Article pickup method, device, electronic device, and computer-readable medium

Abstract

Embodiments of the present disclosure disclose an article pickup method, device, electronic equipment and computer readable medium. A specific embodiment of the method comprises: obtaining article pickup configuration information; pushing pickup attribute information to each target user terminal; controlling a face image acquisition device of an article pickup device to acquire a face image of a user; encrypting and storing the face image in a preset database; generating a credibility corresponding to the user identity information according to a preset credibility model and the user identity information; in response to determining that the credibility is greater than a first preset value and less than or equal to a second preset value, performing the following identity verification processing: obtaining the face image from the preset database; obtaining a reserved face image from the preset database; generating identity verification information; sending the identity verification information to the article pickup device, so that the article pickup device places the pickup article at a preset pickup location. The embodiment improves the security of article pickup.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the field of computer technology, and more specifically to methods, apparatuses, electronic devices, and computer-readable media for retrieving items. Background Technology

[0002] The rise of smart devices and IoT technology has enabled interconnectivity between devices, automating and intelligently dispensing items, providing users with a more convenient service experience. Item dispensing is a technology that uses automated dispensing machines to dispense items. Currently, the common method for dispensing items through automated dispensing machines is: verification is performed directly via a QR code, and after successful verification, the item is dispensed through the machine.

[0003] However, when distributing items using the above method, the following technical problems often arise:

[0004] First, the verification is done directly through the sent QR code. After successful verification, the items are distributed through an automatic dispensing machine. However, the QR code method is not very secure. The QR code can be obtained through a mobile phone or email, which has relatively low physical security. The QR code can be easily stolen by others, which increases the risk of items being claimed by others and thus makes the item collection process less secure.

[0005] Furthermore, when using the preset credibility model of this disclosure to generate credibility for the recipient and distribute items, the following technical problems further exist:

[0006] Second, when generating credibility for users who claim items, it is usually necessary to input the user's dynamic attribute data into a preset credibility model to predict credibility. The user's dynamic attribute data (e.g., claim records, item inflow value operation records) may be affected by external factors (e.g., the user's item inflow value operation behavior may be affected by seasonality, item circulation activities, etc.), which may result in the evaluation results of the preset credibility model being unstable and unreliable, causing legitimate users to be unable to claim items, and thus reducing the user experience.

[0007] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0008] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0009] Some embodiments of this disclosure provide methods, apparatuses, electronic devices, and computer-readable media for retrieving items to address one or more of the technical problems mentioned in the background section above.

[0010] In a first aspect, some embodiments of this disclosure provide a method for retrieving an item. The method includes: acquiring item retrieval configuration information, wherein the item retrieval configuration information includes: retrieval attribute information and retrieval user information; pushing the retrieval attribute information to various target user terminals based on the retrieval user information; controlling a face image acquisition device of an item retrieval device to acquire a user's face image in response to receiving user identity information sent by a user terminal device; encrypting and storing the face image in a preset database in response to receiving the user's face image sent by the face image acquisition device; generating a credibility level corresponding to the user identity information based on a preset credibility model and the user identity information; and performing the following identity verification process based on the user identity information in response to determining that the credibility level is greater than a first preset value and less than or equal to a second preset value: acquiring the face image from the preset database; acquiring a reserved face image corresponding to the user identity information from the preset database; generating identity verification information based on the face image and the reserved face image; and sending the identity verification information to the item retrieval device so that the item retrieval device can place the retrieved item at a preset retrieval location.

[0011] Secondly, some embodiments of this disclosure provide an item retrieval device, the device comprising: an acquisition unit configured to acquire item retrieval configuration information, wherein the item retrieval configuration information includes: retrieval attribute information and retrieval user information; a push unit configured to push the retrieval attribute information to various target user terminals based on the retrieval user information; a control unit configured to control a face image acquisition device of the item retrieval device to acquire a user's face image in response to receiving user identification information sent by a user terminal device; a storage unit configured to encrypt and store the face image of the user in a preset database in response to receiving the face image of the user sent by the face image acquisition device; and a generation unit. The system is configured to generate a credibility level corresponding to the user identity information based on a preset credibility model and the user identity information; the authentication unit is configured to, in response to determining that the credibility level is greater than a first preset value and less than or equal to a second preset value, perform the following authentication processing based on the user identity information: obtain the face image from the preset database; obtain the reserved face image corresponding to the user identity information from the preset database; generate authentication information based on the face image and the reserved face image; and the sending unit is configured to send the authentication information to the item collection device so that the item collection device can place the collected item at a preset collection location.

[0012] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0013] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0014] The above-described embodiments of this disclosure have the following beneficial effects: the item retrieval method of some embodiments of this disclosure improves the security of item retrieval. Specifically, the reason for the poor security of item retrieval is that: directly verifying through a sent retrieval QR code, and distributing the item through an automatic item dispenser after successful verification, is not secure. Retrieval QR codes are usually obtained through mobile phones or emails, resulting in relatively low physical security. Retrieval QR codes are easily stolen, increasing the risk of items being fraudulently claimed by others, thus leading to low security in item retrieval. Based on this, the item retrieval method of some embodiments of this disclosure first obtains item retrieval configuration information, which includes: retrieval attribute information and retrieval user information. Then, based on the retrieval user information, the retrieval attribute information is pushed to each target user terminal. Thus, retrieval attribute information that does not involve user identity information can be sent to each target user terminal. This reduces the possibility of user identity information leakage, thereby reducing the risk of items being fraudulently claimed by others. Next, in response to receiving user identity identification information sent by the user terminal device, the face image acquisition device of the item retrieval device is controlled to acquire the user's face image. Thus, a face image for identity verification can be obtained. Next, in response to receiving the user's facial image sent by the aforementioned facial image acquisition device, the facial image is encrypted and stored in a preset database. This allows for encrypted storage of the facial image for later use. Then, based on a preset credibility model and the aforementioned user identification information, a credibility level corresponding to the aforementioned user identification information is generated. This allows for the generation of credibility based on the user's provided user identification information. Next, in response to determining that the credibility level is greater than a first preset value and less than or equal to a second preset value, the following authentication process is performed based on the aforementioned user identification information: First, the facial image is retrieved from the aforementioned preset database. This allows for the acquisition of the facial image. Second, a reserved facial image corresponding to the aforementioned user identification information is retrieved from the aforementioned preset database. This allows for the generation of a reserved facial image used for authentication information. Third, authentication information is generated based on the aforementioned facial image and the reserved facial image. This allows for the generation of authentication information used for distributing items. Performing authentication processing when the credibility level is greater than the first preset value and less than or equal to the second preset value further improves the security of item distribution. Finally, the aforementioned identity verification information is sent to the item collection device, which then places the item at the preset collection location. Because the collection attribute information pushed to each target user's terminal before the item is distributed through the item collection device does not involve the user's identity information, the possibility of user identity information leakage is reduced, thereby reducing the risk of items being fraudulently claimed by others.Meanwhile, after generating a credibility score corresponding to the aforementioned user identity information, further user authentication is performed based on the credibility score, thereby improving the security of item retrieval. Attached Figure Description

[0015] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0016] Figure 1 This is a flowchart of some embodiments of the item collection method according to this disclosure;

[0017] Figure 2 This is a schematic diagram of the structure of some embodiments of the item dispensing device according to the present disclosure;

[0018] Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0019] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0020] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0021] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0022] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0023] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0024] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] Figure 1 A flow 100 of some embodiments of a method for retrieving items according to this disclosure is shown. The method for retrieving items includes the following steps:

[0026] Step 101: Obtain item redemption configuration information.

[0027] In some embodiments, the executing entity of the item retrieval method (e.g., a computing device) obtains item retrieval configuration information from a preset database. This item retrieval configuration information includes: retrieval attribute information and retrieval user information. The preset database may be a MySQL database. The retrieval attribute information may represent retrieval information indicating the item to be retrieved. This retrieval attribute information may include, but is not limited to, at least one of the following: retrieval time, item name, quantity distributed, and item value. The retrieval user information may represent target user characteristic information. This retrieval user information may include, but is not limited to, at least one of the following: age, gender, and whether the user is a target user (e.g., a member user). The executing entity may be a server that controls the item retrieval device to place the retrieved item at a preset retrieval location. The preset retrieval location may be a specially designed exit or opening on the item retrieval device. For example, the retrieval location may be a pickup slot.

[0028] Step 102: Based on the user information, push the claim attribute information to each target user terminal.

[0029] In some embodiments, the aforementioned executing entity may push the aforementioned claiming attribute information to each target user terminal based on the aforementioned claiming user information.

[0030] In some optional implementations of certain embodiments, the aforementioned execution entity may push the aforementioned claim attribute information to each target user terminal based on the aforementioned claim user information through the following steps:

[0031] The first step is to determine the aforementioned user information as the filtering criteria.

[0032] The second step is to retrieve the target user information set from a pre-set database. Each target user information piece in this set includes at least one of the following: user identification and user age. The target user information in this set can represent a target user (a user who has purchased an item of the same brand as the item being claimed). The user identification can be, but is not limited to, one of the following: phone number or ID card number.

[0033] The third step is to determine the target user information set that meets the above filtering conditions as the user information set to be pushed to. For example, the filtering conditions could be "age 18-30, male, target user". One target user information set could be "age: 22, male, target user, phone number: 1111". Therefore, "age: 22, male, target user, phone number: 1111" meets the filtering condition "age 18-30, male, target user". Thus, "age: 22, male, target user, phone number: 1111" can be considered a user information set to be pushed to.

[0034] Fourth, for each user information in the above set of user information to be pushed to, perform the following push processing:

[0035] The first sub-step is to determine the user identity identifier in the aforementioned user information to be pushed as the user identity identifier to be pushed.

[0036] The second sub-step involves identifying the terminal corresponding to the aforementioned push user identity as the target user terminal.

[0037] The third sub-step involves sending the aforementioned retrieval attribute information to the target user's terminal.

[0038] Step 103: In response to receiving user identification information sent by the user terminal device, control the face image acquisition device of the item collection device to acquire the user's face image.

[0039] In some embodiments, the executing entity may, in response to receiving user identification information sent by the user terminal device, control the facial image acquisition device of the item collection device to acquire the user's facial image. In practice, the user terminal device may scan a QR code on the item collection device to send the user identification information to the server. The user identification information may be information filled in by the user through the user terminal device. For example, the user identification information may include, but is not limited to, at least one of the following: phone number, ID number, or account code. The facial image acquisition device may be a camera or webcam on the item collection device.

[0040] Step 104: In response to receiving the user's face image sent by the face image acquisition device, encrypt and store the face image in a preset database.

[0041] In some embodiments, the execution entity may, in response to receiving a user's face image sent by the face image acquisition device, encrypt and store the face image in a preset database.

[0042] In some optional implementations of certain embodiments, the execution entity may, in response to receiving a user's face image sent by the face image acquisition device, encrypt and store the face image in a preset database by the following steps:

[0043] The first step involves, in response to receiving a user's facial image sent by the aforementioned facial image acquisition device, retrieving encryption algorithm information and initial key information from a preset database. The encryption algorithm information includes at least one of the following: key expansion algorithm information and round function information. The encryption algorithm information can represent an encryption algorithm. For example, it can represent the AES encryption algorithm. The key expansion algorithm information can represent a key expansion algorithm. For example, it can represent the AES key expansion algorithm. The round function information can represent a round function corresponding to the key expansion algorithm information, used for obfuscating and transforming the data to be encrypted.

[0044] The second step is to convert the aforementioned face image into corresponding byte data. In practice, the executing entity can perform Base64 encoding on the face image to obtain the corresponding byte data.

[0045] The third step is to identify the aforementioned byte data as the data to be encrypted.

[0046] The fourth step involves generating a round key sequence corresponding to the initial key information, based on the key expansion algorithm information and the initial key information included in the encryption algorithm information. In practice, the executing entity can expand the initial key corresponding to the initial key information by executing the key expansion algorithm corresponding to the key expansion algorithm information to obtain the round key sequence.

[0047] Fifth step: Based on the first round key information in the round key information sequence and the data to be encrypted, perform the following round key encryption process:

[0048] The first sub-step involves encrypting the data to be encrypted based on the round function information and the first round key information included in the encryption algorithm information, thereby obtaining the round-encrypted data.

[0049] The second sub-step involves removing the first round key information from the round key information sequence in order to update the round key information sequence.

[0050] The third sub-step, in response to determining that the round key information sequence is not empty, performs the following steps:

[0051] Sub-step one: The encrypted data from the previous round is identified as the data to be encrypted, so that the data to be encrypted can be updated.

[0052] Sub-step two involves performing the above round key encryption process again based on the updated round key information sequence and the updated data to be encrypted.

[0053] The fourth sub-step, in response to determining that the round key information sequence is empty, identifies the obtained round encrypted data as encrypted face image data.

[0054] The fifth sub-step involves storing the encrypted facial image data into a preset database.

[0055] Step 105: Generate a credibility level corresponding to the user's identity information based on the preset credibility model and user identity information.

[0056] In some embodiments, the execution entity may generate a credibility level corresponding to the user identity information based on a preset credibility model and the user identity information.

[0057] In some optional implementations of certain embodiments, the execution entity may generate a credibility level corresponding to the user identity information by following the steps of: based on a preset credibility model and the user identity information.

[0058] The first step is to retrieve user attribute data corresponding to the aforementioned identity information from a pre-set database. This user attribute information includes: static user attribute information and dynamic user attribute data. The static user attribute information can be the user's basic information, including but not limited to at least one of the following: age, education level, and occupation. The dynamic user attribute information can represent the corresponding user's historical item value operations and historical collection records. The historical item value operation information can be records of value operations (shopping) performed by the user within a pre-set time period on a pre-set platform. The historical collection information can represent the collection information of items by the user within a pre-set time period.

[0059] The second step involves inputting the aforementioned user attribute data into the pre-defined credibility model to obtain the credibility score corresponding to the user attribute data. The pre-defined credibility model can be a set of formulas for calculating credibility, including judgment logic. For example, the pre-defined credibility model can be "Credit Score = Age Score + Education Score + Value Operation Score". Here, the age score can be assigned based on different age groups, the value operation score can be assigned based on different value operations, and the education score can be assigned based on different education levels. The specific calculation formula can be as follows: Age Score: {If age is less than 15 years old, the score is 5 points; if age is less than 25 years old, the score is 10 points; if age is less than 35 years old, the score is 20 points; if age is less than 45 years old, the score is 15 points; if age is less than 50 years old, the score is 10 ... Score: 10 points; 5 points if age 50 or older. Education Score: 5 points for education level below junior high school; 10 points for education level below high school; 15 points for education level below bachelor's degree; 20 points for education level below postgraduate; 25 points for education level above postgraduate. Value Operations (Monthly): 5 points for one or fewer operations; 10 points for 2-5 operations; 15 points for 5-10 operations; 20 points for more than 10 operations.

[0060] In some optional implementations of certain embodiments, after inputting the aforementioned user attribute data into the aforementioned preset credibility model to obtain the credibility corresponding to the aforementioned user attribute data, the aforementioned execution entity may further perform the following steps:

[0061] The first step, in response to determining that the confidence level is greater than the second preset value, controls the item dispensing device to place the item at a preset dispensing location. In practice, this can be achieved by sending information indicating that the item will be placed at the preset dispensing location to the item dispensing device, thereby controlling the device to place the item at the preset dispensing location.

[0062] Step 106: In response to determining that the credibility is greater than a first preset value and less than or equal to a second preset value, perform the following authentication process based on the user's identity information:

[0063] Step 1061: Obtain a face image from a preset database.

[0064] In some embodiments, the aforementioned executing entity may obtain the aforementioned face image from the aforementioned preset database.

[0065] Step 1062: Obtain the reserved face image corresponding to the user's identity information from the preset database.

[0066] In some embodiments, the execution entity may obtain a reserved face image corresponding to the user identity information from the preset database.

[0067] Step 1063: Generate identity verification information based on the face image and the reserved face image.

[0068] In some embodiments, the aforementioned executing entity may generate authentication information based on the aforementioned face image and the aforementioned reserved face image.

[0069] In some optional implementations of certain embodiments, the aforementioned executing entity may generate authentication information based on the aforementioned face image and the aforementioned reserved face image through the following steps:

[0070] The first step involves performing face detection processing on the aforementioned face image and the aforementioned reserved face image to obtain the face location information of the corresponding face image and the target face location information of the corresponding reserved face image. In practice, the executing entity can use the MTCNN face detection algorithm to perform face detection processing on the aforementioned face image and the aforementioned reserved face image to obtain face location information and target face location information. The aforementioned face location information represents the position of the face in the face image within the aforementioned face image. The aforementioned target face location information represents the position of the face in the reserved face image within the aforementioned reserved face image.

[0071] The second step involves performing face alignment processing on the aforementioned face image based on the face location information, resulting in a face-aligned image corresponding to the face image. In practice, the executing entity can use a face alignment algorithm to perform face alignment on the aforementioned face image based on the face location information, thereby obtaining a face-aligned image.

[0072] The third step involves performing face alignment processing on the reserved face image based on the target face location information, resulting in a reserved face-aligned image corresponding to the reserved face image. In practice, the executing entity can use a face alignment algorithm to perform face alignment on the reserved face image based on the target face location information to obtain the reserved face-aligned image.

[0073] The fourth step involves removing the background from the aforementioned face-aligned image and the aforementioned reserved face-aligned image to obtain the target face-aligned image corresponding to the aforementioned face-aligned image and the target reserved face-aligned image corresponding to the aforementioned reserved face-aligned image. In practice, the executing entity can use a threshold segmentation algorithm (e.g., the GrabCut algorithm) to remove the background from the aforementioned face-aligned image and the aforementioned reserved face-aligned image to obtain the target face-aligned image and the target reserved face-aligned image.

[0074] The fifth step is to perform grayscale processing on the target face alignment image and the target reserved face alignment image to obtain a grayscale face alignment image corresponding to the target face alignment image and a grayscale reserved face alignment image corresponding to the target reserved face alignment image.

[0075] Step 6: Input the aforementioned grayscale face-aligned image and the aforementioned grayscale reserved face-aligned image into the input layer of a pre-trained face comparison model to obtain first initial face feature information corresponding to the aforementioned grayscale face-aligned image and second initial face feature information corresponding to the aforementioned grayscale reserved face-aligned image. The face comparison model includes the aforementioned input layer, feature extraction layer, feature alignment layer, and feature comparison layer. The aforementioned input layer can be an upsampling layer that resizes the image to a preset size. The aforementioned first initial face feature information can represent the grayscale face-aligned image after the image size change. The aforementioned second initial face feature information can represent the grayscale reserved face-aligned image after the image size change.

[0076] Step 7: Output the first initial facial feature information to the feature extraction layer to obtain the first feature extraction information corresponding to the first initial facial feature information. The feature extraction layer can be a convolutional layer for extracting image features. The first feature extraction information can be a facial feature vector corresponding to the first initial facial feature information.

[0077] Step 8: Input the aforementioned second initial facial feature information into the aforementioned feature extraction layer to obtain second feature extraction information corresponding to the aforementioned second initial facial feature information. The aforementioned second feature extraction information can be a facial feature vector corresponding to the aforementioned second initial facial feature information.

[0078] Step nine involves inputting the first and second feature extraction information into the feature alignment layer to obtain first aligned feature information corresponding to the first feature extraction information and second aligned feature information corresponding to the second feature extraction information. The feature alignment layer can be a neural network layer that adjusts the vectors corresponding to the first and second feature extraction information to have similar spatial structures. The first aligned feature information can be a feature vector after feature alignment processing. The second aligned feature information can also be a feature vector after feature alignment processing.

[0079] Step 10: Input the first alignment feature information and the second alignment feature information into the feature comparison layer to obtain feature similarity values. The feature comparison layer can be a metric learning layer used to calculate the similarity between the first alignment feature information and the second alignment feature information. The similarity can be cosine similarity or Euclidean distance.

[0080] Step 11: In response to determining that the similarity value of the aforementioned features is greater than a preset value, the information indicating successful authentication is determined as authentication information. The aforementioned information indicating successful authentication can be represented by the Boolean value True.

[0081] Step 12: In response to determining that the similarity value of the above features is less than or equal to a preset value, the information indicating that the identity information verification failed is identified as identity verification information. The information indicating that the identity information verification failed can be represented by the Boolean value False.

[0082] The above technical solution and its related content, combined with steps 106 to 107, constitute an inventive point of this disclosure, solving the second technical problem mentioned in the background: "When generating credibility for a claiming user, it is usually necessary to input the user's dynamic attribute data into a preset credibility model for credibility prediction. The user's dynamic attribute data (e.g., claiming records, item inflow value operation records) may be affected by external factors (e.g., the user's item inflow value operation behavior may be affected by seasonality, item circulation activities, etc.), resulting in the evaluation results of the preset credibility model being unstable and unreliable, causing legitimate claiming users to be unable to claim items, thereby leading to a reduced user experience." The factors leading to a reduced user experience are often as follows: When generating credibility for a claiming user, it is usually necessary to input the user's dynamic attribute data into a preset credibility model for credibility prediction. The user's dynamic attribute data (e.g., claiming records, item inflow value operation records) may be affected by external factors (e.g., the user's item inflow value operation behavior may be affected by seasonality, item circulation activities, etc.), resulting in the evaluation results of the preset credibility model being unstable and unreliable, causing legitimate claiming users to be unable to claim items, thereby leading to a reduced user experience. Solving the above factors can improve the user experience. To achieve this, firstly, face detection processing is performed on the aforementioned face image and the aforementioned reserved face image to obtain the face position information of the corresponding face image and the target face position information of the corresponding reserved face image. This yields the target face position information representing the position of the face in the reserved face image within the aforementioned reserved face image. Secondly, based on the face position information, face alignment processing is performed on the aforementioned face image to obtain a face-aligned image corresponding to the aforementioned face image. This yields a face-aligned image used to generate the target face-aligned image. Thirdly, based on the target face position information, face alignment processing is performed on the aforementioned reserved face image to obtain a reserved face-aligned image corresponding to the aforementioned reserved face image. This yields a reserved face-aligned image used to generate the target reserved face-aligned image. The fourth step involves removing the background from the aforementioned face-aligned image and the aforementioned reserved face-aligned image to obtain the target face-aligned image corresponding to the aforementioned face-aligned image and the target reserved face-aligned image corresponding to the aforementioned reserved face-aligned image. This allows for background removal of the face-aligned image and the reserved face-aligned image, reducing background interference during comparison. The fifth step involves converting the aforementioned target face-aligned image and the aforementioned target reserved face-aligned image to grayscale to obtain the grayscale face-aligned image corresponding to the aforementioned target face-aligned image and the grayscale reserved face-aligned image corresponding to the aforementioned target reserved face-aligned image. This allows for grayscale conversion of the target face-aligned image and the target reserved face-aligned image.Step 6: Input the aforementioned grayscale face alignment image and the aforementioned grayscale reserved face alignment image into the input layer of the pre-trained face comparison model to obtain first initial face feature information corresponding to the aforementioned grayscale face alignment image and second initial face feature information corresponding to the aforementioned grayscale reserved face alignment image. The face comparison model includes the aforementioned input layer, feature extraction layer, feature alignment layer, and feature comparison layer. The aforementioned input layer can be an upsampling layer that resizes the image to a preset size. Step 7: Output the aforementioned first initial face feature information to the aforementioned feature extraction layer to obtain first feature extraction information corresponding to the aforementioned first initial face feature information. Thus, first feature extraction information used to generate first alignment feature information can be obtained. Step 8: Input the aforementioned second initial face feature information into the aforementioned feature extraction layer to obtain second feature extraction information corresponding to the aforementioned second initial face feature information. Thus, second feature extraction information used to generate second alignment feature information can be obtained. Step 9: Input the first feature extraction information and the second feature extraction information into the feature alignment layer to obtain first alignment feature information corresponding to the first feature extraction information and second alignment feature information corresponding to the second feature extraction information. This yields first alignment feature information and second alignment feature information used to generate feature similarity values. Step 10: Input the first alignment feature information and the second alignment feature information into the feature comparison layer to obtain feature similarity values. This yields the similarity between faces in the reserved face image and faces in the collected face image. Step 11: In response to determining that the feature similarity value is greater than a preset value, determine the information indicating successful identity verification as identity verification information. This yields identity verification information. Step 12: In response to determining that the feature similarity value is less than or equal to a preset value, determine the information indicating unsuccessful identity verification as identity verification information. This yields identity verification information. Combined with step 106, when the confidence level is greater than the first preset value and less than or equal to the second preset value, identity verification processing is performed until step 107, where the identity verification information is sent to the item collection device so that the item collection device can place the collected item at the preset collection location. Because identity verification is performed when the credibility level is greater than the first preset value and less than or equal to the second preset value, the possibility of legitimate users being unable to claim items due to the instability and unreliability of the preset credibility model's evaluation results is reduced. This improves the user experience of the item claiming device.

[0083] Step 107: Send the identity verification information to the item collection device so that the item collection device can place the collected item at the preset collection location.

[0084] In some embodiments, the aforementioned executing entity may send the aforementioned authentication information to the aforementioned item collection device, so that the aforementioned item collection device may place the collected item at a preset collection location.

[0085] The above-described embodiments of this disclosure have the following beneficial effects: the item retrieval method of some embodiments of this disclosure improves the security of item retrieval. Specifically, the reason for the poor security of item retrieval is that: directly verifying through a sent retrieval QR code, and distributing the item through an automatic item dispenser after successful verification, is not secure. Retrieval QR codes are usually obtained through mobile phones or emails, resulting in relatively low physical security. Retrieval QR codes are easily stolen, increasing the risk of items being fraudulently claimed by others, thus leading to low security in item retrieval. Based on this, the item retrieval method of some embodiments of this disclosure first obtains item retrieval configuration information, which includes: retrieval attribute information and retrieval user information. Then, based on the retrieval user information, the retrieval attribute information is pushed to each target user terminal. Thus, retrieval attribute information that does not involve user identity information can be sent to each target user terminal. This reduces the possibility of user identity information leakage, thereby reducing the risk of items being fraudulently claimed by others. Next, in response to receiving user identity identification information sent by the user terminal device, the face image acquisition device of the item retrieval device is controlled to acquire the user's face image. Thus, a face image for identity verification can be obtained. Next, in response to receiving the user's facial image sent by the aforementioned facial image acquisition device, the facial image is encrypted and stored in a preset database. This allows for encrypted storage of the facial image for later use. Then, based on a preset credibility model and the aforementioned user identification information, a credibility level corresponding to the aforementioned user identification information is generated. This allows for the generation of credibility based on the user's provided user identification information. Next, in response to determining that the credibility level is greater than a first preset value and less than or equal to a second preset value, the following authentication process is performed based on the aforementioned user identification information: First, the facial image is retrieved from the aforementioned preset database. This allows for the acquisition of the facial image. Second, a reserved facial image corresponding to the aforementioned user identification information is retrieved from the aforementioned preset database. This allows for the generation of a reserved facial image used for authentication information. Third, authentication information is generated based on the aforementioned facial image and the reserved facial image. This allows for the generation of authentication information used for distributing items. Performing authentication processing when the credibility level is greater than the first preset value and less than or equal to the second preset value further improves the security of item distribution. Finally, the aforementioned identity verification information is sent to the item collection device, which then places the item at the preset collection location. Because the collection attribute information pushed to each target user's terminal before the item is distributed through the item collection device does not involve the user's identity information, the possibility of user identity information leakage is reduced, thereby reducing the risk of items being fraudulently claimed by others.Meanwhile, after generating a credibility score corresponding to the aforementioned user identity information, further user authentication is performed based on the credibility score, thereby improving the security of item retrieval.

[0086] Further reference Figure 2 As an implementation of the methods shown in the figures, this disclosure provides some embodiments of an item dispensing device, which are similar to... Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0087] like Figure 2 As shown, some embodiments of the item retrieval device 200 include: an acquisition unit 201, a push unit 202, a control unit 203, a storage unit 204, a generation unit 205, an authentication unit 206, and a sending unit 207. The acquisition unit 201 is configured to acquire item retrieval configuration information, which includes: retrieval attribute information and retrieval user information. The push unit 202 is configured to push the retrieval attribute information to various target user terminals based on the retrieval user information. The control unit 203 is configured to control the face image acquisition device of the item retrieval device to acquire the user's face image in response to receiving user identity information sent by the user terminal device. The storage unit 204 is configured to encrypt and store the face image in a preset database in response to receiving the user's face image sent by the face image acquisition device. The generation unit 205 is configured to generate an item based on a preset trusted... The credibility model and the aforementioned user identity information are used to generate a credibility level corresponding to the aforementioned user identity information; the authentication unit 206 is configured to, in response to determining that the aforementioned credibility level is greater than a first preset value and less than or equal to a second preset value, perform the following authentication processing based on the aforementioned user identity information: obtain the aforementioned face image from the aforementioned preset database; obtain the reserved face image corresponding to the aforementioned user identity information from the aforementioned preset database; generate authentication information based on the aforementioned face image and the aforementioned reserved face image; the sending unit 207 is configured to send the aforementioned authentication information to the aforementioned item collection device, so that the aforementioned item collection device can place the collected item at a preset collection location.

[0088] It is understandable that the units described in the device 200 are related to the reference. Figure 1 The steps in the method described above correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, and will not be repeated here.

[0089] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0090] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0091] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0092] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0093] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0094] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0095] Computer-readable media may be contained within an electronic device or may exist independently of the electronic device. A computer-readable medium carries one or more programs that, when executed by an electronic device, cause the electronic device to: acquire item collection configuration information, wherein the item collection configuration information includes: collection attribute information and collection user information; push the collection attribute information to each target user terminal based on the collection user information; in response to receiving user identification information sent by a user terminal device, control the face image acquisition device of the item collection device to acquire the user's face image; in response to receiving the user's face image sent by the face image acquisition device, encrypt and store the face image in a preset database; generate a credibility level corresponding to the user identification information based on a preset credibility model and the user identification information; in response to determining that the credibility level is greater than a first preset value and less than or equal to a second preset value, perform the following authentication processing based on the user identification information: acquire the face image from the preset database; acquire a reserved face image corresponding to the user identification information from the preset database; generate authentication information based on the face image and the reserved face image; and send the authentication information to the item collection device so that the item collection device can place the item to be collected at a preset collection location.

[0096] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0098] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit 201, a push unit 202, a control unit 203, a storage unit 204, a generation unit 205, an authentication unit 206, and a sending unit 207. The names of these units do not necessarily limit the specific unit; for example, the storage unit may be described as "a unit that, in response to receiving a user's face image sent by the aforementioned face image acquisition device, encrypts and stores the face image in a preset database."

[0099] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0100] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of technical features, but should also cover other technical solutions formed by arbitrary combinations of technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for receiving items, comprising: Obtain item redemption configuration information, wherein the item redemption configuration information includes: redemption attribute information and redemption user information; Based on the user information received, the receiving attribute information is pushed to each target user terminal; In response to receiving user identification information sent by the user terminal device, the face image acquisition device of the item collection device is controlled to acquire the user's face image; In response to receiving a user's face image sent by the face image acquisition device, the face image is encrypted and stored in a preset database; Based on a preset credibility model and the user identity information, a credibility level corresponding to the user identity information is generated, including: Retrieve user attribute data corresponding to the identity information from a preset database. The user attribute information includes: user static attribute information and user dynamic attribute data. The user static attribute information is the user's basic information, and the user dynamic attribute information is information representing the corresponding user's historical item value operations and historical collection records. The value operation is shopping. The user attribute data is input into the preset credibility model to obtain the credibility corresponding to the user attribute data; In response to determining that the credibility is greater than a first preset value and less than or equal to a second preset value, the following authentication process is performed based on the user identity information: The face image is obtained from the preset database; Obtain the reserved facial image corresponding to the user's identity information from the preset database; Based on the facial image and the reserved facial image, generate identity verification information; In response to determining that the confidence level is greater than the second preset value, the item retrieval device is controlled to place the retrieved item at the preset retrieval location; The authentication information is sent to the item collection device so that the item collection device can place the collected item at the preset collection location.

2. The method according to claim 1, wherein, The item redemption configuration information is generated through the following steps: In response to detecting a selection operation on the attribute information configuration selection control of the claim information configuration page, the attribute information configuration interface is displayed, wherein the attribute information configuration interface includes various attribute information input boxes; In response to detecting input operations in the respective attribute information input boxes, the input information corresponding to each attribute information input box is determined as the attribute information to be claimed; In response to detecting a selection control for receiving user information that is applied to the receiving information configuration page, a receiving user information configuration interface is displayed, wherein the receiving user information configuration interface includes various receiving user information input boxes; In response to detecting an input operation applied to each of the user information input boxes, each input information corresponding to each user information input box is determined as user information; the user information and the user information are determined as item collection configuration information.

3. The method according to claim 1, wherein, The step of pushing the claiming attribute information to each target user terminal based on the claiming user information includes: The user information received is selected as the filtering condition; Obtain a target user information set from a preset database, wherein each target user information set includes at least one of the following: user identity identifier, user age; Each target user information in the target user information set that meets the filtering conditions is determined as the user information set to be pushed; For each user information in the set of user information to be pushed to, perform the following push processing: The user identity identifier in the user information to be pushed is determined as the user identity identifier to be pushed; The terminal corresponding to the push user's identity identifier is identified as the target user terminal; The claim attribute information is sent to the target user terminal.

4. The method according to claim 1, wherein, The step of encrypting and storing the face image in a preset database in response to receiving a user's face image sent by the face image acquisition device includes: In response to receiving a user's face image sent by the face image acquisition device, encryption algorithm information and initial key information are obtained from a preset database, wherein the encryption algorithm information includes at least one of the following: key expansion algorithm information and round function information; The face image is converted into byte data corresponding to the face image; The byte data is identified as the data to be encrypted; Based on the key expansion algorithm information and initial key information included in the encryption algorithm information, a round key information sequence corresponding to the initial key information is generated; Based on the first round key information in the round key information sequence and the data to be encrypted, perform the following round key encryption process: Based on the round function information and the first round key information included in the encryption algorithm information, the data to be encrypted is encrypted to obtain round-encrypted data; Remove the first round key information from the round key information sequence to update the round key information sequence; In response to the determination that the round key information sequence is not empty, the following steps are performed: The encrypted data in the round is identified as the data to be encrypted, and the data to be encrypted is updated accordingly; Based on the updated round key information sequence and the updated data to be encrypted, the round key encryption process is performed again; In response to the determination that the round key information sequence is empty, the obtained round encrypted data is determined to be encrypted face image data; The encrypted facial image data is stored in a preset database.

5. An item dispensing device, comprising: The acquisition unit is configured to acquire item claiming configuration information, wherein the item claiming configuration information includes: claiming attribute information and claiming user information; The push unit is configured to push the claiming attribute information to each target user terminal based on the claiming user information; The control unit is configured to control the facial image acquisition device of the item retrieval device to acquire the user's facial image in response to receiving user identification information sent by the user terminal device; The storage unit is configured to, in response to receiving a user's face image sent by the face image acquisition device, encrypt and store the face image in a preset database; The generation unit is configured to generate a credibility level corresponding to the user identity information based on a preset credibility model and the user identity information, including: obtaining user attribute data corresponding to the identity information from a preset database, wherein the user attribute information includes: user static attribute information and user dynamic attribute data, wherein the user static attribute information is the user's basic information, and the user dynamic attribute information is information representing the corresponding user's historical item value operations and historical redemption records, wherein the value operation is shopping; and inputting the user attribute data into the preset credibility model to obtain the credibility level corresponding to the user attribute data. An authentication unit is configured to, in response to determining that the credibility is greater than a first preset value and less than or equal to a second preset value, perform the following authentication process based on the user identity information: obtain the face image from the preset database; obtain a reserved face image corresponding to the user identity information from the preset database; and generate authentication information based on the face image and the reserved face image. The item placement unit, in response to determining that the confidence level is greater than the second preset value, controls the item retrieval device to place the retrieved item at a preset retrieval location; The sending unit is configured to send the authentication information to the item collection device so that the item collection device can place the collected item at a preset collection location.

6. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 4.

7. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

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