Authority control method and system for school-enterprise information exchange platform

Abstract

The invention relates to the technical field of authority management, in particular to an authority control method and system for a school-enterprise information exchange platform, and the method comprises the steps: firstly, determining recent access attention reflecting the attention of a user to school-enterprise information; determining the platform access frequency similarity among the users according to the similarity of recent access attention among the users; further determining an initial clustering distance between the users according to the overall value centralized distribution condition of the recent access attention of all the marked page types corresponding to the users in combination with the previously calculated platform access frequency similarity; on this basis, the initial clustering is adjusted in combination with the platform access time period overlapping condition between the users, and after clustering is carried out according to the adjusted final clustering distance, the users with similar browsing habits are divided into a platform user clustering cluster; therefore, authority adjustment is carried out according to the overall characteristics of the user authority in the platform user cluster, so that the authority control effect of the school-enterprise information exchange platform is better.

Description

Technical Field

[0001] The present invention relates to the technical field of authority management, and in particular to an authority control method and system for a school-enterprise information exchange platform. Background Art

[0002] The school-enterprise information exchange platform aims to build an interactive bridge between schools and enterprises, and promote exchanges and cooperation in talent training, resource sharing, scientific research cooperation and other aspects. In such a platform, information sharing and management are crucial, especially when it involves the participation of multiple users such as school teachers, students, and corporate employees. Permission control is particularly important. In the school-enterprise information exchange platform, the core task of permission control is to ensure the security, compliance and efficiency of the platform and avoid information leakage and abuse.

[0003] In the permission control of the school-enterprise information exchange platform, the existing technology usually sets the role permissions through the permission control method of role-based access control. Although the corresponding operation permissions can be assigned to different roles, namely students, teachers, enterprise personnel, etc., it is impossible to verify, review and clean up the permissions. This means that the current permission control method cannot clean up users who no longer need to access certain resources or expired permissions, and cannot verify that the access rights of each role match the job responsibilities or learning interests of the role, resulting in the poor effect of the permission control method of role-based access control on the permission control of the school-enterprise information exchange platform. Summary of the invention

[0004] The present application provides a permission control method and system for a school-enterprise information exchange platform. First, according to the uniformity of the length of the user's access time in the time sequence and the overall size, the recent access attention reflecting the user's attention to the school-enterprise information is determined; then, according to the similarity of the recent access attention between users, the platform access frequency similarity between users is determined; then, for each page type, high and low access page types are divided according to the overall numerical concentration distribution of the recent access attention of all marked page types corresponding to the user; thus, according to the association between the high and low access page types between users and the platform access frequency similarity calculated previously, the initial clustering distance between users is determined; and on this basis, the initial clustering is adjusted in combination with the overlap of the platform access time periods between users, and clustering is performed according to the adjusted final clustering distance, so that users with similar browsing habits are divided into a platform user clustering cluster, so that the permission adjustment is performed according to the overall characteristics of the user permissions in the platform user clustering cluster, which solves the problem that the permission control method based on role-based access control has a poor effect on the permission control of the school-enterprise information exchange platform, so that the permission control effect of the school-enterprise information exchange platform is better according to the similarity of the permissions of each user in the platform user clustering cluster.

[0005] The first aspect of the present application provides a permission control method for a school-enterprise information exchange platform, comprising: In the preset historical time period, collect the access time length of each user browsing each page type in the school-enterprise information exchange platform every day; Determine the recent visit attention of each user to each page type based on the uniform distribution of the visit time length of each user to each page type in time series and the overall visit time length; determine the corresponding platform visit frequency similarity based on the similarity between the maximum recent visit attention value corresponding to each user and the maximum recent visit attention value corresponding to other users; According to the overall numerical distribution of recent visit attention of all marked page types corresponding to each user, all marked page types of each user are divided into low-visit page types and high-visit page types; based on the similarity of the platform access frequency, according to the association between each user and other users on the low-visit page types and the high-visit page types, the corresponding initial clustering distance is determined; According to the overlap of platform access time periods between each user and other users, the corresponding distance adjustment coefficient is determined; the final clustering distance after weighted adjustment of the initial clustering distance according to the distance adjustment coefficient is clustered and analyzed on all users to obtain at least two platform user clustering clusters; and the authority control of the school-enterprise information exchange platform is performed according to the similarity of the authority of each user in the platform user clustering cluster.

[0006] In an embodiment of the first aspect of the present application, the process of obtaining the recent visit attention includes: Normalize the average length of time each user spends browsing each page type on all days in a preset historical time period to determine each user's overall attention to each page type; Negative correlation mapping is performed on the standard deviation of the length of time each user spends browsing each page type on all days in a preset historical time period to determine the regular attention each user pays to each page type; The recent visit attention of each user to each page type is determined according to the product of the overall attention and the regular attention.

[0007] In an embodiment of the first aspect of the present application, the process of obtaining the platform access frequency similarity includes: Take each user as the target user in turn, and take each user other than the target user as the comparison user; A negative correlation mapping is performed between the maximum value of the target user's recent visit attention to all page types and the maximum value of each comparison user's recent visit attention to all page types to determine the platform visit frequency similarity between the target user and each comparison user.

[0008] In an embodiment of the first aspect of the present application, the process of obtaining the classification of all marked page types of each user into low-visit page types and high-visit page types according to the overall numerical distribution of recent access attention of all marked page types corresponding to each user includes: For any user: The page type corresponding to the user whose recent visit attention is not 0 is used as the user's marked page type; all marked page types corresponding to the user are arranged in descending order of recent visit attention to determine the user's marked page type sequence; in the marked page type sequence, the difference between the recent visit attention corresponding to each marked page type and the recent visit attention of the next marked page type is used as the attention change value of each marked page type; wherein the attention change value of the last marked page type in the marked page type sequence is set to a preset change value; Filtering out distinguishing page types in the marked page type sequence according to the concentrated distribution of marked page types before and after each marked page type in the marked page type sequence; In the marked page type sequence, all marked page types before the distinguished page type are regarded as the user's high-visit page types; and the distinguished page type and all marked page types after the distinguished page type are regarded as the user's low-visit page types.

[0009] In an embodiment of the first aspect of the present application, the acquisition process of distinguishing page types includes: taking each marked page type in the marked page type sequence as a target page type in turn; In the marked page type, the average of the recent visit attention of all marked page types before the target page type is used as the first attention characteristic value of the target page type; the average of the recent visit attention of the target page type and all marked page types after the target page type is used as the second attention characteristic value of the target page type; according to the difference between the first attention characteristic value and the second attention characteristic value, the characteristic discrimination degree of the target page type is determined; The average of all attention change values ​​of all marked page types before the target page type is used as the first attention change value of the target page type; the average of all attention change values ​​of all marked page types after the target page type is used as the second attention change value of the target page type; the sum of the first attention change value and the second attention change value is negatively correlated to determine the change distinction of the target page type; Determine the final discrimination degree of the target page type according to the product of the feature discrimination degree and the change discrimination degree; count the final discrimination degrees of all marked page types in the marked page type sequence; and use the marked page type with the largest final discrimination degree as the distinguishing page type.

[0010] In an embodiment of the first aspect of the present application, the process of obtaining the initial clustering distance includes: Obtain a low-visit page type set consisting of all low-visit page types of each user, and a high-visit page type set consisting of all high-visit page types; Perform positive correlation mapping on the platform access frequency similarity to determine a low access weight; the low access weight is less than or equal to the platform access frequency similarity; Determine the corresponding low access relevance according to the Jaccard coefficient between the low access page type set of each user and the low access page type set of each other user; determine the low weighted relevance between each user and each other user according to the product between the low access relevance and the low access weight; Determine the corresponding high-visit correlation according to the Jaccard coefficient between each user's high-visit page type set and each other user's high-visit page type set; determine the high-weighted correlation between each user and each other user according to the product of the high-visit correlation and the platform access frequency similarity; A negative correlation mapping is performed on the sum of the low-weighted association and the high-weighted association to determine an initial clustering distance between each user and each other user.

[0011] In an embodiment of the first aspect of the present application, the process of obtaining the distance adjustment coefficient includes: In the preset historical time period, the accumulated value of the time each user spends using the school-enterprise information exchange platform in the preset enterprise working time period of all days is used as the enterprise working time browsing time of each user; the ratio between the enterprise working time browsing time and the total time each user spends browsing the school-enterprise information exchange platform in the preset time period is used as the reference enterprise time ratio of each user; A positive correlation mapping is performed between the reference enterprise time proportion of the target user and the reference enterprise time proportion of each comparison user to determine the distance adjustment coefficient between the target user and the comparison user.

[0012] In an embodiment of the first aspect of the present application, the process of obtaining the final clustering distance includes: The final clustering distance between each user and each other user is determined according to the product of the initial clustering distance and the distance adjustment coefficient.

[0013] In an embodiment of the first aspect of the present application, the process of controlling the authority of the school-enterprise information exchange platform according to the similarity of the authority of each user in the platform user clustering cluster includes: In each platform user cluster, users with the same permission type are regarded as a user set; all user sets in each platform user cluster are counted; All permission types corresponding to the user set with the largest number of users are used as representative permission types for each platform user cluster; other users outside the user set with the largest number of users are used as users to be reviewed; According to the representative authority type, the school-enterprise information exchange platform authority control is performed on all users to be reviewed.

[0014] In a second aspect, the present application provides a permission control system for a school-enterprise information exchange platform, the system comprising: The data collection module is used to collect the access time length of each user browsing each page type in the school-enterprise information exchange platform every day within a preset historical time period; The first determination module is used to determine the recent visit attention of each user to each page type according to the uniform distribution of the access time length of each user to each page type in time series and the overall duration; and determine the corresponding platform access frequency similarity according to the similarity between the maximum recent visit attention value corresponding to each user and the maximum recent visit attention value corresponding to other users; The second determination module is used to divide all the marked page types of each user into low-visit page types and high-visit page types according to the overall numerical distribution of the recent visit attention of all marked page types corresponding to each user; based on the similarity of the platform access frequency, according to the association between each user and other users on the low-visit page types and the high-visit page types, determine the corresponding initial clustering distance; The permission control module is used to determine the corresponding distance adjustment coefficient according to the overlap of the platform access time period between each user and other users; perform cluster analysis on all users according to the final cluster distance after weighted adjustment of the initial cluster distance according to the distance adjustment coefficient to obtain at least two platform user clusters; and perform permission control on the school-enterprise information exchange platform according to the similarity of the permissions of each user in the platform user cluster.

[0015] In a third aspect, the present application provides a computer device, comprising a memory and a processor. The memory is used to store computer program code, and the processor is used to call and run the computer program code from the memory to execute the method of the first aspect of the present application or any embodiment of the first aspect.

[0016] In a fourth aspect, the present application provides a computer program product, comprising a computer program code, which, when executed, performs the method of the first aspect of the present application or any embodiment of the first aspect.

[0017] In a fifth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program code. When the computer program code is executed, the method of the first aspect or any embodiment of the first aspect of the present application is executed.

[0018] This application has the following beneficial effects: When the browsing habits of two users on the school-enterprise information exchange platform are the same or similar, it can be considered that the two users are more likely to belong to the same major or department, and the corresponding permission requirements should be more similar. Therefore, through cluster analysis, the platform users with the same browsing habits can be divided into a cluster, and then the permissions can be adjusted according to the overall characteristics of the permissions of each user in the cluster to improve the effect of permission control on the school-enterprise information exchange platform.

[0019] Regarding the analysis of browsing habits, first of all, from the perspective of platform attention, since the content on the school-enterprise information exchange platform is continuously updated, users in the same major or department who have demand for the school-enterprise information exchange platform usually pay high attention to the required page types, and learn about the relevant content every day and pay attention to whether there is new content released on the corresponding page types; therefore, first of all, based on the uniform distribution of the length of time each user visits each page type in time series and the overall duration, the recent visit attention is determined, and then based on the similarity of the maximum values ​​of the recent visit attention between users, from the perspective of the overall demand for the school-enterprise information exchange platform, the similarity of browsing habits between users is measured by the calculated platform access frequency similarity.

[0020] Secondly, in the dimension of specific page types visited, it is necessary to consider that users will choose page types to browse based on work or study needs; for users with the same browsing habits, the page types they browse usually maintain a high degree of similarity; however, it is necessary to consider that among all page types browsed by each user, there are usually different recent visit attentions; page types with higher recent visit attentions are more important to users and can clearly reflect users' browsing needs; therefore, low-visit page types and high-visit page types can be divided according to the overall numerical distribution of recent visit attentions of all marked page types corresponding to each user; although page types with lower recent visit attentions are less important, they may belong to page types associated with user browsing needs, so further based on the association between each user and other users on low-visit page types and high-visit page types, the browsing habit similarity between users is measured in the dimension of specific page types visited, and on this basis, combined with the previously calculated platform access frequency similarity, the initial clustering distance for clustering analysis is determined in the dimension of browsing habit similarity.

[0021] In addition, in the time dimension, the browsing time periods of users with the same or similar browsing habits are usually the same. Considering that there is usually little overlap between the platform usage time periods of schools and enterprises, the distance adjustment coefficient that characterizes the similarity of browsing habits between users in the time dimension can be determined according to the overlap of platform access time periods between users; the initial clustering distance is further adjusted according to the distance adjustment coefficient to obtain a more accurate final clustering distance, so that the platform user clustering clusters finally divided according to the final clustering distance are more accurate, and the permissions are further adjusted according to the overall characteristics of the permissions of each user in the platform user clustering cluster, thereby improving the effect of permission control of the school-enterprise information exchange platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 A flow chart of a method for controlling the authority of a school-enterprise information exchange platform provided by an embodiment of the present invention; Figure 2 A structural diagram of a permission control system of a school-enterprise information exchange platform provided by one embodiment of the present invention; Figure 3A schematic diagram of the structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the permission control method and system of a school-enterprise information exchange platform proposed by the present invention, its specific implementation method, structure, characteristics and effects, as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment, and the specific features, structures or characteristics in one or more embodiments may be combined in any suitable form. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0025] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0026] The following is a detailed description of a method and system for controlling the authority of a school-enterprise information exchange platform provided by the present invention in conjunction with the accompanying drawings.

[0027] This application embodiment provides a permission control method for a school-enterprise information exchange platform. Figure 1 , which shows a flow chart of a permission control method for a school-enterprise information exchange platform provided by an embodiment of the present invention, the method comprising: Step S101: within a preset historical time period, collect the access time length of each user in the school-enterprise information exchange platform for browsing each page type every day.

[0028] In a specific implementation of an embodiment of the present invention, page types include course notifications, internship opportunities, job advertisements, academic exchanges, and project practices. Implementers can adjust them according to the specific implementation environment. The specific page types can be obtained according to the names of the various sections of the school-enterprise information exchange platform in the specific implementation environment, which will not be further elaborated here. In a specific implementation of an embodiment of the present invention, the preset historical time period is set to within five working days before the current time, which can be adjusted according to the specific implementation environment; the length of time for each user to browse each page type every day within the preset historical time period is obtained through the log information of the school-enterprise information exchange platform. It should be noted that all data obtained in the embodiment of the present invention are data authorized by the user, and the length of the access time is in minutes, which will not be further elaborated here.

[0029] Step S102: Determine each user's recent visit attention to each page type based on the uniform distribution of each user's visit time to each page type in time series and the overall duration; determine the corresponding platform access frequency similarity based on the similarity between the maximum recent visit attention value corresponding to each user and the maximum recent visit attention values ​​corresponding to other users.

[0030] When the browsing habits of two users on the school-enterprise information exchange platform are the same or similar, it can be considered that the two users are more likely to belong to the same major or department, and the corresponding permission requirements should be more similar. Therefore, through cluster analysis, the platform users with the same browsing habits can be divided into a cluster, and then the permissions can be adjusted according to the overall characteristics of the permissions of each user in the cluster to improve the effect of permission control on the school-enterprise information exchange platform.

[0031] Regarding the analysis of browsing habits, first of all, from an overall perspective, since the content on the school-enterprise information exchange platform is continuously updated, users in the same major or department who have demand for the school-enterprise information exchange platform usually pay high attention to the required page types, and learn about relevant content every day and pay attention to whether there is new content released on the corresponding page types; therefore, first of all, based on the uniform distribution of the length of time each user visits each page type in time series and the overall length of time, the recent visit attention is determined, and then based on the similarity of the maximum values ​​of the recent visit attention between users, from the perspective of the overall demand for the school-enterprise information exchange platform, the similarity of browsing habits between users is measured by the calculated platform access frequency similarity.

[0032] Preferably, in some possible implementations of the embodiments of the present invention, the process of acquiring the recent visit attention includes: Normalize the mean of the access time length of each user browsing each page type on all days in the preset historical time period to determine the overall attention of each user to each page type. Negatively correlate the standard deviation of the access time length of each user browsing each page type on all days in the preset historical time period to determine the regular attention of each user to each page type.

[0033] For each page type of each user, the larger the average length of time that users visit this page type in all days, the greater the user's attention demand for this page type in the preset historical time period, that is, the greater the overall attention, the more likely the corresponding page type is to meet the user's browsing needs. In the case of a large overall attention, the closer the length of time that users visit this page type in all days, the more important the corresponding page type is to the user, so that a lot of time is needed to browse it every day, and the more accurate the user's browsing needs reflected by this page type.

[0034] Therefore, the recent visit attention of each user to each page type is further determined based on the product of the overall attention and the regular attention, so that the greater the recent visit attention, the greater the user's demand for the corresponding page type, and the corresponding page type can better reflect the user's browsing habits. In other specific implementations of the embodiments of the present invention, the implementer can also calculate the recent visit attention by other methods based on the correlation, such as by using the mean, the normalized value of the sum, etc. instead of the product for calculation, which will not be further elaborated here.

[0035] In a specific implementation of the embodiment of the present invention, the process of obtaining the recent visit attention is expressed by the formula: ;in, For the User to Recent visit attention of each page type; The number of days in the preset historical time period; For the The user is the first in the preset historical time period Views The length of time each page type is visited; For the User to The overall popularity of each page type; For the Users browsed the first The standard deviation of the visit time of each page type, that is, the regular attention.

[0036] Preferably, in some possible implementations of the embodiments of the present invention, the process of acquiring the platform access frequency similarity includes: Each user is taken as the target user in turn, and each user other than the target user is taken as the comparison user; the difference between the maximum value of the target user's recent visit attention to all page types and the maximum value of the recent visit attention of each comparison user to all page types is negatively correlated and mapped to determine the platform access frequency similarity between the target user and each comparison user. For each user, the greater the recent visit attention, the greater the user's demand for the corresponding page type, and the more the corresponding page type can reflect the user's browsing habits. Therefore, according to the acquisition process of recent visit attention, the greater the corresponding maximum value of recent visit attention, the higher the overall browsing demand of the user's browsing habits, that is, the higher the user's attention to the platform based on browsing needs; if the value between the maximum values ​​of recent visit attention between the target user and the comparison user is closer, it means that the target user and the comparison user are closer in their attention to the school-enterprise information exchange platform in browsing habits, and the browsing habits between the target user and the comparison user are more similar.

[0037] Preferably, in a specific implementation of the embodiment of the present invention, the process of obtaining the platform access frequency similarity includes: ;in, For the The user and the corresponding The similarity of platform access frequency between the compared users; For the The maximum value of a user's recent visit attention to all page types; For the The corresponding user Compare the maximum values ​​of users' recent visit attention to all page types; is the absolute value symbol; is an exponential function with a natural constant as the base. Implementers can adopt other negative correlation mapping methods according to the specific implementation environment, such as , and the reciprocal, where is a hyperbolic tangent function, which will not be further described here.

[0038] Step S103: According to the overall numerical distribution of the recent visit attention of all marked page types corresponding to each user, all marked page types of each user are divided into low-visit page types and high-visit page types; based on the similarity of platform access frequency, the corresponding initial clustering distance is determined according to the association between each user and other users on the low-visit page types and the high-visit page types.

[0039] Secondly, in the dimension of specific page types visited, it is necessary to consider that users will choose page types to browse based on work or study needs; for users with the same browsing habits, the page types they browse usually maintain a high degree of similarity; however, it is necessary to consider that among all page types browsed by each user, there are usually different recent visit attentions; page types with higher recent visit attentions are more important to users and can clearly reflect users' browsing needs; therefore, low-visit page types and high-visit page types can be divided according to the overall numerical distribution of recent visit attentions of all marked page types corresponding to each user; although page types with lower recent visit attentions are less important, they may belong to page types associated with user browsing needs, so further based on the association between each user and other users on low-visit page types and high-visit page types, the browsing habit similarity between users is measured in the dimension of specific page types visited, and on this basis, combined with the previously calculated platform access frequency similarity, the initial clustering distance for clustering analysis is determined in the dimension of browsing habit similarity.

[0040] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the classification of all marked page types of each user into low-visit page types and high-visit page types according to the overall numerical distribution of the recent visit attention of all marked page types corresponding to each user includes: For any user: The page type corresponding to the user whose recent visit attention is not 0 is used as the user's marked page type; all the marked page types corresponding to the user are arranged in descending order of recent visit attention to determine the user's marked page type sequence; in the marked page type sequence, the difference between the recent visit attention corresponding to each marked page type and the recent visit attention of the next marked page type is used as the attention change value of each marked page type; wherein the attention change value of the last marked page type in the marked page type sequence is set to the preset change value.

[0041] Only when the user does not browse the corresponding page type at all, the corresponding recent visit attention will take the value of 0, and the page type that the user has not browsed within the preset historical time period has no analysis value, so the page types marked with a recent visit attention that is not 0 are screened out for analysis. Further, by arranging the marked page types in order from large to small, the obtained marked page type sequence can more clearly show the recent visit attention distribution of the page type, which is convenient for the subsequent distinction between low-visit page types and high-visit page types. In a specific implementation of an embodiment of the present invention, the preset change value is set to 0 to prevent the last marked page type from being meaningless because the attention change value cannot be calculated.

[0042] According to the concentrated distribution of the tagged page types before and after each tagged page type in the tagged page type sequence, the distinguishing page types in the tagged page type sequence are screened out; in the tagged page type sequence, all the tagged page types before the distinguishing page type are regarded as the user's high-visit page types; the distinguishing page type and all the tagged page types after the distinguishing page type are regarded as the user's low-visit page types.

[0043] The distinguishing page type is the page type at the boundary between the low-visit page type and the high-visit page type. The distinguishing page type is used as the boundary, so that the page types on both sides show completely different access characteristics. For the user's high-attention content, the corresponding high-visit page type usually has a large recent visit attention, while for the low-attention content that the user is not keen on, the user usually only pays attention occasionally, and the corresponding recent visit attention is usually relatively small; therefore, for the selection of distinguishing page types, it is necessary to meet the overall obvious difference in attention between the selected low-visit page types and high-visit page types; and the more concentrated the distribution of the recent visit attention of the page types on both sides, the greater the difference between the low-visit page types and the high-visit page types divided by the distinguishing page type, and the more reasonable the selection of the corresponding distinguishing page type.

[0044] Preferably, in some possible implementations of the embodiments of the present invention, the acquisition process of distinguishing page types includes: Taking each marked page type in the marked page type sequence as the target page type in turn; In the marked page type, the average of the recent visit attention of all marked page types before the target page type is used as the first attention characteristic value of the target page type; the average of the recent visit attention of the target page type and all marked page types after the target page type is used as the second attention characteristic value of the target page type; and the characteristic discrimination of the target page type is determined based on the difference between the first attention characteristic value and the second attention characteristic value.

[0045] Since distinguishing page types requires that there is an overall obvious difference in attention between the low-visit page types and the high-visit page types screened out, the greater the difference between the first attention feature value and the second attention feature value, that is, the greater the feature distinction, the greater the overall difference in attention between the low-visit page types and the high-visit page types screened out when the target page type is used as the distinguishing page type, and the more likely the corresponding target page type is to be a distinguishing page type.

[0046] The average of all attention change values ​​of all marked page types before the target page type is taken as the first attention change value of the target page type; the average of all attention change values ​​of the marked page types after the target page type is taken as the second attention change value of the target page type; the sum of the first attention change value and the second attention change value is negatively correlated to determine the change differentiation of the target page type. The more concentrated the distribution of the recent visit attention of the page types on both sides, the more stable the change of attention within the respective ranges on both sides; therefore, when dividing with the target page type as the boundary, the smaller the sum of the first attention change value and the second attention change value, the more stable the change of attention within the respective ranges on both sides of the target page type, and the more consistent with the characteristics of the concentrated distribution of recent visit attention of the page types on both sides; therefore, the greater the change differentiation, the more likely the corresponding target page type is to be a differentiated page type.

[0047] Since the larger the feature discrimination, the larger the change discrimination, the more likely the corresponding target page type is to be a distinguishing page type, that is, when the target page type is the distinguishing page type, the greater the overall difference in the attention of the page types on both sides in the marked page type sequence, therefore, further according to the product of the feature discrimination and the change discrimination, the final discrimination of the target page type is determined; the final discrimination of all marked page types in the marked page type sequence is calculated; and the marked page type with the largest final discrimination is used as the distinguishing page type. It should be noted that, in addition to the product, the implementer can also calculate the final discrimination by other methods according to the correlation, such as the normalized value of the mean and the sum, etc., which will not be further elaborated here.

[0048] In a specific implementation of the embodiment of the present invention, the process of obtaining the final discrimination is expressed by the formula: ;in, For the The first in the sequence of user's tagged page types The final discrimination of the marked page types; is the absolute value symbol; For the The first in the sequence of user's tagged page types The first attention feature value of the marked page type, that is, The average of recent visit attention of all marked page types before the marked page type; For the The first in the sequence of user's tagged page types The second attention feature value of the marked page type, that is, The average of recent visit attention of all tagged page types after the tagged page type (excluding itself); For the The first in the sequence of user's tagged page types The first attention change value of the marked page type; For the The first in the sequence of user's tagged page types The second attention change value of the marked page type; For the The first in the sequence of user's tagged page types The feature discrimination of the marked page type; For the The first in the sequence of user's tagged page types The variation of the marking page type can also be used The first in the sequence of user's tagged page types The change in attention value of the marked page type is replaced by the same essence, which will not be further described here; It is a linear normalization function. The implementer can adjust the normalization method according to the specific implementation environment.

[0049] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the initial clustering distance includes: Obtain a low-visit page type set consisting of all low-visit page types of each user, and a high-visit page type set consisting of all high-visit page types; The platform access frequency similarity is positively correlated and mapped to determine the low access weight; the low access weight is less than or equal to the platform access frequency similarity. In a specific implementation of an embodiment of the present invention, half of the platform access frequency similarity is used as the low access weight. It should be noted that when the platform access frequency similarity is 0, in order to allow the subsequent analysis process to continue, the value of the platform access frequency similarity in the subsequent calculation process is set to 0.1, and the corresponding low access weight is 0.05, which can be adjusted by itself and will not be further elaborated here.

[0050] According to the Jaccard coefficient between each user's low-visit page type set and each other user's low-visit page type set, the corresponding low-visit correlation is determined; according to the product between the low-visit correlation and the low-visit weight, the low-weighted correlation between each user and each other user is determined; according to the Jaccard coefficient between each user's high-visit page type set and each other user's high-visit page type set, the corresponding high-visit correlation is determined; according to the product between the high-visit correlation and the platform access frequency similarity, the high-weighted correlation between each user and each other user is determined. It should be noted that the Jaccard coefficient is a technical means well known to those skilled in the art, and will not be further defined or elaborated here.

[0051] According to the properties of the Jaccard coefficient, the larger the Jaccard coefficient of the low-visit page type set between two users, the higher the overlap of the corresponding two low-visit page type sets, and the more similar the browsing habits of the two users are; the same is true for the high-visit page type set. The larger the Jaccard coefficient of the high-visit page type set between two users, the higher the overlap of the corresponding two high-visit page type sets, and the more similar the corresponding browsing habits are. Therefore, the greater the low-visit correlation and high-visit correlation, the more similar the browsing habits of the two users; and the platform access frequency similarity represents the browsing habit similarity of the two users from the perspective of platform attention, so it is weighted in combination with the platform access frequency similarity. The reason for setting the weight of high-visit correlation greater than that of low-visit correlation is that high-visit page types can better reflect users' browsing habits. The more similar the attention of high-visit page types is, the more similar the users' access habits are in terms of browsing needs, and the more reasonable the measured browsing habit similarity is.

[0052] Therefore, for any two users, the larger the corresponding low-weighted correlation is, the larger the high-weighted correlation is, indicating that the two users are more similar in their attention to page types with different visit degrees, and the corresponding browsing habits are more consistent, which means that the two users are more likely to belong to the same platform user cluster set; therefore, the sum of the low-weighted correlation and the high-weighted correlation is further negatively correlated to determine the initial cluster distance between each user and each other user. The smaller the initial cluster distance is, the more likely it is to be classified into the same cluster, so a negative correlation mapping is performed.

[0053] In a specific implementation of the embodiment of the present invention, the process of obtaining the initial clustering distance is expressed by the formula: ;in, For users and users The initial clustering distance between For users and users The similarity of platform access frequency between them; For users The collection of low-visit page types and users The Jaccard coefficient between the low-visit page type sets, that is, the user and users Low access correlation between them; For users A collection of highly visited page types and users The Jaccard coefficient between the sets of highly visited page types, that is, the user and users High access correlation between For users and users Low access weight between; is a linear normalization function. The implementer can adjust the normalization method according to the specific implementation environment. In addition, implementers can use other negative correlation mapping methods according to the specific implementation environment, such as the inverse of the exponential function with a natural constant as the base, and the reciprocal, where is a hyperbolic tangent function, which will not be further described here.

[0054] Step S104: Determine the corresponding distance adjustment coefficient based on the overlap of platform access time periods between each user and other users; perform cluster analysis on all users based on the final cluster distance after weighted adjustment of the initial cluster distance based on the distance adjustment coefficient to obtain at least two platform user clusters; and perform permission control on the school-enterprise information exchange platform based on the similarity of permissions of each user in the platform user cluster.

[0055] In addition, in the time dimension, the browsing time periods of users with the same or similar browsing habits are usually the same. Considering that there is usually little overlap between the platform usage time periods of schools and enterprises, the distance adjustment coefficient that characterizes the similarity of browsing habits between users in the time dimension can be determined according to the overlap of platform access time periods between users; the initial clustering distance is further adjusted according to the distance adjustment coefficient to obtain a more accurate final clustering distance, so that the platform user clustering clusters finally divided according to the final clustering distance are more accurate, and the permissions are further adjusted according to the overall characteristics of the permissions of each user in the platform user clustering cluster, thereby improving the effect of permission control of the school-enterprise information exchange platform.

[0056] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the distance adjustment coefficient includes: In the preset historical time period, the cumulative value of the time each user uses the school-enterprise information exchange platform in the preset enterprise work time period of all days is used as the enterprise work time browsing time of each user; the ratio between the enterprise work time browsing time and the total time each user browses the school-enterprise information exchange platform in the preset time period is used as the reference enterprise time proportion of each user. The difference between the reference enterprise time proportion of the target user and the reference enterprise time proportion of each comparison user is positively correlated and mapped to determine the distance adjustment coefficient between the target user and the comparison user.

[0057] In a specific implementation of an embodiment of the present invention, the preset enterprise working time period is set to 9:00 a.m. to 6:00 p.m., which can be adjusted according to the specific implementation environment. There are usually differences in the usage habits of the school-enterprise information exchange platform between school and enterprise users. Enterprises usually use it during fixed working hours on weekdays, while students usually use it in their spare time due to daily course arrangements and other reasons, especially during the company's rest time, that is, in the evening or at night. Therefore, the browsing time overlap between enterprise users and student users is usually small, so browsing habits can be distinguished based on the overlap of browsing time periods. Therefore, the greater the difference in the proportion of reference enterprise time between two users, the smaller the overlap of the browsing time periods of the two users, and the greater the corresponding browsing habit differences; therefore, when clustering, it is necessary to divide the two users into different clustering clusters as much as possible based on a larger clustering distance.

[0058] In a specific implementation of the embodiment of the present invention, the process of obtaining the distance adjustment coefficient is expressed by the formula: ;in, For users and users Distance adjustment factor; For users of the company's working hours, that is, the user The accumulated value of the time spent using the school-enterprise information exchange platform during the preset enterprise working hours on all days; For users of the company's working hours, that is, the user The accumulated value of the time spent using the school-enterprise information exchange platform during the preset enterprise working hours on all days within the preset time period; For users The total length of time spent browsing the school-enterprise information exchange platform within the preset time period; For users The total length of time spent browsing the school-enterprise information exchange platform within the preset time period; For users The proportion of time for the reference enterprise; For users The proportion of reference enterprise time; It is a linear normalization function, and the implementer can adjust the normalization method according to the specific implementation environment. After the positive correlation mapping, the distance adjustment coefficient ranges from 1 to 2, so that the initial clustering distance can be adaptively enlarged by the distance adjustment coefficient, thereby increasing the differentiation of users with different browsing habits in the clustering process.

[0059] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the final clustering distance includes: According to the product of the initial cluster distance and the distance adjustment coefficient, the final cluster distance between each user and each other user is determined. In a specific implementation of the embodiment of the present invention, the process of obtaining the final cluster distance is expressed by the formula: ;in, For users and users The final cluster distance between For users and users Distance adjustment factor between For users and users The initial clustering distance between . After weighting the initial clustering distance by the distance adjustment coefficient ranging from 1 to 2, users with different browsing habits can be more distinguished in the clustering process.

[0060] In a specific implementation of the embodiment of the present invention, a k-means clustering analysis algorithm is used to perform clustering analysis according to the final clustering distances between all users, and all users are divided into 20 clusters, and the number of clusters can be adjusted according to the specific implementation environment. It should be noted that the k-means clustering analysis algorithm is a technical means well known to those skilled in the art, and will not be further limited or elaborated here.

[0061] Preferably, in some possible implementations of the embodiments of the present invention, the process of controlling the authority of the school-enterprise information exchange platform according to the similarity of the authority of each user in the platform user clustering cluster includes: In each platform user cluster, users with the same permission type are regarded as a user set; all user sets in each platform user cluster are counted; all permission types corresponding to the user set with the largest number of users are regarded as the representative permission types of each platform user cluster. Each user has certain permissions to use or manage the school-enterprise information exchange platform, but the permission types of different users may be different. For example, user A has system management permissions and content approval permissions, but user B only has content approval permissions. It is considered that user A and user B have different permission types. For user A, system management permissions and content approval permissions belong to the same permission type as a whole.

[0062] After dividing each cluster by browsing habits, the browsing habits of users in the same cluster are usually highly similar. In principle of the authority allocation of the school-enterprise information exchange platform, the authority types of these users should be consistent. Therefore, the authority type with the largest number of people in each platform user cluster is selected as the representative authority type, and then other users outside the user set with the largest number of users are selected as users to be reviewed; the school-enterprise information exchange platform authority control is performed on all users to be reviewed according to the representative authority type. In a specific implementation method of an embodiment of the present invention, it is determined by manual review whether the user to be reviewed can have the representative authority type of the platform user cluster, and the authority is adjusted according to the judgment result, so that the authority control effect of the school-enterprise information exchange platform is better.

[0063] In summary, a permission control method for a school-enterprise information exchange platform first determines the recent access attention that reflects the user's attention to school-enterprise information according to the uniformity of the user's access time length in the time sequence and the overall size; then determines the platform access frequency similarity between users according to the similarity of the recent access attention between users; then, for each page type, high and low access page types are divided according to the overall numerical concentration distribution of the recent access attention of all marked page types corresponding to the user; thus, according to the association between the high and low access page types between users and the platform access frequency similarity calculated previously, the initial clustering distance between users is determined; and on this basis, the initial clustering is adjusted in combination with the overlap of the platform access time periods between users, and clustering is performed according to the adjusted final clustering distance, so that users with similar browsing habits are divided into a platform user clustering cluster, thereby adjusting permissions according to the overall characteristics of user permissions in the platform user clustering cluster, solving the problem that the permission control method based on role-based access control has a poor effect on the permission control of the school-enterprise information exchange platform, so that the permission control effect of the school-enterprise information exchange platform is better according to the similarity of the permissions of each user in the platform user clustering cluster.

[0064] This application also provides a permission control system for the school-enterprise information exchange platform, please refer to Figure 2 , which shows a structural diagram of a permission control system of a school-enterprise information exchange platform provided by an embodiment of the present invention, the system includes: a data collection module 201, a first determination module 202, a second determination module 203 and a permission control module 204.

[0065] The data collection module 201 is used to collect the access time length of each user browsing each page type in the school-enterprise information exchange platform every day within a preset historical time period; The first determination module 202 is used to determine the recent visit attention of each user to each page type according to the uniform distribution of the access time length of each user to each page type in time series and the overall duration; determine the corresponding platform access frequency similarity according to the similarity between the maximum recent visit attention value corresponding to each user and the maximum recent visit attention value corresponding to other users; The second determination module 203 is used to divide all the marked page types of each user into low-visited page types and high-visited page types according to the overall numerical distribution of the recent access attention of all marked page types corresponding to each user; based on the platform access frequency similarity, according to the association between each user and other users on the low-visited page types and the high-visited page types, determine the corresponding initial clustering distance; The permission control module 204 is used to determine the corresponding distance adjustment coefficient according to the overlap of the platform access time period between each user and other users; perform cluster analysis on all users according to the final cluster distance after weighted adjustment of the initial cluster distance according to the distance adjustment coefficient, and obtain at least two platform user clusters; and perform permission control on the school-enterprise information exchange platform according to the similarity of the permissions of each user in the platform user cluster.

[0066] It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above function allocation can be completed by different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the permission control system of a school-enterprise information exchange platform provided in the above embodiment and the permission control method embodiment of a school-enterprise information exchange platform belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0067] The present application also provides a computer device. Figure 3, which shows a schematic diagram of the structure of a computer device provided by an embodiment of the present invention, the computer device includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302, wherein when the processor 302 executes the computer program 303, the computer device can execute any of the aforementioned methods for controlling the authority of the school-enterprise information exchange platform.

[0068] The embodiment of the present application also provides a computer program product. When the computer program product is run on a computer device, the computer device can execute any of the authority control methods for the school-enterprise information exchange platform introduced above.

[0069] An embodiment of the present application also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is executed on a computer device, the computer device can execute any of the aforementioned methods for controlling the authority of the school-enterprise information exchange platform.

[0070] In the embodiments provided in the present application, it should be understood that the provided computer device, computer program product and computer-readable storage medium are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the methods provided above and will not be repeated here.

[0071] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A permission control method for a school-enterprise information exchange platform, characterized in that: The method comprises: In the preset historical time period, collect the access time length of each user browsing each page type in the school-enterprise information exchange platform every day; Determine the recent visit attention of each user to each page type based on the uniform distribution of the visit time length of each user to each page type in time series and the overall visit time length; determine the corresponding platform visit frequency similarity based on the similarity between the maximum recent visit attention value corresponding to each user and the maximum recent visit attention value corresponding to other users; According to the overall numerical distribution of recent visit attention of all marked page types corresponding to each user, all marked page types of each user are divided into low-visit page types and high-visit page types; based on the similarity of the platform access frequency, according to the association between each user and other users on the low-visit page types and the high-visit page types, the corresponding initial clustering distance is determined; According to the overlap of platform access time periods between each user and other users, the corresponding distance adjustment coefficient is determined; the final clustering distance after weighted adjustment of the initial clustering distance according to the distance adjustment coefficient is clustered and analyzed on all users to obtain at least two platform user clustering clusters; and the authority control of the school-enterprise information exchange platform is performed according to the similarity of the authority of each user in the platform user clustering cluster.

2. According to claim 1, the authority control method of the school-enterprise information exchange platform is characterized in that: The process of obtaining the recent visit attention includes: Normalize the average length of time each user spends browsing each page type on all days in a preset historical time period to determine each user's overall attention to each page type; Negative correlation mapping is performed on the standard deviation of the length of time each user spends browsing each page type on all days in a preset historical time period to determine the regular attention each user pays to each page type; The recent visit attention of each user to each page type is determined according to the product of the overall attention and the regular attention.

3. The authority control method of a school-enterprise information exchange platform according to claim 1 is characterized in that: The process of obtaining the platform access frequency similarity includes: Take each user as the target user in turn, and take each user other than the target user as the comparison user; A negative correlation mapping is performed between the maximum value of the target user's recent visit attention to all page types and the maximum value of each comparison user's recent visit attention to all page types to determine the platform visit frequency similarity between the target user and each comparison user.

4. The authority control method of a school-enterprise information exchange platform according to claim 1 is characterized in that: The acquisition process of dividing all marked page types of each user into low-visit page types and high-visit page types according to the overall numerical distribution of recent visit attention of all marked page types corresponding to each user includes: For any user: The page type corresponding to the user whose recent visit attention is not 0 is used as the user's marked page type; all marked page types corresponding to the user are arranged in descending order of recent visit attention to determine the user's marked page type sequence; in the marked page type sequence, the difference between the recent visit attention corresponding to each marked page type and the recent visit attention of the next marked page type is used as the attention change value of each marked page type; wherein the attention change value of the last marked page type in the marked page type sequence is set to a preset change value; Filtering out distinguishing page types in the marked page type sequence according to the concentrated distribution of marked page types before and after each marked page type in the marked page type sequence; In the marked page type sequence, all marked page types before the distinguished page type are regarded as the user's high-visit page types; and the distinguished page type and all marked page types after the distinguished page type are regarded as the user's low-visit page types.

5. The authority control method of a school-enterprise information exchange platform according to claim 4 is characterized in that: The acquisition process of distinguishing page types includes: taking each marked page type in the marked page type sequence as a target page type in turn; In the marked page type, the average of the recent visit attention of all marked page types before the target page type is used as the first attention characteristic value of the target page type; the average of the recent visit attention of the target page type and all marked page types after the target page type is used as the second attention characteristic value of the target page type; according to the difference between the first attention characteristic value and the second attention characteristic value, the characteristic discrimination degree of the target page type is determined; The average of all attention change values ​​of all marked page types before the target page type is used as the first attention change value of the target page type; the average of all attention change values ​​of all marked page types after the target page type is used as the second attention change value of the target page type; the sum of the first attention change value and the second attention change value is negatively correlated to determine the change distinction of the target page type; Determine the final discrimination degree of the target page type according to the product of the feature discrimination degree and the change discrimination degree; count the final discrimination degrees of all marked page types in the marked page type sequence; and use the marked page type with the largest final discrimination degree as the distinguishing page type.

6. The authority control method of a school-enterprise information exchange platform according to claim 1 is characterized in that: The process of obtaining the initial clustering distance includes: Obtain a low-visit page type set consisting of all low-visit page types of each user, and a high-visit page type set consisting of all high-visit page types; Perform positive correlation mapping on the platform access frequency similarity to determine a low access weight; the low access weight is less than or equal to the platform access frequency similarity; Determine the corresponding low access relevance according to the Jaccard coefficient between the low access page type set of each user and the low access page type set of each other user; determine the low weighted relevance between each user and each other user according to the product between the low access relevance and the low access weight; Determine the corresponding high-visit correlation according to the Jaccard coefficient between each user's high-visit page type set and each other user's high-visit page type set; determine the high-weighted correlation between each user and each other user according to the product of the high-visit correlation and the platform access frequency similarity; A negative correlation mapping is performed on the sum of the low-weighted association and the high-weighted association to determine an initial clustering distance between each user and each other user.

7. The authority control method of a school-enterprise information exchange platform according to claim 3 is characterized in that: The process of obtaining the distance adjustment coefficient includes: In the preset historical time period, the accumulated value of the time each user spends using the school-enterprise information exchange platform in the preset enterprise working time period of all days is used as the enterprise working time browsing time of each user; the ratio between the enterprise working time browsing time and the total time each user spends browsing the school-enterprise information exchange platform in the preset time period is used as the reference enterprise time ratio of each user; A positive correlation mapping is performed between the reference enterprise time proportion of the target user and the reference enterprise time proportion of each comparison user to determine the distance adjustment coefficient between the target user and the comparison user.

8. The authority control method of a school-enterprise information exchange platform according to claim 1 is characterized in that: The process of obtaining the final clustering distance includes: The final clustering distance between each user and each other user is determined according to the product of the initial clustering distance and the distance adjustment coefficient.

9. The authority control method of a school-enterprise information exchange platform according to claim 1 is characterized in that: The process of controlling the authority of the school-enterprise information exchange platform according to the similarity of the authority of each user in the platform user clustering cluster includes: In each platform user cluster, users with the same permission type are regarded as a user set; all user sets in each platform user cluster are counted; All permission types corresponding to the user set with the largest number of users are used as representative permission types for each platform user cluster; other users outside the user set with the largest number of users are used as users to be reviewed; According to the representative authority type, the school-enterprise information exchange platform authority control is performed on all users to be reviewed.

10. A permission control system for a school-enterprise information exchange platform, characterized in that: The system comprises: The data collection module is used to collect the access time length of each user browsing each page type in the school-enterprise information exchange platform every day within a preset historical time period; The first determination module is used to determine the recent visit attention of each user to each page type according to the uniform distribution of the access time length of each user to each page type in time series and the overall duration; and determine the corresponding platform access frequency similarity according to the similarity between the maximum recent visit attention value corresponding to each user and the maximum recent visit attention value corresponding to other users; The second determination module is used to divide all the marked page types of each user into low-visit page types and high-visit page types according to the overall numerical distribution of the recent visit attention of all marked page types corresponding to each user; based on the similarity of the platform access frequency, according to the association between each user and other users on the low-visit page types and the high-visit page types, determine the corresponding initial clustering distance; The permission control module is used to determine the corresponding distance adjustment coefficient according to the overlap of the platform access time period between each user and other users; perform cluster analysis on all users according to the final cluster distance after weighted adjustment of the initial cluster distance according to the distance adjustment coefficient to obtain at least two platform user clusters; and perform permission control on the school-enterprise information exchange platform according to the similarity of the permissions of each user in the platform user cluster.

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