A Refinement Method for Trusted Information in the Collaborative Data Space Set of Multiple Value Chains in Manufacturing Enterprises

Abstract

An embodiment of the present invention provides a method for refining trustworthy information in a multi-value chain collaborative data space set of a manufacturing enterprise, including the following steps: obtaining a multi-value chain collaborative data space set of a manufacturing enterprise; performing an analysis of the trustworthy information refinement process based on the multi-value chain collaborative data space set of the manufacturing enterprise; calculating the benefit of refining trustworthy information in the multi-value chain collaborative data space set of the manufacturing enterprise according to the result of the trustworthy information refinement process analysis; obtaining a benefit model based on the benefit of refining trustworthy information; obtaining the utility of refining trustworthy information in the multi-value chain collaborative data space set of the manufacturing enterprise according to the benefit model; constructing a utility model using the utility of refining trustworthy information; determining the constraints on the utility of refining trustworthy information in the multi-value chain collaborative data space set of the manufacturing enterprise; establishing an optimal utility model based on the constraints on the utility of refining trustworthy information and the utility model; if the utility is not optimal, repeat the above steps. It provides effective support for intelligent factory digital set management, knowledge service, intelligent decision-making, etc.

Description

Technical Field

[0001] The present invention relates to the technical field of data management in manufacturing enterprises, and particularly to a method for refining trusted information in a multi-value chain collaborative data space set of manufacturing enterprises. Background Art

[0002] From the existing domestic and foreign research, the research in the field of multi-value chain collaborative data set management in manufacturing enterprises mainly focuses on the issues of co-construction, consensus, co-fusion, co-utilization, and co-governance of multi-value chain collaborative data sets in manufacturing enterprises. Among them, the traceability of multi-value chain collaborative data in manufacturing enterprises under the blockchain environment can provide a decentralized data solution to solve the problem of data co-trust. The data encryption technology for multi-value chain collaborative data in manufacturing enterprises under the blockchain environment can solve the problems of data privacy and information leakage. The construction methods of private chains, alliance chains, and shared chains for multi-value chain collaborative data in manufacturing enterprises under the sovereign blockchain environment can further clarify the data permissions in the data space and achieve hierarchical management, common benefits, and common governance. However, the quality of multi-value chain collaborative data in manufacturing enterprises has not been significantly improved with the application of blockchain technology. After receiving and retrieving data information, individual manufacturing enterprises or organizations have insufficient refined interpretation of this information based on information literacy, which hinders knowledge innovation. At the same time, the utility optimization mechanism for refining multi-value collaborative information in manufacturing enterprises is lacking. Even if information refinement is carried out for knowledge innovation after digital resource aggregation, it still results in a low level of digital resource utilization and poor application effects.

[0003] The applicant has found that there are at least the following problems in the existing technology: the level of digital resource utilization in manufacturing enterprises is low, and the utility optimization mechanism for refining multi-value collaborative information in manufacturing enterprises is lacking. Summary of the Invention

[0004] The technical problem to be solved by the embodiments of the present invention is how to solve the problem of low level of digital resource utilization in manufacturing enterprises.

[0005] To achieve the above object, the embodiments of the present invention provide a method for refining trusted information in a multi-value chain collaborative data space set of manufacturing enterprises, including the following steps:

[0006] Obtain a multi-value chain collaborative data space set of manufacturing enterprises;

[0007] According to the multi-value chain collaborative data space set of manufacturing enterprises, conduct an analysis of the trusted information refinement process;

[0008] According to the result of the analysis of the trusted information refinement process, calculate the trusted information refinement benefit of the multi-value chain collaborative data space set of manufacturing enterprises;

[0009] Obtain a benefit model based on the trusted information refinement benefit of the multi-value chain collaborative data space set of manufacturing enterprises;

[0010] Obtain the refined utility of the credible information of the multi-value chain collaborative data space set of the manufacturing enterprise according to the said revenue model;

[0011] Construct a utility model by using the refined utility of the credible information of the multi-value chain collaborative data space set of the manufacturing enterprise;

[0012] Determine the constraints of the refined utility of the credible information of the multi-value chain collaborative data space set of the manufacturing enterprise;

[0013] Establish an optimal utility model according to the constraints of the refined utility of the credible information of the multi-value chain collaborative data space set of the manufacturing enterprise and the said utility model;

[0014] If the utility is not optimal, repeat the above steps.

[0015] The above technical solution has the following beneficial effects: The present invention proposes a method for refining the credible information of the multi-value chain collaborative data space set of a manufacturing enterprise, proposes an optimal utility model for refining the credible information of the multi-value chain collaborative data set of a manufacturing enterprise in a blockchain environment, explores the relationship between the transaction cost of information refinement, the number of information refinement behaviors and the information refinement utility in a blockchain environment, analyzes the total utility of information producers and information refiners, establishes false information constraints, and solves the optimal utility of information refinement; It can strongly support the implementation path of applying blockchain technology to traditional platforms, helps to construct a credible and traceable mechanism from the production of credible original information to information refinement, and further improves the information production and refinement utility of users, encourages and guides the production of true and credible information, and builds an information platform that goes beyond credibility and has legal binding force; It is of great significance for forming a refined data set of credible information in the multi-value chain collaborative data space of a manufacturing enterprise in a blockchain environment, encouraging and guiding credible information refinement, improving information refinement utility, and providing effective support for intelligent factory digital set management, knowledge service, intelligent decision-making, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0017] Figure 1 is a flowchart of a method for refining the credible information of the multi-value chain collaborative data space set of a manufacturing enterprise provided by an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of a credible information refinement model in a blockchain environment provided by an embodiment of the present invention;

[0019] Figure 3It is the curve of the number of information refiners in the critical state without false information provided by the embodiments of the present invention;

[0020] Figure 4 It is the curve of the maximum number of information refiners provided by the embodiments of the present invention;

[0021] Figure 5 It is the minimum gas curve provided by the embodiments of the present invention;

[0022] Figure 6 It is the maximum information value curve provided by the embodiments of the present invention;

[0023] Figure 7 It is the first utility curve provided by the embodiments of the present invention;

[0024] Figure 8 It is the second utility curve provided by the embodiments of the present invention;

[0025] Figure 9 It is the first maximum utility curve provided by the embodiments of the present invention;

[0026] Figure 10 It is the second maximum utility curve provided by the embodiments of the present invention. Detailed implementation manners

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] The present invention provides a method for refining trusted information in a multi-value chain collaborative data space set of a manufacturing enterprise. In a blockchain environment, as Figure 1 shown, it includes the following steps:

[0029] S101: Obtain the multi-value chain collaborative data space set of the manufacturing enterprise;

[0030] S102: Perform an analysis of the trusted information refinement process according to the multi-value chain collaborative data space set of the manufacturing enterprise;

[0031] S103: Calculate the trusted information refinement benefit of the multi-value chain collaborative data space set of the manufacturing enterprise according to the result of the trusted information refinement process analysis.

[0032] S104: Obtain a benefit model according to the trusted information refinement benefit of the multi-value chain collaborative data space set of the manufacturing enterprise;

[0033] S105: Obtain the refined utility of the credible information of the multi-value chain collaborative data space set of the manufacturing enterprise according to the revenue model;

[0034] S106: Construct a utility model by using the refined utility of the credible information of the multi-value chain collaborative data space set of the manufacturing enterprise;

[0035] S107: Determine the constraints of the refined utility of the credible information of the multi-value chain collaborative data space set of the manufacturing enterprise;

[0036] S108: Establish an optimal utility model according to the constraints of the refined utility of the credible information of the multi-value chain collaborative data space set of the manufacturing enterprise and the utility model;

[0037] S109: If the utility is not optimal, repeat the above steps.

[0038] For forming a refined data set of credible information for the multi-value chain collaboration data space of manufacturing enterprises in a blockchain environment, it is of great significance to incentivize and guide the refinement of credible information, improve the utility of information refinement, and provide effective support for intelligent factory digital set management, knowledge services, intelligent decision-making, etc.

[0039] In a blockchain environment, the key to credible information refinement is the screening and on-chain storage of credible information. The original information or information representing user behavior is recorded in the blockchain distributed ledger according to rules. As Figure 2 shown, subsequent user information behaviors such as information production, transmission, decomposition, sharing, and refinement are all based on the credible information stored on the blockchain. Information dissemination in a blockchain environment depends on existing traditional information platforms. The blockchain only stores the information "transaction", user "identity", and behavior-represented "information" on the chain, while information dissemination, user basic attributes, and user behavior environment are still traditional information platforms. The main bodies of information refinement mainly involve multi-value chain collaborative data information producers of manufacturing enterprises, blockchain information platforms, manufacturing enterprise data information consumers, block storage personnel, and nodes. The credible information refinement process in a blockchain environment is a cyclic process.

[0040] Among them, information refinement refers to the multi-dimensional forms of behavior and cognition of manufacturing enterprises based on information integration, refined interpretation of information, and view cognition, and oriented to information production and knowledge innovation, beyond the levels of information dissemination and knowledge sharing.

[0041] Based on the optimal utility model, identify and optimize the utility of each information refinement subject.

[0042] The revenue model includes: a producer revenue model and a refiner revenue model.

[0043] The producer revenue model is:

[0044]

[0045] Where: t is the time; Δt is the confirmation delay time; the unit is millisecond, microsecond, minute or hour.

[0046] M is the information producer at time t, the information promoter at time t + Δt, and the manufacturing enterprise data consumer at time t + 1;

[0047] U is the manufacturing enterprise data consumer at time t, the information refiner at time t + Δt, and the information producer at time t + 1;

[0048] δ is the user influence; represents the user influence of the information producer M; represents the user influence of the information refiner U; ε is the information revenue additional coefficient, and the unit is 1; ε M represents the information revenue additional coefficient of the information producer M, and the unit is 1; ε U represents the information revenue additional coefficient of the information refiner U, and the unit is 1; ε0 represents the information revenue additional coefficient at t = 0, and the unit is 1; represents the industry environment heat; L0 represents the information value appreciation rate at t = 0; P is the information value, and the unit is yuan.

[0049] The specific refiner revenue model is as follows:

[0050]

[0051] Where: t is the time; Δt is the confirmation delay time; the unit is millisecond, microsecond, minute or hour.

[0052] M is the information producer at time t, the information promoter at time t + Δt, and the manufacturing enterprise data consumer at time t + 1;

[0053] U is the manufacturing enterprise data consumer at time t, the information refiner at time t + Δt, and the information producer at time t + 1;

[0054] δ is the user influence; represents the user influence of the information producer M; represents the user influence of the information refiner U; ε is the information revenue additional coefficient, and the unit is 1; ε M represents the information revenue additional coefficient of the information producer M, and the unit is 1; ε U represents the information revenue additional coefficient of the information refiner U, and the unit is 1; ε0 represents the information revenue additional coefficient at t = 0, and the unit is 1; θ represents the industry environment heat; L0 represents the information value appreciation rate at t = 0; P is the information value, and the unit is yuan; g is the confirmation cost, and the unit is yuan.

[0055] According to the optimal utility model, explore the relationship among the information refinement transaction cost, the number of information refinement behaviors, and the information refinement utility, analyze the total utility of information producers and information refiners, establish false information constraints, and solve the optimal utility of information refinement.

[0056] The optimal utility model is as follows:

[0057]

[0058] where: t is the time, and △t is the confirmation delay time; the unit is milliseconds, microseconds, minutes, or hours. u is the total utility; R M is the income of the false information producer, with the unit of yuan, γ is the change range of the information value appreciation rate, g0 is the blockchain confirmation fee at t = 0, with the unit of yuan; where R U is the income of the false information refiner, with the unit of yuan, σ is the Ethereum question-setting difficulty coefficient, with the unit of 1; α is the deposit protection time limit; Q is a coefficient, with the unit of 1.

[0059] The producer income model includes the income model of the false information producer, specifically:

[0060] R M = (1 + ε m - ε0)(1 - γ)P + m(1 + ε u - ε0)(1 + γ)P - (1 + ε M )L0P - g0

[0061] where, R M is the income of the false information producer, with the unit of yuan, γ is the change range of the information value appreciation rate, g0 is the blockchain confirmation fee at t = 0, with the unit of yuan; ε is the information income additional coefficient, with the unit of 1; ε M represents the information income additional coefficient of information producer M, with the unit of 1; ε U represents the information income additional coefficient of information refiner U, with the unit of 1; ε0 represents the information income additional coefficient at t = 0, with the unit of 1; L0 represents the information value appreciation rate at t = 0; P is the information value, with the unit of yuan.

[0062] The refiner income model includes the income model of the false information refiner, specifically:

[0063] R U = (1 + ε u - ε0)(1 - γ)P - (1 + ε u )L0P - g0 - σ△t

[0064] where R UThe revenue of the false information refiner, in yuan, σ is the Ethereum question difficulty coefficient, △t is the confirmation delay time; γ is the change range of the information value appreciation rate, g0 is the blockchain confirmation fee at t = 0, in yuan; ε is the information revenue additional coefficient, unit is 1; ε U represents the information revenue additional coefficient of information refiner U, unit is 1; ε0 represents the information revenue additional coefficient at t = 0, unit is 1; L0 represents the information value appreciation rate at t = 0; P is the information value, in yuan.

[0065] The data space set includes: a data object set, a data set, and a data service set;

[0066] The data set refers to all the data of the enterprise in the production value chain, manufacturing value chain, sales value chain, and service value chain.

[0067] The present invention proposes a method for refining trusted information of a multi-value chain collaborative data space set of a manufacturing enterprise, proposes an optimal utility model for refining trusted information of a multi-value chain collaborative data set of a manufacturing enterprise in a blockchain environment, explores the relationship between the information refinement transaction cost, the number of information refinement behaviors and the information refinement utility in a blockchain environment, analyzes the total utility of information producers and information refiners, establishes false information constraints, and solves the optimal utility of information refinement; it can strongly support the implementation path of applying blockchain technology to traditional platforms, helps to build a trusted and traceable mechanism from the production of trusted original information to information refinement, and further improves the user information production and information refinement utility, encourages and guides the production of true and trusted information, and builds an information platform that goes beyond trust and has legal binding force; it is of great significance for forming a data set of refined trusted information for the multi-value chain collaborative data space of a manufacturing enterprise in a blockchain environment, encouraging and guiding trusted information refinement, improving the information refinement utility, and providing effective support for intelligent factory digital set management, knowledge service, intelligent decision-making, etc.

[0068] Example 1:

[0069] The present invention provides a method for refining trusted information of a multi-value chain collaborative data space set of a manufacturing enterprise, as Figure 1 shown, including:

[0070] S11. Analysis of the process of refining trusted information of a multi-value chain collaborative data set of a manufacturing enterprise in a blockchain environment;

[0071] S12. Calculate the revenue of refining trusted information of a multi-value chain collaborative data set of a manufacturing enterprise in a blockchain environment;

[0072] S13. Calculate the utility of refining trusted information of a multi-value chain collaborative data set of a manufacturing enterprise in a blockchain environment;

[0073] S14. Determine the utility constraint for refining the trusted information of the multi-value chain collaboration dataset of manufacturing enterprises in the blockchain environment;

[0074] S15. Calculate the optimal utility for refining the trusted information of the multi-value chain collaboration dataset of manufacturing enterprises in the blockchain environment;

[0075] S16. If the utility is not optimal, repeat the above steps S11 - S15.

[0076] Further, the step S11 includes:

[0077] S111. Transformation of the identity of the manufacturing enterprise data consumer into an information refiner;

[0078] S112. Establishment of an information refinement pool for information to be stored on the chain. The information dissemination and sharing mechanism of the manufacturing enterprise data based on the blockchain platform provides a basis for consumers to access information and further refine information;

[0079] S113. Finally, it is the discrimination of the information refinement utility. The block storage operator identifies and optimizes the utility of each information refinement subject based on the optimal utility model for refining trusted information in the blockchain environment.

[0080] Further, the step S12 includes:

[0081] S121. Establish an information value-added revenue model for information producers;

[0082] S122. Establish an information value-added revenue model for information refiners.

[0083] Further, the step S13 includes:

[0084] Establish a relationship model between the total utility of information refinement and the blockchain confirmation fee.

[0085] Further, the step S14 includes:

[0086] S141. Establish a revenue model for false information producers;

[0087] S142. Establish a revenue model for false information refiners.

[0088] Further, the step S15 includes:

[0089] S151. Establish an optimal utility model for information refinement under the trusted information constraint in the blockchain environment.

[0090] The data quality of the multi-value chain collaboration of manufacturing enterprises has not been significantly improved with the application of blockchain technology. After receiving and retrieving data information, individual manufacturing enterprises or organizations have insufficient refined interpretation of this information based on information literacy, which hinders knowledge innovation. At the same time, the mechanism for optimizing the refined utility of multi-value collaboration information in manufacturing enterprises is lacking. Even if information refinement is carried out for knowledge innovation after the aggregation of digital resources, it still leads to a low level of digital resource utilization and poor application effects. Therefore, how to design and propose a method for refining trustworthy information in the collaborative data set of the multi-value chain of manufacturing enterprises in a blockchain environment by expanding information refinement, utility theory, and the application of blockchain technology is a problem that needs to be solved.

[0091] The present invention first proposes a method for refining trustworthy information in the collaborative data set of the multi-value chain of manufacturing enterprises in a blockchain environment, and constructs an optimal utility model for refining trustworthy information in the collaborative data set of the multi-value chain of manufacturing enterprises in a blockchain environment. Through the present invention, a trustworthy information refinement data set in the collaborative data space of the multi-value chain of manufacturing enterprises in a blockchain environment can be formed to encourage and guide trustworthy information refinement, improve the utility of information refinement; promote data interaction and open communication between enterprises, improve enterprise performance and organizational creativity; stimulate enterprises or organizations to make more full use of, analyze, and mine data, improve the multi-dimensional benefits of manufacturing enterprises; provide effective support for digital set management, knowledge services, intelligent decision-making, etc. in intelligent factories, which has important significance.

[0092] The present invention provides a method for refining trustworthy information in the collaborative data set of the multi-value chain of manufacturing enterprises in a blockchain environment. First, analyze the process of refining trustworthy information in the collaborative data set of the multi-value chain of manufacturing enterprises in a blockchain environment; secondly, propose an optimal utility model for refining trustworthy information in the collaborative data set of the multi-value chain of manufacturing enterprises in a blockchain environment, including calculating the benefits of refining trustworthy information in the collaborative data set of the multi-value chain of manufacturing enterprises in a blockchain environment, calculating the utility of refining trustworthy information in the collaborative data set of the multi-value chain of manufacturing enterprises in a blockchain environment, determining the utility constraints of refining trustworthy information in the collaborative data set of the multi-value chain of manufacturing enterprises in a blockchain environment, and calculating the optimal utility of refining trustworthy information in the collaborative data set of the multi-value chain of manufacturing enterprises in a blockchain environment.

[0093] Construct a digital resource aggregation model for the collaborative data space of manufacturing enterprises' multi-value chains

[0094] In the blockchain environment, the key to refining trustworthy information lies in the screening and on-chain storage of trustworthy information. The original information or information representing user behavior is recorded in the blockchain distributed ledger according to rules. Subsequent user information behaviors such as information production, transmission, decomposition, sharing, and refinement are all based on the trustworthy information stored on the blockchain. It should be particularly noted that information dissemination in the blockchain environment relies on existing traditional information platforms. The blockchain only stores on the chain the information "transactions", user "identities", and behavior-representing "information", while information dissemination, user basic attributes, and user behavior environments remain traditional information platforms. For example, the information produced by information producers is stored in the traditional environment of minds. If a user makes an information refinement behavior based on the above information, then the blockchain environment of minds can store this "transaction" that can be used for information traceability on the chain. Based on the above analysis, this application proposes a trustworthy information refinement model in the blockchain environment, as Figure 1 shown.

[0095] The main entities involved in information refinement mainly include data information producers in the multi-value chain collaboration of manufacturing enterprises, blockchain information platforms, data information consumers of manufacturing enterprises, block storage personnel, and nodes. The trustworthy information refinement process in the blockchain environment is a cyclical process. First, there is the transformation of the identity of data consumers of manufacturing enterprises into information refiners. The process is that the information produced by the refiners at the previous moment is stored on the chain and becomes the information producer at the current moment. Ordinary platform manufacturing enterprise data consumers, or some of the information producers at the previous moment, become ordinary platform manufacturing enterprise data consumers at this moment. During the information transmission, decomposition, and sharing processes of other users, they come into contact with the information and generate the motivation for refined interpretation of the information, and then make information refinement behaviors that go beyond information sharing and knowledge sharing, thereby producing information with knowledge innovation value. The above process completes the transformation of the identity from ordinary manufacturing enterprise data consumers to manufacturing enterprise information producers.

[0096] Secondly, it is the establishment stage of the information refinement pool for the information to be stored on the chain. Based on the information dissemination and sharing mechanism of the blockchain platform, the data of manufacturing enterprises provides the basis for consumers to access information and further refine information. When the consumers of manufacturing enterprise data perform information refinement behavior, it can be regarded as a "transaction" reached by the information producer and the information refiner with information as the subject matter. This "transaction" information is temporarily stored in the information refinement pool, waiting for the block storage operator to determine the authenticity of the "transaction", and then handed over to the node for storage on the chain. Then comes the stage of storage on the chain. Transactions with verified true information content, as well as transactions with true form but untrue content due to "false information", will all be stored on the chain. During the storage process, the information refiner needs to pay a certain transaction fee (gas) to incentivize the node to preferentially store the transaction information in the block. The information refinement transaction of the winning party in the competition will be recorded on the main chain of the blockchain. Finally, it is the discrimination of the utility of information refinement. Based on the optimal utility model of trustworthy information refinement in the blockchain environment, the block storage operator identifies and optimizes the utility of each information refinement subject. Due to the constraint of false information, the information production and information refinement cycle process of the information refinement subject with poor information refinement utility will be inhibited and gradually end. While better utility will stimulate the information refinement subject to continuously iterate and optimize the trustworthy information refinement behavior in the blockchain environment.

[0097] Therefore, this application intends to propose an optimal utility model for trustworthy information refinement of the collaborative data set of multiple value chains of manufacturing enterprises in the blockchain environment, explore the relationship between the information refinement transaction fee, the number of information refinement behaviors and the information refinement utility in the blockchain environment, analyze the total utility of information producers and information refiners, establish false information constraints, and solve the optimal utility of information refinement. It can strongly support the implementation path of applying blockchain technology to traditional platforms, help build a trustworthy and traceable mechanism from trustworthy original information production to information refinement, and further improve the utility of user information production and information refinement, incentivize and guide the production of true and trustworthy information, and build an information platform that goes beyond trust and has legal binding force.

[0098] Construct a digital resource aggregation model for the collaborative data space of multiple value chains of manufacturing enterprises

[0099] (1) Information refinement revenue

[0100] Assume that both information producers and manufacturing enterprise data consumers have independent accounts in the blockchain system of the collaborative data set of multiple value chains in manufacturing enterprises, and their identities will change from time t to t+1. Information producers produce information, and manufacturing enterprise data consumers retrieve and browse manufacturing data based on their information needs, and continuously interpret and refine the information at different granularities based on their own information literacy, ultimately achieving information reproduction. For example, information producer M1 produces information Im1. Manufacturing enterprise data consumer U1 retrieves and browses information Im1 at time t, and refines the information Im1 at time t+△t (0<△t<1), producing information Iu1. Then, manufacturing enterprise data consumer U1 is transformed into an information producer at time t+1 and completes the reproduction of information. The reason why the above information reproduction can be successfully completed is that, first of all, the information value of the information Im1 produced by M1 has increased due to the production of the information Iu1 And the information promotion cost F0 paid by M1 for this is less than At the same time, the information refinement cost C0 that U1 is willing to bear is less than the information value of the information Iu1

[0101] The original information produced by information producers and the information reproduced by manufacturing enterprise data consumers after information refinement or interpretation are temporarily stored in the information refinement pool, waiting for the block storage operator to verify the authenticity and consistency of the information and then package it into a block and store it in the blockchain. As time goes by and information is continuously produced, the expected information refinement costs of information producers and manufacturing enterprise data consumers are also constantly changing. Therefore, all participants in information reproduction hope to complete information confirmation in the blockchain as soon as possible. Information producers are willing to pay a certain fee gas to encourage more block storage operators to join the construction of the blockchain, so as to store the information of this reproduction on the chain as soon as possible

[0102] For the convenience of discussion, this application further assumes that the blockchain generation environment for information refinement is a public chain based on Ethereum. After the block storage operator identifies the authenticity and uniqueness of the information, it is handed over to the nodes for "processing". Finally, the winning node packages the information into a block and stores it on the chain. The number of nodes also changes at any time, and the processing difficulty gradually increases. Assume that the blockchain confirmation time limit for information is [0,t]. The original information or reproduced information is temporarily stored in the refinement pool at time 0, and the information refinement cost is The information value is P. Since only the information stored on the blockchain has legal evidentiary effect, there is a certain delay from the production of the information to its formal storage on the blockchain. The blockchain confirmation delay time is △t, where △t ∈ (0, t], and the blockchain evidence preservation duration is α. Then the time limit for the information value to be protected on the blockchain is [△t, α + △t]. Although theoretically, within the time limit of △t, there may be cases where the information is deleted or withdrawn after being uploaded to the blockchain, to simplify the research model and focus on the core research issues, this application assumes that all information cannot be deleted or withdrawn after being uploaded to the blockchain. At the same time, it is also assumed that there is no situation of blockchain fork.

[0103] During the period when the information is protected, the information value P will also have certain changes. Let L t represent the information value appreciation rate during this period, and E(L t ) = L0, with the variation range being γ. Then L t ∈ [L0 - γ, L0 + γ]. Assume that the information promotion cost F t of the information producer at time t = (1 + ε t )E(L t )P, where ε t is the information revenue additional coefficient. Then at t = 0, the information promotion cost F0 that the information producer is willing to pay = (1 + ε t )L0P must be less than the information value-added for the information producer to make the information dissemination behavior and provide information resources for potential information refiners. Assume that the information refinement cost C t of the information refiner at time t = (1 + ε t )E(L t )P, where ε t is the information revenue additional coefficient. Then at t = 0, the information refinement cost C0 that the information refiner is willing to pay = (1 + ε t )L0P must be less than the information value for the user to make the information refinement behavior. Considering that the information value and revenue will be affected by the industry environment heat, which in turn affects the user's expectation of the information refinement cost. When the industry environment heat is relatively high, the information producer and refiner will hope to confirm the information on the blockchain as soon as possible. Therefore, the exponential inversion of the industry environment heat acts on the information revenue additional coefficient ε t .

[0104] Assume that after the information refiner U refines the information of the information producer M at time t0, the reproduced information is stored on the blockchain at time t. The information value P is calculated year-on-year according to the user influence δ t . Then the information value-added revenue of the information producer M is as shown in Formula 1

[0105]

[0106] Similarly, the information refinement benefit of the information refiner U As shown in Formula 2

[0107]

[0108] Based on the previous F0 and E(L t ) expression and the information value distribution rule, Formula 1 can be transformed as follows

[0109]

[0110] Based on the previous C0 and E(L t ) expression and the information value distribution rule, Formula 2 can be transformed as follows

[0111]

[0112] Table 1 Initial Parameter Table of the Model

[0113]

[0114] (2) Information Refinement Utility

[0115] Assume that the utility function of the information producer is u M (·), and the utility function of the information refiner is u U (·). Then the total utility at time t can be expressed as Formula 5

[0116]

[0117] As the information value produced by the information producer and the information refiner accumulates, the utility function shows marginal diminishing. Therefore, this application assumes that the utility functions of the information producer and the information refiner are as shown in Formula 6

[0118] u(x) = 1 - e -x , x ∈ R Formula 6

[0119] Expand the utility functions u M , u U respectively at the target utilities u M0 , u U0 and ignore the terms above the second order. Then the function formulas of u M , u U can be expressed as Formula 7 and Formula 8 respectively, where the value ranges of u M0 , u U0 are all greater than or equal to 0 and less than 1

[0120]

[0121] By simultaneously considering Formulas 3, 4, 5, 7, and 8, the relationship between the total utility u of information refinement and the blockchain confirmation fee g is as shown in Formula 9.

[0122]

[0123] Where:

[0124]

[0125] Q2 = 1 - u u0

[0126] (3) Trusted Information Refinement Constraint

[0127] In reality, there are cases where information is deleted or withdrawn. For example, a user posts false information and deletes the original information after achieving the dissemination goal, or a user withdraws or deletes the information due to an operation error after posting. Although this application assumes that the original information cannot be deleted or withdrawn after being uploaded to the blockchain to demonstrate the legal effect of blockchain evidence storage, within the blockchain confirmation delay time of Δt, the user can still delete or withdraw the information, and the user may obtain benefits. After the producer of false information deletes the information, the purpose is to expand the scope of false dissemination, and moreover, the producer of false information benefits the most and causes the greatest harm. Therefore, within the time of Δt, the minimum incremental benefit of the information producer itself and the maximum information value increment of m information refiners should belong to its benefit category, and what it pays is only the information promotion fee for the information refiners, and the gas paid will not be refunded because it deletes the information. Finally, the benefit R of the producer of false information is obtained M As shown in Formula 10 below.

[0128] R M = (1 + ε m - ε0)(1 - γ)P + m(1 + ε u - ε0)(1 + γ)P - (1 + ε M )L0P - g0 Formula 10

[0129] An information refiner bases on untrusted manufacturing data and conducts information refinement, and its harm has certain limitations. In reality, a certain brand enterprise uses "false enterprise data" for information refinement and deletes the information after achieving the purpose of product sales or brand promotion, which is extremely harmful. Then the profit obtained by m information refiners after deleting the information is the information value added to the information producer, and the cost they pay is mainly gas. In Ethereum, the higher the gas paid by users, the faster the information is stored on the chain. At the same time, over time, the difficulty of Ethereum's problem generation is increasing, and the gas that users need to pay to store information on the chain within the same latency time is also increasing. Assuming that g conforms to the linear relationship of g = g0 + σt, then the total cost of m information stored on the chain within △t time is Furthermore, the profit R of a certain false information refiner is obtained U As shown in Formula 11

[0130] R U =(1 + ε u - ε0)(1 - γ)P - (1 + ε u )L0P - g0 - σ△t Formula 11

[0131] (4) Optimal utility model of information refinement under the constraint of trusted information

[0132] In order to build a multi-value chain collaborative data trusted information refinement utility model for manufacturing enterprises in a blockchain environment, it is necessary to ensure that in any case, information producers and information refiners have no incentive to delete information, or rather, the cost or opportunity cost of deleting information is extremely high. Then for information producers, Formula 12 needs to be satisfied, and for information refiners, Formula 13 needs to be satisfied

[0133]

[0134] After transformation, R can be obtained M ≤ -ln(1 - u M0 ), R U ≤ -ln(1 - u U0 ). Finally, based on Formula 9 and combined with the above derivation process, the optimal utility model of information refinement under the constraint of trusted information in a blockchain environment is established as follows

[0135]

[0136] 3. Simulation research

[0137] To specifically demonstrate the method described in this application, this application uses the data of a certain power equipment co., ltd. as a sample for simulation, and the following uses this specific embodiment to illustrate the effect of the technical solution of this application

[0138] (1) Data source and simulation scheme

[0139] Since Formula 14 cannot obtain an analytical solution, numerical simulation is used to analyze the numerical relationship between gas, the number of information subjects, information value, and utility. The relevant data comes from a certain electric power equipment company and the simulated transaction data of Ethereum. The data collection deadline is 12:00 on June 4, 2022. For data that cannot be obtained, expert interviews are conducted to determine the relevant parameter values. Finally, the model constraint simulation scheme of this application based on the above data setting is shown in Table 2, and the optimal model simulation scheme is shown in Table 3.

[0140] Table 2 Model constraint simulation scheme

[0141]

[0142] Table 3 Optimal model simulation scheme

[0143]

[0144] In order to make the simulation results more representative, some parameter values ​​were dimensionalized. For example, schemes 2 and 4 set the g parameter value to 35, and schemes 1 and 3 used g as a variable to simulate in the range of 0 to infinity. Whether the gas price is 35 US dollars, 35 RMB, or 35 Euros in reality, it does not affect the trend or law reflected in the simulation results. Similarly, parameters such as △t, α, and n were also dimensionalized. Based on the above schemes, numerical simulation was carried out using Matlab software, and the results are shown in Figures 2 to 9 shown.

[0145] (2) Numerical simulation of constraint conditions and discussion of results

[0146] When the initial utility of the information producer changes from 0 to 1, the corresponding minimum gas curve without false information is as follows: Figure 3 As shown. The maximum value of the minimum gas curve is 30.15, and the minimum value is 16.33. This shows that under different initial utilities of information producers, in order to avoid false information, the fee paid by the information producer should be higher than the corresponding gas value. In other words, to upload information to the blockchain environment, a certain amount of money needs to be paid to increase the production cost of false information or spam information. When the benefits of publishing false information or spam information are lower than the gas paid by the information producer, it will play a deterrent role to a certain extent. Moreover, as the initial utility of the information producer increases, the minimum gas value shows a downward trend, and the closer it is to 1, the faster the gas declines. This shows that when information producers can achieve higher utilities by publishing credible information, they can also achieve better false information suppression effects under the condition of lower gas payment amounts.

[0147] Figure 4 It depicts the curve of the number of information refiners in the critical state of no false information as the initial utility of information producers increases. That is, to ensure that information refiners no longer engage in false information production or reproduction through information refinement, the number of information refiners cannot be higher than the corresponding value. In practice, when a producer releases false information, if a large number of users conduct multi-level and multi-faceted elaborate interpretations and fact-deviating interpretations of this false information, it will inevitably promote the widespread spread of false information. Therefore, the fewer information refiners of false information, the better. The average value of their number should be below 39.43, and below 28.01, it has a higher false information suppression effect.

[0148] From Figure 5 and Figure 6 Regarding the simulation results of information refinement constraints, as the initial utility of information refiners increases, the minimum gas value shows a downward trend, while the maximum information value shows an upward trend, and the trends are most obvious when approaching the value of 1. Figure 5 The characteristics of the minimum gas curve are similar to Figure 3 but the gas value is generally much lower. This is because the information reproduction value of information refinement is based on the information value of information producers. For example, if an information refiner forwards the false information of an information producer and interprets one of the viewpoints, the information value brought to the information refiner is lower than that of the information producer. Therefore, even maintaining a relatively low gas level can also play a role in suppressing the false information release behavior of information refiners. And Figure 3 reflects that only when the maximum value of false information is 56.11 and preferably kept below 40.23 can the behavior of information refiners producing false information lose motivation. In other words, when producing positive energy information can bring an information value of more than 56.11 to information refiners, there is no evidence that information refiners will abandon economic interests and actively release false information, thereby chasing economic losses and possibly further bringing institutional, ethical or legal risks. Figure 6

[0149] (3) Numerical simulation of the optimal utility model and result discussion

[0150] Under the above constraints, in order to further analyze how to stimulate the production of credible information by increasing the utility of information refiners and producers, and which factors play a positive role in increasing utility. This application obtained the Figures 7 to 10 results based on Simulation Scenarios 5 to 8, where Figure 7 , Figure 8 The dotted line part of Figure 10 ​The curves close to positive and negative infinity are compressed.

[0151] Figure 7 As t increases, it presents an inverted U shape. When △t takes the value of 0.4, the model reaches the maximum u value of 2.34791 at t=3.2, that is, at the moment of 8 times the on-chain confirmation delay, the total utility of information refinement reaches its peak. This shows that with the joint efforts of information producers and information refiners, after the initial rapid accumulation of total utility, a better information refinement effect has been achieved. However, as time goes by, the cumulative effect of utility begins to slowly decline. Even after reaching a certain time point, continuing to make information refinement behaviors will bring poor results because the value-added benefits of information are less than the gas or information promotion costs paid. From a guidance perspective, early intervention in the information production and refinement process in the early stages of development can achieve better governance and guidance performance.

[0152] The utility of information refinement in a blockchain environment is closely related to the storage latency of the blockchain. Figure 8 It reflects the changes in total utility u under four values ​​of △t: 0, 0.4, 1, and 13. First, it is obvious that as the value of △t increases, the total utility of information refinement shows a downward trend, indicating that as the storage delay of △t on the chain increases, it brings relatively poor information refinement utility, affects the effect of information refinement, and forms an obstacle to information dissemination in the blockchain environment. Secondly, when △t is 13 and t approaches 13, the total utility u is also close to 0. This shows that when the blockchain storage delay reaches a certain level, after a certain moment, the manufacturing enterprise data consumers will have no motivation for information refinement and information production. In addition, the manufacturing enterprise data consumers may also take advantage of this on-chain storage time difference to obtain benefits by publishing a large amount of false information about the manufacturing enterprise quickly and then deleting the false information before on-chain storage. In fact, the congestion of Ethereum not only causes troubles to the construction of information platforms under the blockchain environment, but also brings challenges to the scenario-based application of blockchain technology. Therefore, the information storage delay problem caused by Ethereum congestion, its impact on information utility, and its promotion of the production of false information can help provide a reference for the hypothetical decision-making of shared chain, private chain, and alliance chain information platforms for manufacturing enterprises in the blockchain environment.

[0153] Figure 9It reflects the variation of the total utility of maximum information refinement under different value-taking conditions of the industry environment heat. As the industry environment heat increases, the total utility of maximum information refinement also approximately shows a linear increasing trend. This indicates that in a situation with a relatively high industry environment heat, data consumers of manufacturing enterprises actively participating in the information refinement process will obtain higher benefits and better dissemination effects. As △t changes, the information refinement utility will also change. On the one hand, it is affected by the constraint of false information production. On the other hand, as △t increases, and considering the fact that the Ethereum question generation difficulty gradually increases, the initial value of gas and the change range affected by △t have an impact on the total utility of maximum information refinement.

[0154] To explore the above influence degree and the presented rules, this application plots Figure 10 . When gas is 0, assuming the time delay is 0, the maximum utility is 33.84642, which is the maximum value in the curve. This shows that when the information refinement storage cost is 0, it will bring the optimal stimulation effect to the information refiner, but this stimulation effect is also limited. At the same time, when gas approaches infinity, the total information refinement utility approaches negative infinity. This indicates that when the information refinement cost is very high, the information refinement behavior will no longer occur. When gas is around 37.5, the maximum utility approaches 0, and as gas increases, the total information refinement utility shows a gradually decreasing rule. At the same time, combining Figure 3 and Figure 5 , this application believes that a lower gas will boost the production of false information, and a higher gas may reduce the information refinement utility. Therefore, under the parameter value-taking conditions of Table 2 and Table 3, based on the credible information constraint, the value of gas in the range of 31.5 to 37.5 will have a better total information refinement utility.

[0155] Embodiment 2:

[0156] A method for refining credible information in a multi-value chain collaborative data space set of manufacturing enterprises (a method for refining credible information in a multi-value chain collaborative data set of manufacturing enterprises in a blockchain environment), including:

[0157] Analysis of the process of refining credible information in a multi-value chain collaborative data set of manufacturing enterprises in a blockchain environment;

[0158] Calculating the benefits of refining credible information in a multi-value chain collaborative data set of manufacturing enterprises in a blockchain environment;

[0159] Calculating the utility of refining credible information in a multi-value chain collaborative data set of manufacturing enterprises in a blockchain environment;

[0160] Determining the utility constraint of refining credible information in a multi-value chain collaborative data set of manufacturing enterprises in a blockchain environment;

[0161] Calculate the optimal utility of refining the trustworthy information of the multi-value chain collaborative data set of manufacturing enterprises in the blockchain environment;

[0162] If the utility is not optimal, repeat the above steps.

[0163] Propose a method for refining the trustworthy information of the multi-value chain collaborative data set of manufacturing enterprises in the blockchain environment. For forming a refined data set of trustworthy information in the multi-value chain collaborative data space of manufacturing enterprises in the blockchain environment, motivating and guiding the refinement of trustworthy information, improving the utility of information refinement, and providing effective support for digital set management, knowledge service, intelligent decision-making, etc. in intelligent factories, it has important significance.

[0164] Conduct an analysis of the process of refining the trustworthy information of the multi-value chain collaborative data set of manufacturing enterprises in the blockchain environment. The data set refers to all the data of enterprises in the production value chain, manufacturing value chain, sales value chain, and service value chain; information refinement refers to the multi-dimensional forms of behavior and cognition of manufacturing enterprises based on information integration, refined interpretation of information, and cognitive viewpoints, and oriented towards information production and knowledge innovation and beyond the levels of information dissemination and knowledge sharing.

[0165] In the blockchain environment, the key to trustworthy information refinement is the screening and on-chain storage of trustworthy information. The original information or information representing user behavior is recorded in the blockchain distributed ledger according to rules. Subsequent user information behaviors such as information production, transmission, decomposition, sharing, and refinement are all based on the trustworthy information stored on the blockchain.

[0166] Information dissemination in the blockchain environment depends on existing traditional information platforms. The blockchain only stores information "transactions", user "identities", and behavior-represented "information" on the chain, while information dissemination, user basic attributes, and user behavior environments are still traditional information platforms.

[0167] The main bodies of information refinement mainly involve the producers of multi-value chain collaborative data information of manufacturing enterprises, blockchain information platforms, consumers of manufacturing enterprise data information, block storage personnel, and nodes.

[0168] The process of refining trustworthy information in the blockchain environment is a process of repeated cycles.

[0169] First is the transformation of the identity of manufacturing enterprise data consumers into information refiners; second, it is the establishment of an information refinement pool to be stored on the chain. The information dissemination and sharing mechanism of manufacturing enterprise data based on the blockchain platform provides a basis for consumers to access information and further refine information; finally, it is the discrimination of information refinement utility. The block storage personnel, based on the optimal utility model of refining trustworthy information in the blockchain environment, identify and optimize the utility of each information refinement subject.

[0170] Propose an optimal utility model for refining the trusted information of the multi-value chain collaborative dataset of manufacturing enterprises in the blockchain environment, explore the relationship between the information refinement transaction cost, the number of information refinement behaviors and the information refinement utility in the blockchain environment, analyze the total utility of information producers and information refiners, establish false information constraints, and solve the optimal utility of information refinement.

[0171] Calculate the revenue of refining the trusted information of the multi-value chain collaborative dataset of manufacturing enterprises in the blockchain environment.

[0172] Establish an information value-added revenue model for information producers:

[0173]

[0174] Where M represents the information producer at time t, the information promoter at time t+Δt, and the data consumer of the manufacturing enterprise at time t+1; U represents the data consumer of the manufacturing enterprise at time t, the information refiner at time t+Δt, and the information producer at time t+1; represents the information value-added revenue of information producer M; represents the user influence of information producer M; represents the user influence of information refiner U; ε M represents the information revenue additional coefficient of information producer M; ε U represents the information revenue additional coefficient of information refiner U; ε0 represents the information revenue additional coefficient at t = 0; represents the industry environment heat; L0 represents the information value-added rate at t = 0; P represents the information value.

[0175] Establish an information value-added revenue model for information refiners:

[0176]

[0177] Where represents the information refinement revenue of information refiner U; g represents the blockchain confirmation fee gas.

[0178] Calculate the utility of refining the trusted information of the multi-value chain collaborative dataset of manufacturing enterprises in the blockchain environment.

[0179] Establish a relationship model between the total utility of information refinement and the blockchain confirmation fee:

[0180]

[0181] Where

[0182]

[0183] Q2 = 1 - u u0

[0184] where \(u\) M0 is the utility function value of the information producer at \(t = 0\), and \(u\) u0 is the utility function value of the information refiner at \(t = 0\).

[0185] Determine the utility constraint of the trusted information refinement of the multi - value chain collaborative dataset of manufacturing enterprises in the blockchain environment.

[0186] Establish the revenue model of the false information producer:

[0187] \(R\) M \(=(1 + \varepsilon\) m -\varepsilon_0)(1 - \gamma)P+m(1 + \varepsilon\) u -\varepsilon_0)(1 + \gamma)P-(1 + \varepsilon\) M )L_0P - g_0

[0188] where \(R\) M is the revenue of the false information producer, \(\gamma\) is the change range of the information value - added rate, and \(g_0\) is the blockchain confirmation fee at \(t = 0\).

[0189] Establish the revenue model of the false information refiner:

[0190] \(R\) U \(=(1 + \varepsilon\) u -\varepsilon_0)(1 - \gamma)P-(1 + \varepsilon\) u )L_0P - g_0-\sigma\Delta t

[0191] where \(R\) U is the revenue of the false information refiner, \(\sigma\) is the Ethereum question - setting difficulty coefficient, and \(\Delta t\) is the blockchain confirmation delay time.

[0192] Calculate the optimal utility of the trusted information refinement of the multi - value chain collaborative dataset of manufacturing enterprises in the blockchain environment.

[0193] Establish the optimal utility model of information refinement under the trusted information constraint in the blockchain environment:

[0194]

[0195] where \(u\) is the total utility.

[0196] The present invention proposes a method for refining trusted information in a multi-value chain collaborative data space of manufacturing enterprises, and proposes an optimal utility model for refining trusted information in a multi-value chain collaborative data set of manufacturing enterprises in a blockchain environment. It explores the relationship between the transaction cost of information refinement, the number of information refinement behaviors and the utility of information refinement in a blockchain environment, analyzes the total utility of information producers and information refiners, establishes false information constraints, and solves the optimal utility of information refinement. It can strongly support the implementation path of applying blockchain technology to traditional platforms, help to construct a trusted and traceable mechanism from the production of trusted original information to information refinement, and further improve the utility of user information production and information refinement, encourage and guide the production of true and trusted information, and build an information platform that goes beyond trust and has legal binding force. It is of great significance to form a data set of trusted information refinement in a multi-value chain collaborative data space of manufacturing enterprises in a blockchain environment, encourage and guide trusted information refinement, improve the utility of information refinement, and provide effective support for intelligent factory digital set management, knowledge service, intelligent decision-making, etc.

[0197] It should be understood that the specific order or hierarchy of steps in the disclosure process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The appended method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0198] In the above detailed description, various features are combined in a single embodiment to simplify the present disclosure. This method of disclosure should not be construed as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly recited in each claim. On the contrary, as reflected in the appended claims, the present invention resides in less than all of the features of a single disclosed embodiment. Accordingly, the appended claims are hereby expressly incorporated into the detailed description, where each claim stands on its own as a separate preferred embodiment of the present invention.

[0199] In order to enable any person skilled in the art to implement or use the present invention, the above-described disclosed embodiments have been described. For those skilled in the art; various modifications of these embodiments are obvious, and the general principles defined in this application can also be applied to other embodiments without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments given in this application, but is consistent with the widest scope of the principles and novel features disclosed in this application.

[0200] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but those of ordinary skill in the art should recognize that the various embodiments can be further combined and arranged. Therefore, the embodiments described in this application are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, this term is covered in a manner similar to the term "including" as interpreted when used as a transitional word in a claim. In addition, any use of the term "or" in the specification or claims is intended to mean "non-exclusive or".

[0201] Those skilled in the art can also appreciate that the various illustrative logical blocks, units, and steps listed in the embodiments of the present invention can be implemented by electronic hardware, computer software, or a combination of both. To clearly show the interchangeability of hardware and software, the various illustrative components, units, and steps have been generally described in terms of their functions. Whether such functions are implemented by hardware or software depends on the specific application and the design requirements of the overall system. Those skilled in the art can use various methods to implement the described functions for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of the present invention.

[0202] The various illustrative logical blocks or units described in the embodiments of the present invention can be implemented or operated to perform the described functions by a general-purpose processor, a digital signal processor, an application specific integrated circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of the above designs. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any conventional processor, controller, microcontroller, or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.

[0203] In the embodiments of the present invention, the steps of the methods or algorithms described may be directly embedded in hardware, software modules executed by a processor, or a combination of the two. The software modules may be stored in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium in the art. Exemplarily, the storage medium may be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium may also be integrated into the processor. The processor and the storage medium may be disposed in an ASIC, and the ASIC may be disposed in a user terminal. Optionally, the processor and the storage medium may also be disposed in different components of the user terminal.

[0204] In one or more exemplary designs, the above-described functions described in the embodiments of the present invention may be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, these functions may be stored on a computer-readable medium or transmitted on a computer-readable medium in the form of one or more instructions or codes. A computer-readable medium includes a computer storage medium and a communication medium that facilitates the transfer of a computer program from one place to another. The storage medium may be any available medium accessible by a general or special computer. For example, such a computer-readable medium may include, but is not limited to, RAM, ROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage, or other magnetic storage devices, or any other medium that can be used to carry or store program code in the form of instructions or data structures and other forms readable by a general or special computer, or a general or special processor. In addition, any connection may be appropriately defined as a computer-readable medium. For example, if software is transmitted from a website, server, or other remote resource via a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless means such as infrared, wireless, and microwave, it is also included in the defined computer-readable medium. The disks and discs include compact disks, laser disks, optical discs, DVDs, floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs typically reproduce data optically with a laser. The above combinations may also be included in a computer-readable medium.

[0205] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for refining trusted information in a collaborative data space set of multiple value chains in a manufacturing enterprise, characterized in that, It includes the following steps: Obtain the multi-value chain collaborative data space set of manufacturing enterprises; According to the multi-value chain collaborative data space set of manufacturing enterprises, conduct an analysis of the refined process of trustworthy information; According to the results of the refined process analysis of trustworthy information, calculate the refined benefits of trustworthy information in the multi-value chain collaborative data space set of manufacturing enterprises; Obtain a benefit model based on the refined benefits of trustworthy information in the multi-value chain collaborative data space set of manufacturing enterprises; Obtain the refined utility of trustworthy information in the multi-value chain collaborative data space set of manufacturing enterprises according to the benefit model; Construct a utility model by using the refined utility of trustworthy information in the multi-value chain collaborative data space set of manufacturing enterprises; Determine the constraints on the refined utility of trustworthy information in the multi-value chain collaborative data space set of manufacturing enterprises; Establish an optimal utility model according to the constraints on the refined utility of trustworthy information in the multi-value chain collaborative data space set of manufacturing enterprises and the utility model; If the utility is not optimal, repeat the above steps; The benefit model includes: a producer benefit model and a refiner benefit model; The optimal utility model is: where: t is the moment; △t is the confirmation delay time; u is the total utility; R M is the income of the false information producer, in yuan, γ is the change range of the information value appreciation rate, g0 is the blockchain confirmation fee at t = 0, in yuan; where: R U is the income of the false information refiner, in yuan, σ is the Ethereum question-setting difficulty coefficient, in unit of 1; θ is the industry environment heat, α is the deposit protection time limit; Q is a coefficient, in unit of 1; Where: Q2 = 1 - u u0 ; Among them, u M0 is the target utility of the assumed information producer, u u0 is the target utility of the information refiner, M is the information producer at time t, and U is the data consumer of the manufacturing enterprise at time t; δ is the user influence; represents the user influence of the information producer M; represents the user influence of the information refiner U; ε is the information revenue addition coefficient, ε M represents the information revenue addition coefficient of the information producer M; ε U represents the information revenue addition coefficient of the information refiner U; ε0 represents the information revenue addition coefficient at t = 0; L0 represents the information value appreciation rate at t = 0; P is the information value, with the unit of yuan.

2. The method for refining trustworthy information of a multi-value-chain collaborative data space set of a manufacturing enterprise according to claim 1, wherein Based on the optimal utility model, identify and optimize the utility of each information refinement entity.

3. The method for refining trustworthy information of a multi-value chain collaborative data space set of a manufacturing enterprise according to claim 1, characterized in that, The producer benefit model is: Where: t is the time; △t is the confirmation delay time; M is the information producer at time t, the information promoter at time t + △t, and the manufacturing enterprise data consumer at time t + 1; U is the manufacturing enterprise data consumer at time t, the information refiner at time t + △t, and the information producer at time t + 1; δ is the user influence; represents the user influence of information producer M; represents the user influence of information refiner U; ε is the information benefit additional coefficient, with the unit of 1; ε M represents the information benefit additional coefficient of information producer M, with the unit of 1; ε U represents the information benefit additional coefficient of information refiner U, with the unit of 1; ε0 represents the information benefit additional coefficient at t = 0, with the unit of 1; θ represents the industry environment heat; L0 represents the information value appreciation rate at t = 0; P is the information value, with the unit of yuan.

4. The method for refining trusted information of a multi-value chain collaborative data space set of a manufacturing enterprise according to claim 3, wherein The refiner benefit model is specifically: Where: t is the time; △t is the confirmation delay time; M is the information producer at time t, the information promoter at time t + △t, and the manufacturing enterprise data consumer at time t + 1; U is the manufacturing enterprise data consumer at time t, the information refiner at time t + △t, and the information producer at time t + 1; δ is the user influence; represents the user influence of the information producer M; represents the user influence of the information refiner U; ε is the information benefit additional coefficient, with the unit of 1; ε M represents the information benefit additional coefficient of the information producer M, with the unit of 1; ε U represents the information benefit additional coefficient of the information refiner U, with the unit of 1; ε0 represents the information benefit additional coefficient at t = 0, with the unit of 1; θ represents the industry environment heat; L0 represents the information value appreciation rate at t = 0; P is the information value, with the unit of yuan; g is the confirmation cost, with the unit of yuan.

5. The method for refining trusted information in a multi-value chain collaborative data space set of a manufacturing enterprise according to claim 2, wherein According to the optimal utility model, explore the relationship between the refined transaction cost, the quantity of refined information behaviors and the refined utility, analyze the total utility of information producers and information refiners, establish false information constraints, and solve the optimal refined utility.

6. The method for refining trustworthy information of a multi-value chain collaborative data space set of a manufacturing enterprise according to claim 3, characterized in that, The producer benefit model includes the benefit model of false information producers, specifically: R M = (1 + ε m - ε0)(1 - γ)P + m(1 + ε u - ε0)(1 + γ)P - (1 + ε M )L0P - g0 Among them, R M is the income of the false information producer, in yuan; γ is the change range of the information value appreciation rate; g0 is the blockchain confirmation fee at t = 0, in yuan; ε is the information income additional coefficient, unitless; ε M represents the information income additional coefficient of the information producer M, unitless; ε U represents the information income additional coefficient of the information refiner U, unitless; ε0 represents the information income additional coefficient at t = 0, unitless; L0 represents the information value appreciation rate at t = 0; P is the information value, in yuan.

7. A method for refining trusted information in a multi-value chain collaborative data space set of a manufacturing enterprise according to claim 3, characterized in that The refiner benefit model includes the benefit model of false information refiners, specifically: R U = (1 + ε u - ε0)(1 - γ)P - (1 + ε u )L0P - g0 - σΔt where R U is the income of the false information refiner, in yuan, σ is the Ethereum question difficulty coefficient, and △t is the confirmation delay time; γ is the change range of the information value-added rate, g0 is the blockchain confirmation fee at t = 0, and the unit is yuan; ε is the information benefit additional coefficient, and the unit is 1; ε U It represents the information benefit additional coefficient of the information refiner U, with the unit of 1; ε0 represents the information benefit additional coefficient at t = 0, and the unit is 1; L0 represents the information value-added rate at t = 0; P is the information value, and the unit is yuan.

8. The method for refining trusted information in a multi-value chain collaborative data space set of a manufacturing enterprise according to claim 1, characterized in that, The data space set includes: a data object set, a data set, and a data service set; The data set refers to all data of enterprises in the production value chain, manufacturing value chain, sales value chain, and service value chain.

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