A voting method in standardization of international standardized game

Abstract

The application discloses a kind of voting methods in the standard formulation of international standard game, it is related to the technical field of internet voting, main scheme is: in the voting process, obtain voting validity data, voting anonymity data, encryption algorithm data and voting data by voting system, respectively analyze and obtain voting validity index, anonymity intensity index, encryption efficiency index and consensus stability coefficient;The voting behavior entropy of voting system is obtained by Markov chain Monte Carlo model, and the above data is comprehensively analyzed and calculated to obtain the effectiveness comprehensive value;The encryption efficiency index, anonymity intensity index and effectiveness comprehensive value are compared with the corresponding parameters in the evaluation index threshold set respectively, and the corresponding instruction is executed according to the judgment result. To solve the problem that the voting effectiveness is more one-sided in the prior art, the anonymity security measure has a loophole, the encryption and decryption process takes a long time and lacks the stability of the voting system.

Description

Technical Field

[0001] The present invention relates to the field of Internet voting, and in particular to a voting method in the formulation of standards for international standardized games. Background Art

[0002] In today's era of globalization and rapid technological development, voting methods in international standardization have broad application prospects. As more and more industries and fields pursue globally unified standards, such as communications technology, artificial intelligence, and the Internet of Things, effective voting methods can coordinate the demands of different countries, regions, and stakeholders, ensuring the fairness and scientific nature of the standards-setting process, thereby promoting global trade, technological exchange and cooperation, and promoting the healthy and sustainable development of related industries.

[0003] Voting is an essential and core component of international standardization. Standards development often involves complex conflicts of interest, and all parties must vote to express their acceptance of different standard proposals. A reasonable and fair voting method can foster consensus among all parties, avoid deadlocks or frequent changes in standards development caused by unfair decision-making, ensure the stability and authority of standards, provide clear regulations and guidance for the development of related industries, and ensure consistency and compatibility in the global market.

[0004] However, the existing technology has certain defects, such as:

[0005] Existing voting methods are rather one-sided in judging the validity of votes. They are usually based only on simple voting rules, such as whether format requirements are met, and are unable to comprehensively consider the impact of multiple factors on the validity of votes. This may result in some invalid votes being incorrectly judged or valid votes being ignored due to special circumstances, affecting the accuracy and fairness of the voting results.

[0006] Anonymity safeguards have loopholes, making it difficult to accurately quantify their strength. In complex situations, voters' identities could be maliciously analyzed and tracked. This not only violates their privacy but also potentially prevents them from expressing their true wishes for fear of retaliation or external pressure, undermining the fairness and objectivity of voting.

[0007] The encryption and decryption process is time-consuming and prone to system congestion and delays when processing large amounts of voting data. This not only affects the real-time and efficiency of voting, but also increases the risk of data tampering or leakage, reducing the security and reliability of the voting system.

[0008] Existing voting methods lack comprehensive assessment and consideration of the stability coefficient of the voting system. When faced with complex voting environments and changing voting behaviors, the system is prone to abnormal fluctuations or errors, affecting the smooth progress of the voting process and the credibility of the voting results. Summary of the Invention

[0009] In response to the shortcomings of the existing technology, the present invention provides a voting method for the standard setting of international standardization games, which at least solves one of the problems in the existing technology, such as the one-sided judgment of the validity of the vote, loopholes in the anonymity protection measures, the time-consuming encryption and decryption process, and the lack of stability of the voting system.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: A voting method for the standard setting of an international standardization game, comprising:

[0011] Step 1: During the voting process, the voting validity data is obtained through the voting system, and a voting validity parameter set is obtained based on the voting validity data analysis; the parameters in the voting validity parameter set are comprehensively analyzed to obtain a voting validity index;

[0012] Step 2: Obtain voting anonymity data through the voting system, and obtain an anonymity strength parameter set based on the voting anonymity data analysis; obtain an anonymity strength index by performing a comprehensive analysis on the anonymity strength parameter set;

[0013] Step 3: Obtain the encryption algorithm data of the voting system, and obtain an encryption efficiency parameter set based on the encryption algorithm data analysis; obtain an encryption efficiency index by performing a comprehensive analysis on the encryption efficiency parameter set;

[0014] Step 4: Obtain voting data through the voting system's voting records, and conduct a comprehensive analysis based on the bidding data to obtain the consensus stability coefficient;

[0015] Step 5: Obtain the voting behavior entropy of the voting system by inputting the voting system data into the Markov Chain Monte Carlo model, and calculate the comprehensive effectiveness value by combining the voting behavior entropy, voting effectiveness index, and consensus stability coefficient;

[0016] Step 6: Set the evaluation index threshold set; compare the encryption efficiency index, anonymity strength index and effectiveness comprehensive value with the corresponding parameters in the evaluation index threshold set, and execute the corresponding instructions based on the judgment results.

[0017] In the preferred scheme of the voting method in the standard setting of the above-mentioned international standardization game, the voting validity parameter set includes voting participation P, total number of votes T, number of valid votes V, and average voting results. , standard deviation of voting results , actual voting time t and weight adjustment factor W;

[0018] The voting validity index is obtained by comprehensively analyzing the parameters in the voting validity parameter set. The formula is as follows:

[0019] ,

[0020] in, represents the voting validity index; k represents the time decay coefficient, which controls the influence of time distribution on validity, and its value is [0,1]; is the base duration for voting. is the weight coefficient of voting participation, for The weight coefficient of for The weight coefficient of is the weight coefficient of the weight adjustment factor.

[0021] In the preferred solution of the voting method in the standard setting of the above-mentioned international standardization game, the anonymity strength parameter set includes the total number of voting members N, the comprehensive correlation degree of the equipment , similarity probability of voting pattern , historical voting times and maximum voting times;

[0022] By comprehensively analyzing the parameters in the anonymity strength parameter set, the anonymity strength index is obtained, based on the following formula:

[0023] ,

[0024] in, represents the anonymity strength index; Indicates the historical number of votes cast by the voter with sequence number h, Indicates the maximum number of votes under the current voting mechanism.

[0025] In the preferred embodiment of the voting method in the standard setting of the above-mentioned international standardization game, the encryption efficiency parameter set includes effective key length, algorithm round density, entropy density, peak memory usage, CPU utilization, throughput, and delay tolerance threshold; the specific acquisition method is:

[0026] Obtain the key length of the encryption algorithm of the cryptographic module in the voting system as the effective key length;

[0027] Use hardware performance counters to dynamically collect CPU instruction cycles and obtain the number of core encryption operations required per unit data volume as the algorithm round density.

[0028] The physical entropy source is monitored in real time through the test suite to obtain the entropy value output by the key generator per second as the entropy density;

[0029] Use the operating system resource monitor to record the memory usage curve and obtain the maximum value of the process resident memory during the encryption process, which is used as the peak memory usage.

[0030] By calling the system performance interface to collect process-level CPU usage, we can obtain the percentage of CPU time occupied by encryption tasks as CPU utilization.

[0031] The actual transmission rate of the end-to-end encrypted channel is measured using a network traffic mirroring device to obtain the amount of encrypted data processed per unit time as throughput.

[0032] The maximum encryption processing delay allowed by the application scenario is matched according to the delay classification standard of the transmission protocol as the delay tolerance threshold.

[0033] In the preferred voting method for the standard setting of the above-mentioned international standardization game, the encryption efficiency index is obtained by comprehensively analyzing the set of encryption efficiency parameters, and the formula is as follows:

[0034] ;

[0035] in, represents the encryption efficiency index; Indicates the effective key length; represents the algorithm round density; represents the entropy density; represents the baseline entropy density; Indicates throughput; Indicates the delay tolerance threshold; Indicates the peak memory usage; Indicates CPU utilization; e indicates the base of natural logarithm; express The weight coefficient can be set to 0.4; The weight coefficient of the peak memory usage can be set to 0.3; Indicates the weight coefficient of CPU utilization.

[0036] In the preferred embodiment of the voting method in the standard setting of the above-mentioned international standardization game, the voting data includes:

[0037] The voting rate of support, opposition and abstention is counted through voting records.

[0038] By counting the number of members who have participated in proposals or revisions for multiple times in a row, this number will be counted as the number of actively participating members;

[0039] Calculate the support rate fluctuation value by comparing the difference in support rates between two consecutive rounds of voting , based on the formula: .

[0040] In the preferred voting method for standard setting in the above-mentioned international standardization game, the consensus stability coefficient is obtained by comprehensive analysis of the voting data, and the formula is as follows:

[0041] ;

[0042] in, represents the consensus stability coefficient; represents the support rate of the mth round of voting; m represents the number of the voting round, and n represents the total number of voting rounds; represents the threshold ratio of the mth round of voting; represents the opposition rate in the mth round of voting; represents the abstention rate in the mth round of voting; represents the total number of members voting in round m; Indicates the fluctuation value of the support rate in the mth round of voting; It represents the highest support rate value in all rounds; E represents the number of actively participating members; G represents the total number of stages.

[0043] In the preferred voting method for the standard setting of the above-mentioned international standardization game, the comprehensive effectiveness value is calculated by voting behavior entropy, voting effectiveness index and consensus stability coefficient, based on the following formula:

[0044] ;

[0045] in, Represents the comprehensive value of effectiveness, represents the voting behavior entropy, represents the voting effectiveness index, Represents the controversy value, and the calculation formula is: ; represents the consensus stability coefficient; The weight coefficient representing the entropy of voting behavior; The weight coefficient representing the voting effectiveness index; The weight coefficient representing the consensus stability coefficient; express The weight coefficient of .

[0046] In the preferred embodiment of the voting method in the standard setting of the above-mentioned international standardization game, the method for executing the corresponding instructions according to the judgment result is:

[0047] Set encryption efficiency index threshold, anonymity strength index threshold and effectiveness comprehensive value threshold;

[0048] Compare the encryption efficiency index with the encryption efficiency index threshold. When the encryption efficiency index is greater than or equal to the encryption efficiency index threshold, the encryption efficiency meets the standard. When the encryption efficiency index is less than the encryption efficiency index threshold, the encryption efficiency does not meet the standard and the encryption efficiency management instruction is executed.

[0049] Compare the anonymity strength index with the anonymity strength index threshold. When the anonymity strength index is greater than or equal to the anonymity strength index threshold, the anonymity meets the standard. When the anonymity strength index is less than the anonymity strength index threshold, the anonymity does not meet the standard and the confidentiality management instruction is executed.

[0050] Compare the effectiveness comprehensive value with the effectiveness comprehensive value threshold. When the effectiveness comprehensive value ≥ the effectiveness comprehensive value threshold, the voting validity meets the standard. When the effectiveness comprehensive value < the effectiveness comprehensive value threshold, the voting validity does not meet the standard and the voting procedure needs to be restarted.

[0051] Beneficial effects:

[0052] The present invention provides a voting method for standard setting in international standardization games, which has the following beneficial effects:

[0053] (1) The voting validity data is obtained through the voting system and analyzed to obtain a set of parameters. Then, a comprehensive analysis is performed to obtain the voting validity index, which realizes the multi-dimensional and precise quantification of voting validity. Compared with the traditional voting method, this method is no longer limited to a single voting rule judgment, but comprehensively considers many factors such as voting participation, total number of votes, number of valid votes, mean of voting results, standard deviation of voting results, actual voting time, and weight adjustment factors. For example, in the standard setting voting of the International Organization for Standardization, a vote that may have been judged invalid due to a single factor in the past, such as format incompatibility, can now be more comprehensively evaluated by comprehensively considering many factors, thereby significantly improving the accuracy and fairness of the voting results, ensuring that the decisions made in the standard setting process can truly reflect the wishes and demands of all parties, enhancing the credibility and authority of the voting results, and providing a more reliable foundation for standard setting in the international standardization game.

[0054] (2) By analyzing the anonymity data of voting, we obtain a set of anonymity strength parameters, and conduct a comprehensive analysis to obtain an anonymity strength index, which effectively improves the anonymity protection level of the voting system. In the voting scenario of international standard setting, voters often come from different countries, companies, research institutions, etc., and face complex interest relations and potential external pressure. It can accurately quantify the anonymity strength and ensure that the identity information of voters is strictly protected during the voting process. It greatly enhances the trust of voters in the voting system, allowing them to express their wishes more authentically and freely without having to worry about retaliation or external interference due to voting behavior, thereby improving the objectivity and authenticity of the voting results, and is conducive to forming a fair and just voting environment in the international standardization game.

[0055] (3) In terms of encryption efficiency, by analyzing the encryption algorithm data of the voting system to obtain a set of encryption efficiency parameters, and conducting a comprehensive analysis to obtain an encryption efficiency index, the voting system is encouraged to adopt more efficient and advanced encryption algorithms. For example, the use of lightweight encryption algorithms or parallel encryption processing technology can significantly shorten the encryption and decryption time while ensuring data security, and improve the transmission and processing speed of voting data. For example, in standard-setting voting involving hundreds of member organizations, efficient encryption algorithms can ensure that the voting system can process a large amount of voting data in a short period of time, avoid system congestion and delays caused by the lengthy encryption process, and ensure the real-time and efficiency of voting.

[0056] (4) By inputting the voting system data into the Markov Chain Monte Carlo model to obtain the voting behavior entropy, and combining the voting effectiveness index, anonymity strength index, encryption efficiency index and consensus stability coefficient to calculate the effectiveness comprehensive value, a comprehensive evaluation of the effectiveness comprehensive value is achieved, providing a more accurate basis for voting decisions in standard setting. In the international standardization game, different voting factors have different degrees of influence on voting results, and will change dynamically with the progress of the voting process and changes in the external environment. Through the Markov Chain Monte Carlo model, various uncertain factors and random behaviors in the voting process, such as changes in voter preferences and adjustments to voting strategies, can be fully considered to calculate the voting behavior entropy, thereby more accurately quantifying the complexity and uncertainty of the voting process. At the same time, combined with other key indices for comprehensive evaluation, the effectiveness comprehensive value obtained can comprehensively reflect the overall performance of the voting system and the reliability of the voting results.

[0057] (5) Set a set of evaluation index thresholds and execute corresponding instructions after comparing the relevant index with the threshold, so that the voting system can dynamically adjust and optimize the voting process according to the preset standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1This is a schematic diagram of the steps of a voting method in the standard setting of an international standardized game according to the present invention. DETAILED DESCRIPTION

[0059] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] Example 1:

[0061] See also Figure 1 The present invention provides a voting method for standard setting in an international standardization game, comprising:

[0062] Step 1: During the voting process, the voting validity data is obtained through the voting system, and a set of voting validity parameters is obtained based on the voting validity data analysis.

[0063] Step 101: Obtain the total number of members who should vote and the number of members who actually voted through the member registration system, and calculate voting participation (P) based on the formula: Voting participation (P) = actual number of members who voted / total number of members who should vote. This accurately quantifies the degree of member participation in the voting process. In international standardization, voting participation is a key indicator of the representativeness and comprehensiveness of the voting process. Traditional voting methods often struggle to accurately measure and reflect the relationship between the number of members who actually voted and the total number of members who should vote. This prevents organizers from clearly understanding voting participation and making it difficult to determine whether the voting results are representative and reliable. This technology enables real-time and accurate voting participation statistics, providing organizers with intuitive participation data to help them evaluate the progress of the voting process and promptly identify and address potential participation deficiencies. This improves the transparency and credibility of the voting process, ensures that the standards-setting process fully incorporates the views of all parties, and enhances the authority and influence of the voting results.

[0064] Step 102: During the voting process, a counter is set up in the voting system to count the total number of votes. Each time a valid or invalid voting request is received, this counter is incremented by 1, thereby calculating the total number of votes, T. The voting system's smart contract verifies the validity of the voter's identity certificate using an elliptic curve digital signature (ECDSA) for each vote. Each successful verification and confirmation of validity increments a second counter by 1, and the final value of this second counter becomes the number of valid votes, V. This solution addresses the accuracy and security issues inherent in traditional voting systems when counting votes. In previous voting processes, the lack of effective identity authentication mechanisms and precise counting methods made it easy for duplicate votes and invalid votes to be mistakenly counted as valid votes, distorting voting results and impacting the accuracy of decision-making. This solution, however, leverages smart contracts and ECDSA encryption technology to ensure the authenticity of voter identities and the validity of voting operations. By using two independent counters to count the total and valid votes, it accurately distinguishes between valid and invalid votes, avoiding human intervention and statistical errors. This improves the accuracy and reliability of voting data, providing a solid foundation for subsequent analysis and decision-making.

[0065] It should be noted that a variable uint publictotalVoteCount can be set in the smart contract; at the entrance of the voting function, that is, before the validity verification, the totalVoteCount++ operation can be executed first, so that the number of times all votes are participated in can be counted to obtain the total number of votes; a variable for storing the number of valid votes can be set in the smart contract code, such as uint publicvalidVoteCount;1. In the voting function, when the voter's identity certificate passes the ECDSA verification, the validVoteCount++ operation is executed to count the number of valid votes in real time.

[0066] Step 103: Set the option values ​​of support votes and opposition votes, such as support votes are recorded as 1 and opposition votes are recorded as -1; calculate the average voting results of all valid votes , the formula is: the mean of the voting results = Sum of option values ​​for valid votes / Number of valid votes. This allows for in-depth analysis of the central tendency and dispersion of voting results, providing a powerful tool for assessing the consistency and stability of voting results. In international standardized voting, simply knowing the number of valid votes and the simple majority of voting results is not enough; understanding the distribution characteristics of the voting results is also necessary. By calculating the mean of the voting results, we can intuitively understand the overall trend of support and opposition.

[0067] Step 104: Calculate the standard deviation of the voting results based on the option value of each valid vote and the mean of the voting results, using the following formula: ;in, represents the standard deviation of voting results; Indicates the option value of the i-th valid vote; i represents the sequence number of the valid vote, which is a positive integer, and V is the number of valid votes; represents the mean of the voting results. The standard deviation reflects the degree of disagreement among voters. For example, a small standard deviation indicates relatively consistent voter opinions, making the voting results more convincing. Conversely, a large standard deviation suggests significant disagreement among voters, potentially requiring further communication and coordination. This technical approach helps organizers gain a more comprehensive understanding of the implications of voting results, providing richer information support for subsequent standard-setting decisions, improving the scientific and rational nature of decision-making, and avoiding errors caused by relying solely on superficial voting data.

[0068] Step 105: The voting system obtains the timestamps of the bid closing time and the voting start time to calculate the actual voting duration t. This accurately records the time required for the voting process. In international standardized voting, the appropriateness of the voting duration is crucial for the smooth progress of the voting process and the validity of the voting results. A too short voting duration may prevent some members from fully participating due to time constraints, affecting the representativeness and fairness of the vote. An excessively long voting duration may increase the complexity and management difficulty of the voting process, and may even cause changes in the external environment during the voting period to affect the stability of the voting results.

[0069] Step 106: Set weight coefficients for different types of votes. For example, the weight coefficients for expert votes and ordinary votes can be set to 2 and 1 respectively. The weight adjustment factor is calculated based on the proportion of different types of votes in the total number of votes and the weight coefficients of different types of votes. The formula is as follows: ;W represents the weight adjustment factor, represents the weight coefficient of the j-th category vote; represents the proportion of the jth vote category to the total number of votes; j represents the ordinal number of the voting category, which is a positive integer, and M represents the number of voting categories. This approach fully accounts for the varying importance and influence of different voters in the voting process, resolving the "one-size-fits-all" voting weight distribution problem in traditional voting methods. In international standardization voting, different types of voters (such as experts and ordinary members) often have varying levels of expertise and industry influence, and the weight of their votes also varies. By assigning appropriate weight coefficients to different types of votes and calculating weight adjustment factors based on their respective proportions, we can more accurately reflect the value of each voter's opinions, ensuring that voting results more scientifically and rationally reflect the interests and professional judgment of all parties.

[0070] Step 107: Through voting participation P, total votes T, valid votes V, and voting result mean , standard deviation of voting results , the actual voting time t and the weight adjustment factor W form a set of voting validity parameters.

[0071] Step 108: By comprehensively analyzing the parameters in the voting effectiveness parameter set, before calculating the voting effectiveness index, it is necessary to normalize the parameters involved and eliminate the dimensions of different parameters to facilitate subsequent formula calculations and obtain the voting effectiveness index. The formula is as follows:

[0072] ,

[0073] in, represents the voting validity index; k represents the time decay coefficient, which controls the influence of time distribution on validity, and its value is [0,1]; is the base duration for voting. is the weight coefficient of voting participation, for The weight coefficient of for The weight coefficient of is the weight coefficient of the weight adjustment factor. And The values ​​of each weight coefficient can be determined by training historical voting data through machine learning models (such as random forests) to optimize weight distribution; or they can be set according to organizational rules, such as =0.3, =0.25, =0.25, W=0.2.

[0074] It should be noted that through the Sigmoid function , which can quantify the impact of voting time distribution on effectiveness. For example, if voting is concentrated in a short period of time (t≪t0), the index approaches 0, indicating that voting may be subject to external interference (such as centralized manipulation); if voting is evenly distributed (t≈t0), the index approaches 1, indicating that the voting process is stable; as the value of k increases, the curve of the Sigmoid function becomes steeper, indicating that the impact of time distribution on voting effectiveness is more significant, that is, the larger k is, the greater the discount on the effectiveness of votes that deviate from the reference time. Through the formula It can reflect the discrete degree of voting results. If the index is close to 0 and approaches 1, it means the voting result is clear; if A larger value lowers the index, suggesting possible controversy or strategic voting. The weight adjustment factor allows for dynamic adjustment of the validity index based on voting rules (such as the differentiated weights of expert and ordinary votes), adapting to the needs of different scenarios. By combining valid vote rate, participation, time distribution, result dispersion, and weighting rules, it avoids the limitations of a single dimension.

[0075] This approach can more accurately and comprehensively reflect the true effectiveness of the voting process. For example, in an international standard-setting voting project, if there is high voting participation (e.g., 80%), good consistency in voting results (small standard deviation), a reasonable voting duration, and a reasonable weighting adjustment factor (full consideration of expert opinions, etc.), the VEI index will be correspondingly high, indicating that the voting process was highly effective and the voting results are highly credible. This comprehensive assessment method avoids the one-sided judgment caused by focusing on a single indicator and provides organizers with a more reliable and accurate quantitative indicator of voting effectiveness, helping them better understand the quality of the voting process and the credibility of the results, thereby making more informed decisions about standard-setting and improving the scientific nature and fairness of the standard-setting process.

[0076] Step 2: Obtain voting anonymity data through the voting system, and obtain an anonymity strength parameter set based on voting anonymity data analysis.

[0077] Step 201: Obtain the total number of voting members, N, through the standardization organization's public member registration system or the voting system's member registration system. This provides an accurate reference base for subsequent voting anonymity analysis. In international standardization voting, clearly knowing the total number of voting members is fundamental to assessing the integrity of the voting process and the effectiveness of safeguarding anonymity.

[0078] Step 202: Collect device fingerprint information through the voting system's underlying logs, such as features of at least five dimensions, such as IP address segment, operating system, and the first 24 bits of the terminal MAC address. By comparing the features of each dimension of different voters, the device fingerprint correlation degree is obtained. , when voters h If the feature of the qth dimension is the same as that of voter g, the value is 1, and if it is different, the value is 0; through the device fingerprint association The formula for calculating the comprehensive correlation of devices is: ;in, Indicates voters h The comprehensive correlation degree of the device with voter g; q represents the sequence number of different dimensional features, which is a positive integer; K is the maximum number of dimensional features in the device fingerprint information; h and g are the voters' serial numbers, both of which are positive integers, and in the calculation process, hand g take different serial number values; this approach aims to accurately identify and quantify the correlation between different voter devices, effectively preventing malicious voting linkage and enhancing voting anonymity. In internationally standardized voting scenarios, malicious attackers may leverage multiple devices or disguise device information to link votes, attempting to influence voting results or undermine voting fairness. Traditional voting systems often struggle to effectively identify such linkages, relying solely on a single metric like IP address for preliminary assessment, which can be easily circumvented by attackers. This solution, however, collects device fingerprint information from multiple dimensions and comprehensively calculates correlations, enabling a more comprehensive and in-depth understanding of potential device associations. For example, even if an attacker changes their IP address, specific operating system version information or partial MAC address features may still reveal the device's true association, allowing the system to identify and record it. By quantifying the comprehensive device correlation, the voting system can flag and further scrutinize highly correlated voting behavior, promptly detecting and preventing malicious voting linkage, ensuring the independence and fairness of the voting process. It also helps maintain voting anonymity, preventing attackers from maliciously tracking voters through correlation analysis, enhancing voter trust in the voting system, and promoting a smooth voting process.

[0079] Step 203: Obtain the option sets of several historical votes of each voter from the historical voting record database of the voting system, and calculate the voter's h The probability of having a voting pattern similar to that of voter g is based on the formula: ;in, Indicates voters h The probability of having a voting pattern similar to that of voter g, and Represents voters h and the set of options for voter g's historical votes, and during the calculation process, h and g take different serial number values, represents a smoothing factor to avoid a denominator of zero, and can be taken as 0.1. This allows for in-depth analysis of voter behavior patterns, identifying potential coordinated voting or manipulated voting, and further strengthening the anonymity of voting and the fairness of voting results. In international standardized voting, certain interest groups or malicious manipulators may attempt to manipulate voting results by controlling multiple voting accounts and voting according to a unified voting pattern. Traditional voting systems often have difficulty detecting such hidden coordinated behavior and can only make judgments based on superficial data from a single vote. This solution analyzes a voter's historical voting options and calculates the probability of similar voting patterns, effectively identifying whether there are highly similar voting behavior patterns among voters.

[0080] Step 204: Calculate the historical number of votes for each voter and the maximum number of votes under the current voting mechanism through the historical records of the voting system. For example, the maximum number of votes can be determined by counting the revision rounds of each draft standard. This can effectively monitor and regulate the voting behavior of voters, prevent malicious repeated voting or excessive voting, and ensure the fairness and orderliness of the voting process. In the international standardization voting process, since standard setting often involves multiple rounds of revisions and voting, some voters may attempt to exploit loopholes in the rules to vote repeatedly or vote multiple times beyond a reasonable range in order to amplify their influence or interfere with the voting results. By accurately counting the historical number of votes for each voter and comparing and analyzing it with the maximum number of votes determined by the current voting mechanism, it is possible to monitor in real time whether the voter has exceeded the prescribed number of votes.

[0081] Step 205: The total number of members who should vote, the comprehensive association degree of devices, the similarity probability of voting patterns, the number of historical votes, and the maximum number of votes are aggregated into an anonymity strength parameter set.

[0082] Step 206: By comprehensively analyzing the set of anonymity strength parameters, before calculating the anonymity strength index, it is necessary to normalize the parameters involved and eliminate the dimensions of different parameters to facilitate subsequent formula calculations to obtain the anonymity strength index. The formula is as follows:

[0083] ,

[0084] in, represents the anonymity strength index; Indicates the historical number of votes cast by the voter with sequence number h, Indicates the maximum number of votes under the current voting mechanism.

[0085] It should be noted that the formula uses the inverse function to construct the attenuation model. When the device association and behavior similarity are higher, the individual anonymity is lower. The smaller the value of , and by building a linear attenuation model, the problem of "voting fingerprint" solidification caused by long-term participation is solved. It is suitable for multi-round standard revision scenarios. As the number of votes increases, the impact of historical behavior on current anonymity gradually weakens. That is, the closer the number of historical votes is to the maximum number of votes, the more The smaller the value of .

[0086] The formula comprehensively considers factors such as the overall device correlation, the probability of similar voting patterns, the voter's historical voting history, and the maximum number of votes under the current voting mechanism. The double summation component of the formula reflects the potential risk of identity exposure due to device correlation and similar voting behavior patterns between voters. By taking the inverse of this summation and adding 1 to the inverse, this component reduces the contribution of more highly correlated voter combinations to this component, thereby lowering the overall anonymity strength index. The ratio of historical voting history to maximum voting history reflects the voter's voting activity under the current voting mechanism. The closer this ratio is to 1, the more active the voter, potentially impacting their anonymity. By subtracting this ratio from 1, the anonymity strength index calculation for active voters is appropriately adjusted to reflect the relative difficulty of maintaining their anonymity. Traditional voting anonymity assessment methods often focus on single-dimensional factors, such as determining anonymity strength based solely on the degree of IP address anonymization or simple device information obfuscation. These methods fail to comprehensively and systematically reflect the level of anonymity protection for voters in complex voting environments. This solution constructs an anonymity strength index that incorporates multiple parameters, including device correlation, voting pattern similarity probability, and voting activity. This comprehensively considers factors influencing voter anonymity, including device usage, voting behavior patterns, and voting participation. This addresses the one-sidedness and limitations of traditional methods for quantifying anonymity, providing a comprehensive and systematic quantitative tool for assessing voter anonymity, making the assessment of anonymity strength more scientific, accurate, and reasonable. Based on the results of the anonymity strength index, voting system operators can conduct in-depth analysis of the strengths and weaknesses of voter anonymity protection, thereby optimizing anonymity protection strategies and resource allocation in a targeted manner.

[0087] Step 3: Obtain the encryption algorithm data of the voting system, and obtain the encryption efficiency parameter set based on the encryption algorithm data analysis.

[0088] Step 301: Access the API interface of the cryptographic module in the voting system to read key configuration metadata, including the key length of the encryption algorithm, as the effective key length. For example, when the encryption algorithm is a symmetric encryption algorithm, such as AES-256, the key length is 256. When the encryption algorithm is an asymmetric encryption algorithm, the key length is the key length after equivalent bit conversion. According to the equivalent relationship table provided by cryptographic research and standards (such as NIST SP 800-57), the key length of the asymmetric algorithm is converted to the key length of the symmetric algorithm. For example, RSA-RSA-3072 is converted to a key length of 112 through equivalent bit conversion. This solves the technical difficulty of traditional methods in comparing and evaluating the key security of different types of encryption algorithms, provides an accurate security quantitative indicator for subsequent comprehensive evaluation of encryption efficiency, ensures that the voting system can measure the security of its encryption algorithm based on a unified standard when selecting its security, and thus improves the overall security protection level of the voting system.

[0089] Step 302: Use hardware performance counters (such as Intel VTune) to dynamically collect CPU instruction cycles and obtain the number of core encryption operations required per unit of data (e.g., AES-256 requires 140 round functions per MB) as the algorithm's round density. This accurately measures the computational intensity of the encryption algorithm during actual operation, allowing for real-time and accurate statistics on the number of core encryption operations required per unit of data, providing strong data support for evaluating the encryption algorithm's actual computational efficiency. This not only helps to estimate the encryption process's demand for system computing resources in advance, but also provides a key basis for optimizing encryption algorithm implementation and improving the voting system's data processing efficiency in large-scale voting scenarios. This ensures that the voting system's encryption operations can be completed efficiently within limited computing resources, ensuring a smooth and timely voting process.

[0090] Step 303: Use the NIST SP 800-90B IID test suite to monitor the physical entropy source (e.g., quantum noise sensor) in real time. Obtain the entropy output per second (in bits / s) from the key generator as the entropy density. In voting systems, the quality of key generation directly determines the effectiveness of the encryption algorithm and the security of voting data. This is used to assess the randomness and unpredictability of the key generation process. Real-time monitoring can promptly detect anomalies in the physical entropy source output, ensuring that the key generation process maintains high randomness and unpredictability. For example, if the entropy density shows a downward trend, the system can issue a timely warning and take measures, such as checking the status of the physical entropy source device or adjusting the key generation algorithm parameters. This can prevent security risks caused by insufficient key randomness, effectively ensure the security of the voting system's keys and the confidentiality and integrity of voting data, and enhance the voting system's ability to resist cryptographic attacks.

[0091] Step 304: Use the operating system resource monitor (such as the Linux smem tool) to record the memory usage curve and obtain the maximum value of the process's resident memory during the encryption process, which is used as the peak memory usage. During the operation of the voting system, especially when processing large amounts of voting data encryption tasks, the rational allocation and use of memory resources is crucial. This allows for accurate quantification of the maximum demand for system memory resources by encryption operations. Accurately capturing the peak memory usage during the encryption process provides an important reference for the rational planning and management of system resources.

[0092] Step 305: Process-level CPU usage is collected by calling a system performance interface (such as the Windows PDH API). The percentage of CPU time occupied by the encryption task is obtained as CPU utilization. In a voting system, CPU resources are the core driving force behind the entire system. As a key component of the system, the encryption task's CPU usage directly affects the voting system's concurrent processing capability and response speed.

[0093] Step 306: Use a network traffic mirroring device (such as Spirent TestCenter) to measure the actual transmission rate of the end-to-end encrypted channel and obtain the amount of encrypted data processed per unit time as throughput. This is used to evaluate the data transmission efficiency and performance of the voting system's encrypted channel.

[0094] Step 307: Based on the IETF RFC 7679 delay grading standard for transmission protocols, the maximum allowable encryption processing delay for the application scenario (e.g., T_max = 50ms for real-time communication systems) is automatically matched as the delay tolerance threshold. In international standardized voting, different voting scenarios and service types have significantly different tolerances for encryption processing delay. By automatically matching the delay tolerance threshold based on the standard delay grading, the maximum allowable encryption processing delay for the voting system in a specific application scenario can be accurately determined. This allows optimization of encryption algorithm selection, system configuration, and network transmission strategies to ensure that the encryption process is completed within the specified delay range. This not only improves the adaptability and usability of the voting system in different application scenarios, but also ensures the real-time and smoothness of the voting process.

[0095] Step 308: Summarize the effective key length, algorithm round density, entropy density, memory usage peak, CPU utilization, throughput and delay tolerance threshold into an encryption efficiency parameter set.

[0096] Step 309: Obtain an encryption efficiency index by comprehensively analyzing the encryption efficiency parameter set. Before calculating the encryption efficiency index, the parameters involved need to be normalized and pre-processed to eliminate the dimensions of different parameters to facilitate subsequent formula calculations. The formula is as follows:

[0097] ;

[0098] Among them, represents the encryption efficiency index; represents the effective key length; represents the algorithm round density; represents the entropy value density; represents the reference entropy density, which can be set according to industry standards, such as referring to SO / IEC 18031; represents the throughput; represents the latency tolerance threshold; represents the peak memory occupancy; represents the CPU utilization rate; e represents the base of the natural logarithm; represents the weight coefficient of which can take a value of 0.4; represents the weight coefficient of the peak memory occupancy, which can take a value of 0.3; .

[0099] It should be noted that the effective key length is positively correlated with the security strength, but follows the law of diminishing marginal benefits. It can convert linear growth into sublinear growth and balance security and computational cost. For example, in the key management guidelines of NIST SP 800-57, the equivalent security strength of AES-256 is defined as 256 bits, and that of RSA-3072 is 112 bits. The square root processing reduces the computational penalty of high-round algorithms and avoids excessive bias towards lightweight algorithms (such as 20 rounds of ChaCha20). is used to amplify the negative effect of low-entropy sources. If ED < E0, the term value drops sharply; if ED ≥ E0, the efficiency score is significantly improved; represents the throughput-latency saturation function, which limits the ratio of the throughput (such as 480 MB / s) to the maximum tolerable latency (such as 500 ms) within the interval [0,1]; when is , it reflects linear growth. When is , it avoids over-rewarding ultra-low latency schemes. The addition structure of in the formula denominator can reflect the collaborative consumption effect of memory and CPU. For example, memory leaks lead to frequent CPU scheduling, etc. The square root processing makes the penalty growth rate of resource consumption lower than linear and avoids a single high resource occupancy from overly dragging down the score, such as the high The numerator uses exponential and logarithmic functions to amplify key parameters ED, The contribution of the denominator is to suppress the negative impact of resource consumption, which is closer to the nonlinear characteristics of the actual system. Based on the NIST test data experiment, compared with the traditional linear weighted model (such as ), the sensitivity is improved by about 35%.

[0100] Traditional voting system encryption efficiency assessments often focus on a single metric, such as encryption speed or key length, while ignoring the overall performance of the encryption process. This one-sided assessment can lead to misjudgments of encryption efficiency, impacting the security and performance optimization of the voting system. This solution addresses the inability of traditional methods to comprehensively assess encryption efficiency by integrating multiple key parameters into a set of encryption efficiency parameters and conducting a comprehensive analysis.

[0101] Step 4: Obtain voting data through the voting system's voting records, and conduct a comprehensive analysis based on the bidding data to obtain the consensus stability coefficient;

[0102] Step 401: Count the support rate, opposition rate, and abstention rate of the votes through voting records. For example, in the voting results of the proposal stage or the FDIS stage, the number of support votes, the number of opposition votes, and the number of abstention votes are divided by the total number of voting members.

[0103] Step 402: Count the number of members who have nominated experts to participate multiple times in a row during the proposal or revision process as the number of actively participating members;

[0104] Step 403: Calculate the support rate fluctuation value by comparing the support rate difference between two consecutive rounds of voting , based on the formula: This allows organizers to gain timely insight into changing voting trends, anticipate potential risks, and develop response strategies. For example, if support fluctuations exceed a set threshold, organizers can quickly analyze the cause and implement targeted measures, such as strengthening publicity and promotion, clarifying proposal details, etc., to stabilize voting trends, ensure a smooth voting process, and improve the efficiency and quality of standard setting.

[0105] Step 404: By comprehensively analyzing the voting data, the consensus stability coefficient is obtained. Before calculating the consensus stability index, the parameters involved need to be normalized and pre-processed to eliminate the dimensions of different parameters to facilitate subsequent formula calculations. The formula is as follows:

[0106] ;

[0107] in, represents the consensus stability coefficient; represents the support rate of the mth round of voting; m represents the number of the voting round, and n represents the total number of voting rounds; The threshold ratio for the mth round of voting can be referred to the voting rules in the ISO / IEC Charter. For example, the ISO Charter stipulates that a 2 / 3 majority is required for the proposal stage. ; represents the opposition rate in the mth round of voting; represents the abstention rate in the mth round of voting; represents the total number of members voting in round m; represents the fluctuation value of the support rate in the mth round of voting; = represents the highest support rate among all rounds; E represents the number of actively participating members; and G represents the total number of stages in the proposal or revision process (e.g., proposal stage, DIS stage, FDIS stage, etc.), determined according to the phases of the ISO / IEC standards development process. For example, ISO standards typically go through five stages (NP, CD, DIS, FDIS, Final).

[0108] It is necessary to explain the function of Used to quantify the degree of excess of support over the threshold in each round of voting, It is used to deduct the impact of opposition and abstention, and can reflect whether the proposal is passed with high support in each round, while reducing the weakening of consensus caused by opposition or abstention. With the highest approval rating in history To measure the stability of the support rate, if the support rate fluctuates greatly (such as a sudden drop from 0.7 to 0.5), then The value of some is small, which reduces the overall stability coefficient. The ratio of the number of actively participating members to the total number of stages is used to measure the continuous participation of the proposal. If the proposal continues to receive high participation in multiple stages, it will have a positive impact on the stability coefficient. Introduce sustained engagement to avoid neglecting long-term stability due to short-term high support rates.

[0109] This solution generates a consensus stability coefficient by comprehensively analyzing multi-dimensional data from multiple voting rounds, including approval rates, opposition rates, abstention rates, fluctuations in approval rates, and the number of actively participating members. This overcomes the drawback of traditional methods that fail to fully reflect voting stability. This provides a comprehensive perspective for evaluating voting process stability, helping to accurately determine voting stability trends and ensure a smooth voting process. For example, if a voting round has high approval rates but significant fluctuations and few actively participating members, the consensus stability coefficient can comprehensively reflect potential risks, allowing organizers to intervene in advance and stabilize subsequent voting.

[0110] Step 5: By inputting the voting system data into the Markov Chain Monte Carlo model, the voting behavior entropy of the voting system is obtained through the model, and the comprehensive effectiveness value is calculated by combining the voting behavior entropy, voting effectiveness index and consensus stability coefficient.

[0111] Step 501: Voting system data, such as voter ID, voter voting results, proposal background (including technical field (e.g., ICS classification code), voting time, proposal type (e.g., proposal stage, FDIS stage), and each expert's historical voting record (arranged by time or proposal sequence), is input into a Markov Chain Monte Carlo model. The Markov Chain Monte Carlo model then outputs voting behavior entropy. Traditional voting methods struggle to fully quantify the complexity and uncertainty of voting behavior. They can only perform simple vote counts and trend analysis, but fail to fully capture the randomness and underlying patterns of voting behavior. This solution, by introducing a Markov Chain Monte Carlo model, simulates the stochastic process of voting behavior and calculates voting behavior entropy. This quantifies the uncertainty of voting behavior, provides a deeper understanding of its underlying patterns, and allows for the foreseeing of risks and uncertainties in the voting process. For example, a voting process with high voting behavior entropy may indicate greater uncertainty in the voting results.

[0112] It should be noted that outputting voting behavior entropy through the Markov Chain Monte Carlo model is common knowledge to those skilled in the art and will not be described further.

[0113] Step 502: Calculate the comprehensive effectiveness value using the voting behavior entropy, voting effectiveness index, and consensus stability coefficient. Before calculating the comprehensive effectiveness value, it is necessary to normalize the parameters involved to eliminate the dimensions of different parameters for the convenience of subsequent formula calculations. The formula is as follows:

[0114] ;

[0115] in, Represents the comprehensive value of effectiveness, represents the voting behavior entropy, represents the voting effectiveness index, Represents the controversy value, and the calculation formula is: ; represents the consensus stability coefficient; The weight coefficient representing the entropy of voting behavior; The weight coefficient representing the voting effectiveness index; The weight coefficient representing the consensus stability coefficient; express The weight coefficient of ; and , where the weight coefficient can be set to 0.25 respectively.

[0116] It should be noted that when the ratio of opposing votes to supporting votes is large, the absolute value of the controversy value will be large, resulting in A smaller value of , which reduces the overall effectiveness value, indicates that the voting results are highly controversial and their stability may be affected. This helps organizers promptly address controversial points in the voting process and take measures to promote communication and coordination among all parties, thereby reducing the negative impact of disputes on the voting results and improving their recognition and acceptance.

[0117] Traditional voting evaluation methods focus solely on single-dimensional indicators, such as voting validity or consensus stability, lack a comprehensive assessment of the overall performance of the voting system, and are unable to fully reflect the quality and reliability of the voting process. This solution achieves a comprehensive evaluation of multi-dimensional data by combining voting behavior entropy, voting validity index, consensus stability coefficient, and controversy value to calculate a comprehensive validity value. This accurately determines the overall performance and reliability of the voting system and ensures the credibility of the voting results. For example, if the comprehensive validity value is low, organizers can identify weak links in the voting process based on the performance of each individual index, such as insufficient voting validity, poor consensus stability, or high controversy, and take targeted measures to improve them, thereby improving the overall performance and reliability of the voting system and providing a solid foundation for voting decisions.

[0118] Step 6: Set the evaluation index threshold set; compare the encryption efficiency index, anonymity strength index and effectiveness comprehensive value with the corresponding parameters in the evaluation index threshold set, and execute the corresponding instructions based on the judgment results.

[0119] Step 601: Set the encryption efficiency index threshold, the anonymity strength index threshold, and the effectiveness comprehensive value threshold.

[0120] It should be noted that the encryption efficiency index threshold can be set based on industry standards and best practices. For example, in the financial industry, standards such as the Payment Card Industry Data Security Standard (PCI-DSS) have certain encryption efficiency requirements. For voting systems that process credit card transaction data, the encryption efficiency index threshold can be set based on PCI-DSS requirements. The anonymity strength index threshold can be set based on privacy laws, regulations, and standards. For example, industry-specific privacy standards, such as the Health Insurance Portability and Accountability Act (HIPAA) in the healthcare industry, have detailed provisions for the anonymization of patient information. If the voting system involves healthcare-related voting, such as patient reviews of medical services, the anonymity strength index threshold can be set based on HIPAA requirements. The overall effectiveness threshold can be set based on industry-wide voting system effectiveness data. For example, in the field of e-government voting, successful e-voting projects have shown that setting an overall effectiveness threshold of 0.75 ensures voting security and effectiveness while avoiding issues such as overly complex voting systems or difficulty in passing votes due to excessively high thresholds. Based on these industry experiences and the actual conditions of your own voting system, you can adjust the threshold appropriately. You can also test the overall effectiveness value by simulating different voting scenarios before launching the voting system. For example, a simulated voting environment containing various voting behaviors (such as normal voting, malicious voting, and repeated voting) can be constructed to stress-test the voting system. By adjusting parameters such as voting behavior entropy, voting validity index, anonymity strength index, and encryption efficiency index, the overall validity value of the voting results can be observed. Based on the test results, a threshold can be determined that effectively distinguishes normal from abnormal voting. For example, in a simulated test, when the overall validity value reaches 0.7, the system can correctly identify and eliminate over 90% of abnormal votes while retaining nearly all normal votes. In this case, the threshold can be set to 0.7. This will improve the encryption efficiency of the voting system and ensure a smooth and timely voting process.

[0121] Step 602: Compare the encryption efficiency index with the encryption efficiency index threshold. When the encryption efficiency index ≥ the encryption efficiency index threshold, the encryption efficiency meets the standard. When the encryption efficiency index < the encryption efficiency index threshold, the encryption efficiency does not meet the standard and the encryption efficiency management instruction is executed.

[0122] It should be noted that encryption efficiency management instructions can be key management instructions, which rationally select key length and type. While ensuring security, relatively short but sufficiently secure keys can be chosen. For example, for certain symmetric encryption algorithms, 128-bit or 256-bit keys can be appropriately used. By selecting an appropriate key length, encryption efficiency is improved while meeting security requirements while reducing the complexity of encryption operations. Encryption efficiency management instructions can also serve as data preprocessing instructions, performing preprocessing operations such as compression on the data to be encrypted, reducing the data volume before encryption. Data compression can effectively reduce the size of the encrypted data without affecting the encryption effect, thereby improving encryption efficiency. Hardware acceleration instructions enable hardware encryption acceleration, such as using a dedicated encryption chip or encryption card to perform encryption operations.

[0123] Step 603: Compare the anonymity strength index with the anonymity strength index threshold. When the anonymity strength index is greater than or equal to the anonymity strength index threshold, the anonymity meets the standard. When the anonymity strength index is less than the anonymity strength index threshold, the anonymity does not meet the standard and the confidentiality management instruction is executed.

[0124] It should be noted that confidentiality management instructions can generate anonymous identifiers for voters based on cryptographic algorithms. These identifiers can be updated regularly and have no direct connection to the voter's real identity. For example, cryptographic techniques such as hash functions can be used to create a unique anonymous identifier for each voter. When voting data is recorded, only this anonymous identifier, not the voter's real identity information, is associated with it. The voting system can also be integrated with the Tor (OnionRouter) network or other hybrid network technologies. During the voting process, voters' network requests are routed and encrypted through multiple layers of nodes. The Tor network encapsulates data in multiple layers of encryption. Each time it passes through a node, only enough information to identify the next node is decrypted. This conceals the voter's identifying information, such as their IP address, making it more difficult to track, thereby enhancing anonymity.

[0125] Step 604: Compare the effectiveness comprehensive value with the effectiveness comprehensive value threshold. When the effectiveness comprehensive value ≥ the effectiveness comprehensive value threshold, the voting validity meets the standard. When the effectiveness comprehensive value < the effectiveness comprehensive value threshold, the voting validity does not meet the standard and the voting program needs to be restarted.

[0126] The solution sets a set of evaluation index thresholds, compares the encryption efficiency index, anonymity strength index, and overall effectiveness value with the corresponding thresholds, and executes corresponding instructions based on the comparison results. This mechanism dynamically monitors the performance and security of the voting system, promptly identifying potential issues and implementing adjustments and optimization measures. Reasonable thresholds are set by referencing industry standards, laws and regulations, and best practices to ensure that the voting system operates within a safe, effective, and reliable range. Simulation tests are also used to verify and adjust the thresholds to better meet the needs of actual voting scenarios.

[0127] Example 2:

[0128] The voting system in the standard setting of the international standardization game is used to implement the voting method in the standard setting of the above-mentioned international standardization game.

[0129] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0130] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0131] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A voting method for standard setting in an international standardization game, characterized in that: include: Step 1: During the voting process, the voting validity data is obtained through the voting system, and a set of voting validity parameters is obtained based on the voting validity data analysis; Obtaining a voting effectiveness index by comprehensively analyzing the parameters in the voting effectiveness parameter set; Step 2: Obtain voting anonymity data through the voting system, and obtain an anonymity strength parameter set based on the voting anonymity data analysis; obtain an anonymity strength index by performing a comprehensive analysis on the anonymity strength parameter set; Step 3: Obtain the encryption algorithm data of the voting system, and obtain an encryption efficiency parameter set based on the encryption algorithm data analysis; obtain an encryption efficiency index by performing a comprehensive analysis on the encryption efficiency parameter set; Step 4: Obtain voting data through the voting system's voting records, and conduct a comprehensive analysis based on the bidding data to obtain the consensus stability coefficient; Step 5: Obtain the voting behavior entropy of the voting system by inputting the voting system data into the Markov Chain Monte Carlo model, and calculate the comprehensive effectiveness value by combining the voting behavior entropy, voting effectiveness index, and consensus stability coefficient; Step 6: Set the evaluation index threshold set; compare the encryption efficiency index, anonymity strength index and effectiveness comprehensive value with the corresponding parameters in the evaluation index threshold set, and execute the corresponding instructions based on the judgment results.

2. The voting method for standard setting in an international standardization game according to claim 1, characterized in that: The voting validity parameter set includes voting participation P, total number of votes T, number of valid votes V, and mean value of voting results. , standard deviation of voting results , actual voting time t and weight adjustment factor W; where the mean of voting results The calculation method is as follows: set the option values ​​of support votes and opposition votes, record support votes as 1, and opposition votes as -1, and calculate the average voting results of all valid votes , the formula is: the mean of the voting results = Sum of option values ​​of valid votes / Number of valid votes; Standard deviation of voting results The calculation method is: calculate the standard deviation of the voting results based on the option value of each valid vote and the mean of the voting results, based on the formula: ;in, represents the standard deviation of voting results; Indicates the option value of the i-th valid vote; i represents the sequence number of the valid vote, and V is the number of valid votes; The voting validity index is obtained by comprehensively analyzing the parameters in the voting validity parameter set. The formula is as follows: ， in, represents the voting validity index; k represents the time decay coefficient, which controls the influence of time distribution on validity, and its value is [0,1]; is the base duration for voting. is the weight coefficient of voting participation, for The weight coefficient of for The weight coefficient of is the weight coefficient of the weight adjustment factor.

3. The voting method for setting standards in an international standardization game according to claim 2, characterized in that: The anonymity strength parameter set includes the total number of voting members N, the comprehensive correlation degree of the device , similarity probability of voting pattern , historical voting times and maximum voting times; By comprehensively analyzing the parameters in the anonymity strength parameter set, the anonymity strength index is obtained, based on the following formula: ， in, represents the anonymity strength index; Indicates the historical number of votes cast by the voter with sequence number h, Indicates the maximum number of votes under the current voting mechanism.

4. The voting method for setting standards in an international standardization game according to claim 3, characterized in that: The encryption efficiency parameter set includes effective key length, algorithm round density, entropy density, peak memory usage, CPU utilization, throughput, and delay tolerance threshold. The specific method for obtaining it is: Obtain the key length of the encryption algorithm of the cryptographic module in the voting system as the effective key length; Use hardware performance counters to dynamically collect CPU instruction cycles and obtain the number of core encryption operations required per unit data volume as the algorithm round density. The physical entropy source is monitored in real time through the test suite to obtain the entropy value output by the key generator per second as the entropy density; Use the operating system resource monitor to record the memory usage curve and obtain the maximum value of the process resident memory during the encryption process, which is used as the peak memory usage. By calling the system performance interface to collect process-level CPU usage, we can obtain the percentage of CPU time occupied by encryption tasks as CPU utilization. The actual transmission rate of the end-to-end encrypted channel is measured using a network traffic mirroring device to obtain the amount of encrypted data processed per unit time as throughput. The maximum encryption processing delay allowed by the application scenario is matched according to the delay classification standard of the transmission protocol as the delay tolerance threshold.

5. The voting method for setting standards in an international standardization game according to claim 4, characterized in that: The encryption efficiency index is obtained by comprehensively analyzing the encryption efficiency parameter set. The formula is as follows: ； in, represents the encryption efficiency index; Indicates the effective key length; represents the algorithm round density; represents the entropy density; represents the baseline entropy density; Indicates throughput; Indicates the delay tolerance threshold; Indicates the peak memory usage; Indicates CPU utilization; e indicates the base of natural logarithm; express The weight coefficient is 0.4; Indicates the weight coefficient of the peak memory usage, with a value of 0.3; Indicates the weight coefficient of CPU utilization.

6. The voting method for setting standards in an international standardization game according to claim 5, characterized in that: Voting data includes: Count the approval rate, opposition rate and abstention rate of votes through voting records; By counting the number of members who have participated in proposals or revisions for multiple times in a row, this number will be counted as the number of actively participating members; Calculate the support rate fluctuation value by comparing the difference in support rates between two consecutive rounds of voting , based on the formula: .

7. The voting method for setting standards in an international standardization game according to claim 6, characterized in that: By comprehensively analyzing the voting data, we can obtain the consensus stability coefficient based on the following formula: ； in, represents the consensus stability coefficient; represents the support rate of the mth round of voting; m represents the number of the voting round, and n represents the total number of voting rounds; represents the threshold ratio of the mth round of voting; represents the opposition rate in the mth round of voting; represents the abstention rate in the mth round of voting; represents the total number of members voting in round m; represents the fluctuation value of the support rate in the mth round of voting; It represents the highest support rate value in all rounds; E represents the number of actively participating members; G represents the total number of stages.

8. The voting method for setting standards in an international standardization game according to claim 7, characterized in that: The comprehensive effectiveness value is calculated by voting behavior entropy, voting effectiveness index and consensus stability coefficient. The formula is as follows: ； in, Represents the comprehensive value of effectiveness, represents the voting behavior entropy, represents the voting effectiveness index, Represents the controversy value, and the calculation formula is: ; represents the consensus stability coefficient; The weight coefficient representing the entropy of voting behavior; The weight coefficient representing the voting effectiveness index; The weight coefficient representing the consensus stability coefficient; express The weight coefficient of .

9. The voting method for setting standards in an international standardization game according to claim 8, characterized in that: The method for executing corresponding instructions according to the judgment results is: Set encryption efficiency index threshold, anonymity strength index threshold and effectiveness comprehensive value threshold; Compare the encryption efficiency index with the encryption efficiency index threshold. When the encryption efficiency index is greater than or equal to the encryption efficiency index threshold, the encryption efficiency meets the standard. When the encryption efficiency index is less than the encryption efficiency index threshold, the encryption efficiency does not meet the standard and the encryption efficiency management instruction is executed. Compare the anonymity strength index with the anonymity strength index threshold. When the anonymity strength index is greater than or equal to the anonymity strength index threshold, the anonymity meets the standard. When the anonymity strength index is less than the anonymity strength index threshold, the anonymity does not meet the standard and the confidentiality management instruction is executed. Compare the effectiveness comprehensive value with the effectiveness comprehensive value threshold. When the effectiveness comprehensive value ≥ the effectiveness comprehensive value threshold, the voting validity meets the standard. When the effectiveness comprehensive value < the effectiveness comprehensive value threshold, the voting validity does not meet the standard and the voting procedure needs to be restarted.

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