is the number of time intervals in the current event expression model, is the number of time intervals in the event representation model of the evented target database, is the adjustment factor for the proportional impact of equal time intervals, Adjust the constant for equal time interval proportional effects.

[0013] In some embodiments disclosed in the present invention, an event-based target analysis system based on blockchain data analysis is also disclosed, including: The first module is used to randomly select several monomer blockchains, analyze each monomer blockchain, determine the event data contained in different blocks, determine the timestamp corresponding to each event data, and sort the event data according to the timestamp to form an event data sequence, and build an event expression model according to the event data sequence, and associate the event expression model with the event data sequence; The second module is used to construct an event-goal mapping template, wherein the event-goal mapping template includes a number of implementation goals, and each implementation goal corresponds to an event factor mapping tree, and each branch of the event factor mapping tree includes a number of event factors. If all event factors of the same branch are completely mapped, it is determined that the implementation goal corresponding to the event factor mapping tree is achieved; The third module is used to randomly select several monomer blockchains, and reorganize the corresponding event data sequences according to the order of timestamps to obtain the overall event data sequence, and select the event factor mapping tree that matches the overall event data sequence from several event factor mapping trees, and record the selected event factor mapping tree as the reference event factor mapping tree, and determine the retained reference event factor mapping tree based on the relationship between the actual recorded realization goals and the realization goals of the reference event factor mapping tree, which is recorded as the comparison event factor mapping tree; The fourth module is used to determine several event expression models corresponding to the event factor mapping tree for comparison based on the overall event data sequence corresponding to the event factor mapping tree for comparison, recorded as the event expression model for comparison, and record the combination of the actual implementation goal, the event factor mapping tree for comparison and the several event expression models as the blockchain data group for comparison; The fifth module is used to construct several comparison blockchain data groups into an event-based target analysis database, and use the event-based target database to determine the current implementation goals of the blockchain system.

[0014] The present invention discloses an event-based target analysis method and system based on blockchain data analysis, which relates to the technical field of blockchain data analysis. A clear event expression model is constructed through fine event data extraction and timestamp sorting; an event-target mapping template is established to closely link event factors with specific implementation goals, forming an intuitive event factor mapping tree; the above technical scheme of the present invention not only realizes in-depth analysis of blockchain data, but also accurately reveals the intrinsic connection between events and goals; by constructing an event-based target analysis database, real-time target determination is provided for the blockchain system, which significantly improves the accuracy and application value of data analysis.

[0015] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a method step diagram for an event-based target analysis method based on blockchain data analysis. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0018] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solution of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should be the common meanings understood by the technical personnel described in the present invention.

[0019] Example:

[0020] The purpose of the present invention is to provide an event-based target analysis method and system that can clarify the mapping relationship between events and business goals.

[0021] The present invention discloses an event-based target analysis method based on blockchain data analysis, see Figure 1 ,include: Step S100, randomly select several monomer blockchains, and analyze each monomer blockchain to determine the event data contained in different blocks, and determine the timestamp corresponding to each event data, and sort the event data according to the timestamp to form an event data sequence, and according to the event data sequence, construct an event expression model, and associate the event expression model with the event data sequence.

[0022] The core of this step is to accurately extract event data from several randomly selected monomer blockchains. As a distributed ledger, blockchain records many transactions and events, and each event has a unique timestamp. By parsing these blockchains, event data with timestamps can be obtained. Subsequently, these event data are sorted in the order of timestamps to form a clear event data sequence. This step ensures the timeliness and integrity of the data. On this basis, an event expression model is constructed, which is an abstract representation of the event data sequence and is used to describe the association and characteristics between events. Associating the event expression model with the event data sequence provides a basic framework for subsequent event and target mapping analysis.

[0023] In some embodiments disclosed in the present invention, a method for constructing an event expression model according to an event data sequence includes: Step S101, determine each event data in the event data sequence, construct an event node for each event data, and determine the time interval between different event nodes based on the timestamp corresponding to each event data.

[0024] First, each event data in the event data sequence needs to be analyzed and determined in detail. This includes identifying the type, attributes, and key information of each event. Then, for each determined event data, a corresponding event node is constructed. These nodes are the basic building blocks of the event expression model, and they represent specific events that occurred in the blockchain. In addition, based on the timestamp corresponding to each event data, the time interval between different event nodes needs to be accurately calculated. The timestamp is an important attribute in the blockchain event data. It records the specific time when the event occurred and is the key basis for determining the order and interval of events.

[0025] Step S102, based on the sequence between event nodes and the time interval between different event nodes, the event nodes are connected in sequence, the length of the line segment between the event nodes is determined according to the time interval, and a time expression model is obtained.

[0026] After completing the construction of event nodes and determining the time interval, the construction phase of the time expression model is entered. The core of this step is to connect the event nodes in sequence based on the order relationship between event nodes and the time interval between them. The order of the connection follows the order of events, and the length of the line segment is determined according to the time interval. In this way, the time relationship and order between events can be intuitively displayed through event nodes and the line segments connecting them. The final time expression model not only clearly reflects the temporal relationship between events, but also provides a powerful visual and data analysis tool for subsequent event analysis, target mapping, and decision support.

[0027] Step S200, construct an event-goal mapping template, the event-goal mapping template includes several implementation goals, and each implementation goal corresponds to an event factor mapping tree, each branch of the event factor mapping tree contains several event factors, if all event factors of the same branch are completely mapped, it is determined that the implementation goal corresponding to the event factor mapping tree is achieved.

[0028] This step aims to establish a mapping relationship between events and goals. First, define several implementation goals, which can be key business indicators or desired states. Then, build an event factor mapping tree for each implementation goal. The tree structure is used to describe the event factors and their combinations required to achieve the goal. Event factors are the basic elements that constitute an event. They can be transaction types, transaction amounts, participants, etc. By completely mapping all event factors in the same branch, it can be determined that the implementation goal corresponding to the event factor mapping tree has been achieved. This step provides a clear judgment standard for subsequent goal determination.

[0029] Step S300, randomly select several monomer blockchains, and reorganize the corresponding event data sequences according to the order of timestamps to obtain the overall event data sequence, and screen out the event factor mapping tree that matches the overall event data sequence from several event factor mapping trees, and record the screened event factor mapping tree as the reference event factor mapping tree, and based on the relationship between the actual recorded realization goals and the realization goals of the reference event factor mapping tree, determine the retained reference event factor mapping tree, and record it as the comparison event factor mapping tree.

[0030] The purpose of this step is to filter out the event factor mapping tree related to the predetermined goal from the actual event data sequence. First, the event data sequence of the selected blockchain is reorganized as a whole to ensure the timeliness and consistency of the data. Then, in the constructed event factor mapping tree, find the part that matches the overall event data sequence. By comparing information such as event factors and timestamps, the event factor mapping tree that conforms to the actual situation can be screened out and recorded as the reference event factor mapping tree. Finally, based on the consistency of the actual recorded realization goals with the realization goals of the reference event factor mapping tree, the reference event factor mapping tree that needs to be retained is further determined, that is, the comparison event factor mapping tree. This step provides an accurate data basis for subsequent target analysis.

[0031] In some embodiments disclosed in the present invention, a method for selecting an event factor mapping tree that matches the overall event data sequence from among a plurality of event factor mapping trees includes: Step S301, analyze the event data in the overall event data sequence, determine the event factors corresponding to each event data, and determine the order of the event factors based on the time relationship of the event factors, record the combination of event factors corresponding to each event data as an event factor group monomer, and sort the event factor group monomers based on the order of the event data in the overall event data sequence to obtain an event factor group sequence.

[0032] First, conduct an in-depth analysis of each event data in the overall event data sequence to determine the event factors corresponding to each event data. Event factors are the basic elements that constitute events, and they can reflect the key characteristics of events. Next, according to the temporal relationship of event factors, determine the order of occurrence between them, and combine the event factors corresponding to each event data to form event factor group monomers. Finally, based on the order of event data in the overall event data sequence, sort these event factor group monomers to obtain an ordered event factor group sequence. This step provides an event factor sequence corresponding to the overall event data sequence, laying the foundation for subsequent comparison operations.

[0033] Step S302, performing several random event factor extraction operations on the event factor group sequence, each random event factor extraction operation includes randomly selecting several event factors from the event factor group sequence, and combining them according to the original order of the event factors to obtain a random event factor group.

[0034] After constructing the event factor group sequence, several random event factor extraction operations are performed. Each operation includes randomly selecting several event factors in the event factor group sequence and combining them in their original order to form a random event factor group. The purpose of this step is to generate multiple possible event factor combinations so that the parts that match the event factor mapping tree can be found in the subsequent comparison process. Through multiple random extractions, the probability of finding a matching branch can be increased.

[0035] Step S303, respectively compare the random event factor group with each branch of different event factor mapping trees. If the mapping ratio of the mapped event factors in the branch is greater than or equal to the preset value, the branch is identified as the mapped branch, and the matching sub-parameter of the branch is calculated. Based on the matching sub-parameters corresponding to each branch in each event factor mapping tree, the matching parameters between the event factor mapping tree and the overall event data sequence are determined. If the matching parameters are greater than or equal to the preset value, the two are identified as matching.

[0036] Finally, the generated random event factor group is compared with each branch of different event factor mapping trees. During the comparison process, the mapping ratio of the mapped event factors in the branch is calculated, that is, the degree of overlap between the event factors in the random event factor group and the event factors in the branch. If the mapping ratio of the mapped event factors of a branch is greater than or equal to the preset value, the branch is identified as the mapped branch. Next, the matching sub-parameter of the branch is calculated, which reflects the similarity between the random event factor group and the branch. Finally, based on the matching sub-parameter corresponding to each branch in each event factor mapping tree, the matching parameter between the event factor mapping tree and the overall event data sequence is calculated. If the matching parameter is greater than or equal to the preset value, it is determined that the event factor mapping tree matches the overall event data sequence. This step completes the task of screening out the parts that match the overall event data sequence from several event factor mapping trees, providing an accurate data basis for subsequent target determination and decision support.

[0037] In some embodiments disclosed in the present invention, the expression for calculating the matching parameter between the event factor mapping tree and the overall event data sequence is: .

[0038] Among them, W is the anastomosis parameter, L is the branch anastomosis influence adjustment coefficient, is the mapping ratio of the event factors mapped in the i-th branch of the event factor mapping tree, is the mapping probability weight adjustment coefficient of the i-th branch of the event factor mapping tree, c is the branch matching influence adjustment constant, and n is the total number of branches in the event factor mapping tree.

[0039] The determination method includes calculating the ratio of the number of times the i-th branch is mapped to the total number of times the branches in the event factor mapping tree are mapped, and determining based on the ratio of the number of times.

[0040] Step S400, based on the overall event data sequence corresponding to the comparison event factor mapping tree, several event expression models corresponding to the comparison event factor mapping tree are determined, recorded as the comparison event expression model, and the combination of the actual implementation goal, the comparison event factor mapping tree and the several event expression models is recorded as the comparison blockchain data group.

[0041] This step is based on the previously selected comparison event factor mapping tree to further determine the corresponding event expression model. These event expression models are specific descriptions and representations of the event factors in the comparison event factor mapping tree. Then, the actual implementation goals, the comparison event factor mapping tree, and the corresponding event expression models are combined to form a comparison blockchain data group. This data group contains all the key information and event factors required to achieve a certain implementation goal, providing comprehensive data support for subsequent goal determination and decision support.

[0042] Step S500, constructing a plurality of comparison blockchain data groups into an event-based target analysis database, and using the event-based target database to determine the current implementation goal of the blockchain system.

[0043] This step is the final output and application link of the entire technical solution. First, multiple comparison blockchain data groups are integrated together to build an event-based target analysis database. This database contains a wealth of historical data and event factor mapping relationships, which can be used to accurately determine the current implementation goals of the blockchain system. By querying the comparison blockchain data group in the database, you can find the part that best matches the current event data sequence, and use this to determine whether the current system has achieved the predetermined implementation goals. This step provides a scientific basis and data support for the operational decisions of the blockchain system.

[0044] In some embodiments disclosed in the present invention, a method for constructing a plurality of comparison blockchain data sets into an event-based target analysis database includes: Step S501: Analyze each comparison blockchain data group, determine the feature labels of the comparison factor mapping tree and the feature labels of the event expression model, combine the feature labels of the comparison factor mapping tree and the feature labels of the event expression model into a retrieval label group, and establish an association relationship between the retrieval label group and the comparison blockchain data group.

[0045] Step S502: construct a plurality of search tag groups into a search tag set.

[0046] In some embodiments disclosed in the present invention, the feature labels of the event expression model include the time length of the event expression model and the number of event nodes in the time expression model.

[0047] The feature labels of the factor mapping tree for comparison include the number of branches and the number of event factors.

[0048] In some embodiments disclosed in the present invention, the method of using the evented target database to determine the current implementation goal of the blockchain system includes: Step S503, randomly intercept the current monomer blockchain of the blockchain system, analyze each current monomer blockchain, and build a current event expression model and a current event data sequence based on the analysis results.

[0049] Step S504, reorganize a number of current event data sequences as a whole to obtain a current overall data sequence, and construct a current event factor group sequence based on the current overall data sequence, and determine a corresponding current comparison event factor mapping tree based on the current event factor group sequence.

[0050] Step S505, compare the current comparison event factor mapping tree with the comparison event factor mapping tree in the event-based target database to obtain a first matching parameter, compare the current event expression model with the comparison event expression model in the event-based target database to obtain a second matching parameter, and determine a comprehensive matching parameter based on the first matching parameter and the second matching parameter. If the comprehensive matching parameter is greater than or equal to a preset value, the implementation target in the corresponding comparison blockchain data group is determined to be the current implementation target of the blockchain system.

[0051] In some embodiments disclosed in the present invention, the method for determining the comprehensive matching parameter includes: Step S5051, compare the current comparison event factor mapping tree with the comparison event factor mapping tree in the event-based target database, determine the branch occupancy ratio of all branches occupied by mutually equivalent mapped branches, and determine the first matching parameter based on the branch occupancy ratio.

[0052] Step S5052, compare the current event expression model with the event expression model in the event target database, determine the time interval between event nodes, and compare them with each other to determine the time interval ratio between each pair of time intervals. If the time interval ratio is within a preset range, the time interval is recognized as an equivalent time interval. Based on the ratio of the equivalent time interval to all time intervals, the second matching parameter is determined.

[0053] Among them, the expression for calculating the comprehensive matching parameter is: .

[0054] Among them, F is the comprehensive matching parameter, is the weight adjustment coefficient of the first corresponding sub-parameter, is the weight adjustment coefficient of the second corresponding sub-parameter, The branch occupancy ratio of all branches occupied by the mapped branches that are equivalent to each other, The number of branches in the tree mapped with the event factor for the current alignment, is the number of branches of the event expression model in the evented target database, is the branch occupancy ratio impact adjustment coefficient, The branch occupancy ratio affects the adjustment constant, is the t-th pair of time interval equivalence judgment function. If the time interval ratio is within the preset range, then Output 1, otherwise output 0, T is the total logarithm used to judge the time intervals that are equal to each other, is the number of time intervals in the current event expression model, is the number of time intervals in the event representation model of the evented target database, is the adjustment factor for the proportional impact of equal time intervals, Adjust the constant for equal time interval proportional effects.

[0055] In some embodiments disclosed in the present invention, an event-based target analysis system based on blockchain data analysis is also disclosed, including: The first module is used to randomly select several monomer blockchains, analyze each monomer blockchain, determine the event data contained in different blocks, determine the timestamp corresponding to each event data, and sort the event data according to the timestamp to form an event data sequence, and build an event expression model according to the event data sequence, and associate the event expression model with the event data sequence; The second module is used to construct an event-goal mapping template, wherein the event-goal mapping template includes a number of implementation goals, and each implementation goal corresponds to an event factor mapping tree, and each branch of the event factor mapping tree includes a number of event factors. If all event factors of the same branch are completely mapped, it is determined that the implementation goal corresponding to the event factor mapping tree is achieved; The third module is used to randomly select several monomer blockchains, and reorganize the corresponding event data sequences according to the order of timestamps to obtain the overall event data sequence, and select the event factor mapping tree that matches the overall event data sequence from several event factor mapping trees, and record the selected event factor mapping tree as the reference event factor mapping tree, and determine the retained reference event factor mapping tree based on the relationship between the actual recorded realization goals and the realization goals of the reference event factor mapping tree, which is recorded as the comparison event factor mapping tree; The fourth module is used to determine several event expression models corresponding to the event factor mapping tree for comparison based on the overall event data sequence corresponding to the event factor mapping tree for comparison, recorded as the event expression model for comparison, and record the combination of the actual implementation goal, the event factor mapping tree for comparison and the several event expression models as the blockchain data group for comparison; The fifth module is used to construct several comparison blockchain data groups into an event-based target analysis database, and use the event-based target database to determine the current implementation goals of the blockchain system.

[0056] The present invention discloses an event-based target analysis method and system based on blockchain data analysis, which relates to the technical field of blockchain data analysis. A clear event expression model is constructed through fine event data extraction and timestamp sorting; an event-target mapping template is established to closely link event factors with specific implementation goals, forming an intuitive event factor mapping tree; the above technical scheme of the present invention not only realizes in-depth analysis of blockchain data, but also accurately reveals the intrinsic connection between events and goals; by constructing an event-based target analysis database, real-time target determination is provided for the blockchain system, which significantly improves the accuracy and application value of data analysis.

[0057] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. An event-based target analysis method based on blockchain data analysis, characterized in that: include: Randomly select several monomer blockchains, analyze each monomer blockchain, determine the event data contained in different blocks, determine the timestamp corresponding to each event data, and sort the event data according to the timestamp to form an event data sequence, and build an event expression model according to the event data sequence, and associate the event expression model with the event data sequence; Constructing an event-goal mapping template, wherein the event-goal mapping template includes a number of implementation goals, and each implementation goal corresponds to an event factor mapping tree, wherein each branch of the event factor mapping tree includes a number of event factors, and if all event factors of the same branch are completely mapped, it is determined that the implementation goal corresponding to the event factor mapping tree is achieved; Randomly select a number of single blockchains, and reorganize the corresponding event data sequences according to the order of timestamps to obtain the overall event data sequence, and select an event factor mapping tree that matches the overall event data sequence from a number of event factor mapping trees, and record the selected event factor mapping tree as a reference event factor mapping tree, and determine the reference event factor mapping tree to be retained based on the relationship between the actual recorded realization goals and the realization goals of the reference event factor mapping tree, and record it as the comparison event factor mapping tree; Based on the overall event data sequence corresponding to the comparison event factor mapping tree, several event expression models corresponding to the comparison event factor mapping tree are determined, recorded as comparison event expression models, and the combination of the actual implementation goal, the comparison event factor mapping tree and several event expression models is recorded as the comparison blockchain data group; Several comparison blockchain data sets are constructed into an event-based target analysis database, and the event-based target database is used to determine the current implementation goals of the blockchain system.

2. The event-based target analysis method based on blockchain data analysis according to claim 1 is characterized in that: According to the event data sequence, the method of constructing the event expression model includes: Determine each event data in the event data sequence, construct an event node for each event data, and determine the time interval between different event nodes based on the timestamp corresponding to each event data; Based on the sequence between event nodes and the time interval between different event nodes, the event nodes are connected in sequence, and the length of the line segments between the event nodes is determined according to the time interval to obtain a time expression model.

3. The event-based target analysis method based on blockchain data analysis according to claim 1 is characterized in that: The method of selecting an event factor mapping tree that matches the overall event data sequence from among a plurality of event factor mapping trees includes: Analyze the event data in the overall event data sequence, determine the event factor corresponding to each event data, and determine the order of the event factors based on the time relationship of the event factors, record the combination of event factors corresponding to each event data as an event factor group monomer, and sort the event factor group monomers based on the order of the event data in the overall event data sequence to obtain an event factor group sequence; Performing several random event factor extraction operations on the event factor group sequence, each random event factor extraction operation includes randomly selecting several event factors from the event factor group sequence, and combining them according to the original order of the event factors to obtain a random event factor group; The random event factor group is compared with each branch of different event factor mapping trees respectively. If the mapping ratio of the mapped event factors in the branch is greater than or equal to the preset value, the branch is identified as the mapped branch, and the matching sub-parameter of the branch is calculated. Based on the matching sub-parameters corresponding to each branch in each event factor mapping tree, the matching parameters between the event factor mapping tree and the overall event data sequence are determined. If the matching parameters are greater than or equal to the preset value, the two are considered to match.

4. The event-based target analysis method based on blockchain data analysis according to claim 3 is characterized in that: The expression for calculating the matching parameter between the event factor mapping tree and the overall event data sequence is: ； Among them, W is the anastomosis parameter, L is the branch anastomosis influence adjustment coefficient, is the mapping ratio of the event factors mapped in the i-th branch of the event factor mapping tree, is the mapping probability weight adjustment coefficient of the i-th branch of the event factor mapping tree, c is the branch matching influence adjustment constant, and n is the total number of branches in the event factor mapping tree; The determination method includes calculating the ratio of the number of times the i-th branch is mapped to the total number of times the branches in the event factor mapping tree are mapped, and determining based on the ratio of the number of times.

5. The event-based target analysis method based on blockchain data analysis according to claim 1 is characterized in that: The method of constructing a plurality of comparison blockchain data sets into an event-based target analysis database includes: Analyze each comparison blockchain data group, determine the feature labels of the comparison factor mapping tree and the feature labels of the event expression model, combine the feature labels of the comparison factor mapping tree and the feature labels of the event expression model into a retrieval label group, and establish an association relationship between the retrieval label group and the comparison blockchain data group; A plurality of search tag groups are constructed as a search tag set.

6. The event-based target analysis method based on blockchain data analysis according to claim 5 is characterized in that: The feature labels of the event expression model include the time length of the event expression model and the number of event nodes in the time expression model; The feature labels of the factor mapping tree for comparison include the number of branches and the number of event factors.

7. The event-based target analysis method based on blockchain data analysis according to claim 1 is characterized in that: Methods for using the event-based target database to determine the current goals of the blockchain system include: Randomly intercept the current monomer blockchain of the blockchain system, analyze each current monomer blockchain, and build the current event expression model and current event data sequence based on the analysis results; Reorganize a number of current event data sequences as a whole to obtain a current overall data sequence, and construct a current event factor group sequence based on the current overall data sequence, and determine a corresponding current comparison event factor mapping tree based on the current event factor group sequence; The current comparison event factor mapping tree is compared with the comparison event factor mapping tree in the event-based target database to obtain a first matching parameter, and the current event expression model is compared with the comparison event expression model in the event-based target database to obtain a second matching parameter. Based on the first matching parameter and the second matching parameter, a comprehensive matching parameter is determined. If the comprehensive matching parameter is greater than or equal to a preset value, the implementation target in the corresponding comparison blockchain data group is determined to be the current implementation target of the blockchain system.

8. The event-based target analysis method based on blockchain data analysis according to claim 7 is characterized in that: Methods for determining comprehensive compliance parameters include: Compare the current comparison event factor mapping tree with the comparison event factor mapping tree in the evented target database to determine the branch occupancy ratio of all branches occupied by mutually equivalent mapped branches, and determine the first matching parameter based on the branch occupancy ratio; Compare the current event expression model with the event expression model in the event target database, determine the time intervals between event nodes, and compare them with each other to determine the time interval ratio between each pair of time intervals. If the time interval ratio is within a preset range, the time interval is considered to be an equivalent time interval. Based on the equivalent time interval ratio relative to all time intervals, determine the second matching parameter; Among them, the expression for calculating the comprehensive matching parameter is: ； Among them, F is the comprehensive matching parameter, is the weight adjustment coefficient of the first corresponding sub-parameter, is the weight adjustment coefficient of the second corresponding sub-parameter, The branch occupancy ratio of all branches occupied by the mapped branches that are equivalent to each other, The number of branches in the tree mapped with the event factor for the current alignment, is the number of branches of the event expression model in the evented target database, is the branch occupancy ratio impact adjustment coefficient, The branch occupancy ratio affects the adjustment constant, is the t-th pair of time interval equivalence judgment function. If the time interval ratio is within the preset range, then Output 1, otherwise output 0, T is the total logarithm used to judge the time intervals that are equal to each other, is the number of time intervals in the current event expression model, is the number of time intervals in the event representation model of the evented target database, is the adjustment coefficient for the proportional impact of equal time intervals, Adjust the constant for equal time interval proportional effects.

9. The event-based target analysis system based on blockchain data analysis is characterized by: include: The first module is used to randomly select several monomer blockchains, analyze each monomer blockchain, determine the event data contained in different blocks, determine the timestamp corresponding to each event data, and sort the event data according to the timestamp to form an event data sequence, and build an event expression model according to the event data sequence, and associate the event expression model with the event data sequence; The second module is used to construct an event-goal mapping template, wherein the event-goal mapping template includes a number of implementation goals, and each implementation goal corresponds to an event factor mapping tree, and each branch of the event factor mapping tree includes a number of event factors. If all event factors of the same branch are completely mapped, it is determined that the implementation goal corresponding to the event factor mapping tree is achieved; The third module is used to randomly select several monomer blockchains, and reorganize the corresponding event data sequences according to the order of timestamps to obtain the overall event data sequence, and select the event factor mapping tree that matches the overall event data sequence from several event factor mapping trees, and record the selected event factor mapping tree as the reference event factor mapping tree, and determine the retained reference event factor mapping tree based on the relationship between the actual recorded realization goals and the realization goals of the reference event factor mapping tree, which is recorded as the comparison event factor mapping tree; The fourth module is used to determine several event expression models corresponding to the event factor mapping tree for comparison based on the overall event data sequence corresponding to the event factor mapping tree for comparison, recorded as the event expression model for comparison, and record the combination of the actual implementation goal, the event factor mapping tree for comparison and the several event expression models as the blockchain data group for comparison; The fifth module is used to construct several comparison blockchain data groups into an event-based target analysis database, and use the event-based target database to determine the current implementation goals of the blockchain system.

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