Method and device for managing intelligent dense cabinets based on AI model

Abstract

The invention discloses a method and device for managing intelligent dense cabinets based on an AI model, and relates to the field of artificial intelligence. According to the method, when it is detected that a user exists in a preset range, all mobile sensors are adjusted to be in a working state, and all areas of the intelligent dense cabinet are set to be in a locked state; in response to an operation that a first user requests to view a file, determining a corresponding authorized area and an unauthorized area in the intelligent dense cabinet according to the permission of the first user, adjusting a mobile sensor in the authorized area to be in a standby state, and releasing a locking state of the authorized area; when the mobile sensor in the unauthorized area detects a mobile state, giving an alarm; and when the user does not exist in the preset range, all the mobile sensors are adjusted to be in a standby state, and the locking states of all the areas of the intelligent dense cabinet are relieved. By implementing the technical scheme provided by the invention, the working states of the sensors in different areas are adjusted according to the user permission, and energy is saved to the greatest extent under the condition of ensuring the permission.

Description

Technical Field

[0001] This application relates to the technical field of artificial intelligence, and particularly to a method, system, electronic device, and storage medium for managing intelligent compact filing cabinets based on an AI model. Background Art

[0002] With the development of technology, AI (Artificial Intelligence) models have demonstrated powerful capabilities in data processing, pattern recognition, and predictive analysis. Electric intelligent compact filing cabinets involve a large amount of sensor data and business data, and effective data processing and analysis methods are required to improve management efficiency and the stability of the equipment.

[0003] Currently, there are problems such as low efficiency and dependence on manual intervention in the management method of compact filing cabinets. In addition, there is no method in the existing technology to adjust the states of sensors corresponding to authorized and unauthorized areas in the compact filing cabinet according to user permissions.

[0004] Therefore, a method for managing compact filing cabinets through an AI model is needed. Summary of the Invention

[0005] This application provides a method, system, electronic device, and storage medium for managing intelligent compact filing cabinets based on an AI model. By managing the intelligent compact filing cabinets through the AI model, the working states of sensors and the locking states of areas can be automatically adjusted, reducing the need for manual intervention and improving management efficiency. Through identity authentication and permission control, it can be ensured that only authorized users can access the corresponding areas of the intelligent compact filing cabinets, enhancing the security of files.

[0006] In the first aspect of this application, a method for managing intelligent compact filing cabinets based on an AI model is provided, which is applied to an intelligent compact filing cabinet management platform. The method includes: When an infrared sensor of the intelligent compact filing cabinet detects that there is a user within a preset range, all mobile sensors are adjusted from the standby state to the working state through the AI model, and all areas of the intelligent compact filing cabinet are set in the locked state, where the mobile sensors are used to detect the movement state of the corresponding areas of the intelligent compact filing cabinet, and the locked state means that reading and movement are not allowed; In response to the operation of the first user requesting to view a file, the first user is authenticated to determine the permissions of the first user; According to the permissions of the first user, the corresponding authorized and unauthorized areas in the intelligent compact filing cabinet are determined, and the mobile sensors in the authorized areas are adjusted from the working state to the standby state through the AI model, and the locked state of the authorized areas is released; When the mobile sensors in the unauthorized areas detect a movement state, an alarm is issued; When the infrared sensor detects that there is no user within the preset range, all mobile sensors are adjusted from the working state to the standby state through the AI model, and the locking states of all areas of the intelligent compact shelves are released.

[0007] By adopting the above technical solution, the intelligent compact shelves can be managed based on the AI model, which can automatically adjust the working states of the sensors and the locking states of the areas, reduce the need for manual intervention, and improve the management efficiency. Through identity authentication and permission control, it can be ensured that only authorized users can access the corresponding areas of the intelligent compact shelves, enhancing the security of the files. The AI model can intelligently adjust the states of the mobile sensors according to the behaviors and needs of the users, thereby reducing energy consumption.

[0008] Optionally, determining the corresponding authorized areas and unauthorized areas in the intelligent compact shelves according to the permissions of the first user includes: When there are multiple first users, the first permission and the second permission are the permissions corresponding to any two of the multiple first users, and the authorized area corresponding to the first permission is larger than the authorized area corresponding to the second permission. The corresponding authorized areas and unauthorized areas in the intelligent compact shelves are determined according to the second permission.

[0009] By adopting the above technical solution, according to the permission levels of the users, the intelligent compact shelves can dynamically adjust the sizes of the authorized areas to meet the needs of different users. This can not only reasonably allocate resources but also improve the utilization rate of resources. By determining the authorized areas and unauthorized areas with the minimum permissions, it can effectively prevent low-permission users from entering high-permission areas, ensuring the security of the files to the greatest extent.

[0010] Optionally, the method further includes: In response to a second user's request to open a target area in the unauthorized area, the second user, who is any one of the multiple first users, is authenticated again; When the permission of the second user can open the target area in the unauthorized area, the target area in the unauthorized area is adjusted to an authorized area through the AI model, and the locking state of the target area is released; When the permission of the second user cannot open the target area in the unauthorized area, an alarm is issued.

[0011] By adopting the above technical solution, the step of introducing the second user authentication is added, which further enhances the flexibility of permission management. Whether to authorize access to the target area in the unauthorized area can be determined according to the permissions of the second user, thereby providing more refined and personalized permission management. During the process of the second user authentication, the identity and permissions of the user can be verified again to ensure that only users with corresponding permissions can access the target area in the unauthorized area, improving the security of the system. When the permissions of the second user cannot open the target area in the unauthorized area, an alarm can be issued in a timely manner to remind the administrator to take corresponding measures to prevent potential security risks and misoperations. By dynamically adjusting the target area in the unauthorized area to an authorized area, the system can reasonably allocate and manage resources according to actual needs, improving resource utilization and the overall performance of the system.

[0012] Optionally, after adjusting the target area in the unauthorized area to an authorized area through the AI model and unlocking the target area, the method further includes: Collecting an image of the target area and determining whether a third user other than the second user enters the target area during the period when the second user stays in the target area; When a third user enters the target area, authenticating the third user; When the permissions of the third user cannot open the target area, issuing an alarm.

[0013] By adopting the above technical solution, collecting images and determining whether a third user enters the target area can further enhance the security of the system. If it is found that the third user does not have permission to access the target area, the system can issue an alarm in a timely manner to prevent potential security risks and misoperations. By collecting images of the target area in real time, the access situation of the target area can be monitored in real time, and abnormal behaviors and potential security threats can be detected in a timely manner. This real-time monitoring ability can improve the response speed and accuracy of the system.

[0014] Optionally, the method further includes: Collecting operation data, where the operation data includes device temperature, ambient temperature, ambient humidity, current, and voltage. The operation data includes first operation data when there is a user within a preset range, second operation data when there is no user within a preset range, and fault data, and dividing the operation data into a training set and a test set; Updating the AI model through the training set and the test set.

[0015] By adopting the above technical solution, collecting operation data and dividing it into a training set and a test set, the AI model can be continuously updated and optimized. The training set is used to train the AI model, while the test set is used to evaluate the performance of the model. In this way, the accuracy and adaptability of the model can be continuously improved, thereby improving the management efficiency and security of the intelligent intensive cabinet. The collected operation data includes device temperature, ambient temperature, ambient humidity, current, voltage, etc. These data provide detailed information about the operation status of the intelligent intensive cabinet. By analyzing these data, the performance, energy consumption, and potential faults or problems of the device can be understood, and corresponding measures can be taken in a timely manner for maintenance or improvement.

[0016] Optionally, the method further includes: Obtain the current operation data, and analyze the current operation data through the AI model to predict potential faults.

[0017] By adopting the above technical solution, the current operation data can be obtained in real time and analyzed using the AI model, and potential faults or problems can be predicted. This prediction ability can help take measures in advance for repair or component replacement, avoid equipment failures during operation, and improve the reliability and stability of the system. By discovering and handling problems in advance, it is possible to avoid sudden equipment failures during operation that lead to downtime. This helps ensure the continuous operation of the system and reduce production losses. By predicting potential faults, unnecessary regular maintenance and inspections can be reduced. Only when the AI model predicts potential problems is targeted maintenance or inspection required, thereby reducing maintenance costs.

[0018] Optionally, the method further includes: After predicting potential faults, adjust the operation parameters or working mode of the intelligent intensive cabinet through the AI model to reduce the probability of faults occurring.

[0019] By adopting the above technical solution, after predicting potential faults, adjusting the operation parameters or working mode of the intelligent intensive cabinet through the AI model can proactively take measures to reduce the probability of faults occurring. This proactive prevention strategy helps reduce the likelihood of faults occurring and improve the stability and reliability of the system. The AI model can adjust the operation parameters or working mode according to the predicted faults to optimize the allocation and use of resources. By adjusting the operation parameters or working mode of the intelligent intensive cabinet through the AI model, the service life of the equipment can be extended. Appropriate parameter adjustment and mode switching can reduce the wear and burden on the equipment, reduce the frequency of faults occurring, and thus extend the service life of the equipment. Through the prediction and adjustment of the AI model, manual intervention and troubleshooting time can be reduced. When the system automatically takes measures to prevent potential faults, staff can focus more on other tasks, improving the overall work efficiency.

[0020] In a second aspect of the present application, a system for managing intelligent compact cabinets based on an AI model is provided, including a startup module, an authentication module, an authorization module, an alarm module, and a standby module, wherein: A startup module is configured to adjust all mobile sensors from standby to working state through an AI model when the infrared sensor of the smart compact cabinet detects the presence of a user within a preset range, and set all areas of the smart compact cabinet to a locked state, wherein the mobile sensor is used to detect the mobile state of the corresponding area of the smart compact cabinet, and the locked state means that reading and movement cannot be performed; an authentication module, configured to, in response to a first user's request to view a file, authenticate the first user to determine the first user's authority; An authorization module is configured to determine the corresponding authorized area and unauthorized area in the smart compact cabinet according to the authority of the first user, and adjust the mobile sensor of the authorized area from the working state to the standby state through the AI model, and release the locking state of the authorized area; An alarm module configured to generate an alarm when a motion sensor in the unauthorized area detects a motion state; The standby module is configured to adjust all mobile sensors from the working state to the standby state through the AI model and release the locking state of all areas of the smart compact cabinet when the infrared sensor detects that there is no user within the preset range.

[0021] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Based on the AI model, the intelligent cabinet can be managed to automatically adjust the working status of the sensor and the locking status of the area, reduce the need for manual intervention, and improve management efficiency; 2. Through identity authentication and permission control, it can ensure that only authorized users can access the corresponding areas of the smart cabinet, enhancing the security of files; 3. The AI model can intelligently adjust the state of the mobile sensor according to the user's behavior and requirements, thereby reducing energy consumption. Description of the Drawings

[0024] Figure 1 is a schematic flowchart of a method for managing an intelligent compactus based on an AI model disclosed in an embodiment of the present application; Figure 2 is a schematic block diagram of a system for managing an intelligent compactus based on an AI model disclosed in an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0025] Description of the reference numerals: 201, start module; 202, authentication module; 203, authorization module; 204, alarm module; 205, standby module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Embodiments

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0027] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, the use of words such as "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0028] In the description of the embodiments of the present application, the meaning of the term "a plurality" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0029] An AI model refers to a mathematical model that uses methods in the fields of mathematics, statistics, computer science, and machine learning to analyze, process, predict, and optimize data with certain regularity and predictability. There are many types of AI models, including decision trees, naive Bayes, K-Nearest Neighbors (KNN), random forests, etc. These models have achieved remarkable success in data preprocessing, classification, prediction, etc. With the continuous development of artificial intelligence technology, AI models will be applied in more fields and bring more extensive and profound impacts.

[0030] This embodiment discloses a method for managing an intelligent compactus based on an AI model, which is applied to an intelligent compactus management platform. Figure 1 It is a schematic flowchart of the method for managing an intelligent compactus based on an AI model disclosed in the embodiments of the present application. As Figure 1 shown, it includes the following steps: S110. When an infrared sensor of the intelligent compactus detects that there is a user within a preset range, all mobile sensors are adjusted from the standby state to the working state through the AI model, and all areas of the intelligent compactus are set in a locked state, where the mobile sensors are used to detect the movement state of the corresponding areas of the intelligent compactus, and the locked state means that reading and movement are not allowed. To save energy, when there is no user within the preset range of the intelligent compactus, the mobile sensors can be set to the standby state. When the infrared sensor of the intelligent compactus detects that there is a user within the preset range, all mobile sensors are adjusted from the standby state to the working state through the AI model, and all areas of the intelligent compactus are set in a locked state. The mobile sensors are used to detect the movement state of the corresponding areas of the intelligent compactus. When the mobile sensors are in the working state, if the intelligent compactus in the area corresponding to the mobile sensors moves, the movement state can be detected by the mobile sensors. When the mobile sensors are in the standby state, the movement state cannot be detected. When all areas of the intelligent compactus are in the locked state, electronic files cannot be read or paper files cannot be taken by moving.

[0031] S120. In response to the operation of the first user requesting to view a file, authenticate the first user to determine the permissions of the first user. When receiving a request from the first user to view a file, authenticate the first user to determine the permissions of the first user. If the first user has no permission to view the file requested to be viewed, directly display the words "No permission to view" and record the request. If the first user has permission to view the file requested to be viewed, subsequent steps can be executed.

[0032] S130. Determine the authorized area and unauthorized area in the intelligent compact rack according to the permission of the first user, and adjust the motion sensors in the authorized area from the working state to the standby state through the AI model, and unlock the authorized area; For example, the intelligent compact rack is divided into six areas: 1, 2, 3, 4, 5, and 6. The permissions can be set corresponding to 1-6. That is, the authorized areas corresponding to permission 4 are 1, 2, 3, and 4, and the unauthorized areas are 5 and 6. When the permission of the first user is 4, the motion sensors in areas 1-4 are adjusted from the working state to the standby state, and the locked state of areas 1-4 is unlocked.

[0033] Optionally, the determining the authorized area and unauthorized area in the intelligent compact rack according to the permission of the first user includes: When there are multiple first users, the first permission and the second permission are the permissions corresponding to any two first users among the multiple first users, and the authorized area corresponding to the first permission is larger than the authorized area corresponding to the second permission. Determine the authorized area and unauthorized area in the intelligent compact rack according to the second permission.

[0034] When there are multiple users at the same time, the authorized area and unauthorized area in the intelligent compact rack can be determined according to the user with the lowest permission. For example, when there are users A, B, and C at the same time, the permission of user A is 2, the permission of user B is 3, and the permission of user C is 4. Then, the authorized area and unauthorized area are determined according to the permission of user A. That is, the authorized areas are 1 and 2, and the unauthorized areas are 3, 4, 5, and 6.

[0035] According to the permission level of the user, the intelligent compact rack can dynamically adjust the size of the authorized area to meet the needs of different users. This can not only reasonably allocate resources but also improve the utilization rate of resources. By determining the authorized area and unauthorized area with the minimum permission, it can effectively prevent low-permission users from entering high-permission areas and ensure the security of files to the greatest extent.

[0036] Optionally, the method further includes: In response to a second user's request to open a target area in the unauthorized area, authenticate the second user again. The second user is any one of the multiple first users; When the permission of the second user can open the target area in the unauthorized area, adjust the target area in the unauthorized area to an authorized area through the AI model, and unlock the locked state of the target area; When the permission of the second user cannot open the target area in the unauthorized area, give an alarm.

[0037] Continuing with the previous example, the second user is User C in the previous example. User C wants to view the files in Area 4. Although User C has the permission to view the files in Area 4, since Area 4 is divided into an unauthorized area at this time, User C needs to be authenticated again. After User C passes the authentication, Area 4 is temporarily adjusted to an authorized area, and the locked state of Area 4 is lifted. If User B wants to view the files in Area 4, since User B does not have the permission to view the files in Area 4, an alarm is directly issued.

[0038] Before responding to the request of the second user, the second user will be authenticated again. This dual authentication mechanism enhances security, ensuring that only users with the corresponding permissions can access the target area in the unauthorized area. According to the permissions of the second user, the permission status of the target area in the unauthorized area can be dynamically adjusted. When the permissions of the second user are sufficient to open the target area, the target area is adjusted from the unauthorized area to the authorized area, and the locked state is lifted, allowing the user to perform operations. This dynamic permission management enables the system to flexibly adjust access permissions according to the actual situation of the user's permissions, improving the adaptability and efficiency of the system. When the permissions of the second user cannot open the target area in the unauthorized area, the system will issue an alarm in a timely manner. This timely response and handling helps to detect and handle potential security risks or abnormal situations in a timely manner, improving the reliability and stability of the system.

[0039] Optionally, after adjusting the target area in the unauthorized area to the authorized area through the AI model and lifting the locked state of the target area, the method further includes: Collecting an image of the target area and determining whether a third user other than the second user enters the target area during the stay of the second user in the target area; When a third user enters the target area, authenticating the third user; When the permissions of the third user cannot open the target area, issuing an alarm.

[0040] To prevent an illegal user from entering the target area during the stay of a legitimate user in the target area, an image of the target area can be collected, and it can be determined through image recognition whether there is an illegal user breaking in. For example, when User C is staying in Area 4 and User B enters Area 4, then User B is authenticated through image recognition, and it is found that User B does not have the permission to enter Area 4, so an alarm is issued.

[0041] By collecting images of the target area in real time, it is possible to monitor the access situation of the target area in real time, timely detect abnormal behaviors and potential security threats. This real-time monitoring ability improves the monitoring efficiency and accuracy of the system, and helps to timely detect and handle potential security risks. It is possible to manage the access situations of multiple users (such as the second user and the third user) in the target area at the same time. This multi-user management ability enables the system to better handle complex user interactions and access requests, and improves the maintainability and efficiency of the system. When the third user enters the target area, the third user will be authenticated. If the third user's permission cannot open the target area, an alarm will be issued. This permission verification and alarm mechanism helps to improve the security and reliability of the system, and timely detect and handle unauthorized access requests.

[0042] S140. When the motion sensor in the unauthorized area detects a motion state, an alarm is issued. When the intelligent filing cabinet in the unauthorized area moves, the motion sensor will detect this motion state, indicating that a user has opened the intelligent filing cabinet with unauthorized access, and then an alarm is directly issued.

[0043] S150. When the infrared sensor detects that there is no user within the preset range, all motion sensors are adjusted from the working state to the standby state through the AI model, and the locked states of all areas of the intelligent filing cabinet are released.

[0044] When there is no user within the preset range, the intelligent filing cabinet is reset, all motion sensors are adjusted from the working state to the standby state, and the locked states of all areas of the intelligent filing cabinet are released to reduce energy consumption.

[0045] Optionally, the method further includes: Collecting operation data, where the operation data includes device temperature, ambient temperature, ambient humidity, current, and voltage. The operation data includes first operation data when there is a user within the preset range, second operation data when there is no user within the preset range, and fault data, and dividing the operation data into a training set and a test set. Updating the AI model through the training set and the test set.

[0046] Collect operation data through sensors and monitoring devices, including device temperature, ambient temperature, ambient humidity, current, voltage, etc. These data can be divided into three categories: First, the first operation data when there are users within the preset range; second, the second operation data when there are no users within the preset range; third, fault data. Preprocess the collected operation data, including data cleaning, outlier handling, missing value filling, etc., to ensure the accuracy and integrity of the data. Divide the preprocessed operation data into a training set and a test set. The training set is used to train the AI model, while the test set is used to evaluate the performance and accuracy of the AI model. Use the training set to train and update the AI model. Through training, the AI model can learn the associations and patterns between the operation data and the state of the intelligent density cabinet. Use the test set to evaluate the trained AI model. By comparing the prediction results of the AI model for the test set with the actual operation data, the performance and accuracy of the AI model can be evaluated. According to the evaluation results, the AI model can be further optimized and adjusted. Feed back the evaluation results to the AI model and continuously update and optimize the AI model. Through continuous learning and updating, the AI model can better adapt to the changes in the actual operation environment and improve the accuracy and stability of the prediction.

[0047] By collecting operation data, the system can understand the operation status of the device and environmental conditions in real time. These data provide a rich information source for the training and updating of the AI model, helping to improve the accuracy and reliability of the AI model. By continuously updating and optimizing the AI model, the intelligent level of the system is improved. The AI model can better adapt to different operation environments and conditions, intelligently manage the device, and improve the operation efficiency and stability of the device.

[0048] Optionally, the method further includes: Obtain the current operation data and analyze the current operation data through the AI model to predict potential faults.

[0049] Obtain the current operation data, where the current operation data includes device temperature, ambient temperature, ambient humidity, current, and voltage. The AI model calculates the probability of the device failing based on the current operation data by analyzing and learning the patterns and rules in the historical data. When the probability of a device failing is greater than a threshold (e.g., 80%), pay close attention to the device.

[0050] By collecting the operation data of the device in real time and analyzing it using an AI model, the system can achieve real-time monitoring of the device's operation status. Once a potential fault is detected, the system can issue a warning in a timely manner to notify relevant personnel to take measures to reduce the risk of the fault occurring. The fault prediction method based on the AI model can discover patterns and regularities in the data by learning and analyzing historical data, thereby accurately predicting potential faults. At the same time, through continuous monitoring and optimization, the accuracy and stability of the prediction can be further improved.

[0051] Optionally, the method further includes: After predicting a potential fault, adjust the operation parameters or working mode of the intelligent intensive cabinet through the AI model to reduce the probability of the fault occurring.

[0052] Analyze the current operation data in real time through the AI model to predict potential faults. For example, when the AI model detects abnormal current fluctuations or abnormal temperature increases, it may indicate that a certain component may malfunction. Once a potential fault is predicted, the operation parameters or working mode of the intelligent intensive cabinet will be automatically adjusted immediately. For example, if it is predicted that a certain component may malfunction, the system can reduce the workload of the component or temporarily shut down the component to reduce the probability of the fault occurring. After adjusting the operation parameters or working mode, the system will continuously monitor the operation status of the intelligent intensive cabinet and provide real-time feedback to the AI model. If the fault still occurs or the situation continues to deteriorate, the system will immediately issue a warning and take further countermeasures. The AI model will learn based on the latest operation data and feedback information, and continuously optimize its prediction and adjustment strategies. This helps to improve the self-adaptability and long-term stability of the system.

[0053] By predicting potential faults and timely adjusting the operation parameters or working mode of the intelligent intensive cabinet, preventive maintenance can be achieved, and the probability of faults occurring can be reduced. This method helps to reduce unexpected downtime and improve the reliability and stability of the device. The AI model can automatically adjust the operation parameters or working mode of the intelligent intensive cabinet based on real-time monitoring data and historical operation data. This self-adaptive adjustment ability enables the system to better adapt to different operating environments and conditions, and improve the adaptability and operating efficiency of the device. By predicting potential faults and adjusting the operation parameters or working mode, resource allocation can be optimized, energy consumption and resource consumption can be reduced without degrading the device performance. This helps to improve the economic efficiency and sustainable development ability of the enterprise.

[0054] This embodiment also discloses a system for managing an intelligent intensive cabinet based on an AI model, Figure 2 is a schematic diagram of the modules of the system for managing an intelligent intensive cabinet based on an AI model disclosed in the embodiments of the present application, as Figure 2As shown, the system includes a startup module 201, an authentication module 202, an authorization module 203, an alarm module 204 and a standby module 205, wherein: The start module 201 is configured to adjust all mobile sensors from the standby state to the working state through the AI model when the infrared sensor of the smart compact cabinet detects the presence of a user within a preset range, and set all areas of the smart compact cabinet to a locked state, wherein the mobile sensor is used to detect the mobile state of the corresponding area of the smart compact cabinet, and the locked state means that reading and movement cannot be performed; An authentication module 202 is configured to perform identity authentication on a first user to determine the authority of the first user in response to a request by the first user to view a file; The authorization module 203 is configured to determine the corresponding authorized area and unauthorized area in the smart compact cabinet according to the authority of the first user, and adjust the mobile sensor of the authorized area from the working state to the standby state through the AI model, and release the locking state of the authorized area; An alarm module 204 is configured to generate an alarm when the motion sensor in the unauthorized area detects a motion state; The standby module 205 is configured to adjust all mobile sensors from the working state to the standby state through the AI model and release the locking state of all areas of the smart compact cabinet when the infrared sensor detects that there is no user within the preset range.

[0055] Optionally, the authorization module 203 is further configured to: When there are multiple first users, the first permission and the second permission are the permissions corresponding to any two first users among the multiple first users, the authorized area corresponding to the first permission is larger than the authorized area corresponding to the second permission, and the corresponding authorized area and unauthorized area in the smart compact cabinet are determined according to the second permission.

[0056] Optionally, the system further comprises a detection module, wherein the detection module is configured to: In response to a second user requesting to open a target area in the unauthorized area, performing identity authentication on the second user again, the second user being any one of the plurality of first users; When the second user has the authority to open the target area in the unauthorized area, the target area in the unauthorized area is adjusted to an authorized area through the AI model, and the locking state of the target area is released; When the authority of the second user is not enough to open the target area in the unauthorized area, an alarm is issued.

[0057] Optionally, the system further includes an image recognition module, which is configured to: Collect an image of the target area, and determine whether a third user other than the second user enters the target area during the period when the second user stays in the target area; When a third user enters the target area, authenticate the third user; When the permission of the third user cannot open the target area, give an alarm.

[0058] Optionally, the system further includes a model module, which is configured to: Collect operation data, where the operation data includes device temperature, ambient temperature, ambient humidity, current, and voltage. The operation data includes first operation data when there is a user within a preset range, second operation data when there is no user within a preset range, and fault data, and divide the operation data into a training set and a test set; Update the AI model through the training set and the test set.

[0059] Optionally, the system further includes a prediction module, which is configured to: Obtain current operation data, and analyze the current operation data through an AI model to predict potential faults.

[0060] Optionally, the system further includes an adjustment module, which is configured to: After predicting potential faults, adjust the operation parameters or working mode of the intelligent intensive cabinet through the AI model to reduce the probability of faults occurring.

[0061] It should be noted that: when the device provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual application, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be repeated here.

[0062] This embodiment also discloses an electronic device. Referring to Figure 3 , the electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0063] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0064] Among them, the user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may further include standard wired interfaces and wireless interfaces.

[0065] Among them, the network interface 304 may optionally include standard wired interfaces and wireless interfaces (such as WI-FI interfaces).

[0066] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and circuits. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305, it performs various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately through a single chip.

[0067] Among them, the memory 305 may include a random access memory (RAM), or may also include a read-only memory (ROM). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. As shown in the figure, the memory 305, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for the method of managing an intelligent intensive cabinet based on an AI model.

[0068] In Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; while the processor 301 can be used to call the application program for the method of managing an intelligent intensive cabinet based on an AI model stored in the memory 305. When executed by one or more processors 301, the electronic device is caused to execute the method of one or more of the above embodiments.

[0069] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0070] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0071] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some service interfaces. The indirect couplings or communication connections of devices or units can be electrical or other forms.

[0072] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0073] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0074] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory 305. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. And the aforementioned memory 305 includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0075] The foregoing are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and the practice of the present disclosure, those skilled in the art will readily think of other implementation schemes of the present disclosure. The present application aims to cover any variations, uses, or adaptive changes of the present disclosure, and these variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for managing intelligent intensive cabinets based on an AI model, characterized in that, Applied to the intelligent compact cabinet management platform, the method includes: When the infrared sensor of the smart cabinet detects the presence of a user within a preset range, all mobile sensors are adjusted from standby to working state through the AI model, and all areas of the smart cabinet are set to a locked state, wherein the mobile sensor is used to detect the mobile state of the corresponding area of the smart cabinet, and the locked state means that reading and movement cannot be performed; In response to a first user's request to view a file, authenticating the first user to determine the first user's authority; Determine the corresponding authorized area and unauthorized area in the smart compact cabinet according to the authority of the first user, and adjust the mobile sensor of the authorized area from the working state to the standby state through the AI model, and release the locking state of the authorized area; When the motion sensor in the unauthorized area detects a moving state, an alarm is issued; When the infrared sensor detects that there is no user within the preset range, all mobile sensors are adjusted from the working state to the standby state through the AI model, and the locking state of all areas of the smart compact cabinet is released.

2. The method for managing an intelligent intensive cabinet based on an AI model according to claim 1, wherein, The determining, according to the authority of the first user, the corresponding authorized area and unauthorized area in the smart compact cabinet comprises: When there are multiple first users, the first permission and the second permission are the permissions corresponding to any two first users among the multiple first users, the authorized area corresponding to the first permission is larger than the authorized area corresponding to the second permission, and the corresponding authorized area and unauthorized area in the smart compact cabinet are determined according to the second permission.

3. The method for managing an intelligent intensive cabinet based on an AI model according to claim 2, wherein The method further comprises: In response to a second user requesting to open a target area in the unauthorized area, performing identity authentication on the second user again, the second user being any one of the plurality of first users; When the second user has the authority to open the target area in the unauthorized area, the target area in the unauthorized area is adjusted to an authorized area through the AI model, and the locking state of the target area is released; When the authority of the second user is not enough to open the target area in the unauthorized area, an alarm is issued.

4. The method for managing an intelligent intensive cabinet based on an AI model according to claim 3, wherein After adjusting the target area in the unauthorized area to an authorized area through the AI model and releasing the locking state of the target area, the method further includes: capturing an image of the target area, and determining whether a third user other than the second user enters the target area while the second user stays in the target area; When a third user enters the target area, performing identity authentication on the third user; When the third user's authority is not sufficient to open the target area, an alarm is issued.

5. The method for managing an intelligent intensive cabinet based on an AI model according to claim 1, wherein, The method further comprises: Collecting operation data, the operation data including device temperature, ambient temperature, ambient humidity, current and voltage, the operation data including first operation data when a user exists within a preset range, second operation data when no user exists within the preset range, and fault data, and dividing the operation data into a training set and a test set; The AI model is updated using the training set and the test set.

6. The method for managing an intelligent intensive cabinet based on an AI model according to claim 5, wherein The method further comprises: Acquire current operating data, and analyze the current operating data through an AI model to predict potential failures.

7. The method for managing an intelligent intensive cabinet based on an AI model according to claim 6, wherein, The method further comprises: After predicting a potential failure, the AI model is used to adjust the operating parameters or working mode of the smart compact cabinet to reduce the probability of failure.

8. A system for managing intelligent intensive cabinets based on an AI model, characterized in that, It includes a startup module, an authentication module, an authorization module, an alarm module and a standby module, among which: A startup module is configured to adjust all mobile sensors from standby to working state through an AI model when the infrared sensor of the smart compact cabinet detects the presence of a user within a preset range, and set all areas of the smart compact cabinet to a locked state, wherein the mobile sensor is used to detect the mobile state of the corresponding area of the smart compact cabinet, and the locked state means that reading and movement cannot be performed; an authentication module, configured to, in response to a first user's request to view a file, authenticate the first user to determine the first user's authority; An authorization module is configured to determine the corresponding authorized area and unauthorized area in the smart compact cabinet according to the authority of the first user, and adjust the mobile sensor of the authorized area from the working state to the standby state through the AI model, and release the locking state of the authorized area; An alarm module configured to generate an alarm when a motion sensor in the unauthorized area detects a motion state; The standby module is configured to adjust all mobile sensors from the working state to the standby state through the AI model and release the locking state of all areas of the smart compact cabinet when the infrared sensor detects that there is no user within the preset range.

9. An electronic device, characterized in that, It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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