A data integrity auditing matching method based on psychological perception and preference

Abstract

The application claims a data integrity audit matching method based on psychological perception and preference, comprising the following steps: S1: calculating the subjective and objective weights of each evaluation index, and calculating the subjective and objective combination weights of the index based on the weights, and then calculating the weight of each audit scheme by using the improved Topsis comprehensive evaluation method; S2: calculating the user preference weight by using the triangular fuzzy number and Topsis method according to the user preference set, and integrating the weights of the two; S3: quantitatively calculating the comprehensive preference value of both parties according to the demand preference of the audit task and the execution preference of the audit resource, and calculating the preference utility value matrix of both parties; S4: calculating the perception utility value matrix of both parties based on the improved disappointment theory; S5: standardizing the perception utility value matrix, taking the maximization of the standard perception utility value of both parties as the optimization goal, constructing a double-objective optimization model, and solving the model. The application effectively improves the comprehensive satisfaction of both parties in the case of considering the psychological factors and preferences of both parties.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data integrity audit and bilateral matching, and particularly relates to a method for bilateral matching of user audit tasks and audit schemes (resources) in consideration of psychological perception and preferences of both parties before data integrity audit. BACKGROUND

[0002] In recent years, cloud computing has made significant progress, providing flexible and reliable computing and storage solutions for numerous users. With the rapid development of 5G and mobile network technologies, the diversity and complexity of online applications have been greatly improved, including interactive network games, virtual reality (VR), video analysis, and natural language processing. However, these online applications are very sensitive to latency, and there is often high and unstable latency between cloud services and end users. Therefore, application service providers urgently need to deploy applications near users to meet the increasingly stringent requirements for latency. Edge computing is considered a key technology for reducing latency, which is a distributed computing model whose core concept is to push computing power and data storage to the network edge to reduce latency during data transmission and improve system response speed. In the mobile edge computing (MEC) scenario, in order to enable terminal devices to directly access data, edge nodes will usually pre-load part of the data or services from the central cloud server. However, in the edge computing environment, edge servers may be subject to internal attacks or external failures, which may result in compromised integrity of edge data, and therefore the security of edge data has also attracted increasing attention.

[0003] Data integrity detection technology is an effective means for checking the integrity of data. Users can use this technology to verify the integrity of data stored at the edge, ensuring the security and availability of data. However, existing data integrity audit schemes often focus on improving audit efficiency, reliability of audit results, and subsequent functional expansion, while ignoring the adaptability of a single audit strategy and not considering how to match the most suitable audit scheme according to the needs and preferences of each task when handling multiple user audit tasks. On the one hand, a single data integrity audit scheme is difficult to cope with the rapid changes of massive data in the edge environment and the diversity of user preferences. On the other hand, in current bilateral matching research, the psychological impact of individuals on matching results and the preference needs of both parties are often overlooked. Therefore, how to further improve the overall satisfaction of both parties in consideration of their psychological perception and preferences needs to be addressed.

[0004] Therefore, the present application proposes a data integrity audit matching method based on psychological perception and preferences.

[0005] Through retrieval, application publication number CN117114280A belongs to the field of bilateral matching. The method includes proposing a research institute team-science and technology talent bilateral matching method based on prospect theory, and the steps are as follows: a new research institute team and science and technology talent first-stage elimination matching method is constructed, and a first-stage elimination matching model M-1 is constructed; a new research institute team and science and technology talent second-stage selection matching is constructed, and a second-stage selection matching model M-2 is constructed. The invention considers reference points and psychological factors of decision makers, combines prospect theory and bilateral matching, and considers two stages of bilateral matching, the first stage considers benefits and costs, and the second stage considers the satisfaction of both the team and the talent.

[0006] The difference from the above method is that the present application realizes the bilateral satisfaction matching of audit tasks and audit resources, and quantitatively calculates the comprehensive preference value of both parties according to the demand preference of the audit task and the execution preference of the audit resource by describing the attributes of the audit task and resource scheme, and considering the preference demand of both parties in the matching process. At the same time, the disappointment theory is improved, in the construction of the happiness function, the disappointment avoidance effect is considered, that is, the disappointment avoidance parameter is introduced to modify the happiness value, and in the calculation of the perceived utility value matrix, the subjective characteristics of both parties are considered, instead of completely rational individuals, and finally the maximum of the standard perceived utility value of both parties is taken as the optimization objective, and the linear weighting method is used to convert the double-objective optimization model into a single-objective optimization model for solving. SUMMARY

[0007] The present application aims to solve the problems of the above prior art. A data integrity audit matching method based on psychological perception and preference is proposed. The technical scheme of the present application is as follows:

[0008] A data integrity audit matching method based on psychological perception and preference, comprising the following steps:

[0009] S1, calculating the subjective and objective weights ω of each evaluation index s and ω o , and calculating the combined weight ω of each index according to the subjective and objective weights of the index c , and then calculating the weight ω of each audit scheme by using the improved Topsis comprehensive evaluation method a ;

[0010] S2, constructing a weighted judgment matrix H ij according to the user preference set y[M] and y[N][M], calculating a comprehensive non-fuzzy evaluation matrix E ij using the mean area method according to the weighted judgment matrix, calculating the weight ω of each audit scheme considering the user preference demand by using Topsis b , and integrating ω aand ω b The weight of both, the maximum weight ω of each audit scheme f ;

[0011] S3, according to the calculated maximum weight of each audit scheme, based on the matching both sides of the demand for preference, quantitative calculation of audit task to audit resources and audit resources to the comprehensive preference value of audit task tp ij and rp ij , and construct the preference order matrix TPO and RPO, and according to the preference utility function, the preference order matrix is converted into the preference utility value matrix TPU and RPU;

[0012] S4, using the improved disappointment theory (i.e. the preference order value of both sides is quantitatively calculated, and in constructing the happy function, the patent considers the disappointment avoidance effect, and in calculating the perceived utility value matrix of both sides, the subjective characteristics of the matching sides are considered, instead of completely rational individuals), respectively construct the disappointment function and the happy function form considering the disappointment avoidance effect, calculate the comprehensive loss value matrix d(r ij ), d`(t ij ) and the comprehensive happy value matrix e(r ij ), e`(t ij ), then correct the preference utility value to obtain the perceived utility value matrix u(r ij ), u`(t ij );

[0013] S5, the perceived utility value matrix of both sides is standardized to standard perceived utility value and Maximize the optimization objective, and construct a double objective optimization model and solve it.

[0014] Further, in step S1, the subjective weight and objective weight of the evaluation index and the subjective and objective combination weight are calculated, and then the weight of each audit scheme is calculated based on the improved Topsis comprehensive evaluation method, which specifically includes:

[0015] S11. According to the data results of the twelve expert questionnaire survey forms obtained by investigation, the subjective weight of the evaluation index is calculated by using the improved G1 order relation analysis method, that is, the opinions of multiple experts are considered in the calculation of the subjective weight of the index by using the G1 order relation analysis method, which is more in line with the objectivity of group decision-making and avoids the deviation that may be caused by single expert subjective weighting.

[0016] S12. According to the data integrity audit scheme attribute set, the objective weight of the evaluation index is calculated by using the improved Critic objective weight method, that is, when the index variability is calculated by using the Critic method, the influence of the average value on the index variability is considered, so that the calculation of the variability coefficient is more reasonable and accurate.

[0017] S13. The subjective and objective combined weight of each index is calculated in a linear weighting manner.

[0018] S14. According to the subjective and objective combined weight, the weight of each audit scheme is calculated by using the improved Topsis comprehensive evaluation method, that is, when the weighted Euclidean distance of each audit scheme and the positive ideal value and the negative ideal value is calculated, the subjective and objective combined weight of the index is combined, so that the rationality and accuracy of the weight calculation result are effectively improved.

[0019] Further, in the step S2, the weight of each audit scheme considering the user preference demand and the final weight of each audit scheme obtained by aggregating the two weights are calculated, and specifically comprising:

[0020] S21. Based on the triangular fuzzy number, the user language evaluation is fuzzified and quantified, the weighted judgment matrix is calculated according to the user preference set, and the comprehensive fuzzy evaluation matrix is obtained;

[0021] S22. The mean area method is used to defuzzify the comprehensive fuzzy evaluation matrix to obtain a comprehensive non-fuzzy evaluation matrix, and the Topsis comprehensive evaluation method is used to calculate the weight of each audit scheme considering the user preference demand;

[0022] S23. The final weight of each audit scheme is calculated by using the linear weighting method to aggregate the two weights.

[0023] Further, in the step S3, the comprehensive preference value of the audit task to the audit resource and the audit resource to the audit task is quantitatively calculated, and the preference utility value of both sides is calculated based on the preference utility function, and specifically comprising:

[0024] S31. The attributes of the user audit task and the audit resource are described, the preference values of both sides are quantitatively calculated according to the preference demands of both sides, the comprehensive preference value of both sides is calculated by using the linear weighting method, and the preference order value is calculated according to the individual comprehensive preference value;

[0025] S32. According to the matching preference order matrix of both sides, the preference utility function is constructed, the preference utility value is calculated, and the preference utility value matrix of the audit task to the audit resource and the audit resource to the audit task is obtained.

[0026] Further, in the step S4, the comprehensive loss value and the comprehensive pleasure value matrix of both sides are calculated, and the preference utility value is corrected to obtain the perceived utility value matrix, and specifically comprising:

[0027] S41. Based on the improved disappointment theory, a disappointment function is constructed, and the subject A i is matched with the subject B j , and the loss value of the subject B k that is not matched with the subject B ij , then the comprehensive loss value d(r ) of the audit task to the audit resource is an arithmetic mean value, and in the same way, a happy function is constructed, and the comprehensive happy value matrix of the matched two parties is calculated under the consideration of the disappointment avoidance effect;

[0028] S42. According to the comprehensive happy value and the comprehensive loss value matrix of the matched two parties, the preference utility value matrix of the two parties is corrected to obtain the perceived utility value matrix of the audit task to the audit resource and the audit resource to the audit task.

[0029] Further, in the step S5, the perceived utility value matrix is standardized, a double-objective optimization model is constructed with the maximum standard perceived utility value as an optimization objective, and the double-objective optimization model is solved, and the step specifically includes:

[0030] S51. The perceived utility value is standardized, a double-objective optimization model is constructed with the maximum standard perceived utility value of the two parties as an optimization objective, and a linear weighting method is used to convert the double-objective optimization model into a single-objective optimization model;

[0031] S52. The KM algorithm is used to find an optimal matching result.

[0032] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the user data integrity audit matching method based on psychological perception and preference according to any one of the embodiments when executing the program.

[0033] A non-transitory computer readable storage medium has a computer program stored thereon, and the computer program implements the user data integrity audit matching method based on psychological perception and preference according to any one of the embodiments when executed by a processor.

[0034] A computer program product includes a computer program, and the computer program implements the user data integrity audit matching method based on psychological perception and preference according to any one of the embodiments when executed by a processor.

[0035] The advantages and beneficial effects of the present application are as follows:

[0036] 1、The subjective weight of the evaluation index is calculated by using the improved G1 sequence relation analysis method in step S1 of the present application, the opinions of multiple experts are comprehensively considered, which is more in line with the objectivity of group decision-making and avoids the deviation caused by the subjective weighting of a single expert; when the Critic method is used to calculate the index variability, the influence of the average value on the index variability is considered, so that the calculation of the variability coefficient is more reasonable and accurate; finally, the improved Topsis comprehensive evaluation method is used to calculate the weight of each audit scheme by combining the calculated subjective and objective combined weights, thereby effectively improving the rationality and accuracy of the weight calculation result.

[0037] 2、In step S3 of the present application, the user's preference weight is further considered when calculating the maximum weight of the audit scheme, that is, the weight of each audit scheme considering the user's preference demand calculated by using the triangular fuzzy number and Topsis in step S2 is aggregated according to the user's preference set.

[0038] 3、In step S4 of the present application, the attributes of the audit task and resource scheme are described, the comprehensive preference value of both sides is quantitatively calculated according to the audit task demand preference and the audit resource execution preference, and the preference demand of both sides is considered in the bilateral matching process. In addition, in the subsequent matching, the improved disappointment theory is used to consider the psychological factors of both sides.

[0039] 4、In step S6 of the present application, the bilateral matching problem of multi-objective optimization is ingeniously converted into a single-objective optimization problem by using linear weighting, and a double-objective optimization model is constructed to find the optimal matching. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 is the flow chart of the whole preferred embodiment provided by the present application;

[0041] Figure 2 is the system architecture diagram;

[0042] Figure 3 is the flow chart of the audit scheme evaluation model considering the user's preference based on triangular fuzzy number;

[0043] Figure 4 is the bilateral matching strategy flow chart of the audit task and audit resource based on psychological perception and preference. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings of the embodiments of the present application. The described embodiments are only a part of the embodiments of the present application.

[0045] The technical solution of the present application to solve the above technical problems is:

[0046] 1. To address the issue that existing data integrity audit strategies neglect the adaptability of a single audit strategy, and that a single audit strategy is difficult to adapt to the rapid changes and data diversity of massive data in edge environments, this paper proposes an audit scheme evaluation model based on triangular fuzzy numbers that takes into account user preferences. The model calculates the weight of each audit scheme and ranks them according to the weight to select the audit scheme that is more in line with user preferences.

[0047] 2. Existing data integrity auditing strategies do not consider how to match the most suitable auditing solution based on the needs and preferences of each user when handling multiple user auditing tasks. Furthermore, in current two-sided matching algorithms, the psychological impact of the matching result on individuals and the preferences and needs of both parties are often overlooked. Therefore, this paper proposes a two-sided matching strategy for auditing tasks and auditing resources based on psychological perception and preferences. Using improved disappointment theory, the standard perceived utility value matrices of auditing tasks to auditing resources and auditing resources to auditing tasks are calculated separately. Finally, a bi-objective optimization model is constructed with the goal of maximizing the standard perceived utility value (satisfaction) of both parties, and then solved.

[0048] like Figure 1 As shown, the present invention provides a data integrity auditing matching method based on psychological perception and preferences, characterized by comprising the following steps:

[0049] S1. Calculate the subjective and objective weights ω for each evaluation indicator. s and ω o And based on the subjective and objective weights of the indicators, calculate the combined weight ω of each indicator. c Then, the weight ω of each audit plan is calculated using the improved Topsis comprehensive evaluation method. a ;

[0050] S2. Construct a weighted judgment matrix H based on user preference sets y[M] and y[N][M]. ij Based on the weighted judgment matrix, the comprehensive non-fuzzy evaluation matrix E is calculated using the mean area method. ij Topsis is used to calculate the weight ω of each audit scheme that takes into account user preference requirements. b Gathering ω a and ω b The weights of these two factors are used to obtain the final weight ω for each audit scheme. f .

[0051] S3. Based on the final weight of each audit plan, and considering the preferences of both parties, quantitatively calculate the comprehensive preference value tp of the audit task for audit resources and the audit resources for audit tasks. ij and rp ijThe preference order matrices TPO and RPO are constructed, and then the preference order matrices are converted into preference utility value (satisfaction) matrices TPU and RPU according to the preference utility function.

[0052] S4: Using the improved disappointment theory, we construct the disappointment function and the pleasure function considering the disappointment aversion effect, respectively, and calculate the combined loss matrix d(r) of audit task to audit resources and audit resources to audit task. ij ), d`(t ij ) and the comprehensive happiness value matrix e(r ij ), e`(t ij Furthermore, taking into account the psychological perceptions of both parties, the preference utility values ​​are adjusted to obtain the perceived utility value matrix u(r). ij ), u`(t ij ).

[0053] S5: Standardize the perceived utility value matrix of both parties to obtain the standard perceived utility value. and To maximize the optimization objective, a bi-objective optimization model is constructed and solved.

[0054] In this example, in step S1, the subjective weight ω of each evaluation indicator is calculated using the following method. s :

[0055] S11: First, we construct the evaluation index system for the comprehensive evaluation model. This paper adopts the following seven indicators as evaluation indicators: computational efficiency, storage efficiency, time complexity, communication efficiency, security, accuracy, and functionality. To avoid the bias that may be caused by subjective weighting by a single expert, this paper uses the G1 order relation analysis method to calculate the subjective weight ω. s The opinions of multiple experts were considered. Each indicator was scored using expert ratings, assigning scores of 90, 80, 70, 60, and 50 respectively, categorized as very important, important, moderately important, unimportant, and not considered. Then, based on data from twelve expert questionnaires, the importance of each evaluation indicator was calculated as follows: for example, the score for the "computation efficiency indicator" was calculated as: 9 / 12 × 90 + 3 / 12 × 80 + 0 / 12 × 70 + 0 / 12 × 60 + 0 / 12 × 50 = 87.5 points. The order of the indicators was then determined based on their scores. After determining the order relationship between the indicators, calculate the C of two adjacent indicators. j and C j+1 The relative importance of r β r β The larger the value, the higher the value of index C. j Comparison Index C j+1The greater the importance, because this paper considers the subjective will of multiple experts, so the relative importance of the index assigned to the expert k = 1, 2, …, n, is represented as This paper selects 6 experts from the above 12 experts to do this work, and the final index relative importance r β The calculation formula is as follows:

[0056]

[0057] According to the r β value determined by the decision maker, the subjective weight of the index is calculated by the following two formulas:

[0058]

[0059] ω β-1 = ω β * r β #(3)

[0060] Where β = n, n-1, …, 2, and

[0061] In this example, in step S1, the objective weight ω o of each evaluation index is calculated as follows:

[0062] S12: Considering that the evaluation indexes adopted in this paper are qualitative indexes, it is necessary to quantify the qualitative indexes before using the Critic method to calculate the objective weight ω o of the indexes. This paper uses the 1-9 scale method to assign values to each index according to excellent, good, medium, poor, and very poor by expert scoring, with scores of 9, 7, 5, 3, and 1 respectively. Twelve experts are selected to score and quantify the qualitative indexes of each scheme, for example, the final score of audit scheme one in computing efficiency is: (5+9+7+3+3+1+1+9+7+3+5+9) / 12 = 5.17 points. According to the data from the twelve expert questionnaires, the original data matrix X of i evaluation samples and j evaluation indexes is calculated. In order to eliminate the influence of different dimensions on the evaluation results, the following formula is used for index normalization:

[0063]

[0064] To calculate the variability of the index, this paper considers the influence of the average value of the index on the variability of the index, and introduces the coefficient of variation to replace the index variability S j . The larger the coefficient of variation, the greater the numerical difference of the index, and the stronger the evaluation intensity of the index itself, and the higher the weight. The calculation formula of the coefficient of variation is as follows:

[0065]

[0066] wherein, represents the average value of the index.

[0067] Then the index conflict R is calculated, and the index conflict R j The calculation formula is as follows:

[0068]

[0069] wherein, r ij represents the correlation coefficient between the ith index and the jth index, the stronger the correlation coefficient with other indexes, the smaller the conflict of the index with other indexes, and the lower the weight.

[0070] Further, based on the index variability and the index conflict, the index information amount C j is calculated, and according to the information amount C j , the index objective weight ω o is calculated, and the calculation formula is as follows:

[0071] C j = S j *R j #(9)

[0072]

[0073] wherein, the larger the index information amount C j , the greater the role of the jth evaluation index in the entire evaluation method, and the higher the weight.

[0074] In this example, in the step S1, the following method is used to calculate the subjective and objective combined weight ω c of each evaluation index:

[0075] S13: The subjective and objective combined weight of the evaluation index is calculated by linear weighting, and the calculation formula is as follows:

[0076]

[0077] In this example, in the step S1, the following method is used to calculate the weight ω a of each audit scheme:

[0078] S14: According to the subjective and objective combined weight, the weight of each audit scheme is calculated by using the improved Topsis comprehensive evaluation method, and the optimal ideal value and the worst ideal value of each index of the audit scheme are determined first, and the calculation formula is as follows:

[0079]

[0080] Then the weighted Euclidean distance of each scheme from the ideal and the nadir values is calculated, and the formula is as follows:

[0081]

[0082] According to the weighted Euclidean distance, the score of each scheme before normalization is calculated, and the formula is as follows:

[0083]

[0084] After normalization, the weight ω of each audit scheme is obtained a , and the formula is as follows:

[0085]

[0086] In this example, the weight ω of each audit scheme considering user preference requirements in step S2 is calculated as follows: b

[0087] S21: According to the user preference set, the user's language evaluation is fuzzified and quantified by using triangular fuzzy numbers. The correspondence between the user language evaluation level and the triangular fuzzy number used in this paper is shown in the following table:

[0088] Excellent Good Medium Poor Very poor (0.8,0.9,1) (0.6,0.7,0.8) (0.4,0.5,0.6) (0.2,0.3,0.4) (0.1,0.2,0.3)

[0089] According to the above table, the user's fuzzy language evaluation of the pros and cons of each audit scheme is converted into a triangular fuzzy number, denoted as R ij =(a ij ,b ij ,c ij ). Thus, all the fuzzy evaluation values of the user for N audit schemes can be represented by a triangular fuzzy matrix R=[R ij ]. Then the triangular fuzzy evaluation matrix is normalized to obtain the normalized matrix The formula is as follows:

[0090]

[0091] Secondly, the user's evaluation of the importance of each index is converted into a triangular fuzzy number, and a triangular fuzzy weight matrix W=[ω j ] is obtained. The normalized matrix is obtained by normalizing it, and the formula is as follows:

[0092]

[0093] According to the user evaluation triangular fuzzy matrix and the triangular fuzzy weight matrix, a weighted judgment matrix H=(H ij ) is constructed, where​ The calculation formula is as follows:

[0094]

[0095] S22: Then, the mean area method is used to defuzzify the weighted judgment matrix to obtain a comprehensive non-fuzzy evaluation matrix E=(e ij ), and the calculation formula is as follows:

[0096]

[0097] S23: Thus, according to the comprehensive non-fuzzy evaluation matrix, the Topsis comprehensive evaluation method is used to calculate the weight ω b of each audit scheme considering the user preference requirements, and finally the linear weighting method is used to calculate the final weight ω f of each audit scheme:

[0098]

[0099] In this example, in the step S3, the following method is used to quantify the comprehensive preference values of the audit task to the audit resource and the audit resource to the audit task and calculate the preference utility values of both sides based on the preference utility function:

[0100] S31. The attributes of the task requirements and the resource schemes are described, and the user audit task m j (j=1, 2, …, m) is represented by a two-dimensional vector T(t w , t cos ), and the audit strategy (resource) n i (i=1, 2, …, n) is represented by a two-dimensional vector T(r w , r cos ), and then according to the audit task requirement preference and the audit resource execution preference, the comprehensive preference values of both sides are quantitatively calculated, and the calculation formula is as follows:

[0101] The preference requirement of the audit task to the audit resource in terms of weight is that the weight of the required resource must meet the demand of its own weight, and the higher the resource weight, the more preferred by the audit task:

[0102]

[0103] The preference requirement of the audit task to the audit resource in terms of service price is that the price of the required resource is within the range that the user can afford, and the lower the price of the resource, the more preferred by the task:

[0104]

[0105] The audit resource's preference for the audit task in weight aspect is that the lower the weight required by the task is, the more the resource can satisfy the task, and the more the resource is preferred:

[0106]

[0107] The audit resource's preference for the audit task in service price aspect is that the lower the price required by the task is, the more the resource can satisfy the task, and the more the resource is preferred:

[0108]

[0109] Then the linear weighting method is used to calculate the comprehensive preference value of both sides, the preference order value is calculated according to the individual comprehensive preference value, and the comprehensive preference value calculation method of the audit task for the audit resource:

[0110]

[0111] Similarly, the comprehensive preference value calculation method of the audit resource for the audit task:

[0112]

[0113] S32: According to the comprehensive preference value of both sides, the preference order matrix TPO and RPO of both sides is obtained. In order to better represent the preference degree of one subject to another subject, and decrease with the increase of the preference order, the following preference utility function is constructed in this paper to convert the preference order value into the preference utility value, and the preference utility value matrix TPU and RPU of both sides is obtained:

[0114]

[0115] In this example, in step S4, the following method is used to calculate the comprehensive loss value and comprehensive pleasure value matrix of the matching parties and correct the preference utility value, and obtain the perceived utility value matrix u(r ij ), u`(t ij ):

[0116] S41: In order to consider the psychological factors of the matching parties in the matching process, the following loss function is constructed based on the improved disappointment theory:

[0117] D(x) = 1 - α x #(30)

[0118] Wherein, α represents the concave-convex degree of the disappointment function, the larger α represents the smaller disappointment degree of the subject, and the smaller α represents the larger disappointment degree of the subject. Then the subject A i matches with the subject B i instead of Bk The missing value d of matching k (r ij ), the calculation formula is as follows:

[0119]

[0120] Further, the comprehensive missing value D(r ij ) of the audit task to the audit resource is calculated as follows:

[0121]

[0122] In constructing the happy function, this paper considers the loss avoidance effect, introduces the loss avoidance parameter γ to modify the happy value, and the happy function form is as follows:

[0123] E(X)=γ(1-β x )#(33)

[0124] Where, β represents the concave-convex degree of the happy function, the larger β represents the smaller happy degree of the subject, and the smaller β represents the larger happy degree. Then the happy value e i (r ij ) of the subject A i matching with the subject B k instead of matching with B k , the calculation formula is as follows:

[0125]

[0126] Further, the comprehensive happy value e(r ij ) of the audit task to the audit resource is calculated as follows:

[0127]

[0128] S42: According to the preference utility value, the comprehensive missing value and the comprehensive happy value of the audit task to the audit resource, the perceived utility value u(r i ) of the subject A j matching with the subject B ij is calculated, that is, the modified preference utility value under the premise of considering the psychological perception of both parties. This paper introduces the parameter θ on this basis, considers the subjective will of both parties, and is not a completely rational subject, which is more in line with the objective reality, and the calculation formula is as follows:

[0129] u(r ij )=v(r uj )-θ*d(r ij )+(1-θ)*e(r ij )#(36)

[0130] Similarly, the perceived utility value of the audit resource to the audit task is calculated as:

[0131] u`(t ij )=v(t ij )-θ*d`(t ij )+(1-θ)*e`(t ij )#(37)

[0132] In this example, the step S5, the following method is used to standardize the perceived utility value matrix, and to maximize the two standard perceived utility values as the optimization objective, to construct a bi-objective optimization model, and to solve:

[0133] S51: The following formula is used to standardize the perceived utility value, and a linear weighted method is used to convert the bi-objective optimization model into a single objective optimization model, and the calculation formula is as follows:

[0134]

[0135] Where x ij represents a 0-1 variable, and respectively represent the standard perceived utility value (satisfaction) matrix of the audit task and the audit resource, in the matrix When r ij is the maximum value of the column or in the matrix When t ij is the maximum value of the row, the corresponding x ij takes 1, otherwise 0. f ij represents the coefficient matrix calculated based on the two standard perceived utility value matrices, and ω1+ω2=1.

[0136] S52: This paper uses KM algorithm to find the optimal matching result, and the optimal result is: max Z, that is, the comprehensive satisfaction of both parties reaches the highest.

[0137] The following is a specific example:

[0138] According to the data results of the expert questionnaire, the importance score of each evaluation index is calculated, and finally according to the score ranking, the order relationship between each index is as follows:

[0139] Computing efficiency (CE) > storage efficiency (SE) > time complexity (TC) > communication efficiency (COE) > security (SEC) > accuracy (AC) > functionality (FN)

[0140] The final relative importance of the index r β is calculated as follows:

[0141] [r1] [r2] [r3] [r4] [r5] [r6] 1.50 1.40 1.33 1.23 1.20 1.16

[0142] Similarly, according to the data results of the expert questionnaire, the subjective weight ω s , objective weight ω o , and combined weight ω c of each index are calculated as follows:

[0143] CE SE TC COE SEC AC FN s ]]> ​ 0.308 0.206 0.147 0.110 0.090 0.075 0.064 o ]]> ​ 0.204 0.098 0.175 0.108 0.180 0.115 0.120 c ]]> ​ 0.256 0.152 0.161 0.109 0.135 0.095 0.092

[0144] According to the user preference set, the weight ω a , ω b , and ω f of each type of audit scheme are calculated as follows:

[0145] Scheme one Scheme two Scheme three Scheme four a ]]> ​ 0.063 0.143 0.136 0.141 b ]]> ​ 0.066 0.150 0.142 0.150 f ]]> ​ 0.064 0.146 0.139 0.145 Scheme five Scheme six Scheme seven Scheme eight <![CDATA[ω a ]]> 0.141 0.127 0.119 0.130 b ]]> ​ 0.133 0.126 0.113 0.120 f ]]> ​ 0.137 0.126 0.116 0.125

[0146] Suppose that in a matching pair of an audit task and an audit resource, the number of audit tasks is 11 and the number of audit resources is 8. According to the preference requirements of both parties, the comprehensive preference values of both parties are quantitatively calculated, and the preference sequence ranking results of the audit task to the audit resource are shown in the following table:

[0147] ​ [ t2 ] [ t3 ] [ t4 ] [t5] [t6] ​ [t8] [t9] [cat 10 ]]> [CAT 11 ]]> [r1] 1 1 3 1 1 1 3 1 1 1 1 [r2] 6 3 6 2 2 3 6 2 2 6 2 [r3] 5 4 5 3 3 4 5 3 3 2 3 [r4] 8 5 8 4 8 5 8 4 4 8 4 [r5] 2 6 1 5 4 6 1 5 5 3 5 [r6] 3 7 2 6 5 7 2 6 6 4 6 [r7] 7 2 7 7 6 2 7 7 7 7 7 [r8] 4 8 4 8 7 8 4 8 8 8 8

[0148] The preference sequence ranking results of the audit resource to the audit task are shown in the following table:

[0149] ​ [ t2 ] [ t3 ] [ t4 ] [t5] [t6] [t7] [t8] [t9] [cat 10 ]]> [cat 11 ]]> [r1] 9 1 10 2 11 8 3 4 5 6 7 [r2] 9 4 10 1 11 8 7 5 2 3 6 [r3] 9 4 10 1 11 7 8 6 2 3 5 [r4] 9 4 7 1 11 10 6 5 2 3 8 [r5] 9 6 10 1 11 4 8 7 2 5 3 [r6] 9 6 10 1 11 5 8 7 2 3 4 [r7] 9 4 10 1 11 8 6 5 3 2 7 [r8] 9 4 10 1 11 7 8 6 2 3 5

[0150] According to the preference sequence matrix, the standard perceived utility value matrix of the audit task to the audit resource and the standard perceived utility value matrix of the audit resource to the audit task are calculated based on the improved disappointment theory. Based on the above two matrices, the coefficient matrix f ij is obtained as follows:

[0151] ​ [ t2 ] [ t3 ] [ t4 ] [ t5 ] [t6] ​ [t8] [t9] [CAT 10 ]]> [cat 11 ]]> [r1] 0.993 1.0 0.5 0.5 0.5 0.609 1.0 0.623 0.958 1.0 1.0 [r2] 0.331 0.633 0.176 0.208 0.240 0.278 0.666 0.716 0.726 0.364 0.463 [r3] 0.241 0.590 0.195 0.116 0.145 0.289 0.585 0.621 0.617 0.572 0.485 [r4] 0.134 0.429 0.211 0.568 0.083 0.243 0.551 0.573 0.477 0.423 0.352 [r5] 0.057 0.035 0.341 0.037 0.536 0.696 0.384 0.3 0.494 0.430 0.744 [r6] 0.538 0.021 0.254 0.022 0.238 0.402 0.504 0.164 0.459 0.376 0.634 [r7] 0.521 0.361 0.515 0.005 0.031 0.506 0.496 0.116 0.515 0.469 0.454 [r8] 0.510 0.210 0.210 0 0.025 0.567 0.476 0.125 0.511 0.397 0.517

[0152] Based on the above coefficient matrix, in order to maximize the value of the objective function, the optimal matching result is (r6, t1), (r7, t3), (r4, t4), (r8, t6), (r3, t8), (r2, t9), (r1, t 10 ), (r5, t 11 ), and the tasks t2 and t5 have no resource matching. The comprehensive satisfaction degree of the matching result of both parties is 5.28.

[0153] The application aims at the problem that the adaptability of a single audit strategy is ignored in the existing data integrity audit strategy, and proposes an audit scheme evaluation model based on triangular fuzzy numbers considering user preferences, in which the subjective weights of evaluation indexes are calculated by using an improved G1 sequence relation analysis method, the opinions of multiple experts are comprehensively considered, which is more in line with the objectivity of group decision-making and avoids the deviation caused by the subjective weighting of a single expert, the influence of the average value on the index variability is considered when the Critic method is used to calculate the index variability, so that the calculation of the variability coefficient is more reasonable and accurate, and finally the improved Topsis comprehensive evaluation method is used to calculate the weights of each audit scheme by combining the calculated subjective and objective combination weights, so that the rationality and accuracy of the weight calculation result are effectively improved.In addition, compared with other evaluation models, the application considers the personalized demand weight of users in the comprehensive evaluation, which can more accurately reflect the actual needs and preferences of users, provide more personalized services and further improve the decision quality.Meanwhile, the application proposes a double-sided matching method based on psychological perception and preference, aiming at the problem that the existing data integrity audit strategy does not consider how to match the most suitable audit scheme according to the demand preferences of each task when processing multiple user audit tasks, and the psychological impact of the matching result on individuals and the preference demands of the matching parties are often easily ignored in the current double-sided matching algorithm, the standard perception utility value matrix of the audit task to the audit resource and the audit resource to the audit task is calculated based on the improved disappointment theory, and finally a double-objective optimization model is constructed by taking the maximization of the standard perception utility values of both parties as the optimization goal, and the model is solved.

[0154] The systems, apparatuses, modules or units illustrated by the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions.

[0155] Computer readable media includes permanent and non-permanent, removable and non-removable media, which can be implemented by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition in this paper, computer readable medium does not include transitory computer readable medium, such as modulated data signals and carriers.

[0156] It is also to be noted that the terms "comprising", "including", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a..." does not, without further restriction, exclude the existence of additional elements of a like kind in the process, method, article, or apparatus that comprises the element.

[0157] The above examples are to be understood only as illustrative of the application and not a limitation of the scope of protection of the application. After reading the description of the application, a person skilled in the art can make various modifications or alterations to the application, and these equivalent changes and modifications also fall within the scope of protection defined by the claims of the application.

Claims

1. A data integrity auditing matching method based on psychological perception and preferences, characterized in that, Includes the following steps: S1. Calculate the subjective weight ω for each evaluation indicator. s and objective weight ω o And based on the subjective and objective weights of the indicators, calculate the combined weight ω for each indicator. c Then, the weight ω of each audit plan is calculated using the improved Topsis comprehensive evaluation method. a ; S2. Based on the user preference set, construct a weighted judgment matrix. Based on the weighted judgment matrix, calculate the comprehensive non-fuzzy evaluation matrix using the mean area method. Calculate the weight ω of each audit scheme considering user preference requirements using Topsis. b Gathering ω a and ω b The weights of these two factors are used to obtain the final weight ω for each audit scheme. f Specifically, it includes: S21. Based on triangular fuzzy numbers, user language evaluations are fuzzified and quantified. According to the user preference set, a weighted judgment matrix is ​​calculated to obtain a comprehensive fuzzy evaluation matrix. S22. The mean area method is used to defuzzify the comprehensive fuzzy evaluation matrix to obtain the comprehensive non-fuzzy evaluation matrix. The Topsis comprehensive evaluation method is used to calculate the weights of each audit scheme that take into account user preference requirements. S23. Using the linear weighting method, combine the weights of the two to calculate the final weight of each audit plan; S3. Based on the final weight of each audit plan, and considering the preferences of both parties, quantitatively calculate the comprehensive preference value tp of the audit task for audit resources and the audit resources for audit tasks. ij and rp ij The preference order matrices TPO and RPO are constructed, and then the preference order matrices are converted into preference utility value matrices TPU and RPU according to the preference utility function. S4. Using the improved disappointment theory, construct a disappointment function and a pleasure function considering the disappointment aversion effect, respectively. Calculate the comprehensive disappointment matrix and comprehensive pleasure matrix of the audit task versus audit resources and the audit resources versus audit task, respectively. Then, modify the preference utility matrix to obtain the perceived utility matrix for both parties, specifically including: S41. Based on the improved disappointment theory, construct the disappointment function and calculate the subject A. i Regarding subject B j Matching instead of subject B k The matched missing value is then the overall missing value d(r) of the audit task for audit resources. ij This is an arithmetic mean. At the same time, a happiness function is constructed, and the comprehensive happiness value matrix of the matched parties is calculated under the consideration of the disappointment avoidance effect. S42. Based on the combined pleasure and disappointment matrices of the matched parties, the preference utility value matrices of the parties are corrected to obtain the perceived utility value matrices of the audit task to the audit resources and the audit resources to the audit task. S5. Standardize the perceived utility value matrix of both parties to obtain the standard perceived utility value. and To maximize the optimization objective, a bi-objective optimization model is constructed and solved.

2. The data integrity audit matching method based on psychological perception and preference according to claim 1, characterized in that, In step S1, the subjective weight ω of each evaluation indicator is calculated. s and objective weight ω o And based on the subjective and objective weights of the indicators, calculate the combined weight ω for each indicator. c Then, the weight ω of each audit plan is calculated using the improved Topsis comprehensive evaluation method. a ; Specifically, it includes: S11. Based on the data from the twelve expert questionnaires obtained in the survey, the subjective weights of the evaluation indicators were calculated using the improved G1 order relation analysis method. S12. Based on the attribute set of the data integrity audit scheme, calculate the objective weights of the evaluation indicators using the improved Critic objective weighting method; S13. Calculate the combined subjective and objective weights of each indicator using a linear weighting method; S14. Calculate the weight of each audit scheme using the improved Topsis comprehensive evaluation method.

3. The data integrity audit matching method based on psychological perception and preference according to claim 1, characterized in that, In step S3, based on the final weight of each audit scheme and the preferences of both parties, the comprehensive preference value tp of the audit task for audit resources and the audit resources for audit tasks is quantitatively calculated. ij and rp ij The preference order matrices TPO and RPO are constructed, and then, based on the preference utility function, the preference order matrices are converted into preference utility value matrices TPU and RPU; specifically including: S31. Describe the attributes of user audit tasks and audit resources, quantify and calculate the respective preference values ​​based on the preference needs of both parties, and calculate the comprehensive preference value of both parties using a linear weighting method, and calculate the preference order value based on the individual's comprehensive preference value; S32. Based on the preference order matrix of the matched parties, construct the preference utility function, calculate the preference utility value, and obtain the preference utility value matrix of the audit task to the audit resource and the audit resource to the audit task.

4. The data integrity audit matching method based on psychological perception and preference according to claim 1, characterized in that, In step S5, the perceived utility value matrix is ​​standardized, and a bi-objective optimization model is constructed with the goal of maximizing the standard perceived utility value. This model is then solved, specifically including: S51. Standardize the perceived utility value, construct a bi-objective optimization model with the goal of maximizing the standard perceived utility value of both parties, and use the linear weighting method to convert the bi-objective optimization model into a single-objective optimization model. S52. Use the KM algorithm to find the optimal matching result.

5. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the user data integrity audit matching method based on psychological perception and preference as described in any one of claims 1 to 4.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the user data integrity audit matching method based on psychological perception and preference as described in any one of claims 1 to 4.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the user data integrity audit matching method based on psychological perception and preference as described in any one of claims 1 to 4.

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