Abnormal handling method and handling device for vending machine

Abstract

The present invention relates to the technical field of vending machine equipment, and in particular provides an abnormality handling method and processing equipment for vending machines, the method comprising: detecting the operation status of the vending machine in real time through an information acquisition module, and issuing an abnormality alarm message if the operation is abnormal; upon receiving the abnormality alarm message, retrieving the corresponding abnormality alarm classification table according to the preset alarm classification information, and finding the set processing solution by comparison; responding and processing according to the processing solution, saving the abnormal alarm and processing records, and feeding back the processing result information. The equipment comprises an information acquisition module, an operation diagnosis module, an alarm classification query module, a solution determination module, a response processing module, and a storage module; and is used to implement the aforementioned processing method. The present invention judges the operation abnormality by detecting data, and improves the processing intelligence by processing separately according to the abnormality type, thereby reducing the vending machine time and maintaining long-term transaction services.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic vending machine equipment, and in particular to an abnormality handling method and handling equipment for an automatic vending machine. Background Art

[0002] Vending machines are unattended devices that have developed rapidly in recent years because they can provide convenient transaction services. On the one hand, they have created an unmanned retail business model, and on the other hand, they can meet consumers' temporary or urgent needs.

[0003] As a device, a vending machine may experience operational abnormalities (such as equipment failure) during use due to various reasons. Since there is no one on duty, the operational abnormalities may not be handled for a long time, making the device unable to be used normally and unable to achieve the above purpose.

[0004] Existing vending machines can only handle single equipment failures (such as a shipment being stuck) or send an alarm to alert maintenance personnel. This means they cannot flexibly address the diversity of operational anomalies and their handling is not intelligent enough. Therefore, there is a need for highly intelligent technical solutions to address vending machine anomalies. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides an abnormality handling method for a vending machine, comprising:

[0006] S100: The information collection module detects the operation status of the vending machine in real time, and issues an abnormal alarm message if the operation is abnormal;

[0007] S200: When receiving abnormal alarm information, the corresponding abnormal alarm classification table is retrieved according to the preset alarm classification information, and the set processing solution is found through comparison;

[0008] S300: Respond and process according to the processing plan, save abnormal alarm and processing records, and feedback processing result information.

[0009] Optionally, step S100 includes:

[0010] Obtaining consumer operation request information and real-time response information of vending machines;

[0011] Determine a preset response action based on the operation request information;

[0012] Verify whether the real-time response information matches the response action. If they match, it indicates normal operation. If they do not match, it indicates abnormal operation.

[0013] Optionally, the information collection module detects the operation status of the vending machine in real time, including:

[0014] By shooting the current image and performing image recognition, it is used to determine whether there is anyone operating the device.

[0015] Collect the user's operations on the vending machine, including but not limited to product selection, payment, and pickup;

[0016] Collect the internal temperature of the vending machine and the operating information of the temperature control equipment;

[0017] Collect payment information;

[0018] Collect the shipping response information of the vending machine, which includes but is not limited to the shipping information of the storage bin outlet, the shipping information of the cargo channel and the shipping information of the pickup cavity.

[0019] Optionally, the image recognition method for the current image is as follows:

[0020] Establish a coordinate system;

[0021] Pre-process each frame of the current image, and identify the consumer's hand and the hand position in each pre-processed frame;

[0022] The hand position is imported into the coordinate system according to the acquisition time of each frame image, and the hand position of each frame image corresponding to the consumer is fitted and connected in the coordinate system in sequence to obtain the coordinate fitting trajectory of the hand movement;

[0023] Construct a motion tracking function based on the coordinate fitting trajectory of the hand movement, calculate the derivative value of the motion tracking function of each hand position corresponding to the point in the coordinate system, and select the hand position corresponding to the point whose derivative value difference with the next adjacent point exceeds a preset threshold as the action segmentation point;

[0024] Using the image frames corresponding to the action segmentation points to segment each frame in the current image, forming multiple groups of image frames;

[0025] Each set of image frames and their corresponding coordinate fitting trajectory lines are analyzed to identify whether each consumer's action belongs to the operation of the vending machine.

[0026] Optionally, preprocessing includes:

[0027] Perform grayscale processing on each frame of the current image to obtain the corresponding grayscale processed image frame;

[0028] Based on each pixel point in the grayscale processed image frame, the product of the R channel pixel value and the R channel weighted value, the product of the G channel pixel value and the G channel weighted value, and the product of the B channel pixel value and the B channel weighted value are added to obtain the grayscale value of the pixel point, and a Gaussian filtering algorithm is used to combine the inherent variation of the image window and the total variation of the image window to form a structure and texture decomposition regularizer for smoothing the image pixel point to obtain a smoothed image frame;

[0029] The smoothed image frame is subjected to image enhancement processing, that is, supervised model training is performed on the smoothed image frame using an image enhancement model to obtain an image enhanced image frame.

[0030] The present invention also provides an abnormality handling device for a vending machine, comprising:

[0031] An information collection module is used to detect the operation status of the vending machine in real time, including the consumer's operation request information and the vending machine's real-time response information;

[0032] The operation diagnosis module is used to evaluate the operation of the vending machine and issue an abnormal alarm message if the operation is abnormal;

[0033] The alarm classification query module, when receiving abnormal alarm information, retrieves the corresponding abnormal alarm classification table according to the preset alarm classification information;

[0034] Solution determination module, which finds the set treatment solution through comparison;

[0035] The response processing module responds and processes according to the processing plan and feeds back the processing result information;

[0036] Storage module, saves abnormal alarm and processing records.

[0037] Optionally, run the diagnostic module including:

[0038] A preset response determination submodule is used to determine a preset response action according to the operation request information;

[0039] The verification submodule is used to verify whether the real-time response information matches the response action. If they match, it indicates normal operation; if they do not match, it indicates abnormal operation;

[0040] The abnormal information sending submodule is used to generate and send abnormal alarm information when an abnormal operation occurs.

[0041] Optionally, a communication module is also included, which is used to connect to the network to transmit abnormal alarm information and processing result information to a set terminal device.

[0042] Optionally, a transaction interruption module is also included. The transaction interruption module is used to forcibly interrupt the transaction and return the money paid by the consumer when an abnormal alarm message is received.

[0043] Optionally, it also includes a restart module and a software repair module;

[0044] The restart module is used to automatically cut off and restore power when the operating abnormality is of software type, and restore the device to default settings by restarting and initializing.

[0045] The software repair module is used to call the backup software to repair or reinstall the software when there is still a software type operation abnormality after the restart module is restarted.

[0046] The abnormality handling method and processing equipment of the vending machine of the present invention make operation judgments through detection data. When an operation abnormality occurs, the processing plan in the classification table is called according to the abnormality classification, and the abnormality type is handled separately to improve the flexibility and adaptability of various operation abnormalities, improve the processing intelligence, respond and handle according to the processing plan, and save and feedback the abnormality and processing situation. For example, for software-type operation abnormalities, the device restart processing method can be used first. If the restart still cannot solve the problem, the software is repaired or updated to make the software run normally; for hardware-type or unrecognizable abnormalities, maintenance personnel intervention is generally required. Therefore, the transaction is forced to be interrupted first, the amount paid by the consumer is returned, and then the abnormal alarm situation is fed back to remind maintenance personnel to intervene in time, thereby reducing the time of the vending machine and maintaining long-term transaction services.

[0047] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0048] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 This is a flow chart of a method for handling an exception in a vending machine according to an embodiment of the present invention;

[0051] Figure 2This is a flow chart of a method for determining an operation abnormality adopted in an embodiment of the abnormality handling method for a vending machine of the present invention;

[0052] Figure 3 The figure is a schematic diagram of an abnormality handling device for a vending machine according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0054] like Figure 1 As shown, an embodiment of the present invention provides a method for handling an exception of a vending machine, comprising:

[0055] S100: The information collection module detects the operation status of the vending machine in real time, and issues an abnormal alarm message if the operation is abnormal;

[0056] S200: When receiving abnormal alarm information, the corresponding abnormal alarm classification table is retrieved according to the preset alarm classification information, and the set processing solution is found through comparison;

[0057] S300: Respond and process according to the processing plan, save abnormal alarm and processing records, and feedback processing result information.

[0058] The working principle and beneficial effects of the above technical solution are as follows: This solution uses detection data to make operational judgments. When an operational anomaly occurs, the solution retrieves the processing solution in the classification table according to the anomaly classification, and processes each anomaly type separately, thereby improving the flexibility and adaptability of various operational anomalies and improving the intelligent processing level. The solution responds and processes according to the processing solution, and saves and feedbacks the anomaly and processing status. For example, for software-type operational anomalies, the device can be restarted first. If the restart still cannot solve the problem, the software can be repaired or updated to ensure normal software operation. For hardware-type or unrecognizable anomalies, maintenance personnel are generally required to intervene. Therefore, the transaction is first forcibly interrupted and the amount paid by the consumer is refunded. Then, the abnormal alarm situation is fed back to remind maintenance personnel to intervene in time, thereby reducing the vending machine time and maintaining long-term transaction services. Among them, operational anomalies may include: the vending machine operates on its own when no one is operating; when someone is operating, it does not respond to the consumer's operation or responds incorrectly.

[0059] In one embodiment, Figure 2 As shown, step S100 includes:

[0060] S110: Acquire the consumer's operation request information and the vending machine's real-time response information;

[0061] S120: Determine a preset response action according to the operation request information;

[0062] S130: Verify whether the real-time response information matches the response action. If they match, it indicates normal operation; if they do not match, it indicates abnormal operation.

[0063] The working principle and beneficial effects of the above technical solution are as follows: this solution obtains the consumer's operation request information and the real-time response information of the vending machine, determines the preset response action for the operation request, verifies whether the real-time response action matches the preset response action, and uses the matching result to judge whether there is an operation abnormality and send an abnormal alarm information; the operation diagnosis of this solution not only focuses on the device itself, but also associates it with the consumer's operation, making the operation status judgment more comprehensive and accurate, and can cover the application scenarios of vending machines.

[0064] In one embodiment, the information collection module detects the operation status of the vending machine in real time, including:

[0065] By shooting the current image and performing image recognition, it is used to determine whether there is anyone operating the device.

[0066] Collect the user's operations on the vending machine, including but not limited to product selection, payment, and pickup;

[0067] Collect the internal temperature of the vending machine and the operating information of the temperature control equipment;

[0068] Collect payment information;

[0069] Collect the shipping response information of the vending machine, which includes but is not limited to the shipping information of the storage bin outlet, the shipping information of the cargo channel and the shipping information of the pickup cavity.

[0070] The working principle and beneficial effects of the above technical solution are as follows: This solution comprehensively detects and judges the operating status of the vending machine through the above-mentioned multi-dimensional data collection; the detection not only focuses on the consumer's actions, but also on the operating response of various parts of the vending machine itself, ensuring the accuracy and comprehensiveness of the operating status judgment, which is conducive to further improving the operating utilization rate of the equipment.

[0071] In one embodiment, the image recognition method for the current image is as follows:

[0072] Establish a coordinate system;

[0073] Pre-process each frame of the current image, and identify the consumer's hand and the hand position in each pre-processed frame;

[0074] The hand position is imported into the coordinate system according to the acquisition time of each frame image, and the hand position of each frame image corresponding to the consumer is fitted and connected in the coordinate system in sequence to obtain the coordinate fitting trajectory of the hand movement;

[0075] Construct a motion tracking function based on the coordinate fitting trajectory of the hand movement, calculate the derivative value of the motion tracking function of each hand position corresponding to the point in the coordinate system, and select the hand position corresponding to the point whose derivative value difference with the next adjacent point exceeds a preset threshold as the action segmentation point;

[0076] Using the image frames corresponding to the action segmentation points to segment each frame in the current image, forming multiple groups of image frames;

[0077] Each set of image frames and their corresponding coordinate fitting trajectory lines are analyzed to identify whether each consumer's action belongs to the operation of the vending machine.

[0078] The working principle and beneficial effects of the above technical solution are as follows: this solution can improve image quality and eliminate noise interference through image shooting and frame image preprocessing; through frame image time series analysis, combined with coordinate tools, action trajectory fitting and analysis are performed to achieve real-time tracking of consumer actions, which can improve the accuracy of operational action recognition, and can accurately identify different actions and the purpose (function) of each action, realizing anthropomorphic management of action recognition and improving the intelligence level of action recognition.

[0079] In one embodiment, pre-processing includes:

[0080] Perform grayscale processing on each frame of the current image to obtain the corresponding grayscale processed image frame;

[0081] Based on each pixel point in the grayscale processed image frame, the product of the R channel pixel value and the R channel weighted value, the product of the G channel pixel value and the G channel weighted value, and the product of the B channel pixel value and the B channel weighted value are added to obtain the grayscale value of the pixel point, and a Gaussian filtering algorithm is used to combine the inherent variation of the image window and the total variation of the image window to form a structure and texture decomposition regularizer for smoothing the image pixel point to obtain a smoothed image frame;

[0082] The smoothed image frame is subjected to image enhancement processing, that is, supervised model training is performed on the smoothed image frame using an image enhancement model to obtain an image enhanced image frame.

[0083] The working principle and beneficial effects of the above technical solution are as follows: This solution can well protect image details and image clarity by performing grayscale processing, smoothing processing and image enhancement processing on each frame of the current image, has a good inhibitory effect on artifacts in the image, can improve the accuracy and clarity of each image frame, and increase the accuracy of consumer action recognition and positioning tracking.

[0084] like Figure 3 As shown, an embodiment of the present invention provides an exception handling device for a vending machine, comprising:

[0085] The information collection module 10 is used to detect the operation status of the vending machine in real time, including the consumer's operation request information and the real-time response information of the vending machine;

[0086] The operation diagnosis module 20 is used to evaluate the operation of the vending machine and issue an abnormal alarm message if the operation is abnormal;

[0087] The alarm classification query module 30 retrieves the corresponding abnormal alarm classification table according to the preset alarm classification information when receiving abnormal alarm information;

[0088] The solution determination module 40 finds the set processing solution through comparison;

[0089] The response processing module 50 responds and processes according to the processing plan and feeds back the processing result information;

[0090] The storage module 60 stores abnormal alarm and processing records.

[0091] The working principle and beneficial effects of the above technical solution are as follows: this solution detects data through the information collection module and performs operation judgment through the operation diagnosis module. When an operation abnormality occurs, the alarm classification query module retrieves the classification table according to the abnormality classification, and the solution determination module obtains the processing solution from the classification table. The flexibility and adaptability of various operation abnormalities are improved by processing them separately according to the abnormality type, and the processing intelligence is improved. The response processing module responds and processes according to the processing solution, and feedbacks the abnormality and processing status. The storage module saves the abnormal alarm and processing records; for example, for software-type operation abnormalities, you can first use the device restart processing method. If the restart still cannot solve the problem, the software is repaired or updated to make the software run normally; for hardware-type or unrecognizable abnormality types, maintenance personnel generally need to intervene. Therefore, the transaction is forced to be interrupted first, the amount paid by the consumer is returned, and then the abnormal alarm situation is fed back to remind maintenance personnel to intervene in time; operation abnormalities include: when there is no operation, the vending machine moves on its own; when there is someone operating, there is no response to the consumer's operation or an incorrect response.

[0092] In one embodiment, running the diagnostic module includes:

[0093] A preset response determination submodule is used to determine a preset response action according to the operation request information;

[0094] The verification submodule is used to verify whether the real-time response information matches the response action. If they match, it indicates normal operation; if they do not match, it indicates abnormal operation;

[0095] The abnormal information sending submodule is used to generate and send abnormal alarm information when an abnormal operation occurs.

[0096] The working principle and beneficial effects of the above technical solution are as follows: this solution determines the preset response action for the operation request by setting a preset response determination submodule, a verification submodule and an abnormal information sending submodule in the operation diagnosis module, verifies whether the real-time response action matches the preset response action, judges whether there is an operation abnormality based on the matching result, and sends an abnormal alarm message; the operation diagnosis of this solution not only focuses on the device itself, but also associates it with the consumer's operation, making the operation status judgment more comprehensive and accurate, and can cover the application scenarios of vending machines.

[0097] In one embodiment, a communication module is further included, and the communication module is used to connect to the network to transmit abnormal alarm information and processing result information to a set terminal device.

[0098] The working principle and beneficial effects of the above technical solution are as follows: This solution sets up a communication module, connects to the network, and transmits abnormal alarm information and processing result information to the set terminal device, so that maintenance personnel can remotely and timely obtain abnormal information of the equipment, make timely judgments and responses, maximize the maintenance of normal operation of the equipment, and facilitate consumers to purchase the required items; through remote transmission, maintenance personnel inspections can be reduced, abnormally operating equipment can be accurately located, maintenance costs can be reduced, and efficiency can be improved.

[0099] In one embodiment, a transaction interruption module is further included. The transaction interruption module is used to forcibly interrupt the transaction and return the amount paid by the consumer when receiving abnormal alarm information.

[0100] The working principle and beneficial effects of the above technical solution are as follows: This solution sets a transaction interruption module to forcibly interrupt the transaction and return the amount paid by the consumer when the vending machine has an abnormal operation, which can avoid transaction disputes and improve consumer satisfaction.

[0101] In one embodiment, it further includes a restart module and a software repair module;

[0102] The restart module is used to automatically cut off and restore power when the operating abnormality is of software type, and restore the device to default settings by restarting and initializing.

[0103] The software repair module is used to call the backup software to repair or reinstall the software when there is still a software type operation abnormality after the restart module is restarted.

[0104] The working principle and beneficial effects of the above technical solution are as follows: This solution sets up a restart module and a software repair module. For software-type operating anomalies, it uses automatic power-off restart or combined with software repair or reinstallation to automatically handle them, thereby eliminating the impact of software-type anomalies. It can further reduce the number of on-site maintenance times by maintenance personnel, save maintenance costs, and ensure that the equipment can provide transaction services for a long time.

[0105] In one embodiment, the restart module is provided with a software fault prediction model, which uses the following formula to predict the time when a software type of abnormal operation occurs:

[0106]

[0107] In the above formula, T represents the predicted time of the next software type operation abnormality; T n Indicates the last time when a software type operation anomaly occurred; n indicates the number of software type operation anomalies that have occurred; t i,i+1 represents the interval between the i-th and i+1-th software type operation anomalies; m represents the number of differences between adjacent intervals; Δt j Indicates the difference in duration between the jth adjacent intervals; Δt j+1 Indicates the difference in duration between the j+1th adjacent intervals;

[0108] The restart module selects an appropriate time period to power off and restart the device based on the predicted time of software operation anomalies, automatically cuts off and restores power, and restores the device to default settings through restart and initialization.

[0109] The working principle and beneficial effects of the above technical solution are as follows: This solution sets up a software fault prediction model, and selects a suitable time period to power off and restart the equipment according to the predicted time of software type operation anomalies, so as to prevent the occurrence of software type operation anomalies in advance and ensure the long-term effective operation of the equipment; the software fault prediction model adopted combines the trend that the interval time of software type operation anomalies occurring with long-term use of equipment software gradually shortens, thereby improving the accuracy of the prediction; on the one hand, this solution can reduce the number of regular restarts and increase the life of the equipment; on the other hand, it can reduce or even prevent the occurrence of software type operation anomalies and continuously ensure the effective operation of the equipment.

[0110] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for handling abnormalities in a vending machine, characterized in that: include: S100: The information collection module detects the operation status of the vending machine in real time, and issues an abnormal alarm message if the operation is abnormal; S200: When receiving abnormal alarm information, the corresponding abnormal alarm classification table is retrieved according to the preset alarm classification information, and the set processing solution is found through comparison; S3 00: Respond and process according to the processing plan, save abnormal alarm and processing records, and feedback processing result information; The exception handling method further includes: When the operating anomaly is software-related, the system automatically cuts off and restores power, and restores the device to default settings by restarting and initializing it. If the software still runs abnormally after restarting, use the backup software to repair or reinstall the software. In addition, a software fault prediction model is provided. The software fault prediction model uses the following formula to predict the time when a software type of abnormal operation occurs: In the above formula, Indicates the predicted time of the next occurrence of software type operation abnormality; Indicates the last time a software type operation exception occurred; Indicates the number of times a software type operation anomaly has occurred; Indicates the Second and The duration between occurrences of software-type operational anomalies; Indicates the number of differences between adjacent interval durations; Indicates the The difference in duration between adjacent intervals; Indicates the The difference in duration between adjacent intervals; Based on the predicted time of software abnormality, the system selects an appropriate time period to power off and restart the device, automatically cuts off and restores power, and restores the device to default settings through restart and initialization.

2. The abnormality handling method for a vending machine according to claim 1, characterized in that: Step S100 includes: Obtaining consumer operation request information and real-time response information of vending machines; Determine a preset response action based on the operation request information; Verify whether the real-time response information matches the response action. If they match, it indicates normal operation. If they do not match, it indicates abnormal operation.

3. The abnormality handling method for a vending machine according to claim 1, characterized in that: The information collection module detects the operation status of the vending machine in real time, including: By shooting the current image and performing image recognition, it is used to determine whether there is anyone operating the device. Collect the user's operations on the vending machine, including but not limited to product selection, payment, and pickup; Collect the internal temperature of the vending machine and the operating information of the temperature control equipment; Collect payment information; Collect the shipping response information of the vending machine, which includes but is not limited to the shipping information of the storage bin outlet, the shipping information of the cargo channel and the shipping information of the pickup cavity.

4. The abnormality handling method for a vending machine according to claim 3, characterized in that: The image recognition method for the current image is as follows: Establish a coordinate system; Pre-process each frame of the current image, and identify the consumer's hand and the hand position in each pre-processed frame; The hand position is imported into the coordinate system according to the acquisition time of each frame image, and the hand position of each frame image corresponding to the consumer is fitted and connected in the coordinate system in sequence to obtain the coordinate fitting trajectory of the hand movement; Construct a motion tracking function based on the coordinate fitting trajectory of the hand movement, calculate the derivative value of the motion tracking function of each hand position corresponding to the point in the coordinate system, and select the hand position corresponding to the point whose derivative value difference with the next adjacent point exceeds a preset threshold as the action segmentation point; Using the image frames corresponding to the action segmentation points to segment each frame in the current image, forming multiple groups of image frames; Each set of image frames and their corresponding coordinate fitting trajectory lines are analyzed to identify whether each consumer's action belongs to the operation of the vending machine.

5. The abnormality handling method for a vending machine according to claim 4, characterized in that: Preprocessing includes: Perform grayscale processing on each frame of the current image to obtain the corresponding grayscale processed image frame; Based on each pixel point in the grayscale processed image frame, the product of the R channel pixel value and the R channel weighted value, the product of the G channel pixel value and the G channel weighted value, and the product of the B channel pixel value and the B channel weighted value are added to obtain the grayscale value of the pixel point, and a Gaussian filtering algorithm is used to combine the inherent variation of the image window and the total variation of the image window to form a structure and texture decomposition regularizer for smoothing the image pixel point to obtain a smoothed image frame; The smoothed image frame is subjected to image enhancement processing, that is, supervised model training is performed on the smoothed image frame using an image enhancement model to obtain an image frame after image enhancement.

6. An abnormality handling device for a vending machine, characterized in that: include: An information collection module is used to detect the operation status of the vending machine in real time, including the consumer's operation request information and the vending machine's real-time response information; The operation diagnosis module is used to evaluate the operation of the vending machine and issue an abnormal alarm message if the operation is abnormal; The alarm classification query module, when receiving abnormal alarm information, retrieves the corresponding abnormal alarm classification table according to the preset alarm classification information; Solution determination module, which finds the set treatment solution through comparison; The response processing module responds and processes according to the processing plan and feeds back the processing result information; Storage module, saves abnormal alarm and processing records; The exception handling device also includes a restart module and a software repair module; The restart module is used to automatically cut off and restore power when the operating abnormality is of software type, and restore the device to default settings by restarting and initializing. The software repair module is used to call the backup software to repair or reinstall the software if the software type still has abnormal operation after restarting; The restart module is equipped with a software fault prediction model, which uses the following formula to predict the time when a software type of abnormal operation will occur: In the above formula, Indicates the predicted time of the next occurrence of software type operation abnormality; Indicates the last time a software type operation exception occurred; Indicates the number of times a software type operation anomaly has occurred; Indicates the Second and The duration between occurrences of software-type operational anomalies; Indicates the number of differences between adjacent interval durations; Indicates the The difference in duration between adjacent intervals; Indicates the The difference in duration between adjacent intervals; The restart module selects an appropriate time period to power off and restart the device based on the predicted time of software operation anomalies, automatically cuts off and restores power, and restores the device to default settings through restart and initialization.

7. The abnormality handling device for a vending machine according to claim 6, characterized in that: The operational diagnostics module includes: A preset response determination submodule is used to determine a preset response action according to the operation request information; The verification submodule is used to verify whether the real-time response information matches the response action. If they match, it indicates normal operation; if they do not match, it indicates abnormal operation; The abnormal information sending submodule is used to generate and send abnormal alarm information when an abnormal operation occurs.

8. The abnormality handling device for a vending machine according to claim 6, characterized in that: It also includes a communication module, which is used to connect to the network to transmit abnormal alarm information and processing result information to the set terminal device.

9. The abnormality handling device for a vending machine according to claim 6, characterized in that: It also includes a transaction interruption module, which is used to forcibly interrupt the transaction and return the money paid by the consumer when receiving abnormal alarm information.

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