Control method, system and device for intelligent vending cabinet

Abstract

The application discloses a control method, system and device of an intelligent vending cabinet, the method comprising: receiving opening cabinet verification information generated by the intelligent vending cabinet based on a user terminal, and verifying the opening cabinet verification information; when the opening cabinet verification information is verified, controlling the intelligent vending cabinet to open the cabinet door; continuously acquiring selection commodity data generated by a sensor in the intelligent vending cabinet when a user selects a commodity, and continuously monitoring a cabinet door closing signal; when the cabinet door closing signal is received, generating to-be-paid order information according to the selection commodity data; sending the to-be-paid order information to the user terminal, and generating a consumption voucher and pushing the consumption voucher to the user terminal when payment completion information fed back by the user terminal is received. The application can open the cabinet door, and the user can independently select a commodity, thereby avoiding the defects of the traditional push-down vending mode, and significantly improving the poor flexibility of selection and purchase.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and in particular to a control method, system and device for an intelligent vending machine. Background Technology

[0002] As self-service retail and other scenarios upgrade towards unmanned, self-service, and intelligent models, the industry has placed higher demands on the flexibility of smart vending machines in terms of self-selection.

[0003] Currently, existing smart vending machine control solutions typically rely on users selecting and paying for products on the product display screen of the vending machine, and then using a mechanical push structure to force the products down to the retrieval port to complete the delivery. However, this model has a rigid selection process, and users cannot directly touch or view the actual products. They can only passively rely on the information on the product display screen to select products, resulting in a poor shopping experience. Furthermore, users can only select a single or fixed combination of products at a time, which is difficult to meet diverse purchasing needs.

[0004] Therefore, existing smart vending machine control solutions suffer from a lack of flexibility in selection. Summary of the Invention

[0005] The main purpose of this application is to propose a control method, system and device for intelligent vending machines, which aims to solve the problem of poor purchasing flexibility.

[0006] To achieve the above objectives, this application proposes a control method for an intelligent vending machine, comprising: Receive the opening verification information generated by the user terminal based on the smart vending machine, and verify the opening verification information; When the verification of the vending machine information is passed, the smart vending machine is controlled to open its door. The system continuously acquires product selection data generated by sensors in the smart vending machine when a user selects a product, and continuously listens for the door closing signal. Upon receiving the cabinet door closing signal, an order information to be paid is generated based on the selected product data; The system sends the order information to be paid to the user's client, and generates a consumption voucher and pushes it to the user's client upon receiving payment completion information from the user's client.

[0007] In some embodiments, the sensor includes at least a vision sensor; the continuous acquisition of product selection data generated by the sensors in the smart vending machine when a user selects a product includes: Control the visual sensor to continuously collect video data of the user's actions; The user actions are obtained by parsing the motion video data, and the user actions include pick-up actions and / or put-back actions. Determine whether the user action includes a putback action; If the user action does not include a putback action, the selected product data is generated based on the product image of the retrieval action in the action video data; If the user action includes a putback action, anti-tampering detection is performed based on the product images of the take-up and put-back actions in the action video data, and the selected product data is generated when the detection passes.

[0008] In some embodiments, the sensor further includes an optical sensor and a gravity sensor, the optical sensor and the gravity sensor being configured on the screen pusher of the smart vending machine; the step of performing anti-tampering detection based on the product images of the picking and putting actions in the motion video data, and generating the selected product data when the detection passes, includes: Extract all product images from the motion video data during the playback actions, and product images from the pick-up actions preceding each playback action. For any return action and its preceding take action, the SKU information of the returned product and the SKU information of the take action are determined from the preset SKU information database based on the product image of the return action and the product image of the take action, and the consistency between the returned product SKU information and the take product SKU information is compared. Acquire the displacement data of the slide rail being picked up and the displacement data of the slide rail being put back, collected by the optical sensor, and compare whether the displacement data of the slide rail being picked up and the displacement data of the slide rail being put back are consistent; The weight data before being picked up and after being put back, collected by the gravity sensor, are obtained, and the weight data before being picked up and after being put back are compared to see if they are consistent. If all three comparisons corresponding to the return actions and their preceding pick-up actions are consistent, the detection passes, and the selected product data is generated based on the product images of pick-up actions that do not have corresponding return actions. If any return action is inconsistent with any of the three comparisons corresponding to its previous take action, the detection fails, a transaction anomaly alarm is generated and pushed to the management terminal.

[0009] In some embodiments, generating a consumption voucher and pushing it to the user terminal upon receiving payment completion information from the user terminal includes: Continuously monitor the payment anomaly information reported by the user terminal, as well as the payment completion information returned by the user terminal; When the payment error information is received but the payment completion information is not received, the payment error log is extracted, and the payment error is repaired according to the payment error log; When the payment completion information is received and the payment error information is not received, the consumption voucher is generated based on the payment completion information and pushed to the user terminal.

[0010] In some embodiments, before receiving the vending machine verification information generated by the user terminal based on the smart vending machine, the method further includes: Receive the installation and debugging request sent by the smart vending machine, the installation and debugging request including a unique identifier, installation location information and hardware configuration parameters; A management file for the smart vending machine is created based on the unique identifier and the installation location information, and the management file is added to the preset vending machine management list. The calibration command for the sensor is generated based on the hardware configuration parameters, and the calibration command is sent to the smart vending machine. Continuously monitor the calibration completion command fed back by the smart vending machine; Upon receiving the calibration completion instruction, a debugging completion instruction is sent to the smart vending machine.

[0011] In some embodiments, sending a debugging completion instruction to the smart vending machine includes: Obtain the SKU information of the products sold by the smart vending machine configured on the management terminal; The vision sensor is controlled to collect images of the products already on the shelves in the smart vending machine; The SKU information of the products already listed is determined from the preset SKU information database based on the images of the products already listed; Compare whether the SKU information of the products for sale is consistent with the SKU information of the products already listed; When the SKU information of the product for sale is inconsistent with the SKU information of the products already listed, an error message is generated and pushed to the management terminal; When the SKU information of the product for sale matches the SKU information of the product already on the shelves, the debugging completion command is sent to the smart vending machine.

[0012] In some embodiments, after controlling the visual sensor to acquire images of the products already on the shelves in the smart vending machine, the method further includes: Acquire real-time slide rail displacement data collected by the optical sensor and real-time weight data collected by the gravity sensor; The system detects whether the smart vending machine is out of stock based on the images of the products already on the shelves, the real-time slide rail displacement data, and the real-time weight data. If the smart vending machine is not out of stock, the step of detecting whether the smart vending machine is out of stock will continue to be executed; If the smart vending machine is out of stock, an out-of-stock message is generated and sent to the management terminal.

[0013] In some embodiments, after generating a consumption voucher and pushing it to the user terminal upon receiving payment completion information from the user terminal, the method further includes: Collect all consumption vouchers within a preset time period; Based on all consumption vouchers within a preset time period and the SKU information of the listed products, product sales are analyzed to obtain product sales data. Based on the product sales data, a product listing / delisting recommendation is generated. The recommendations for listing and delisting products are sent to the management terminal.

[0014] This application further proposes a control system for an intelligent vending machine, which includes a user terminal, an intelligent vending machine, and a server; the server is capable of executing the control method for the intelligent vending machine described above.

[0015] This application further proposes a control device for an intelligent vending machine, including: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that are executed by the at least one processor, which enable the at least one processor to perform the control method for the smart vending machine described above.

[0016] This application's technical solution receives and verifies the opening verification information generated by the user terminal based on the smart vending machine. Upon successful verification, the smart vending machine opens its door. It continuously acquires product selection data generated by sensors within the machine as the user selects items and continuously monitors for door closing signals. Upon receiving a door closing signal, it generates a pending payment order based on the selected product data. This pending payment order is sent to the user terminal, and a payment completion receipt is generated and pushed to the user terminal upon receiving payment completion feedback. By verifying the user terminal's opening verification information, the machine opens upon successful verification, allowing the user to select products independently. Combined with the product selection data collected by sensors within the machine, and the calculation of the pending payment order after the door closes, automatic settlement is achieved. This avoids the limitations of traditional push-to-open vending models, significantly improving purchasing flexibility. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of the control method for the intelligent vending machine according to this application; Figure 2 This is a flowchart illustrating another embodiment of the control method for the intelligent vending machine of this application; Figure 3This is a flowchart illustrating another embodiment of the control method for the intelligent vending machine of this application; Figure 4 This is a flowchart illustrating another embodiment of the control method for the intelligent vending machine of this application; Figure 5 This is a flowchart illustrating another embodiment of the control method for the intelligent vending machine of this application; Figure 6 This is a flowchart illustrating another embodiment of the control method for the intelligent vending machine of this application; Figure 7 This is a flowchart illustrating another embodiment of the control method for the intelligent vending machine of this application; Figure 8 This is a flowchart illustrating another embodiment of the control method for the intelligent vending machine of this application; Figure 9 This is a schematic diagram of the structure of an embodiment of the intelligent vending machine control system of this application; Figure 10 This is a schematic diagram of the structure of an embodiment of the control device for the intelligent vending machine of this application.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0021] It should also be noted that when a component is described as "fixed to" or "set on" another component, it can be directly on the other component or there may be an intervening component present. When a component is described as "connected to" another component, it can be directly connected to the other component or there may be an intervening component present.

[0022] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0023] This application proposes a control method for an intelligent vending machine, referring to... Figure 1 , Figure 1 This is a flowchart illustrating one embodiment of the control method for the smart vending machine according to this application. In some embodiments, the control method for the smart vending machine includes: Step S110: Receive the opening verification information generated by the user terminal based on the smart vending machine, and verify the opening verification information. Step S120: When the verification of the vending machine information is passed, control the smart vending machine to open the door. Step S130: Continuously acquire product selection data generated by the sensors in the smart vending machine when the user selects products, and continuously listen for the door closing signal; Step S140: Upon receiving a cabinet door closing signal, generate pending payment order information based on the selected product data; Step S150: Send the order information to be paid to the user terminal, and generate a consumption voucher and push it to the user terminal when the user terminal sends back payment completion information.

[0024] In this embodiment, the control method for the smart vending machine can be configured as software or a program, or it can be packaged as executable firmware, driver-level plugins, or modular tools; then it is configured onto a server so that the server can run the control method for the smart vending machine. Alternatively, it can be configured onto other computer devices so that the computer devices can run the control method for the smart vending machine.

[0025] It is understandable that the control system of a smart vending machine includes a user terminal, the smart vending machine itself, and a server. When the server runs the control method for the smart vending machine, it can remotely manage and control the machine based on user requests, or it can autonomously manage and control the machine remotely. The control system for the smart vending machine can be applied to scenarios such as self-service retail. Administrators can deploy a batch of smart vending machines in various scenarios according to site layout and operational needs. Administrators can be the back-end management personnel of the smart vending machine control system.

[0026] When the server is running the control method for the smart vending machine, it can monitor the user terminal in real time. When the user terminal sends the opening verification information generated by the smart vending machine to the server, the server can receive the opening verification information generated by the user terminal and then verify it. For example, the user refers to a shopper. The user terminal can be the user's mobile device (e.g., a mobile phone) or a touch screen installed on the smart vending machine that includes a facial recognition verification module. The smart vending machine can be configured with a QR code containing information about the smart vending machine (e.g., identity information). The user can scan the QR code using their mobile device, then authorize their account, verify their identity, etc., bind the smart vending machine, trigger an opening application, generate corresponding opening verification information, and then send the opening verification information to the server. At this time, the server can receive the opening verification information generated by the user terminal based on the smart vending machine. Of course, users can also trigger identity verification processes on the touchscreen display. The facial recognition and verification module can then collect the user's facial features and verify the account based on those features. After successful verification, the user logs into the corresponding account, binds the smart vending machine, triggers an opening application, generates corresponding opening verification information, and sends this information to the server. The server can then receive the opening verification information generated by the user based on the smart vending machine. This opening verification information may include the vending machine's unique identifier and the user's identity information.

[0027] After receiving the vending machine verification information, the server can retrieve the pre-stored vending machine whitelist and the credit permissions corresponding to the user's identity information to verify the verification information and determine whether the smart vending machine is in a normal usable state and whether the user has legitimate access rights. If the verification fails, the server can refuse to open the smart vending machine door.

[0028] Once the verification information is successfully verified, the server can control the smart vending machine to open its door.

[0029] After the smart vending machine opens its door, it continuously acquires product selection data generated by sensors within the machine as the user selects items, and continuously monitors for the door closing signal. For example, the sensors could be located inside the smart vending machine and be able to detect changes in the products inside in real time; such as vision sensors. After the door opens, the user can select and take products according to their needs. While the user is selecting and taking products, the server can control the vision sensors to continuously capture video of the user's selection process and use this video as product selection data. After the door opens, the server can also continuously monitor for the door closing signal.

[0030] Smart vending machines can also be equipped with voice announcements. After the machine door is opened, a voice message can play, "Please close the door after taking the item." The user can then manually close the door after taking the item. Alternatively, visual sensors can be used to detect whether the user has moved away from an area that could prevent the door from closing automatically; if so, the door will close automatically. The door can also be equipped with sensors, such as pressure sensors; when the pressure sensor detects the door closing, it triggers a closing signal, which is then uploaded to the server. The server then receives the closing signal.

[0031] Upon receiving a locker door closing signal, the server can generate a pre-payment order based on the selected items. For example, upon receiving the locker door closing signal, the server can determine that the user has finished selecting items. At this point, the server can analyze the selected items to determine the items chosen, calculate the total amount, and thus generate the pre-payment order.

[0032] After generating the order information to be paid, the server can send it to the user's device. Upon receiving payment completion information from the user, the server can generate a receipt and push it to the user's device. For example, after sending the order information to the user's device, the server can also monitor for payment completion information. After receiving the order information, the user can make the payment on their mobile phone. After payment, the user can send payment completion information to the server. At this point, the server can receive the payment completion information from the user. Upon receiving the payment completion information from the user, the server can generate a receipt based on the order information and the payment completion information, and then push the receipt to the user's device.

[0033] The technical solution of this application verifies the user's cabinet opening verification information. When the verification is successful, the cabinet door is opened, allowing the user to select goods independently. Combined with the data collected by the sensors inside the cabinet, the system calculates and generates the order information to be paid after the cabinet door is closed, so as to complete the automatic settlement. This avoids the drawbacks of the traditional push-to-drop sales mode, which restricts the selection of goods, and significantly improves the flexibility of selection.

[0034] Reference Figure 2 , Figure 2 This is a flowchart illustrating another embodiment of the control method for the smart vending machine according to this application. In some embodiments, the sensor includes at least a vision sensor; the aforementioned continuous acquisition of product selection data generated by the sensors in the smart vending machine when a user selects a product includes: Step S160: Control the visual sensor to continuously collect video data of the user's actions; Step S161: Parse the motion video data to obtain user actions, including pick-up and / or put-back actions; Step S162: Determine whether the user action includes a putback action; Step S163: If the user action does not include the put-back action, then generate the selected product data based on the product image of the retrieval action in the action video data. Step S164: If the user action includes a put-back action, anti-swapping detection is performed based on the product images of the take-up and put-back actions in the action video data, and product selection data is generated when the detection passes.

[0035] In this embodiment, as Figure 2 As shown, during step S130, the visual sensor can be controlled to continuously collect video data of the user's actions. The sensor includes at least one or more visual sensors, and the collection range of the visual sensors covers at least all areas inside the smart vending machine, as well as a portion of the area outside the vending machine door (e.g., the area extending outwards from the door within 0.5 meters). This ensures that the visual sensor can completely collect video data of the user's actions when selecting goods.

[0036] The server can control visual sensors to continuously collect video data of the user's actions. For example, after the smart vending machine opens its door, the user can select and take products according to their needs. While the user is selecting and taking products, the server can control the visual sensors to record video data of the user's actions throughout the entire process, including taking the products and putting them back.

[0037] After obtaining the motion video data, the user's actions can be analyzed to obtain the user's actions. These actions include picking up and / or putting back actions. For example, the server can be configured with image recognition and behavior analysis models; these models can be trained by the administrator according to actual needs. The server can call the image recognition and behavior analysis models to perform frame-by-frame analysis, image feature extraction, and target behavior recognition of the motion video data; through multi-dimensional feature analysis such as limb movement trajectories, changes in product position, and object occlusion relationships, it can accurately distinguish and identify the user's actions within the display case. These user actions include picking up and / or putting back actions. A user may perform a single picking up action during a single product selection process, or a combination of picking up and putting back actions may occur simultaneously.

[0038] After receiving the user's action, it can be determined whether the user's action includes a put-back action.

[0039] If the user's action does not include a put-back action, the selected product data can be generated based on the product image of the pick-up action in the action video data. For example, when the user's action does not include a put-back action, the server can extract the product image of the pick-up action (i.e., the image of the user picking up the product) from the action video data, and then generate the selected product data based on the product image of the pick-up action.

[0040] If the user's action includes a put-back action, anti-tampering detection can be performed based on the product images of the pick-up and put-back actions in the action video data. If the detection passes, selected product data is generated. For example, when the user's action includes a put-back action, the server can extract the product image of the pick-up action (i.e., the image of the user picking up the product) and the product image of the put-back action (i.e., the image of the user putting the product back) from the action video data. Then, anti-tampering detection is performed on the product images of the pick-up and put-back actions to prevent the user from swapping products. If the detection passes, selected product data is generated based on the last product selected by the user.

[0041] Reference Figure 3 , Figure 3 This is a flowchart illustrating another embodiment of the control method for the smart vending machine according to this application. In some embodiments, the sensor further includes an optical sensor and a gravity sensor, which are configured on the screen pusher of the smart vending machine. The aforementioned anti-tampering detection based on the product images of the picking and putting-back actions in the motion video data, and generating selected product data when the detection passes, includes: Step S170: Extract all product images of the placement actions and the product images of the pick-up actions before each placement action from the motion video data. Step S171: For any return action and its preceding take action, determine the return product SKU information and take product SKU information from the preset SKU information database based on the product image of the return action and the product image of the take action, and compare whether the return product SKU information and the take product SKU information are consistent. Step S172: Obtain the pick-up slide rail displacement data and return slide rail displacement data collected by the optical sensor, and compare whether the pick-up slide rail displacement data and return slide rail displacement data are consistent. Step S173: Obtain the weight data before picking up and the weight data after putting back collected by the gravity sensor, and compare whether the weight data before picking up and the weight data after putting back are consistent. Step S174: If all the comparisons of the put-back actions and the three corresponding actions of the previous take-up actions are consistent, the detection is passed, and the selected product data is generated based on the product images of the take-up actions that do not have corresponding put-back actions. In step S175, if any return action is inconsistent with any of the three comparisons corresponding to its previous take action, the detection fails, a transaction anomaly alarm is generated and pushed to the management terminal.

[0042] In this embodiment, as Figure 3 As shown, during step S164, product images of all return actions and product images of the retrieval actions preceding each return action can be extracted from the motion video data. The sensors include optical sensors and gravity sensors, which are configured on the display pusher of the smart vending machine. There can be one or more retrieval actions, and there can also be one or more return actions; the number of retrieval actions can be greater than or equal to the number of return actions.

[0043] The server can extract product images from all playback actions and product images from the take-up actions preceding each playback action from the motion video data. For example, the server can chronologically iterate through all playback actions in the motion video data during the user's current shopping process; for each independent product playback action, it accurately captures the product image for that playback action (playback product image) and simultaneously retrieves the product image from the take-up action preceding that playback action (take-up product image). Following the "take-up first, put-back later" action sequence, the take-up product image and the playback product image are bound and paired one-to-one, forming multiple sets of take-up and playback product image samples. The binding and pairing must ensure product consistency. For example, a smart vending machine contains: Product A, Product B, and Product C. The user takes Product A first, then Product B, but when putting the product back, the user puts back Product B first, then Product A, and finally takes back Product C. Therefore, in the binding and pairing process, it is necessary to pair the image of taking product A with the image of putting product A back, and to pair the image of taking product B with the image of putting product B back.

[0044] For any return action and its preceding retrieval action, the server retrieves the SKU information of the returned and retrieved products from a pre-defined SKU database based on the product images from the return and retrieval actions, and compares the returned and retrieved SKU information for consistency. For example, the server can be configured with a pre-defined SKU database, which stores various product parameters such as appearance features, packaging styles, specifications, and category identifiers. This database can be customized by the administrator. The server can call the pre-defined SKU database to perform feature retrieval and intelligent matching on the paired retrieved and returned product images, identifying and determining the corresponding retrieved and returned SKU information. Then, it compares the returned and retrieved SKU information for consistency. This determines whether the returned product is the same compliant product as the initially retrieved product, achieving the first layer of visual verification against substitution and preventing the unauthorized return or replacement of non-compliant or off-site products.

[0045] For any return action and its preceding pick-up action, the system acquires and compares the displacement data of the pick-up and return slide rails, collected by optical sensors, to determine if they match. For example, a product can be placed on a display pusher. An optical sensor integrated into the pusher monitors the extension, displacement, and positional shift of the product placement slide rail in real time. During the user's product pick-up phase, the system synchronously collects and records the pick-up slide rail displacement data; after the user completes the product return operation, it collects the return slide rail displacement data in real time. The pick-up and return slide rail displacement data collected by the optical sensors can be uploaded to a server. The server can then obtain these data and compare them to determine if they match. Verify whether the slide rail movement during product retrieval matches the slide rail reset movement after product return. This mechanical displacement verification checks whether the product has been completely returned to its original position and whether there are any abnormal behaviors such as missing, incorrect, or replaced items, forming a second layer of slide rail displacement verification.

[0046] For any return action and the preceding retrieval action, the system acquires weight data before retrieval and after return from the gravity sensor, and compares the two data for consistency. For example, a product can be placed on a display pusher. A gravity sensor integrated into the pusher collects real-time data on the weight it carries; before the product is retrieved, the weight data is collected; after the user returns the product and it is stable, the weight data is collected. This data is uploaded to a server. The server then obtains this data and compares it for consistency. By comparing the weight values ​​before and after the return operation, the system determines whether the shelf load remains consistent, thus identifying any fraudulent activities such as product unpacking damage, partial removal, or replacement of products with inconsistent weights. This provides a third layer of precise verification from a weight perspective.

[0047] If all three comparisons corresponding to the return actions and their preceding take actions are consistent, the detection passes. Product selection data is then generated based on the product images of take actions without corresponding return actions. For example, for each take-and-place pairing behavior during this shopping process, if the product SKU information comparison results, the slide rail displacement data comparison results, and the weight data comparison results are consistent, the return action of that pairing behavior is deemed compliant, without any issues of substitution or abnormal damage. When the return actions of all pairing behaviors are compliant, the overall anti-tampering detection is deemed passed. Then, product selection data is generated based on the product images of take actions without corresponding return actions. For example, the smart vending machine contains: Product A, Product B, and Product C. The user takes Product A first, then Product B, but when returning, the user first returns Product B, then Product A, and finally takes Product C; product selection data can then be generated based on the image of Product C. In a special case, if a user puts product A back but does not take product C, the generated product selection data can be null if there is no corresponding take action without a put-back action, meaning the user did not take any product.

[0048] If any return action is inconsistent with any of the three comparisons corresponding to its preceding take action, the detection fails, generating a transaction anomaly alarm and pushing it to the management terminal. For example, during multi-dimensional comparison, if any set of take-and-place pairings shows inconsistencies in product SKU information, slide rail displacement data comparison results, or weight data comparison results, the detection is deemed a failure, indicating abnormal transaction behavior such as product swapping, misplacement, unsealing and theft, or unauthorized replacement. The server can record the anomaly time, anomaly type, corresponding product information, and sensor anomaly data to generate transaction anomaly alarms, which are then pushed to the management terminal. The smart vending machine's control system can also include a management terminal. The management terminal can be integrated with the server or separate. Administrators can view transaction anomaly alarms through the management terminal, enabling source tracing, verification, and intervention, effectively curbing cheating and reducing equipment damage and operational risks.

[0049] Reference Figure 4 , Figure 4 This is a flowchart illustrating another embodiment of the control method for the smart vending machine of this application. In some embodiments, the aforementioned step of generating a consumption voucher and pushing it to the user terminal upon receiving payment completion information from the user terminal includes: Step S180: Continuously monitor payment error information reported by the user terminal and payment completion information returned by the user terminal; Step S181: When a payment error message is received but a payment completion message is not received, extract the payment error log and repair the payment error based on the payment error log. Step S182: When payment completion information is received and no payment error information is received, a consumption voucher is generated based on the payment completion information and pushed to the user terminal.

[0050] In this embodiment, as Figure 4 As shown, during step S150, the server can continuously monitor payment error information reported by the user and payment completion information reported by the user. For example, after sending the order information to be paid to the user, the server can establish a long-polling monitoring mechanism to connect the data interaction interface between the user and the third-party payment channel in real time, continuously monitoring two types of key information: one is payment error information actively reported by the user, which can include payment timeouts, failed deductions, duplicate deductions, network interruptions, payment channel errors, and other abnormal feedback; the other is payment completion information reported by the user based on the order information to be paid after the payment process has been completed normally.

[0051] When a payment error message is received but no payment completion message is received, the payment error log is retrieved, and the payment error is repaired based on the payment error log. For example, if the server receives the payment error message first but does not receive the payment completion message, it is determined that the payment process for this transaction has failed. The server can automatically retrieve the complete payment error log corresponding to this order. The payment error log records key traceability data such as the time of the error, the error code, the interaction message, the order number, the payment channel, and the operation behavior. Based on the payment error log, the cause of the failure is located, and targeted error repair is performed, including resetting the order status, refreshing payment permissions, reversing invalid deductions, retrying the payment interface, and protecting the order pending, etc., to eliminate the payment link failure, ensure that the order can be paid normally again, and avoid problems such as transaction freeze, order cancellation, and fund account disorder caused by a single payment failure.

[0052] Upon receiving payment completion information and no payment error information, the server generates a consumption voucher based on the payment completion information and pushes the voucher to the user's terminal. For example, if the server does not detect any payment error information throughout the process and receives the payment completion information from the user's terminal, it determines that the deduction was successful and the transaction was fulfilled. The server can integrate related data such as order details, product SKU information, transaction amount, payment time, unique identifier (the unique identifier of the smart vending machine), transaction serial number, and after-sales identification to automatically generate a standardized consumption voucher in a structured manner. The generated consumption voucher is then pushed to the corresponding user's terminal, providing the user with a record of the transaction and facilitating subsequent after-sales inquiries, order verification, and traceability of consumption records, thus completing the closed-loop management of the entire self-service transaction.

[0053] Reference Figure 5 , Figure 5 This is a flowchart illustrating another embodiment of the control method for the smart vending machine according to this application. In some embodiments, before receiving the opening verification information generated by the user terminal based on the smart vending machine, the method further includes: Step S190: Receive an installation and debugging request sent by the smart vending machine. The installation and debugging request includes a unique identifier, installation location information, and hardware configuration parameters. Step S191: Establish a management file for the smart vending machine based on the unique identifier and installation location information, and add the management file to the preset vending machine management list; Step S192: Generate a calibration command for the sensor based on the hardware configuration parameters and send the calibration command to the smart vending machine; Step S193: Continuously listen for the calibration completion command from the smart vending machine; Step S194: Upon receiving the calibration completion instruction, send a debugging completion instruction to the smart vending machine.

[0054] In this embodiment, as Figure 5 As shown, before executing step S111, the smart vending machine needs to be installed first. The system receives the installation and debugging request sent by the smart vending machine. This request includes a unique identifier, installation location information, and hardware configuration parameters. For example, the administrator can choose a suitable location to install the smart vending machine based on the actual situation. After the smart vending machine completes on-site hardware installation, line connection, and power-on, it automatically sends an installation and debugging request to the server. At this point, the server can receive the installation and debugging request from the smart vending machine.

[0055] The installation and commissioning request includes a unique identifier, installation location information, and hardware configuration parameters. The unique identifier can be a unique, non-repeatable number specific to the smart vending machine. The installation location information includes the address and site information of the smart vending machine's deployment location. The hardware configuration parameters include the model, parameters, communication protocols, and installation location information of all hardware components installed in the smart vending machine, such as visual sensors, optical sensors, gravity sensors, display pushers, and pressure sensors.

[0056] Upon receiving an installation and debugging request, a management file for the smart vending machine can be created based on its unique identifier and installation location information, and then added to the preset vending machine management list. For example, after receiving the installation and debugging request, the server will use the unique identifier as the primary key, combined with the reported installation location information and hardware configuration parameters, to create a dedicated and independent digital management file for the smart vending machine. The management file can synchronously reserve supporting data modules such as device operating status, calibration records, transaction data, inventory information, operation and maintenance logs, and anomaly records, enabling traceability and controllability throughout the entire lifecycle of the smart vending machine. Once the management file is created, it is automatically added to the preset vending machine management list, completing the online registration of the smart vending machine and enabling centralized and large-scale remote control of multiple smart vending machines.

[0057] The server generates calibration instructions for the sensors based on the hardware configuration parameters and sends these instructions to the smart vending machines. For example, based on the hardware configuration parameters, the server matches the corresponding sensor's calibration standards and debugging rules to generate specific calibration instructions, avoiding compatibility errors with general instructions. The calibration instructions cover all core sensing modules of the vending machine, such as the image parameters and recognition threshold calibration for the vision sensor, the slide rail displacement reference and ranging accuracy calibration for the optical sensor, the weight zero point and load reference calibration for the gravity sensor, and synchronously adapt the operating parameter calibration for the display pusher. After the calibration instructions are generated, they are sent to the corresponding smart vending machines.

[0058] After receiving the calibration command, the smart vending machine automatically enters a full sensor self-test calibration process to eliminate recognition errors caused by hardware installation deviations and environmental interference, ensuring the accuracy of data for subsequent product recognition, retrieval behavior monitoring, and anti-tampering detection. Once the sensors are calibrated, the smart vending machine sends a calibration completion command to the server.

[0059] After sending the calibration command to the smart vending machine, the server can continuously monitor for calibration completion commands from the vending machine. If no calibration completion command is received, the server can continue to monitor for such commands. Once the smart vending machine sends the calibration completion command to the server, the server will receive the calibration completion command.

[0060] Upon receiving the calibration completion command, the server issues a debugging completion command to the smart vending machine. For example, upon receiving the calibration completion command, the server can confirm that the smart vending machine's hardware status is normal, the sensor module accuracy meets the standard, and it meets the conditions for normal transaction services. Then, it can issue a debugging completion command to the smart vending machine. After receiving the debugging completion command, the smart vending machine automatically exits the installation and debugging mode, switches to normal standby service state, and grants users the right to open the vending machine and select products. This completes the entire process of device deployment and pre-debugging, and it can now execute the subsequent user opening, product selection, and settlement transaction logic normally.

[0061] Reference Figure 6 , Figure 6 This is a flowchart illustrating another embodiment of the control method for the smart vending machine according to this application. In some embodiments, the aforementioned issuance of the debugging completion command to the smart vending machine includes: Step S200: Obtain the SKU information of the smart vending machine configured on the management terminal; Step S201: Control the vision sensor to collect images of the products already on the shelves in the smart vending machine; Step S202: Determine the SKU information of the products already listed from the preset SKU information database based on the images of the products already listed; Step S203: Compare whether the SKU information of the products for sale is consistent with the SKU information of the products already listed; Step S204: When the SKU information of the product for sale is inconsistent with the SKU information of the products already listed, an error message is generated and pushed to the management terminal. Step S205: When the SKU information of the product for sale is consistent with the SKU information of the products already on the shelves, a debugging completion instruction is sent to the smart vending machine.

[0062] In this embodiment, as Figure 6As shown, during step S194, the SKU information of the smart vending machines configured by the management terminal can be obtained first. The server can first obtain the SKU information of the smart vending machines configured by the management terminal. For example, the administrator can pre-configure a list of permitted products for each smart vending machine based on the sales location, business plan, and product distribution strategy; then configure the corresponding SKU information based on this product list; and then send the SKU information to the server through the management terminal. At this point, the server can obtain the SKU information of the smart vending machines configured by the management terminal.

[0063] After obtaining the SKU information of the products for sale, the server can control the visual sensors to capture images of the products already on the shelves in the smart vending machine. For example, after configuring a list of products allowed to be sold for the smart vending machine, the administrator can also put the corresponding products onto the display pushers of the smart vending machine based on this list. Then, the server can control the visual sensors to capture images of the products already on the shelves in the smart vending machine.

[0064] After obtaining images of the products already on the shelves, the server determines the SKU information of these products from a pre-set SKU database. For example, the server parses the collected images of the products already on the shelves, identifies each type of product actually displayed in the smart vending machine, and matches and determines the corresponding SKU information from the pre-set SKU database.

[0065] After obtaining the SKU information of the products for sale and the SKU information of the products already listed, you can compare whether the SKU information of the products for sale is consistent with the SKU information of the products already listed.

[0066] When the SKU information of a product being sold differs from the SKU information of products already on the shelves, an error message can be generated and pushed to the management terminal. For example, if the SKU information of a product being sold differs from the SKU information of products already on the shelves, the server can determine that the actual products on the shelves in the smart vending machine are different from the product list configured by the administrator. At this time, the server will generate an error message based on the unique identifier of the smart vending machine, the abnormal product SKU information, and the type of error; then, the error message will be pushed to the management terminal in real time, reminding the administrator to promptly investigate, rectify, and adjust the product display to ensure standardized operation and management.

[0067] When the SKU information of the product for sale matches the SKU information of the products already on the shelves, a debugging completion command can be sent to the smart vending machine. For example, when the SKU information of the product for sale matches the SKU information of the products already on the shelves, the server can determine that the actual products on the shelves in the smart vending machine are the same as the product list configured by the administrator. At this time, the server can send a debugging completion command to the smart vending machine. After receiving the debugging completion command, the smart vending machine automatically exits the installation and debugging mode, switches to normal standby service state, and grants users the right to open the vending machine and select products. This completes the entire process of device deployment and pre-debugging, and the subsequent user vending machine opening, product selection, and settlement transaction logic can be executed normally.

[0068] Reference Figure 7 , Figure 7 This is a flowchart illustrating another embodiment of the control method for the smart vending machine according to this application. In some embodiments, after the aforementioned control vision sensor acquires images of the products already stocked in the smart vending machine, the method further includes: Step S210: Obtain real-time slide rail displacement data collected by the optical sensor and real-time weight data collected by the gravity sensor; Step S211: Detect whether the smart vending machine is out of stock based on the images of the products already on the shelves, real-time slide rail displacement data, and real-time weight data; Step S212: If the smart vending machine is not out of stock, continue to execute the step of detecting whether the smart vending machine is out of stock. Step S213: If the smart vending machine is out of stock, generate out-of-stock information and send it to the management terminal.

[0069] In this embodiment, as Figure 7 As shown, after executing step S201, out-of-stock detection can also be performed. The server can acquire real-time slide rail displacement data collected by the optical sensor and real-time weight data collected by the gravity sensor. For example, the optical sensor can collect the extension and retraction state and displacement of the product slide rail on the display pusher in real time, thereby obtaining real-time slide rail displacement data, and then upload the real-time slide rail displacement data to the server. At the same time, the gravity sensor can collect the real-time load weight of the display pusher in real time, thereby obtaining real-time weight data, and then upload the real-time weight data to the server. Then, the server can obtain the real-time slide rail displacement data collected by the optical sensor and the real-time weight data collected by the gravity sensor.

[0070] After obtaining images of the products already on the shelves, real-time sliding rail displacement data, and real-time weight data, the smart vending machine can detect whether it is out of stock. For example, a smart vending machine includes multiple display pushers used to display products. The server uses visual recognition of the images of the products already on the shelves to initially determine whether there are obvious empty spaces on each display pusher and whether the product display density is lower than a preset threshold. Secondly, combining real-time sliding rail displacement data from optical sensors, if the displacement of a certain sliding rail does not reach the standard displacement value when the shelf is full, it is determined that the display pusher may be out of stock. Finally, the real-time weight data collected by the gravity sensor is compared with the standard weight data when the display pusher is full; if the weight difference exceeds a preset out-of-stock weight threshold, the possibility of an out-of-stock situation is further confirmed. Only when the results of visual recognition, sliding rail displacement, and weight detection all point to an empty product is the corresponding display pusher ultimately determined to be out of stock.

[0071] If the smart vending machine is not out of stock, the process of checking for stock shortages can continue. For example, if visual recognition, slide rail displacement, and weight detection all indicate that the products on each display pusher in the smart vending machine are sufficient and there is no stock shortage, the server can continue to execute the process of checking for stock shortages.

[0072] If a smart vending machine is out of stock, an out-of-stock notification can be generated and sent to the management system. For example, if a display pusher is found to be out of stock, an out-of-stock notification can be generated based on key data such as the smart vending machine's unique identifier, installation location information, the out-of-stock display pusher's serial number, product SKU information, out-of-stock time, and historical replenishment cycle. This notification is then sent to the management system. Administrators can then use this information to coordinate replenishment plans, optimize replenishment routes, ensure the smart vending machine's continuous and normal operation, and improve the user shopping experience.

[0073] Reference Figure 8 , Figure 8 This is a flowchart illustrating another embodiment of the control method for the smart vending machine of this application. In some embodiments, after generating a consumption voucher and pushing it to the user terminal upon receiving payment completion information from the user terminal, the method further includes: Step S220: Collect all consumption vouchers within a preset time period; Step S221: Analyze product sales based on all consumption vouchers and SKU information of listed products within a preset time period to obtain product sales data; Step S222: Generate product listing and delisting recommendations based on product sales data; Step S223: Send the product listing / delisting recommendations to the management terminal.

[0074] In this embodiment, as Figure 8 As shown, after executing step S150, all consumption vouchers within a preset time period can also be collected. The server collects all consumption vouchers within the preset time period. The preset time period can be customized by the administrator; for example, it can be one day, one week, or one month.

[0075] After obtaining all purchase receipts, sales data can be analyzed based on all receipts and SKU information of listed products within a preset time period. For example, the server will match the collected receipts with the SKU information of the smart vending machine to build a multi-dimensional sales analysis model: on the one hand, it will statistically analyze the total sales volume, average daily sales volume, and peak-hour sales distribution of each product category within a preset time period according to the SKU information of listed products, identifying best-selling and slow-moving products; on the other hand, it will analyze the market acceptance of different types of products by combining attributes such as product specifications and price ranges; at the same time, it will compare the inventory consumption rate and replenishment cycle of each product to calculate the product turnover efficiency. Through multi-dimensional cross-analysis, product sales data will be generated, including key indicators such as sales ranking, sales share, turnover days, and supply-demand balance of each listed product SKU.

[0076] After obtaining product sales data, recommendations for product listing and delisting can be generated based on this data. For example, based on product sales data, the server recommends the following: for products with high sales ranking, high turnover efficiency, and strong user demand, it recommends increasing the number of listings or maintaining their listing status; for slow-moving products with zero sales within a preset time and a turnover period exceeding a threshold, it recommends delisting or adjusting their display position; for products with moderate sales but high profit margins, it recommends optimizing the listing ratio; at the same time, combining regional consumption characteristics and historical data trends, it recommends introducing similar popular alternative products; the recommendation content clearly indicates the reason for the recommendation (e.g., if the average daily sales are less than 1 unit, it is recommended to delist).

[0077] After generating product listing and delisting recommendations, these recommendations can be sent to the management terminal. For example, the server sends the generated product listing and delisting recommendations to the management terminal. Upon receiving the recommendations, the management terminal will display a pop-up reminder or mark the task as pending in the operations management interface. Administrators can directly view the product listing and delisting recommendations and the sales data behind them, quickly making decisions on delisting, delisting, and replenishment without the need for manual statistical analysis, significantly improving operational efficiency and enabling refined and data-driven operations of the smart vending machine.

[0078] The technical solution of this application verifies the user's cabinet opening verification information. When the verification is successful, the cabinet door is opened, allowing the user to select goods independently. Combined with the data collected by the sensors inside the cabinet, the system calculates and generates the order information to be paid after the cabinet door is closed, so as to complete the automatic settlement. This avoids the drawbacks of the traditional push-to-drop sales mode, which restricts the selection of goods, and significantly improves the flexibility of selection.

[0079] This application further proposes a control system for an intelligent vending machine, referring to... Figure 9 , Figure 9 This is a schematic diagram of the structure of an embodiment of the control system of the smart vending machine of this application. In some embodiments, the control system of the smart vending machine includes a user terminal, a smart vending machine, and a server; the server is capable of executing the control method of the smart vending machine described above.

[0080] In this embodiment, as Figure 9 As shown, the control system of the smart vending machine includes a user terminal, the smart vending machine itself, and a server. When the server runs the control method for the smart vending machine, it can remotely manage and control the smart vending machine based on user requests, or it can autonomously manage and control the smart vending machine remotely. The control system of the smart vending machine can be applied to scenarios such as self-service retail. Administrators can deploy a batch of smart vending machines in various scenarios according to site layout and operational needs. The administrator can be the back-end management personnel of the smart vending machine control system.

[0081] This application further proposes a control device for an intelligent vending machine, referring to... Figure 10 , Figure 10 This is a schematic diagram of the structure of a control device for a smart vending machine according to an embodiment of this application. In some embodiments, the control device for the smart vending machine includes: At least one processor; and, A memory that is communicatively connected to at least one processor; wherein, The memory stores instructions that are executed by the at least one processor to enable the at least one processor to perform the control method of the smart vending machine as described above.

[0082] In this embodiment, refer to Figure 10 In this application embodiment, the control device for the smart vending machine can be a processor capable of running the control method for the smart vending machine; there is at least one processor. For example... Figure 10As shown, the control device of the smart vending machine may include: a processor 1001 (e.g., CPU), a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit, such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0083] Those skilled in the art will understand that Figure 10 The control device structure of the smart vending machine shown does not constitute a limitation on the control device of the smart vending machine. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0084] like Figure 10 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and computer programs.

[0085] exist Figure 10 In the control device of the smart vending machine shown, the network interface 1004 is mainly used to connect to the back-end server and communicate data with the back-end server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; and the processor 1001 can be used to call the computer program stored in the memory 1005. When the computer program is called and executed by the processor 1001, it implements the steps of the above-mentioned control method of the smart vending machine.

[0086] The above description is only a part or preferred embodiment of the present invention. Neither the text nor the drawings should limit the scope of protection of the present invention. All equivalent structural transformations made using the content of the present invention specification and drawings under the overall concept of the present invention, or direct / indirect applications in other related technical fields, are included within the scope of protection of the present invention.

Claims

1. A control method for an intelligent vending machine, characterized in that, include: Receive the opening verification information generated by the user terminal based on the smart vending machine, and verify the opening verification information; When the verification of the vending machine information is passed, the smart vending machine is controlled to open its door. The system continuously acquires product selection data generated by sensors in the smart vending machine when a user selects a product, and continuously listens for the door closing signal. Upon receiving the cabinet door closing signal, an order information to be paid is generated based on the selected product data; The system sends the order information to be paid to the user's client, and generates a consumption voucher and pushes it to the user's client upon receiving payment completion information from the user's client.

2. The control method for the intelligent vending machine according to claim 1, characterized in that, The sensor includes at least a vision sensor; the continuous acquisition of product selection data generated by the sensors in the smart vending machine when a user selects a product includes: Control the visual sensor to continuously collect video data of the user's actions; The user actions are obtained by parsing the motion video data, and the user actions include pick-up actions and / or put-back actions. Determine whether the user action includes a putback action; If the user action does not include a putback action, the selected product data is generated based on the product image of the retrieval action in the action video data; If the user action includes a putback action, anti-tampering detection is performed based on the product images of the take-up and put-back actions in the action video data, and the selected product data is generated when the detection passes.

3. The control method for the intelligent vending machine according to claim 2, characterized in that, The sensor also includes an optical sensor and a gravity sensor, which are configured on the screen pusher of the smart vending machine; the step of performing anti-tampering detection based on the product images of the picking and putting-back actions in the motion video data, and generating the selected product data when the detection passes, includes: Extract all product images from the motion video data during the playback actions, and product images from the pick-up actions preceding each playback action. For any return action and its preceding take action, the SKU information of the returned product and the SKU information of the take action are determined from the preset SKU information database based on the product image of the return action and the product image of the take action, and the consistency between the returned product SKU information and the take product SKU information is compared. Acquire the displacement data of the slide rail being picked up and the displacement data of the slide rail being put back, collected by the optical sensor, and compare whether the displacement data of the slide rail being picked up and the displacement data of the slide rail being put back are consistent; The weight data before being picked up and after being put back, collected by the gravity sensor, are obtained, and the weight data before being picked up and after being put back are compared to see if they are consistent. If all three comparisons corresponding to the return actions and their preceding pick-up actions are consistent, the detection passes, and the selected product data is generated based on the product images of pick-up actions that do not have corresponding return actions. If any return action is inconsistent with any of the three comparisons corresponding to its previous take action, the detection fails, a transaction anomaly alarm is generated and pushed to the management terminal.

4. The control method for the intelligent vending machine according to claim 3, characterized in that, The step of generating a consumption voucher and pushing it to the user terminal upon receiving payment completion information from the user terminal includes: Continuously monitor the payment anomaly information reported by the user terminal, as well as the payment completion information returned by the user terminal; When the payment error information is received but the payment completion information is not received, the payment error log is extracted, and the payment error is repaired according to the payment error log; When the payment completion information is received and the payment error information is not received, the consumption voucher is generated based on the payment completion information and pushed to the user terminal.

5. The control method for the intelligent vending machine according to claim 4, characterized in that, Before receiving the unlocking verification information generated by the user terminal based on the smart vending machine, the process also includes: Receive the installation and debugging request sent by the smart vending machine, the installation and debugging request including a unique identifier, installation location information and hardware configuration parameters; A management file for the smart vending machine is created based on the unique identifier and the installation location information, and the management file is added to the preset vending machine management list. The calibration command for the sensor is generated based on the hardware configuration parameters, and the calibration command is sent to the smart vending machine. Continuously monitor the calibration completion command fed back by the smart vending machine; Upon receiving the calibration completion instruction, a debugging completion instruction is sent to the smart vending machine.

6. The control method for the intelligent vending machine according to claim 5, characterized in that, The step of issuing the debugging completion instruction to the smart vending machine includes: Obtain the SKU information of the products sold by the smart vending machine configured on the management terminal; The vision sensor is controlled to collect images of the products already on the shelves in the smart vending machine; The SKU information of the products already listed is determined from the preset SKU information database based on the images of the products already listed; Compare whether the SKU information of the products for sale is consistent with the SKU information of the products already listed; When the SKU information of the product for sale is inconsistent with the SKU information of the products already listed, an error message is generated and pushed to the management terminal; When the SKU information of the product for sale matches the SKU information of the product already on the shelves, the debugging completion command is sent to the smart vending machine.

7. The control method for the intelligent vending machine according to claim 6, characterized in that, After controlling the visual sensor to acquire images of the products already on the shelves in the smart vending machine, the method further includes: Acquire real-time slide rail displacement data collected by the optical sensor and real-time weight data collected by the gravity sensor; The system detects whether the smart vending machine is out of stock based on the images of the products already on the shelves, the real-time slide rail displacement data, and the real-time weight data. If the smart vending machine is not out of stock, the step of detecting whether the smart vending machine is out of stock will continue to be executed; If the smart vending machine is out of stock, an out-of-stock message is generated and sent to the management terminal.

8. The control method for the intelligent vending machine according to claim 7, characterized in that, After generating a consumption voucher and pushing it to the user's terminal upon receiving payment completion information from the user's terminal, the process further includes: Collect all consumption vouchers within a preset time period; Based on all consumption vouchers within a preset time period and the SKU information of the listed products, product sales are analyzed to obtain product sales data. Based on the product sales data, a product listing / delisting recommendation is generated. The recommendations for listing and delisting products are sent to the management terminal.

9. A control system for an intelligent vending machine, characterized in that, The control system of the smart vending machine includes a user terminal, a smart vending machine, and a server; the server is capable of executing the control method of the smart vending machine as described in any one of claims 1 to 8.

10. A control device for an intelligent vending machine, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that are executed by the at least one processor to enable the at least one processor to perform the control method of the smart vending machine according to any one of claims 1 to 8.

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