Storage cabinet storage early warning system based on visual identification technology

Abstract

The invention belongs to the technical field of storage early warning, and relates to a storage cabinet storage early warning system based on a visual identification technology, and the system comprises an article image collection module which collects multi-angle images of articles in a storage cabinet; and the article abnormal degree judgment module analyzes the capacity ratio and the abnormal article information by using the article image and the placement duration to obtain an abnormal degree index so as to judge whether the article is abnormal or not and perform early warning. The article compatibility degree analysis module analyzes a compatibility index by collecting contact data between articles, judges whether the compatibility condition is abnormal or not, and then gives an early warning. The environment abnormity degree analysis module collects environment humidity and dust and sulfur dioxide concentration, analyzes humidity and pollution indexes to obtain an environment abnormity index, and performs early warning after environment abnormity is judged. And the storage early warning degree analysis module analyzes the early warning index of the storage cabinet in combination with the article abnormity, the compatibility degree and the environment abnormity index, and judges whether to carry out early warning on the storage state or not.

Description

Technical Field

[0001] This invention belongs to the field of storage early warning technology, and relates to a storage early warning system for lockers based on visual recognition technology. Background Technology

[0002] With the rapid development of information technology, especially the significant advancements in computer vision, image processing, and artificial intelligence, various industries are actively exploring the application of new technologies to improve operational efficiency, enhance security, and improve user experience. In the field of locker management, traditional management methods face numerous challenges, such as low efficiency of manual management, uneven utilization of storage space, insufficient security, and difficulty in real-time monitoring of item status. Therefore, locker storage early warning systems based on visual recognition technology have emerged as an innovative solution to these problems.

[0003] For example, Chinese invention publication CN111915809A discloses a locker management method, device, equipment, and readable storage medium, relating to the field of computer technology. The method includes: acquiring user data, including facial data and access data; when the access data is stored data, sending the user data to a management server; receiving a judgment result from the management server; when the judgment result indicates that the pre-stored data contains facial data, acquiring user request data and sending the user data and request data to a payment server; receiving a payment confirmation request from the payment server, acquiring the payment data responded by the user according to the payment confirmation request, and sending the payment data to the payment server; receiving deduction information from the payment server, and generating an opening instruction based on the deduction information. This solves the problems of low convenience and security of lockers in existing technologies, and the inability to achieve personalized payment, thus improving the user experience.

[0004] The existing technologies have the following problems: 1. The existing technologies do not have an automated early warning function. When the items in the locker exceed the preset capacity or there is an abnormal situation, an alarm cannot be issued in time, which may result in safety hazards not being dealt with in a timely manner.

[0005] 2. Existing technologies, when assessing the storage conditions of items in lockers, often only consider simple physical attributes such as the size and shape of the items, neglecting the physical parameters involved in the actual contact between the items. Because these physical parameters are not comprehensively considered, existing technologies struggle to accurately measure the degree of interaction between items. Ignoring these physical parameters may lead to unnecessary wear, compression, or deformation of items during storage. These physical effects may gradually accumulate, eventually causing damage to the items, affecting their usability, or resulting in economic losses. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background technology, a storage locker early warning system based on visual recognition technology is proposed.

[0007] The objective of this invention can be achieved through the following technical solution: This invention provides a storage locker early warning system based on visual recognition technology, comprising: an item image acquisition module: used to acquire multiple angle images of each item stored in the locker.

[0008] The item anomaly assessment module analyzes the storage locker's capacity ratio, the number of abnormal items, and their storage duration based on multiple angle images and data collected from each item. This analysis, in turn, determines the anomaly index of the stored items. It can determine whether there are any abnormalities in the items stored in the lockers and then issue an early warning.

[0009] Item Compatibility Analysis Module: This module collects data on the contact area, contact pressure, and contact time between each item stored in the locker and other items, and analyzes the compatibility index between the items stored in the locker. It can determine whether the compatibility of items stored in the locker is abnormal and then issue an early warning.

[0010] The environmental anomaly analysis module is used to collect humidity data inside the lockers and analyze the humidity level index; it also collects dust and sulfur dioxide concentration data to analyze the pollution level index; and further analyzes the environmental anomaly index within the lockers. It can determine whether there are any abnormalities in the environment inside the locker and then issue an early warning.

[0011] Storage warning level analysis module: This module analyzes the warning level index of the locker by considering the abnormality index and compatibility index of the items stored in the locker, as well as the abnormality index of the environment inside the locker, and then determines whether to issue a warning for the storage status of the locker.

[0012] Database: Used to store the allowable capacity percentage, allowable storage time, and allowable number of abnormal items in the lockers, as well as the allowable dust concentration and allowable sulfur dioxide concentration in the lockers.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention can estimate the volume of each item in the locker by using image processing technology, thereby helping managers to understand the space utilization of the locker. This helps to optimize the layout and allocation of the lockers, improve space utilization, and reduce storage problems caused by insufficient space or improper allocation. Combining the placement time of the items and the number of abnormal items, the items stored in the locker are detected for abnormality from the two dimensions of time and content. This multi-dimensional analysis method can more fully identify potential problems and risks, and improve the accuracy and effectiveness of early warning. When the system detects that there is an abnormality in the items stored in the locker, it can automatically trigger the early warning mechanism and notify the manager in a timely manner. This automated early warning method can greatly shorten the response time, enabling the manager to take measures to solve the problem quickly and prevent the problem from escalating.

[0014] (2) This invention analyzes the capacity ratio, number of abnormal items, and placement time of items stored in the locker based on multiple angle images of each item stored in the locker and the collection of placement time data. This analysis, in turn, determines the degree of abnormality of the items stored in the locker, allowing for the early detection of potential mutual influence issues between items. For example, for easily damaged items such as precision instruments and fragile items, their storage positions can be adjusted promptly to prevent damage due to incompatibility with other items, thereby improving the storage quality of the items in the locker.

[0015] (3) By monitoring the ambient humidity, dust concentration, and sulfur dioxide concentration inside the locker in real time, this invention can quickly detect potential environmental problems, such as mold caused by excessive humidity, hygiene problems caused by dust accumulation, and the impact of excessive harmful gases on stored items. Humidity and pollution are important factors leading to damage to items inside the locker. Through timely analysis and early warning, effective measures such as dehumidification, cleaning, and ventilation can be taken to prevent items from getting damp, corroded, or damaged by harmful gases.

[0016] (4) By comprehensively considering the degree of abnormality of the items stored in the locker, the compatibility between items, and the environmental conditions inside the locker, this invention can more fully and accurately assess the current status of the locker. This comprehensive assessment method helps to identify potential problems and risks, avoids the one-sidedness that may result from a single indicator assessment, and, based on a warning degree index derived from multi-factor comprehensive analysis, can more accurately determine whether a warning needs to be issued for the locker. This accurate warning mechanism can reduce false alarms and missed alarms, and improve management efficiency and user experience. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram showing the connections of the various modules in the system of the present invention.

[0019] Figure 2 This is a flowchart illustrating the system of the present invention.

[0020] Figure 3 This is a schematic diagram illustrating the steps of the system of the present invention to obtain the volume, storage time, and number of times each item is placed in the locker. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 and Figure 2 As shown, the present invention provides a storage locker early warning system based on visual recognition technology, including: an item image acquisition module, an item anomaly determination module, an item compatibility analysis module, an environmental anomaly analysis module, a storage early warning analysis module, and a database.

[0023] The item anomaly determination module is connected to the item image acquisition module, the item compatibility analysis module, and the database. The item compatibility analysis module is connected to the item anomaly determination module, the environmental anomaly analysis module, and the database. The environmental anomaly analysis module is connected to the item compatibility analysis module, the storage early warning analysis module, and the database. The database is connected to the item anomaly determination module, the item compatibility analysis module, and the environmental anomaly analysis module.

[0024] Item image acquisition module: used to acquire multiple angle images of the items stored in the locker.

[0025] As a preferred embodiment, the specific process of acquiring multiple angle images of each item stored in the locker is as follows: using a rotatable camera whose rotation and position can be precisely controlled, multiple images are taken from different positions inside the locker, thus acquiring multiple angle images of each item stored in the locker.

[0026] The item anomaly assessment module analyzes the storage locker's capacity ratio, the number of abnormal items, and their storage duration based on multiple angle images and data collected from each item. This analysis, in turn, determines the anomaly index of the stored items. It can determine whether there are any abnormalities in the items stored in the lockers and then issue an early warning.

[0027] Please see Figure 3 As shown, further, the analysis of the capacity ratio of the items stored in the locker, the number of abnormal items, and the storage time includes: A1, obtaining the volume and category of each item stored in the locker from multiple angle images of each item stored in the locker using an image processing algorithm.

[0028] A2. Sum the volumes of all items stored in the locker to obtain the total volume of the items stored in the locker.

[0029] A3. Extract the total volume of the locker's contents from the database. Divide the total volume of the items stored in the locker by the total volume of the locker's contents to obtain the percentage of the locker's contents relative to its capacity. This percentage is denoted as A3. .

[0030] A4. Based on the known abnormal items, obtain the preset categories for each abnormal item. Compare the categories of each item with the preset categories for each abnormal item, filter and count the number of abnormal items, and denot them as [missing information]. .

[0031] A5. Scan the barcodes of each item stored in the locker using a scanning device, and record the timestamps of when each item was placed in the locker and when it was taken out.

[0032] A6. Compare the timestamps of when each item was removed from the locker with the timestamps of when it was placed to obtain the storage time of each item. Substitute these timestamps into the average calculation formula to obtain the total storage time of the items in the locker, denoted as _____. .

[0033] As a preferred embodiment, the specific process of obtaining the volume of each item stored in the locker is as follows: using edge detection and corner detection algorithms, the edges and key feature points of each item are extracted from multiple angle images of each item stored in the locker. Using feature matching algorithms, such as SIFT, SURF, and ORB, feature points of the same item in multiple angle images are matched to establish a correspondence between feature points. Based on the matched feature points and known camera parameters, such as focal length and optical center position, the position and attitude of the camera corresponding to multiple angle images are calculated using camera pose estimation methods, such as PnP algorithm and ICP algorithm. Then, the three-dimensional coordinates of each item stored in the locker are calculated using three-dimensional reconstruction technology, and a three-dimensional point cloud of each item stored in the locker is constructed to generate a three-dimensional model of each item stored in the locker.

[0034] If the 3D model of an item stored in the locker is a closed 3D model, the volume of the item stored in the locker is calculated by numerical integration; if the 3D model of an item stored in the locker is not a closed model, the volume of the item stored in the locker is approximated by estimating or filling in the missing parts, and then the volume of each item stored in the locker is obtained.

[0035] The specific process for obtaining the category of each item stored in the locker is as follows: the 3D model of the known item category is matched with the 3D model of each item stored in the locker to obtain the category of each item stored in the locker.

[0036] The specific abnormal items are: flammable and explosive materials and contraband.

[0037] The flammable and explosive materials include: explosives such as explosives, detonators, and fuses; and flammable materials such as fireworks, matches, and lighters.

[0038] The prohibited items include: firearms, ammunition and replica firearms; controlled knives such as knives, daggers and triangular knives; crossbows, crossbow bolts and other lethal weapons; wild animals and their products (such as ivory, rhinoceros horn, pangolins, etc.).

[0039] Furthermore, the specific formula for calculating the anomaly index of the items stored in the locker is as follows: ,in , and These represent the percentage of allowed capacity, allowed storage time, and allowed number of abnormal items stored in the lockers, respectively, extracted from the database. , and These represent the weights corresponding to the deviation values ​​of the storage capacity percentage, storage time, and number of abnormal items in the locker, respectively. .

[0040] The , and These values ​​can be set to 0.35, 0.35, and 0.3 respectively. Capacity percentage is a crucial indicator for assessing whether the locker is overcrowded or underutilized. If the items in the locker exceed its allowed capacity percentage, it may prevent other users from using the locker or affect its normal opening and closing. Placement time is an important indicator for assessing whether items have been forgotten or left unclaimed. Items that remain unclaimed for an extended period may indicate that users have forgotten about them or that other abnormalities exist. The number of abnormal items refers to the number of items in the locker that do not meet regulations or pose safety hazards. While the number of abnormal items significantly impacts the overall safety and user experience of the locker, in some cases, it may not be as directly related to the locker's immediate availability as capacity percentage and placement time. Therefore, the weights corresponding to the deviation values ​​of the capacity percentage and placement time of the stored items are greater than the weight corresponding to the deviation value of the number of abnormal items.

[0041] The specific process for determining whether there is an anomaly in the items stored in the locker is as follows: the anomaly index of the items stored in the locker is compared with a preset anomaly index threshold. If the anomaly index of the items stored in the locker is greater than or equal to the preset anomaly index threshold, then there is an anomaly in the items stored in the locker, and an early warning is issued.

[0042] This invention utilizes image processing technology to estimate the volume of each item within a locker, helping managers understand the space utilization of the lockers. This facilitates optimizing locker layout and allocation, improving space utilization, and reducing storage problems caused by insufficient space or improper allocation. By combining the storage time of items and the number of abnormal items, the system detects anomalies in the lockers from both time and content dimensions. This multi-dimensional analysis method can more fully identify potential problems and risks, improving the accuracy and effectiveness of early warnings. When the system detects anomalies in the items stored in a locker, it automatically triggers an early warning mechanism to promptly notify the manager. This automated early warning method significantly shortens response time, enabling managers to quickly take measures to resolve problems and prevent their escalation.

[0043] Item Compatibility Analysis Module: This module collects data on the contact area, contact pressure, and contact time between each item stored in the locker and other items, and analyzes the compatibility index between the items stored in the locker. It can determine whether the compatibility of items stored in the locker is abnormal and then issue an early warning.

[0044] Furthermore, the analysis of the compatibility index between items stored in the locker includes: recording the contact area, contact pressure, and contact time between each item stored in the locker and other items, respectively. , and ,in Indicates the number stored in the locker The item's corresponding number, .

[0045] Analyze the wear and tear index between the items stored in the locker and other items. , ,in This indicates the number of items stored in the preset locker. The wear coefficient between an item and other items , and These represent the number of items stored in the preset lockers. The permissible contact area, permissible contact pressure, and permissible contact time between an item and other items.

[0046] As a preferred embodiment, the permissible contact area, permissible contact pressure, and permissible contact time between the items stored in the locker and other items are determined by those skilled in the art.

[0047] The shape, size, weight, and surface characteristics of different items determine the permissible contact area between them and other items. For example, large, heavy objects may require a larger contact area to distribute pressure, while small, light objects can be stored stably on a smaller contact area.

[0048] Different items have different material strengths and different abilities to withstand external pressure. Therefore, the permissible contact area between different items and other items needs to be determined based on the material strength of the items.

[0049] Different items have different sensitivities to prolonged contact with other items. For example, perishable foods and chemical reagents require different permissible contact times with other items, which need to be determined based on their properties.

[0050] The maximum wear index among the items stored in the locker is selected from the wear indexes between the items stored in the locker and other items. This maximum wear index is then used as the wear index among the items stored in the locker, denoted as _____. .

[0051] Analyze the degree of compression between the items stored in the locker and other items. , ,in This indicates the number of items stored in the preset locker. The compression coefficient between an item and other items.

[0052] The maximum compression index among the items stored in the locker is selected from the compression indexes between the items stored inside and other items. This maximum compression index is then used as the compression index among the items stored in the locker, denoted as _____. .

[0053] Analyze the compatibility index between items stored in the locker. , ,in and This represents the permissible wear and tear index and permissible crush index among the items stored in the locker, extracted from the database. and These represent the weights corresponding to the wear and tear index deviation values ​​and the compression index deviation values ​​among the items stored in the locker, respectively. .

[0054] As a preferred embodiment, the and These values ​​can be set to 0.5 and 0.5 respectively. In many cases, items stored in lockers may be affected by both wear and tear and compression. Therefore, the weights corresponding to the wear index deviation value and compression index deviation value between items stored in lockers are set to equal weights.

[0055] The specific process of collecting the contact pressure between the items stored in the locker and other items is as follows: inside the locker, the area where the items are placed is divided into several small units based on the size of the locker and the size and quantity of the items that are usually stored.

[0056] Pressure sensors are placed at key locations within each small unit (such as corners and areas where items are easily squeezed together). For example, if most of the stored items are cuboids, pressure sensors are placed at adjacent positions on each face of the cuboids to detect the contact pressure with other items in different directions. For storage areas with irregular shapes, pressure sensors are flexibly arranged according to the contact points and contact methods of the items to collect the contact pressure between the items stored in the locker and other items.

[0057] The specific process for collecting the contact area between each item stored in the locker and other items is as follows: An image processing algorithm is used to identify the outlines of each item and other items, as well as their contact areas, in multiple angle images of each item stored in the locker. The contact pressure between each item and other items is combined with the outlines of the corresponding items and other items and their contact areas. If the contact pressure is uniformly distributed within the contact area, the geometric area of ​​the contact area is directly used as the contact area between each item stored in the locker and other items. If the pressure distribution is uneven, mathematical methods such as integration are used, combined with the pressure distribution function and the shape of the contact area, to obtain the contact area between each item stored in the locker and other items.

[0058] The specific process for collecting the contact time between each item stored in the locker and other items is as follows: when contact between an item and other items is detected by image processing algorithm and pressure sensor data, a timestamp is recorded as the start time of the contact between the item and other items. When the contact ends, a timestamp is also recorded as the end time of the contact between the item and other items.

[0059] By comparing the end time of contact between an item and other items with the start time, the contact time between the item stored in the locker and other items can be obtained, and thus the contact time between each item stored in the locker and other items can be obtained.

[0060] The compatibility index between the items stored in the locker is compared with a preset compatibility threshold. If the compatibility index between the items stored in the locker is less than the preset compatibility threshold, then the compatibility of the items stored in the locker is abnormal.

[0061] This invention analyzes the storage capacity ratio, the number of abnormal items, and their storage duration based on multiple angle images of the items stored in the locker. This analysis, combined with the time each item has been stored, allows for the assessment of an abnormality index, enabling the early detection of potential inter-item interference. For example, for fragile items such as precision instruments or breakable goods, their storage positions can be adjusted promptly to prevent damage due to incompatibility with other items, thereby improving the overall storage quality.

[0062] The environmental anomaly analysis module is used to collect humidity data inside the lockers and analyze the humidity level index; it also collects dust and sulfur dioxide concentration data to analyze the pollution level index; and further analyzes the environmental anomaly index within the lockers. It can determine whether there are any abnormalities in the environment inside the locker and then issue an early warning.

[0063] Furthermore, the analysis of the humidity level index inside the locker includes: recording the humidity level inside the locker as... Analyze the humidity index inside the locker , ,in This indicates the set allowable ambient humidity level inside the locker.

[0064] In a preferred embodiment, the ambient humidity inside the locker is collected by an ambient humidity sensor.

[0065] Furthermore, the analysis of the environmental pollution level index inside the locker includes: collecting the dust concentration inside the locker using a particulate matter monitor, and recording it as... The sulfur dioxide concentration inside the locker was then collected using a sulfur dioxide gas analyzer and recorded as follows: .

[0066] Analyze the environmental pollution level index inside the lockers , ,in and These represent the allowable dust concentration and allowable sulfur dioxide concentration in the locker, extracted from the database, respectively. and These represent the weights corresponding to the set deviation values ​​of dust concentration and sulfur dioxide concentration in the locker, respectively. .

[0067] As a preferred embodiment, the and The values ​​can be set to 0.6 and 0.4 respectively. Dust is one of the most common contaminants inside lockers, affecting not only their cleanliness but also potentially contaminating or damaging stored items. Dust accumulation can also affect the locker's opening, closing, and sealing performance. While sulfur dioxide is a harmful gas, its concentration inside lockers is usually not high enough to directly harm human health. However, its presence can still cause corrosion or discoloration of sensitive items such as metal products and textiles. Therefore, the weighting of the dust concentration deviation value inside the locker is greater than that of the sulfur dioxide concentration deviation value.

[0068] Furthermore, determining whether there are any abnormalities in the environment inside the locker includes: analyzing the degree of environmental abnormality index inside the locker. , ,in and These represent the weights corresponding to the set humidity level index and pollution level index inside the locker, respectively. .

[0069] The system compares the environmental anomaly index inside the locker with a preset environmental anomaly index threshold. If the environmental anomaly index inside the locker is greater than the preset environmental anomaly index threshold, then an environmental anomaly is detected inside the locker, and an early warning is issued.

[0070] This invention enables rapid detection of potential environmental problems by real-time monitoring of humidity, dust concentration, and sulfur dioxide concentration within the storage cabinet. These problems include mold growth due to excessive humidity, hygiene issues caused by dust accumulation, and the impact of excessive harmful gases on stored items. Humidity and pollution are significant factors leading to damage to items within the storage cabinet. Timely analysis and early warning systems allow for effective measures such as dehumidification, cleaning, and ventilation to prevent items from becoming damp, corroded, or damaged by harmful gases.

[0071] Storage warning level analysis module: This module analyzes the warning level index of the locker by considering the abnormality index and compatibility index of the items stored in the locker, as well as the abnormality index of the environment inside the locker, and then determines whether to issue a warning for the storage status of the locker.

[0072] Furthermore, the specific formula for calculating the early warning level index of the analytical locker is as follows: ,in and These represent the weights corresponding to the abnormality index and compatibility index of the items stored in the locker, respectively. This indicates the weight corresponding to the set index of the degree of environmental abnormality within the locker. .

[0073] As a preferred embodiment, the , and The anomaly index, which can be set to 0.5, 0.3, and 0.2 respectively, reflects whether there are any anomalies in the items inside the locker that may directly threaten the locker's safety or compliance. Therefore, the anomaly index should generally be given a higher weight. The compatibility index assesses the risk of interactions between items inside the locker, such as physical damage or chemical reactions. While this risk may not be as direct and immediate as that of anomalies, it is equally important because it can lead to long-term damage or safety hazards. The environmental anomaly index reflects whether the environment in which the locker is located is suitable for storing items. Unsuitable environmental conditions may cause items to be damaged or deteriorated, thus increasing the risk. However, compared to the anomalies and compatibility issues of the items themselves, environmental factors may not be the most direct threat.

[0074] Furthermore, the determination of whether to issue a warning for the storage status of the locker includes: comparing the warning level index of the locker with a preset warning level index threshold; if the warning level index of the locker is greater than the preset warning level index threshold, then an warning is issued for the storage status of the locker.

[0075] This invention, by comprehensively considering the degree of abnormality of the items stored in the locker, the compatibility between items, and the environmental conditions inside the locker, can more fully and accurately assess the current status of the locker. This comprehensive assessment method helps to identify potential problems and risks, avoids the one-sidedness that may result from a single indicator assessment, and, based on a warning level index derived from multi-factor comprehensive analysis, can more accurately determine whether a locker needs to issue a warning. This accurate warning mechanism can reduce false alarms and missed alarms, improving management efficiency and user experience.

[0076] Database: Used to store the allowable capacity percentage, allowable storage time, and allowable number of abnormal items in the lockers, as well as the allowable dust concentration and allowable sulfur dioxide concentration in the lockers.

[0077] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A storage locker early warning system based on visual recognition technology, characterized in that: The method includes: Item image acquisition module: used to acquire multiple angle images of the items stored in the locker; The item anomaly assessment module analyzes the storage locker's capacity ratio, the number of abnormal items, and their storage duration based on multiple angle images and data collected from each item. This analysis, in turn, determines the anomaly index of the stored items. It can determine whether there are any abnormalities in the items stored in the lockers and then issue an early warning; Item Compatibility Analysis Module: This module collects data on the contact area, contact pressure, and contact time between each item stored in the locker and other items, and analyzes the compatibility index between the items stored in the locker. It determines whether there are any abnormalities in the compatibility of the items stored in the locker, and then issues an early warning; The environmental anomaly analysis module is used to collect humidity data inside the lockers and analyze the humidity level index; it also collects dust and sulfur dioxide concentration data to analyze the pollution level index; and further analyzes the environmental anomaly index within the lockers. It can determine whether there are any abnormalities in the environment inside the locker and then issue an early warning; Storage warning level analysis module: It is used to analyze the warning level index of the locker by using the abnormality index and compatibility index of the items stored in the locker and the abnormality index of the environment inside the locker, and then to determine whether to issue a warning for the storage status of the locker. Database: Used to store the allowable capacity percentage, allowable storage time, and allowable number of abnormal items in the lockers; allowable dust concentration and allowable sulfur dioxide concentration in the lockers; and allowable wear and tear index and allowable compression index between the items stored in the lockers.

2. The storage locker early warning system based on visual recognition technology according to claim 1, characterized in that: The analysis includes the percentage of storage space occupied by items in the lockers, the number of unusual items, and the duration of storage. A1. Obtain the volume and category of each item stored in the locker from multiple angle images of each item stored in the locker using image processing algorithms; A2. Sum the volumes of all items stored in the locker to obtain the total volume of the items stored in the locker. A3. Extract the total volume of the locker's contents from the database. Divide the total volume of the items stored in the locker by the total volume of the locker's contents to obtain the percentage of the locker's contents relative to its capacity. This percentage is denoted as A3. ; A4. Based on the known abnormal items, obtain the preset categories for each abnormal item. Compare the categories of each item with the preset categories for each abnormal item, filter and count the number of abnormal items, and denot them as [missing information]. ; A5. Scan the barcodes of each item stored in the locker using a scanning device, and record the timestamps of when each item was placed in the locker and when it was taken out. A6. Compare the timestamps of when each item was removed from the locker with the timestamps of when it was placed to obtain the storage time of each item. Substitute these timestamps into the average calculation formula to obtain the total storage time of the items in the locker, denoted as _____. .

3. A storage locker early warning system based on visual recognition technology according to claim 2, characterized in that: The specific formula for calculating the abnormality index of the items stored in the locker is as follows: ,in , and These represent the percentage of allowed capacity, allowed storage time, and allowed number of abnormal items stored in the lockers, respectively, extracted from the database. , and These represent the weights corresponding to the deviation values ​​of the storage capacity percentage, storage time, and number of abnormal items in the locker, respectively. .

4. The storage locker early warning system based on visual recognition technology according to claim 1, characterized in that: The specific process for determining whether there are any abnormalities in the items stored in the locker is as follows: The system compares the anomaly index of the items stored in the locker with a preset anomaly index threshold. If the anomaly index of the items stored in the locker is greater than or equal to the preset anomaly index threshold, then the items stored in the locker are considered abnormal, and an early warning is issued.

5. A storage locker early warning system based on visual recognition technology according to claim 1, characterized in that: The compatibility index between items stored in the locker is analyzed, including: The contact area, contact pressure, and contact time between each item stored in the locker and other items are recorded as follows: , and ,in Indicates the number stored in the locker The item's corresponding number, ; Analyze the wear and tear index between the items stored in the locker and other items. , ,in This indicates the number of items stored in the preset locker. The wear coefficient between an item and other items , and These represent the number of items stored in the preset lockers. The permissible contact area, permissible contact pressure, and permissible contact time between an item and other items; The maximum wear index among the items stored in the locker is selected from the wear indexes between the items stored in the locker and other items. This maximum wear index is then used as the wear index among the items stored in the locker, denoted as _____. ; Analyze the degree of compression between the items stored in the locker and other items. , ,in This indicates the number of items stored in the preset locker. The compression coefficient between an item and other items; The maximum compression index among the items stored in the locker is selected from the compression indexes between the items stored inside and other items. This maximum compression index is then used as the compression index among the items stored in the locker, denoted as _____. ; Analyze the compatibility index between items stored in the locker. , ,in and This represents the permissible wear and tear index and permissible crush index among the items stored in the locker, extracted from the database. and These represent the weights corresponding to the wear and tear index deviation values ​​and the compression index deviation values ​​among the items stored in the locker, respectively. ; The compatibility index between the items stored in the locker is compared with a preset compatibility threshold. If the compatibility index between the items stored in the locker is less than the preset compatibility threshold, then the compatibility of the items stored in the locker is abnormal.

6. A storage locker early warning system based on visual recognition technology according to claim 1, characterized in that: The analysis of the humidity level index inside the locker includes: Record the ambient humidity inside the locker as . Analyze the humidity index inside the locker , ,in This indicates the set allowable ambient humidity level inside the locker.

7. A storage locker early warning system based on visual recognition technology according to claim 6, characterized in that: The analysis of the environmental pollution level index inside the locker includes: The dust concentration inside the locker was collected using a particulate matter monitor and recorded as follows: The sulfur dioxide concentration inside the locker was then collected using a sulfur dioxide gas analyzer and recorded as follows: ; Analyze the environmental pollution level index inside the lockers , ,in and These represent the allowable dust concentration and allowable sulfur dioxide concentration in the locker, extracted from the database, respectively. and These represent the weights corresponding to the set deviation values ​​of dust concentration and sulfur dioxide concentration in the locker, respectively. .

8. A storage locker early warning system based on visual recognition technology according to claim 7, characterized in that: The determination of whether there are any abnormalities in the environment inside the locker includes: Analyze the degree of environmental anomaly index inside the locker , ,in and These represent the weights corresponding to the set humidity level index and pollution level index inside the locker, respectively. ; The system compares the environmental anomaly index inside the locker with a preset environmental anomaly index threshold. If the environmental anomaly index inside the locker is greater than the preset environmental anomaly index threshold, then an environmental anomaly is detected inside the locker, and an early warning is issued.

9. A storage locker early warning system based on visual recognition technology according to claim 1, characterized in that: The specific formula for calculating the early warning level index of the analytical locker is as follows: ,in and These represent the weights corresponding to the abnormality index and compatibility index of the items stored in the locker, respectively. This indicates the weight corresponding to the set index of the degree of environmental abnormality within the locker. .

10. A storage locker early warning system based on visual recognition technology according to claim 1, characterized in that: The determination of whether to issue an early warning for the storage status of the locker includes: The warning level index of the locker is compared with the preset warning level index threshold. If the warning level index of the locker is greater than the preset warning level index threshold, a warning is issued for the storage status of the locker.

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