Voting method and system in standard formulation of international standardized game

Abstract

The invention discloses a voting method and system in standard formulation of an international standardized game, and relates to the technical field of internet voting, and the main scheme is as follows: in the voting process, voting validity data, voting anonymity data, encryption algorithm data and voting data are obtained through the voting system; respectively analyzing to obtain a voting validity index, an anonymity strength index, an encryption efficiency index and a formula stability coefficient; the voting behavior entropy of the voting system is obtained through a Markov chain Monte Carlo model, and the data are comprehensively analyzed to calculate an effective comprehensive value; and respectively comparing the encryption efficiency index, the anonymity strength index and the effectiveness comprehensive value with corresponding parameters in the evaluation index threshold set, and executing a corresponding instruction according to a judgment result. The method and the device are used for solving the problems of one-sided voting validity judgment, vulnerability of anonymity safeguard measures, long time consumption in encryption and decryption processes, lack of stability of a voting system and the like in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of Internet voting, and in particular to a voting method and system for 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: 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.

[0005] 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.

[0006] 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.

[0007] 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

[0008] In view of the deficiencies of the prior art, the present invention provides a voting method in the standard setting of international standardization games, which at least solves one of the problems in the prior art, such as relatively one-sided judgment of voting validity, loopholes in anonymity protection measures, long time-consuming encryption and decryption processes, and lack of stability of the voting system.

[0009] To achieve the above objectives, the present invention is realized through the following technical solutions: A voting method in the standard setting of international standardization games, including: Step 1: During the voting process, obtain voting validity data through the voting system, and obtain a set of voting validity parameters based on the analysis of the voting validity data; obtain a voting validity index through comprehensive analysis of the parameters in the set of voting validity parameters; Step 2: Obtain voting anonymity data through the voting system, and obtain a set of anonymity strength parameters based on the analysis of the voting anonymity data; obtain an anonymity strength index through comprehensive analysis of the set of anonymity strength parameters; Step 3: Obtain the encryption algorithm data of the voting system, and obtain a set of encryption efficiency parameters based on the analysis of the encryption algorithm data; obtain an encryption efficiency index through comprehensive analysis of the set of encryption efficiency parameters; Step 4: Obtain voting data through the voting records of the voting system, and obtain a formula stability coefficient through comprehensive analysis based on the bid data; Step 5: Input the data of the voting system into the Markov chain Monte Carlo model to obtain the voting behavior entropy of the voting system, and calculate the effectiveness comprehensive value by combining the voting validity index, anonymity strength index, encryption efficiency index, and consensus stability coefficient; Step 6: Set a set of evaluation index thresholds; compare the encryption efficiency index, anonymity strength index, and effectiveness comprehensive value with the corresponding parameters in the set of evaluation index thresholds respectively, and execute corresponding instructions according to the judgment results.

[0010] In the preferred embodiment of the above voting method in the standard setting of international standardization games, the set of voting validity parameters includes voting participation rate P, total number of votes T, number of valid votes V, average voting result , standard deviation of voting results , actual voting duration t, and weight adjustment factor W; Obtain a voting validity index through comprehensive analysis of the parameters in the set of voting validity parameters, and the formula is as follows: , where, represents the voting validity index; k represents the time decay coefficient, which controls the influence intensity of time distribution on validity, and its value range is [0,1]; is the weight coefficient of voting participation, is 's weight coefficient; is 's weight coefficient; is the weight coefficient of the weight adjustment factor.

[0011] In the preferred scheme of the voting method in the above-mentioned standard setting of the international standardization game, the set of anonymity strength parameters includes the total number N of members to vote, the comprehensive device correlation , the similarity probability of voting patterns , the number of historical votes, and the maximum number of votes; By comprehensively analyzing the parameters in the set of anonymity strength parameters, the anonymity strength index is obtained, and the basis formula is as follows: , where, represents the anonymity strength index; represents the number of historical votes of the voter numbered h, represents the maximum number of votes under the current voting mechanism.

[0012] In the preferred scheme of the voting method in the above-mentioned standard setting of the international standardization game, the set of encryption efficiency parameters includes the effective key length, the algorithm round density, the entropy value density, the peak memory occupancy, the CPU utilization rate, the throughput, and the latency tolerance threshold; the specific acquisition method is: Obtain the key length of the encryption algorithm of the password module in the voting system as the effective key length; Use the hardware performance counter to dynamically collect the number of CPU instruction cycles, and obtain the number of core encryption operations required for a unit of data volume as the algorithm round density; Through the test suite, monitor the physical entropy source in real time, and obtain the entropy value output by the key generator per second as the entropy value density; Record the memory usage curve through the operating system resource monitor, and obtain the maximum value of the process resident memory during the encryption process as the peak memory occupancy; Collect the CPU usage rate at the process level by calling the system performance interface, and obtain the proportion of the CPU time occupied by the encryption task as the CPU utilization rate; Measure the actual transmission rate of the end-to-end encryption channel through the network traffic mirroring device, and obtain the amount of encrypted data processed per unit time as the throughput; Match the maximum encryption processing latency allowed by the application scenario according to the delay classification standard of the transmission protocol as the latency tolerance threshold.

[0013] In the preferred solution of the voting method in the standard setting of the above international standardization game, by comprehensively analyzing the encryption efficiency parameter set, the encryption efficiency index is obtained, and the basis formula is as follows: ; 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; represents the throughput; represents the delay 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;

[0014] In the preferred solution of the voting method in the standard setting of the above international standardization game, the voting data includes: By counting the voting record, the support rate, opposition rate and abstention rate of the vote are statistically analyzed.

[0015] By counting the number of members who participate continuously during the proposal or revision process, it is used as the number of actively participating members; By comparing the difference in support rates between two consecutive rounds of voting, the support rate fluctuation value is calculated , and the basis formula is: .

[0016] In the preferred solution of the voting method in the standard setting of the above international standardization game, by comprehensively analyzing the voting data, the consensus stability coefficient is obtained, and the basis formula is as follows: ; Among them, represents the consensus stability coefficient; represents the support rate of the m-th round of voting; m represents the serial number of the voting round, and n represents the total number of voting rounds; represents the threshold ratio of the m-th round of voting; represents the opposition rate of the m-th round of voting; represents the abstention rate of the m-th round of voting; represents the total number of members in the m-th round of voting; represents the support rate fluctuation value of the m-th round of voting; represents the highest support rate value in all rounds; E represents the number of actively participating members; G represents the total number of stages.

[0017] In the preferred solution of the voting method in the standard setting of the above international standardization game, the comprehensive effectiveness value is calculated through the voting behavior entropy, the voting effectiveness index, and the consensus stability coefficient. The basis formula is as follows: ; Among them, represents the comprehensive effectiveness value, represents the voting behavior entropy, represents the voting effectiveness index, represents the controversy degree value, and the calculation formula is: ; represents the consensus stability coefficient; represents the weight coefficient of the voting behavior entropy; represents the weight coefficient of the voting effectiveness index; represents the weight coefficient of the consensus stability coefficient; represents the weight coefficient of

[0018] In the preferred solution of the voting method in the standard setting of the above international standardization game, the method for executing corresponding instructions according to the judgment result is as follows: Set the encryption efficiency index threshold, the anonymity strength index threshold, and the comprehensive effectiveness value threshold; 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 execute the encryption efficiency management instruction; Compare the anonymity strength index with the anonymity strength index threshold. When the anonymity strength index ≥ the anonymity strength index threshold, the anonymity meets the standard; when the anonymity strength index < the anonymity strength index threshold, the anonymity does not meet the standard, and execute the confidentiality management instruction; Compare the comprehensive effectiveness value with the comprehensive effectiveness value threshold. When the comprehensive effectiveness value ≥ the comprehensive effectiveness value threshold, the voting effectiveness meets the standard. When the comprehensive effectiveness value < the comprehensive effectiveness value threshold, the voting effectiveness does not meet the standard, and it is necessary to restart the voting procedure.

[0019] Beneficial effects:

[0020] The present invention provides a voting method in the standard setting of an international standardization game, having the following beneficial effects: (1)The voting validity data is obtained through the voting system and analyzed to obtain a set of parameters, and then a comprehensive analysis is carried out to obtain the voting validity index, realizing the multi-dimensional and accurate quantification of voting validity. Compared with traditional voting methods, this method is no longer limited to a single voting rule judgment, but comprehensively considers many factors such as voting participation rate, total number of votes, number of valid votes, average voting result, standard deviation of voting results, actual voting duration, and weight adjustment factor. For example, in the voting for standard setting by the International Organization for Standardization, votes that may have been judged invalid due to a single factor in the past, such as non-compliance with format, can now be more comprehensively evaluated for voting validity by considering many factors, thus significantly improving the accuracy and fairness of voting results, ensuring that the decisions in the standard-setting process can truly reflect the wishes and demands of all parties, enhancing the credibility and authority of voting results, and providing a more reliable basis for standard setting in international standardization games.

[0021] (2)By analyzing the voting anonymity data, a set of anonymity strength parameters is obtained, and a comprehensive analysis is carried out to obtain the anonymity strength index, effectively improving the anonymity guarantee level of the voting system. In the scenario of international standard-setting voting, voters often come from different countries, enterprises, research institutions, etc., facing complex interest relationships and potential external pressures. The ability to accurately quantify the anonymity strength ensures that the identity information of voters is strictly protected during the voting process. This greatly enhances the trust of voters in the voting system, enabling them to express their wishes more truly and freely without worrying about retaliation or external interference due to their voting behavior, thereby improving the objectivity and authenticity of voting results and facilitating the formation of a fair and just voting environment in international standardization games.

[0022] (3)In terms of encryption efficiency, by analyzing the encryption algorithm data of the voting system, a set of encryption efficiency parameters is obtained, and a comprehensive analysis is carried out to obtain the encryption efficiency index, prompting the voting system to adopt more efficient and advanced encryption algorithms. For example, the adoption of lightweight encryption algorithms or parallel encryption processing technologies can significantly shorten the encryption and decryption time while ensuring data security, and improve the transmission and processing speed of voting data. In the standard-setting voting involving hundreds of member organizations, an efficient encryption algorithm can ensure that the voting system processes a large amount of voting data in a short time, avoiding system congestion and delays caused by excessive encryption time consumption, and guaranteeing the real-time and efficient nature of voting.

[0023] (4) By inputting the data of the voting system 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 comprehensive effectiveness value, the comprehensive evaluation of the comprehensive effectiveness value is realized, 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 the voting results, and will change dynamically with the progress of the voting process and the changes in the external environment. Through the Markov Chain Monte Carlo model, various uncertain factors and random behaviors in the voting process can be fully considered, such as the preference changes of voters, voting strategy adjustments, etc., to calculate the voting behavior entropy, and then more accurately quantify the complexity and uncertainty of the voting process. At the same time, through comprehensive evaluation in combination with other key indexes, the obtained comprehensive effectiveness value can comprehensively reflect the overall performance of the voting system and the reliability of the voting results.

[0024] (5) Set the evaluation index threshold set, compare the relevant indexes with the thresholds and execute the corresponding instructions, so that the voting system can dynamically adjust and optimize the voting process according to the preset criteria. Brief Description of the Drawings

[0025] Figure 1 It is a schematic diagram of the steps of a voting method in the standard setting of an international standardization game of the present invention. Detailed Embodiments

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] Embodiment 1:

[0028] Please refer to Figure 1 , the present invention provides a voting method in the standard setting of an international standardization game, including: Step 1: During the voting process, obtain the voting effectiveness data through the voting system, and obtain the voting effectiveness parameter set based on the analysis of the voting effectiveness data.

[0029] Step 101: Obtain the total number of members eligible to vote and the actual number of members who voted through the member registration system, and calculate the voting participation rate P. The formula is: Voting participation rate P = Actual number of members who voted / Total number of members eligible to vote. This can accurately quantify the participation degree of members in the voting process. In the standard-setting voting of international standardized games, the voting participation rate is an important indicator to measure the representativeness and comprehensiveness of the voting process. Traditional voting methods often have difficulty accurately counting and reflecting the relationship between the actual number of members participating in the vote and the total number of members eligible to vote, resulting in the organizer being unable to clearly understand the participation situation of the voting process and difficult to judge whether the voting result has sufficient representativeness and credibility. Through this technical means, the voting participation rate can be counted in real time and accurately, providing intuitive participation data for the organizer, helping it evaluate the progress of the voting process, timely discover and solve potential participation deficiencies, thereby improving the transparency and credibility of the voting process, ensuring that all parties' opinions can be fully absorbed in the standard-setting process, and enhancing the authority and influence of the voting result.

[0030] Step 102: During the voting process, set a counter in the voting system to count the total number of votes. Each time a voting request (valid request and invalid request) is received, this counter is incremented by 1 to count the total number of votes T; the smart contract of the voting system verifies the validity of the voter's identity certificate through Elliptic Curve Digital Signature (ECDSA) for each vote. Each time a successful verification and confirmation of a valid vote occurs, a second counter is incremented by 1, and the final value of the second counter is the number of valid votes V. This solves the problems of accuracy and security in counting the number of votes in traditional voting systems. In previous voting processes, due to the lack of effective identity verification mechanisms and precise counting and statistical means, situations such as repeated voting and invalid votes being miscounted as valid votes were likely to occur, resulting in distorted voting results and affecting the accuracy of decision-making. However, this solution uses smart contracts and ECDSA encryption technology to ensure the authenticity of the voter's identity and the effectiveness of the voting operation. By using two independent counters to count the total number of votes and the number of valid votes respectively, it can accurately distinguish between valid votes and invalid votes, avoiding human intervention and data statistical errors, improving the accuracy and reliability of the voting data, and providing a solid data foundation for subsequent voting result analysis and decision-making.

[0031] It should be noted that a variable uint public totalVoteCount can be set in the smart contract. At the entrance of the voting function, that is, before the validity verification, the operation totalVoteCount++ is executed first, so that the number of all participating votes can be counted, and thus the total number of votes can be obtained. A variable for storing the number of valid votes, such as uint public validVoteCount, can be set in the smart contract code. In the voting function, when the identity certificate of the voter passes the ECDSA verification, the operation validVoteCount++ is executed to count the number of valid votes in real time.

[0032] Step 103: Set the option values for the support votes and opposition votes. For example, the support vote is recorded as 1 and the opposition vote is recorded as -1; calculate the mean value of the voting results of all valid votes. , and the formula is: Mean value of voting results = Sum of the option values of valid votes / Number of valid votes. It can deeply analyze the central tendency and dispersion degree of the voting results, providing a powerful tool for evaluating the consistency and stability of the voting results. In international standardized voting, just knowing the number of valid votes and the simple majority of the voting results is not enough. It is also necessary to understand the distribution characteristics of the voting results. By calculating the mean value of the voting results, the overall tendency of support and opposition can be intuitively seen.

[0033] Step 104: Calculate the standard deviation of the voting results based on the option value of each valid vote and the mean value of the voting results. The formula is: ; where represents the standard deviation of the voting results; represents the option value of the i-th valid vote; i represents the serial number of the valid vote, taking positive integers, and V is the numerical value of the number of valid votes; represents the mean value of the voting results. The standard deviation reflects the degree of divergence of the voters' opinions. For example, if the standard deviation is small, it means that the voters' views are more consistent and the voting results are more persuasive; on the contrary, if the standard deviation is large, it indicates that there are large differences among the voters, and further communication and coordination may be needed. This technical means helps the organizer to more comprehensively understand the connotation of the voting results, provides richer information support for the subsequent standard-setting decision-making, improves the scientificity and rationality of the decision-making, and avoids decision-making mistakes caused by one-sided reliance on the surface data of the voting results.

[0034] Step 105: Obtain the timestamps of the bid end time and the vote start time through the voting system to calculate the actual voting duration t, which can accurately record the time required for the voting process. In international standardized voting, the rationality of the voting duration has an important impact on the smooth progress of the voting process and the validity of the voting results. An overly short voting duration may cause some members not to be able to fully participate due to time constraints, affecting the representativeness and fairness of the voting; while an overly long voting duration may increase the complexity and management difficulty of the voting process, and may even lead to changes in the external environment during the voting period affecting the stability of the voting results.

[0035] Step 106: Set the 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. Calculate the weight adjustment factor according to 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: ; where W represents the weight adjustment factor, represents the weight coefficient of the j-th type of vote; represents the proportion of the j-th type of vote in the total number of votes; j represents the serial number of the vote type, taking positive integer values, and M represents the number of vote types. It can fully consider the differences in the importance and influence of different voters in the voting process and solve the "one-size-fits-all" voting weight allocation problem in traditional voting methods. In international standardized voting, different types of voters (such as experts, ordinary members, etc.) often have different professional knowledge levels and industry influences, and the importance of their voting opinions also varies. By setting reasonable weight coefficients for different types of votes and calculating the weight adjustment factor according to their proportions, the value of the opinions of various voters can be more accurately reflected, and the voting results can more scientifically and reasonably reflect the interests and professional judgments of all parties.

[0036] Step 107: Form a voting validity parameter set through the voting participation rate P, the total number of votes T, the number of valid votes V, the mean value of the voting results 、the standard deviation of the voting results 、the actual voting duration t, and the weight adjustment factor W.

[0037] Step 108: Through comprehensive analysis of the parameters in the voting validity parameter set, before calculating the voting validity index, it is necessary to perform normalization parameter preprocessing on the involved parameters to eliminate the dimensions of different parameters for subsequent formula calculations, and obtain the voting validity index. The formula is as follows: , where, represents the voting validity index; k represents the time decay coefficient, which controls the influence intensity of the time distribution on the validity, taking values in [0,1]; is the weight coefficient of the voting participation rate, is 's weight coefficient; is 's weight coefficient; is the weight coefficient of the weight adjustment factor. And , the value of each weight coefficient can be obtained by training historical voting data through a machine learning model (such as a random forest) to optimize the weight allocation; or set according to organizational rules, such as =0.3, =0.25, =0.25, W = 0.2.

[0038] It should be noted that through the Sigmoid function , the impact of the voting time distribution on the validity can be quantified. For example, if the voting is concentrated within a short period of time (t ≪ t0), the exponent approaches 0, indicating that the voting may be affected by external interference (such as centralized manipulation); if the voting distribution is balanced (t ≈ t0), the exponent 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 the time distribution on the voting validity is more significant, that is, the larger the k, the greater the discount on the validity of the vote deviating from the reference time. Through the formula can reflect the dispersion degree of the voting results. If is close to 0, the exponent approaches 1, indicating that the voting results are clear; if is larger, the exponent decreases, suggesting that there may be disputes or strategic voting. The weight adjustment factor allows for dynamically adjusting the validity index according to voting rules (such as the differential weights of expert votes and ordinary votes) to adapt to different scenario requirements. By combining the valid vote rate, participation rate, time distribution, result dispersion, and weight rules, the limitations of a single dimension are avoided.

[0039] The solution can more accurately and comprehensively reflect the true validity of the voting process. For example, in an international standard - setting voting project, if there is a high voting participation rate (such as 80%), good consistency in voting results (small standard deviation), reasonable voting duration, and reasonable setting of the weight adjustment factor (fully considering expert opinions, etc.), the VEI index will be relatively high, indicating a high validity of this voting process and strong credibility of the voting results. This comprehensive evaluation method avoids one - sided judgments caused by only focusing on a single indicator, provides a more reliable and accurate quantitative indicator of voting validity for the organizer, helps it better grasp the quality of the voting process and the credibility of the results, and thus makes more informed standard - setting decisions, improving the scientificity and fairness of the standard - setting process.

[0040] Step 2: Obtain voting anonymity data through the voting system and obtain a set of anonymity strength parameters based on the analysis of the voting anonymity data.

[0041] Step 201: Obtain the total number of members N who should vote through the member registration form publicly available by the standardization organization or the member registration system of the voting system. This provides an accurate reference base for subsequent voting anonymity analysis. In the standard-setting voting of international standardization games, clearly knowing the total number of members who should vote is the basis for evaluating the integrity of the voting process and the effectiveness of anonymity protection.

[0042] Step 202: Collect device fingerprint information through the underlying log collection device of the voting system, such as at least five dimensions of features including IP address segments, operating systems, the first 24 bits of the terminal MAC address, etc. By comparing the features of each dimension of different voters, obtain the device fingerprint correlation degree. , when the voter h and the q-th dimension feature of voter g are the same, take the value of 1; when they are different, take the value of 0; through the device fingerprint correlation degree Calculate the comprehensive device correlation degree, and the formula based on it is: ; where represents the comprehensive device correlation degree between voter h and voter g; q represents the serial number of different dimension features, taking positive integer values, and K is the maximum value of the types of dimension features in the device fingerprint information; h and g are both the serial numbers of voters, both taking positive integer values, and during the calculation process, h and g take different serial number values; aiming to accurately identify and quantify the correlation between different voters' devices, thereby effectively preventing malicious associated voting behavior and enhancing the level of voting anonymity protection. In the international standardization voting scenario, there may be malicious attackers using multiple devices or disguising device information for associated voting, attempting to influence the voting result or undermine the fairness of the voting. Traditional voting systems often have difficulty effectively identifying such associated behaviors and can only make a preliminary judgment based on a single dimension such as the IP address, which is easily circumvented by attackers. However, this solution can more comprehensively and deeply reveal the potential correlation between devices by collecting device fingerprint information of multiple dimensions and comprehensively calculating the correlation degree. For example, even if the attacker changes the IP address, the specific version information of its operating system, some features of the MAC address, etc. may still expose the true correlation of the device, thus being recognized and recorded by the system. Through the quantitative calculation of the comprehensive device correlation degree, the voting system can mark and further review voting behaviors with high correlation degrees, timely discover and prevent malicious associated voting behaviors, ensure the independence and fairness of the voting process, and at the same time help maintain the anonymity of the voting, prevent attackers from maliciously tracking voters' identities through correlation analysis, enhance voters' trust in the voting system, and promote the smooth progress of the voting process.

[0043] 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 voterh The probability of similarity with the voting pattern of voter g is based on the following formula: ; where represents voter h The probability of similarity with the voting pattern of voter g and respectively represent the set of options for the historical votes of voter h and voter g. And during the calculation process, h and g take different serial numbers. represents the smoothing factor, which is used to avoid a zero denominator and can take a value of 0.1. It can deeply analyze the voting behavior patterns of voters, identify potential coordinated voting or controlled voting behaviors, and further strengthen the guarantee of voting anonymity and the fairness of voting results. In international standardized voting, there may be some interest groups or malicious manipulators who attempt to control multiple voting accounts and vote according to a unified voting pattern to achieve the purpose of manipulating the voting results. Traditional voting systems usually have difficulty detecting such hidden coordinated behaviors and can only judge based on the surface data of single votes. This solution can effectively identify whether there are highly similar voting behavior patterns among voters by analyzing the set of historical voting options of voters and calculating the probability of similarity of voting patterns.

[0044] Step 204: Calculate the historical voting times of each voter and the maximum voting times under the current voting mechanism through the historical records of the voting system. For example, determine the maximum voting times by counting the revision rounds of each draft standard. It can effectively monitor and regulate the voting behaviors of voters, prevent malicious repeated voting or over-limit voting behaviors, and ensure the fairness and orderliness of the voting process. During the international standardized voting process, since standard formulation often involves multiple rounds of revision and voting, some voters may attempt to take advantage of rule loopholes to conduct repeated voting or vote multiple times beyond a reasonable range to amplify their own influence or interfere with the voting results. By accurately counting the historical voting times of each voter and comparing and analyzing them with the maximum voting times determined by the current voting mechanism, it can be monitored in real time whether voters exceed the specified voting times limit.

[0045] Step 205: Aggregate the total number of eligible voters, the comprehensive device correlation degree, the probability of similarity of voting patterns, the historical voting times, and the maximum voting times into an anonymity strength parameter set.

[0046] Step 206: Through comprehensive analysis of the anonymity strength parameter set, before calculating the anonymity strength index, it is necessary to perform normalized parameter preprocessing on the involved parameters to eliminate the dimensions of different parameters for the convenience of subsequent formula calculation, and obtain the anonymity strength index. The formula is as follows: , where represents the anonymity strength index; represents the historical voting times of the voter with serial number h, represents the maximum voting times under the current voting mechanism.

[0047] It should be noted that the formula constructs a decay model using the reciprocal function. When the device correlation and behavior similarity are higher, the individual anonymity is lower. In the formula the smaller the value, and by constructing a linear decay model, the problem of "voting fingerprint" solidification caused by long-term participation is solved. It is applicable to the scenario of multi-round standard revision. As the number of votes increases, the influence of historical behavior on the current anonymity gradually weakens. That is, the closer the values of the historical voting times and the maximum voting times are, the smaller the value of in the formula.

[0048] The formula comprehensively considers factors such as the comprehensive device correlation, the similarity probability of voting patterns, the historical voting times of voters, and the maximum voting times under the current voting mechanism. The double summation part in the formula can reflect the potential identity exposure risk caused by device correlation and similar voting behavior patterns among voters. By taking the reciprocal of it, adding 1, and then taking the reciprocal again, the contribution of the voter combination with stronger correlation to this part becomes smaller, thereby reducing the overall anonymity strength index; while the ratio part of the historical voting times and the maximum voting times reflects the voting activity of voters under the current voting mechanism. The closer this ratio is to 1, the more active the voter is, and their anonymity may be affected to a certain extent. By subtracting this ratio from 1, active voters are appropriately adjusted in the calculation of the anonymity strength index to reflect the relative difficulty of their anonymity protection. Traditional voting anonymity evaluation methods often only focus on single-dimensional factors, such as judging the anonymity strength only based on the degree of anonymization of the IP address or simple fuzzification of device information, and cannot comprehensively and systematically reflect the anonymity protection level of voters in a complex voting environment. This solution constructs an anonymity strength index including multiple-dimensional parameters such as comprehensive device correlation, similarity probability of voting patterns, and voting activity degree, which can comprehensively consider the anonymity influencing factors of voters in aspects such as device use, voting behavior patterns, and voting participation degree, solves the one-sidedness and limitations of traditional methods in anonymity quantification, provides a comprehensive and systematic quantification tool for voting anonymity evaluation, and makes the evaluation of anonymity strength more scientific, accurate, and reasonable. Based on the evaluation results of the anonymity strength index, the voting system operator can deeply analyze the advantages and disadvantages of voter anonymity protection, and thus optimize the anonymity protection strategy and resource allocation targeted.

[0049] Step 3: Obtain the encryption algorithm data of the voting system, and obtain the encryption efficiency parameter set based on the analysis of the encryption algorithm data.

[0050] Step 301: Obtain the API interface of the password module in the voting system to read the 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 the key length of AES-256 = 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 cryptography research and standards (such as NIST SP 800-57), convert the key length of the asymmetric algorithm to the key length of the symmetric algorithm. For example, RSA-RSA-3072 has a key length = 112 after equivalent bit conversion. This solves the technical problem in traditional methods of difficult comparison and evaluation of the key security of different types of encryption algorithms, provides an accurate security quantification index for subsequent comprehensive evaluation of encryption efficiency, ensures that the voting system can measure its security based on a unified standard when selecting an encryption algorithm, and thus improves the overall security protection level of the voting system.

[0051] Step 302: Dynamically collect the number of CPU instruction cycles using a hardware performance counter (such as Intel VTune) to obtain the number of core encryption operations required for a unit data volume (such as AES-256 requires 140 round functions to be executed per MB), as the algorithm round density. It can accurately measure the computational intensity of the encryption algorithm during actual operation, and then statistically obtain the number of core encryption operations required for a unit data volume in real-time and accurately, thus providing strong data support for evaluating the actual computational efficiency of the encryption algorithm. This not only helps to predict in advance the demand of the encryption process for system computing resources, but also provides a key basis for optimizing the implementation of the encryption algorithm and improving the data processing efficiency of the voting system in large-scale voting scenarios, ensuring that the encryption operations of the voting system can be efficiently completed within limited computing resources, and guaranteeing the fluency and timeliness of the voting process.

[0052] Step 303: Real-time monitor the physical entropy source (such as a quantum noise sensor) through the IID test suite of NIST SP 800-90B to obtain the entropy value output by the key generator per second (unit: bit / s), as the entropy density. In the voting system, the quality of key generation directly determines the effectiveness of the encryption algorithm and the security of voting data. It is used to evaluate the randomness and unpredictability of the key generation process. By adopting the real-time monitoring method, abnormal situations in the output of the physical entropy source can be detected in a timely manner, ensuring that the key generation process continuously maintains high randomness and unpredictability. For example, if the entropy density shows a downward trend, the system can give a timely warning and take measures, such as checking the status of the physical entropy source device or adjusting the parameters of the key generation algorithm, thereby avoiding security risks caused by insufficient key randomness, effectively guaranteeing the security of keys in the voting system and the confidentiality and integrity of voting data, and enhancing the ability of the voting system to resist cryptographic attacks.

[0053] Step 304: Record the memory usage curve through the operating system resource monitor (such as the smem tool in Linux), and obtain the maximum value of the process resident memory during the encryption process as the memory occupancy peak. During the operation of the voting system, especially when dealing with a large number of voting data encryption tasks, the reasonable allocation and use of memory resources are crucial, which can accurately quantify the maximum demand of the encryption operation for the system memory resources. Precisely capturing the peak memory occupancy during the encryption process provides an important reference for the reasonable planning and management of system resources.

[0054] Step 305: Collect the CPU usage rate at the process level by calling the system performance interface (such as the PDH API in Windows), and obtain the proportion of CPU time occupied by the encryption task as the CPU utilization rate. In the voting system, the CPU resources are the core driving force to support the operation of the entire system. As a key part of the system, the occupancy of the encryption task on the CPU resources directly affects the concurrent processing ability and response speed of the voting system.

[0055] Step 306: Measure the actual transmission rate of the end-to-end encryption channel through a network traffic mirroring device (such as Spirent TestCenter), and obtain the amount of encrypted data processed per unit time as the throughput. It is used to evaluate the data transmission efficiency and performance of the encryption channel in the voting system.

[0056] Step 307: Automatically match the maximum encryption processing delay allowed by the application scenario according to the delay classification standard of the transmission protocol in IETF RFC 7679 (such as T_max = 50ms in the real-time communication system) as the delay tolerance threshold. In international standardized voting, the tolerance of different voting scenarios and business types to the encryption processing delay varies greatly. By automatically matching the delay tolerance threshold according to the standard delay classification, the maximum allowed encryption processing delay of the voting system in a specific application scenario can be accurately determined, and based on this, the selection of encryption algorithms, system configuration, and network transmission strategies can be optimized to ensure that the encryption processing 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 guarantees the real-time and smoothness of the voting process.

[0057] Step 308: Aggregate the effective key length, algorithm round density, entropy value density, memory occupancy peak, CPU utilization rate, throughput, and delay tolerance threshold into an encryption efficiency parameter set.

[0058] Step 309: Through comprehensive analysis of the encryption efficiency parameter set, obtain the encryption efficiency index. Before calculating the encryption efficiency index, it is necessary to perform normalized parameter preprocessing on the parameters involved to eliminate the dimensions of different parameters for subsequent formula calculations. The formula is as follows: ; 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; .

[0059] It should be noted that the effective key length is positively correlated with the security strength, but follows the law of diminishing marginal returns. It can transform 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 then , reflecting linear growth. When then , avoiding over-rewarding of ultra-low latency schemes. The addition structure of the denominator in the formula can reflect the cooperative 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 of the GPU encryption card. The numerator uses exponential and logarithmic functions to amplify the key parameters ED, For the contribution, the denominator uses the square root to suppress the negative impact of resource consumption, which is closer to the non-linear characteristics of the actual system; based on the experiment with NIST test data, the sensitivity is increased by about 35% compared with the traditional linear weighted model (such as ).

[0060] The evaluation of the encryption efficiency of traditional voting systems often focuses on a single indicator, such as only paying attention to the encryption speed or the key length, ignoring the overall performance of the encryption process. This one-sided evaluation may lead to misjudgment of the encryption efficiency and affect the security and performance optimization direction of the voting system. This solution solves the problem that traditional methods cannot comprehensively evaluate the encryption efficiency by integrating multiple key parameters into a set of encryption efficiency parameters and conducting comprehensive analysis.

[0061] Step 4: Obtain voting data through the voting records of the voting system, and conduct comprehensive analysis based on the bid data to obtain the formula stability coefficient; Step 401: Count the support rate, opposition rate, and abstention rate of the vote through the voting records. For example, in the voting results of the proposal stage or the FDIS stage, the number of support votes, opposition votes, and abstention votes are respectively divided by the total number of voting members.

[0062] Step 402: During the proposal or revision process, count the number of members who continuously nominate experts to participate as the number of actively participating members; Step 403: Calculate the support rate fluctuation value by comparing the difference in support rates between two consecutive rounds of voting , and the formula is as follows: ; enabling the organizer to promptly insight into the trend changes during the voting process, anticipate potential risks in advance and formulate coping strategies. For example, if the support rate fluctuation value exceeds the set threshold, the organizer can quickly analyze the reasons and take targeted measures, such as strengthening publicity and promotion, clarifying the details of the plan, etc., to stabilize the voting trend, ensure the smooth progress of the voting process, and improve the efficiency and quality of standard formulation.

[0063] Step 404: Through comprehensive analysis of the voting data, obtain the consensus stability coefficient. Before calculating the consensus stability index, it is necessary to perform normalization parameter preprocessing on the involved parameters to eliminate the dimensions of different parameters for the convenience of subsequent formula calculation. The formula is as follows: ; where represents the consensus stability coefficient; represents the support rate of the m-th round of voting; m represents the serial number of the voting round, and n represents the total number of voting rounds; represents the threshold ratio of the m-th round of voting, which can refer 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, then ; represents the opposition rate of the m-th round of voting; represents the abstention rate of the m-th round of voting; represents the total number of members in the m-th round of voting; represents the support rate fluctuation value of the m-th round of voting; represents the highest support rate value among all rounds; E represents the number of actively participating members; G represents the total number of stages, that is, the total number of stages in the proposal or revision process (such as the proposal stage, DIS stage, FDIS stage, etc.), which is determined according to the stage division of the ISO / IEC standard development process. For example, ISO standards usually go through 5 stages (NP, CD, DIS, FDIS, Final).

[0064] It should be noted that in the formula is used to quantify the degree of over-fulfillment of the support rate over the threshold in each round of voting, is used to deduct the influence of opposition and abstention, and can reflect whether the proposal passes with a high support rate in each round, while reducing the weakening of consensus caused by opposition or abstention. Through the support rate fluctuation value and the historical highest support rate the ratio is used to measure the stability of the support rate. If the support rate fluctuates greatly (such as dropping suddenly from 0.7 to 0.5), then the value of the part is small, reducing the overall stability coefficient. By the ratio of the number of actively participating members to the total number of stages, it is used to measure the continuous participation degree of the proposal. If the proposal continuously obtains a high participation degree in multiple stages, it has a positive impact on the stability coefficient. By introducing the parameter into the continuous participation degree, it avoids ignoring the long-term stability due to a short-term high support rate.

[0065] This solution forms a consensus stability coefficient by comprehensively analyzing multi-dimensional data such as the support rate, opposition rate, abstention rate, support rate fluctuation value, and the number of actively participating members in multiple rounds of voting, solving the drawback that traditional methods cannot comprehensively reflect the voting stability. It provides a comprehensive perspective for the stability assessment of the voting process, helps to accurately judge the voting stability trend, and ensures the smooth progress of the voting process. For example, if the support rate in a certain round of voting is high but fluctuates greatly and the number of actively participating members is small, the consensus stability coefficient can comprehensively reflect the potential risks, and the organizer can intervene in advance to stabilize the subsequent voting.

[0066] Step Five: By inputting the data of the voting system into the Markov chain Monte Carlo model, obtain the voting behavior entropy of the voting system through the model, and calculate the effectiveness comprehensive value by combining the voting effectiveness index, anonymity strength index, encryption efficiency index, and consensus stability coefficient.

[0067] Step 501: Input the voting system data, such as the voter ID, the voting results of the voters, the proposal background (including the technical field (e.g., ICS classification code), voting time, proposal type (e.g., proposal stage, FDIS stage, etc.)), and the historical voting records of each expert (arranged in chronological order or proposal order) into the Markov chain Monte Carlo model, and output the voting behavior entropy through the Markov chain Monte Carlo model. Traditional voting methods are difficult to comprehensively quantify the complexity and uncertainty of voting behavior. They can only perform simple vote counting and trend analysis, and cannot deeply capture the randomness and potential change rules of voting behavior. By introducing the Markov chain Monte Carlo model in this solution, the random process of voting behavior is simulated, and the voting behavior entropy is calculated, so as to quantify the uncertainty of voting behavior, deeply understand the potential change rules of voting behavior, and predict the risks and uncertainties in the voting process in advance. For example, a voting process with a higher voting behavior entropy may mean that there is a greater uncertainty in the voting result.

[0068] It should be noted that outputting the voting behavior entropy through the Markov chain Monte Carlo model is common knowledge for those skilled in the art, so it will not be described further.

[0069] Step 502: Calculate the comprehensive effectiveness value through the voting behavior entropy, voting effectiveness index, and consensus stability coefficient. Before calculating the comprehensive voting effectiveness value, it is necessary to perform normalized parameter preprocessing on the involved parameters to eliminate the dimensions of different parameters for the convenience of subsequent formula calculations. The formula is as follows: ; Among them, represents the comprehensive effectiveness value, represents the voting behavior entropy, represents the voting effectiveness index, represents the controversy value, and the calculation formula is: ; represents the consensus stability coefficient; represents the weight coefficient of the voting behavior entropy; represents the weight coefficient of the voting effectiveness index; represents the weight coefficient of the consensus stability coefficient; represents the weight coefficient of , and

[0070] It should be noted that when the ratio of the number of against votes to the number of support votes is large, the absolute value of the controversy value will be large, resulting in The value is small, thus reducing the comprehensive effectiveness value, indicating that there are significant disputes in the voting results and the stability may be affected. This helps the organizers to promptly pay attention to the controversial points in the voting, take measures to promote communication and coordination among all parties, reduce the negative impact of disputes on the voting results, and enhance the recognition and acceptance of the voting results.

[0071] Traditional voting evaluation methods only focus on single - dimensional indicators, such as voting effectiveness or consensus stability, lacking a comprehensive evaluation of the overall performance of the voting system and unable to fully reflect the quality and reliability of the voting process. This solution calculates the comprehensive effectiveness value by combining voting behavior entropy, voting effectiveness index, consensus stability coefficient, and controversy degree value, achieving a comprehensive evaluation of multi - dimensional data integration. It accurately judges the overall performance and reliability of the voting system, ensuring the credibility of the voting results. For example, if the comprehensive effectiveness value is low, the organizer can, based on the performance of each individual index, identify the weak links in the voting process, such as insufficient voting effectiveness, poor consensus stability, or excessive controversy, and take targeted measures for improvement, thereby enhancing the overall performance and reliability of the voting system and providing a solid foundation for voting decisions.

[0072] Step Six: Set the set of evaluation index thresholds; compare the encryption efficiency index, anonymity strength index, and comprehensive effectiveness value with the corresponding parameters in the set of evaluation index thresholds respectively, and execute corresponding instructions according to the judgment results.

[0073] Step 601: Set the encryption efficiency index threshold, anonymity strength index threshold, and comprehensive effectiveness value threshold.

[0074] It should be noted that the encryption efficiency index threshold can be set with reference to industry standards and best practices. For example, in the financial industry, specifications such as the Payment Card Industry Data Security Standard (PCI - DSS) have certain requirements for encryption efficiency. For a voting system that processes credit card transaction data, the encryption efficiency index threshold can be set with reference to the requirements of PCI - DSS. The anonymity strength index threshold can be set with reference to privacy protection laws, regulations, and standards. For example, in some industry - specific privacy standards, such as the Health Insurance Portability and Accountability Act (HIPAA) in the medical industry, there are detailed regulations on the anonymization of patient information. If the voting system involves voting related to healthcare, such as patients' evaluation votes on medical services, the anonymity strength index threshold can be set according to the requirements of HIPAA. The effectiveness comprehensive value threshold can be set with reference to the effectiveness data of voting systems in the industry. For example, in the field of e - government voting, the experience of other successful e - voting projects shows that setting the effectiveness comprehensive value threshold to 0.75 can ensure the security and effectiveness of voting while avoiding problems such as an overly complex voting system or difficult voting approval due to too high a threshold. Based on these industry experiences and combined with the actual situation of its own voting system, the threshold can be adjusted appropriately. Before the voting system is launched, different voting scenarios can be simulated to test the effectiveness comprehensive value. For example, a simulated voting environment containing various voting behaviors (such as normal voting, malicious voting, repeated voting, etc.) can be constructed to conduct a stress test on the voting system. By adjusting parameters such as the voting behavior entropy, voting effectiveness index, anonymity strength index, and encryption efficiency index, observe the change in the effectiveness comprehensive value of the voting results. According to the test results, determine a threshold that can effectively distinguish normal voting from abnormal voting situations. For example, in the simulation test, when the effectiveness comprehensive value reaches 0.7, the system can correctly identify and eliminate more than 90% of the abnormal votes while retaining almost all normal votes, then the threshold can be set to 0.7. Furthermore, it can improve the encryption efficiency of the voting system and ensure the fluency and timeliness of the voting process.

[0075] 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 execute the encryption efficiency management instruction.

[0076] It should be noted that the encryption efficiency management instruction can be a key management instruction. By reasonably selecting the key length and type, and on the premise of ensuring security, a relatively short but sufficiently secure key can be selected. For example, for some symmetric encryption algorithms, 128-bit or 256-bit keys can be appropriately used. By choosing the appropriate key length, while meeting the security requirements, the complexity of the encryption operation can be reduced and the encryption efficiency can be improved. The encryption efficiency management instruction can also be used as a data preprocessing instruction to perform preprocessing operations such as compressing the data to be encrypted. After reducing the data volume, the data is encrypted. By compressing the data, the scale of the data to be encrypted can be effectively reduced without affecting the encryption effect, thereby improving the encryption efficiency. The hardware acceleration instruction enables the hardware encryption acceleration function, such as using a dedicated encryption chip or encryption card to perform the encryption operation.

[0077] Step 603: Compare the anonymity strength index with the anonymity strength index threshold. When the anonymity strength index ≥ the anonymity strength index threshold, the anonymity meets the standard; when the anonymity strength index < the anonymity strength index threshold, the anonymity does not meet the standard, and the confidentiality management instruction is executed; It should be noted that the confidentiality management instruction can generate an anonymous identity identifier based on an encryption algorithm for the voter. The identifier can be updated regularly and has no direct association with the voter's true identity. For example, encryption technologies such as hash functions are used to create a unique anonymous identifier for each voter. When the voting data is recorded, only this anonymous identifier is associated, rather than the voter's true identity information. The voting system can also be integrated with the Tor (The Onion Router) network or other hybrid network technologies. During the voting process, the network requests of the voters are routed and encrypted through multiple nodes; the Tor network encapsulates the data in multiple encryption layers, and each time it passes through a node, only enough information is decrypted to determine the next node. This can hide the identity information such as the IP address of the voter and increase the difficulty of tracking, thereby enhancing anonymity.

[0078] Step 604: Compare the comprehensive validity value with the comprehensive validity value threshold. When the comprehensive validity value ≥ the comprehensive validity value threshold, the voting validity meets the standard; when the comprehensive validity value < the comprehensive validity value threshold, the voting validity does not meet the standard, and the voting procedure needs to be restarted.

[0079] The solution monitors the performance and security of the voting system in a timely manner by setting a set of evaluation index thresholds, comparing the encryption efficiency index, anonymity strength index, and effectiveness comprehensive value with the corresponding thresholds respectively, and executing corresponding instructions according to the comparison results. By referring to industry standards, laws and regulations, and best practices, reasonable thresholds are set to ensure that the voting system operates within a safe, effective, and reliable range. At the same time, simulation tests are used to verify and adjust the thresholds to make them more in line with the requirements of the actual voting scenario.

[0080] Embodiment 2: A voting system in the standard setting of the international standardization game, used to implement the voting method in the standard setting of the above international standardization game.

[0081] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will realize that the units and algorithm steps of the 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 executed in hardware or software depends on the specific application and design constraints of the technical solution.

[0082] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application.

Claims

1. A voting method in the standard setting of an international standardized game, characterized in that, Including: Step 1: During the voting process, obtain voting validity data through the voting system, and analyze the voting validity parameters set based on the voting validity data analysis; Obtain the voting validity index by comprehensively analyzing the parameters in the voting validity parameters set; Step 2: Obtain voting anonymity data through the voting system, and analyze the anonymity strength parameters set based on the voting anonymity data analysis; obtain the anonymity strength index by comprehensively analyzing the anonymity strength parameters set; Step 3: Obtain the encryption algorithm data of the voting system, and analyze the encryption efficiency parameters set based on the encryption algorithm data analysis; obtain the encryption efficiency index by comprehensively analyzing the encryption efficiency parameters set; Step 4: Obtain voting data through the voting records of the voting system, and conduct comprehensive analysis based on the tender data to obtain the formula stability coefficient; Step 5: Input the data of the voting system into the Markov chain Monte Carlo model to obtain the voting behavior entropy of the voting system, and calculate the effectiveness comprehensive value by combining the voting validity index, anonymity strength index, encryption efficiency 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 respectively, and execute the corresponding instructions according to the judgment results.

2. The voting method in the standard setting of an international standardized game according to claim 1, wherein The set of voting validity parameters includes voting participation P, total number of votes T, number of valid votes V, mean value of voting results , standard deviation of voting results , actual voting duration t, and weight adjustment factor W; Obtain the voting validity index by comprehensively analyzing the parameters in the voting validity parameters set. The formula is as follows: ， Among them, represents the voting validity index; k represents the time decay coefficient, which controls the influence intensity of the time distribution on the validity, and its value ranges from [0, 1]; is the weight coefficient of the voting participation degree, is 's weight coefficient; is 's weight coefficient; is the weight coefficient of the weight adjustment factor.

3. The voting method in the standard setting of an international standardized game according to claim 2, characterized in that, The set of anonymity strength parameters includes the total number of members N to be voted, the comprehensive device correlation , the similarity probability of voting patterns , the number of historical votes, and the maximum number of votes; Obtain the anonymity strength index by comprehensively analyzing the parameters in the anonymity strength parameters set. The formula is as follows: ， Among them, represents the anonymity strength index; represents the historical voting times of the voter with the serial number h, represents the maximum voting times under the current voting mechanism.

4. The voting method in the standard setting of an international standardized game according to claim 3, characterized in that, The encryption efficiency parameters set includes the effective key length, algorithm round density, entropy value density, peak memory occupancy, CPU utilization rate, throughput, and latency tolerance threshold; the specific obtaining method is: Obtain the key length of the encryption algorithm of the password module in the voting system as the effective key length; Dynamically collect the CPU instruction cycle count using the hardware performance counter, and obtain the number of core encryption operations required for a unit data volume as the algorithm round density; Real-time monitor the physical entropy source through the test suite, and obtain the entropy value output by the key generator per second as the entropy value density; Record the memory usage curve through the operating system resource monitor, and obtain the maximum value of the process resident memory during the encryption process as the peak memory occupancy; Collect the process-level CPU usage rate by calling the system performance interface, and obtain the proportion of the CPU time occupied by the encryption task as the CPU utilization rate; Measure the actual transmission rate of the end-to-end encryption channel through the network traffic mirroring device, and obtain the amount of encrypted data processed per unit time as the throughput; Match the maximum encryption processing latency allowed by the application scenario according to the delay classification standard of the transmission protocol as the latency tolerance threshold.

5. The voting method in the standard setting of an international standardized game according to claim 4, characterized in that, Obtain the encryption efficiency index by comprehensively analyzing the encryption efficiency parameters 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 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.

6. The voting method in standard setting of the international standardization game according to claim 5, characterized in that, The voting data includes: Statistically calculate the support rate, opposition rate, and abstention rate of the vote through the voting records; During the proposal or revision process, statistically calculate the number of members participating continuously as the number of actively participating members; Calculate the support rate fluctuation value by comparing the differences in support rates between two consecutive rounds of voting , and the formula used is: .

7. The voting method in the standard setting of an international standardized game according to claim 6, characterized in that, By comprehensively analyzing the voting data, the consensus stability coefficient is obtained, and the formula is as follows: ； Among them, represents the consensus stability coefficient; represents the support rate of the m-th round of voting; m represents the serial number of the voting round, and n represents the total number of voting rounds; represents the threshold ratio of the m-th round of voting; represents the opposition rate of the m-th round of voting; represents the abstention rate of the m-th round of voting; represents the total number of members in the m-th round of voting; represents the fluctuation value of the support rate of the m-th round of voting; represents the highest support rate value among all rounds; E represents the number of actively participating members; G represents the total number of stages.

8. A voting method in the standard setting of an international standardized game according to claim 7, characterized in that, The comprehensive effectiveness value is calculated through the voting behavior entropy, the voting effectiveness index, and the consensus stability coefficient, and the formula is as follows: ； Among them, represents the comprehensive validity value, represents the voting behavior entropy, represents the voting validity index, represents the controversy value, and the calculation formula is: ; represents the consensus stability coefficient; represents the weight coefficient of the voting behavior entropy; represents the weight coefficient of the voting validity index; represents the weight coefficient of the consensus stability coefficient; represents 's weight coefficient.

9. The voting method in the standard setting of an international standardized game according to claim 8, characterized in that, The method for executing corresponding instructions according to the judgment result is: Set the encryption efficiency index threshold, the anonymity strength index threshold, and the comprehensive effectiveness value threshold; 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 execute the encryption efficiency management instruction; Compare the anonymity strength index with the anonymity strength index threshold. When the anonymity strength index ≥ the anonymity strength index threshold, the anonymity meets the standard; when the anonymity strength index < the anonymity strength index threshold, the anonymity does not meet the standard, and execute the confidentiality management instruction; Compare the comprehensive effectiveness value with the comprehensive effectiveness value threshold. When the comprehensive effectiveness value ≥ the comprehensive effectiveness value threshold, the voting effectiveness meets the standard. When the comprehensive effectiveness value < the comprehensive effectiveness value threshold, the voting effectiveness does not meet the standard, and the voting procedure needs to be restarted.

10. A voting system in the standard setting of an international standardized game, characterized in that, A voting method in the standard formulation of an international standard game for implementing any one of the above claims 1-9.

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