Access frequency optimization method and device based on fuzzy algorithm, equipment and medium

Abstract

The application relates to the technical field of IT application, and provides an access frequency optimization method and device based on a fuzzy algorithm, equipment and a medium, the method comprising the following steps: determining various user features influencing the interface access frequency of accessing various application information systems; calculating a frequency optimization value of the interface access frequency of accessing various application information systems according to a fuzzy algorithm combined with the various user features of the user; and generating a differentiated interface access control strategy of various users accessing various application information systems according to the frequency optimization value of the interface access frequency of various application information systems. The access frequency optimization method based on the fuzzy algorithm provided in the application makes the fuzzy control algorithm and the various user features of the interface access frequency of the user accessing various application information systems, formulates a differentiated interface access control strategy of different users accessing various application information systems at different interface access frequencies, reduces invalid interface access quantity, and improves system resource utilization.

Description

Technical Field

[0001] This application relates to the field of IT application technology, and in particular to a method, apparatus, device and medium for optimizing access frequency based on fuzzy algorithm. Background Technology

[0002] With the development of government and enterprise informatization, the number of internal information systems within government and enterprises is increasing. One of the main functions of a government and enterprise office portal information system is to centralize data or business processes from multiple application information systems, enabling government and enterprise users to uniformly handle business and pending tasks from multiple application information systems within the office portal information system, without needing to log in to each application information system separately. Government and enterprise office portal information systems typically use interface access methods to centrally display pending tasks and business processes from various application information systems, facilitating unified processing by government and enterprise users. There are various forms of interface access between the government and enterprise office portal information system and other application information systems, the main form being that the office portal system actively initiates queries, calling the query interfaces provided by other application information systems.

[0003] In the aforementioned interface access methods, after a user logs into the government and enterprise office portal information system, the existing query and feedback methods mainly involve the portal continuously accessing and querying various application information systems at a fixed interface access frequency to obtain the logged-in user's business and pending tasks in the corresponding application information systems. The portal also needs to configure corresponding interface access server resources based on the access frequency and the number of centralized application systems to ensure the stable operation of the portal. However, the fixed interface access frequency does not fully consider the user characteristics and relevance of government and enterprise business in various application information systems. For example, the enterprise's contract system is closely related to the legal department, and its business data volume will significantly exceed that of other business departments. Similarly, the MIS system is closely related to the finance department, and its business data volume will significantly exceed that of other business departments.

[0004] Therefore, the fixed interface access frequency of each application information system will lead to a large number of invalid interface query accesses, resulting in low utilization of system resources. Summary of the Invention

[0005] This application provides a method, apparatus, device, and medium for optimizing access frequency based on fuzzy algorithm, aiming to reduce the number of invalid interface accesses and improve system resource utilization.

[0006] In a first aspect, this application provides an access frequency optimization method based on a fuzzy algorithm, comprising:

[0007] Identify the various user characteristics that influence the frequency of user access to the interfaces of various application information systems.

[0008] Based on the fuzzy algorithm and the user characteristics of each user in each of the application information systems, the frequency optimization value of the user's access frequency to the interface of each of the application information systems is calculated.

[0009] Based on the user's various frequency optimization values, generate differentiated interface access control policies for the user's access to each of the application information systems.

[0010] In one embodiment, calculating the frequency optimization value of the user's access frequency to the interfaces of each of the application information systems based on a fuzzy algorithm and the user's characteristics in each of the application information systems includes:

[0011] The fuzzy algorithm is used to convert the various user characteristics that affect the access frequency of the interfaces of the various application information systems into various fuzzy variables, and to calculate the original value range of each fuzzy variable in each application information system.

[0012] Based on the original value range of each fuzzy variable in each of the application information systems, the target value range of each fuzzy variable in each of the application information systems is determined by a preset feature coefficient algorithm.

[0013] The original value ranges of each of the fuzzy variables are mapped to obtain the range mapping values ​​of each of the fuzzy variables on the target value range;

[0014] By using a preset fuzzy membership function to fuzzify the original value range of each fuzzy variable, the classification level and fuzzy rule table of each fuzzy variable are determined.

[0015] Substitute the range mapping values ​​of each of the fuzzy variables into the fuzzy rule table to perform fuzzy decision-making and output the graded decision values.

[0016] By combining the preset anti-fuzzy algorithm with the grading decision value, the frequency optimization value of the user's access frequency to the interface of each application information system is output.

[0017] The step of fuzzifying the original value range of each fuzzy variable by using a preset fuzzy membership function to determine the level and fuzzy rule table to which each fuzzy variable belongs includes:

[0018] The original value range of each fuzzy variable is fuzzified by the preset fuzzy membership function to obtain the range to be classified for each different numerical range of each fuzzy variable.

[0019] The ranges of each fuzzy variable to be classified are sorted in ascending order of numerical range to obtain the classification level of each fuzzy variable.

[0020] The fuzzy rule table is obtained by combining the various levels of each fuzzy variable according to preset rules.

[0021] The step of substituting the range mapping values ​​of each of the fuzzy variables into the fuzzy rule table to perform fuzzy decision-making and outputting graded decision values ​​includes:

[0022] Based on the range to be classified of each fuzzy variable and its corresponding classification level, determine the classification value of the range mapping value of each fuzzy variable.

[0023] The tiered values ​​of each fuzzy variable are input into the fuzzy rule table for fuzzy decision-making, and the tiered decision values ​​are output.

[0024] The step of determining the target value range of each fuzzy variable in each application information system based on the original value range of each fuzzy variable in each application information system through a preset feature coefficient algorithm includes:

[0025] Determine the number of historical transactions for each of the fuzzy variables in the application information system, and determine the mapping ratio of each of the fuzzy variables based on the number of historical transactions for each of the fuzzy variables;

[0026] According to the mapping ratio of each fuzzy variable, the original value range of each fuzzy variable is mapped to obtain the target value range of each fuzzy variable.

[0027] The step of mapping the original value ranges of each of the fuzzy variables to obtain the range mapping value of each of the fuzzy variables on the target value range includes:

[0028] Determine the coefficients to be mapped within the target value range of each of the fuzzy variables;

[0029] The coefficients to be mapped of each of the fuzzy variables are mapped for the first time to the target value range of each of the fuzzy variables, so as to obtain the target mapping coefficients of each of the fuzzy variables.

[0030] The target mapping coefficients of each fuzzy variable are mapped a second time to the original value range of each fuzzy variable to obtain the range mapping value of each fuzzy variable.

[0031] The process of converting user characteristics affecting the access frequency of interfaces to various application information systems into fuzzy variables using the fuzzy algorithm includes:

[0032] Identify the various user characteristics that affect the frequency of user access to the interfaces of various application information systems, wherein the user characteristics include job title characteristics, department characteristics, and interface access interval.

[0033] The job title characteristics are converted into a first fuzzy variable using a fuzzy algorithm, the department characteristics are converted into a second fuzzy variable, and the interface access interval is converted into a third fuzzy variable.

[0034] Secondly, this application also provides an access frequency optimization device based on a fuzzy algorithm, comprising:

[0035] The determination module is used to determine various user characteristics that affect the frequency of user access to the interfaces of various application information systems.

[0036] The calculation module is used to calculate the frequency optimization value of the user's access frequency to the interface of each application information system based on the fuzzy algorithm and the user characteristics of each user in each application information system.

[0037] The generation module is used to generate differentiated interface access control policies for the user's access to each of the application information systems based on the user's various frequency optimization values.

[0038] Thirdly, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the access frequency optimization method based on fuzzy algorithm described in the first aspect.

[0039] Fourthly, this application also provides a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including a computer program, which, when executed by the processor, implements the access frequency optimization method based on fuzzy algorithm described in the first aspect.

[0040] Fifthly, this application also provides a computer program product, the computer program product comprising a computer program, which, when executed by the processor, implements the access frequency optimization method based on fuzzy algorithm described in the first aspect.

[0041] The access frequency optimization method, apparatus, device, and medium based on fuzzy algorithm provided in this application optimize the access frequency by using fuzzy control algorithm and user characteristics of the interface access frequency of users accessing various application information systems. Different interface access control strategies are formulated for different users to access various application information systems with different interface access frequencies, thereby reducing the amount of invalid interface access and improving the system resource utilization. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating the access frequency optimization method based on fuzzy algorithm provided in this application;

[0044] Figure 2 This is a schematic diagram of the access frequency optimization device based on fuzzy algorithm provided in this application;

[0045] Figure 3 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] The following is combined with Figures 1 to 3 This application describes the access frequency optimization method, apparatus, device, and medium based on fuzzy algorithm provided in this application. Figure 1 This is a flowchart illustrating the access frequency optimization method based on fuzzy algorithm provided in this application; Figure 2 This is a schematic diagram of the access frequency optimization device based on fuzzy algorithm provided in this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in this application.

[0048] This application provides an embodiment of an access frequency optimization method based on a fuzzy algorithm. It should be noted that although the logical order is shown in the flowchart, under certain data conditions, the steps shown or described may be completed in a different order than that shown here.

[0049] This application uses an electronic device as the execution subject for example. This application uses an office portal information system as one of the manifestations of the electronic device, but it does not limit it.

[0050] Specifically, refer to Figure 1 , Figure 1This is a flowchart illustrating the access frequency optimization method based on fuzzy algorithm provided in this application. The access frequency optimization method based on fuzzy algorithm provided in this application includes:

[0051] Step S10: Determine the user characteristics that affect the frequency of user access to the interfaces of various application information systems.

[0052] It should be noted that the office portal information system in this application embodiment is mainly used for government and enterprise office portal information systems. After logging into the office portal information system, users can access all application information systems. It can be understood that the office portal information system is the main system, and each application information system is a subsystem. One main system can connect with multiple subsystems through interfaces, or it can be understood that the office portal information system connects and communicates with multiple application information systems through multiple interfaces.

[0053] Furthermore, after a user logs into the office portal information system, the system needs to identify the various factors influencing the frequency of the user's access to the interfaces of various application information systems. These factors are essentially the user characteristics that affect the frequency of the user's access to the interfaces of various application information systems. This can be further understood as the office portal information system acquiring the various user characteristics that influence the frequency of the user's access to the interfaces of various application information systems. These user characteristics include, but are not limited to, access permission characteristics, access level characteristics, job title characteristics, departmental characteristics, and interface access intervals.

[0054] In one embodiment, user A's user characteristics in application information system 1 are: supervisor position, level 3 access level, and authorized access. Alternatively, user A's user characteristics in application information system 1 may be: supervisor position, level 3 access level, and no access.

[0055] Step S20: Based on the fuzzy algorithm and the user characteristics of each user in each of the application information systems, calculate the frequency optimization value of the user's access frequency to the interface of each of the application information systems.

[0056] Furthermore, the office portal information system uses a fuzzy algorithm to convert each user characteristic into fuzzy variables, where the fuzzy variables are the parameter variables of the fuzzy control algorithm. Next, the office portal information system uses the fuzzy algorithm combined with these fuzzy variables to calculate the optimized frequency values ​​for user access to the interfaces of various application information systems. This can be further understood as the office portal information system determining the optimized frequency values ​​for accessing the interfaces of different application information systems based on the user characteristics of the user in different application information systems, and then using these optimized frequency values ​​to access different application information systems, as described in steps S201 to S205.

[0057] Step S30: Based on the user's various frequency optimization values, generate differentiated interface access control policies for the user's access to each of the application information systems.

[0058] Furthermore, the office portal information system optimizes user access frequency across different application information systems and formulates differentiated interface access control strategies for users to access various application information systems at different interface access frequencies.

[0059] This can be further understood as follows: when the same user accesses different application information systems, the frequency of access to the interfaces of these systems will differ because the user characteristics of the user are different in each system. Conversely, if the user characteristics of the same user are identical across several application information systems, then the frequency of access to the interfaces of those specific application information systems will be the same.

[0060] To further understand this, when different users access different application information systems, their access frequencies to the interfaces of these systems differ because their user characteristics differ depending on whether they are within the same or different application information systems. Conversely, if several different users have identical user characteristics within the same application information system, their access frequencies to that system's interface will be the same. Similarly, if several different users have identical user characteristics across several different application information systems, their access frequencies to those systems will also be the same.

[0061] This embodiment provides an access frequency optimization method based on fuzzy algorithms. In the process of access frequency optimization based on fuzzy algorithms, by using fuzzy control algorithms and user characteristics of the interface access frequencies of various application information systems, differentiated interface access control strategies are formulated for different users to access various application information systems at different interface access frequencies. This reduces the amount of invalid interface access, significantly reduces the number of accesses to the interfaces of various application information systems, improves system resource utilization, and enhances system stability. Furthermore, this embodiment sets different interface access frequencies for different users, rationally utilizes interface carrying capacity, optimizes system utilization, meets the access needs of more application information systems, and ensures system availability and user experience.

[0062] This can be further understood as follows: Imagine a company has a centralized portal system that connects to over 20 other systems. Company employees can access these systems simply by logging into the centralized portal system, and can also extract necessary tasks from these systems. Therefore, the centralized portal system needs to interface with these other systems. The centralized portal system collects these tasks from the other systems. A common strategy is that after a user logs into the centralized portal system, it periodically accesses one or more of the 20 systems (e.g., every 3 seconds) based on the logged-in username, collecting the user's tasks and uploading them to the centralized portal system. The user can then view and process these tasks on the centralized portal system. Using the fuzzy algorithm-based access frequency optimization method in steps S10 to S20, the interface access frequency of the centralized portal system collecting the user's tasks from the 20 other systems is obtained. In one embodiment, if user A is a finance personnel, the centralized portal system determines, based on user A's user characteristics, that the system accesses the ERP system once every 10 seconds, the customer management system once every 5 minutes, and the OA system once every 2 minutes. A differentiated interface access control strategy is formulated for the user to access various application information systems at different interface access frequencies. This effectively reduces the number of invalid interface accesses, greatly reduces the number of accesses to various application information system interfaces, improves system resource utilization, and enhances system stability.

[0063] Further, steps S201 to S206 are described as follows:

[0064] Step S201: The user characteristics that affect the access frequency of the interfaces of the various application information systems are converted into various fuzzy variables by the fuzzy algorithm, and the original value range of each fuzzy variable in each application information is calculated.

[0065] Furthermore, the office portal information system converts each user's characteristics into fuzzy variables, as described in steps S2011 to S2012. Simultaneously, the office portal information system calculates the original value range of each fuzzy variable within each application information.

[0066] Step S202: Based on the original value range of each fuzzy variable in each application information system, the target value range of each fuzzy variable in each application information system is determined by a preset feature coefficient algorithm.

[0067] Furthermore, the office portal information system calculates and determines the target value range of each fuzzy variable based on the original value range of each fuzzy variable, as described in steps S2021 to S2022.

[0068] Step S203: Map the original value range of each of the fuzzy variables to obtain the range mapping value of each of the fuzzy variables on the target value range.

[0069] Furthermore, the office portal information system uses a preset feature coefficient algorithm combined with the original value ranges of the role and dept fuzzy variables to map any coefficient within the target value range of the role and dept fuzzy variables, thereby determining the range mapping value of the role and dept fuzzy variables. In one embodiment, the original value range of the role fuzzy variable is [1, n], the target value range of the role fuzzy variable is [1, s], and the coefficient to be mapped in the role fuzzy variable is s1. The office portal information system then maps the coefficient to be mapped s1 to the original value range [1, n], and the preset feature coefficient algorithm for obtaining the range mapping value n1 is: n1 = (s1 * (1 / s)) / (1 / n). This is as described in steps S2031 to S2033.

[0070] Step S204: The original value range of each fuzzy variable is fuzzified by a preset fuzzy membership function to determine the grade level and fuzzy rule table to which each fuzzy variable belongs.

[0071] It should be noted that the purpose of fuzzification processing using fuzzy membership functions in this application embodiment is to classify the value ranges of the role fuzzy variable and the dept fuzzy variable according to a certain membership relationship, and to determine the number of classification levels for the role fuzzy variable and the dept fuzzy variable. In this application embodiment, the number of classification levels is taken as 3 levels, namely high level H, medium level M, and low level L, but this is not limited and can be set according to actual conditions. Fuzzy membership functions include, but are not limited to, triangular fuzzy membership functions and trapezoidal fuzzy membership functions. This application embodiment uses the triangular fuzzy membership function as an example. The triangular fuzzy membership function is determined by three parameters a, b, and c, where parameters a and c determine the feet of the triangle, and parameter b determines the peak of the triangle. The expression of the triangular fuzzy membership function is: trimf(x[a,b,c]), where x is the independent variable. The output of the triangular fuzzy membership function is the classification of the fuzzy variable. The formula of the triangular fuzzy membership function is as follows:

[0072]

[0073] Therefore, the office portal information system uses a triangular fuzzy membership function to fuzzify the original value range of the `role` fuzzy variable, dividing the original value range of the `role` fuzzy variable into high, medium, and low levels, which also categorizes the original value range of the job / position characteristics into high, medium, and low levels. Furthermore, the office portal information system uses a triangular fuzzy membership function to fuzzify the original value range of the `dept` fuzzy variable, dividing the original value range of the `dept` fuzzy variable into high, medium, and low levels, which also categorizes the original value range of the department characteristics into high, medium, and low levels. Finally, the office portal information system uses a triangular fuzzy membership function to divide the target value range of the `t` fuzzy variable into high, medium, and low levels, which also categorizes the interface access interval into high, medium, and low levels. Furthermore, the office portal information system establishes a fuzzy rule table based on the high-end, medium-end, and low-end fuzzy variables of role, dept, and t, and combines it with the input expert historical experience, as described in steps S2041 to S2043.

[0074] Step S205: Substitute the range mapping values ​​of each of the fuzzy variables into the fuzzy rule table to make fuzzy decisions and output the graded decision values.

[0075] The office portal information system inputs the range mapping values ​​of the role fuzzy variable and the dept fuzzy variable into the fuzzy rule table for fuzzy decision-making, and outputs the tier decision value to which the range mapping values ​​of the role fuzzy variable and the dept fuzzy variable belong. The tier decision value can be high-end, medium-end or low-end, as described in steps S2051 to S2052.

[0076] Step S206: Perform anti-fuzzing processing by combining the preset anti-fuzzing algorithm with the grading decision value, and output the frequency optimization value of the user's access frequency to the interface of each application information system.

[0077] It should be noted that defuzzification transforms fuzzy quantities into precise quantities, which means deriving the mapping from fuzzy sets to ordinary sets and finding the most representative value within the ranges of low-level (L), medium-level (M), and high-level (H). The preset defuzzification algorithm in this application embodiment is illustrated using the centroid defuzzification algorithm, but it is not intended to be limiting.

[0078] Furthermore, after the office portal information system determines the tier decision values ​​for each application information system, it uses a centroid defuzzification algorithm to defuzzify these values, calculating the definite values ​​under three fuzzy states: low (L), medium (M), and high (H), representing the interface access frequency of each application information system under user characteristics. The specific process of the centroid defuzzification algorithm is as follows: the centroid of the area enclosed by the membership function curve generated by the triangular fuzzy membership function and the horizontal coordinate is taken as the final output value v0 of the fuzzy inference. v0 is the definite value. The formula for the centroid defuzzification algorithm is shown below:

[0079]

[0080] In one embodiment, the fuzzy variable t of a certain application information system is a low-level L. Therefore, the expression of the triangular fuzzy membership function is: trimf(t[50,200,200]). The fuzzy function is defuzzified by the centroid defuzzification algorithm. The centroid of the area enclosed by the membership function curve and the horizontal coordinate is taken as the final output value, which means that the interface access frequency is about 167 seconds.

[0081] Furthermore, a comparative analysis between this application and existing technologies is as follows: With 100 concurrent users and 0.2 accesses per user per second, calculated based on four times the peak user volume, the existing technology yields an average interface access frequency of (100*0.2) / 4 = 5 times / second. The present application's embodiment yields an average interface access frequency of (50*0.2) / 4 + 50 / 167 = 2.8 times / second. Here, the concurrent user count is 50 based on a 50% proportion of ordinary employees, and 167 represents the interface access frequency, or the user interface access interval. For the existing technology: users access the interface at the same frequency, resulting in more interface calls and slower system response during peak hours. For this application: the interface call frequency decreases from 5 times / second to 2.8 times / second. Since ordinary employees account for more than 50% of the enterprise, they generally have little to-do information or infrequent access. The interface access interval is approximately 167 seconds, significantly reducing the interface access frequency compared to the original fixed 20-second interval.

[0082] This application embodiment uses fuzzy control algorithms and user characteristics of the frequency of user access to various application information systems to formulate differentiated interface access control strategies for users to access different application information systems at different interface access frequencies. This effectively reduces the number of invalid interface accesses, greatly reduces the number of accesses to the interfaces of various application information systems, improves system resource utilization, and enhances system stability.

[0083] Further, steps S2011 to S2012 are described as follows:

[0084] Step S2011: Determine the user characteristics that affect the frequency of user access to the interfaces of various application information systems, wherein the user characteristics include job title characteristics, department characteristics, and interface access interval.

[0085] Step S2012: The job position feature is converted into a first fuzzy variable using a fuzzy algorithm, the department feature is converted into a second fuzzy variable, and the interface access interval is converted into a third fuzzy variable.

[0086] Specifically, the office portal information system determines various user characteristics that affect the frequency of user access to the interfaces of various application information systems. These user characteristics include, but are not limited to, access permission characteristics, access level characteristics, job title characteristics, departmental characteristics, and interface access intervals. This application embodiment only uses job title characteristics, departmental characteristics, and interface access intervals as examples. Further, the office portal information system converts the job title characteristic into a first fuzzy variable, represented by the fuzzy variable 'role'. Simultaneously, the office portal information system converts the departmental characteristic into a second fuzzy variable, represented by the fuzzy variable 'dept'. Also, the office portal information system converts the interface access interval into a third fuzzy variable, represented by the fuzzy variable 't'.

[0087] The embodiments of this application employ fuzzy algorithms to provide fuzzy variables for subsequent processes, thereby ensuring the accuracy of data in subsequent processes.

[0088] Further, steps S2021 to S2022 are described as follows:

[0089] Step S2021: Determine the number of historical transactions for each of the fuzzy variables in the application information system, and determine the mapping ratio of each of the fuzzy variables based on the number of historical transactions for each of the fuzzy variables.

[0090] Step S2022: Map the fuzzy variables within their original value ranges according to their mapping ratios to obtain the target value ranges for each fuzzy variable.

[0091] Furthermore, the office portal information system needs to determine the target value range of the role fuzzy variable and the dept fuzzy variable based on their original value ranges, as follows: The office portal information system determines the original value range of the job position characteristics as [1, n] and the original value range of the department characteristics as [1, m].

[0092] Furthermore, the office portal information system determines the historical transaction volume of job position characteristics in the application information system, and determines the mapping ratio of job position characteristics based on the historical transaction volume of job position characteristics in the application information system, such as a mapping ratio of s. Finally, the office portal information system maps the original value range [1, n] of job position characteristics according to the mapping ratio s, to obtain the target value range of job position characteristics as [1, s], that is, the target value range of the role fuzzy variable is [1, s]. In one embodiment, if the original value range of job position characteristic A is defined as [1, 20], and the historical transaction volume of job position characteristic A in a certain application information system is 10, then the mapping ratio s = 10 is determined for job position characteristic A, and the target value range of job position characteristic A is mapped to [1, 10].

[0093] Furthermore, the office portal information system determines the historical transaction count of the department characteristic in the application information system and the number of employees in the department characteristic, and divides the historical transaction count of the department characteristic by the number of employees to obtain the corresponding calculation result. Next, the office portal information system determines the mapping ratio of the department characteristic based on the calculation result, such as a mapping ratio of k. Finally, the office portal information system maps the original value range [1, m] of the department characteristic according to the mapping ratio k, obtaining the target value range of the department characteristic as [1, k], that is, the target value range of the fuzzy variable dept is [1, k]. For example, if the original value range of department characteristic B is defined as [1, 10], the historical transaction count of department characteristic B in a certain application information system is 50, and the number of employees in the department characteristic B is 10, then the mapping ratio k = 5 is calculated for department characteristic B, and the target value range of department characteristic B is mapped to [1, 5].

[0094] Furthermore, the office portal information system determines the maximum interface capacity of each application information system and the number of concurrent users of the office portal information system. Next, based on the maximum interface capacity and the number of concurrent users, the office portal information system calculates the maximum access frequency of each application information system and determines the minimum interface access interval based on the maximum access frequency. Finally, the office portal information system sets the target value range for each application information system based on the minimum interface access interval, i.e., the target value range of the fuzzy variable t, where the maximum value in the target value range does not exceed the maximum access frequency. In one embodiment, if a certain enterprise has 500 users, the number of concurrent users is 100, and the maximum interface capacity of a certain application information system is 20 times / second, the maximum access frequency of this application information system is calculated to be 0.2 times / second based on the maximum interface capacity and the number of concurrent users. Therefore, based on the maximum access frequency of 0.2 times / second, the minimum interface access interval is determined to be 5 seconds, and the target value range for this application information system can be set to [5, 200].

[0095] Based on the different job positions and departments in the application system, and combined with the historical business volume of the job positions and departments, the target value range of the job positions and departments is determined, which ensures the accuracy of the target value range and provides data conditions for calculating the accurate interface access frequency.

[0096] Further, steps S2031 to S2033 are described as follows:

[0097] Step S2031: Determine the coefficients to be mapped in the target value range of each of the fuzzy variables;

[0098] Step S2032: The coefficients to be mapped of each of the fuzzy variables are mapped to the target value range of each of the fuzzy variables for the first time to obtain the target mapping coefficients of each of the fuzzy variables.

[0099] Step S2033: The target mapping coefficients of each fuzzy variable are mapped a second time with the original value range of each fuzzy variable to obtain the range mapping value of each fuzzy variable.

[0100] Specifically, the office portal information system determines the coefficients to be mapped within the target value range of the fuzzy variables `role` and `dept`. Next, the system performs a first mapping between the coefficients to be mapped of the `role` fuzzy variable and its target value range, obtaining the target mapped coefficients of the `role` fuzzy variable. Simultaneously, the system performs a first mapping between the coefficients to be mapped of the `dept` fuzzy variable and its target value range, obtaining the target mapped coefficients of the `dept` fuzzy variable. Finally, the system performs a second mapping between the target mapped coefficients of the `role` fuzzy variable and its original value range, obtaining the range mapped value of the `role` fuzzy variable. Simultaneously, the system performs a second mapping between the target mapped coefficients of the `dept` fuzzy variable and its original value range, obtaining the range mapped value of the `dept` fuzzy variable. The specific scaling formula is n1 = (s1 * (1 / s)) / (1 / n), where n1 is the range mapped value, s1 is the coefficient to be mapped, 1 / s is the target value range, and 1 / n is the original value range. Furthermore, for the t-fuzzy variable, the t-fuzzy variable maintains the target value range.

[0101] In this embodiment, the coefficients to be mapped for each fuzzy variable are mapped twice, with the target value range and the original value range, to ensure the accuracy of the range mapping values ​​of the fuzzy variables and provide data conditions for calculating the accurate interface access frequency.

[0102] Further, steps S2041 to S2043 are described as follows:

[0103] Step S2041: The original value range of each fuzzy variable is fuzzified by the preset fuzzy membership function to obtain the range to be classified for each different numerical range of each fuzzy variable.

[0104] Step S2042: Sort the ranges of each fuzzy variable to be classified according to the numerical range from small to large, and obtain the classification level of each fuzzy variable.

[0105] Step S2043: Combine the various levels of each fuzzy variable according to preset rules to obtain the fuzzy rule table.

[0106] Specifically, the office portal information system uses a triangular fuzzy membership function to fuzzify the original value range of the role fuzzy variable, splitting the original value range of the role fuzzy variable into multiple unclassified ranges with different numerical ranges. In this embodiment, there are three unclassified ranges. Next, the office portal information system sorts the unclassified ranges of the role fuzzy variable in ascending order of numerical range to obtain the classification levels of the role fuzzy variable. Alternatively, in this embodiment, the classification can also be performed in descending order of numerical range. In one embodiment, the original value range of the role fuzzy variable is [1,20]. The original value range [1,20] is split into [1,9], [10,14], and [15,20]. Based on the ascending order of numerical range, [1,9] is classified as low-level, [10,14] as medium-level, and [15,20] as high-level.

[0107] Furthermore, the office portal information system uses a triangular fuzzy membership function to fuzzify the original value range of the fuzzy variable `dept`, splitting it into multiple ranges of different numerical values ​​to be classified. Then, the system sorts these ranges in ascending order of numerical value to obtain the classification levels for the fuzzy variable `dept`. In one embodiment, the original value range of the fuzzy variable `dept` is [1,7]. This range is split into [1,2], [3,5], and [6,7]. Based on the ascending order of numerical value, [1,2] is classified as low, [3,5] as medium, and [6,7] as high.

[0108] Furthermore, the office portal information system uses a triangular fuzzy membership function to fuzzify the original value range of the fuzzy variable t, splitting the original value range of the fuzzy variable t into multiple unclassified ranges with different numerical ranges. Then, the office portal information system sorts the unclassified ranges of the fuzzy variable t in ascending order of numerical range to obtain the classification levels of the fuzzy variable t. In one embodiment, the original value range of the fuzzy variable t is [5,200]. The original value range [5,200] is split into [5,99], [100,149], and [150,200]. Based on the ascending order of numerical range, [5,99] is classified as low-level, [100,149] as medium-level, and [150,200] as high-level.

[0109] Furthermore, the office portal information system establishes a fuzzy rule table based on the high-end H, mid-end M, and low-end L of the role fuzzy variable, and the high-end H, mid-end M, and low-end L of the t fuzzy variable, and combines it with the input expert historical experience, as shown in Table 1. Table 1 is the fuzzy rule table.

[0110] Table 1 Fuzzy Rule Table

[0111]

[0112] This can be understood as follows: In a certain application information system, when the fuzzy variable `dept` is low-level L and the fuzzy variable `role` is low-level L, then the fuzzy variable `t` is low-level L; when the fuzzy variable `dept` is low-level L and the fuzzy variable `role` is medium-level M, then the fuzzy variable `t` is low-level L; when the fuzzy variable `dept` is low-level L and the fuzzy variable `role` is high-level H, then the fuzzy variable `t` is medium-level M; when the fuzzy variable `dept` is medium-level M and the fuzzy variable `role` is low-level L, then the fuzzy variable `t` is low-level L; when the fuzzy variable `dept` is... If the fuzzy variable for the role is "Mid-range M", then the fuzzy variable for the t variable is "Mid-range M". If the fuzzy variable for the dept variable is "Mid-range M" and the fuzzy variable for the role is "High-end", then the fuzzy variable for the t variable is "High-end H". If the fuzzy variable for the dept variable is "High-end H" and the fuzzy variable for the role is "Low-range L", then the fuzzy variable for the t variable is "Mid-range M". If the fuzzy variable for the dept variable is "High-end H" and the fuzzy variable for the role is "Mid-range M", then the fuzzy variable for the t variable is "High-end H". If the fuzzy variable for the dept variable is "High-end H" and the fuzzy variable for the role is "High-end H", then the fuzzy variable for the t variable is "High-end H".

[0113] The embodiments of this application establish a fuzzy rule table based on the number of levels of the role fuzzy variable, the dept fuzzy variable, and the t fuzzy variable, and in combination with the historical experience of experts, so that the fuzzy rule table is more representative and accurate.

[0114] Further, steps S2051 to S2052 are described as follows:

[0115] Step S2051: Determine the range mapping value of each fuzzy variable based on the range to be classified of each fuzzy variable and its corresponding classification level.

[0116] Step S2052: Input the tiered values ​​of each fuzzy variable into the fuzzy rule table for fuzzy decision-making, and output the tiered decision value.

[0117] Specifically, the office portal information system determines the various to-be-classified ranges of the fuzzy variable 'role' and its corresponding class levels. Based on which to-be-classified range the range mapping value of the fuzzy variable 'role' belongs to, the system determines the class level of the range mapping value of the fuzzy variable 'role'. Further, the office portal information system determines the various to-be-classified ranges of the fuzzy variable 'dept' and its corresponding class levels. Based on which to-be-classified range the range mapping value of the fuzzy variable 'dept' belongs to, the system determines the class level of the range mapping value of the fuzzy variable 'dept'. In one embodiment, the role fuzzy variable [1,9] represents low level, [10,14] represents medium level and [15,20] represents high level, and the dept fuzzy variable [1,2] represents low level, [3,5] represents medium level and [6,7] represents high level. The range mapping value of the role fuzzy variable in a certain application information system is 1 and the range mapping value of the dept fuzzy variable is 3. Then, the range mapping value 1 of the role fuzzy variable is determined to be low level L, and the range mapping value 3 of the dept fuzzy variable is determined to be medium level M. Fuzzy decision is performed according to the fuzzy rule table in Table 1, and the t fuzzy variable is obtained as low level L, that is, the classification decision value is low level L.

[0118] This application embodiment uses a precise fuzzy rule table to make fuzzy decisions on the range mapping values ​​of each fuzzy variable, and outputs precise grading decision values, providing data conditions for calculating accurate interface access frequencies.

[0119] Furthermore, the access frequency optimization device based on fuzzy algorithm provided in this application is described as follows. The access frequency optimization device based on fuzzy algorithm and the access frequency optimization method based on fuzzy algorithm can be referred to each other accordingly.

[0120] like Figure 2 As shown, Figure 2 This is a schematic diagram of the access frequency optimization device based on fuzzy algorithm provided in this application. The access frequency optimization device based on fuzzy algorithm includes:

[0121] Module 201 is used to determine various user characteristics that affect the frequency of user access to the interfaces of various application information systems.

[0122] The calculation module 202 is used to calculate the frequency optimization value of the user's access frequency to the interface of each application information system based on the fuzzy algorithm and the user characteristics of each user in each application information system.

[0123] The generation module 203 is used to generate differentiated interface access control policies for the user's access to various application information systems based on the user's frequency optimization values.

[0124] Furthermore, the computing module 202 is also used for:

[0125] The fuzzy algorithm is used to convert the various user characteristics that affect the access frequency of the interfaces of the various application information systems into various fuzzy variables, and to calculate the original value range of each fuzzy variable in each application information system.

[0126] Based on the original value range of each fuzzy variable in each of the application information systems, the target value range of each fuzzy variable in each of the application information systems is determined by a preset feature coefficient algorithm.

[0127] The original value ranges of each of the fuzzy variables are mapped to obtain the range mapping values ​​of each of the fuzzy variables on the target value range;

[0128] By using a preset fuzzy membership function to fuzzify the original value range of each fuzzy variable, the classification level and fuzzy rule table of each fuzzy variable are determined.

[0129] Substitute the range mapping values ​​of each of the fuzzy variables into the fuzzy rule table to perform fuzzy decision-making and output the graded decision values.

[0130] By combining the preset anti-fuzzy algorithm with the grading decision value, the frequency optimization value of the user's access frequency to the interface of each application information system is output.

[0131] Furthermore, the computing module 202 is also used for:

[0132] The original value range of each fuzzy variable is fuzzified by the preset fuzzy membership function to obtain the range to be classified for each different numerical range of each fuzzy variable.

[0133] The ranges of each fuzzy variable to be classified are sorted in ascending order of numerical range to obtain the classification level of each fuzzy variable.

[0134] The fuzzy rule table is obtained by combining the various levels of each fuzzy variable according to preset rules.

[0135] Furthermore, the computing module 202 is also used for:

[0136] Based on the range to be classified of each fuzzy variable and its corresponding classification level, determine the classification value of the range mapping value of each fuzzy variable.

[0137] The tiered values ​​of each fuzzy variable are input into the fuzzy rule table for fuzzy decision-making, and the tiered decision values ​​are output.

[0138] Furthermore, the computing module 202 is also used for:

[0139] Identify the various user characteristics that affect the frequency of user access to the interfaces of various application information systems, wherein the user characteristics include job title characteristics, department characteristics, and interface access interval.

[0140] The job title characteristics are converted into a first fuzzy variable using a fuzzy algorithm, the department characteristics are converted into a second fuzzy variable, and the interface access interval is converted into a third fuzzy variable.

[0141] Furthermore, module 201 is also used for:

[0142] Determine the number of historical transactions for each of the fuzzy variables in the application information system, and determine the mapping ratio of each of the fuzzy variables based on the number of historical transactions for each of the fuzzy variables;

[0143] According to the mapping ratio of each fuzzy variable, the original value range of each fuzzy variable is mapped to obtain the target value range of each fuzzy variable.

[0144] Furthermore, the computing module 202 is also used for:

[0145] Determine the coefficients to be mapped within the target value range of each of the fuzzy variables;

[0146] The coefficients to be mapped of each of the fuzzy variables are mapped for the first time to the target value range of each of the fuzzy variables, so as to obtain the target mapping coefficients of each of the fuzzy variables.

[0147] The target mapping coefficients of each fuzzy variable are mapped a second time to the original value range of each fuzzy variable to obtain the range mapping value of each fuzzy variable.

[0148] The specific embodiments of the access frequency optimization device based on fuzzy algorithm provided in this application are basically the same as the embodiments of the access frequency optimization method based on fuzzy algorithm described above, and will not be repeated here.

[0149] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute an access frequency optimization method based on a fuzzy algorithm, the method including:

[0150] Identify the various user characteristics that influence the frequency of user access to the interfaces of various application information systems.

[0151] Based on the fuzzy algorithm and the user characteristics of each user in each of the application information systems, the frequency optimization value of the user's access frequency to the interface of each of the application information systems is calculated.

[0152] Based on the user's various frequency optimization values, generate differentiated interface access control policies for the user's access to each of the application information systems.

[0153] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0154] On the other hand, this application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer is able to execute the access frequency optimization method based on fuzzy algorithm provided by the above methods. The method includes:

[0155] Identify the various user characteristics that influence the frequency of user access to the interfaces of various application information systems.

[0156] Based on the fuzzy algorithm and the user characteristics of each user in each of the application information systems, the frequency optimization value of the user's access frequency to the interface of each of the application information systems is calculated.

[0157] Based on the user's various frequency optimization values, generate differentiated interface access control policies for the user's access to each of the application information systems.

[0158] In another aspect, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform the aforementioned access frequency optimization methods based on fuzzy algorithms, the method comprising:

[0159] Identify the various user characteristics that influence the frequency of user access to the interfaces of various application information systems.

[0160] Based on the fuzzy algorithm and the user characteristics of each user in each of the application information systems, the frequency optimization value of the user's access frequency to the interface of each of the application information systems is calculated.

[0161] Based on the user's various frequency optimization values, generate differentiated interface access control policies for the user's access to each of the application information systems.

[0162] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0163] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a non-transitory computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing access frequency based on fuzzy algorithm, characterized in that, include: Identify the various user characteristics that influence the frequency of user access to the interfaces of various application information systems. Based on a fuzzy algorithm and considering the user characteristics in each of the application information systems, the frequency optimization value for the user's access frequency to the interfaces of each application information system is calculated. This includes: converting the user characteristics affecting the access frequency to the interfaces of each application information system into fuzzy variables using the fuzzy algorithm, and calculating the original value range of each fuzzy variable in each application information system; determining the target value range of each fuzzy variable in each application information system based on the original value range of each fuzzy variable in each application information system using a preset feature coefficient algorithm; mapping the original value range of each fuzzy variable to obtain a range mapping value of each fuzzy variable on the target value range; fuzzifying the original value range of each fuzzy variable using a preset fuzzy membership function to determine the tier number and fuzzy rule table to which each fuzzy variable belongs; substituting the range mapping value of each fuzzy variable into the fuzzy rule table for fuzzy decision-making and outputting the tier decision value; and performing defuzzification processing using a preset antifuzzy algorithm combined with the tier decision value to output the frequency optimization value for the user's access frequency to the interfaces of each application information system. The step of mapping the original value ranges of each fuzzy variable to obtain the range mapping value of each fuzzy variable in the target value range includes: determining the coefficients to be mapped in the target value range of each fuzzy variable; performing a first mapping between the coefficients to be mapped of each fuzzy variable and the target value range of each fuzzy variable to obtain the target mapping coefficients of each fuzzy variable; and performing a second mapping between the target mapping coefficients of each fuzzy variable and the original value range of each fuzzy variable to obtain the range mapping value of each fuzzy variable. Based on the user's various frequency optimization values, generate differentiated interface access control policies for the user's access to each of the application information systems.

2. The access frequency optimization method based on fuzzy algorithm according to claim 1, characterized in that, The step of fuzzifying the original value range of each fuzzy variable by using a preset fuzzy membership function to determine the level and fuzzy rule table to which each fuzzy variable belongs includes: The original value range of each fuzzy variable is fuzzified by the preset fuzzy membership function to obtain the range to be classified for each different numerical range of each fuzzy variable. The ranges of each fuzzy variable to be classified are sorted in ascending order of numerical range to obtain the classification level of each fuzzy variable. The fuzzy rule table is obtained by combining the various levels of each fuzzy variable according to preset rules.

3. The access frequency optimization method based on fuzzy algorithm according to claim 2, characterized in that, The step of substituting the range mapping values ​​of each of the fuzzy variables into the fuzzy rule table to perform fuzzy decision-making and outputting graded decision values ​​includes: Based on the range to be classified of each fuzzy variable and its corresponding classification level, determine the classification value of the range mapping value of each fuzzy variable. The tiered values ​​of each fuzzy variable are input into the fuzzy rule table for fuzzy decision-making, and the tiered decision values ​​are output.

4. The access frequency optimization method based on fuzzy algorithm according to claim 1, characterized in that, The step of determining the target value range of each fuzzy variable in each application information system based on the original value range of each fuzzy variable in each application information system through a preset feature coefficient algorithm includes: Determine the number of historical transactions for each of the fuzzy variables in the application information system, and determine the mapping ratio of each of the fuzzy variables based on the number of historical transactions for each of the fuzzy variables; According to the mapping ratio of each fuzzy variable, the original value range of each fuzzy variable is mapped to obtain the target value range of each fuzzy variable.

5. The access frequency optimization method based on fuzzy algorithm according to claim 1, characterized in that, The process of converting user characteristics affecting the access frequency of interfaces to various application information systems into fuzzy variables using the fuzzy algorithm includes: Identify the various user characteristics that affect the frequency of user access to the interfaces of various application information systems, wherein the user characteristics include job title characteristics, department characteristics, and interface access interval. The job title characteristics are converted into a first fuzzy variable using a fuzzy algorithm, the department characteristics are converted into a second fuzzy variable, and the interface access interval is converted into a third fuzzy variable.

6. An access frequency optimization device based on a fuzzy algorithm, characterized in that... include: The determination module is used to determine various user characteristics that affect the frequency of user access to the interfaces of various application information systems. The calculation module is used to calculate the frequency optimization value of the user's access frequency to the interfaces of each of the application information systems based on a fuzzy algorithm and the user characteristics of each user in each application information system. This includes: converting the user characteristics affecting the access frequency to the interfaces of each application information system into fuzzy variables using the fuzzy algorithm, and calculating the original value range of each fuzzy variable in each application information system; determining the target value range of each fuzzy variable in each application information system based on the original value range of each fuzzy variable in each application information system using a preset feature coefficient algorithm; mapping the original value range of each fuzzy variable to obtain a range mapping value of each fuzzy variable on the target value range; fuzzifying the original value range of each fuzzy variable using a preset fuzzy membership function to determine the tier number and fuzzy rule table to which each fuzzy variable belongs; substituting the range mapping value of each fuzzy variable into the fuzzy rule table for fuzzy decision-making and outputting the tier decision value; and performing defuzzification processing using a preset antifuzzy algorithm combined with the tier decision value to output the frequency optimization value of the user's access frequency to the interfaces of each application information system. The step of mapping the original value ranges of each fuzzy variable to obtain the range mapping value of each fuzzy variable in the target value range includes: determining the coefficients to be mapped in the target value range of each fuzzy variable; performing a first mapping between the coefficients to be mapped of each fuzzy variable and the target value range of each fuzzy variable to obtain the target mapping coefficients of each fuzzy variable; and performing a second mapping between the target mapping coefficients of each fuzzy variable and the original value range of each fuzzy variable to obtain the range mapping value of each fuzzy variable. The generation module is used to generate differentiated interface access control policies for the user's access to each of the application information systems based on the user's various frequency optimization values.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the access frequency optimization method based on fuzzy algorithm as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the access frequency optimization method based on fuzzy algorithm as described in any one of claims 1 to 5.

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