Method and system for dynamically adjusting user permission based on multi-dimensional perception

Abstract

The invention discloses a user permission dynamic adjustment method and system based on multi-dimensional perception, and the method comprises the steps: obtaining multi-dimensional operation data of a to-be-accessed device, multi-dimensional behavior data of a current user in the to-be-accessed device, and multi-dimensional permission change data corresponding to the current user, obtaining a multi-dimensional perception feedback matrix between the current user and the to-be-accessed device; acquiring an elastic permission adjustment factor corresponding to the current user according to the multi-dimensional perception feedback matrix; adjusting a pre-constructed permission adjustment grid model according to the multi-dimensional perception feedback matrix to obtain a multi-dimensional permission adjustment grid model corresponding to the current user; according to the multi-dimensional perception feedback matrix, the elastic permission adjustment factor and the multi-dimensional permission adjustment grid model, the current permission value of the current user is dynamically adjusted, a real-time permission value corresponding to the current user is obtained, and the flexibility and accuracy of permission adjustment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device security management, and particularly relates to a user permission dynamic adjustment method and system based on multi-dimensional perception. BACKGROUND

[0002] With the application of various intelligent measurement devices in the fields of power consumption information collection, digital instrument metering, industrial automation and power system state monitoring, efficient and safe management and maintenance of the intelligent measurement devices become an important process to ensure the safety and stability of the system. In the process of safe management of the intelligent measurement devices, user permission management as the core part of the intelligent measurement device software management becomes a crucial problem.

[0003] The existing user permission management method is mostly based on static role authorization and permission allocation. This traditional method cannot effectively cope with the complex and changeable business requirements and environmental changes in intelligent device management. User behavior, device state, business cycle and other factors are constantly changing, and the traditional permission management method often cannot respond to these changes in time, resulting in excessive permissions, excessive rigidity or security risks. In addition, the existing method usually adopts a fixed permission allocation method, which cannot dynamically adjust user permissions according to real-time feedback, is prone to over-authorization or insufficient authorization of permissions, and lacks a real-time response mechanism based on behavior feedback and device state changes; the existing technology often ignores the audit and traceability function in the permission change process, and lacks comprehensive permission adjustment records and compliance protection. SUMMARY

[0004] In order to solve the above technical problems, the present application discloses a user permission dynamic adjustment method and system based on multi-dimensional perception, which is used to improve the flexibility and accuracy of permission management.

[0005] In order to achieve the above purpose, the present application discloses a user permission dynamic adjustment method based on multi-dimensional perception, comprising:

[0006] Obtaining multi-dimensional operation running data of a to-be-accessed device, multi-dimensional behavior data of a current user in the to-be-accessed device and multi-dimensional permission change data corresponding to the current user, to obtain a multi-dimensional perception feedback matrix between the current user and the to-be-accessed device;

[0007] Obtaining an elastic permission adjustment factor corresponding to the current user according to the multi-dimensional perception feedback matrix;

[0008] Adjusting a pre-constructed permission adjustment grid model according to the multi-dimensional perception feedback matrix to obtain a multi-dimensional permission adjustment grid model corresponding to the current user;

[0009] According to the multi-dimensional perception feedback matrix, the elastic permission adjustment factor and the multi-dimensional permission adjustment grid model, the current permission value of the current user is dynamically adjusted to obtain the real-time permission value corresponding to the current user.

[0010] The application discloses a multi-dimensional perception-based user permission dynamic adjustment method, which can flexibly change the user permission by comprehensively considering the multi-dimensional permission change data of the user, the multi-dimensional operation and running data of the equipment and the multi-dimensional operation and running data between the user and the equipment, thereby improving the flexibility and accuracy of the permission adjustment.

[0011] As a preferred example, the real-time running data of the to-be-accessed equipment, the operation behavior data of the current user in the to-be-accessed equipment and the permission change data corresponding to the current user are acquired to construct the multi-dimensional perception feedback matrix between the current user and the to-be-accessed equipment, including:

[0012] The running state of the to-be-accessed equipment is monitored in real time to extract the multi-dimensional operation and running data corresponding to the to-be-accessed equipment;

[0013] The multi-dimensional operation behavior data and the multi-dimensional permission change data of the current user in the software platform corresponding to the to-be-accessed equipment are collected;

[0014] The historical permission change cumulative amount corresponding to the current user and the current permission value corresponding to the current user are acquired according to the multi-dimensional permission change data;

[0015] The running score value and the running weight coefficient corresponding to each dimension of the running data, the operation score value and the operation weight coefficient corresponding to each dimension of the operation behavior data and the permission change score value and the permission change coefficient corresponding to each dimension of the permission change data are acquired;

[0016] According to the historical permission change cumulative amount, the current permission value, the running score value, the running weight coefficient, the operation score value, the operation weight coefficient, the permission change score value and the permission change coefficient, a multi-dimensional perception feedback matrix between the current user and the device to be accessed is constructed.

[0017] In the above scheme, firstly, the multi-dimensional perception data corresponding to the user and the device respectively is collected to ensure accurate reflection of the changes of user behavior and device state, and then a multi-dimensional perception feedback matrix is established for the current user according to the collected multi-dimensional perception data. In the construction process, the user's behavior, the device's working state, the historical permission change and other information are considered. The multi-dimensional perception feedback matrix of each user is obtained by weighting and combining different dimensions of information, which accurately reflects the influence of the user on the system, and then the accuracy of subsequent permission adjustment is improved according to the influence.

[0018] As a preferred example, the obtaining of the running score value and the running weight coefficient corresponding to each dimension of the running data, the operation score value and the operation weight coefficient corresponding to each dimension of the operation behavior data, and the permission change score value and the permission change coefficient corresponding to each dimension of the permission change data includes:

[0019] For any one dimension of the running data or any one dimension of the operation behavior data or any one dimension of the permission change data;

[0020] According to the dimension of the running data, a running quantitative scoring function corresponding to the running data and a pre-constructed running weight coefficient are called, and the running data is normalized scored by the running quantitative scoring function to obtain the running score value corresponding to the running data;

[0021] According to the dimension of the operation behavior data, an operation behavior quantitative scoring function corresponding to the operation behavior data and a pre-constructed operation behavior weight coefficient are called, and the operation behavior data is normalized scored by the operation behavior quantitative scoring function to obtain the operation behavior score value corresponding to the operation behavior data;

[0022] According to the dimension of the permission change data, a permission change quantitative scoring function corresponding to the permission change data and a pre-constructed permission change weight coefficient are called, and the permission change data is normalized scored by the permission change quantitative scoring function to obtain the permission change score value corresponding to the permission change data.

[0023] In the scheme, the data of each dimension is normalized by quantifying the scoring function to solve the problem of unified evaluation of multi-source heterogeneous data, realize multi-dimensional environment perception of users and devices, and improve the accuracy of multi-dimensional perception feedback matrix construction. Then, the weight coefficient reflecting the influence of each dimension data on the adjustment of the right is called to improve the accuracy of multi-dimensional perception feedback matrix construction by combining the weight coefficient and the normalized score value, and then improve the accuracy of right adjustment.

[0024] As a preferred example, the calculation expression of the multi-dimensional perception feedback matrix is as follows:

[0025]

[0026] Wherein, the F u represents the multi-dimensional perception feedback matrix between the user u and the device to be accessed; the w i represents the weight coefficient corresponding to the data of the i-th dimension; the f u,i (t) represents the score value obtained by normalizing the data of the i-th dimension of the user u at time t; the n is the total number of the dimensions; the C u (t) is the historical right change accumulation of the user u at time t; the P u (t) represents the current right value of the user u; and the p represents the preset elastic factor.

[0027] In the scheme, the mathematical expression corresponding to the constructed multi-dimensional perception feedback matrix is used to realize the data input and the running of the mathematical expression, realize the automatic acquisition of the multi-dimensional perception feedback matrix, and guarantee the efficiency and accuracy of the generation of the multi-dimensional perception feedback matrix.

[0028] As a preferred example, the elastic right adjustment factor corresponding to the current user is obtained according to the multi-dimensional perception feedback matrix, including:

[0029] Obtain a plurality of users corresponding to the device to be accessed and the multi-dimensional perception feedback matrix corresponding to each of the users;

[0030] According to the preset right adjustment factor table, obtain the factor value and factor weight corresponding to each of the influence factors affecting the weight adjustment;

[0031] According to a plurality of the multi-dimensional perception feedback matrices, the factor value, and the factor weight, obtain the elastic right adjustment factor corresponding to the current user.

[0032] In the above scheme, based on the calculation result of the multi-dimensional perception feedback matrix, the elastic permission adjustment factor corresponding to the current user is calculated. The elastic permission adjustment factor is a transformation of the multi-dimensional perception feedback matrix, which determines the amplitude of user permission adjustment by adjusting the sensitivity of different dimensions to permission. In order to make the permission adjustment flexible to respond to changes in various environments and business needs, multiple influencing factors are introduced to ensure the flexibility of permission adjustment, which represents the dynamic state of the user in the permission adjustment, and then the flexibility and accuracy of the dynamic adjustment of user permission are realized through the elastic permission adjustment factor.

[0033] As a preferred example, the calculation expression of the elastic permission adjustment factor is:

[0034]

[0035] Wherein, the EPAF u (t) is the elastic permission adjustment factor of the user u at time t; the max(F u (t)) represents the largest multi-dimensional perception feedback matrix in the plurality of multi-dimensional perception feedback matrices; the F u (t) represents the multi-dimensional perception feedback matrix between the current user u and the device to be accessed; the a j is the factor weight of the jth influencing factor; the E j (t) is the factor value of the jth influencing factor; the β represents the pre-constructed global bias constant; and the m represents the total number of factors.

[0036] In the above scheme, the elastic permission adjustment factor reflecting the permission adjustment range of the current user is calculated through the multi-dimensional perception feedback matrix corresponding to multiple users and the values and weights of the influencing factors affecting the permission adjustment of the current user, which can improve the accuracy and flexibility of permission adjustment. Secondly, the mathematical expression of the constructed elastic permission adjustment factor can effectively realize automatic data processing and data processing accuracy.

[0037] As a preferred example, the pre-constructed permission adjustment grid model is adjusted according to the multi-dimensional perception feedback matrix to obtain a multi-dimensional permission adjustment grid model corresponding to the current user, which includes:

[0038] Obtaining the division number of the grid in the pre-constructed permission adjustment grid model and the permission adjustment coefficient corresponding to each grid;

[0039] According to the division number, the permission adjustment coefficient and the multi-dimensional perception feedback matrix, a multi-dimensional permission adjustment grid model corresponding to the current user is constructed.

[0040] In the scheme, the multi-dimensional permission adjustment grid model is constructed, so that the permission adjustment strategy can be optimized in different grid regions with fine granularity during the permission adjustment process, thereby ensuring high precision and high adaptability of the permission adjustment.

[0041] As a preferred example, the calculation expression of the multi-dimensional permission adjustment grid model is:

[0042]

[0043] Among them, represent the multi-dimensional permission adjustment grid model of the current user u; the N represents the number of grid divisions; the γ k represent the weight adjustment coefficient corresponding to the kth grid.

[0044] In the scheme, from the calculation expression of the multi-dimensional permission adjustment grid model, it can be seen that the multi-dimensional permission adjustment grid model maps data into multiple grids, and adjusts user permissions through different optimization calculation methods; the construction of the grid analyzes the feedback results of each dimension, and applies different weight coefficients in different feedback intervals, thereby dynamically adjusting the allocation strategy of the permission; the quantized feedback value of each user is weighted, and the corresponding adjustment is given according to the grid region it is in, so that the permission management can flexibly cope with the changing environment, and ensure the intelligence and efficiency of the permission adjustment.

[0045] As a preferred example, the calculation expression of the real-time permission value is as follows, including:

[0046]

[0047] Among them, the NP u represent the real-time permission value corresponding to the current user u.

[0048] In the scheme, the multi-dimensional perception feedback matrix in the calculation expression of the real-time permission value represents the behavior of the user in different dimensions, and the elastic permission adjustment factor adjusts the flexibility of the user's permission according to the historical behavior and the current environment, and the multi-dimensional optimization adjustment grid further optimizes the precision of the permission adjustment, so that the influence of each dimension on the final decision is reasonably balanced. Reflects the final adjustment state of the user's permission, which may be upgraded, downgraded or kept unchanged, so as to ensure that adaptive permission adjustment can be continuously made in a changing environment, and the flexibility and accuracy of the permission adjustment are improved.

[0049] On the other hand, the application discloses a user permission dynamic adjustment system based on multi-dimensional perception, which comprises a multi-dimensional perception module, an elastic adjustment module, a grid optimization module and a permission adjustment module.

[0050] The multi-dimensional perception module is used for acquiring multi-dimensional operation running data of a to-be-accessed device, multi-dimensional behavior data of a current user in the to-be-accessed device and multi-dimensional permission change data corresponding to the current user, so as to acquire a multi-dimensional perception feedback matrix between the current user and the to-be-accessed device.

[0051] The elastic adjustment module is used for acquiring an elastic permission adjustment factor corresponding to the current user according to the multi-dimensional perception feedback matrix.

[0052] The grid optimization module is used for adjusting a pre-constructed permission adjustment grid model according to the multi-dimensional perception feedback matrix, so as to obtain a multi-dimensional permission adjustment grid model corresponding to the current user.

[0053] The permission adjustment module is used for dynamically adjusting a current permission value of the current user according to the multi-dimensional perception feedback matrix, the elastic permission adjustment factor and the multi-dimensional permission adjustment grid model, so as to obtain a real-time permission value corresponding to the current user.

[0054] The application discloses a user permission dynamic adjustment system based on multi-dimensional perception, which can flexibly change user permission by comprehensively considering multi-dimensional permission change data of a user, multi-dimensional operation running data of a device and multi-dimensional operation running data between the user and the device, so as to improve flexibility and accuracy of permission adjustment. First, a multi-dimensional perception feedback matrix between the user and the device is constructed according to acquired multi-dimensional data, so that the multi-dimensional perception feedback matrix can dynamically adapt to changes of the user and the device, so that the permission adjustment has environment perception ability, and the accuracy and flexibility of the permission adjustment are improved. Then, an elastic permission adjustment factor used for determining an adjustment range of user permission is determined according to the multi-dimensional perception feedback matrix, so that the flexibility and accuracy of the permission adjustment are determined. Then, a pre-constructed permission adjustment grid model is adjusted according to the multi-dimensional perception feedback matrix, so that a multi-dimensional permission adjustment grid model corresponding to the current user is obtained, so that the environment perception ability of the permission adjustment is improved by using the multi-dimensional permission adjustment grid model, and the current permission value of the current user is dynamically adjusted by using the multi-dimensional perception feedback matrix, the elastic permission adjustment factor and the multi-dimensional permission adjustment grid model, so that the flexibility and accuracy of the permission adjustment are improved. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative effort.

[0056] Figure 1This is a flowchart of a method for dynamically adjusting user permissions based on multi-dimensional perception disclosed in an embodiment of the present invention;

[0057] Figure 2 This is a structural diagram of a system for dynamically adjusting user permissions based on multi-dimensional perception disclosed in an embodiment of the present invention;

[0058] Figure 3 This is a flow chart of a method for dynamically adjusting user permissions based on multi-dimensional perception disclosed in another embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] Example 1

[0061] Reference Figure 1 To address the technical issues in the prior art of low flexibility and accuracy of permission adjustment due to static permission adjustment or fixed permission allocation, this embodiment provides a method for dynamically adjusting user permissions based on multi-dimensional perception, including:

[0062] Step 101: Obtain multi-dimensional operation data of the device to be accessed, multi-dimensional behavior data of the current user in the device to be accessed, and multi-dimensional permission change data corresponding to the current user to obtain a multi-dimensional perception feedback matrix between the current user and the device to be accessed.

[0063] In this embodiment, this step mainly includes: real-time monitoring of the operating status of the device to be accessed to extract multi-dimensional operation data corresponding to the device to be accessed; collecting multi-dimensional operation behavior data and multi-dimensional permission change data of the current user in the software platform corresponding to the device to be accessed; obtaining the historical permission change accumulation corresponding to the current user and the current permission value corresponding to the current user based on the multi-dimensional permission change data; obtaining the operation score value and operation weight coefficient corresponding to the operation data in each dimension, the operation score value and operation weight coefficient corresponding to the operation behavior data in each dimension, and the permission change score value and permission change coefficient corresponding to the permission change data in each dimension; constructing a multi-dimensional perception feedback matrix between the current user and the device to be accessed based on the historical permission change accumulation, the current permission value, the operation score value, the operation weight coefficient, the operation score value, the operation weight coefficient, the permission change score value and the permission change coefficient.

[0064] In an embodiment of the present embodiment, for any one-dimensional operation data or any one-dimensional operation behavior data or any one-dimensional permission change data; according to the dimension of the operation data, the operation data corresponding operation quantization scoring function and pre-constructed operation weight coefficient are called, and the operation data is normalized and scored by the operation quantization scoring function to obtain the operation score value corresponding to the operation data; according to the dimension of the operation behavior data, the operation behavior data corresponding operation behavior quantization scoring function and pre-constructed operation behavior weight coefficient are called, and the operation behavior data is normalized and scored by the operation behavior quantization scoring function to obtain the operation behavior score value corresponding to the operation behavior data; according to the dimension of the permission change data, the permission change data corresponding permission change quantization scoring function and pre-constructed permission change weight coefficient are called, and the permission change data is normalized and scored by the permission change quantization scoring function to obtain the permission change score value corresponding to the permission change data.

[0065] In an embodiment of the present embodiment, the calculation expression of the multi-dimensional perception feedback matrix is as follows:

[0066]

[0067] Wherein, the F u represents the multi-dimensional perception feedback matrix between the user u and the device to be accessed; the w i represents the weight coefficient corresponding to the i-th dimension data; the f u,i (t) represents the score value obtained after the i-th dimension data of the user u at time t is normalized and scored; the n is the total number of the dimensions; the C u (t) is the historical permission change cumulative amount of the user u at time t; the P u (t) represents the current permission value of the user u; the p represents the preset elastic factor.

[0068] In the embodiment, the above steps first collect multi-dimensional perception data corresponding to the user and the device respectively to ensure accurate reflection of the change of the user behavior and the device state, and then a multi-dimensional perception feedback matrix is established for the current user according to the collected multi-dimensional perception data. In the construction process, the user behavior, the working state of the device, the historical permission change and other information are considered. Each multi-dimensional perception feedback matrix of the user is obtained by weighting and combining information of different dimensions, accurately reflects the influence of the user on the system, and then improves the accuracy of subsequent permission adjustment according to the influence. In which, the data of each dimension is normalized by a quantitative scoring function to solve the problem of difficult unified evaluation of multi-source heterogeneous data, realize multi-dimensional environment perception of the user and the device, and improve the accuracy of the multi-dimensional perception feedback matrix construction. Then, the weight coefficient reflecting the influence of each dimension data on the permission adjustment is called to improve the accuracy of the multi-dimensional perception feedback matrix construction by combining the weight coefficient and the normalized score value, and then improve the accuracy of the permission adjustment. At the same time, the mathematical expression corresponding to the multi-dimensional perception feedback matrix realizes the automatic acquisition of the multi-dimensional perception feedback matrix through the data input and the operation of the mathematical expression, ensures the efficiency and accuracy of the multi-dimensional perception feedback matrix generation.

[0069] Step 102: obtaining the elastic permission adjustment factor corresponding to the current user according to the multi-dimensional perception feedback matrix.

[0070] In the embodiment, the step mainly includes: obtaining a plurality of users corresponding to the to-be-accessed device and the multi-dimensional perception feedback matrix corresponding to each of the users; obtaining the factor value and the factor weight corresponding to each of a plurality of influence factors affecting the weight adjustment according to a preset permission adjustment factor table; and obtaining the elastic permission adjustment factor corresponding to the current user according to the plurality of multi-dimensional perception feedback matrices, the factor value and the factor weight.

[0071] In an embodiment of the embodiment, the calculation expression of the elastic permission adjustment factor is:

[0072]

[0073] Among them, the EPAF u (t) is the elastic permission adjustment factor of the user u at time t; the max(F u (t)) represents the largest multi-dimensional perception feedback matrix in the plurality of multi-dimensional perception feedback matrices; the F u (t) represents the multi-dimensional perception feedback matrix between the current user u and the to-be-accessed device; the a j is the factor weight of the jth influence factor; the E j(t) is the factor value of the jth influencing factor; the β represents a pre-constructed global bias constant; the m represents the total number of factors of the influencing factor.

[0074] In the embodiment, the step calculates the elastic permission adjustment factor corresponding to the current user based on the calculation result of the multi-dimensional perception feedback matrix. The elastic permission adjustment factor is a deformation of the multi-dimensional perception feedback matrix, and determines the amplitude of user permission adjustment by adjusting the sensitivity of different dimensions to the permission. In order to make the permission adjustment flexibly respond to the changes of various environment and business requirements, multiple influencing factors are introduced to ensure the flexibility of the permission adjustment, and the dynamic state of the user in the permission adjustment is represented, and then the flexibility and accuracy of the dynamic adjustment of the user permission are realized through the elastic permission adjustment factor. The elastic permission adjustment factor reflecting the adjustment range of the current user permission is calculated through the multi-dimensional perception feedback matrix corresponding to multiple users, the values and weights of the influencing factors affecting the permission adjustment of the current user, which can improve the accuracy and flexibility of the permission adjustment. Secondly, the mathematical expression of the elastic permission adjustment factor can effectively realize the automatic processing of data and the accuracy of data processing.

[0075] Step 103: adjusting the pre-constructed permission adjustment grid model according to the multi-dimensional perception feedback matrix to obtain the multi-dimensional permission adjustment grid model corresponding to the current user.

[0076] In the embodiment, the step mainly includes: obtaining the division number of the grid in the pre-constructed permission adjustment grid model and the permission adjustment coefficient corresponding to each grid; constructing the multi-dimensional permission adjustment grid model corresponding to the current user according to the division number, the permission adjustment coefficient and the multi-dimensional perception feedback matrix.

[0077] In an embodiment of the embodiment, the calculation expression of the multi-dimensional permission adjustment grid model is:

[0078]

[0079] wherein, represents the multi-dimensional permission adjustment grid model of the current user u; the N represents the division number of the grid; the γ k represents the weight adjustment coefficient corresponding to the kth grid.

[0080] In the embodiment, the above step enables the optimization of the strategy of the adjustment of the permission in the different grid areas of the fine granularity in the process of the adjustment of the permission by constructing the multi-dimensional permission adjustment grid model, thereby ensuring the high precision and high adaptability of the adjustment of the permission. It can be known from the calculation expression of the multi-dimensional permission adjustment grid model that the multi-dimensional permission adjustment grid model maps the data into multiple grids and adjusts the user permission by different optimization calculation modes; the construction of the grid dynamically adjusts the allocation strategy of the permission by analyzing the feedback result of each dimension and applying different weight coefficients in different feedback intervals; the quantized feedback value of each user is weighted, and the corresponding adjustment is given according to the grid area where the user is located, so that the permission management can flexibly cope with the changing environment and ensure the intelligentization and high efficiency of the adjustment of the permission.

[0081] Step 104: dynamically adjusting the current permission value of the current user according to the multi-dimensional perception feedback matrix, the elastic permission adjustment factor and the multi-dimensional permission adjustment grid model, to obtain the real-time permission value corresponding to the current user.

[0082] In the embodiment, the calculation expression of the real-time permission value is as follows, including:

[0083]

[0084] The NP u represents the real-time permission value corresponding to the current user u.

[0085] In the embodiment, the multi-dimensional perception feedback matrix in the calculation expression of the real-time permission value in the above step represents the behavior performance of the user in different dimensions, the elastic permission adjustment factor adjusts the flexibility of the permission according to the historical behavior and the current environment, and the multi-dimensional optimization adjustment grid further optimizes the precision of the adjustment of the permission, thereby ensuring that the influence of each dimension on the final decision is reasonably balanced. The final adjustment state of the user permission is reflected, which may be upgraded, downgraded or kept unchanged, thereby ensuring that adaptive permission adjustment can be continuously made in the changing environment and the flexibility and accuracy of the adjustment of the permission are improved.

[0086] With reference to Figure 2 , the embodiment further provides a user permission dynamic adjustment system based on multi-dimensional perception, including a multi-dimensional perception module 201, an elastic adjustment module 202, a grid optimization module 203 and a permission adjustment module 204.

[0087] The multi-dimensional perception module 201 is used to acquire the multi-dimensional operation running data of a to-be-accessed device, the multi-dimensional behavior data of a current user in the to-be-accessed device and the multi-dimensional permission change data corresponding to the current user, so as to acquire a multi-dimensional perception feedback matrix between the current user and the to-be-accessed device.

[0088] The elastic adjustment module 202 is configured to obtain an elastic permission adjustment factor corresponding to the current user according to the multi-dimensional perception feedback matrix.

[0089] The grid optimization module 203 is configured to adjust a pre-constructed permission adjustment grid model according to the multi-dimensional perception feedback matrix to obtain a multi-dimensional permission adjustment grid model corresponding to the current user.

[0090] The permission adjustment module 204 is configured to dynamically adjust a current permission value of the current user according to the multi-dimensional perception feedback matrix, the elastic permission adjustment factor, and the multi-dimensional permission adjustment grid model to obtain a real-time permission value corresponding to the current user.

[0091] The embodiment discloses a multi-dimensional perception-based user permission dynamic adjustment method and system. The method and system improve the flexibility and accuracy of permission adjustment by comprehensively changing the multi-dimensional permission data of the user, the multi-dimensional operation and running data of the device, and the multi-dimensional operation and running data between the user and the device. First, a multi-dimensional perception feedback matrix between the user and the device is constructed according to the obtained multi-dimensional data, so that the permission adjustment has environmental perception ability and the accuracy and flexibility of the permission adjustment are improved. Then, an elastic permission adjustment factor for determining the adjustment range of the user permission is determined according to the multi-dimensional perception feedback matrix, so that the flexibility and accuracy of the permission adjustment are determined. Then, a pre-constructed permission adjustment grid model is adjusted according to the multi-dimensional perception feedback matrix to obtain a multi-dimensional permission adjustment grid model corresponding to the current user. The environmental perception of the permission adjustment is improved through the multi-dimensional permission adjustment grid model. The current permission value of the current user is dynamically adjusted by using the multi-dimensional perception feedback matrix, the elastic permission adjustment factor, and the multi-dimensional permission adjustment grid model, so that the flexibility and accuracy of the permission adjustment are improved.

[0092] Embodiment two

[0093] In the fields of power utilization information collection, digital instrument metering, industrial automation, and power system state monitoring, the application of intelligent measurement equipment has formed a certain scale, and the software management of intelligent measurement equipment has become increasingly complex. Intelligent measurement equipment undertakes important real-time data collection, transmission, processing, and control functions. These devices not only need efficient management and maintenance, but also must ensure the security and stability of the system. However, the effective management and control of intelligent measurement equipment software have not been realized in the prior art, which has affected the promotion and application of intelligent measurement equipment and restricted the development of new-generation intelligent measurement system construction. Under this background, user permission management, as the core part of intelligent measurement equipment software management, has become a crucial issue.

[0094] Existing user permission management methods are mostly based on static role authorization and permission allocation. This traditional approach cannot effectively respond to complex and changing business needs and environmental changes in intelligent device management. User behavior, device status, business cycle, and other factors are constantly changing, and the traditional permission management approach often cannot respond to these changes in a timely manner, resulting in excessive permissions, excessive rigidity, or security risks.

[0095] To solve the above technical problems, the embodiment discloses a user permission dynamic adjustment method based on multi-dimensional perception, which can adjust the user's access permission in real time according to the user's behavior change, device working state, and historical permission, etc. Factors, so as to realize more intelligent, flexible and efficient permission management. Specifically, the method can refer to Figure 3 , mainly including the following steps:

[0096] Step 301: Obtain multi-dimensional running data of the device to be accessed and multi-dimensional operation behavior data and multi-dimensional historical permission change data of the current user from the software platform corresponding to the device to be accessed.

[0097] In this embodiment, this step mainly includes: real-time monitoring of the real-time running state of the device to be accessed to collect multi-dimensional running data of the device to be accessed; wherein the multi-dimensional running data includes device online state data showing whether the device is online, device health state data showing the health state of the device such as normal, warning, failure, etc. and device online upgrade state data showing whether the device is in upgrading.

[0098] Then, the multi-dimensional operation behavior data of the current user in the software platform is collected from the software platform corresponding to the device to be accessed, that is, a third-party platform for user login, access, control, and operation of the device to be accessed. Among them, the operation behavior data includes access frequency data, access content data, operation type (such as query, modification, deletion, etc.) data, stay time data, and interaction mode data, etc.

[0099] At the same time, the historical permission change data corresponding to the current user recorded in the software platform can also be obtained. Among them, the historical permission change data includes permission change time data, permission change type (such as permission upgrade, downgrade) data, operator information, and permission change reason data, etc.

[0100] Preferably, after collecting the required multi-dimensional data, all collected multi-dimensional data can be cleaned and standardized pretreated to remove noise and invalid information in the data, etc., to ensure accurate reflection of changes in user behavior and device status.

[0101] Step 302: constructing a multi-dimensional perception feedback matrix between the current user and the device to be accessed according to the multi-dimensional running data, the multi-dimensional operation behavior data and the multi-dimensional historical permission change data.

[0102] In the embodiment, the running score value and the running weight coefficient corresponding to the running data of each dimension, the operation score value and the operation weight coefficient corresponding to the operation behavior data of each dimension, and the permission change score value and the permission change coefficient corresponding to the permission change data of each dimension are obtained; and the multi-dimensional perception feedback matrix between the current user and the device to be accessed is constructed according to the historical permission change cumulative amount, the current permission value, the running score value, the running weight coefficient, the operation score value, the operation weight coefficient, the permission change score value and the permission change coefficient.

[0103] In an implementation manner of the embodiment, the mathematical expression of the multi-dimensional perception feedback matrix is as follows:

[0104]

[0105] In the formula, the F u represents the multi-dimensional perception feedback matrix between the user u and the device to be accessed; the w i represents the weight coefficient corresponding to the data of the i-th dimension, indicates the contribution degree of different dimensions to the user feedback matrix, is obtained through experiments, and the value range is [0, 1]; the f u,i (t) represents the score value obtained after the data of the i-th dimension of the user u at the time t is normalized, for example, the operation frequency, the behavior category times or the satisfaction score of the user at a specific time are counted, and the value is normalized to 0 to 1, reflecting the activity degree or positive feedback of the user in the current dimension; the n is the total number of the dimensions; the C u (t) is the historical permission change cumulative amount of the user u at the time t, measuring the size of the permission currently possessed by the user; the P u (t) represents the current permission value of the user u; the p represents a preset elastic factor, used for controlling the influence degree of the historical permission change on the current multi-dimensional perception feedback matrix of the user, and can be preset according to the sensitivity of the permission adjustment.

[0106] In the embodiment, the F u is a feedback matrix obtained by weighted fusion of the obtained data of each dimension, such as the small type data of each dimension, such as the running data of each dimension and the operation behavior of each dimension. Each perception dimension (such as the access frequency and the device health state) has an independent effect in the F u , and a comprehensive feedback result is obtained after weighting.

[0107] When calibrating the weight coefficient of data corresponding to each perception dimension through experiments, the following methods can be used for experimental calibration, such as real-time feedback adjustment: adjusting the weight according to the influence degree of a certain dimension on permission adjustment at the current time. For example, when the health status of the device is in the "warning" state, the weight of this dimension is increased. Based on historical data and empirical model: through historical data analysis, the importance of different dimensions in different situations is determined. For specific use scenarios, the weight coefficient can be adjusted through pre-set rules or machine learning models. Adaptive weight adjustment: through reinforcement learning or other adaptive algorithms, the weights of each dimension are automatically adjusted, so that they can be flexibly adjusted according to historical performance and real-time data feedback.

[0108] Further, when normalizing the data of each dimension, a unified standard (such as a design function) can be used to quantify the score. For each perception dimension, a scoring function can be designed to quantify the feedback of this dimension into a standardized score. For example, the visit frequency scoring function in user behavior data is visit_count / 50, which converts the user's visit frequency (i.e. the number of times the user accesses the system within a certain period of time) into a score between 0 and 1, reflecting the user's activity. The user's visit frequency (visit_count) is collected to reflect the user's activity level. The following rules can be set: if the visit frequency is very low (e.g. visit_count≤5), the score value is low (close to 0). If the visit frequency is moderate (e.g. visit_count is between 6 and 20), the score value is high. If the visit frequency is very high (e.g. visit_count≥21), the score value is 1. An example of the scoring function is:

[0109]

[0110] where max_visit_count is the maximum value of the visit frequency, and the score is 1 when it exceeds this value. Assuming that the maximum visit frequency is 50, the scoring formula is:

[0111] If visit_count=5, the score value is

[0112] If visit_count=20, the score value is

[0113] If visit_count=50, the score value is

[0114] Thus, the score of the access frequency varies linearly between 0 and 1, reflecting the user activity. The device is scored according to its health status (e.g., normal, warning, failure, etc.). The health status of the device can have multiple levels, such as normal, warning, failure, etc. Each status corresponds to a score value, usually a lower value is assigned to the normal state (indicating that the device is stable), a medium value is assigned to the warning state (indicating that the device may have a problem), and a higher value is assigned to the failure state (indicating that the device may have a serious problem). An example of the score criteria of the score function set for the device is: normal: the device is running normally, the score value is 0.3; warning: the device has a slight failure or warning, the score value is 0.5; failure: the device has a serious failure, the score value is 0.8. An example of the score function is:

[0115]

[0116] The device state score is expressed by a discrete value mapping to represent the health degree of the device. If the device is in a failure state, its score will be higher, and the system will pay more attention to the device, which may need to adjust the related permissions or access strategy. The user history permission change score function is to evaluate the user's permission change history according to the user's historical permission change records (such as the number of permission changes, the amplitude of permission increase / decrease, etc.), and then affect the decision of permission adjustment. If the user's permission changes frequently and the amplitude of the change is large (for example, the number of permission upgrades is large), the score value is high, indicating that the user has a greater impact on the system, and the system may need to adjust its permissions more frequently. If the user's permission changes less or the amplitude of the change is small, the score value is low. An example of the score criteria is: no permission change: score value is 0; less permission change (e.g., 1 to 3 times): score value is 0.3; frequent permission change (e.g., 4 times or more): score value is 0.6; large amplitude of permission increase / decrease (e.g., from the lowest permission to higher permission): score value is 1. An example of the score function is:

[0117] Assuming that the number of historical permission changes and the change amplitude affect the score, the following rules are used:

[0118]

[0119] The greater the frequency or amplitude of the user's historical permission change, the higher the score value, reflecting the user's impact on permission management.

[0120] It should be noted that for historical permission change data, the number of permission changes or the amplitude of permission changes (e.g., from low permission to high permission) can be scored.C u(t) is not the number of times of the permission change, but the cumulative amount of the historical permission change of the user u at time t, that is, the cumulative effect of recording all the permission changes of the user in history. This measure is extracted through the log record in the permission management system, and the type, frequency, amplitude and other factors of the historical permission change are considered. For example, if the user has experienced multiple permission promotion and demotion, then C u (t) will comprehensively reflect the cumulative effect of these changes, not just the number of changes. p is a flexible factor, which can have an interval and can be adjusted according to actual needs. The value of p usually depends on the permission sensitivity of the system and the required flexibility: if the system needs to be more sensitive to the historical impact of permission changes (i.e. it is desired that the historical permission change has a greater impact on the current permission adjustment), a smaller p value can be selected; if it is desired that the system has less impact on the historical permission change (for example, the historical permission change should not excessively affect the current permission adjustment), a larger p value can be selected. Therefore, the value of p can be set according to business requirements at the design time, and can be adjusted according to the actual operation of the system, allowing flexible response to permission adjustment requirements in different scenarios.

[0121] In this embodiment, by constructing a multi-dimensional perception feedback matrix of the user, the multi-dimensional information is fused into a comprehensive feedback model, and each information dimension in the system is weighted to synthesize a feedback matrix, so that the system can determine in real time whether the permission needs to be adjusted according to the user's behavior and device state.

[0122] Step 303: constructing an elastic permission adjustment factor corresponding to the current user according to the multi-dimensional perception feedback matrix.

[0123] In this embodiment, based on the calculation result of the multi-dimensional perception feedback matrix, an elastic permission adjustment factor is calculated, which is a transformation of the multi-dimensional perception feedback matrix. By adjusting the sensitivity of different dimensions to the permission, the amplitude of the user permission adjustment is determined; in order to make the permission adjustment process flexible to respond to changes in various environmental and business requirements, multiple influence factors are introduced to ensure the flexibility of the permission adjustment, which represents the dynamic state of the user in the system, including: device state, user behavior, historical permission change, business cycle.

[0124] The operation behavior data, running data and historical permission change data of different dimensions are comprehensively weighed, and finally an extremely adaptive elastic permission adjustment factor is obtained. The calculation formula of the elastic permission adjustment factor is:

[0125]

[0126] Wherein, EPAF u(t) is the elasticity permission adjustment factor of user u at time t, which represents the flexibility and sensitivity of user permission adjustment, and is the basis for final adjustment of user permission; max(F u (t)) refers to the maximum multi-dimensional perception feedback matrix among the multi-dimensional perception feedback matrices corresponding to the multiple users of the current device to be accessed, as the normalization factor; a j is the weight coefficient of the jth influencing factor, representing the sensitivity of the factor to permission adjustment, obtained through experiments; E j (t) is the value of the jth influencing factor, such as the online status of the device, the activity of the user, etc., which is dynamically updated according to the real-time state of the system; β is the global bias constant, representing the influence of global adjustment, obtained through experiments; m is the total number of factors affecting user permission adjustment.

[0127] It should be noted that the influencing factors refer to other variables in addition to user behavior and device status that may affect user permission adjustment during the permission adjustment process. For example: the health status of the device (such as whether the device is online, the failure condition of the device, etc.), the activity of the user (such as whether the user frequently operates), the business cycle (such as working hours, holidays, etc.), E j (t) is the value of the influencing factor at time t, which can be obtained by monitoring the device, user behavior, system settings, etc.

[0128] In this embodiment, the calculation of the elasticity permission adjustment factor makes the permission adjustment process more flexible and can adapt to the needs of users in different environments. By dynamically adjusting the influence of each factor, the system can intelligently decide whether to adjust the permission according to different use scenarios.

[0129] In one embodiment of the present embodiment, in the calculation formula of the elasticity permission adjustment factor, the influencing factors refer to various factors related to permission adjustment, such as external or system state, which affect the flexibility or sensitivity of the permission adjustment process. These factors may come from certain dimensions in the feedback matrix, but are not limited to this. For example: the online status of the device, the activity of the user, the influence of historical permission changes, the fluctuation of the business cycle, the current load of the system, etc. The perception dimensions in the feedback matrix are mainly user behavior, device status, permission changes, etc., which reflect the comprehensive behavior of users in the system through feedback values (such as access frequency, device health status, etc.). The weight coefficient can be obtained in the following ways: expert experience and feedback: based on the experience of system designers or business experts, manually assign weights to each influencing factor. Machine learning method: use supervised learning or reinforcement learning algorithm, train the model based on historical data, let the model automatically learn the weight of each factor, which can automatically identify the most influential factors and adjust the weight coefficient in a large amount of data.

[0130] Preferably, when calibrating the factor value and factor weight of each influencing factor through experiments, historical permission adjustment data is collected, the influence of each factor on permission adjustment is calculated through machine learning, and the weight of each factor is determined according to the analysis result j The method of obtaining β can be based on experimental tuning: adjusting the value of β through multiple experiments so that the system performs well in different scenarios. For example, different user behaviors and device states can be simulated to observe the effect of the system's permission adjustment, and the value of β can be adjusted according to the results.

[0131] Preferably, when adjusting the value of β, an initial value of β is set, multiple experiments are performed, the value of β is adjusted, the response of the system and the effect of the permission adjustment are observed, and the value of β that best meets the system performance and security requirements is selected.

[0132] It should be noted that when adjusting permissions, the factor values and factor weights of the influencing factors are dynamically updated, such as real-time data of device status, user behavior, etc. For example, whether the device is online, whether the permission has been changed, whether the user is active, etc. Dynamic calculation: once the real-time data changes, the system will immediately recalculate the relevant influencing factors. For example: if the device changes from "healthy" to "faulty", the corresponding influencing factor (device health status) will decrease. If the user frequently performs a certain operation within a period of time, the user activity level influencing factor will increase. If the user's permission has changed significantly, the historical permission change influencing factor on the current decision will change. Adaptive adjustment: the calculation of influencing factors can also be updated through adaptive models. For example, the system can dynamically adjust the weights and calculation methods of the influencing factors according to historical data and real-time feedback to ensure that they always meet the system's needs. Dynamic updating steps: collect real-time data (such as device status, user behavior, etc.) regularly. Update the values of each influencing factor according to the collected data. Use the updated influencing factors to calculate the elastic permission adjustment factor to ensure the flexibility and accuracy of the system's permission adjustment decision.

[0133] Step 304: Adjust the pre-constructed grid model according to the multi-dimensional perception feedback matrix to construct a multi-dimensional permission adjustment grid model corresponding to the current user.

[0134] In this embodiment, the grid model is adjusted to construct a multi-dimensional permission adjustment grid model corresponding to the current user, thereby improving the accuracy of permission adjustment. The multi-dimensional permission adjustment grid model dynamically adjusts the allocation strategy of permissions according to user behavior feedback to adjust user permissions, ensuring the intelligence and efficiency of permission management.

[0135] In this embodiment, in order to improve the accuracy of the permission adjustment, a grid model is designed to map the collected multi-dimensional data into multiple regions (grids), and the user permissions are adjusted through different optimization calculation methods; the construction of the grid is through the analysis of the feedback results of each dimension, and different weight coefficients are applied in different feedback intervals, so as to dynamically adjust the allocation strategy of the permissions; the quantized feedback value of each user is weighted, and the corresponding adjustment is given according to the grid area it is in, so that the permission management can flexibly cope with the changing environment, and ensure the intelligence and efficiency of the system. The calculation formula of the multi-dimensional permission adjustment grid model is:

[0136]

[0137] wherein, is the optimization adjustment grid of the user u, and the final result of the user permission adjustment is obtained after multi-dimensional optimization; N is the size of the grid, indicating that the feedback of the user is divided into how many areas for optimization; γ k is the adjustment coefficient of the kth grid area, used to adjust the weight of the feedback in this area, which is obtained through experiments. By constructing the multi-dimensional optimization adjustment grid, the system can optimize the permission adjustment strategy in the fine-grained feedback area, thereby ensuring the high precision and high adaptability of the permission adjustment. Wherein, when calibrating the adjustment coefficient corresponding to each of the grid areas in the experiment, first, a plurality of perception dimensions are divided into discrete intervals to construct a multi-dimensional grid, and each grid represents a combination of user states; then, based on the historical data, the adjustment success rate, behavior consistency, system performance improvement and user satisfaction in each grid are calculated, and these indicators are normalized and fused into the grid feedback score γ k according to the preset weight; finally, the effectiveness of the feedback weight is verified through simulation test or gray deployment, and the score model or weight parameter is fine-tuned according to the effect, so as to realize the accurate modeling and dynamic optimization of the feedback influence of each grid area.

[0138] Step 305: dynamically adjusting the current permission value of the current user according to the multi-dimensional perception feedback matrix, the elastic permission adjustment factor and the multi-dimensional permission adjustment grid model, to obtain the real-time permission value corresponding to the current user.

[0139] In this embodiment, after the calculation of the multi-dimensional perception feedback matrix, the elastic permission adjustment factor and the multi-dimensional permission adjustment grid model, the real-time permission value of the user is finally calculated to provide the most appropriate permission adjustment scheme for the user by integrating all the calculation results. The multi-dimensional perception feedback matrix in the formula represents the user's behavior performance in different dimensions, while the elastic permission adjustment factor adjusts the flexibility of its permission according to historical behavior and current environment, and the multi-dimensional permission adjustment grid model further optimizes the precision of permission adjustment, ensuring that the influence of each dimension on the final decision is reasonably balanced. Reflects the final adjustment state of the user's permission, which may be upgraded, downgraded or remain unchanged, ensuring that the system can continuously make adaptive adjustments in a changing environment. The formula of the final permission adjustment decision:

[0140]

[0141] where NP u is the final permission value of the user u, indicating whether the permission is adjusted, deciding whether the user obtains permission upgrade, downgrade or keeps the status quo. The final permission adjustment decision is the result obtained by considering all factors, which can accurately reflect the influence of the user's current state and feedback on the permission, ensuring reasonable permission adjustment in various complex scenarios.

[0142] In this embodiment, the user's permission settings are updated according to the calculated final permission value, which means that the permission adjustment method provided in this embodiment will automatically adjust the user's access permissions according to the user's behavior and state. For example, if NP u indicates the upgrade of user permission, the user will be given more operation permissions; if it indicates the downgrade of permission, the system will limit the user's access to certain functions.

[0143] In an embodiment of this embodiment, after ensuring the permission update, the changes generated by the system can be synchronized in real time to all related system components, including device end, application end and backend service. Through real-time synchronization, it ensures that the change of user access permission will not cause any lag or incorrect access control. When the user attempts to access resources, the device to be accessed or the corresponding software system, etc. performs permission verification according to the new permission value. The system checks whether the user's current permission allows it to perform a certain operation. For example, when the user attempts to access a specific function, modify data or view sensitive information, the system will verify its current permission and decide whether to allow the operation according to the new permission settings, and record the history log of each permission adjustment, ensuring that all permission adjustments can meet the established security and management specifications.

[0144] The embodiment discloses a user permission dynamic adjustment method based on multi-dimensional perception, considers user behavior, device working state, historical permission change and other information, and constructs a multi-dimensional perception feedback matrix corresponding to each user by weighting and combining different dimensions of information (such as operation frequency, feedback quality, device online state and the like), which reflects the influence of the user on the system. By constructing the multi-dimensional perception feedback matrix of the user, the operation behavior of the user in the platform, the device state and the historical permission change information are comprehensively captured, so that the permission adjustment is more accurate; by weighting and processing the feedback of different dimensions, it is ensured that the system can dynamically respond based on the actual behavior of the user and the system state, and the limitations of the traditional static permission management method are avoided. Then, based on the calculation result of the multi-dimensional perception feedback matrix, an elastic permission adjustment factor is calculated, which is a deformation of the multi-dimensional perception feedback matrix. By adjusting the sensitivity of different dimensions to the permission, the amplitude of user permission adjustment is determined; in order to enable the system to flexibly respond to changes in various environments and business needs, multiple influence factors are introduced to ensure the flexibility of permission adjustment, which represents the dynamic state of the user in the system. The elastic permission adjustment factor makes the permission management have high adaptability, and by introducing multiple influence factors (such as device state, user behavior, historical permission change and the like), the user permission can be flexibly adjusted according to real-time feedback, and the security and stability of the system in different business scenarios are ensured; the introduction of the elastic factor enables the system not only to respond to the current behavior of the user, but also to consider the long-term influence of historical permission changes and environmental changes on the permission, thereby avoiding simple and mechanical permission management. In order to improve the precision of permission adjustment, a multi-dimensional permission adjustment grid model is designed, which maps the behavior feedback data of the user into multiple regions (grids), and adjusts the user permission through different optimization calculation methods; the construction of the grid analyzes the feedback results of each dimension, and applies different weight coefficients in different feedback intervals, so as to dynamically adjust the allocation strategy of the permission; the weighted value of the quantitative feedback value of each user is adjusted according to the grid area it is in, so that the permission management can flexibly cope with the changing environment, and the intelligence and efficiency of the system are ensured. The multi-dimensional permission adjustment grid model further improves the precision of permission adjustment. In the fine-grained optimization area, the system can accurately match the relationship between user permission and behavior mode, device state and the like by adjusting the different weights of each feedback dimension, effectively improving the flexibility and efficiency of the permission adjustment strategy, and ensuring that the system can make the most appropriate permission decision according to the dynamic changes of different users. Finally, after the calculation of the multi-dimensional perception feedback matrix, the elastic permission adjustment factor and the multi-dimensional optimization grid, the system will finally calculate the dynamic permission adjustment strategy of the user, and provide the most appropriate permission adjustment scheme for the user by comprehensively considering all the calculation results.

[0145] The above-described specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above-described specific embodiments are merely examples of the present application and are not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for dynamically adjusting user rights based on multi-dimensional perception, characterized in that: include: Acquire multi-dimensional operation data of the device to be accessed, multi-dimensional behavior data of the current user in the device to be accessed, and multi-dimensional permission change data corresponding to the current user, so as to obtain a multi-dimensional perception feedback matrix between the current user and the device to be accessed; Obtaining an elastic authority adjustment factor corresponding to the current user according to the multidimensional perception feedback matrix; Adjusting a pre-built permission adjustment grid model according to the multi-dimensional perception feedback matrix to obtain a multi-dimensional permission adjustment grid model corresponding to the current user; The current permission value of the current user is dynamically adjusted according to the multi-dimensional perception feedback matrix, the elastic permission adjustment factor and the multi-dimensional permission adjustment grid model to obtain a real-time permission value corresponding to the current user.

2. The method for dynamically adjusting user rights based on multi-dimensional perception according to claim 1, characterized in that: The acquiring of real-time operation data of the device to be accessed, operation behavior data of the current user in the device to be accessed, and permission change data corresponding to the current user to construct a multi-dimensional perception feedback matrix between the current user and the device to be accessed includes: Monitor the operating status of the device to be accessed in real time to extract multi-dimensional operation data corresponding to the device to be accessed; Collect multi-dimensional operation behavior data and multi-dimensional permission change data of the current user in the software platform corresponding to the device to be accessed; Obtaining the cumulative amount of historical permission changes corresponding to the current user and the current permission value corresponding to the current user according to the multi-dimensional permission change data; Obtaining the operation score value and operation weight coefficient corresponding to the operation data in each dimension, the operation score value and operation weight coefficient corresponding to the operation behavior data in each dimension, and the permission change score value and permission change coefficient corresponding to the permission change data in each dimension; A multidimensional perception feedback matrix between the current user and the device to be accessed is constructed based on the accumulated historical permission changes, the current permission value, the operation score value, the operation weight coefficient, the operation score value, the operation weight coefficient, the permission change score value and the permission change coefficient.

3. The method for dynamically adjusting user rights based on multi-dimensional perception according to claim 2, characterized in that: The obtaining of the operation score value and the operation weight coefficient corresponding to the operation data in each dimension, the operation score value and the operation weight coefficient corresponding to the operation behavior data in each dimension, and the permission change score value and the permission change coefficient corresponding to the permission change data in each dimension includes: For the operation data of any dimension, the operation behavior data of any dimension, or the permission change data of any dimension; Calling an operation quantitative scoring function and a pre-built operation weight coefficient corresponding to the operation data according to the dimension of the operation data, and performing normalized scoring on the operation data using the operation quantitative scoring function to obtain an operation score value corresponding to the operation data; Calling an operation behavior quantitative scoring function and a pre-built operation behavior weight coefficient corresponding to the operation behavior data according to the dimension of the operation behavior data, and performing normalized scoring on the operation behavior data using the operation behavior quantitative scoring function to obtain an operation behavior score value corresponding to the operation behavior data; According to the dimension of the permission change data, the permission change quantitative scoring function and the pre-built permission change weight coefficient corresponding to the permission change data are called, and the permission change data is normalized and scored by the permission change quantitative scoring function to obtain the permission change score value corresponding to the permission change data.

4. A method for dynamically adjusting user rights based on multi-dimensional perception according to any one of claims 2-3, characterized in that: The calculation expression of the multi-dimensional perception feedback matrix is ​​as follows: Among them, the F u Represents the multi-dimensional perception feedback matrix between user u and the device to be accessed; the w i Represents the weight coefficient corresponding to the data of the i-th dimension; the f u,i (t) represents the score value obtained after normalizing the score of the data of the i-th dimension of user u within time t; n is the total number of dimensions; C u (t) is the cumulative amount of historical permission changes of user u at time t; u (t) represents the current authority value of user u; and p represents the preset elasticity factor.

5. The method for dynamically adjusting user rights based on multi-dimensional perception according to claim 4 is characterized in that: The obtaining, according to the multi-dimensional perception feedback matrix, the elastic authority adjustment factor corresponding to the current user includes: Acquire multiple users corresponding to the device to be accessed and the multi-dimensional perception feedback matrix corresponding to each of the users; Obtaining the factor value and factor weight corresponding to each of the influencing factors affecting the weight adjustment according to the preset authority adjustment factor table; The elastic authority adjustment factor corresponding to the current user is obtained according to the multiple multi-dimensional perception feedback matrices, the factor values, and the factor weights.

6. The method for dynamically adjusting user rights based on multi-dimensional perception according to claim 5, characterized in that: The calculation expression of the elastic authority adjustment factor is: Wherein, the EPAF u (t) is the elastic authority adjustment factor of user u at time t; the max(F u (t)) represents the largest multi-dimensional perception feedback matrix among multiple multi-dimensional perception feedback matrices; the F u (t) represents the multi-dimensional perception feedback matrix between the current user u and the device to be accessed; α j is the factor weight of the j-th influencing factor; the E j (t) is the factor value of the j-th influencing factor; the β represents a pre-built global bias constant; and the m represents the total number of factors of the influencing factor.

7. The method for dynamically adjusting user rights based on multi-dimensional perception according to claim 5, characterized in that: The step of adjusting the pre-built permission adjustment grid model according to the multi-dimensional perception feedback matrix to obtain the multi-dimensional permission adjustment grid model corresponding to the current user includes: Obtain the number of grid divisions in the pre-built permission adjustment grid model and the permission adjustment coefficient corresponding to each grid; A multi-dimensional authority adjustment grid model corresponding to the current user is constructed according to the number of divisions, the authority adjustment coefficient, and the multi-dimensional perception feedback matrix.

8. The method for dynamically adjusting user rights based on multi-dimensional perception according to claim 7, characterized in that: The calculation expression of the multi-dimensional permission adjustment grid model is: in, represents the multi-dimensional permission adjustment grid model of the current user u; N represents the number of grid divisions; γ k Represents the weight adjustment coefficient corresponding to the k-th grid.

9. The method for dynamically adjusting user rights based on multi-dimensional perception according to claim 8, characterized in that: The calculation expression of the real-time permission value is as follows: Wherein, the NP u Represents the real-time permission value corresponding to the current user u.

10. A user authority dynamic adjustment system based on multi-dimensional perception, characterized in that: Includes multi-dimensional perception module, elastic adjustment module, grid optimization module and authority adjustment module; The multi-dimensional perception module is used to obtain multi-dimensional operation data of the device to be accessed, multi-dimensional behavior data of the current user in the device to be accessed, and multi-dimensional permission change data corresponding to the current user, so as to obtain a multi-dimensional perception feedback matrix between the current user and the device to be accessed; The elastic adjustment module is used to obtain the elastic authority adjustment factor corresponding to the current user according to the multi-dimensional perception feedback matrix; The grid optimization module is used to adjust the pre-built permission adjustment grid model according to the multi-dimensional perception feedback matrix to obtain the multi-dimensional permission adjustment grid model corresponding to the current user; The authority adjustment module is used to dynamically adjust the current authority value of the current user according to the multi-dimensional perception feedback matrix, the elastic authority adjustment factor and the multi-dimensional authority adjustment grid model to obtain a real-time authority value corresponding to the current user.

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