A blockchain-based enterprise data processing method

Abstract

This invention relates to the field of blockchain applications, specifically to a method for processing enterprise data based on a blockchain platform. The method includes: acquiring attribute data from multiple blockchain platforms and using commonly used attributes of the blockchain platforms as evaluation attributes; evaluating the evaluation attributes of the blockchain platforms using various fuzzy numbers based on the attribute data to obtain an original evaluation matrix; calculating the relative proximity of each blockchain platform based on the original evaluation matrix and selecting the blockchain platform with the highest relative proximity as a candidate blockchain platform; using the candidate blockchain platform to receive data provided by the enterprise and preprocessing the data before it is uploaded to the blockchain; and adding the preprocessed data to the local ledger on the candidate blockchain platform to complete the enterprise data upload operation. This invention can quickly select the optimal blockchain platform from numerous blockchain platforms, improving the processing speed of enterprise data and reducing enterprise costs.

Description

Technical Field

[0001] This invention relates to the field of blockchain applications, and in particular to a method for processing enterprise data based on a blockchain platform. Background Technology

[0002] With the advent of the internet age, blockchain technology has made significant progress in just ten years since its inception in 2009. It has been tested in industries such as finance, supply chain, the Internet of Things, intellectual property protection, real estate, luxury goods, and food and drug traceability. In terms of blockchain platform development, there are now multiple public blockchain development and application platforms, including Bitcoin, Ethereum, EOS, and Hyperledger, providing a convenient and fast foundation for rapid blockchain development and deployment. On the Ethereum application platform, there are currently 2,667 applications (DApps), over 4,200 deployed smart contracts, and more than 27,000 daily active users, forming a robust blockchain distributed application ecosystem.

[0003] Currently, more and more enterprises are applying blockchain to process enterprise data. However, there are many blockchain platforms on the market, with as many as 20 common ones. Most blockchain platforms have complex functions, numerous modules, complex configuration parameters, and rapid version updates. It is difficult for general enterprise platform administrators to understand these numerous blockchain platforms in a short period of time. For enterprises, how to quickly select a blockchain platform to process enterprise data presents a serious challenge.

[0004] Existing blockchain platforms primarily use fuzzy numbers to evaluate multiple blockchains and select the optimal one as a candidate for processing enterprise data. However, using a single type of fuzzy number cannot clearly describe the attribute information of a blockchain platform. Furthermore, the selection of a blockchain platform does not take into account the enterprise's own needs, resulting in inefficient data processing and wasted resources. Summary of the Invention

[0005] To better address the problem that existing technologies cannot clearly describe the attribute information of a blockchain platform using a single type of fuzzy number, and that the selection of a blockchain platform does not take into account the specific needs of the enterprise, resulting in inefficient data processing and wasted resources, this invention proposes a blockchain-based enterprise data processing method. The method includes the following steps:

[0006] S1: Obtain attribute data from multiple blockchain platforms and use the commonly used attributes of the blockchain platforms as evaluation attributes. The commonly used attributes of the blockchain platforms are those shared by multiple blockchain platforms.

[0007] S2: Based on the attribute data of the blockchain platform, use various fuzzy numbers to evaluate the evaluation attributes of the blockchain platform to obtain the original evaluation matrix;

[0008] S3: Calculate the relative proximity of each blockchain platform based on the original evaluation matrix, and select the blockchain platform with the highest relative proximity as the candidate blockchain platform;

[0009] S4: Use an alternative blockchain platform to receive enterprise data provided by the enterprise and preprocess the enterprise data before putting it on the blockchain;

[0010] S5: The alternative blockchain platform adds the pre-processed enterprise data to the local ledger, completing the on-chain operation of enterprise data.

[0011] The present invention has at least the following beneficial effects

[0012] This invention uses attribute data from blockchain platforms and various fuzzy numbers to evaluate the platform's attributes, accurately describing the platform's information. By calculating the relative fit of the blockchain platforms, it can accurately combine the platform's attributes with the enterprise's needs to select the optimal platform. Processing enterprise data through the optimal platform can improve processing speed, reduce costs, and save computing resources. Attached Figure Description

[0013] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Please see Figure 1 This invention proposes an enterprise data processing method based on a blockchain platform, comprising the following steps:

[0016] S1: Obtain attribute data from multiple blockchain platforms and use the commonly used attributes of the blockchain platforms as evaluation attributes. The commonly used attributes of the blockchain platforms are those shared by multiple blockchain platforms.

[0017] Preferably, the evaluation attributes of a blockchain platform include: scalability, consensus mechanism, privacy, resilience, interoperability, popularity, performance efficiency, reliability, programming language diversity, cost, compatibility, and ease of use; wherein, cost and ease of use are cost-type attributes; scalability, consensus mechanism, privacy, resilience, interoperability, popularity, performance efficiency, reliability, programming language diversity, and compatibility are benefit-type attributes.

[0018] S2: Based on the attribute data of the blockchain platform, use various fuzzy numbers to evaluate the evaluation attributes of the blockchain platform to obtain the original evaluation matrix.

[0019] Preferably, the various fuzzy numbers include: real numbers, interval numbers, intuitive fuzzy numbers, triangular fuzzy numbers, trapezoidal fuzzy numbers, and a set of hesitant fuzzy language terms.

[0020] Preferably, the step of evaluating the evaluation attributes of the blockchain platform using multiple fuzzy numbers based on the platform's attribute data includes:

[0021] The evaluation of a blockchain platform is based on its privacy protection capabilities, the types of consensus mechanisms it supports, its market popularity, and the types of programming languages ​​it supports. Real numbers are used to assess privacy, consensus mechanisms, popularity, and programming language diversity. Privacy refers to the platform's ability to protect privacy; consensus mechanisms refer to the types of consensus mechanisms it supports; popularity reflects its market appeal; and programming language diversity refers to the types of programming languages ​​it supports. Privacy protection capabilities can be determined through third-party testing platforms. For consensus mechanism evaluation, the platform's support for nine consensus mechanisms (PoW, PoS, DPoS, PBFT, DBFT, POA, FBA, POET, and DAG) is considered. Supporting nine mechanisms earns a score of 9, eight earns 8, seven earns 7, and so on. Supporting one earns 1, and supporting zero earns 0. The platform's attribute data includes its support for these evaluation attributes, its overall ranking across major networks, and scores from third-party testing agencies.

[0022] For example, to evaluate the popularity of the attribute, we consider the ranking of 6 blockchain platforms in the market. According to their ranking from smallest to largest, they are assigned values ​​of 10, 8, 6, 4, 2, and 0 respectively. According to the latest survey, the ranking of the 6 blockchain platforms in the market is A1 > A4 > A2 > A6 > A3 > A5, where A1 represents the first blockchain platform obtained.

[0023] For example, regarding the diversity of programming languages, consider the support of various blockchain platforms for seven programming languages: Solidity, Python, Golang, Java, Javascript, C#, and C++. A score of 7 is given when 7 languages ​​are supported, a score of 6 is given when 6 languages ​​are supported, and so on, with a score of 0 when 0 languages ​​are supported.

[0024] Similarly, scalability, performance efficiency, and ease of use can be evaluated using interval numbers based on the differences between blockchain platforms on different operating systems, the impact of different network performance on blockchain platforms, and the varying difficulty of using blockchain platforms for different users. Among these, scalability is uncertain due to the impact of differences in operating systems and other environments on blockchain platforms, so interval numbers are used for evaluation. For example, for different computing devices, such as computers with the same operating system, the efficiency of blockchain platforms will vary under the same operating system due to the influence of hardware conditions such as computing resources. Performance efficiency is uncertain due to the influence of network performance and other factors, so interval numbers are used for evaluation. The difficulty of using blockchain platforms varies for different users, which is also uncertain, so interval numbers are used for evaluation.

[0025] Interoperability is evaluated using intuitionistic fuzzy numbers based on user satisfaction with the blockchain platform. The interoperability of the blockchain platform can be considered from two aspects: satisfaction and dissatisfaction. User satisfaction with the blockchain platform corresponds to the membership degree and non-membership degree of the intuitionistic fuzzy numbers, hence the use of intuitionistic fuzzy numbers for evaluation.

[0026] The reliability of a blockchain platform is evaluated using triangular fuzzy numbers based on its revenue and scale. As the platform upgrades, its maturity, revenue, and scale will change, meaning that the reliability of a blockchain platform has a lower and upper limit. Therefore, triangular fuzzy numbers are used for evaluation.

[0027] Based on the ability of a blockchain platform to resist and recover after being attacked, a trapezoidal fuzzy number is used to evaluate its resilience. Resilience refers to the ability of a blockchain platform to resist and recover after being attacked, and has the largest possible interval and upper limit, hence the use of a trapezoidal fuzzy number for evaluation.

[0028] The cost and compatibility of the blockchain platform are evaluated using a hesitant and fuzzy terminology set, taking into account the supported operating systems, databases, and functions. Since cost involves numerous factors and is difficult to evaluate using explicit numerical values, a hesitant and fuzzy terminology set is employed. Similarly, due to the differences in supported operating systems, databases, and other functions, compatibility is difficult to evaluate based on explicit numerical values; therefore, a hesitant and fuzzy terminology set is used. In this embodiment, the terminology set S = {s0 = extremely poor, s1 = very poor, s2 = poor, s3 = moderate, s4 = good, s5 = very good, s6 = excellent}.

[0029] Similarly, based on the attribute data of the blockchain platform, various fuzzy numbers can be used to evaluate the evaluation attributes of the blockchain platform, resulting in the original evaluation matrix:

[0030] Original evaluation matrix

[0031]

[0032] Among them, a ij Represents the i-th blockchain platform A in the original evaluation matrix. i The j-th evaluation attribute C j The obtained evaluation is given by i∈{1,2,...,m}, j∈{1,2,...,n}, where m represents the number of blockchain platforms and n represents the number of evaluation attributes for the blockchain platforms. In this invention, only 12 commonly used blockchain platform attributes are selected as evaluation attributes. Those skilled in the art can select more attributes as evaluation attributes based on this invention and use appropriate fuzzy numbers to evaluate the selected evaluation attributes.

[0033] S3: Calculate the relative proximity of each blockchain platform based on the original evaluation matrix, and select the blockchain platform with the highest relative proximity as the candidate blockchain platform;

[0034] Preferably, the calculation of the relative proximity of each blockchain platform based on the original evaluation matrix includes:

[0035] S31: Based on the type of evaluation attributes of the blockchain platform, the evaluations in the original evaluation matrix are made consistent and normalized using a standardized formula to obtain a standardized evaluation matrix;

[0036] Preferably, the process of standardizing and normalizing the evaluations in the original evaluation matrix into standardized evaluations includes:

[0037] When the evaluation attributes of a blockchain platform are privacy, consensus mechanism, popularity, or programming language diversity:

[0038]

[0039] Among them, b ij Indicates a ij Standardized evaluation, a ij Represents the i-th blockchain platform A in the original evaluation matrix. i The j-th evaluation attribute C j The evaluation received C represents the j-th evaluation attribute C of all blockchain platforms in the original evaluation matrix. j The minimum value obtained in the evaluation, C represents the j-th evaluation attribute C of all blockchain platforms in the original evaluation matrix. j The maximum value obtained in the evaluation should be noted. It's important to note that in this invention, user evaluations of privacy, consensus mechanisms, popularity, or programming language diversity are expressed using fuzzy numbers of real-valued type, such as a. 22 =5 indicates that the blockchain platform supports 5 consensus mechanisms;

[0040] For example: Regarding a in the original rating matrix 23 =5 is processed for standardization and normalization, a 23 The standardized evaluation is

[0041] When the evaluation attributes of a blockchain platform are scalability, performance efficiency, or ease of use:

[0042]

[0043] Among them, b ij Indicates a ij Standardized evaluation, a ij =[α1 ij α2 ij ], a ij Represents the i-th blockchain platform A in the original evaluation matrix. i The j-th evaluation attribute C j The evaluation obtained, α1 ij For a ij The first term, α2 ij For a ij The second item, α1 represents the evaluation obtained from the j-th evaluation attribute of all blockchain platforms in the original evaluation matrix. ij The minimum value, The α2 is the evaluation obtained for the j-th evaluation attribute of all blockchain platforms in the original evaluation matrix. ijThe maximum value of is important to note. In this invention, when users consider scalability, performance efficiency, or ease of use, real numbers cannot accurately express information about these parameters. Therefore, in this invention, users use interval numbers to represent scalability, performance efficiency, or ease of use, for example, a. 41 = [8, 9] indicates that the user's evaluation of the scalability of the 4th blockchain platform indicates that the scalability of the blockchain platform is between [8, 9]. There are a total of 10 scalability levels. The number of levels can be selected by people in the field. Among them, 8 represents the lower limit of the scalability level score and 9 represents the upper limit of the scalability level score.

[0044] For example: a in the original evaluation matrix 41 =[α1 41 α2 41 The values ​​[8, 9] are standardized. Since the evaluation attribute C1 is benefit-type, we have...

[0045] When the evaluation attribute of a blockchain platform is interoperability:

[0046] a ij =<α1 ij α2 ij >,a ij Represents the i-th blockchain platform A in the original evaluation matrix. i The j-th evaluation attribute C j The evaluation obtained will be a ij Convert to interval number [α1] ij ,1-α2 ij ], then for the interval number [α1 ij ,1-α2 ij After standardization and normalization, we obtain a. ij Standardized evaluation b ij Finally, b ij It is then transformed into the form of intuitionistic fuzzy numbers; where α1 ij For a ij The first term, α2 ij For a ij Secondly, it's important to note that in this invention, when users evaluate interoperability, the evaluation is based on their satisfaction with the blockchain platform. Since user satisfaction corresponds to the membership and non-membership degrees of an intuitionistic fuzzy number, the interoperability evaluation is based on an intuitionistic fuzzy number, for example, represented as a... 55 =<α1 55 α2 55> indicates that the interoperability of the fifth blockchain platform is <0.4, 0.3>, where 0.4 represents the membership value given by users when evaluating the interoperability of the blockchain platforms, and 0.3 represents the non-membership value given by users when evaluating the interoperability of the blockchain platforms.

[0047] For example: a in the original evaluation matrix 55 =<α1 55 α2 55 The expression >=<0.4,0.3> is subjected to consistency and normalization processing, and a is then processed. 55 Converted to interval numbers:

[0048] a 55 =[α1 55 ,1-α2 55 = [0.4, 0.7]

[0049] have Converted to an intuitive fuzzy number b 55 =<0, 0.4>;

[0050] When the evaluation attribute of a blockchain platform is reliability:

[0051]

[0052] Among them, b ij Indicates a ij Standardized evaluation, a ij =(α1) ij α2 ij α3 ij ), a ij Represents the i-th blockchain platform A in the original evaluation matrix. i The j-th evaluation attribute C j The evaluation obtained, α1 ij For a ij The first term, α2 ij For a ij The second term, α3 ij For a ij The third item, The evaluation α1 obtained for the j-th evaluation attribute of all blockchain platforms in the original evaluation matrix. ij The minimum value, The α3 is the evaluation obtained for the j-th evaluation attribute of all blockchain platforms in the original evaluation matrix. ij The maximum value, for example, a 28 =(α1) 28 α2 28 α3 28= (3, 4, 5) means that the reliability of the second blockchain platform is (3, 4, 5), where 3 represents the most conservative estimate given by users when evaluating the reliability of the blockchain platform, 4 represents the most likely estimate given by users when evaluating the reliability of the blockchain platform, and 5 represents the most positive estimate given by users when evaluating the reliability of the blockchain platform.

[0053] For example: a in the original evaluation matrix 28 =(α1) 28 α2 28 α3 28 Standardizing (3, 4, 5) gives us:

[0054] When the evaluation attribute of a blockchain platform is resilience:

[0055]

[0056] Among them, b ij Indicates that for a ij Standardized evaluation after consistency and normalization processing, a ij =(α1) ij α2 ij α3 ij α4 ij ), a ij Represents the i-th blockchain platform A in the original evaluation matrix. i The j-th evaluation attribute C j The evaluation obtained, α1 ij For a ij The first term, α2 ij For a ij The second term, α3 ij For a ij The third term, α4 ij For a ij The fourth item, The evaluation α1 obtained for the j-th evaluation attribute of all blockchain platforms in the original evaluation matrix. ij The minimum value, The α4 is the evaluation obtained for the j-th evaluation attribute of all blockchain platforms in the original evaluation matrix. ij The maximum value, for example, a 64 =(α1) 64 α2 64 α3 64 α4 64= (3, 4, 5, 7) means that the resilience of the 6th blockchain platform is (3, 4, 5, 7), where 3 represents the most conservative estimate given by users when evaluating the resilience of the blockchain platform, 4 and 5 represent the most likely range estimates given by users when evaluating the resilience of the blockchain platform, which is between 4 and 5, and 7 represents the most positive estimate given by users when evaluating the resilience of the blockchain platform.

[0057] For example: a in the original evaluation matrix 64 =(α1) 64 α2 64 α3 64 α4 64 Standardizing (3, 4, 5, 7) gives us:

[0058] When the evaluation attributes of a blockchain platform are cost and compatibility:

[0059]

[0060]

[0061] a ij ={α1 ij α2 ij ,...αk ij , ..., αT ij}

[0062] {α1 ij α2 ij ,...αk ij , ..., αT ij}={s l s l+1 , ..., s l+d}, T=d+1, α1 ij =s l ,

[0063] α2 ij =s l+1 , ..., αT ij =s l+d α1 ij <α2 ij <...<αT ij s l <s l+1 <...<αT ij ,

[0064] {s l s l+1 , ..., s l+d}∈{s0=very poor, s1=very poor, ..., s3=moderate, s4=good, s5=very good, s6=excellent}

[0065] Among them, b ij Indicates that for a ij Standardized evaluation after consistency and normalization processing, a ij Represents the i-th blockchain platform A in the original evaluation matrix. i The j-th evaluation attribute C j The evaluation obtained, αk ij For a ij The kth term, αT ij For a ij The last term, 1≤k≤T∈[1,6], f(a ij ) represents the language scale function. The evaluation f(a) of the j-th evaluation attribute of all blockchain platforms in the original evaluation matrix. ij The minimum value of ) The evaluation f(a) of the j-th evaluation attribute of all blockchain platforms in the original evaluation matrix. ij The maximum value of ), for example, a 310 ={α1 ij α2 ij}={s4,s5} represents the cost of the third blockchain platform as {s4,s5}, where s4 represents the lower limit of the user's evaluation of the cost of the third blockchain platform as s4=good, and s5 represents the upper limit of the user's evaluation of the cost of the third blockchain platform as s5=very good;

[0066] For example: a in the original evaluation matrix 310 ={α1 ij α2 ij The values ​​of} = {s4, s5} are standardized. Due to evaluation attribute C 10 It is cost-oriented, and has

[0067] Preferably, the standardized evaluation matrix includes:

[0068] Standardized evaluation matrix

[0069]

[0070]

[0071] Among them, b ij Represents the i-th blockchain platform A in the standardized evaluation matrix. i The j-th evaluation attribute C in jThe obtained standardized evaluations, through the consistency and normalization of the evaluations in the original evaluation matrix, can make the data more standardized and save computation time.

[0072] S32: Obtain the positive and negative ideal solutions for each evaluation attribute of the blockchain platform based on the standardized evaluation matrix;

[0073] The standardized evaluation b of the j-th evaluation attribute in the standardized evaluation matrix. ij The maximum value of each item is used as the j-th evaluation attribute C. j The positive ideal solution is the standardized evaluation b of the j-th evaluation attribute in the standardized evaluation matrix. ij The minimum value of each item is used as the j-th evaluation attribute C of the blockchain platform. j For example, for evaluation attribute C1, the maximum value of the first term of all standardized evaluations under evaluation attribute C1 is 0.75, and the maximum value of the second term is 1.0. Therefore, the positive ideal solution of evaluation attribute C1 is [0.75, 1.0]. The minimum value of the first term of all standardized evaluations under evaluation attribute C1 is 0, and the minimum value of the second term is 0.25. Therefore, the negative ideal solution of evaluation attribute C1 is [0, 0.25].

[0074] Positive / negative ideal solutions for blockchain platform evaluation attributes:

[0075] Positive / Negative Ideal Solutions for Evaluation Attributes of Blockchain Platforms

[0076]

[0077] Among them, C j + Let C represent the positive ideal solution of the j-th evaluation attribute of the blockchain platform. j - Let represent the negative ideal solution of the j-th evaluation attribute of the blockchain platform.

[0078] S33: Calculate the distance between the standardized evaluation and the positive and negative ideal solutions of the evaluation attributes in the standardized evaluation matrix using the distance formula;

[0079] Preferably, the step of calculating the distance between the standardized evaluation and the positive ideal solution of the evaluation attribute in the standardized evaluation matrix using the distance formula includes:

[0080] When standardization evaluation b ij When evaluating factors such as privacy, consensus mechanism, popularity, programming language diversity, cost, compatibility, scalability, performance efficiency, ease of use, interoperability, or reliability in a standardized manner:

[0081]

[0082] in, Indicates standardized evaluation b ij With the j-th evaluation attribute positive ideal solution The distance, αk′ ij b ij The kth term, βk j express The k-th term; when N=1, the standardized evaluation b ij Standardized evaluations for privacy, consensus mechanisms, popularity, programming language diversity, cost, or compatibility; when N=2, the standardized evaluation b ij Standardized evaluations are obtained for scalability, performance efficiency, ease of use, or interoperability; when N=3, the standardized evaluation b ij For the standardized evaluation of reliability, λ represents a random parameter, and the value of λ is randomly selected by those skilled in the art based on actual needs;

[0083] For example: when N=1, b in the standardized evaluation matrix 33 =0.8333 and the ideal solution The distance is:

[0084]

[0085] When standardization evaluation b ij For standardized evaluation of toughness:

[0086]

[0087] in, Indicates standardized evaluation b ij With the j-th evaluation attribute positive ideal solution The distance, α1′ ij For b ij The first term, β1 j for The first term, α2′ ij For b ij The second term, β2 j for The second term, α3 ij ′ for b ij The third term, β3 j for The third term, α4′ ij For b ij The 4th term, β4 j for The 4th term, αk′ ij b ij The kth term, βk j for The k-th term, λ, represents a random parameter, and the value of λ is randomly selected by those skilled in the art based on actual needs.

[0088] Preferably, the step of calculating the distance between the standardized evaluation and the negative ideal solution in the standardized evaluation matrix using the distance formula includes:

[0089] When standardization evaluation b ij When evaluating factors such as privacy, consensus mechanism, popularity, programming language diversity, cost, compatibility, scalability, performance efficiency, ease of use, interoperability, or reliability in a standardized manner:

[0090]

[0091] in, Indicates standardized evaluation b ij With the j-th evaluation attribute negative ideal solution The distance, αk′ ij b ij The kth term, βk′ j C represents j - The k-th term; when N=1, the standardized evaluation b ij Standardized evaluations are obtained for privacy, consensus mechanisms, popularity, programming language diversity, cost, or compatibility; when N=2, the standardized evaluation b ij Standardized evaluations are obtained for scalability, performance efficiency, ease of use, or interoperability; when N=3, the standardized evaluation b ij For the standardized evaluation of reliability, λ represents a random parameter, the value of which is randomly selected by those skilled in the art based on actual needs:

[0092] For example, when N=2, b in the standardized evaluation matrix 61 = [0.25, 0.75] and the negative ideal solution C1 - The distance between [0, 0.25] is:

[0093]

[0094] When standardization evaluation b ij For standardized evaluation of toughness:

[0095]

[0096] in, Indicates standardized evaluation b ij With the j-th evaluation attribute negative ideal solution The distance, α1′ ij For b ij The first term, β1' j for The first term, α2′ ij For b ij The second term, β2′ j for The second term, α3 ij ′ for b ij The third term, β3′ j for The third term, α4′ ij For b ij The 4th term, β4′ j for The 4th term, αk′ ij b ij The kth term, βk′ j express The k-th term, λ, represents a random parameter, and the value of λ is randomly selected by those skilled in the art based on actual needs.

[0097] For example: the standardized evaluation of resilience in the standardized evaluation matrix, b. 64 = (0.125, 0.25, 0.375, 0.625) and the negative ideal solution The distance is:

[0098]

[0099] S34: Convert the standardized evaluations in the standardized evaluation matrix into real numbers to obtain the information content of the standardized evaluations;

[0100] When the evaluation attributes of a blockchain platform are privacy, consensus mechanism, popularity, or programming language diversity, the information content of the standardized evaluation in the standardized evaluation matrix is:

[0101]

[0102] in, Indicates standardized evaluation b ij Information content, C j + Let represent the positive ideal solution for the j-th evaluation attribute of the blockchain platform;

[0103] For example: b in the standardized evaluation matrix 33 =0.8333 information content is

[0104] When the evaluation attributes of a blockchain platform are scalability, performance efficiency, and ease of use, the amount of information in the standardized evaluation matrix is:

[0105]

[0106] in, Indicates standardized evaluation b ij Information content, b ij =[α1′ ij α2′ ij ], α1′ ij b ij The first term, α2′ ij b ij The second item, β1 j express The first term, β2 j express The second item;

[0107] For example: b in the standardized evaluation matrix 11 The information content of [0.5, 0.75] is

[0108] When the evaluation attribute of a blockchain platform is interoperability, the information content of the standardized evaluation in the standardized evaluation matrix is:

[0109]

[0110] in, Indicates standardized evaluation b ij Information content, b ij =<α1′ ij α2′ ij >,α1′ ij b ij The first term, α2′ ij b ij The second item, β1 j express The first term, β2 j express The second item;

[0111] For example: b in the standardized evaluation matrix 25 The information content of <0.2, 0.4> is

[0112] When the evaluation attribute of a blockchain platform is reliability, the information content of the standardized evaluation in the standardized evaluation matrix includes:

[0113]

[0114] in, Indicates standardized evaluation b ij Information content, b ij =(α1′) ijα2′ ij ,α3′ ij ), α1′ ij b ij The first term, α2′ ij b ij The second term, α3′ ij b ij The third item, β1 j express The first term, β2 j express The second term, β3 j express The third item;

[0115] For example: b in the standardized evaluation matrix 28 The information content of (0.4286, 0.5714, 0.7143) is

[0116] When the evaluation attribute of a blockchain platform is resilience, the information content of the standardized evaluation in the standardized evaluation matrix includes:

[0117]

[0118] in, Indicates standardized evaluation b ij Information content, b ij =(α1′) ij α2′ ij ,α3′ ij α4′ ij ), α1′ ij b ij The first term, α2′ ij b ij The second term, α3′ ij b ij The third term, α4′ ij b ij The fourth item, β1 j express The first term, β2 j express The second term, β3 j express The third term, β4 j express The fourth item;

[0119] For example: b in the standardized evaluation matrix 34 The information content of (0.25, 0.375, 0.5, 0.625) is

[0120]

[0121] When the evaluation attribute of a blockchain platform is cost or compatibility, the information content of the standardized evaluation in the standardized evaluation matrix includes:

[0122]

[0123] in, Indicates standardized evaluation b ij Information content, C j + Let represent the positive ideal solution for the j-th evaluation attribute of the blockchain platform;

[0124] For example: b in the standardized evaluation matrix 111 The information content of 0.7143 is:

[0125]

[0126] S35: Based on the frequency of user use of each evaluation attribute of the blockchain platform, the evaluation attributes of the blockchain platform are divided into multiple levels. The subjective weight of the evaluation attributes of the blockchain platform is determined using the Analytic Hierarchy Process (AHP). The objective weight of the evaluation attributes of the blockchain platform is calculated using the entropy weight method based on the information content of the standardized evaluation. The comprehensive weight of the evaluation attributes of the blockchain platform is calculated using the combined weighting method based on the subjective and objective weights of the evaluation attributes of the blockchain platform.

[0127] The objective weights of the evaluation attributes of the blockchain platform include:

[0128]

[0129]

[0130]

[0131] Where, p ij Indicates standardized evaluation b ij Information content and the j-th evaluation attribute C j The ratio of the sum of information content of all standardized evaluations, e j Indicates the evaluation attribute C j The entropy value, where m is the number of blockchain platforms and n represents the number of evaluation attributes for each blockchain platform. Indicates standardized evaluation b ij The amount of information, To evaluate the objective weight of attribute j, e represents the natural base.

[0132] The subjective weights of the evaluation attributes of the blockchain platform include:

[0133] The evaluation attributes of the blockchain platform are divided into Q levels, and the subjective weights of the evaluation attributes at different levels of the blockchain platform are determined using the Analytic Hierarchy Process (AHP).

[0134] The subjective weights for determining the evaluation attributes at different levels of the blockchain platform using the Analytic Hierarchy Process (AHP) include:

[0135] (1) Based on the level of the evaluation attributes of the blockchain platform, compare the evaluation attributes of the blockchain platform at different levels pairwise to obtain the priority comparison matrix, and perform consistency verification on the priority comparison matrix.

[0136]

[0137] Among them, c a’b’ This indicates the relative importance of the evaluation attributes of a blockchain platform at level a' to those at level b', and it possesses the following properties:

[0138]

[0139] Among them, c a’b’ The rules for selecting the value are as follows:

[0140]

[0141] The consistency check of the priority comparison matrix includes:

[0142] A: Calculate the largest eigenvalue λ of the priority comparison matrix. max .

[0143] B: Determine the consistency index:

[0144]

[0145] When CI = 0, it means that the priority comparison matrix is ​​consistent. The smaller the CI, the better the consistency. In this embodiment, Q is 12.

[0146] C: The corresponding random consistency index RI is obtained by averaging through random simulation. The value of RI is obtained through querying.

[0147] Random Consistency Index

[0148]

[0149] D: Calculate the consistency ratio:

[0150]

[0151] If CR≤0.1, the priority comparison matrix is ​​determined. If CR>0.1, the priority comparison matrix is ​​reconstructed until CR≤0.1.

[0152] (2) Normalize the priority comparison matrix to obtain the normalized priority comparison matrix:

[0153]

[0154] Where Q represents the number of evaluation attribute levels for the blockchain platform.

[0155] (3) Add the columns of the normalized priority comparison matrix and calculate the eigenvectors of the priority comparison matrix:

[0156]

[0157]

[0158] in, This represents the eigenvector of the priority comparison matrix, where Q is the number of evaluation attribute levels for the blockchain platform.

[0159] (4) The feature vector Normalization yields the subjective weights of the blockchain platform evaluation attributes at level a':

[0160]

[0161] in, The subjective weight of the evaluation attribute of the blockchain platform at level a'.

[0162] Based on the subjective weights of the blockchain platform evaluation attributes at level a', the subjective weights of each evaluation attribute are obtained.

[0163] The comprehensive weighting of the evaluation attributes of the blockchain platform includes:

[0164]

[0165] in, Let represent the overall weight of the j-th evaluation attribute, and n represent the number of evaluation attributes of the blockchain platform. This represents the subjective weight of the j-th evaluation attribute. Let θ represent the objective weight of the j-th evaluation attribute, and let θ be a random parameter belonging to [0, 1]. In this embodiment, θ is 0.6.

[0166] S36: Calculate the relative proximity of the blockchain platform based on the distance between the standardized evaluation and the positive and negative ideal solutions, as well as the comprehensive weight of the evaluation attributes, and sort the blockchain platforms from largest to smallest relative proximity value.

[0167] The relative proximity of the blockchain platform includes:

[0168]

[0169] Among them, RC i Let W represent the relative proximity to the i-th blockchain platform; n represents the number of evaluation attributes; W j C b represents the overall weight of the j-th evaluation attribute; ij This represents the standardized evaluation of the j-th evaluation attribute of the i-th blockchain platform; Let represent the positive ideal solution of the j-th evaluation attribute of the blockchain platform; Let represent the negative ideal solution of the j-th evaluation attribute of the blockchain platform; b ij and The distance; b ij and The distance.

[0170] S4: Use an alternative blockchain platform to receive enterprise data provided by the enterprise and preprocess the enterprise data before putting it on the blockchain.

[0171] Preferably, the data provided by the enterprise includes user data, marketing data, sales data, and financial data;

[0172] Preferably, the preprocessing of the data provided by the enterprise before it is uploaded to the blockchain includes:

[0173] First, the data is processed using an edge computing system on a server or IoT device to obtain the transaction to be signed. Then, the data is hashed and the hash is signed. Finally, the processed data is sent to the blockchain node.

[0174] S51: Use the client of the alternative blockchain platform as a monitoring node, and randomly select a node from the consensus nodes as the master node through the monitoring node;

[0175] S52: The master node receives the pre-processed data provided by the enterprise, sorts it, packages it into a pre-block, and sends it to each consensus node;

[0176] S53: Each consensus node receives the preparatory block sent by the master node, verifies and votes on each transaction in the preparatory block, and sends the voting results to the master node;

[0177] S54: The master node counts the voting results. If the number of consensus nodes' votes exceeds a set threshold, the transaction is verified as a valid transaction; otherwise, the transaction is verified as invalid.

[0178] S55: The master node reorganizes the transaction information in the preparatory block, selects the transactions that have passed the vote and adds them to the formal block, and returns the transaction information that is not verified to the monitoring node.

[0179] S56: The master node broadcasts the generated official block to the consensus node and the storage node. The consensus node receives the official block and adds it to its own ledger, and returns the reception information to the master node; the storage node receives the official block and stores the official block information, and returns the storage information to the master node.

[0180] S57: When the number of successfully stored messages received by the master node exceeds the set threshold, a new block is generated, and the new block is added to the blockchain to return the transaction success information to the monitoring node.

[0181] S58: The monitoring node calculates the reputation value of the consensus node and the storage node based on the data processing speed of each node in this round of block generation, and reclassifies the consensus node and the storage node according to the set reputation value threshold.

[0182] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1.A blockchain platform-based enterprise data processing method, characterized in that, The method comprises the following steps: S1: Obtain attribute data of a plurality of blockchain platforms, and use common attributes of the blockchain platforms as evaluation attributes, wherein the common attributes of the blockchain platforms are attributes common to the plurality of blockchain platforms; The evaluation attributes of the blockchain platforms comprise scalability, consensus mechanism, privacy, resilience, interoperability, popularity, performance efficiency, reliability, programming language diversity, cost, compatibility, and use difficulty, wherein the cost and use difficulty are cost-type attributes; the scalability, consensus mechanism, privacy, resilience, interoperability, popularity, performance efficiency, reliability, programming language diversity, and compatibility are benefit-type attributes; S2: According to the attribute data of the blockchain platforms, use a plurality of fuzzy numbers to evaluate the evaluation attributes of the blockchain platforms respectively, to obtain an original evaluation matrix; The plurality of fuzzy numbers comprise real numbers, interval numbers, intuitionistic fuzzy numbers, triangular fuzzy numbers, trapezoidal fuzzy numbers, and hesitant fuzzy language term sets; The evaluation of the evaluation attributes of the blockchain platforms using the plurality of fuzzy numbers respectively comprises: The privacy, consensus mechanism, popularity, and programming language diversity are evaluated using real numbers; The scalability, performance efficiency, and use difficulty are evaluated using interval numbers; The interoperability is evaluated using intuitionistic fuzzy numbers; The reliability is evaluated using triangular fuzzy numbers; The resilience is evaluated using trapezoidal fuzzy numbers; The cost and compatibility are evaluated using hesitant fuzzy language term sets; S3: Calculate the relative closeness of each blockchain platform according to the original evaluation matrix, and select the blockchain platform with the highest relative closeness as a candidate blockchain platform; The specific steps of calculating the relative closeness of each blockchain platform according to the original evaluation matrix comprise: S31: According to the types of the evaluation attributes of the blockchain platforms, use a standardization formula to unify and normalize the evaluations in the original evaluation matrix into standardized evaluations, to obtain a standardized evaluation matrix; S32: Obtain the positive ideal solution and the negative ideal solution of each evaluation attribute of the blockchain platforms according to the standardized evaluation matrix; S33: Use a distance formula to calculate the distances between the standardized evaluations in the standardized evaluation matrix and the positive ideal solution and the negative ideal solution of the evaluation attributes; S34: Convert the standardized evaluations in the standardized evaluation matrix into real numbers, to obtain the information amount of the standardized evaluations; S35: According to the use frequency of each evaluation attribute of the blockchain platforms by a user, divide the evaluation attributes of the blockchain platforms into a plurality of levels, determine the subjective weight of the evaluation attributes of the blockchain platforms by using an analytic hierarchy process (AHP), calculate the objective weight of the evaluation attributes of the blockchain platforms by using an entropy weight method according to the information amount of the standardized evaluations, and calculate the comprehensive weight of the evaluation attributes of the blockchain platforms by using a combination weighting method according to the subjective weight and the objective weight of the evaluation attributes of the blockchain platforms; The objective weight of the evaluation attributes of the blockchain platforms comprises: ，0≤ ≤1 ，0≤ ≤1 wherein, denotes the information amount of the standardized evaluation of the jth evaluation attribute , and the ratio of the information amount of all standardized evaluations , and the entropy value of the evaluation attribute , m is the number of blockchain platforms, and n denotes the number of evaluation attributes of the blockchain platforms, denotes the information amount of the standardized evaluation , is the objective weight of the evaluation attribute j, denotes the natural base; The determination of the subjective weight of the evaluation attributes of the blockchain platforms by using the AHP comprises: The blockchain platform evaluation attributes are divided into Q levels, the priority comparison matrix is obtained by comparing the blockchain platform evaluation attributes of different levels with each other according to the levels of the blockchain platform evaluation attributes, and the priority comparison matrix is subjected to consistency check; The priority comparison matrix is subjected to normalization processing to obtain a normalized priority comparison matrix; The normalized priority comparison matrix is added to calculate the eigenvector of the priority comparison matrix; The characteristic vector is normalized to obtain the subjective weight of the blockchain platform evaluation attribute of the first level: in, Representing the Subjective weighting of evaluation attributes for blockchain platforms at each level. Indicates level The evaluation attributes of blockchain platforms relative to their ratings The importance of evaluation attributes for blockchain platforms; According to the subjective weight of the evaluation attribute of the blockchain platform of the first level, the subjective weight of each evaluation attribute is obtained ; S36: The relative closeness of the blockchain platform is calculated according to the distance between the standardized evaluation and the positive ideal solution and the negative ideal solution, and the comprehensive weight of the evaluation attributes, and the blockchain platforms are sorted in descending order of the relative closeness; The comprehensive weight of the evaluation attributes includes: wherein, denotes the comprehensive weight of the jth evaluation attribute, and n denotes the number of evaluation attributes of the blockchain platform, denotes the subjective weight of the jth evaluation attribute, denotes the objective weight of the jth evaluation attribute, is a random parameter belonging to [0, 1]; The relative closeness of the blockchain platform includes: wherein, is the relative closeness of the i-th blockchain platform; n represents the number of evaluation attributes; represents the comprehensive weight of the j-th evaluation attribute; represents the normalized evaluation of the j-th evaluation attribute of the i-th blockchain platform; represents the positive ideal solution of the j-th evaluation attribute of the blockchain platform; represents the negative ideal solution of the j-th evaluation attribute of the blockchain platform; represents and the distance between represents and the distance between S4: The candidate blockchain platform receives the enterprise data provided by the enterprise and pre-processes the enterprise data before being uploaded to the chain; S5: The candidate blockchain platform adds the pre-processed enterprise data to the local ledger to complete the enterprise data uploading operation. 2.The enterprise data processing method based on a blockchain platform according to claim 1, characterized in that, The operation of adding the pre-processed enterprise data to the local ledger to complete the enterprise data uploading operation includes: S51: The client of the candidate blockchain platform is used as a monitoring node, and a node is randomly selected as a master node in the consensus node through the monitoring node; S52: The master node receives the pre-processed enterprise data and sorts it, and packs it into a preliminary block to send to each consensus node; S53: Each consensus node receives the preliminary block sent by the master node, verifies and votes on each transaction information in the preliminary block, and sends the voting result to the master node; S54: The master node counts the voting results, and if the number of passed information of the consensus node exceeds the set threshold, the transaction is verified as a legal transaction; otherwise, the transaction is verified as illegal; S55: The master node reorganizes the transaction information of the preliminary block, selects the transactions that pass the voting, adds them to the formal block, and returns the transaction information that is not verified as legal to the monitoring node; S56: The master node broadcasts the generated formal block to the consensus nodes and storage nodes, the consensus nodes receive the formal block and add it to the ledger they save, and return the receiving information to the master node; the storage nodes accept the formal block and store the formal block information, and return the successful storage information to the master node; S57: When the number of successful storage information received by the master node exceeds the set threshold, a new block is generated, and the new block is added to the blockchain and returns the transaction successful uploading information to the monitoring node; S58: The monitoring node calculates the reputation values of the consensus nodes and the storage nodes according to the data processing speed of each node in this round of block generation, and redivides the consensus nodes and the storage nodes according to the set reputation value threshold.

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