CN117150533A - Enterprise content management authority management and control method and device - Google Patents
Enterprise content management authority management and control method and device Download PDFInfo
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Abstract
The application provides a method and a device for managing and controlling enterprise content management authorities, which relate to enterprise authority management and comprise the steps of sending enterprise content management authority information and enterprise internal role information to a hierarchical analysis model for hierarchical analysis, and carrying out authority allocation based on a result obtained by the hierarchical analysis to obtain management authority information corresponding to all roles; generating intelligent contract information corresponding to each management authority information based on the management authority information corresponding to all roles; transmitting the role information of the preset staff and the management authority information corresponding to all roles to the trained neural network model for authority allocation to obtain the management authority information corresponding to each staff; the management authority information and the intelligent contract information corresponding to each employee are sent to a preset authority management and control platform for verification and adjustment until the authorities possessed by all employees are the same as the preset targets.
Description
Technical Field
The application relates to the technical field of enterprise rights management, in particular to a method and a device for managing and controlling enterprise content management rights.
Background
Currently, as the complexity and data volume of enterprise content management increases, enterprises need an effective rights management method to ensure sensitive information protection and reasonable rights distribution of enterprise content. However, there are some problems with the conventional enterprise content management rights management methods. First, these methods typically rely on manual authority settings, which are prone to leaks and errors. Secondly, due to the complexity of roles in enterprises and the diversity of authority relationships, the traditional method often cannot accurately perform authority allocation, so that security risks and low working efficiency are caused, and therefore, an allocation method and an allocation device capable of improving the authority allocation efficiency and quality are urgently needed, so that the rights and interests of the enterprises are guaranteed.
Disclosure of Invention
The application aims to provide a method and a device for managing and controlling enterprise content management authorities, so as to solve the problems. In order to achieve the above purpose, the technical scheme adopted by the application is as follows:
in a first aspect, the present application provides a method for managing and controlling enterprise content management rights, including:
acquiring enterprise content management authority information and enterprise internal role information;
transmitting the enterprise content management authority information and the enterprise internal role information to a hierarchical analysis model for hierarchical analysis, and performing authority allocation based on the result obtained by the hierarchical analysis to obtain management authority information corresponding to all roles;
sending the management authority information corresponding to all roles to an intelligent contract generation module for processing to obtain intelligent contract information corresponding to each management authority information;
transmitting the role information of the preset staff and the management authority information corresponding to all roles to the trained neural network model for authority allocation to obtain the management authority information corresponding to each staff;
and sending the management authority information corresponding to each employee and the intelligent contract information corresponding to each management authority information to a preset authority management and control platform for verification and adjustment until the authorities possessed by all employees are the same as the preset targets.
In a second aspect, the present application further provides an enterprise content management authority management and control device, including:
the acquisition unit is used for acquiring enterprise content management authority information and enterprise internal role information;
the analysis unit is used for sending the enterprise content management authority information and the enterprise internal role information to the hierarchical analysis model for hierarchical analysis, and performing authority allocation based on the result obtained by the hierarchical analysis to obtain management authority information corresponding to all roles;
the processing unit is used for sending the management authority information corresponding to all roles to the intelligent contract generation module for processing to obtain intelligent contract information corresponding to each management authority information;
the distribution unit is used for sending the role information of the preset staff and the management authority information corresponding to all roles to the trained neural network model for authority distribution to obtain the management authority information corresponding to each staff;
and the adjusting unit is used for sending the management authority information corresponding to each employee and the intelligent contract information corresponding to each management authority information to a preset authority management and control platform for verification and adjustment until the authorities possessed by all employees are the same as the preset targets.
The beneficial effects of the application are as follows:
the application carries out hierarchical analysis on all functions in an enterprise and internal roles of a clustered enterprise by establishing a hierarchical analysis model, carries out authority allocation based on analysis results, rapidly and effectively distributes management authorities to each role, further realizes specific authority control by utilizing an intelligent contract technology, prevents authority abuse, carries out authority allocation on each employee based on the association degree of the role corresponding to each employee and the management authority, prevents the employees of different departments of the same level from being distributed to the same authorities, leads to authority abuse, carries out analysis and calculation through a neural network to achieve the purpose of intelligent allocation, and finally optimizes and updates the neural network model and intelligent contracts by continuously collecting and analyzing authority use conditions so as to adapt to the change and development of the enterprise.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an enterprise content management and authority control method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an enterprise content management authority management and control device according to an embodiment of the present application.
In the figure, 701, an acquisition unit; 702. an analysis unit; 703. a processing unit; 704. a distribution unit; 705. an adjusting unit; 7021. a first analysis subunit; 7022. a second analysis subunit; 7023. a first computing subunit; 7024. a third analysis subunit; 7025. a fourth analysis subunit; 7026. a second computing subunit; 7027. a fifth analysis subunit; 7031. a first processing unit; 7032. a second processing unit; 7041. a first training subunit; 7042. a second training subunit; 7043. a third training subunit; 7044. a first allocation subunit; 7045. a second allocation subunit; 7051. obtaining a subunit; 7052. a sixth analysis subunit; 7053. and a third processing subunit.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1:
the embodiment provides a management and control method for enterprise content management rights.
Referring to fig. 1, the method is shown to include steps S100, S200, S300, S400, and S500.
S100, acquiring enterprise content management authority information and enterprise internal role information;
it will be appreciated that in this step, the enterprise content management authority information and the enterprise internal role information are determined and input based on the company architecture, wherein each company is different in architecture, all management authorities are different, and internal role information is also different.
S200, sending the enterprise content management authority information and the enterprise internal role information to a hierarchical analysis model for hierarchical analysis, and performing authority allocation based on a result obtained by the hierarchical analysis to obtain management authority information corresponding to all roles;
it will be appreciated that the enterprise content management rights information and the enterprise internal role information are hierarchically distributed by hierarchical analysis in this step, in which step S200 includes
S201, analyzing all elements in enterprise content management authority information and enterprise internal role information, and establishing a hierarchical structure model;
it can be understood that this step classifies and analyzes all elements in the enterprise content management authority information and the enterprise internal role information into a hierarchical structure model formed sequentially from top to bottom. For example, the enterprise internal role information comprises the types of functions, departments, groups and the like, and the enterprise content management rights comprise the types of function level management rights, department management rights, group management rights and the like.
S202, element information of each level in the hierarchical structure model is subjected to layer-by-layer pairwise comparison, and a discrimination matrix is normalized and constructed based on comparison results to obtain at least two discrimination matrices;
it can be understood that the step compares every two factors layer by layer through the hierarchical structure model to obtain the relation of relative importance degree, wherein the elements refer to a certain role of the role information in the enterprise or a certain authority in the enterprise content management authority, each index is scored by using a 1-9 scale method, and a discrimination matrix is obtained after normalization processing, and is shown as follows:
;
wherein: a is a discrimination matrix;the importance ratio of the element i and the element j of the current level to the previous level is scaled; i and j are respectively different kinds of elements; n is the dimension of the hierarchical model.
S203, carrying out feature vector calculation and maximum feature value calculation on all the discrimination matrixes to obtain consistency indexes;
it can be understood that the normalization processing is performed on the discrimination matrix according to the column vector to obtain a normalized matrix; and adding the normalized matrixes according to rows to obtain feature vectors, and calculating the maximum feature value based on the feature vectors.
And S204, under the condition that the consistency index is met, sorting all elements of each level based on the feature vector and the maximum feature value to obtain element sorting information in enterprise content management authority information and element sorting information in enterprise internal role information.
It can be understood that in order to ensure the rationality of the solution weight, the step needs to perform consistency test on the discrimination matrix, in the step, the discrimination matrix is illustrated to meet the consistency index at the moment, and the solution weight is illustrated to be reasonable, after the discrimination matrix passes the consistency test, the step also obtains the index weight by adopting a geometric average method to obtain the feature vector corresponding to the maximum feature value, wherein each element corresponding value is the weight of each factor of the layer, the sorting is performed based on the size of the weight value, and the index for measuring the deviation consistency of the discrimination matrix is obtained based on the maximum feature value and the following formula:
;
wherein: r is a consistency index;the maximum eigenvalue of the matrix is judged; n is the order of the discrimination matrix; e is an average random consistency index;
it is understood that step S200 in this step further includes step S205, step S206, and step S207.
Step S205, carrying out association analysis on the result obtained by the hierarchical analysis, wherein the element ordering information in the enterprise content management authority information and the element ordering information in the enterprise internal role information are subjected to dimensionless processing, so as to obtain the dimensionless authority element ordering information and the dimensionless role element ordering information;
it can be understood that, in order to avoid that the magnitude difference is too large and the subsequent processing error is too large, each sample element is subjected to dimensionless processing before data calculation, so as to eliminate the dimensionality difference between different data, and the formula of the mean conversion method is as follows:;
wherein:is a dimensionless treated parameter, +.>Ordering information for enterprise content management rights information or a certain element within enterprise content management rights information, +.>Sorting a sample mean of information for the enterprise content management rights information or for a certain element within the enterprise content management rights information; />Sample standard deviation of ordering information for a particular element within the enterprise content management rights information or within a particular element within the enterprise content management rights information.
Step S206, carrying out association calculation on the dimensionless right element ordering information and the dimensionless role element ordering information to obtain an association value of the dimensionless right element ordering information and the dimensionless role element ordering information;
it can be understood that in this step, the correlation value is calculated according to the following correlation calculation formula, which is shown below;
;
;
wherein:a relation coefficient of the ordering information f of a certain role element after dimensionless treatment relative to the ordering information k of a certain authority element after dimensionless treatment; f is the non-dimensionalized ordering information of a certain character element; k is the ordering information of a certain authority element after dimensionless treatment;/>a sequence of ordered elements for a role that is dimensionless; />A sequencing sequence for sequencing a non-dimensionalized authority element; />Taking 0-1 as a resolution coefficient; />The relevance corresponding to a role element t after dimensionless treatment; t is the non-dimensionalized character element type; h is the kind of the non-dimensionalized authority element; n is the total number of samples of the dimensionless role element; />A relation coefficient of the non-dimensionalized certain character element ordering information f relative to the non-dimensionalized certain authority element ordering information h;
step S207, determining management authority information corresponding to all roles based on the relevance value, wherein the management authority information corresponding to each role is obtained by mapping authority elements and role elements of which the relevance value is larger than a preset threshold.
It can be understood that the management authority corresponding to each role is determined by performing association analysis based on the weight of each element, so that the condition that the authority is abused or the distribution is not corresponding is prevented.
And S300, sending the management authority information corresponding to all roles to an intelligent contract generation module for processing to obtain intelligent contract information corresponding to each management authority information.
It can be understood that, in this step, specific rights control is implemented by using an intelligent contract technology, so as to prevent rights from being abused, wherein an intelligent contract is built by using variable information, function information and business logic information of the intelligent contract in management rights information, and an intelligent contract to be built is built quickly, wherein in this step, when each piece of management rights information corresponds to one piece of intelligent contract information, and when the management rights information is triggered, the corresponding intelligent contract information is triggered, and whether staff has access rights is judged according to preset business logic, so that the purpose of controlling rights based on the intelligent contract is achieved. It is understood that step S300 includes step S301 and step S302.
Step S301, the management authority information corresponding to all roles is sent to a Bert model for pre-training treatment, and feature sentences of associated authority information corresponding to all roles are generated, wherein the feature sentences are business logic information comprising variable information, function information and intelligent contracts in the management authority information corresponding to all roles;
it can be understood that the pre-training and recognition analysis processing are performed on the management authority information corresponding to all roles through the Bert model, and sentences containing feature words are rapidly determined, wherein the feature words are feature words containing variable information, function information and business logic information of intelligent contracts in the management authority information corresponding to all roles, and further provide basis for the generation of the following intelligent contracts.
Step S302, intelligent contract information corresponding to management authority information corresponding to all roles is generated based on the characteristic sentences, wherein each management authority information corresponds to one intelligent contract information, when the management authority information is triggered, the corresponding intelligent contract information is triggered, and whether the employee has the intelligent contract information of the access authority is judged according to preset business logic.
It can be understood that the intelligent contract information corresponding to the management authority information corresponding to all roles is generated through the feature sentences, so that when an employee uses enterprise content, the enterprise content which is not outside the authority range of the employee cannot be observed or modified, and management and control of the employee authority and employee rights cannot be abused.
S400, sending preset role information of staff and management authority information corresponding to all roles to the trained neural network model for authority allocation to obtain management authority information corresponding to each staff;
it can be understood that in this step, management rights are allocated to all employees through the neural network, where the neural network is trained based on the historical allocation information, and then the trained neural network is optimally selected by using a particle swarm optimization algorithm, so as to achieve the purpose of determining an optimal allocation scheme, prevent rights allocation from not meeting requirements, and reduce the number of times of rights allocation, where in this step, step S400 includes steps S401, S402, S403, S404, and S405.
Step S401, taking role information of preset historical staff and management authority information corresponding to the preset historical roles as training sets according to the sequence of recognition time, and taking the management authority information corresponding to the preset historical staff as verification sets;
it can be understood that the step divides the historical data into a training set and a verification set, ensures that the training of the neural network can reach a preset target, and can be verified through the verification set after the training is completed.
Step S402, transmitting all training sets to a BP neural network model for matching degree calculation, and obtaining the matching degree value of the role information of each historical employee and the management authority information corresponding to each historical role;
it can be understood that in this step, matching degree calculation is performed on role information of the preset historical staff and management authority information corresponding to the preset historical roles, and then distribution relation of authorities is predicted based on the matching degree, so that each management authority can be accurately distributed to the corresponding staff, and distribution efficiency and quality are improved.
Wherein A is the matching degree value of the role information of each history employee and the management authority information corresponding to each history role,representing historic staffThe number of i-th keywords in the character information of (a),/, and (b)>The number of the ith keywords in the management authority information corresponding to the history role is represented, and n represents the total number of the keywords, wherein the keywords are keywords which can represent employee role information or management authority information, such as president, all authority management, and the like.
Step S403, optimizing the BP neural network model based on a particle swarm optimization algorithm, and sending the role information of each historical employee and the matching degree value of the management authority information corresponding to each historical role to the optimized BP neural network model for calculation, and determining the maximum matching degree value of the role information of each historical employee and the management authority information corresponding to each historical role;
the step can be understood to screen each matching degree value through a particle swarm optimization algorithm, select the largest matching degree value, and then determine the role information of each historical employee and the management authority information corresponding to each historical role according to the largest matching degree value.
Step S404, based on the distribution result of the maximum matching degree value, distributing the management authority information corresponding to the maximum matching degree value to the historical staff corresponding to the maximum matching degree value as the management authority information of the historical staff;
and step 405, comparing the verification set with the distribution result, judging whether the distribution result of the verification set is consistent, and if so, obtaining the constructed neural network model.
It can be understood that the step verifies whether the matching degree value selected by the BP neural network is the optimal matching degree value through the verification set, if not, the parameters of the BP neural network are adjusted,
s500, sending the management authority information corresponding to each employee and the intelligent contract information corresponding to each management authority information to a preset authority management and control platform for verification and adjustment until the authorities possessed by all employees are the same as the preset targets.
It can be understood that the purpose of adapting to the change and development of the enterprise is achieved by optimizing and updating the neural network model and the intelligent contract by continuously collecting and analyzing the rights usage in this step, wherein step S500 includes step S501, step S502 and step S503.
Step S501, acquiring abnormal information of the authority control platform and satisfaction information of each employee of the authority control platform;
it can be understood that the authority service condition is collected and analyzed in real time in the step, and then the authority management and control platform is uploaded for analysis.
Step S502, analyzing the contribution degree of each piece of abnormal information to the satisfaction information according to a relation model between the abnormal information of the authority management and control platform and the satisfaction information based on a regression analysis method, and obtaining management authority information corresponding to each employee and verification results of intelligent contract information corresponding to each management authority information;
it will be appreciated that in this step, regression analysis is a statistical method for exploring the correlation between a certain plurality of independent variables (e.g., abnormality information of a plurality of kinds of authority management platforms) and dependent variables (satisfaction information, etc.), and creating a model to describe the correlation. Based on this model, the contribution of each influencing factor can be analyzed.
And step S503, adjusting the management authority information corresponding to each employee according to the verification result until the authorities possessed by all employees are the same as the preset targets.
It can be understood that in this step, according to the management authority information corresponding to each employee and the use result of the intelligent contract information corresponding to each management authority information, it is determined whether the authority possessed by all employees is the same as the predetermined target, and the authority possessed by all employees is modified by adjusting the management authority information corresponding to each employee and the intelligent contract information corresponding to each management authority information.
It can be understood that, firstly, the reasons that the permissions possessed by all employees are different from the preset targets are determined according to the abnormal information and the employee satisfaction information of a plurality of types of the permission management and control platform, and then, the related data of the associated permissions are adjusted according to the preset parameter setting rules, so that the permissions possessed by all employees are changed. The preset parameter setting rules comprise: and deleting the authority of the staff when the authority of the staff is greater than or equal to a set value, and determining the authority of the staff to be provided and increasing when the authority of the staff is less than the set value.
Example 2:
as shown in fig. 2, the present embodiment provides an enterprise content management authority management apparatus, which includes an acquisition unit 701, an analysis unit 702, a processing unit 703, an allocation unit 704, and an adjustment unit 705.
An acquiring unit 701, configured to acquire enterprise content management authority information and enterprise internal role information;
the analysis unit 702 is configured to send the enterprise content management authority information and the enterprise internal role information to a hierarchical analysis model for hierarchical analysis, and perform authority allocation based on a result obtained by the hierarchical analysis, so as to obtain management authority information corresponding to all roles;
the analysis unit 702 includes a first analysis subunit 7021, a second analysis subunit 7022, a first calculation subunit 7023, and a third analysis subunit 7024.
A first analysis subunit 7021, configured to analyze enterprise content management authority information and all elements in the enterprise internal role information, and establish a hierarchical structure model;
the second analysis subunit 7022 is configured to perform layer-by-layer pairwise comparison on element information of each level in the hierarchical structure model, and normalize and construct a discrimination matrix based on a comparison result, so as to obtain at least two discrimination matrices;
a first calculating subunit 7023, configured to perform feature vector calculation and maximum feature value calculation on all the discrimination matrices to obtain a consistency index;
and a third analysis subunit 7024, configured to, when the consistency index is satisfied, sort all the elements of each level based on the feature vector and the maximum feature value, so as to obtain element sorting information in the enterprise content management authority information and element sorting information in the enterprise internal role information.
The analysis unit 702 further includes a fourth analysis subunit 7025, a second calculation subunit 7026, and a fifth analysis subunit 7027.
A fourth analysis subunit 7025, configured to perform association analysis on a result obtained by the hierarchical analysis, where the element ordering information in the enterprise content management authority information and the element ordering information in the enterprise internal role information are subjected to dimensionless processing, so as to obtain dimensionless authority element ordering information and dimensionless role element ordering information;
a second calculating subunit 7026, configured to perform a correlation calculation on the dimensionless right element ranking information and the dimensionless role element ranking information, so as to obtain a correlation value of the dimensionless right element ranking information and the dimensionless role element ranking information;
and a fifth analysis subunit 7027, configured to determine management authority information corresponding to all roles based on the association value, where the management authority information corresponding to each role is obtained by mapping authority elements and role elements that have association values greater than a preset threshold.
A processing unit 703, configured to send management authority information corresponding to all roles to the intelligent contract generation module for processing, so as to obtain intelligent contract information corresponding to each management authority information;
the processing unit 703 includes a first processing unit 7031 and a second processing unit 7032.
The first processing unit 7031 is configured to send management authority information corresponding to all roles to a Bert model for performing pre-training processing, and generate feature sentences of associated authority information corresponding to all roles, where the feature sentences include variable information, function information and business logic information of intelligent contracts in the management authority information corresponding to all roles;
the second processing unit 7032 is configured to generate, based on the feature statement, intelligent contract information corresponding to management authority information corresponding to all roles, where each management authority information corresponds to one piece of intelligent contract information, and when the management authority information is triggered, the corresponding intelligent contract information is triggered, and determine whether the employee has the intelligent contract information of the access authority according to a preset business logic.
The allocation unit 704 is configured to send preset role information of employees and management authority information corresponding to all roles to the trained neural network model for authority allocation, so as to obtain management authority information corresponding to each employee;
wherein the allocation unit 704 comprises a first training subunit 7041, a second training subunit 7042, a third training subunit 7043, a first allocation subunit 7044, and a second allocation subunit 7045.
A first training subunit 7041, configured to use role information of a preset historical employee and management authority information corresponding to the preset historical role as a training set according to an identification time sequence, and use management authority information corresponding to the preset historical employee as a verification set;
the second training subunit 7042 is configured to send all training sets to the BP neural network model for matching degree calculation, so as to obtain a matching degree value of the role information of each historical employee and the management authority information corresponding to each historical role;
the third training subunit 7043 is configured to optimize the BP neural network model based on a particle swarm optimization algorithm, send the matching degree value of the role information of each historical employee and the management authority information corresponding to each historical role to the optimized BP neural network model for calculation, and determine the maximum matching degree value of the role information of each historical employee and the management authority information corresponding to each historical role;
a first allocation subunit 7044, configured to obtain an allocation result based on the maximum matching degree value, where management authority information corresponding to the maximum matching degree value is allocated to a historical employee corresponding to the maximum matching degree value as management authority information of the historical employee;
and the second allocation subunit 7045 is configured to compare the verification set with the allocation result, determine whether the allocation result of the verification set is consistent, and if so, obtain the constructed neural network model.
And the adjusting unit 705 is configured to send the management authority information corresponding to each employee and the intelligent contract information corresponding to each management authority information to a preset authority management and control platform for verification and adjustment until the authorities possessed by all employees are the same as the preset targets.
The adjustment unit 705 includes an acquisition subunit 7051, a sixth analysis subunit 7052, and a third processing subunit 7053.
An obtaining subunit 7051, configured to obtain exception information of the rights management and control platform and satisfaction information of each employee of the rights management and control platform;
a sixth analysis subunit 7052, configured to analyze, based on a regression analysis method, a contribution degree of each piece of abnormal information to the satisfaction information according to a relationship model between the abnormal information and the satisfaction information of the rights management and control platform, so as to obtain management rights information corresponding to each employee and a verification result of intelligent contract information corresponding to each management rights information;
and the third processing subunit 7053 is configured to adjust the management authority information corresponding to each employee according to the verification result until the authority possessed by all employees is the same as the predetermined target.
It should be noted that, regarding the apparatus in the above embodiments, the specific manner in which the respective modules perform the operations has been described in detail in the embodiments regarding the method, and will not be described in detail herein.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. A method for managing and controlling enterprise content management rights, comprising:
acquiring enterprise content management authority information and enterprise internal role information;
transmitting the enterprise content management authority information and the enterprise internal role information to a hierarchical analysis model for hierarchical analysis, and performing authority allocation based on the result obtained by the hierarchical analysis to obtain management authority information corresponding to all roles;
sending the management authority information corresponding to all roles to an intelligent contract generation module for processing to obtain intelligent contract information corresponding to each management authority information;
transmitting the role information of the preset staff and the management authority information corresponding to all roles to the trained neural network model for authority allocation to obtain the management authority information corresponding to each staff;
and sending the management authority information corresponding to each employee and the intelligent contract information corresponding to each management authority information to a preset authority management and control platform for verification and adjustment until the authorities possessed by all employees are the same as the preset targets.
2. The method for managing and controlling enterprise content management rights according to claim 1, wherein said sending the enterprise content management rights information and the enterprise internal role information to a hierarchical analysis model for hierarchical analysis comprises:
analyzing enterprise content management authority information and all elements in the enterprise internal role information, and establishing a hierarchical structure model;
element information of each level in the hierarchical structure model is subjected to layer-by-layer pairwise comparison, and a discrimination matrix is normalized and constructed based on comparison results to obtain at least two discrimination matrices;
performing feature vector calculation and maximum feature value calculation on all the discrimination matrixes to obtain consistency indexes;
and under the condition that the consistency index is met, sorting all elements of each layer based on the feature vector and the maximum feature value to obtain element sorting information in enterprise content management authority information and element sorting information in enterprise internal role information.
3. The enterprise content management and authority control method according to claim 1, wherein the performing authority allocation based on the result obtained by the hierarchical analysis to obtain management authority information corresponding to all roles includes:
performing association analysis on the result obtained by the hierarchical analysis, wherein element ordering information in enterprise content management authority information and element ordering information in enterprise internal role information are subjected to dimensionless processing, and the dimensionless authority element ordering information and the dimensionless role element ordering information are obtained;
performing association calculation on the dimensionless right element ordering information and the dimensionless role element ordering information to obtain an association degree value of the dimensionless right element ordering information and the dimensionless role element ordering information;
and determining management authority information corresponding to all roles based on the relevance value, wherein the management authority information corresponding to each role is obtained by mapping authority elements and role elements of which the relevance value is larger than a preset threshold.
4. The enterprise content management and authority control method according to claim 1, wherein the sending the management authority information corresponding to all roles to the intelligent contract generation module for processing to obtain the intelligent contract information corresponding to each management authority information includes:
transmitting the management authority information corresponding to all roles to a Bert model for pre-training treatment, and generating characteristic sentences of the associated authority information corresponding to all roles, wherein the characteristic sentences comprise variable information, function information and business logic information of intelligent contracts in the management authority information corresponding to all roles;
and generating intelligent contract information corresponding to management authority information corresponding to all roles based on the characteristic statement, wherein each management authority information corresponds to one intelligent contract information, when the management authority information is triggered, the corresponding intelligent contract information is triggered, and judging whether the employee has the intelligent contract information of the access authority according to preset business logic.
5. The enterprise content management authority control method according to claim 1, wherein the trained neural network model construction method comprises:
taking role information of preset historical staff and management authority information corresponding to the preset historical roles as training sets according to the sequence of the identification time, and taking the management authority information corresponding to the preset historical staff as verification sets;
all training sets are sent to a BP neural network model for matching degree calculation, so that the matching degree value of the role information of each historical employee and the management authority information corresponding to each historical role is obtained;
optimizing the BP neural network model based on a particle swarm optimization algorithm, sending the role information of each historical employee and the matching degree value of the management authority information corresponding to each historical role to the optimized BP neural network model for calculation, and determining the maximum matching degree value of the role information of each historical employee and the management authority information corresponding to each historical role;
obtaining an allocation result based on the maximum matching degree value, wherein management authority information corresponding to the maximum matching degree value is allocated to a historical employee corresponding to the maximum matching degree value as management authority information of the historical employee;
comparing the verification set with the distribution result, judging whether the distribution result of the verification set is consistent, and if so, obtaining the constructed neural network model.
6. An enterprise content management rights management and control apparatus, comprising:
the acquisition unit is used for acquiring enterprise content management authority information and enterprise internal role information;
the analysis unit is used for sending the enterprise content management authority information and the enterprise internal role information to the hierarchical analysis model for hierarchical analysis, and performing authority allocation based on the result obtained by the hierarchical analysis to obtain management authority information corresponding to all roles;
the processing unit is used for sending the management authority information corresponding to all roles to the intelligent contract generation module for processing to obtain intelligent contract information corresponding to each management authority information;
the distribution unit is used for sending the role information of the preset staff and the management authority information corresponding to all roles to the trained neural network model for authority distribution to obtain the management authority information corresponding to each staff;
and the adjusting unit is used for sending the management authority information corresponding to each employee and the intelligent contract information corresponding to each management authority information to a preset authority management and control platform for verification and adjustment until the authorities possessed by all employees are the same as the preset targets.
7. The enterprise content management right management and control apparatus according to claim 6, wherein the analysis unit includes:
the first analysis subunit is used for analyzing the enterprise content management authority information and all elements in the enterprise internal role information and establishing a hierarchical structure model;
the second analysis subunit is used for carrying out layer-by-layer pairwise comparison on the element information of each level in the hierarchical structure model, normalizing the comparison result and constructing a discrimination matrix to obtain at least two discrimination matrices;
the first calculating subunit is used for carrying out eigenvector calculation and maximum eigenvalue calculation on all the judging matrixes to obtain consistency indexes;
and the third analysis subunit is used for sequencing all elements of each layer based on the feature vector and the maximum feature value under the condition that the consistency index is met, so as to obtain element sequencing information in enterprise content management authority information and element sequencing information in enterprise internal role information.
8. The enterprise content management right management and control apparatus according to claim 6, wherein the analysis unit further comprises:
a fourth analysis subunit, configured to perform association analysis on a result obtained by the hierarchical analysis, where the element ordering information in the enterprise content management authority information and the element ordering information in the enterprise internal role information are subjected to dimensionless processing, so as to obtain dimensionless authority element ordering information and dimensionless role element ordering information;
the second calculation subunit is used for carrying out association calculation on the dimensionless right element ordering information and the dimensionless role element ordering information to obtain an association degree value of the dimensionless right element ordering information and the dimensionless role element ordering information;
and a fifth analysis subunit, configured to determine management authority information corresponding to all roles based on the association value, where the management authority information corresponding to each role is obtained by mapping authority elements and role elements that have association values greater than a preset threshold.
9. The enterprise content management rights management apparatus of claim 6, wherein the processing unit comprises:
the first processing unit is used for sending the management authority information corresponding to all the roles to the Bert model for pre-training treatment, and generating characteristic sentences of the associated authority information corresponding to all the roles, wherein the characteristic sentences are business logic information comprising variable information, function information and intelligent contracts in the management authority information corresponding to all the roles;
the second processing unit is used for generating intelligent contract information corresponding to management authority information corresponding to all roles based on the characteristic sentences, wherein each management authority information corresponds to one intelligent contract information, when the management authority information is triggered, the corresponding intelligent contract information is triggered, and whether the staff has the intelligent contract information of the access authority is judged according to preset business logic.
10. The enterprise content management right management and control apparatus according to claim 6, wherein the distribution unit comprises:
the first training subunit is used for taking the role information of the preset historical staff and the management authority information corresponding to the preset historical role as a training set according to the sequence of the identification time, and taking the management authority information corresponding to the preset historical staff as a verification set;
the second training subunit is used for sending all training sets to the BP neural network model for matching degree calculation to obtain the matching degree value of the role information of each historical employee and the management authority information corresponding to each historical role;
the third training subunit is used for optimizing the BP neural network model based on a particle swarm optimization algorithm, sending the role information of each historical employee and the matching degree value of the management authority information corresponding to each historical role to the optimized BP neural network model for calculation, and determining the maximum matching degree value of the role information of each historical employee and the management authority information corresponding to each historical role;
the first allocation subunit is used for obtaining an allocation result based on the maximum matching degree value, wherein the management authority information corresponding to the maximum matching degree value is allocated to the historical staff corresponding to the maximum matching degree value as the management authority information of the historical staff;
and the first distribution subunit is used for comparing the verification set with the distribution result, judging whether the distribution result of the verification set is consistent, and if so, obtaining the constructed neural network model.
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