Article deposit management system based on Internet of Things

Abstract

The invention relates to the technical field of data processing, in particular to an article deposit management system based on the Internet of Things, which comprises a data acquisition module, an authentication deposit module, a deposit space distribution module and a deposit environment sensing module. The system manages the locker subsequently, authenticates the identity of the user through the authentication and storage module, realizes high-precision identification according to double authentication of the iris and palmprint of the user, and adjusts the double authentication through environment information to realize high-precision identification. The storage space of the storage cabinet is intelligently distributed through the storage space distribution module, the distribution of the storage cabinet space is precisely corrected through the storage environment sensing module, the situation that the storage space is unreasonably utilized is avoided, it is guaranteed that a user obtains a proper storage environment for storing articles, and reasonable distribution of the storage space is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to an article storage management system based on Internet of Things. BACKGROUND

[0002] Most of the traditional article storage management systems adopt bar code identification. Although the bar code identification method is relatively convenient, the bar code is easy to be damaged and lost. Once the bar code is damaged, the identity cannot be accurately identified, and the normal collection of the article is affected. Moreover, these traditional methods lack effective prevention mechanisms for the situation of using other people's storage cards to collect articles, and the security is low.

[0003] For example, a Chinese patent with publication number CN112184392A discloses a method for reporting the storage space state of an intelligent storage cabinet, which comprises the following steps: after the server receives the operation request sent by the mobile terminal, the intelligent storage cabinet opens the box door of the specified storage space according to the instruction of the server, and reports the state of the storage space to the server, so that the server synchronizes the state of the storage space in the server with the state of the storage space in the intelligent storage cabinet; the mobile terminal queries the server regularly to obtain the state of the storage space in the server; when the state of the storage space on the server does not match the state of the storage space on the intelligent storage cabinet, the mobile terminal reports the server that the state of the storage space is abnormal. The present application also relates to a device for implementing the above method. The method and device for reporting the storage space state of the intelligent storage cabinet have the following beneficial effects: they do not affect the use of the intelligent storage cabinet, and increase the safety of the articles in the cabinet. However, the patent cannot be applied to storage places with high storage safety requirements. When important articles are stored, it is difficult to avoid the situation that the important articles are lost due to the misoperation of others or the inaccurate identification of the storage person by the storage system. Moreover, the storage area for important articles is relatively single, and the storage space is not dynamically adjusted according to factors such as the size and weight of the articles, so it is difficult to meet the storage requirements of important articles. SUMMARY

[0004] Therefore, the present application provides an article storage management system based on Internet of Things, which overcomes the problems of low storage safety of valuable articles and mismatch of storage space for valuable articles caused by low identification accuracy of the storage system and unreasonable allocation of storage space in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides an article storage management system based on Internet of Things, which comprises: a data acquisition module, configured to acquire storage information and environmental information; The authentication and storage module is configured to obtain an identity similarity of a user feature vector according to a user iris and a user palmprint in the storage information, and to open a storage permission for the user according to the identity similarity of the user feature vector, and is further configured to adjust a process of obtaining the identity similarity of the user feature vector according to a light condition, and to judge a security demand condition according to a storage cabinet security demand value, and to optimize an adjustment process of the process of obtaining the identity similarity of the user feature vector according to a judgment result. The storage space allocation module is configured to intelligently allocate a storage space according to a dynamic programming algorithm when the storage permission is opened for the user, and to calibrate a judgment process of a storage cabinet space allocation condition according to an important index of a storage object. The storage environment sensing module is configured to judge the storage cabinet space allocation condition according to a space utilization rate in the storage information, and to optimize the dynamic programming algorithm according to a judgment result, and is further configured to judge an abnormal condition of the storage cabinet according to a number of times of the abnormal condition of the storage cabinet, and to correct the judgment process of the storage cabinet space allocation condition according to a judgment result.

[0006] Further, the authentication and storage module obtains an iris feature vector H of the user iris in the storage information by using an iris feature vector extraction method, obtains a palmprint feature vector Y of the user palmprint in the storage information by using a palmprint feature vector extraction method, and calculates a comprehensive user feature vector K according to the iris feature vector H and the palmprint feature vector Y.

[0007] Further, the authentication and storage module obtains an identity similarity A of the comprehensive user feature vector and a comprehensive user feature vector in a storage database by using a Hamming distance method, compares the identity similarity A of the comprehensive user feature vector with a preset identity similarity A0, 0.83≤A0≤0.91, judges an identity similarity condition according to a comparison result, and opens the storage permission for the user according to a judgment result.

[0008] Further, the authentication and storage module calculates a light condition Qt according to a light intensity value Qg and a light angle value Qj in the environment information, compares the light condition Qt with a preset light condition Qt0, 0.73≤Qt0≤0.93, judges a light condition according to a comparison result, and adjusts a calculation process of the comprehensive user feature vector according to a judgment result.

[0009] Further, the authentication and storage module inputs a passenger flow and an environment structure diagram in the environment information into an expert judgment model, outputs a storage cabinet security demand value F, compares the storage cabinet security demand value with a preset demand value F0, 0.67≤F0≤0.88, judges a security demand condition according to a comparison result, and optimizes an adjustment process of the calculation process of the comprehensive user feature vector according to a judgment result.

[0010] Further, the storage space allocation module intelligently allocates the storage space according to the storage information by using a dynamic programming algorithm.

[0011] Further, the storage space allocation module inputs the storage object picture in the storage information into a storage object recognition model, outputs a storage object importance index Jz, compares the storage object importance index Jz with a preset storage object importance index Jz0, 0.42≤Jz0≤0.56, judges the importance of the storage object according to the comparison result, and calibrates the judgment process of the safety demand condition according to the judgment result.

[0012] Further, the storage space perception module compares the space utilization rate Ly in the storage information with a preset storage space utilization rate Ly0, 70%≤Ly0≤88%, judges the utilization of the storage space according to the comparison result, and records the utilization of the storage cabinet according to the judgment result.

[0013] Further, the storage space perception module further compares the low utilization rate number Lc of the storage cabinet in the historical utilization rate data with a preset low utilization rate number Lc0, 3 times / day≤Lc0≤5 times / day, judges the storage cabinet space allocation according to the comparison result, and optimizes the dynamic programming algorithm according to the judgment result.

[0014] Further, the storage space perception module compares the storage cabinet switching current Ik in the storage information with a preset storage cabinet minimum switching current Ikmin and a preset storage cabinet maximum switching current Ikmax, Ikmin=600mA, Ikmax=800mA, judges the storage cabinet current condition according to the comparison result, and records the storage cabinet current condition according to the judgment result. The storage space perception module compares the number of abnormal conditions of the storage cabinet current Ck with a preset number of abnormal conditions of the storage cabinet Ck0, 3 times≤Ck0≤5 times, judges the abnormal condition of the storage cabinet according to the comparison result, and corrects the judgment process of the storage cabinet space allocation according to the judgment result.

[0015] Compared with the prior art, the beneficial effects of the present application are that the system is applied to an airport storage management terminal, the system collects storage information and environmental information through a data acquisition module, so as to manage the storage cabinet subsequently, the system also authenticates the user identity through an authentication storage module, and realizes high-precision identification according to the dual authentication of the user iris and the user palmprint, and meanwhile, the weight of the user iris and the user palmprint identification is adjusted according to the light intensity value, the light angle value, the passenger flow and the environmental structure diagram in the environmental information, so as to adapt to accurate identification in multiple environments and multiple situations, and when important articles are faced, the weight of the user iris identification is adjusted to be high, the precision of the user identification is improved, so as to realize the security guarantee of important article storage, improve the safety of important article storage, the system also intelligently allocates the storage space of the storage cabinet through a storage space allocation module, improves the rationality of the storage space allocation, and meanwhile, the safety demand situation of the stored articles is further calibrated through the identification of the important index of the stored articles, so as to guarantee the safety of the valuable article storage, increase the storage security guarantee, realize high-safety storage, the system also judges the storage cabinet space allocation situation through a storage environment sensing module, avoids the unreasonable utilization of the storage space, realizes the rational allocation of the storage space, guarantees that the user stored articles obtain suitable storage environment, avoids the damage of the stored articles due to small space, and improves the rationality of the storage space allocation. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 It is a structural schematic view of the article storage management system based on the Internet of Things. DETAILED DESCRIPTION

[0017] In order to make the purpose and advantages of the present application more clear and explicit, the present application is further described below in combination with examples; it should be understood that the specific examples described herein are only used to explain the present application, and do not limit the present application.

[0018] The preferred embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application, and are not intended to limit the protection scope of the present application.

[0019] It should be noted that in the description of the present application, the terms "upper", "lower", "left", "right", "inner", "outer" and the like indicate the direction or positional relationship terms based on the direction or positional relationship shown in the drawings, which are only for the convenience of description, and do not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0020] Moreover, it needs to be explained that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "linking" should be understood in a broad sense, for example, can be fixed connection, can also be detachable connection, or integrally connected, can be mechanical connection, can also be electrical connection, can be directly connected, can also be indirectly connected through intermediate medium, can be internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0021] Please refer to Figure 1 As shown in the figure, it is the structure schematic diagram of the article storage management system based on the Internet of Things in the embodiment, the system comprises: The data acquisition module is used to collect the storage information and the environmental information; The authentication storage module is used to obtain the identity similarity of the user feature vector according to the user iris and the user palmprint in the storage information, and to open the storage permission for the user according to the identity similarity of the user feature vector, and is also used to adjust the process of obtaining the identity similarity of the user feature vector according to the light condition, and to judge the security demand condition according to the storage cabinet security demand value, and to optimize the adjustment process of the process of obtaining the identity similarity of the user feature vector according to the judgment result, the authentication storage module is connected with the data acquisition module; The storage space allocation module is used to intelligently allocate the storage space according to the dynamic programming algorithm when the storage permission is opened for the user, and to calibrate the judgment process of the security demand condition according to the important index of the stored articles, the storage space allocation module is connected with the authentication storage module; The storage environment sensing module is used to judge the storage cabinet space allocation condition according to the space utilization rate in the storage information, and to optimize the dynamic programming algorithm according to the judgment result, and is also used to judge the abnormal condition of the storage cabinet according to the number of times of the current abnormal condition of the storage cabinet, and to correct the judgment process of the storage cabinet space allocation condition according to the judgment result, the storage environment sensing module is connected with the storage space allocation module.

[0022] Specifically, the system is applied to high-security demand storage environment management terminals such as jewelry storage management terminals, auction item storage management terminals and shared transaction storage management terminals, the system collects storage information and environment information through a data acquisition module to manage the storage cabinet subsequently, the system also authenticates the user identity through an authentication storage module, and realizes high-precision identification according to the dual authentication of the user's iris and the user's palm print, and adjusts the weight of the user's iris and the user's palm print identification according to the light intensity value, the light angle value, the passenger flow and the environment structure diagram in the environment information, to adapt to accurate identification in multiple environments and multiple situations, and when facing important items, the weight of the user's iris identification is adjusted to be high, to improve the accuracy of user identification, to realize the security guarantee of important item storage, to improve the security of important item storage, the system also intelligently allocates the storage space of the storage cabinet through a storage space allocation module, to improve the rationality of the storage space allocation, at the same time, through the identification of the important index of the stored items, the safety demand situation of the stored items is further calibrated, to ensure the safety of the valuable item storage, to increase the security guarantee of the storage, to realize high-security storage, the system also judges the storage cabinet space allocation situation through a storage environment sensing module, to avoid the situation that the storage space utilization is unreasonable, to realize the rational allocation of the storage space, to ensure that the user's stored items obtain a suitable storage environment, to avoid the damage of the stored items due to small space, to improve the rationality of the storage space allocation.

[0023] Specifically, the data collection module collects the registration information and the environmental information through an iris collection device, a near-infrared light sensor, a light intensity sensor, an angle sensor, an internal camera, a current sensor and an external camera. The registration information includes a user iris, a user palmprint, a registration object picture, a space utilization rate, a locker switch current, a locker size required by the user i, a value obtained by distributing objects to the user i, a total allocated space, a new total allocated space after distribution, a combination state of the current idle locker of different sizes and a new locker state after distribution. The data collection module collects the user iris through the iris collection device. The data collection module collects the user palmprint through the near-infrared light sensor. The data collection module collects the locker switch current through the current sensor. The data collection module collects the registration object picture through the external camera. The data collection module collects the space utilization rate through the internal camera. The data collection module collects the value obtained by distributing objects to the user i, the locker size required by the user i, the total allocated space, the new total allocated space after distribution, the combination state of the current idle locker of different sizes and the new locker state after distribution through the internal camera. The environmental information includes an illumination intensity value, an illumination angle, a passenger flow and an environmental structure diagram. The data collection module collects the illumination intensity value through the light intensity sensor. The data collection module collects the illumination angle through the angle sensor. The data collection module collects the passenger flow and the environmental structure diagram through the external camera.

[0024] Specifically, the authentication registration module obtains an iris feature vector H of the user iris in the registration information by using an iris feature vector extraction method, obtains a palmprint feature vector Y of the user palmprint in the registration information by using a palmprint feature vector extraction method, calculates a comprehensive user feature vector K according to the iris feature vector H and the palmprint feature vector Y, and sets , , as a weight coefficient of the iris feature vector, , as a weight coefficient of the palmprint feature vector.

[0025] Specifically, the iris feature vector extraction method refers to a feature vector conversion method of extracting the user iris as a feature vector. The embodiment does not limit the specific implementation of the iris feature vector extraction method. A person skilled in the art can set it according to actual needs, for example, a Hough transform can be used to find the inner and outer boundaries of the user iris, to determine the center of the user iris, the radius rs of the inner boundary of the user iris and the radius re of the outer boundary of the user iris. The palmprint feature vector extraction method refers to a conversion method of converting the user palmprint into a palmprint feature vector. The palmprint feature vector extraction method includes: Step B1: Convert the collected user palm print into grayscale image pixel values ​​(Gray), and set... R is the pixel value corresponding to the red channel of the color image, G is the pixel value corresponding to the green channel of the color image, and B is the pixel value corresponding to the blue channel of the color image. Step B2: Use median filtering to denoise the pixel values ​​of the grayscale image to obtain the denoised palmprint grayscale image values. The median filtering is a non-linear filtering method that sorts the pixel values ​​in the filtering window and takes the median value as the new value of the center pixel of the window. Step B3: Normalize the image values ​​of the palmprint grayscale image after noise reduction to obtain the normalized value Gg, and set... Where Graymin is the minimum gray value of the palmprint image, and Graymax is the minimum gray value of the palmprint image. Step B4: The normalized value Gg is vectorized using principal component analysis to obtain the palmprint feature vector Y. The principal component analysis method is a data analysis method mainly used for feature extraction of data. This embodiment does not limit the specific application of the principal component analysis method. Those skilled in the art can set it according to their needs, as long as the vectorization requirement of the normalized value is met. The user's iris refers to the biometric feature of the user's eyes, and the user's palm print refers to the lines on the surface of the user's palm, which are formed by the combined effects of genetic factors and various factors during embryonic development, and have individual specificity and stability.

[0026] Specifically, by combining the user's iris and palm print, the number of recognition dimensions and features is increased, which can more accurately distinguish different individuals, improve the accuracy of unlocking recognition, reduce the false recognition rate and the rejection rate, and when one feature is stolen or forged, the other feature is difficult to counterfeit at the same time, which can effectively prevent illegal unlocking, increase the security and reliability of the system, and better protect the user's property and privacy.

[0027] Specifically, the authentication storage module uses the Hamming distance method to obtain the identity similarity A between the comprehensive user feature vector and the comprehensive user feature vector in the storage database. It then compares the identity similarity A with a preset identity similarity A0, where 0.83 ≤ A0 ≤ 0.91. Based on the comparison result, it judges the identity similarity and grants storage permissions to the user according to the judgment result. When A < A0, the authentication storage module determines that the identity similarity is not similar and does not grant storage permissions to the user. When A is greater than or equal to A0, the authentication storage module determines that the identity similarity condition is similar, opens the storage permission for the user, and matches the user identity to obtain a user type, and feeds back the user type to the storage space allocation module, wherein the user type includes a general user and a member user.

[0028] Specifically, the Hamming distance method refers to an information checking method for measuring the difference between two data, and the specific use of the Hamming distance method is not limited in the embodiment, and a person skilled in the art can set it according to the needs, as long as the calculation requirement of the identity similarity of the user feature vector is met. The storage database refers to a database recording the user information of all stored objects, and the storage database includes recorded historical comprehensive user feature vectors. The identity similarity refers to the similarity between the comprehensive user feature vector and the recorded historical comprehensive user feature vector in the storage database. The user identity refers to the user information corresponding to the user iris and user palm print. The preset identity similarity refers to a preset value for judging the identity similarity condition. The identity similarity condition refers to the similarity between the user feature vector and the feature vector in the database. The identity similarity condition includes an identity similarity condition that is not similar and an identity similarity condition that is similar.

[0029] Specifically, the authentication storage module can identify whether the user is a storage cabinet storage user through the judgment of the identity similarity condition, and determine whether to open the storage cabinet according to the identification result, thereby improving the security of the storage.

[0030] Specifically, the authentication storage module also marks the user iris and the user palm print of the same user collected in the time length t as the user for identity recognition in the same time period, and counts the number of users for identity recognition in the same time period to obtain the number of simultaneous identity recognition users. The specific value of the time length t is not limited in the embodiment, and a person skilled in the art can set it according to the actual needs, such as setting t = 120 seconds.

[0031] Specifically, the authentication storage module calculates the illumination condition Qt according to the light intensity value Qg and the light angle value Qj in the environmental information, sets Qt = 0.7 * Qg + 0.3 * Qj, compares the illumination condition Qt with the preset illumination condition Qt0, 0.73 ≤ Qt0 ≤ 0.93, judges the illumination condition according to the comparison result, and adjusts the calculation process of the comprehensive user feature vector according to the judgment result, wherein: When Qt is greater than or equal to Qt0, the authentication storage module determines that the illumination condition is qualified, and does not adjust the calculation process of the comprehensive user feature vector; When Qt < Qt0, the authentication storage module determines that the light condition is unqualified, and adjusts the weight coefficient of the palmprint feature vector and the weight coefficient of the iris feature vector , obtains the adjusted weight coefficient of the palmprint feature vector and the adjusted weight coefficient of the iris feature vector , sets , replaces the weight coefficient of the palmprint feature vector with the adjusted weight coefficient of the palmprint feature vector , replaces the weight coefficient of the iris feature vector with the adjusted weight coefficient of the iris feature vector , and recalculates the comprehensive user feature vector K.

[0032] Specifically, the light intensity value refers to the light intensity outside the storage cabinet, which is assigned according to the interval of 0-1 in this embodiment. The light angle value refers to the light angle outside the storage cabinet, which is assigned according to the interval of 0-1 in this embodiment. The light condition refers to the light condition outside the storage cabinet, which includes the light condition being qualified and the light condition being unqualified.

[0033] Specifically, the authentication storage module adjusts the weight coefficient of the palmprint feature vector to increase it and reduces the weight coefficient of the iris feature vector to avoid collecting too many iris feature vectors in the case of unqualified light condition, which leads to low user recognition accuracy of the system.

[0034] Specifically, the authentication storage module inputs the crowd and environmental structure diagram in the environmental information into the expert judgment model, outputs the storage cabinet safety demand value F, compares the storage cabinet safety demand value F with the preset demand value F0, 0.67 ≤ F0 ≤ 0.88, and judges the safety demand condition according to the comparison result, and optimizes the adjustment process of the calculation process of the comprehensive user feature vector according to the judgment result, wherein: When F ≤ F0, the authentication storage module determines that the safety demand condition is low, and does not optimize the adjustment process of the calculation process of the comprehensive user feature vector; When F > F0, the authentication storage module determines that the safety demand condition is high, and optimizes the adjustment process of the calculation process of the comprehensive user feature vector, optimizes the adjusted weight coefficient of the iris feature vector and the adjusted weight coefficient of the palmprint feature vector , and obtains the optimized weight coefficient of the iris feature vector and the optimized weight coefficient of the palmprint feature vector And the weight coefficient of the optimized palmprint feature vector , set , the weight coefficient of the adjusted iris feature vector is replaced by the weight coefficient of the optimized iris feature vector , the weight coefficient of the adjusted palmprint feature vector is replaced by the weight coefficient of the optimized palmprint feature vector , and the comprehensive user feature vector K is recalculated.

[0035] Specifically, the expert determination model refers to a deep learning model taking the flow of people and the environmental structure diagram as input and taking the safe demand value of the storage cabinet as output. The expert determination model is constructed by the expert determination model construction method. The expert determination model construction method comprises: Step C10, arranging the historical flow of people and the environmental structure diagram in the expert database and the safe demand value of the storage cabinet corresponding to the flow of people and the environmental structure diagram; Step C20, 70% of the data in the expert database is divided into an expert determination training set and 30% of the data in the expert database is divided into an expert determination verification set; Step C30, selecting a multi-layer perceptron as the expert determination model, initializing the weights and biases of the expert determination model, inputting the data in the expert determination training set into the expert determination model, and calculating the output of the expert determination model; Step C40, calculating the loss function value according to the output of the expert determination model and the label, calculating the gradient by the back propagation algorithm, and updating the weights and biases of the expert determination model, repeating the process of forward propagation, loss function calculation and back propagation; Step C50, and the accuracy of the expert determination model is tested by the expert determination verification set, and the expert determination model with an accuracy of 90% is output; The flow of people refers to the flow of personnel around the storage cabinet in a day, the environmental structure diagram refers to the environmental structure diagram around the storage cabinet, the safe demand value F of the storage cabinet refers to the demand value of the storage cabinet for protection safety, the preset demand value refers to a preset value for judging the safety demand situation, and the safety demand situation refers to the safety demand situation of the storage cabinet according to the surrounding environment. The safety demand situation includes safety demand low and safety demand high.

[0036] Specifically, the authentication storage module optimizes the weight coefficient of the adjusted iris feature vector by determining the safety demand situation, increases the proportion of the iris feature vector in the comprehensive user feature vector, collects more user iris when the safety demand situation is determined as high, and thus improves the accuracy of user identification of the system.

[0037] Specifically, the register space allocation module intelligently allocates the register space according to the register information by using a dynamic programming algorithm, and the dynamic programming algorithm includes: Step S1, setting an allocation state dp[i][j][k], i is the i-th user, i=1, 2, 3...m, m is the user order, j is the current free combination state of different size register cabinets, and k is the total allocated space; Step S2, constructing state transition equations according to the allocation, and the state transition equations include a first state transition equation and a second state transition equation, wherein: When the allocation is not to allocate the items of the user i into the register cabinet, the register space allocation module constructs a first state transition equation dp[i][j][k]1 according to the allocation state dp[i][j][k], and sets dp[i][j][k]1=dp[i-1][j][k]; When the allocation is to allocate the items of the user i into the register cabinet, the register space allocation module constructs a second state transition equation dp[i][j][k]2 according to the allocation state dp[i][j][k], and sets dp[i][j][k]2=max(dp[i-1][j][k], dp[i-1][j'][k']+vi), wherein vi is the value obtained by allocating the items to the user i, si is the required size of the register cabinet of the user i, j' is the new register cabinet state after allocation, k' is the new total allocated space after allocation, and k' is set to k+si.

[0038] Specifically, the dynamic programming algorithm refers to an algorithm strategy for solving optimization problems, the core principle of which is to decompose a complex problem into a series of interrelated sub-problems, solve the sub-problems, and use the solutions of the sub-problems to construct the solution of the original problem, thereby avoiding a large number of repeated calculations of the state of the storage cabinet. The combination state of the current idle different size storage cabinets refers to the combination of all storage cabinets of different sizes. The embodiment does not limit the specific representation of the combination state of the current idle different size storage cabinets, and those skilled in the art can set it according to actual needs, such as representing the combination state of the current idle different size storage cabinets by binary. The total allocated space refers to the total space obtained by adding the spaces of all allocated storage cabinets. The size of the storage cabinet required by the user i refers to the size of the storage cabinet required by the user i to store the items. The size of the storage cabinet required by the user i is determined by the volume of the items stored by the user i. The new storage cabinet state after allocation refers to the state of the remaining idle storage cabinets after the user i's items are allocated to the storage cabinets. The new total allocated space after allocation refers to the total size of all allocated storage cabinets after the user i's items are allocated to the storage cabinets. The value obtained by allocating the items to the user i refers to a quantitative indicator for measuring the importance of the items to the space allocation target. The embodiment does not limit the specific determination standard of the value obtained by allocating the items to the user i, and those skilled in the art can set it according to actual needs, such as that the stored items have a clear market price or economic value, which can be directly used as the value obtained by allocating the items to the user i.

[0039] Specifically, the storage space allocation module can control the storage information of all storage cabinets through the dynamic programming algorithm and reasonably allocate the storage cabinets to fully utilize the space of each storage cabinet.

[0040] Specifically, the storage space allocation module compares the number of simultaneously identified users Sy with the preset number of simultaneously identified users Sy0, 10≤Sy0≤20, and judges the demand situation of the storage cabinet according to the comparison result, and adjusts the dynamic programming algorithm according to the judgment result, wherein: When Sy≤Sy0, the storage space allocation module determines that the demand situation of the storage cabinet is low, and does not adjust the dynamic programming algorithm; When Sy> Sy0, the storage space allocation module determines that the demand situation of the storage cabinet is high, and adjusts the dynamic programming algorithm, the adjustment method being: The storage users of the number of simultaneously identified users are preferentially allocated to the storage space, and after the member users are allocated, the storage users of the number of simultaneously identified users are allocated to the storage space. If the member user allocation is completed, the storage space allocation module determines j=0, and stops calculating the storage space for the general user.

[0041] Specifically, the preset number of simultaneous identity recognition users refers to a preset value for judging the storage cabinet demand situation, and the storage cabinet demand situation refers to the demand amount of the storage cabinet by multiple users at the same time. The storage cabinet demand situation includes low demand and high demand.

[0042] Specifically, the storage space allocation module determines the storage cabinet demand situation, and in the case of high demand, the use demand of members is preferentially met, and in the case of insufficient storage cabinets, the calculation of the storage space for the general user is stopped in advance, thereby reducing unnecessary waiting time of the user and early planning of the allocation of the storage cabinet.

[0043] Specifically, the storage space allocation module inputs the storage object picture in the storage information into the storage object recognition model, outputs the storage object importance index Jz, compares the storage object importance index Jz with the preset storage object importance index Jz0, 0.42≤Jz0≤0.56, and judges the importance of the storage object according to the comparison result, and adjusts the judgment process of the safety demand situation according to the judgment result, wherein: When Jz≤Jz0, the storage space allocation module determines that the importance of the storage object is not important, and does not adjust the judgment process of the safety demand situation; When Jz>Jz0, the storage space allocation module determines that the importance of the storage object is important, and adjusts the judgment process of the safety demand situation, adjusts the preset demand value F0 through the adjustment coefficient γ=1-(Jz-Jz0 / Jz0) to obtain the adjusted preset demand value F01, sets F01=F0×γ, replaces the preset demand value F0 with the adjusted preset demand value F01, and rejudges the safety demand situation.

[0044] Specifically, the storage object recognition model refers to a convolutional neural network model with a storage object picture as input and a storage object importance index as output. The storage space allocation module constructs the storage object recognition model through a storage object recognition model construction method, and the storage object recognition model construction method includes: 70% of the data recognition learning sample dataset is divided into a data recognition training set, 15% into a data recognition validation set, and 15% into a data recognition test set. A convolutional neural network (CNN) model is trained using the training set, and its weights are updated using a backpropagation algorithm. After each epoch, the CNN model is validated using the validation set to obtain a validation loss value and a validation accuracy. When the validation loss value and the validation accuracy meet preset validation conditions, the CNN model is tested using the test set to obtain a validation test accuracy. When the validation test accuracy reaches a preset accuracy, the CNN model is output as a registered object recognition model. The data recognition learning sample dataset refers to the learning dataset used to train the registered object recognition model. The data recognition learning sample dataset includes historical registered object images and the historical registered object importance index corresponding to those images. An epoch refers to the process of the convolutional neural network model completing one forward and backward propagation on the data recognition training set. The validation loss value refers to the loss value of the loss function in the convolutional neural network model when the data recognition validation set is input into the model for validation. The validation accuracy rate refers to the ratio of the number of data recognition results output by the model that match the data recognition results in the data recognition validation set to the total number of samples in the data recognition validation set. The preset validation condition refers to the condition that the validation loss value does not decrease and the validation accuracy does not increase for five consecutive epochs. The recognition test accuracy rate refers to the ratio of the number of data test results output by the model that match the data recognition results in the data recognition test set to the total number of samples in the data recognition test set. The preset accuracy rate refers to the preset value for the recognition test accuracy rate at which the convolutional neural network model reaches the output standard, such as a preset accuracy rate of 98%. The importance index of the stored item refers to the score of the importance of the stored item. The preset importance index of the stored item refers to the preset value used to judge the importance of the stored item. The importance of the stored item refers to the importance of the stored item. The importance of the stored item includes the stored item being unimportant and the stored item being important.

[0045] Specifically, the storage space allocation module adjusts the preset requirement value by judging the importance of the stored items, thereby reducing the preset requirement value. This makes it easier to identify stored items as important items, improves their storage security, makes identity verification more difficult and accurate, and thus increases the security of storing valuable items.

[0046] Specifically, the storage space sensing module compares the space utilization rate Ly in the storage information with the preset storage space utilization rate Ly0, where 70%≤Ly0≤88%. Based on the comparison result, it judges the utilization of the storage space and records the storage cabinet utilization based on the judgment result. When Ly > Ly0, the storage space sensing module determines that the utilization rate of the storage space is high and does not record the storage cabinet utilization. When Ly≤Ly0, the storage space sensing module determines that the storage space utilization is low, records the storage cabinet utilization as one instance of low utilization, and also compares the number of times the storage cabinet has been used (Lc) with the preset number of times it has been used (Lc0) in the historical utilization data, where 3 times / day ≤ Lc0 ≤ 5 times / day. Based on the comparison result, the module judges the storage cabinet space allocation and optimizes the dynamic programming algorithm accordingly. When Lc≤Lc0, the storage space sensing module determines that the storage cabinet space allocation is normal and does not optimize the dynamic programming algorithm. When Lc > Lc0, the storage space sensing module determines that the storage cabinet space allocation is abnormal and optimizes the dynamic programming algorithm. The allocation state dp[i][j][k] is optimized by the algorithm optimization coefficient Ω = (Ly - Ly0) / (Lc - Lc0) to obtain the optimized allocation state dp[i][j][k]'. The allocation state dp[i][j][k]' is set to dp[i][j][k] - ((Ly - Ly0) / (Lc - Lc0)), and the allocation state dp[i][j][k] is replaced with the optimized allocation state dp[i][j][k]'. The first state transition equation and the second state transition equation are then redefined.

[0047] Specifically, the space utilization rate refers to the proportion of space occupied by stored items in the locker; the preset storage space utilization rate is a preset value used to judge the utilization of storage space; the storage space utilization refers to the utilization of storage space by stored items in the locker; the storage space utilization includes high utilization and low utilization; the historical utilization data refers to the total data recorded by the storage space sensing module regarding the utilization of the locker; the number of times the locker has a low utilization rate refers to the number of times the locker is recorded as having a low utilization rate within a day; the preset number of times the locker has a low utilization rate is a preset value used to judge the space allocation of the locker; the locker space allocation refers to the allocation of the locker space within a day in which it is reasonably utilized; the locker space allocation includes normal space allocation and abnormal space allocation.

[0048] Specifically, the storage space sensing module judges the utilization of the storage space of the lockers by monitoring the space utilization rate and records the utilization of the lockers. When the number of times the storage locker has a low utilization rate in the historical utilization rate data is greater than the preset number of times it has a low utilization rate, the storage space sensing module determines that the storage locker space allocation is abnormal, that is, the allocation of the storage lockers is unreasonable, and optimizes the dynamic programming algorithm to make the allocation of the storage lockers more reasonable and more in line with the actual volume of the stored items.

[0049] Specifically, the storage space sensing module compares the storage cabinet switching current Ik in the storage information with the preset minimum storage cabinet switching current Ikmin and the preset maximum storage cabinet switching current Ikmax, where Ikmin = 600mA and Ikmax = 800mA. Based on the comparison result, it judges the storage cabinet current situation and records the judgment result. When Ikmin<Ik≤Ikmax, the storage space sensing module determines that the storage cabinet current is normal and does not record the storage cabinet current. When Ikmin=Ik, the storage space sensing module determines that the storage cabinet current is normal and does not record the storage cabinet current. When Ikmin>Ik, the register space sensing module determines that the current condition of the register cabinet is abnormal, records the current condition of the register cabinet, and records it as a current abnormality. When Ikmax < Ik, the storage space sensing module determines that the storage cabinet current is abnormal, records the storage cabinet current, and records it as a current abnormality.

[0050] Specifically, the locker switch current refers to the current magnitude when controlling the locker to open and close; the preset minimum locker switch current refers to a preset minimum value used to judge the locker current status; the preset maximum locker switch current refers to a preset maximum value used to judge the locker current status; the locker current status refers to the normal and abnormal conditions of the locker switch current, including normal and abnormal conditions.

[0051] Specifically, the storage space sensing module monitors the current status of the storage cabinets, records abnormal current conditions, and adjusts subsequent plans based on the number of abnormal current conditions to enhance the rationality of space allocation.

[0052] Specifically, the storage space sensing module compares the number of abnormal storage cabinet current events Ck with the preset number of abnormal storage cabinet events Ck0, where 3 events ≤ Ck0 ≤ 5 events. Based on the comparison result, it judges the abnormality of the storage cabinet and corrects the judgment process of storage cabinet space allocation based on the judgment result. When Ck≤Ck0, the storage space sensing module determines that the abnormal situation of the storage cabinet is infrequent and does not correct the judgment process of storage cabinet space allocation. When Ck > Ck0, the storage space sensing module determines that the abnormal situation of the storage cabinet is abnormally frequent, and corrects the judgment process of storage cabinet space allocation by using a correction coefficient ES = 1.4 - 0.4 × e -（Ck-Ck0） The preset number of low utilizations Lc0 is corrected to obtain the corrected number of low utilizations Lc0k. Lc0k is set to Lc0×ES. The preset number of low utilizations Lc0 is replaced with the corrected number of low utilizations Lc0k, and the storage cabinet space allocation is re-evaluated.

[0053] Specifically, the number of abnormal current conditions in the storage cabinet refers to the number of times the current condition of the storage cabinet is determined to be abnormal. The preset number of abnormal storage cabinet conditions refers to the abnormal value used to judge the abnormal condition of the storage cabinet. The abnormal condition of the storage cabinet refers to whether the abnormal condition of the storage cabinet is frequent. The abnormal condition of the storage cabinet includes the abnormal condition of the storage cabinet being infrequent and the abnormal condition of the storage cabinet being frequent.

[0054] Specifically, the storage space sensing module monitors abnormal conditions of the storage cabinets. When abnormal conditions occur frequently, it corrects the judgment process of storage cabinet space allocation and increases the preset number of low utilization rates. This reduces the difficulty of judging the storage cabinet space allocation and avoids the possibility that low storage cabinet utilization is caused by too many abnormal current conditions, thereby improving the rationality of storage cabinet space allocation.

[0055] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An Internet of Things-based article storage management system, characterized by comprising: The system comprises: a data acquisition module for acquiring the registration information and the environmental information; an authentication registration module for obtaining the identity similarity of the user feature vector according to the user iris and the user palmprint in the registration information, and opening the registration permission for the user according to the identity similarity of the user feature vector, and for adjusting the process of obtaining the identity similarity of the user feature vector according to the illumination condition, and for judging the security demand condition according to the locker security demand value, and optimizing the adjustment process of the process of obtaining the identity similarity of the user feature vector according to the judgment result; a registration space allocation module for intelligently allocating the registration space according to the dynamic programming algorithm when the registration permission is opened for the user, and calibrating the judgment process of the security demand condition according to the registration object importance index; a registration environment sensing module for judging the locker space allocation condition according to the space utilization rate in the registration information, and optimizing the dynamic programming algorithm according to the judgment result, and for judging the abnormal condition of the locker according to the number of times of the current abnormal condition of the locker, and correcting the judgment process of the locker space allocation condition according to the judgment result.

2. The Internet of Things based item storage management system as claimed in claim 1, wherein, The authentication registration module obtains the iris feature vector H of the user iris in the registration information by using the iris feature vector extraction method, obtains the palmprint feature vector Y of the user palmprint in the registration information by using the palmprint feature vector extraction method, and calculates the comprehensive user feature vector K according to the iris feature vector H and the palmprint feature vector Y. 3.The Internet-of-Things based item storage management system according to claim 2, wherein, The authentication registration module obtains the identity similarity A of the comprehensive user feature vector in the registration database by using the Hamming distance method, compares the identity similarity A of the comprehensive user feature vector with the preset identity similarity A0, 0.83≤A0≤0.91, judges the identity similarity condition according to the comparison result, and opens the registration permission for the user according to the judgment result.

4. The Internet of Things-based item storage management system according to claim 3, wherein, The authentication registration module calculates the illumination condition Qt according to the illumination intensity value Qg and the illumination angle value Qj in the environmental information, compares the illumination condition Qt with the preset illumination condition Qt0, 0.73≤Qt0≤0.93, judges the illumination condition according to the comparison result, and adjusts the calculation process of the comprehensive user feature vector according to the judgment result. 5.The Internet-of-Things based item storage management system according to claim 4, wherein, The authentication registration module inputs the passenger flow and the environmental structure diagram in the environmental information into the expert judgment model, outputs the locker security demand value F, compares the locker security demand value with the preset demand value F0, 0.67≤F0≤0.88, judges the security demand condition according to the comparison result, and optimizes the adjustment process of the calculation process of the comprehensive user feature vector according to the judgment result. 6.The Internet-of-Things based item storage management system according to claim 5, wherein, The registration space allocation module intelligently allocates the registration space according to the registration information by using the dynamic programming algorithm. 7.The Internet-of-Things based item storage management system according to claim 6, wherein, The storage space allocation module inputs the storage object picture in the storage information into the storage object recognition model, outputs a storage object importance index Jz, compares the storage object importance index Jz with a preset storage object importance index Jz0, 0.42≤Jz0≤0.56, judges the importance of the storage object according to the comparison result, and calibrates the judgment process of the safety demand condition according to the judgment result. 8.The Internet-of-Things based item storage management system according to claim 7, wherein, The storage space perception module compares the space utilization rate Ly in the storage information with a preset storage space utilization rate Ly0, 70%≤Ly0≤88%, judges the utilization of the storage space according to the comparison result, and records the utilization of the storage cabinet according to the judgment result. 9.The Internet-of-Things based item storage management system according to claim 8, wherein, The storage space perception module also compares the low utilization rate times Lc of the storage cabinet in the historical utilization rate data with a preset low utilization rate times Lc0, 3 times / day≤Lc0≤5 times / day, judges the storage cabinet space allocation according to the comparison result, and optimizes the dynamic programming algorithm according to the judgment result. 10.The Internet-of-Things based item storage management system according to claim 9, wherein, The storage space perception module compares the storage cabinet switching current Ik in the storage information with a preset minimum storage cabinet switching current Ikmin and a preset maximum storage cabinet switching current Ikmax, Ikmin=600mA, Ikmax=800mA, judges the storage cabinet current condition according to the comparison result, records the storage cabinet current condition according to the judgment result, compares the number of abnormal conditions of the storage cabinet current Ck with a preset number of abnormal conditions of the storage cabinet Ck0, 3 times≤Ck0≤5 times, judges the abnormal condition of the storage cabinet according to the comparison result, and corrects the judgment process of the storage cabinet space allocation according to the judgment result.

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