An intelligent management method and system for item storage cabinets based on the Internet of Things and artificial intelligence

Abstract

The present invention discloses an intelligent management method and system for an item storage cabinet based on the Internet of Things and artificial intelligence, and relates to the field of intelligent storage management technology, including collecting information on items stored in an item storage cabinet, assigning RFID tags to stored items, and binding the information on stored items to a QR code; binding user information and recording interaction information between users and stored items; analyzing the demand for items in the item storage cabinet for replenishment based on the interaction information between users and stored items, and monitoring the validity period of items in the item storage cabinet; and displaying the information on stored items in the item storage cabinet in real time and recording it in a database. The present invention improves the efficiency of the item storage cabinet in collecting information on stored items, and predicts item demand by registering user identities, using the Bayesian theorem for identity authentication, and recording transaction information between users and stored items, thereby effectively improving the accuracy of user identity authentication and ensuring that the number of items in the item storage cabinet meets demand.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent storage management, and in particular to an intelligent management method and system for item storage cabinets based on the Internet of Things and artificial intelligence. Background Art

[0002] With the rapid development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, intelligent item storage and management systems have been widely used in many fields, especially in warehousing, logistics, retail, medical and other industries. As an intelligent management device, item storage cabinets combine the real-time data collection of IoT technology and the efficient data processing capabilities of artificial intelligence, promoting the intelligent process of item management. Existing item storage cabinet systems usually rely on RFID tags, barcodes, facial recognition, voice recognition and other technologies to manage items and user identities. RFID technology can realize the automatic recording of item access information, while facial recognition and voice recognition are used to improve the convenience and security of identity authentication. The widespread application of these technologies has effectively improved the efficiency of item management, reduced labor costs, and improved user experience. However, the existing technology is difficult to achieve comprehensive and accurate identity authentication when users authenticate their identities, and the accuracy of identity authentication and the adaptive ability of the system are also insufficient. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides an intelligent management method and system for item storage cabinets based on the Internet of Things and artificial intelligence, which solves the problem that the prior art is difficult to achieve comprehensive and accurate identity authentication when users authenticate their identities, and the accuracy of identity authentication and the system's adaptability are also insufficient.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides an intelligent management method for item storage cabinets based on the Internet of Things and artificial intelligence, which includes collecting information on items stored in the item storage cabinet, assigning RFID tags to the stored items, and binding the stored item information to a QR code; binding user information and recording interaction information between the user and the stored items; analyzing the demand for items in the item storage cabinet based on the interaction information between the user and the stored items and replenishing them, and monitoring the validity period of items in the item storage cabinet; displaying the information on items stored in the item storage cabinet in real time and recording it in a database.

[0007] As a preferred solution of the intelligent management method of item storage cabinets based on the Internet of Things and artificial intelligence described in the present invention, wherein: the collection of item information stored in the item storage cabinet, the assignment of RFID tags to the stored items, and the binding of the stored item information to the QR code refers to obtaining the item information to generate an item ID before storing the item in the item storage cabinet, and binding it through the RFID tag, and synchronously generating a QR code to bind the item information and the item ID. The item storage cabinet obtains the stored item information through the RFID tag and the QR code scanning, and the built-in camera in the item storage cabinet captures the stored item image, uses the pre-trained YOLO model to identify the stored item image to determine the type of stored item, and matches the item information according to the type of stored item.

[0008] As a preferred solution of the intelligent management method of the item storage cabinet based on the Internet of Things and artificial intelligence described in the present invention, wherein: the binding of user information and recording of the interaction information between the user and the stored items refers to the user entering identity information for identity registration when using the item storage cabinet for the first time, the item storage cabinet collects the user's facial image and voice data through a camera and a microphone, processes the facial image through a pre-trained convolutional neural network to extract the facial feature vector f, uses a pre-trained deep neural network to process the voice data to extract the voice feature vector s, and generates a comprehensive feature vector h through weighted fusion:

[0009] ;

[0010] in is the fusion weight;

[0011] The comprehensive feature vector h is recorded as a user identity feature and bound to the user identity information, and the interaction information between the user and the stored items in the item storage cabinet is recorded;

[0012] The user identity feature is used to verify the user identity. When the user needs to authenticate the identity, the item storage cabinet re-collects the user's facial image and voice data to generate a verification comprehensive feature vector , and verify the comprehensive feature vector The verified comprehensive feature vector is calculated based on Bayesian theorem The posterior probability distribution of ;

[0013] Define the user identity recorded in the item storage cabinet as m, and verify the comprehensive feature vector The corresponding user potential identity is M, and the probability of successful verification is calculated by the likelihood function and sample data ,in Is the result of the authentication. If successful, is 1, otherwise it is 0. is the potential identity of the user at time t;

[0014] Calculate the state transition probability of the user identity from time t to time t+1 through sample data and the probability of the authentication result at time t ,in is the potential identity of the user at time t+1, For other users, is the belief at time t;

[0015] Update user beliefs based on Bayesian theorem:

[0016] in is the belief of user identity m at time t+1;

[0017] Define a belief bias function based on the updated user belief and value function :

[0018] ;

[0019] ;

[0020] in is the probability of identity authentication result at time t+1;

[0021] Based on belief bias function and value function Calculate the user verification success result :

[0022] ;

[0023] Get the result of successful user verification Compare with the verification threshold, if the user verification is successful If it is greater than or equal to the verification threshold, the user identity verification is considered to have passed, otherwise it is considered to have failed.

[0024] As a preferred solution of the intelligent management method of item storage cabinets based on the Internet of Things and artificial intelligence of the present invention, wherein: the analysis of item storage cabinet item demand for replenishment based on the interaction information between the user and the stored items refers to constructing an LSTM network, defining the LSTM network input as the interaction information between the user and the stored items, and the output as the predicted demand for stored items, using a training set to train the LSTM network, and using a cross entropy loss function and an Adam optimizer to iteratively optimize the LSTM network parameters;

[0025] The interaction information between users and stored items is input into the trained LSTM network to obtain the future demand for stored items. The item storage cabinet automatically generates an inventory replenishment list based on the stored item information and future demand and sends it to the staff for replenishment of stored items.

[0026] The interaction information between the user and the stored items includes the interaction information between the individual user and the stored items and the interaction information between the collective user and the stored items. The future demand for the stored items includes the future demand of the individual user and the future demand of the collective user. The future demand of the individual user is obtained by inputting the interaction information between the individual user and the stored items into the trained LSTM network, and the future demand of the collective user is obtained by inputting the interaction information between the collective user and the stored items into the trained LSTM network.

[0027] As a preferred solution of the intelligent management method of item storage cabinets based on the Internet of Things and artificial intelligence described in the present invention, wherein: the monitoring of the validity period of items in the item storage cabinet refers to the item storage cabinet obtaining the stored item information through an RFID tag, monitoring the validity period of the stored items in real time according to the stored item information, and marking the stored items that have exceeded the validity period, reminding the staff to replace the marked stored items.

[0028] As a preferred solution of the intelligent management method of item storage cabinets based on the Internet of Things and artificial intelligence described in the present invention, the real-time display of the information of stored items in the item storage cabinet refers to the item storage cabinet displaying the information of stored items in real time, and calculating the remaining quantity and validity period distribution of the stored items to form a visual chart for synchronous display; when the stored items in the item storage cabinet change, the stored item information and the visual chart are synchronously updated to form an update record.

[0029] As a preferred solution of the intelligent management method of item storage cabinets based on the Internet of Things and artificial intelligence described in the present invention, wherein: the recording in the database means that the item storage cabinet stores the item information and update records in the local database, the local database sorts the stored data according to the timestamp, and regularly performs integrity and security checks on the stored data, and the local database is connected to the cloud to back up the stored data.

[0030] In a second aspect, the present invention provides an intelligent management system for item storage cabinets based on the Internet of Things and artificial intelligence, comprising:

[0031] The tag generation module is used to collect the information of the stored items and generate RFID tags for binding, and simultaneously generate QR codes for binding the stored item information;

[0032] A user record module, which is used to bind user identity information for identity recognition and record the interaction information between the user and the stored items according to the user identity information;

[0033] The item detection module is used to predict the demand for items in the item storage cabinet based on the interaction information between the user and the stored items, replenish the stored items, and monitor the validity period of the items in real time to mark and remind;

[0034] The display storage module is used to display item information in real time and store it in the local database.

[0035] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the intelligent management method of item storage cabinets based on the Internet of Things and artificial intelligence as described in the first aspect of the present invention.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the intelligent management method of item storage cabinets based on the Internet of Things and artificial intelligence as described in the first aspect of the present invention.

[0037] The beneficial effects of the present invention are as follows: the present invention improves the efficiency of the item storage cabinet in collecting the information of the stored items by acquiring the information of the stored items and binding the RFID tags, and predicts the demand for items by registering the identity of the user, adopting the Bayesian theorem for identity authentication and recording the transaction information between the user and the stored items, thereby effectively improving the accuracy of the user identity authentication and ensuring that the number of items in the item storage cabinet meets the demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0039] Figure 1 This is a flow chart of the intelligent management method of item storage cabinets based on the Internet of Things and artificial intelligence in Example 1.

[0040] Figure 2 This is a system / schematic diagram of an intelligent management system for item storage cabinets based on the Internet of Things and artificial intelligence in Example 1. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0043] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0044] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides an intelligent management method for item storage cabinets based on the Internet of Things and artificial intelligence, comprising the following steps:

[0045] S1. Collect information about items stored in the item storage cabinet, assign RFID tags to the stored items, and bind the stored item information to a QR code;

[0046] Specifically, collecting information on items stored in an item storage cabinet, assigning RFID tags to the stored items, and binding the stored item information to a QR code means obtaining item information to generate an item ID before storing the item in the item storage cabinet, binding it through an RFID tag, and synchronously generating a QR code to bind the item information and the item ID. The item storage cabinet obtains the stored item information through RFID tag and QR code scanning, and a built-in camera in the item storage cabinet captures images of stored items, uses a pre-trained YOLO model to identify the stored item images to determine the type of stored item, and matches the item information according to the type of stored item.

[0047] The combination of RFID tags and QR codes can quickly identify and record item information, greatly improving the efficiency of item management and avoiding the problems of manual intervention and time delay in traditional barcode scanning methods. During the storage and retrieval of items, the system can update the item status and inventory data in real time, reducing the error rate and processing time. The automated application of RFID tags and QR codes reduces the need for manual intervention and the error rate of manual entry, thereby reducing labor costs. The YOLO model is used to identify the images of stored items and determine the types of stored items. The information of stored items can be monitored when RFID tags and QR codes cannot be added to the stored items, thereby improving the applicability of item storage cabinets.

[0048] S2. Bind user information and record interaction information between the user and the stored items;

[0049] Specifically, binding user information and recording the interaction information between the user and the stored items means that the user enters the identity information for identity registration when using the item storage cabinet for the first time. The item storage cabinet collects the user's facial image and voice data through the camera and microphone, and processes the facial image through the pre-trained convolutional neural network to extract the facial feature vector f, including the user's facial structure, expression, key points and other information, which can effectively express the user's identity characteristics. The pre-trained deep neural network is used to process the voice data to extract the voice feature vector s. The voice feature can include pitch, speaking speed, timbre and other information, which is also a unique representation of the user's identity. The comprehensive feature vector h is generated through weighted fusion:

[0050] ;

[0051] in To fuse the weights, through training data acquisition, the contribution of facial and voice features in identity authentication can be flexibly adjusted according to the feature data of different users. The comprehensive feature vector can more comprehensively represent the user's identity and improve the accuracy and robustness of verification;

[0052] The fusion of facial and voice information reduces the misrecognition rate of a single technology in complex situations, thereby improving the security and stability of the system. The weighted fusion feature vector method can optimize the weights of facial and voice features through training to adapt to the feature differences of different users and changes in usage scenarios.

[0053] The comprehensive feature vector h is recorded as a user identity feature and bound to the user identity information, and the interaction information between the user and the stored items in the item storage cabinet is recorded;

[0054] User identity features are only used to verify the user's identity. Users do not need to verify when using the storage cabinet on a daily basis. Instead, they access the cabinet through normal access methods, such as NFC, work card, QR code scanning, facial recognition, etc. When normal access methods fail, user identity verification is required;

[0055] The user identity feature is used to verify the user identity. When the user needs to authenticate the identity, the item storage cabinet re-collects the user's facial image and voice data to generate a verification comprehensive feature vector , and verify the comprehensive feature vector The verified comprehensive feature vector is calculated based on Bayesian theorem The posterior probability distribution of ;

[0056] Define the user identity recorded in the item storage cabinet as m, and verify the comprehensive feature vector The corresponding user potential identity is M, and the probability of successful verification is calculated by the likelihood function and sample data ,in Is the result of the authentication. If successful, is 1, otherwise it is 0. is the potential identity of the user at time t;

[0057] Calculate the state transition probability of the user identity from time t to time t+1 through sample data and the probability of the authentication result at time t ,in is the potential identity of the user at time t+1, For other users, is the belief at time t, from the initial belief start;

[0058] Update user beliefs based on Bayesian theorem:

[0059] in is the belief of user identity m at time t+1;

[0060] The belief update formula is based on Bayes' theorem. It calculates the posterior probability distribution of the user's identity through sample data, combines the new verification comprehensive feature vector with the initial belief, and dynamically adjusts the estimation of the user's identity. By calculating the likelihood function and the probability of successful verification, combined with the state transition probability of the user's identity and historical verification results, the system can adaptively adjust the accuracy and stability of identity authentication.

[0061] Through Bayesian theorem, the system can adjust its estimate of the user's identity based on new verification data and historical data during each identity authentication. This dynamic update allows the system to better adapt to changes in user identity and reduce verification errors caused by physiological or behavioral changes. Through belief updates, the system can make full use of past identity authentication data, improve the stability of identity authentication, and avoid over-reliance on fluctuations in single verification data. By calculating the probability of successful verification and the posterior distribution, the system can provide a credibility assessment for each verification, helping the system make more rational verification decisions.

[0062] Define a belief bias function based on the updated user belief and value function :

[0063] ;

[0064] ;

[0065] in is the probability of identity authentication result at time t+1;

[0066] Based on belief bias function and value function Calculate the user verification success result :

[0067] ;

[0068] Get the result of successful user verification Compare with the verification threshold, if the user verification is successful If it is greater than or equal to the verification threshold, the user identity verification is considered to have passed, otherwise it is considered to have failed.

[0069] In the intelligent management of item storage cabinets, a technical solution combining multimodal verification and Bayesian theorem is adopted, which improves the identity authentication accuracy and system robustness of the item storage cabinet. Through the weighted fusion of facial and voice features, the user identity verification process is more stable and intelligent, and combined with Bayesian belief updating, the system's adaptability can be improved. These innovative steps effectively solve the shortcomings of the existing technology in terms of identity authentication accuracy and intelligent item management, and have significant practical application value, especially in the fields of high security requirements and intelligent management.

[0070] S3, analyzing the demand for items in the item storage cabinet based on the interaction information between the user and the stored items, and monitoring the validity period of the items in the item storage cabinet;

[0071] Specifically, analyzing the demand for items in the item storage cabinet according to the interaction information between the user and the stored items and replenishing the items refers to constructing an LSTM network, defining the LSTM network input as the interaction information between the user and the stored items, and the output as the predicted demand for the stored items, using the training set to train the LSTM network, and using the cross entropy loss function and the Adam optimizer to iteratively optimize the LSTM network parameters;

[0072] The interaction information between users and stored items is input into the trained LSTM network to obtain the future demand for stored items. The item storage cabinet automatically generates an inventory replenishment list based on the stored item information and future demand and sends it to the staff for replenishment of stored items.

[0073] The interaction information between the user and the stored items includes the interaction information between the individual user and the stored items and the interaction information between the collective user and the stored items. The future demand for the stored items includes the future demand of the individual user and the future demand of the collective user. The future demand of the individual user is obtained by inputting the interaction information between the individual user and the stored items into the trained LSTM network, and the future demand of the collective user is obtained by inputting the interaction information between the collective user and the stored items into the trained LSTM network.

[0074] The user collective refers to all users in the organization or department to which the user belongs.

[0075] Through the prediction of the LSTM model, the system can adjust the inventory of items more accurately to avoid inventory shortages or surpluses caused by inaccurate predictions. This can not only reduce inventory management costs, but also improve user experience and ensure that items are always available when needed. The LSTM network can dynamically optimize the model as new user interaction data is continuously added, making demand forecasts more accurate. As time goes by, the system will capture the patterns between user behavior and item demand more and more accurately, further improving the accuracy of the prediction. By combining the demand prediction results of the LSTM network with the automatically generated replenishment list, the present invention realizes the automation of inventory management. The system can automatically generate a replenishment list based on the predicted demand and send it to the staff for replenishment, reducing the time and errors of manual operations and improving inventory management efficiency.

[0076] Furthermore, monitoring the validity period of items in the item storage cabinet means that the item storage cabinet obtains the stored item information through the RFID tag, monitors the validity period of the stored items in real time according to the stored item information, and marks the stored items that have exceeded the validity period, reminding the staff to replace the marked stored items.

[0077] By combining RFID tags with item expiration date information, the present invention realizes automatic monitoring of item expiration dates. The system obtains detailed information of items, including expiration date data, by reading RFID tags, thereby realizing all-weather, seamless monitoring of item expiration dates. Compared with manual inspection, automatic monitoring significantly improves management efficiency and reduces human omissions. Through the automatic marking and reminder function of expired items, staff can receive immediate notification of expired item processing to avoid continued use or storage of expired items due to negligence. The reminder function can effectively reduce the losses and potential risks caused by expired items, and ensure the real-time and accuracy of item management.

[0078] S4, displaying the information of items stored in the item storage cabinet in real time and recording it in the database;

[0079] Specifically, real-time display of information of stored items in an item storage cabinet means that the item storage cabinet displays the information of stored items in real time, calculates the remaining quantity and validity period distribution of the stored items to form a visual chart for synchronous display, and when the stored items in the item storage cabinet change, the stored item information and the visual chart are synchronously updated to form an update record.

[0080] RFID technology is used to collect and dynamically update item information in real time. The quantity, expiration date and other data of items in the item storage cabinet are always kept up to date. Real-time updates can promptly discover problems such as insufficient inventory and expired items, avoiding risks such as inventory imbalance and continued use of expired items due to data lags. This is especially important for storing sensitive items (such as food and medicines) and can ensure the quality and safety of items. By calculating the remaining quantity and expiration date distribution and generating visual charts, managers can intuitively view the inventory status and expiration date information of various items. This graphical display greatly simplifies the data analysis process and improves the transparency of item management. Item storage cabinets can adjust inventory management strategies based on real-time calculated data, replenish shortages or replace expired items in a timely manner, ensure that inventory is always in the best condition, and avoid waste and waste of expired items. Through the synchronous update mechanism, the item information and visual charts in the item storage cabinet are always consistent, avoiding management errors caused by data update delays or conflicts.

[0081] Furthermore, recording in a database means that the item storage cabinet stores item information and update records in a local database, the local database sorts the stored data according to timestamps, and regularly performs integrity and security checks on the stored data, and the local database is connected to the cloud to back up the stored data.

[0082] This embodiment also provides an intelligent management system for item storage cabinets based on the Internet of Things and artificial intelligence, including:

[0083] The tag generation module is used to collect the information of the stored items and generate RFID tags for binding, and simultaneously generate QR codes for binding the stored item information;

[0084] A user record module, which is used to bind user identity information for identity recognition and record the interaction information between the user and the stored items according to the user identity information;

[0085] The item detection module is used to predict the demand for items in the item storage cabinet based on the interaction information between the user and the stored items, replenish the stored items, and monitor the validity period of the items in real time to mark and remind;

[0086] The display storage module is used to display item information in real time and store it in the local database.

[0087] This embodiment also provides a computer device, which is suitable for the case of an intelligent management method for item storage cabinets based on the Internet of Things and artificial intelligence, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the intelligent management method for item storage cabinets based on the Internet of Things and artificial intelligence proposed in the above embodiment.

[0088] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0089] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the intelligent management method of an item storage cabinet based on the Internet of Things and artificial intelligence proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0090] In summary, the present invention improves the efficiency of the item storage cabinet in collecting the information of stored items by acquiring the information of stored items and binding the RFID tags, and predicts the demand for items by registering the identity of the user, adopting the Bayesian theorem for identity authentication, and recording the transaction information between the user and the stored items, thereby effectively improving the accuracy of the user identity authentication and ensuring that the number of items in the item storage cabinet meets the demand.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent management method for item storage cabinets based on the Internet of Things and artificial intelligence, characterized in that: include, Collect information about items stored in the item storage cabinet, assign RFID tags to the stored items, and bind the stored item information to a QR code; Bind user information and record the interaction information between users and stored items; Analyze the demand for items in the item storage cabinet based on the interaction information between the user and the stored items, and monitor the validity period of the items in the item storage cabinet; Display the information of items stored in the item storage cabinet in real time and record it in the database; The binding of user information and recording of interaction information between the user and the stored items refers to the user entering identity information for identity registration when using the item storage cabinet for the first time. The item storage cabinet collects the user's facial image and voice data through a camera and a microphone, processes the facial image through a pre-trained convolutional neural network to extract a facial feature vector f, and uses a pre-trained deep neural network to process the voice data to extract a voice feature vector s, and generates a comprehensive feature vector h through weighted fusion: ； in is the fusion weight; The comprehensive feature vector h is recorded as a user identity feature and bound to the user identity information, and the interaction information between the user and the stored items in the item storage cabinet is recorded; The user identity feature is used to verify the user identity. When the user needs to authenticate the identity, the item storage cabinet re-collects the user's facial image and voice data to generate a verification comprehensive feature vector , and verify the comprehensive feature vector The verification comprehensive feature vector is calculated based on Bayesian theorem The posterior probability distribution of ; Define the user identity recorded in the item storage cabinet as m, and verify the comprehensive feature vector The corresponding user potential identity is M, and the probability of successful verification is calculated by the likelihood function and sample data ,in Is the result of the authentication. If successful, is 1, otherwise it is 0. is the potential identity of the user at time t; Calculate the state transition probability of the user identity from time t to time t+1 through sample data and the probability of the authentication result at time t in is the potential identity of the user at time t+1, For other users, is the belief at time t; Update user beliefs based on Bayesian theorem: in is the belief of user identity m at time t+1; Define a belief deviation function based on the updated user belief and value function : ； ； in is the probability of identity authentication result at time t+1; Based on belief bias function and value function Calculate the user verification success result : ； Get the result of successful user verification Compare with the verification threshold, if the user verification is successful If it is greater than or equal to the verification threshold, the user identity verification is considered to have passed, otherwise it is considered to have failed.

2. The method for intelligent management of item storage cabinets based on the Internet of Things and artificial intelligence as claimed in claim 1, characterized in that: The collecting of information on items stored in the item storage cabinet, assigning RFID tags to the stored items, and binding the stored item information to a QR code means obtaining the item information to generate an item ID before storing the item in the item storage cabinet, binding the item ID through the RFID tag, and synchronously generating a QR code to bind the item information and the item ID. The item storage cabinet obtains the stored item information through scanning the RFID tag and the QR code, and a built-in camera in the item storage cabinet captures images of the stored items, uses a pre-trained YOLO model to identify the stored item images and determine the types of the stored items, and matches the item information according to the types of the stored items.

3. The intelligent management method for item storage cabinets based on the Internet of Things and artificial intelligence as claimed in claim 2, characterized in that: Analyzing the demand for items in the item storage cabinet according to the interaction information between the user and the stored items and replenishing the items refers to constructing an LSTM network, defining the LSTM network input as the interaction information between the user and the stored items, and the output as the predicted demand for the stored items, training the LSTM network using a training set, and iteratively optimizing the LSTM network parameters using a cross entropy loss function and an Adam optimizer; The interaction information between users and stored items is input into the trained LSTM network to obtain the future demand for stored items. The item storage cabinet automatically generates an inventory replenishment list based on the stored item information and future demand and sends it to the staff for replenishment of stored items. The interaction information between the user and the stored items includes the interaction information between the individual user and the stored items and the interaction information between the collective user and the stored items. The future demand for the stored items includes the future demand of the individual user and the future demand of the collective user. The future demand of the individual user is obtained by inputting the interaction information between the individual user and the stored items into the trained LSTM network, and the future demand of the collective user is obtained by inputting the interaction information between the collective user and the stored items into the trained LSTM network. The user collective refers to all users in the organization or department to which the user belongs.

4. The method for intelligent management of item storage cabinets based on the Internet of Things and artificial intelligence as claimed in claim 3, characterized in that: The monitoring of the validity period of items in the item storage cabinet refers to the item storage cabinet obtaining the stored item information through the RFID tag, monitoring the validity period of the stored items in real time according to the stored item information, marking the stored items that have exceeded the validity period, and reminding the staff to replace the marked stored items.

5. The method for intelligent management of item storage cabinets based on the Internet of Things and artificial intelligence as claimed in claim 4, characterized in that: The real-time display of the information of stored items in the item storage cabinet means that the item storage cabinet displays the information of stored items in real time, calculates the remaining quantity and validity period distribution of the stored items to form a visual chart for synchronous display, and when the stored items in the item storage cabinet change, the stored item information and the visual chart are synchronously updated and an update record is formed.

6. The method for intelligent management of item storage cabinets based on the Internet of Things and artificial intelligence as claimed in claim 5, characterized in that: The record in the database means that the item storage cabinet stores the item information and update records in the local database. The local database sorts the stored data according to the timestamp and regularly performs integrity and security checks on the stored data. The local database is connected to the cloud to back up the stored data.

7. An intelligent management system for item storage cabinets based on the Internet of Things and artificial intelligence, based on the intelligent management method for item storage cabinets based on the Internet of Things and artificial intelligence according to any one of claims 1 to 6, characterized in that: include, The tag generation module is used to collect the information of the stored items and generate RFID tags for binding, and simultaneously generate QR codes for binding the stored item information; A user record module, which is used to bind user identity information for identity recognition and record the interaction information between the user and the stored items according to the user identity information; The item detection module is used to predict the demand for items in the item storage cabinet based on the interaction information between the user and the stored items, replenish the stored items, and monitor the validity period of the items in real time to mark and remind; The display storage module is used to display item information in real time and store it in the local database.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent management method of item storage cabinets based on the Internet of Things and artificial intelligence as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the intelligent management method of item storage cabinets based on the Internet of Things and artificial intelligence as described in any one of claims 1 to 6 are implemented.

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