Remote control method of distribution cabinet based on user behavior analysis and intelligent distribution cabinet

Abstract

The present invention relates to the technical fields of intelligent power distribution systems, artificial intelligence, etc., and provides a remote control method for a distribution cabinet based on user behavior analysis and an intelligent distribution cabinet. By configuring a user behavior recording module, different user behavior permissions are assigned to different user roles. The user behavior permissions include access behavior permissions to the intelligent distribution cabinet system and remote operation behavior permissions to remotely control the distribution cabinet when the distribution cabinet is abnormal. The access behavior data of the intelligent distribution cabinet system and the remote operation behavior data when the distribution cabinet is abnormal are obtained to input into a trained user behavior analysis AI model to analyze the access habits and remote operation habits of different user roles, adjust the user behavior permissions of different user roles, and dynamically manage the access behaviors and remote operation behaviors of different user roles, thereby dynamically managing user behavior permissions and improving the flexibility of user behavior permission management and system security.

Description

Technical Field

[0001] The present invention relates to the technical fields of intelligent power distribution systems, artificial intelligence, and more particularly to a remote control method for a power distribution cabinet based on user behavior analysis and an intelligent power distribution cabinet. Background Art

[0002] Currently, while smart PDC systems support system access and remote control of PDC anomalies, user permission management relies primarily on static settings, which lacks flexibility. Static permissions cannot adapt to changes in user responsibilities or dynamic adjustments to business needs, resulting in excessive or insufficient permissions, impacting system security and operational efficiency. Furthermore, static user permission management lacks behavioral monitoring and analysis, making it impossible to promptly detect abnormal operations or potential security threats. Furthermore, the inability to dynamically adjust permissions makes them susceptible to abuse or exploitation by malicious users, increasing the risk of system attacks or misoperations. When permissions need to be adjusted, operations and maintenance personnel must manually adjust them, which consumes human resources and is prone to human error.

[0003] In summary, the user authority management technology of the existing intelligent distribution cabinet system has technical problems such as lack of flexibility in authority setting, inability to timely detect abnormal operations or potential security threats, and manpower-consuming and prone to human errors in authority adjustment. Summary of the Invention

[0004] In response to the deficiencies in the above-mentioned prior art, the present invention provides a remote control method for a distribution cabinet and an intelligent distribution cabinet based on user behavior analysis, so as to dynamically manage user behavior permissions and improve the flexibility of user behavior permission management and system security.

[0005] In a first aspect, the present invention provides a method for remotely controlling a power distribution cabinet based on user behavior analysis, comprising:

[0006] A user behavior recording module is configured to record the user behavior of different user roles in the intelligent distribution cabinet system. Different user roles are assigned different user behavior permissions, including access behavior permissions to access the intelligent distribution cabinet system and remote operation behavior permissions to remotely control the distribution cabinet when the distribution cabinet is abnormal;

[0007] Obtain the access behavior data of the different user roles accessing the intelligent power distribution cabinet system and the remote operation behavior data of remotely controlling the power distribution cabinet when the power distribution cabinet is abnormal, recorded by the user behavior recording module, and input the access behavior data and the remote operation behavior data of the different user roles into the trained user behavior analysis AI model to analyze the access habits and remote operation habits of the different user roles;

[0008] According to the access habits and remote operation habits of the different user roles obtained by analyzing the user behavior analysis AI model, the user behavior permissions of the different user roles are adjusted to dynamically manage the access behaviors and remote operation behaviors of the different user roles.

[0009] In a second aspect, the present invention provides an intelligent power distribution cabinet, which is remotely controlled using the above-mentioned power distribution cabinet remote control method based on user behavior analysis.

[0010] Compared with the prior art, the present invention has the following beneficial effects:

[0011] The present invention provides a remote control method for a distribution cabinet and an intelligent distribution cabinet based on user behavior analysis. A user behavior recording module is configured to record the user behavior of different user roles in the intelligent distribution cabinet system. Different user behavior permissions are assigned to different user roles. The user behavior permissions include access behavior permissions to the intelligent distribution cabinet system and remote operation behavior permissions to remotely control the distribution cabinet when the distribution cabinet is abnormal. The access behavior data of the different user roles accessing the intelligent distribution cabinet system and the remote operation behavior data of the remote control of the distribution cabinet when the distribution cabinet is abnormal recorded by the user behavior recording module are obtained. The access behavior data and the remote operation behavior data of the different user roles are input into a trained user behavior analysis AI model to analyze the access habits and remote operation habits of the different user roles. According to the access habits and remote operation habits of the different user roles obtained by the user behavior analysis AI model, the user behavior permissions of the different user roles are adjusted to dynamically manage the access behaviors and remote operation behaviors of the different user roles, thereby dynamically managing user behavior permissions and improving the flexibility of user behavior permission management and system security. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings described herein are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0013] Figure 1 This is a flow chart of a method for remotely controlling a power distribution cabinet based on user behavior analysis according to an embodiment of the present invention;

[0014] Figure 2 This is a schematic diagram of a system architecture in which an intelligent distribution cabinet is remotely controlled using a distribution cabinet remote control method based on user behavior analysis in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0016] See also Figure 1-Figure 2 , an embodiment of the present invention provides a distribution cabinet remote control method based on user behavior analysis and an intelligent distribution cabinet. The intelligent distribution cabinet is controlled using a distribution cabinet remote control method based on user behavior analysis. The distribution cabinet remote control method based on user behavior analysis may include step S101, step S102, and step S103. Step S101, step S102, and part or all of step S103 may be run on the server side, by configuring a user behavior recording module for recording user behaviors of different user roles in the intelligent distribution cabinet system, and different user behavior permissions are assigned to different user roles. The user behavior permissions include access behavior permissions to the intelligent distribution cabinet system and remote operation behavior permissions for remotely controlling the distribution cabinet when the distribution cabinet is abnormal, and the user behavior recording module records the different user roles' access to the intelligent distribution cabinet system. The access behavior data of the distribution cabinet system and the remote operation behavior data of the remote control of the distribution cabinet when the distribution cabinet is abnormal, the access behavior data and the remote operation behavior data of the different user roles are input into the trained user behavior analysis AI model to analyze the access habits and remote operation habits of the different user roles, and according to the access habits and remote operation habits of the different user roles obtained by the user behavior analysis AI model, the user behavior permissions of the different user roles are adjusted to dynamically manage the access behaviors and remote operation behaviors of the different user roles, thereby dynamically managing user behavior permissions and improving the flexibility of user behavior permission management and system security.

[0017] The remote control method for a power distribution cabinet based on user behavior analysis includes:

[0018] S101. Configure a user behavior recording module for recording user behaviors of different user roles in the intelligent power distribution cabinet system. Different user roles are assigned different user behavior permissions, including access permissions to the intelligent power distribution cabinet system and remote operation permissions for remotely controlling the power distribution cabinet when an abnormality occurs in the power distribution cabinet.

[0019] S102. Obtain access behavior data of different user roles accessing the intelligent power distribution cabinet system and remote operation behavior data of remotely controlling the power distribution cabinet when the power distribution cabinet is abnormal, recorded by the user behavior recording module, and input the access behavior data and remote operation behavior data of the different user roles into a trained user behavior analysis AI model to analyze the access habits and remote operation habits of the different user roles;

[0020] S103. Adjust the user behavior permissions of the different user roles based on the access habits and remote operation habits of the different user roles obtained by analyzing the user behavior analysis AI model to dynamically manage the access behaviors and remote operation behaviors of the different user roles.

[0021] It should be noted that in traditional intelligent power distribution cabinet systems, user permissions are often statically configured, making it difficult to dynamically adjust them based on actual user behavior or changes in responsibilities. This can easily lead to irrational permission allocation, such as excessive or insufficient permissions, impacting system security or normal operation and maintenance. Furthermore, a lack of behavior monitoring and analysis prevents effective monitoring of users' daily access and remote operation behaviors, making it difficult to promptly identify anomalies. Furthermore, manual management costs are high, and permission changes often rely on manual intervention, which can easily lead to operational errors and delays. In step S101 of this embodiment, a user behavior recording module is configured, and access and remote operation permissions are set. Different users (administrators, maintenance personnel, business management users, etc.) have different responsibilities within the intelligent power distribution cabinet system, necessitating a detailed distinction between access and remote operation permissions. Access permissions correspond to user operations such as browsing, viewing, and data querying the system; remote operation permissions correspond to control operations (such as starting and stopping the power supply, adjusting parameters, etc.) that can be performed on the distribution cabinet in the event of anomalies. Understandably, traditional intelligent power distribution cabinet systems fail to differentiate and fail to meet practical needs. In step S101, by classifying user behavior permissions into two levels: access behavior permissions and remote operation behavior permissions, user behavior can be dynamically managed more flexibly. In step S102, user behavior data is acquired and input into a user behavior analysis AI model for analysis, addressing the lack of behavior monitoring and analysis in traditional intelligent distribution cabinet systems. In traditional systems, user access and remote operation behaviors cannot be automatically captured or rely on manual log review. In step S102, a user behavior recording module configured within the system automatically captures access and remote operation behavior data and then inputs it into the AI ​​model for in-depth analysis. Based on the learning and recognition capabilities of the user behavior analysis AI model, the AI ​​model can analyze the access and remote operation habits of different user roles. By analyzing the access and remote operation habits of different user roles, it can determine whether the current permission allocation matches actual operational needs or whether there are risks. In step S103, user behavior permissions are dynamically adjusted based on the analysis results, thereby dynamically managing the access and remote operation behaviors of different user roles. User responsibilities may change with project scale or staffing. If some operations and maintenance personnel require additional or higher remote operation permissions in the short term, the system can automatically increase or decrease these permissions based on the legitimacy of the user's actions. This prevents the security risks associated with ordinary business management users gaining excessive remote operation permissions, while also eliminating the high maintenance costs and error-prone issues of frequent manual permission changes. It's understood that different user roles can access the intelligent distribution cabinet system through intelligent user terminals and remotely control the distribution cabinet. The intelligent distribution cabinet system can run on the server, and the distribution cabinet can communicate with the server.

[0022] In some preferred embodiments, the access behavior permission includes the access behavior permission level and the access behavior permission content, and the level of the access behavior permission determines the amount of the access behavior permission content; the remote operation behavior permission includes the remote operation behavior permission level and the remote operation behavior permission content, and the level of the remote operation behavior permission determines the amount of the remote operation behavior permission content. It should be noted that in this embodiment, by dividing each type of permission in the access behavior permission and remote operation behavior permission into two parts, the permission level and the permission content, the correspondence between high and low levels and the executable operation scope (content) is clarified. With the expansion of the functions of the intelligent distribution cabinet system or the change of business needs, different levels can correspond to different depths or different types of access and remote operation content without having to completely reconstruct the permission system. When the user behavior analysis AI model recognizes that a user needs to be upgraded or downgraded, it can not only quickly adjust its level, but also synchronously change the associated permission content to achieve refined dynamic management of permissions.

[0023] In some preferred embodiments, the different user roles include administrators, operation and maintenance personnel, and business management users; the user behavior permission level of the administrator is higher than the user behavior permission level of the operation and maintenance personnel, and the user behavior permission level of the operation and maintenance personnel is higher than the user behavior permission level of the business management user. It should be noted that in a typical business scenario of an intelligent power distribution cabinet system, common user roles include administrators, operation and maintenance personnel, and business management users. In this embodiment, by stipulating the order of permission levels between different user roles, the system can be designed and used in a more consistent manner with the actual operation and maintenance process, that is, the administrator has the highest authority and can perform comprehensive management and respond to emergencies; the operation and maintenance personnel have medium authority and handle daily operation and maintenance and some operations; and the business management user only performs the minimum operations related to the business. This hierarchical allocation of permission levels can ensure security (preventing the abuse of high permissions) and efficiency (avoiding cumbersome application processes as much as possible within the scope of authorization).

[0024] In some preferred embodiments, the administrator's remote operation permissions include remote power on / off of the power distribution cabinet, remote parameter adjustment, remote system reset, and remote alarm handling; the operations and maintenance personnel's remote operation permissions include remote power on / off of the power distribution cabinet, remote parameter adjustment, and remote system reset; and the business management user's remote operation permissions include remote parameter adjustment related to the business management user's daily business needs. It should be noted that administrators typically require the highest permissions to handle emergency or major changes (such as system resets and alarm handling). Operations and maintenance personnel can perform more technical operations (such as power on / off, parameter adjustment, and system resets) but do not need to manage alarm policies or core configuration policies. Business management users only need to perform limited remote parameter adjustments related to business needs and do not need to engage in core technical operations, thereby reducing operational errors and security risks. In this embodiment, by clarifying the specific remote operations that different user roles can perform, systematic management of remote operation behaviors of different user roles can be achieved, thereby improving the security and reliability of the intelligent power distribution cabinet system.

[0025] In some preferred embodiments, the administrator's access permissions include viewing and modifying system configurations, viewing all operational data, operation logs, and alarm records, creating, deleting, or adjusting user account information, and managing and adjusting the security policies of the intelligent power distribution cabinet system. The operations and maintenance personnel's access permissions include viewing system operational data and logs, viewing and maintaining device parameters, and downloading or exporting operation reports for fault analysis. The business management user's access permissions include viewing business-related power distribution status and device usage statistics, and querying and exporting business reports. It should be noted that different roles have different focuses on the intelligent power distribution cabinet system. Administrators need to have comprehensive control over system configuration, user management, security policies, etc., and therefore have the widest access scope. Operations and maintenance personnel focus on maintenance, inspection, and troubleshooting, and need to view logs and related device parameters. Business management users only need to understand business-related data or statistical information, avoiding security risks caused by obtaining excessive technical access permissions. In this embodiment, by clearly distinguishing access scopes, the possibility of system misoperation or unauthorized access is minimized while ensuring efficient collaboration.

[0026] In some preferred embodiments, the user behavior analysis AI model utilizes a supervised learning model, employing either a decision tree model or a random forest model. It should be noted that both decision tree and random forest models are supervised learning models. Both offer good interpretability (e.g., visualization of decision paths and feature importance), making them easier for system operators to understand. User behavior analysis typically involves multi-dimensional features (e.g., access time, access frequency, frequency of abnormal operations, user role category, etc.). Decision tree and random forest models are well-suited to identifying and leveraging the complex relationships between these features for classification or anomaly detection.

[0027] In some preferred embodiments, before training the user behavior analysis AI model, a training dataset, a validation dataset, and a test dataset are first obtained. The training dataset, validation dataset, and test dataset are obtained by: collecting access behavior data and remote operation behavior data of different user roles from the intelligent power distribution cabinet system to obtain raw data of the access behavior and remote operation behavior of the different user roles; preprocessing the raw data of the access behavior and remote operation behavior of the different user roles to obtain preprocessed access behavior data and remote operation behavior data of the different user roles; and classifying and labeling the preprocessed access behavior data and remote operation behavior data of the different user roles to obtain the training dataset, validation dataset, and test dataset. It should be noted that machine learning in the industrial field requires a clear data management process: first, raw data is collected, then preprocessed (format conversion, deduplication, missing value processing, etc.), and finally, divided into training, validation, and test sets according to specific rules. This process ensures that subsequent model training, validation, and testing are conducted within the same feature space and consistent data distribution, avoiding problems such as data leakage and inconsistent distribution. By setting up training sets, validation sets, and test sets separately, we can continuously evaluate whether the model is overfitting during the training process and test its generalization ability on the final test set.

[0028] In some preferred embodiments, after obtaining the training data set, the verification data set, and the test data set, if the supervised learning model uses a decision tree model, training the user behavior analysis AI model includes: inputting the training data set into the decision tree model for model training, so that the decision tree model learns the characteristic patterns of different user roles in access behavior and remote operation behavior, and generates a preliminary user behavior analysis model; using the verification data set to perform performance evaluation on the preliminary user behavior analysis model, calculating the accuracy, recall rate, and F1 score of the preliminary user behavior analysis model on the verification data set, and adjusting the depth of the decision tree model according to the accuracy, recall rate, and F1 score to improve the accuracy and generalization ability of the preliminary user behavior analysis model, and obtain an optimized user behavior analysis model; using the test data set to perform a final evaluation on the optimized user behavior analysis model to obtain the trained user behavior analysis AI model. It should be noted that one of the common hyperparameters of a decision tree is the depth of the tree. If the depth is too large, it is prone to overfitting, and if the depth is too small, it is prone to underfitting. In this example, the model is evaluated using a validation set, and the decision tree depth is dynamically adjusted based on metrics such as precision, recall, and F1 score, ensuring a balance between model accuracy and generalization. Furthermore, a final evaluation of the test dataset yields a trained AI model for user behavior analysis, confirming its reliability in real-world scenarios and meeting the system's requirements for high security and low false positive rates.

[0029] In some preferred embodiments, after obtaining the training dataset, the validation dataset, and the test dataset, if the supervised learning model uses a random forest model, training the user behavior analysis AI model includes: inputting the training dataset into the random forest model for model training, so that the random forest model learns the characteristic patterns of different user roles in access behavior and remote operation behavior, and generates a preliminary user behavior analysis model; using the validation dataset to evaluate the performance of the preliminary user behavior analysis model, calculating the accuracy, recall rate, and F1 score of the preliminary user behavior analysis model on the validation dataset, and adjusting the number of trees in the random forest model based on the accuracy, recall rate, and F1 score to improve the accuracy and generalization ability of the preliminary user behavior analysis model, thereby obtaining an optimized user behavior analysis model; using the test dataset to perform a final evaluation on the optimized user behavior analysis model to obtain the trained user behavior analysis AI model. It should be noted that the random forest model is composed of multiple decision trees, and the number of trees is its important hyperparameter. If the number of trees is too small, the model is prone to underfitting; if the number of trees is too large, training time and resource consumption may increase. In this embodiment, the number of trees is dynamically adjusted based on validation set metrics (precision, recall, and F1 score) to achieve an optimal balance for industrial scenarios. Random forests, through ensemble learning, can mitigate the overfitting problem of single decision trees. In complex distribution cabinet scenarios, random forests can handle more features and are more robust to outliers.

[0030] In some further embodiments, when adjusting the user behavior permissions of different user roles based on the access habits of the different user roles obtained by the user behavior analysis AI model, the method includes: comparing the user access habits output by the user behavior analysis AI model with the access behavior range pre-set by the system; when it is identified that the user access habits are not within the access behavior range, determining that the user behavior permissions of the user role need to be changed; after determining that the user behavior permissions need to be changed, generating a corresponding access permission upgrade or downgrade plan based on the differentiated needs of different user roles in terms of access behavior, the access permission upgrade or downgrade plan includes the items that need to be opened or revoked in the specific access behavior permission content; adjusting the user behavior permissions of the corresponding user role according to the access permission upgrade or downgrade plan to dynamically manage the access behavior of the corresponding user role. It should be noted that in this embodiment, by discovering abnormal access habits and automatically changing the execution permissions, the refined management needs of the smart distribution cabinet system in terms of accessing data, viewing information, browsing configurations, etc. can be met. Among them, comparing the user's access habits with the access behavior range pre-set by the system can clearly identify abnormal situations. In addition, the need to change permissions is determined based on the identification results. When there is a need for change, an upgrade or downgrade plan is generated to accurately divide which specific access permissions to open or revoke. Ultimately, the permission change is executed and takes effect, thus forming a closed-loop management from monitoring to execution, ensuring timely adjustment and controllability of access permissions.

[0031] In some further embodiments, adjusting the user behavior permissions of different user roles based on the remote operation habits of the different user roles analyzed by the user behavior analysis AI model includes: comparing the remote operation habits output by the user behavior analysis AI model with the system's pre-set abnormal response operation behavior range; when it is determined that the remote operation habits are not within the abnormal response operation behavior range, determining that the user behavior permissions of the user role need to be changed; after determining that the user behavior permissions need to be changed, generating a corresponding remote operation permission upgrade or downgrade plan based on the differentiated needs of different user roles in terms of remote operation behavior, the remote operation permission upgrade or downgrade plan including specific items to be opened or revoked in the remote operation behavior permission content; and adjusting the user behavior permissions of the corresponding user role based on the remote operation permission upgrade or downgrade plan to dynamically manage the remote operation behavior of the corresponding user role. In this embodiment, by dynamically adjusting the permissions of remote operation behaviors (such as starting and stopping the power supply of the distribution cabinet, adjusting parameters, resetting the system, handling abnormal alarms, etc.), the intelligent distribution cabinet system can distinguish between two types of behaviors with different security risk levels: daily viewing / access and abnormal response operations, thereby achieving more refined permission management. Remote operation often carries a higher security risk. Improper operation can lead to equipment damage or safety incidents. In this embodiment, by comparing remote operation habits with the range of abnormal response operations, potential risks can be promptly identified and prevented, and operational permissions can be dynamically restricted or revoked.

[0032] In some further embodiments, when remote operations are performed on the same operation item within the remote operation behavior permissions of different user roles due to exception handling, the administrator's remote exception control operation takes precedence over the operation and maintenance personnel's remote exception control operation. When remote operations are performed on the same operation item within the remote operation behavior permissions of multiple user roles due to exception handling, the user trust scores of the multiple user roles are analyzed, and the remote exception control operation of users with higher user trust scores takes precedence over the remote exception control operation of users with lower user trust scores. Furthermore, when analyzing the user trust scores of the multiple user roles, the user behavior analysis AI model performs an assessment based on the access habits and remote operation habits. It should be noted that in traditional intelligent power distribution cabinet systems, once a fault or anomaly occurs, multiple users (e.g., administrators and operation and maintenance personnel) may simultaneously attempt remote operations to troubleshoot or handle the exception. However, there is often controversy or confusion over which operation should be executed first, especially in the case of concurrent operations. Without priority management, the system may face duplicate or conflicting operations. Simultaneous execution of the same remote operation by different users may result in conflicting operation commands and even leave the power distribution cabinet in an uncertain state. In this embodiment, remote exception control operations are performed based on role priority, with administrators taking precedence over operations and maintenance personnel. Administrators typically assume higher-level management and decision-making responsibilities and have full control over system configuration and management. In exceptional circumstances, the system often requires a swift and comprehensive response. Administrators, familiar with the system's global configuration, security policies, and emergency procedures, are better positioned to optimize operations from a global perspective than operations and maintenance personnel. Therefore, when multiple roles simultaneously initiate the same remote exception control operation, prioritizing the administrator's operation can reduce the number of steps involved in the decision-making process, expedite the handling process, and improve the success rate of system emergency response. When multiple user roles of the same priority level perform the same operation, remote exception control operations are prioritized based on user trust scores. User trust scores are comprehensively assessed by a user behavior analysis AI model based on the user's historical access and remote operation behaviors. A higher score indicates a more reliable user in their previous operations (e.g., fewer errors, fewer exceptions, and a closer match with their role permissions). By assigning higher priority to highly trusted users and prioritizing their commands in emergency situations, we can reduce the negative impact of commands from users with incorrect operations or poor safety records on the system. This has direct implications for ensuring the safety and stability of the smart distribution cabinet system and, to a certain extent, encourages users to maintain good operating habits, creating positive feedback. Furthermore, after analyzing user access and remote operation habits, the user behavior analysis AI model can further assess the user's trustworthiness.

[0033] It should be pointed out that the above embodiments are only preferred specific implementation methods of the present invention, and the protection scope of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A remote control method for a power distribution cabinet based on user behavior analysis, characterized in that: include: A user behavior recording module is configured to record the user behavior of different user roles in the intelligent distribution cabinet system. Different user roles are assigned different user behavior permissions, including access behavior permissions to access the intelligent distribution cabinet system and remote operation behavior permissions to remotely control the distribution cabinet when the distribution cabinet is abnormal; Obtain the access behavior data of the different user roles accessing the intelligent power distribution cabinet system and the remote operation behavior data of remotely controlling the power distribution cabinet when the power distribution cabinet is abnormal, recorded by the user behavior recording module, and input the access behavior data and the remote operation behavior data of the different user roles into the trained user behavior analysis AI model to analyze the access habits and remote operation habits of the different user roles; Adjust the user behavior permissions of the different user roles based on the access habits and remote operation habits of the different user roles obtained by the user behavior analysis AI model to dynamically manage the access behaviors and remote operation behaviors of the different user roles; When adjusting the user behavior permissions of the different user roles based on the access habits of the different user roles obtained by the user behavior analysis AI model, it includes: comparing the user access habits output by the user behavior analysis AI model with the access behavior range preset by the system; when it is identified that the user access habits are not within the access behavior range, determining that the user behavior permissions of the user role need to be changed; after determining that the user behavior permissions need to be changed, generating corresponding access permission upgrade or downgrade plans based on the differentiated needs of different user roles in terms of access behavior, the access permission upgrade or downgrade plan includes the items that need to be opened or withdrawn in the specific access behavior permission content; adjusting the user behavior permissions of the corresponding user role based on the access permission upgrade or downgrade plan limit, so as to dynamically manage the access behavior of the corresponding user role; compare the remote operation habits output by the user behavior analysis AI model with the abnormal response operation behavior range preset by the system; when it is identified that the remote operation habits are not within the abnormal response operation behavior range, determine that the user behavior permissions of the user role need to be changed; after determining that the user behavior permissions need to be changed, generate corresponding remote operation permission upgrade or downgrade plans based on the differentiated needs of different user roles in remote operation behavior, and the remote operation permission upgrade or downgrade plans include specific items that need to be opened or withdrawn in the remote operation behavior permission content; according to the remote operation permission upgrade or downgrade plan, adjust the user behavior permissions of the corresponding user role to dynamically manage the remote operation behavior of the corresponding user role.

2. The method for remote control of a power distribution cabinet based on user behavior analysis according to claim 1, characterized in that: The access behavior permission includes the access behavior permission level and the access behavior permission content. The level of the access behavior permission determines the amount of the access behavior permission content. The remote operation behavior authority includes a remote operation behavior authority level and remote operation behavior authority content. The remote operation behavior authority level determines the amount of the remote operation behavior authority content.

3. The method for remote control of a power distribution cabinet based on user behavior analysis according to claim 2, characterized in that: The different user roles include administrators, operation and maintenance personnel, and business management users; the user behavior authority level of the administrator is higher than the user behavior authority level of the operation and maintenance personnel, and the user behavior authority level of the operation and maintenance personnel is higher than the user behavior authority level of the business management user.

4. The method for remote control of a power distribution cabinet based on user behavior analysis according to claim 3, characterized in that: The administrator's remote operation authority includes remote start and stop of the power supply of the distribution cabinet, remote parameter adjustment, remote system reset and remote alarm processing; the operation and maintenance personnel's remote operation authority includes remote start and stop of the power supply of the distribution cabinet, remote parameter adjustment and remote system reset; the business management user's remote operation authority includes remote parameter adjustment related to the daily business needs of the business management user.

5. The method for remote control of a power distribution cabinet based on user behavior analysis according to claim 3, characterized in that: The administrator's access rights include viewing and modifying system configurations, viewing all operating data, operation logs and alarm records, creating, deleting or adjusting user account information, and managing and adjusting the security policy of the intelligent distribution cabinet system; the operation and maintenance personnel's access rights include viewing system operating data and logs, viewing and maintaining equipment parameters, and downloading or exporting operation reports for fault analysis; the business management user's access rights include viewing business-related power distribution status, equipment usage statistics, and querying and exporting business reports.

6. The method for remote control of a power distribution cabinet based on user behavior analysis according to any one of claims 1 to 5, characterized in that: The user behavior analysis AI model adopts a supervised learning model, and the supervised learning model uses a decision tree model or a random forest model.

7. The method for remote control of a power distribution cabinet based on user behavior analysis according to claim 6, characterized in that: Before training the user behavior analysis AI model, a training data set, a verification data set and a test data set are first obtained. The methods for obtaining the training data set, the verification data set and the test data set include: collecting the access behavior data and remote operation behavior data of different user roles from the intelligent distribution cabinet system to obtain the original data of the access behavior and remote operation behavior of different user roles; preprocessing the original data of the access behavior and remote operation behavior of the different user roles to obtain the preprocessed access behavior data and remote operation behavior data of different user roles; classifying and labeling the preprocessed access behavior data and remote operation behavior data of different user roles to obtain the training data set, the verification data set and the test data set.

8. The method for remote control of a power distribution cabinet based on user behavior analysis according to claim 7, characterized in that: After obtaining the training data set, the verification data set and the test data set, if the supervised learning model uses a decision tree model, training the user behavior analysis AI model includes: inputting the training data set into the decision tree model for model training, so that the decision tree model learns the characteristic patterns of different user roles in access behavior and remote operation behavior, and generates a preliminary user behavior analysis model; using the verification data set to perform performance evaluation on the preliminary user behavior analysis model, calculating the accuracy, recall rate and F1 score of the preliminary user behavior analysis model on the verification data set, and adjusting the depth of the decision tree model according to the accuracy, recall rate and F1 score to improve the accuracy and generalization ability of the preliminary user behavior analysis model to obtain an optimized user behavior analysis model; using the test data set to perform a final evaluation on the optimized user behavior analysis model to obtain the trained user behavior analysis AI model.

9. The method for remote control of a power distribution cabinet based on user behavior analysis according to claim 7, characterized in that: After obtaining the training data set, the verification data set and the test data set, if the supervised learning model uses a random forest model, training the user behavior analysis AI model includes: inputting the training data set into the random forest model for model training, so that the random forest model learns the characteristic patterns of different user roles in access behavior and remote operation behavior, and generates a preliminary user behavior analysis model; using the verification data set to perform performance evaluation on the preliminary user behavior analysis model, calculating the accuracy, recall rate and F1 score of the preliminary user behavior analysis model on the verification data set, and adjusting the number of trees of the random forest model according to the accuracy, recall rate and F1 score to improve the accuracy and generalization ability of the preliminary user behavior analysis model, and obtain an optimized user behavior analysis model; using the test data set to perform a final evaluation on the optimized user behavior analysis model to obtain the trained user behavior analysis AI model.

10. An intelligent power distribution cabinet, characterized in that: The intelligent power distribution cabinet is remotely controlled using the power distribution cabinet remote control method based on user behavior analysis as described in any one of claims 1 to 9.

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