CN114861185B - Consensus mechanism processing method and device for enterprise-level ledger - Google Patents
Consensus mechanism processing method and device for enterprise-level ledger Download PDFInfo
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Abstract
The invention relates to the technical field of ledger management, which is used for solving the problem that the existing consensus mechanism processing method for enterprise-level ledgers cannot carry out abnormity defense on enterprises in a targeted manner, in particular to a consensus mechanism processing method and a device for enterprise-level ledgers, which are used for monitoring and analyzing the consensus mechanism processing environment of the industry to which the enterprises belong: marking an enterprise as an analysis object, marking the industry to which the analysis object belongs as an analysis industry, carrying out numerical calculation on node data, total accident data and near accident data of the analysis industry to obtain an industry coefficient, and marking the analysis industry as a normal industry or an abnormal industry according to the numerical value of the industry coefficient; the method and the system make a follow-up defense strategy of the enterprise by processing the monitoring and analyzing result of the environment through a consensus mechanism, perform overall evaluation on the data security of the enterprise in the analysis industry, and further perform targeted defense scheme design when the overall data security does not meet the requirement so as to ensure that the data security of the analysis industry is improved on the whole.
Description
Technical Field
The invention relates to the technical field of standing book management, in particular to a consensus mechanism processing method and device for enterprise-level standing books.
Background
The machine account originally refers to an account book which is placed on a machine for people to read, and is called as a machine account, later, through continuous evolution, some data are also called as machine accounts in modern enterprise management, such as fixed asset management machine accounts, enterprise safety production machine accounts, personnel management machine accounts, commodity purchase and sale machine accounts and the like, and the machine accounts are not only some statistics, but also comprise some files, work plans, work reports, work summaries and related data, are classified and arranged into book cost, and are convenient for daily reference and higher-level inspection at ordinary times.
The existing consensus mechanism processing method for enterprise-level ledgers can only perform reason analysis under the condition of inconsistent operation generally, but cannot predict abnormal computer nodes which may appear subsequently and reasons causing the abnormality by combining the operation environment of the industry to which the enterprise belongs and the qualification of the enterprise, so that the abnormality defense cannot be performed in a targeted manner, and the safety of ledger information is low.
In view of the above technical problem, the present application proposes a solution.
Disclosure of Invention
The invention aims to solve the problem that the existing consensus mechanism processing method for the enterprise-level ledger cannot specifically defend the enterprise from abnormity, and provides a consensus mechanism processing method and a device for the enterprise-level ledger.
The purpose of the invention can be realized by the following technical scheme: the consensus mechanism processing method for the enterprise-level standing book comprises the following steps of:
the method comprises the following steps: monitoring and analyzing a consensus mechanism processing environment of the industry to which the enterprise belongs: marking an enterprise as an analysis object, marking the industry to which the analysis object belongs as an analysis industry, carrying out numerical calculation on node data, total accident data and near accident data of the analysis industry to obtain an industry coefficient, and marking the analysis industry as a normal industry or an abnormal industry according to the numerical value of the industry coefficient;
step two: carrying out defense analysis on an analysis object in an abnormal industry: the method comprises the steps that numerical calculation is carried out on floor data, investment data and investor data of an analysis object to obtain an asset coefficient ZC, an asset range is generated through the asset coefficient ZC, a similar object is obtained through the asset range, defense information of the analysis object is obtained through the similar object, and defense scheme is generated through the defense information to carry out defense protection on the analysis object;
step three: verifying and analyzing the effectiveness of the defense scheme generated by the standing book management platform: when operation inconsistency occurs in the analysis object, the factor nodes are marked as conforming nodes or non-conforming nodes, the reasons corresponding to the reason values are marked as conforming reasons or non-conforming reasons, the matching coefficients are obtained by numerically calculating the total occurrence frequency of the operation inconsistency, the number of the conforming nodes and the occurrence frequency of the conforming reasons, and whether the accuracy of the defense information meets the requirements or not is judged according to the numerical value of the matching coefficients.
As a preferred embodiment of the present invention, in the first step, the analysis industry node data is the total number of computer nodes with inconsistent operation results in the analysis industry; the failure total data of the analysis industry is the total times of inconsistent operation results in the analysis industry; the recent data of the analysis industry is the times of inconsistent operation results of the analysis industry within L1 days.
As a preferred embodiment of the present invention, the process of marking the analysis industry as a normal industry or an abnormal industry in the step one comprises: acquiring an industry threshold value through a storage module, and comparing an industry coefficient of an analysis industry with the industry threshold value:
if the industry coefficient is smaller than an industry threshold value, judging that the industry environment of the analysis industry meets the requirement, and marking the corresponding analysis industry as a normal industry;
if the industry coefficient is larger than or equal to the industry threshold value, the industry environment of the analysis industry is judged to be not satisfied with the requirements, the corresponding analysis industry is marked as an abnormal industry, the industry analysis module sends the defense analysis signal to the standing book management platform, and the standing book management platform sends the abnormal analysis signal to the defense analysis module after receiving the defense analysis signal.
As a preferred embodiment of the present invention, the floor area data of the analysis object in the step two is a total floor area value of all office areas of the analysis object; the capital data of the analysis object is the registered capital value of the analysis object; the human-centered data of the analysis object is the worker-worker value registered for the analysis object.
As a preferred embodiment of the present invention, the process of acquiring the asset range in step two includes: obtaining asset thresholds ZCmin and ZCmax by the formulae ZCmin = a1 ZC and ZCmax = a2 ZC, where ZCmin is the minimum asset threshold, ZCmax is the maximum asset threshold, and a1 and a2 are scaling factors; forming an asset range by the maximum asset threshold ZCmax and the minimum asset threshold ZCmin;
the acquisition process of the similar objects comprises the following steps: and acquiring asset coefficients of all industries in the analysis industry, and marking enterprises with the asset coefficients within the asset range as similar objects.
As a preferred embodiment of the present invention, the defense information in the step two includes an abnormal node set, a consistent value, and a defense value;
the acquisition process of the abnormal node set comprises the following steps: marking computer nodes with inconsistent operations of similar objects as marked nodes, acquiring the times of inconsistent operations of the marked nodes and marking the times as operation values, acquiring operation threshold values through a storage module, and marking the marked nodes with the operation values smaller than the operation threshold values as normal nodes; marking the marked nodes with the operation values not less than the operation threshold as abnormal nodes, and marking the set formed by the abnormal nodes as an abnormal node set;
the acquisition process of the consistent value and the defense value comprises the following steps: acquiring a reason value for the inconsistency of operation of the marked nodes, wherein the acquisition process of the reason value comprises the following steps:
if the reason that the operation of the marked nodes is inconsistent is that the server is down, the corresponding reason value is 1;
if the reason that the operation of the marked nodes is inconsistent is that the communication protocol is unreliable, the corresponding reason value is 2;
if the reason for the inconsistency of the operation of the marked nodes is message delay or loss, the corresponding reason value is 3;
if the reason that the operation of the marked nodes is inconsistent is that the information is tampered, the corresponding reason value is 4;
establishing a reason set for the reason values with inconsistent operation appearing in all the marked nodes, carrying out variance calculation on the reason set to obtain a reason expression value, obtaining a reason expression threshold value through a storage module, and comparing the reason expression value with the reason expression threshold value:
if the reason expression value is smaller than the reason expression threshold value, judging that the reasons of inconsistent operation have consistency, wherein the value of the consistency value is 1, and marking the reason value with the largest occurrence frequency in the reason set as a defense value;
and if the reason expression value is larger than or equal to the reason expression threshold value, judging that the reasons are inconsistent in operation, wherein the consistency value is 0, and marking the reason value with the largest occurrence frequency in the reason set as a defense value.
As a preferred embodiment of the present invention, the process of marking the factor nodes as conforming nodes or non-conforming nodes in the step three comprises: marking computer nodes causing operation inconsistency as factor nodes, judging whether the factor nodes are subsets of the abnormal node set, and if so, marking the factor nodes as conforming nodes; if not, marking the factor node as a non-conforming node;
the process of marking the reason corresponding to the reason value as the reason or not includes: acquiring a cause value causing inconsistent operation, judging whether the cause value is the same as the defense value, and if so, marking a cause corresponding to the cause value as a conforming cause; if not, marking the reason corresponding to the reason value as the non-conformity reason.
As a preferred embodiment of the present invention, the determination process of whether the accuracy of the defense information in step three meets the requirement includes: obtaining a matching threshold value through a storage module, and comparing a matching coefficient of an analysis object with the matching threshold value: if the matching coefficient is smaller than the matching threshold value, judging that the accuracy of the defense information does not meet the requirement, generating update information by a verification module and sending the update information to a standing book management platform, sending the update information to a defense analysis module after the standing book management platform receives the update information, and updating the defense information of an analysis object after the defense analysis module receives the update information; and if the matching coefficient is greater than or equal to the matching threshold, judging that the accuracy of the defense information meets the requirement, generating a verification qualified signal by the verification module, and sending the verification qualified signal to the standing book management platform.
The consensus mechanism processing device for the enterprise-level ledger comprises a ledger management platform, wherein the ledger management platform is in communication connection with an industry analysis module, a defense analysis module, a verification module and a storage module;
the industry analysis module is used for monitoring and analyzing the consensus mechanism processing environment of the industry to which the enterprise belongs;
the defense analysis module performs defense analysis on the enterprise after receiving the defense analysis signal;
the verification module is used for verifying and analyzing the effectiveness of the defense scheme generated by the standing book management platform.
Compared with the prior art, the invention has the beneficial effects that:
1. monitoring and analyzing the consensus mechanism processing environment of the industry to which the enterprise belongs through the industry analysis module, formulating a subsequent defense strategy of the enterprise through the monitoring and analyzing result of the consensus mechanism processing environment, and simultaneously performing overall evaluation on the data security of the enterprise in the analysis industry through the numerical value of the industry coefficient, and then performing targeted defense scheme design when the data security does not meet the requirement overall so as to ensure that the data security of the analysis industry is improved overall.
2. A specific defense scheme is generated for the enterprise by combining the industrial data security and the asset data of the enterprise through the defense analysis module, the follow-up attacked computer nodes and modes of the enterprise are predicted through the defense information, and then the defense scheme can be appointed to prevent in advance through the prediction result, so that the data security of the enterprise is further ensured.
3. The accuracy of the defense information can be verified and analyzed through the verification module, the computer nodes which are subsequently attacked by enterprises are compared with the mode and the defense information, the consistency of parameters of which the defense information is inconsistent with the actually appeared operation result is judged, when the parameter deviation of the defense information inconsistent with the actually appeared operation result is large, the defense scheme can be dynamically adjusted in time, and the defense scheme can be continuously implemented.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The trust of the blockchain is mainly embodied in that users distributed in the blockchain do not need to trust the other party of the transaction or trust a centralized mechanism, and the transaction can be realized only by trusting a software system under a blockchain protocol.
Example one
Referring to fig. 1, the consensus mechanism processing apparatus for enterprise-level ledgers includes a ledger management platform, and the ledger management platform is communicatively connected with an industry analysis module, a defense analysis module, a verification module, and a storage module.
The industry analysis module is used for monitoring and analyzing the consensus mechanism processing environment of the industry to which the enterprise belongs: marking an enterprise as an analysis object, marking the industry to which the analysis object belongs as an analysis industry, and acquiring node data JD, total data GZ and near data GJ of the analysis industry, wherein the node data JD of the analysis industry is the total number of computer nodes with inconsistent operation results in the analysis industry; the total failure data GZ of the analysis industry is the total times of inconsistent operation results in the analysis industry; the analysis industry recent data GJ is the times of inconsistent operation results in the analysis industry within L1 days, L1 is a numerical constant, and the numerical value of L1 is set by a manager; obtaining an industry coefficient HY of the analysis industry through a formula HY = alpha 1 × JD + alpha 2 × GZ + alpha 3 × GJ, wherein the industry coefficient is a numerical value for reflecting the quality of the whole operation environment in the analysis industry, and the higher the numerical value of the industry coefficient is, the worse the whole operation environment of a sharing mechanism in the analysis industry is; wherein alpha 1, alpha 2 and alpha 3 are all proportionality coefficients, and alpha 1 is more than alpha 2 and more than alpha 3 is more than 1; acquiring an industry threshold HYmax through a storage module, and comparing an industry coefficient HY of an analysis industry with the industry threshold HYmax: if the industry coefficient HY is less than an industry threshold HYmax, judging that the industry environment of the analysis industry meets the requirements, and marking the corresponding analysis industry as a normal industry; if the industry coefficient HY is greater than or equal to an industry threshold HYmax, judging that the industry environment of the analysis industry does not meet the requirements, marking the corresponding analysis industry as an abnormal industry, sending a defense analysis signal to the standing book management platform by the industry analysis module, and sending the abnormal analysis signal to the defense analysis module after the standing book management platform receives the defense analysis signal; the subsequent defense strategy of the enterprise is formulated by processing the monitoring and analyzing result of the environment through a consensus mechanism, meanwhile, the data security of the enterprise in the analysis industry is integrally evaluated through the numerical value of the industry coefficient, and then the defense scheme is designed in a targeted manner when the data security integrally does not meet the requirement, so that the data security of the analysis industry is improved on the whole.
The defense analysis module performs defense analysis on the enterprise after receiving the defense analysis signal: acquiring floor data ZD, investment data ZZ and person-pouring data ZR of an analysis object, wherein the floor data ZD of the analysis object is the total floor area value of all office areas of the analysis object; the data ZZ of the analysis object is the registered fund value of the analysis object; the person-of-interest data ZR of the analysis object is the worker numerical value registered by the analysis object; obtaining an asset coefficient ZC of an analysis object by a formula ZC = beta 1 × ZD + beta 2 × ZZ + beta 3 × ZR, wherein beta 1, beta 2 and beta 3 are proportionality coefficients, and beta 1 > beta 2 > beta 3 > 1; obtaining asset thresholds ZCmin and ZCax through formulas ZCmin = a1 × ZC and ZCax = a2 × ZC, wherein ZCmin is a minimum asset threshold, ZCax is a maximum asset threshold, a1 and a2 are proportionality coefficients, a1 is more than or equal to 0.75 and less than or equal to 0.85, and a2 is more than or equal to 1.25; forming an asset range by the maximum asset threshold ZCmax and the minimum asset threshold ZCmin, acquiring asset coefficients of all industries in the analysis industry, and marking enterprises with the asset coefficients in the asset range as similar objects; marking computer nodes with inconsistent operations of similar objects as marked nodes, acquiring the times of inconsistent operations of the marked nodes and marking the times as operation values, acquiring operation threshold values through a storage module, and marking the marked nodes with the operation values smaller than the operation threshold values as normal nodes; marking the marked nodes with the operation values not less than the operation threshold as abnormal nodes, and marking the set formed by the abnormal nodes as an abnormal node set; acquiring a reason value for the inconsistency of operation of the marked nodes, wherein the acquisition process of the reason value comprises the following steps: if the reason that the operation of the marked nodes is inconsistent is that the server is down, the corresponding reason value is 1; if the reason that the operation of the marked nodes is inconsistent is that the communication protocol is unreliable, the corresponding reason value is 2; if the reason for the inconsistent operation of the marked nodes is message delay or loss, the corresponding reason value is 3; if the reason that the operation of the marked nodes is inconsistent is that the information is tampered, the corresponding reason value is 4; establishing a reason set for the reason values with inconsistent operation appearing in all the marked nodes, carrying out variance calculation on the reason set to obtain a reason expression value, obtaining a reason expression threshold value through a storage module, and comparing the reason expression value with the reason expression threshold value: if the reason expression value is smaller than the reason expression threshold, it is determined that the reasons for operation inconsistency are consistent, the value of the consistent value is 1, and when the value of the consistent value is 1, it indicates that the reasons for operation results inconsistency of the existing similar objects are consistent, and the reasons corresponding to the defense values at this time belong to the most common and concentrated reasons in the analysis industry, so that the prevention of the reasons corresponding to the defense values needs to be strengthened; marking the reason value with the most occurrence times in the reason set as a defense value; if the reason expression value is larger than or equal to the reason expression threshold value, judging that the reasons are inconsistent in operation, wherein the consistency exists, the value of the consistency value is 0, and the reason value with the largest occurrence frequency in the reason set is marked as a defense value; the defense analysis module sends defense information to the standing book management platform, the standing book management platform generates a defense scheme for the analysis object after receiving the defense information, the standing book management platform performs consensus mechanism defense on the analysis object through the defense scheme, and the defense information comprises an abnormal node set, a consistent value and a defense value; and predicting subsequent computer nodes and modes attacked by the enterprise through the defense information, and further appointing a defense scheme to prevent in advance through a prediction result, so that the data security of the enterprise is further ensured.
The verification module is used for verifying and analyzing the effectiveness of the defense scheme generated by the standing book management platform: when the operation of the analysis object is inconsistent, obtaining computer nodes causing the operation inconsistency and marking the computer nodes as factor nodes, judging whether the factor nodes are subsets of the abnormal node set, and if so, marking the factor nodes as conforming nodes; if not, marking the factor node as a non-conforming node; acquiring a cause value causing inconsistent operation, judging whether the cause value is the same as the defense value, and if so, marking the cause corresponding to the cause value as a conforming cause; if not, marking the reason corresponding to the reason value as a non-conformity reason; after the platform account management platform receives the defense information, the total times of analyzing the phenomenon that the operation of the objects is inconsistent are marked as YB, the number of the conforming nodes is marked as FJ, and the times of conforming reasons are marked as FY; obtaining a matching coefficient PP of an analysis object through a formula PP = t1 FJ/YB + t2 FY/YB, wherein the matching coefficient is a numerical value reflecting the matching degree of the parameters inconsistent with the actual operation and the defense information, and the larger the numerical value of the matching coefficient is, the higher the matching degree of the parameters inconsistent with the actual operation and the defense information is, the higher the matching degree of the defense scheme and the enterprise is; wherein t1 and t2 are both proportionality coefficients, and t1 > t2 > 1; acquiring a matching threshold value PPmin through a storage module, and comparing a matching coefficient PP of an analysis object with the matching threshold value PPmin: if the matching coefficient PP is smaller than the matching threshold value PPmin, judging that the accuracy of the defense information does not meet the requirement, generating update information by a verification module and sending the update information to a standing book management platform, sending the update information to a defense analysis module after the standing book management platform receives the update information, and updating the defense information of an analysis object after the defense analysis module receives the update information; if the matching coefficient PP is larger than or equal to the matching threshold value PPmin, judging that the accuracy of the defense information meets the requirement, generating a verification qualified signal by a verification module, and sending the verification qualified signal to the ledger management platform; the method comprises the steps that computer nodes of an enterprise, which are subsequently attacked, are compared with mode and defense information, so that the consistency of parameters of which the defense information is inconsistent with an actually-appearing operation result is judged, when the parameter deviation of the defense information inconsistent with the actually-appearing operation result is large, a defense scheme can be dynamically adjusted in time, and the sustainable implementation of the defense scheme is guaranteed.
Example two
Referring to fig. 2, the consensus mechanism processing method for enterprise-level ledger includes the following steps:
the method comprises the following steps: monitoring and analyzing a consensus mechanism processing environment of the industry to which the enterprise belongs: marking an enterprise as an analysis object, marking the industry to which the analysis object belongs as an analysis industry, carrying out numerical calculation on node data, total accident data and near accident data of the analysis industry to obtain an industry coefficient, marking the analysis industry as a normal industry or an abnormal industry according to the numerical value of the industry coefficient, and carrying out overall evaluation on the data security of the enterprise in the analysis industry according to the numerical value of the industry coefficient;
step two: carrying out defense analysis on an analysis object in an abnormal industry: the method comprises the steps that numerical calculation is carried out on floor data, investment data and investor data of an analysis object to obtain an asset coefficient ZC, an asset range is generated by the asset coefficient ZC, a similar object is obtained through the asset range, defense information of the analysis object is obtained through the similar object, computer nodes and modes of an enterprise which are subsequently attacked are predicted through the defense information, and then a defense scheme can be appointed through a prediction result to prevent in advance;
step three: verifying and analyzing the effectiveness of the defense scheme generated by the standing book management platform: when operation inconsistency occurs in an analysis object, marking factor nodes as conforming nodes or non-conforming nodes, marking reasons corresponding to reason values as conforming reasons or non-conforming reasons, carrying out numerical calculation on the total occurrence frequency of the operation inconsistency, the number of conforming nodes and the occurrence frequency of the conforming reasons to obtain matching coefficients, judging whether the accuracy of defense information meets requirements or not through the numerical value of the matching coefficients, and when the accuracy of the defense information does not meet the requirements, dynamically adjusting a defense scheme in time to ensure that the defense scheme can be continuously implemented.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions; such as: the formula HY = α 1 × JD + α 2 × GZ + α 3 × GJ; collecting multiple groups of sample data and setting corresponding industry coefficients for each group of sample data by technicians in the field; substituting the set industrial coefficient and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of alpha 1, alpha 2 and alpha 3 which are 5.28, 3.54 and 2.21 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding industry coefficient preliminarily set by a person skilled in the art for each group of sample data; as long as the proportional relation between the parameters and the quantized numerical values is not influenced, for example, the industry coefficient is in direct proportion to the numerical value of the node data;
when the system is used, the common recognition mechanism processing environment of the industry to which an enterprise belongs is monitored and analyzed, an industry coefficient is obtained by carrying out numerical calculation on node data, total fault data and near fault data of the analysis industry, and the analysis industry is marked as a normal industry or an abnormal industry through the numerical value of the industry coefficient; carrying out defense analysis on an analysis object in an abnormal industry, acquiring a similar object through an asset range, acquiring defense information of the analysis object through the similar object, and generating a defense scheme through the defense information to carry out defense protection on the analysis object; verifying and analyzing the effectiveness of the defense scheme generated by the standing book management platform: when operation inconsistency occurs in an analysis object, marking factor nodes as conforming nodes or non-conforming nodes, marking reasons corresponding to reason values as conforming reasons or non-conforming reasons, carrying out numerical calculation on the total occurrence frequency of the operation inconsistency, the number of conforming nodes and the occurrence frequency of the conforming reasons to obtain matching coefficients, judging whether the accuracy of defense information meets requirements or not through the numerical value of the matching coefficients, and when the accuracy of the defense information does not meet the requirements, dynamically adjusting a defense scheme in time to ensure that the defense scheme can be continuously implemented.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. The consensus mechanism processing method for the enterprise-level standing book is characterized by comprising the following steps of:
the method comprises the following steps: monitoring and analyzing a consensus mechanism processing environment of the industry to which the enterprise belongs: marking an enterprise as an analysis object, marking the industry to which the analysis object belongs as an analysis industry, carrying out numerical calculation on node data, total accident data and near accident data of the analysis industry to obtain an industry coefficient, and marking the analysis industry as a normal industry or an abnormal industry according to the numerical value of the industry coefficient;
step two: carrying out defense analysis on an analysis object in an abnormal industry: the method comprises the steps that numerical calculation is carried out on floor data, investment data and investor data of an analysis object to obtain an asset coefficient ZC, an asset range is generated through the asset coefficient ZC, a similar object is obtained through the asset range, defense information of the analysis object is obtained through the similar object, and defense scheme is generated through the defense information to carry out defense protection on the analysis object;
step three: verifying and analyzing the effectiveness of the defense scheme generated by the standing book management platform: when operation inconsistency occurs in an analysis object, marking factor nodes as conforming nodes or non-conforming nodes, marking reasons corresponding to the reason values as conforming reasons or non-conforming reasons, obtaining matching coefficients by carrying out numerical calculation on the total occurrence frequency of the operation inconsistency, the number of the conforming nodes and the occurrence frequency of the conforming reasons, and judging whether the accuracy of defense information meets requirements or not through the numerical value of the matching coefficients;
analyzing the node data of the industry in the first step to obtain the total number of computer nodes with inconsistent operation results in the industry; the failure total data of the analysis industry is the total times of inconsistent operation results in the analysis industry; the recent data of the analysis industry is the times of inconsistent operation results of the analysis industry within L1 days;
in the second step, the floor area data of the analysis object is the total floor area value of all office areas of the analysis object; the funding data of the analysis object is the registered fund value of the analysis object; the annotator data of the analysis object is the worker numerical value registered by the analysis object;
the acquisition process of the similar objects in the second step comprises the following steps: and acquiring asset coefficients of all industries in the analysis industry, and marking enterprises with the asset coefficients within the asset range as similar objects.
2. The consensus mechanism processing method for enterprise-level ledgers according to claim 1, wherein the process of marking an analysis industry as a normal industry or an abnormal industry in step one comprises: acquiring an industry threshold value through a storage module, and comparing an industry coefficient of an analysis industry with the industry threshold value:
if the industry coefficient is smaller than an industry threshold value, judging that the industry environment of the analysis industry meets the requirement, and marking the corresponding analysis industry as a normal industry;
if the industry coefficient is larger than or equal to the industry threshold value, the industry environment of the analysis industry is judged to be not satisfied with the requirements, the corresponding analysis industry is marked as an abnormal industry, the industry analysis module sends the defense analysis signal to the standing book management platform, and the standing book management platform sends the abnormal analysis signal to the defense analysis module after receiving the defense analysis signal.
3. The method according to claim 1, wherein the process of acquiring the asset range in the second step comprises: obtaining asset thresholds ZCmin and ZCmax by the formulae ZCmin = a1 ZC and ZCmax = a2 ZC, where ZCmin is the minimum asset threshold, ZCmax is the maximum asset threshold, and a1 and a2 are scaling factors; the asset range is formed by a maximum asset threshold ZCmax and a minimum asset threshold ZCmin.
4. The consensus mechanism processing method for enterprise level ledgers of claim 3, wherein the defense information in step two comprises an abnormal node set, a consensus value, and a defense value;
the acquisition process of the abnormal node set comprises the following steps: marking computer nodes with inconsistent operations of similar objects as marked nodes, acquiring the times of inconsistent operations of the marked nodes and marking the times as operation values, acquiring operation threshold values through a storage module, and marking the marked nodes with the operation values smaller than the operation threshold values as normal nodes; marking the marked nodes with the operation values not less than the operation threshold value as abnormal nodes, and marking the set formed by the abnormal nodes as an abnormal node set;
the acquisition process of the consistent value and the defense value comprises the following steps: acquiring a reason value for the inconsistency of operation of the marked nodes, wherein the acquisition process of the reason value comprises the following steps:
if the reason that the operation of the marked nodes is inconsistent is that the server is down, the corresponding reason value is 1;
if the reason that the operation of the marked nodes is inconsistent is that the communication protocol is unreliable, the corresponding reason value is 2;
if the reason for the inconsistency of the operation of the marked nodes is message delay or loss, the corresponding reason value is 3;
if the reason that the operation of the marked nodes is inconsistent is that the information is tampered, the corresponding reason value is 4;
establishing a reason set for the reason values with inconsistent operation appearing in all the marked nodes, carrying out variance calculation on the reason set to obtain a reason expression value, obtaining a reason expression threshold value through a storage module, and comparing the reason expression value with the reason expression threshold value:
if the reason expression value is smaller than the reason expression threshold value, judging that the reasons with inconsistent operation are consistent, wherein the value of the consistent value is 1, and marking the reason value with the largest occurrence frequency in the reason set as a defense value;
and if the reason expression value is larger than or equal to the reason expression threshold value, judging that the reasons are inconsistent in operation, wherein the consistency value is 0, and marking the reason value with the largest occurrence frequency in the reason set as a defense value.
5. The method of claim 1, wherein the step of marking the factor nodes as conforming nodes or non-conforming nodes comprises: marking computer nodes causing operation inconsistency as factor nodes, judging whether the factor nodes are subsets of the abnormal node set, and if so, marking the factor nodes as conforming nodes; if not, marking the factor node as a non-conforming node;
the process of marking the reason corresponding to the reason value as the reason or not includes: acquiring a cause value causing inconsistent operation, judging whether the cause value is the same as the defense value, and if so, marking the cause corresponding to the cause value as a conforming cause; if not, marking the reason corresponding to the reason value as the non-conformity reason.
6. The method for processing the consensus mechanism of the enterprise-level standing book as claimed in claim 5, wherein the step three of determining whether the accuracy of the defense information meets the requirement comprises: obtaining a matching threshold value through a storage module, and comparing a matching coefficient of an analysis object with the matching threshold value: if the matching coefficient is smaller than the matching threshold value, judging that the accuracy of the defense information does not meet the requirement, generating update information by a verification module and sending the update information to a standing book management platform, sending the update information to a defense analysis module after the standing book management platform receives the update information, and updating the defense information of an analysis object after the defense analysis module receives the update information; and if the matching coefficient is greater than or equal to the matching threshold, judging that the accuracy of the defense information meets the requirement, generating a verification qualified signal by the verification module, and sending the verification qualified signal to the standing book management platform.
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