A remote management system for vending machines based on the Internet of Things

Abstract

The present invention relates to the technical field of vending machine management, and specifically discloses a remote management system for vending machines based on the Internet of Things, which is used to solve the problem that frozen food is stored for a long time in existing ice cream vending machines, and frost is easily accumulated in the refrigerated area, but frequent defrosting and cleaning increase the operating cost and maintenance workload, resulting in challenges in stability and maintenance of existing ice cream vending machines. The present invention monitors the temperature parameters and environmental parameters of the refrigerated area inside the vending machine in real time through a sensor network, discretizes the refrigerated area inside the vending machine into an internal three-dimensional grid, simulates the temperature distribution of the refrigerated area inside the vending machine to obtain the temperature fluctuation range parameters of the refrigerated area inside the vending machine, and obtains the temperature fluctuation range parameters of the vending machine environment, respectively predicts the frost prediction value of the refrigerated area and calculates the frost severity of the refrigerated area inside the vending machine, thereby improving the operating efficiency, stability and user experience of the vending machine.

Description

Technical Field

[0001] The present invention relates to the technical field of vending machine management, and more specifically, to an automatic vending machine remote management system based on the Internet of Things. Background Art

[0002] Traditional ice cream sales methods usually rely on fixed stores and manual services, which have problems such as high costs and limited coverage. For this reason, ice cream vending machines came into being to provide a more convenient and efficient sales method. With the diversification of sales products, especially the increasing pursuit of people's quality of life, vending machines need to provide a more stable high and low temperature storage environment than beverage vending machines. In order to match the stable shipment requirements of special products or packaging, fast and stable intelligent control is required. However, the storage of existing ice cream requires a constant low-temperature environment. The stability and efficiency of the refrigeration system are the key to ensuring the quality of ice cream. At present, ice cream vending machines usually use compressor refrigeration technology, combined with high-efficiency thermal insulation materials, to ensure that the ice cream is always in the optimal temperature range during storage. However, ice cream needs to be stored at a specific temperature to ensure that its taste and quality are not affected. Therefore, the temperature control system must be efficient and stable. Any temperature fluctuation will cause product quality problems. In particular, maintaining the stable quality of ice cream in a high temperature environment is still a difficult problem. Since ice cream vending machines store frozen food for a long time, frost is prone to accumulate in the refrigerated area, which further affects the newly released ice cream and affects food safety. However, frequent defrosting and cleaning increase operating costs and maintenance workload, resulting in challenges in stability and maintenance of existing ice cream vending machines. In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a remote management system for automatic vending machines based on the Internet of Things, which simulates the temperature distribution of the refrigerated area inside the vending machine to obtain the temperature fluctuation range parameters of the refrigerated area inside the vending machine, and predicts the frost prediction value of the refrigerated area and calculates the severity of frost in the refrigerated area inside the vending machine, thereby improving the operating efficiency, stability and user experience of the vending machine to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A remote management system for automatic vending machines based on the Internet of Things includes a number of vending machine terminal devices, a sensor network management center, a first parameter acquisition module, a second parameter acquisition module, a refrigeration stability management module and an early warning module. The first parameter acquisition module is used to obtain temperature parameters of a refrigerated area inside the vending machine and ambient temperature parameters of the vending machine through a temperature sensor, and to obtain real-time image data of the refrigerated area inside the vending machine through a camera; the second parameter acquisition module is used to obtain standard image data when there is no frost in the refrigerated area inside the vending machine; the refrigeration stability management module includes a refrigerated space division unit, a temperature fluctuation analysis unit, and a frost prediction unit; the refrigerated space division unit is used to discretize the refrigerated area inside the vending machine into an internal three-dimensional grid, and the node spacing of the internal three-dimensional grid is , and , the time step is , the number of time steps is , and obtain the internal three-dimensional grid at x, and the number of grid points in the z direction, where the number of grid points in the x direction is , The number of grid points in the direction is , the number of grid points in the z direction is The temperature fluctuation analysis unit is used to simulate the temperature distribution of the refrigerated area inside the vending machine to obtain the temperature fluctuation range parameters of the refrigerated area inside the vending machine, and to obtain the temperature fluctuation range parameters of the vending machine environment; the frost prediction unit is used to extract the temperature fluctuation range parameters of the refrigerated area inside the vending machine and the temperature fluctuation range parameters of the environment, and import the temperature fluctuation range parameters of the refrigerated area inside the vending machine and the temperature fluctuation range parameters of the environment into the frost prediction model of the refrigerated area to predict the frost prediction value of the refrigerated area. The formula of the frost prediction model of the refrigerated area is:

[0006] ;

[0007] Where: is the frost prediction value for cold storage areas, To achieve the desired simulation duration The total number of time steps within , is the temperature fluctuation parameter of the vending machine environment at the n+1th time step, is the temperature fluctuation parameter of the vending machine environment at the nth time step, is the mean value of the temperature fluctuation parameter of the vending machine environment, is the temperature fluctuation parameter of the refrigerated area inside the vending machine at the n+1th time step, is the temperature fluctuation parameter of the refrigerated area inside the vending machine at the nth time step, is the mean value of the temperature fluctuation parameters in the refrigerated area inside the vending machine.

[0008] As a further solution of the present invention, the temperature fluctuation analysis unit includes a first temperature fluctuation analysis subunit and a second temperature fluctuation analysis subunit; the first temperature fluctuation analysis subunit is used to simulate the temperature distribution of the refrigerated area inside the vending machine to obtain the temperature fluctuation range parameters of the refrigerated area inside the vending machine, and the specific steps are:

[0009] Step 11: Set the temperature distribution of the entire refrigerated area inside the vending machine at time t=0 ,in, The position of the three-dimensional grid inside the vending machine at time t=0 is The temperature, The position of the 3D grid inside the vending machine is The initial temperature distribution function at is the initial temperature distribution function, is the index of the internal 3D grid in the x direction, used to locate the x coordinate. The index of the internal 3D grid in the y direction, used to locate the y coordinate. It is the index of the internal 3D grid in the z direction, used to locate the z coordinate;

[0010] Step 12, the heat conduction equation of the refrigerated area inside the vending machine is:

[0011] ;

[0012] Where: is the temperature, For time, is the thermal diffusion coefficient, is the rate of change of temperature with time, is the second-order partial derivative of temperature in the x-direction of the internal three-dimensional grid, is the second-order partial derivative of temperature in the y direction of the internal three-dimensional grid, is the second-order partial derivative of temperature in the z direction of the internal three-dimensional grid;

[0013] The finite difference method is used to discretize the heat conduction equation of the refrigerated area inside the vending machine, and the heat conduction discretization equation of the refrigerated area inside the vending machine is obtained:

[0014] ;

[0015] Where: The position of the 3D grid inside the vending machine at the n+1th time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is Temperature;

[0016] Step 13, the heat conduction discretization equation of the refrigerated area inside the vending machine is sorted out to obtain:

[0017] ;

[0018] Thus, the temperature fluctuation parameter of the refrigerated area inside the vending machine is obtained at the nth time step. ;

[0019] Step 14: Use the discretized heat conduction equation of the refrigerated area inside the vending machine to calculate the heat conduction equation of the refrigerated area inside the vending machine through the time step Perform iterative calculations until the required simulation time is reached , thereby obtaining the temperature fluctuation range parameters of the refrigerated area inside the vending machine within the required simulation time.

[0020] As a further solution of the present invention, the second temperature fluctuation analysis subunit is used to obtain the temperature fluctuation range parameter of the vending machine environment, and the specific steps are:

[0021] Step 21, extracting the ambient temperature parameters of the vending machine within the simulation time, wherein the initial ambient temperature parameters of the vending machine are , based on the time step Calculate the temperature fluctuation parameters of the vending machine environment:

[0022] ;

[0023] Where: is the temperature fluctuation parameter of the vending machine environment at the nth time step, is the ambient temperature parameter of the vending machine at the nth time step, is the ambient temperature parameter of the vending machine at the n+1th time step;

[0024] Step 22, by time step Iterate and calculate the temperature fluctuation parameters of the vending machine environment until the required simulation time is reached , thereby obtaining the temperature fluctuation range parameters of the vending machine environment within the required simulation time.

[0025] As a further solution of the present invention, the refrigeration stability management module also includes a frost determination unit and a refrigerated space identification unit; the frost determination unit includes a first frost determination sub-unit and a second frost determination sub-unit.

[0026] As a further solution of the present invention, the refrigerated space identification unit is used to obtain standard image data when there is no frost in the refrigerated area inside the vending machine, and to obtain real-time image data of the refrigerated area inside the vending machine within a fixed period when there is frost in the refrigerated area, and to obtain pixel value parameters of each pixel point in the real-time image data to form pixel value range parameters, and to construct a frost severity calculation formula based on the frost prediction value of the refrigerated area and the pixel value range parameters. The frost severity calculation formula of the refrigerated area inside the vending machine is:

[0027] ;

[0028] Where: is the severity of frost in the refrigerated area inside the vending machine. is the number of pixels of the real-time image data of the refrigerated area inside the vending machine, is the pixel value of the pth pixel in the real-time image data of the refrigerated area inside the vending machine, is the pixel value of the pth pixel in the standard image data when there is no frost in the refrigerated area inside the vending machine, is the maximum pixel value of each pixel in the real-time image data of the refrigerated area inside the vending machine, is the minimum pixel value of each pixel in the real-time image data of the refrigerated area inside the vending machine, is the maximum pixel value of each pixel in the standard image data when there is no frost in the refrigerated area inside the vending machine, is the minimum pixel value of each pixel in the standard image data when there is no frost in the refrigerated area inside the vending machine, Adjustment parameter to prevent the denominator from being zero.

[0029] As a further scheme of the present invention, the first frost determination subunit is used to extract a frost prediction value for the cold storage area, and compare the frost prediction value for the cold storage area with a preset frost prediction threshold for the cold storage area; if the frost prediction value for the cold storage area is greater than or equal to the preset frost prediction threshold for the cold storage area, then frost exists in the cold storage area; if the frost prediction value for the cold storage area is less than the preset frost prediction threshold for the cold storage area, then there is no frost in the cold storage area.

[0030] As a further solution of the present invention, the second frost determination subunit is used to extract the severity of frost in the refrigerated storage area inside the vending machine, and compare the severity of frost in the refrigerated storage area inside the vending machine with a preset frost severity threshold; if the severity of frost in the refrigerated storage area inside the vending machine is greater than or equal to the preset frost severity threshold, the frost in the refrigerated storage area inside the vending machine needs to be cleaned; if the severity of frost in the refrigerated storage area inside the vending machine is less than the preset frost severity threshold, the frost in the refrigerated storage area inside the vending machine does not need to be cleaned.

[0031] The technical effects and advantages of an automatic vending machine remote management system based on the Internet of Things of the present invention are as follows: the present invention monitors the temperature parameters and environmental parameters of a cold storage area inside a vending machine in real time through a sensor network, discretizes the cold storage area inside the vending machine into an internal three-dimensional grid, simulates the temperature distribution of the cold storage area inside the vending machine to obtain the temperature fluctuation range parameters of the cold storage area inside the vending machine, and obtains the temperature fluctuation range parameters of the vending machine environment, respectively predicts the frost prediction value of the cold storage area and calculates the frost severity of the cold storage area inside the vending machine, thereby improving the operating efficiency, stability and user experience of the vending machine, reducing the frequency of manual inspection and maintenance, and saving human resources and operating costs; through intelligent management and real-time monitoring, the cold storage area inside the vending machine is kept within the optimal temperature range, reducing equipment wear and failure rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a schematic diagram of the structure of an automatic vending machine remote management system based on the Internet of Things provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solution in the present invention. Obviously, the described technical solution is only a part of the present invention, not all of it. Based on the technical solution in the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0034] Figure 1 FIG. 1 shows a schematic diagram of a vending machine remote management system based on the Internet of Things provided by the first embodiment of the present invention. Figure 1 As shown, an Internet of Things-based automatic vending machine remote management system in this embodiment includes several vending machine terminal devices, a sensor network management center, a first parameter acquisition module, a second parameter acquisition module, a refrigeration stability management module and an early warning module;

[0035] A number of vending machine terminal devices are connected to the sensor network management center, the sensor network management center is connected to the first parameter acquisition module, the first parameter acquisition module and the second parameter acquisition module are respectively connected to the refrigeration stability management module, and the refrigeration stability management module is connected to the early warning module;

[0036] The vending machine terminal device is used to interact with the user and provide automatic vending services for ice cream products;

[0037] The sensor network management center is used to monitor the temperature parameters and environmental parameters of the refrigeration area inside the vending machine in real time through the sensor network, and store the internal parameters and environmental parameters of the vending machine;

[0038] The first parameter acquisition module is used to obtain the temperature parameters of the refrigerated area inside the vending machine and the ambient temperature parameters of the vending machine through the temperature sensor, and to obtain the real-time image data of the refrigerated area inside the vending machine through the camera;

[0039] The second parameter acquisition module is used to acquire standard image data when there is no frost in the refrigerated area inside the vending machine;

[0040] The refrigeration stability management module is used to analyze the temperature fluctuations in the refrigerated area inside the vending machine and make frost predictions;

[0041] The early warning module is used to issue an alarm response when frost exists in the refrigerated area of ​​the vending machine and the frost in the refrigerated area inside the vending machine needs to be cleared.

[0042] The refrigeration stability management module includes a cold storage space division unit, a temperature fluctuation analysis unit, a frost prediction unit, a frost determination unit and a cold storage space identification unit; the cold storage space division unit is connected to the temperature fluctuation analysis unit, the temperature fluctuation analysis unit is connected to the frost prediction unit, the frost prediction unit and the cold storage space identification unit are respectively connected to the frost determination unit;

[0043] The temperature fluctuation analysis unit includes a first temperature fluctuation analysis subunit and a second temperature fluctuation analysis subunit;

[0044] The frost determination unit includes a first frost determination sub-unit and a second frost determination sub-unit.

[0045] The refrigerated space division unit is used to discretize the refrigerated area inside the vending machine into an internal three-dimensional grid. The node spacing of the internal three-dimensional grid is , and , the time step is , the number of time steps is , and obtain the internal three-dimensional grid at x, and the number of grid points in the z direction, where the number of grid points in the x direction is , The number of grid points in the direction is , the number of grid points in the z direction is ;

[0046] The temperature fluctuation analysis unit is used to simulate the temperature distribution of the refrigerated area inside the vending machine to obtain the temperature fluctuation range parameters of the refrigerated area inside the vending machine, and to obtain the temperature fluctuation range parameters of the vending machine environment;

[0047] The frost prediction unit is used to extract the temperature fluctuation range parameters of the refrigerated area inside the vending machine and the temperature fluctuation range parameters of the environment, and import the temperature fluctuation range parameters of the refrigerated area inside the vending machine and the temperature fluctuation range parameters of the environment into the frost prediction model of the refrigerated area to predict the frost prediction value of the refrigerated area. The formula of the frost prediction model of the refrigerated area is:

[0048] ;

[0049] Where: is the frost prediction value for cold storage areas, To achieve the desired simulation duration The total number of time steps within , is the temperature fluctuation parameter of the vending machine environment at the n+1th time step, is the temperature fluctuation parameter of the vending machine environment at the nth time step, is the mean value of the temperature fluctuation parameter of the vending machine environment, is the temperature fluctuation parameter of the refrigerated area inside the vending machine at the n+1th time step, is the temperature fluctuation parameter of the refrigerated area inside the vending machine at the nth time step, is the mean value of the temperature fluctuation parameters in the refrigerated area inside the vending machine.

[0050] The frost determination unit is used to determine whether there is frost in the refrigerated area of ​​the vending machine and whether the frost in the refrigerated area inside the vending machine needs to be cleaned when there is frost in the refrigerated area;

[0051] The refrigerated space identification unit is used to obtain standard image data when there is no frost in the refrigerated area inside the vending machine, and to obtain real-time image data of the refrigerated area inside the vending machine within a fixed period when there is frost in the refrigerated area, and to obtain pixel value parameters of each pixel point in the real-time image data to form pixel value range parameters. A frost severity calculation formula is constructed based on the frost prediction value of the refrigerated area and the pixel value range parameters. The frost severity calculation formula of the refrigerated area inside the vending machine is:

[0052] ;

[0053] Where: is the severity of frost in the refrigerated area inside the vending machine. is the number of pixels of the real-time image data of the refrigerated area inside the vending machine, is the pixel value of the pth pixel in the real-time image data of the refrigerated area inside the vending machine, is the pixel value of the pth pixel in the standard image data when there is no frost in the refrigerated area inside the vending machine, is the maximum pixel value of each pixel in the real-time image data of the refrigerated area inside the vending machine, is the minimum pixel value of each pixel in the real-time image data of the refrigerated area inside the vending machine, is the maximum pixel value of each pixel in the standard image data when there is no frost in the refrigerated area inside the vending machine, is the minimum pixel value of each pixel in the standard image data when there is no frost in the refrigerated area inside the vending machine, Adjustment parameter to prevent the denominator from being zero.

[0054] It should be noted that the standard image data when there is no frost in the refrigerated area inside the vending machine is the same as the number of pixel points of the real-time image data of the refrigerated area inside the vending machine extracted within a fixed period when there is frost in the refrigerated area; if there are different situations, the standard image data when there is no frost in the refrigerated area inside the vending machine is adjusted through cropping, scaling and other methods to ensure that the standard image data when there is no frost in the refrigerated area inside the vending machine is the same as the number of pixel points of the real-time image data of the refrigerated area inside the vending machine extracted within a fixed period when there is frost in the refrigerated area.

[0055] The internal cold storage area is discretized into a three-dimensional grid through the cold storage space division unit to achieve accurate simulation and monitoring of temperature distribution. The temperature fluctuation analysis unit obtains and analyzes the temperature fluctuation range parameters in real time to ensure that the cold storage area is maintained within the optimal temperature range. Through intelligent prediction and automatic identification of frost, frequent defrosting and cleaning work is reduced, the labor maintenance cost and equipment operation cost are reduced, the defrosting time is automatically optimized, the power consumption is reduced, and the energy utilization efficiency is improved. The damage to the equipment caused by frost accumulation is reduced, the equipment failure rate is reduced, and the service life of the vending machine is extended. The reduction in maintenance workload also reduces the wear and tear caused by frequent maintenance. The cold storage space identification unit constructs a frost severity calculation formula by comparing standard image data and real-time image data, accurately calculates the frost severity, and improves the accuracy and reliability of frost identification. The Internet of Things technology and intelligent algorithms are used to achieve accurate management and optimization of the cold storage area of ​​the vending machine, which greatly improves the operating efficiency, stability and user experience of the equipment, while reducing operating costs and maintenance workload.

[0056] The first temperature fluctuation analysis subunit is used to simulate the temperature distribution of the refrigerated area inside the vending machine to obtain the temperature fluctuation range parameters of the refrigerated area inside the vending machine. The specific steps are:

[0057] Step 11: Set the temperature distribution of the entire refrigerated area inside the vending machine at time t=0 ,in, The position of the three-dimensional grid inside the vending machine at time t=0 is The temperature, The position of the 3D grid inside the vending machine is The initial temperature distribution function at is the initial temperature distribution function, is the index of the internal 3D grid in the x direction, used to locate the x coordinate. The index of the internal 3D grid in the y direction, used to locate the y coordinate. It is the index of the internal 3D grid in the z direction, used to locate the z coordinate;

[0058] Step 12, the heat conduction equation of the refrigerated area inside the vending machine is:

[0059] ;

[0060] Where: is the temperature, For time, is the thermal diffusion coefficient, is the rate of change of temperature with time, is the second-order partial derivative of temperature in the x-direction of the internal three-dimensional grid, is the second-order partial derivative of temperature in the y direction of the internal three-dimensional grid, is the second-order partial derivative of temperature in the z direction of the internal three-dimensional grid;

[0061] The finite difference method is used to discretize the heat conduction equation of the refrigerated area inside the vending machine, and the heat conduction discretization equation of the refrigerated area inside the vending machine is obtained:

[0062] ;

[0063] Where: The position of the 3D grid inside the vending machine at the n+1th time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is Temperature;

[0064] Step 13, the heat conduction discretization equation of the refrigerated area inside the vending machine is sorted out to obtain:

[0065] ;

[0066] Thus, the temperature fluctuation parameter of the refrigerated area inside the vending machine is obtained at the nth time step. ;

[0067] Step 14: Use the discretized heat conduction equation of the refrigerated area inside the vending machine to calculate the heat conduction equation of the refrigerated area inside the vending machine through the time step Perform iterative calculations until the required simulation time is reached , thereby obtaining the temperature fluctuation range parameters of the refrigerated area inside the vending machine within the required simulation time.

[0068] By setting the initial temperature distribution and heat conduction equation, the system can accurately simulate the temperature distribution of the refrigerated area inside the vending machine, discretize the heat conduction equation using the finite difference method to ensure the accuracy and precision of the calculation; obtain the temperature changes of the three-dimensional grid inside the vending machine in real time, dynamically monitor the temperature fluctuations, and

[0069] Through iterative calculations, the system can continuously monitor temperature fluctuations and provide real-time data support. Through the temperature fluctuation range parameters, it can predict frost conditions in advance to avoid the impact of frost on the vending machine. Through accurate frost prediction, it can determine whether defrosting operations are needed, thereby reducing unnecessary defrosting times, which not only reduces power consumption but also reduces maintenance costs. Through continuous monitoring and optimized management, it reduces the damage of frost to the internal components of the vending machine and extends the service life of the equipment. Through precise control of temperature fluctuations in the refrigerated area, the system can effectively save energy, improve energy utilization efficiency, reduce unnecessary cooling and heating operations, and reduce overall energy consumption. Through accurate temperature distribution simulation and real-time dynamic monitoring, effective management of the refrigerated area inside the vending machine is achieved, which greatly improves the operating efficiency, stability and user experience of the vending machine, while reducing maintenance costs and energy consumption.

[0070] The second temperature fluctuation analysis subunit is used to obtain the temperature fluctuation range parameter of the vending machine environment, and the specific steps are as follows:

[0071] Step 21, extracting the ambient temperature parameters of the vending machine within the simulation time, wherein the initial ambient temperature parameters of the vending machine are , based on the time step Calculate the temperature fluctuation parameters of the vending machine environment:

[0072] ;

[0073] Where: is the temperature fluctuation parameter of the vending machine environment at the nth time step, is the ambient temperature parameter of the vending machine at the nth time step, is the ambient temperature parameter of the vending machine at the n+1th time step;

[0074] Step 22, by time step Iterate and calculate the temperature fluctuation parameters of the vending machine environment until the required simulation time is reached , thereby obtaining the temperature fluctuation range parameters of the vending machine environment within the required simulation time.

[0075] Extracting and analyzing the ambient temperature parameters of the vending machine enables the system to fully understand the impact of the external environment on the refrigerated area. Through the temperature fluctuation parameters, the system can identify the laws of ambient temperature changes and prevent possible negative impacts in advance. Incorporating ambient temperature fluctuation parameters into the frost prediction model improves the accuracy of frost prediction. By combining internal and external temperature data, the system can more accurately predict the timing and severity of frost. According to the ambient temperature fluctuations, the operating parameters of the refrigeration system are dynamically adjusted to optimize energy use, reduce unnecessary energy consumption caused by ambient temperature fluctuations, and improve energy utilization efficiency. The operating strategy is automatically adjusted according to the changes in ambient temperature to improve the adaptability of the vending machine in different environments. It can comprehensively monitor and analyze the temperature fluctuations in the environment where the vending machine is located, optimize equipment operation and maintenance strategies, improve equipment operation efficiency and stability, reduce energy consumption and maintenance costs, extend equipment life, and at the same time improve user experience and satisfaction.

[0076] The first frost discrimination subunit is used to extract the frost prediction value of the cold storage area, compare the frost prediction value of the cold storage area with the preset frost prediction threshold of the cold storage area, if the frost prediction value of the cold storage area is greater than or equal to the preset frost prediction threshold of the cold storage area, then there is frost in the cold storage area; if the frost prediction value of the cold storage area is less than the preset frost prediction threshold of the cold storage area, then there is no frost in the cold storage area;

[0077] The second frost determination subunit is used to extract the severity of frost in the refrigerated area inside the vending machine, and compare the severity of frost in the refrigerated area inside the vending machine with a preset frost severity threshold. If the severity of frost in the refrigerated area inside the vending machine is greater than or equal to the preset frost severity threshold, the frost in the refrigerated area inside the vending machine needs to be cleared; if the severity of frost in the refrigerated area inside the vending machine is less than the preset frost severity threshold, the frost in the refrigerated area inside the vending machine does not need to be cleared.

[0078] The embodiment of the present invention monitors the temperature parameters and environmental parameters of the internal refrigerated area of ​​the vending machine in real time through a sensor network, discretizes the internal refrigerated area of ​​the vending machine into an internal three-dimensional grid, simulates the temperature distribution of the internal refrigerated area of ​​the vending machine to obtain the temperature fluctuation range parameters of the internal refrigerated area of ​​the vending machine, and obtains the temperature fluctuation range parameters of the vending machine environment, and respectively predicts the frost prediction value of the refrigerated area and calculates the frost severity of the internal refrigerated area of ​​the vending machine, thereby improving the operating efficiency, stability and user experience of the vending machine, reducing the frequency of manual inspection and maintenance, and saving human resources and operating costs; through intelligent management and real-time monitoring, the internal refrigerated area of ​​the vending machine is maintained within the optimal temperature range, reducing equipment wear and failure rate.

[0079] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0080] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A remote management system for automatic vending machines based on the Internet of Things, comprising a number of vending machine terminal devices, a sensor network management center, a first parameter acquisition module, a second parameter acquisition module, a refrigeration stability management module and an early warning module, characterized in that: The first parameter acquisition module is used to obtain the temperature parameters of the refrigerated area inside the vending machine and the ambient temperature parameters of the vending machine through the temperature sensor, and obtain the real-time image data of the refrigerated area inside the vending machine through the camera; the second parameter acquisition module is used to obtain the standard image data when there is no frost in the refrigerated area inside the vending machine; the refrigeration stability management module includes a refrigerated space division unit, a temperature fluctuation analysis unit, and a frost prediction unit; the refrigerated space division unit is used to discretize the refrigerated area inside the vending machine into an internal three-dimensional grid, and the internal three-dimensional grid node spacing is , and , the time step is , the number of time steps is , and obtain the internal three-dimensional grid at x, and the number of grid points in the z direction, where the number of grid points in the x direction is , The number of grid points in the direction is , the number of grid points in the z direction is The temperature fluctuation analysis unit is used to simulate the temperature distribution of the refrigerated area inside the vending machine to obtain the temperature fluctuation range parameters of the refrigerated area inside the vending machine, and to obtain the temperature fluctuation range parameters of the vending machine environment; the frost prediction unit is used to extract the temperature fluctuation range parameters of the refrigerated area inside the vending machine and the temperature fluctuation range parameters of the environment, and import the temperature fluctuation range parameters of the refrigerated area inside the vending machine and the temperature fluctuation range parameters of the environment into the frost prediction model of the refrigerated area to predict the frost prediction value of the refrigerated area. The formula of the frost prediction model of the refrigerated area is: ； Where: is the frost prediction value for cold storage areas, To achieve the desired simulation duration The total number of time steps within , is the temperature fluctuation parameter of the vending machine environment at the n+1th time step, is the temperature fluctuation parameter of the vending machine environment at the nth time step, is the mean value of the temperature fluctuation parameter of the vending machine environment, is the temperature fluctuation parameter of the refrigerated area inside the vending machine at the n+1th time step, is the temperature fluctuation parameter of the refrigerated area inside the vending machine at the nth time step, is the mean value of the temperature fluctuation parameter of the refrigerated area inside the vending machine; The temperature fluctuation analysis unit includes a first temperature fluctuation analysis subunit and a second temperature fluctuation analysis subunit; the first temperature fluctuation analysis subunit is used to simulate the temperature distribution of the refrigerated area inside the vending machine to obtain the temperature fluctuation range parameters of the refrigerated area inside the vending machine, and the specific steps are: Step 11: Set the temperature distribution of the entire refrigerated area inside the vending machine at time t=0 ,in, The position of the three-dimensional grid inside the vending machine at time t=0 is The temperature, The position of the 3D grid inside the vending machine is The initial temperature distribution function at is the initial temperature distribution function, is the index of the internal 3D grid in the x direction, used to locate the x coordinate. The index of the internal 3D grid in the y direction, used to locate the y coordinate. It is the index of the internal 3D grid in the z direction, used to locate the z coordinate; Step 12, the heat conduction equation of the refrigerated area inside the vending machine is: ； Where: is the temperature, For time, is the thermal diffusion coefficient, is the rate of change of temperature with time, is the second-order partial derivative of temperature in the x-direction of the internal three-dimensional grid, is the second-order partial derivative of temperature in the y direction of the internal three-dimensional grid, is the second-order partial derivative of temperature in the z direction of the internal three-dimensional grid; The finite difference method is used to discretize the heat conduction equation of the refrigerated area inside the vending machine, and the heat conduction discretization equation of the refrigerated area inside the vending machine is obtained: ； Where: The position of the 3D grid inside the vending machine at the n+1th time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is The temperature, The position of the 3D grid inside the vending machine at the nth time step is Temperature; Step 13, the heat conduction discretization equation of the refrigerated area inside the vending machine is sorted out to obtain: ； Thus, the temperature fluctuation parameter of the refrigerated area inside the vending machine is obtained at the nth time step. ; Step 14: Use the discretized heat conduction equation of the refrigerated area inside the vending machine to calculate the heat conduction equation of the refrigerated area inside the vending machine through the time step Perform iterative calculations until the required simulation time is reached , thereby obtaining the temperature fluctuation range parameters of the refrigerated area inside the vending machine within the required simulation time.

2. According to the Internet of Things-based vending machine remote management system according to claim 1, it is characterized in that: The second temperature fluctuation analysis subunit is used to obtain the temperature fluctuation range parameter of the vending machine environment, and the specific steps are as follows: Step 21, extracting the ambient temperature parameters of the vending machine within the simulation time, wherein the initial ambient temperature parameters of the vending machine are , based on the time step Calculate the temperature fluctuation parameters of the vending machine environment: ； Where: is the temperature fluctuation parameter of the vending machine environment at the nth time step, is the ambient temperature parameter of the vending machine at the nth time step, is the ambient temperature parameter of the vending machine at the n+1th time step; Step 22, by time step Iterate and calculate the temperature fluctuation parameters of the vending machine environment until the required simulation time is reached , thereby obtaining the temperature fluctuation range parameters of the vending machine environment within the required simulation time.

3. According to the Internet of Things-based vending machine remote management system of claim 1, it is characterized in that: The refrigeration stability management module also includes a frost determination unit and a refrigerated space identification unit; the frost determination unit includes a first frost determination sub-unit and a second frost determination sub-unit.

4. According to the Internet of Things-based vending machine remote management system of claim 3, it is characterized in that: The refrigerated space identification unit is used to obtain standard image data when there is no frost in the refrigerated area inside the vending machine, and to obtain real-time image data of the refrigerated area inside the vending machine within a fixed period when there is frost in the refrigerated area, and to obtain pixel value parameters of each pixel point in the real-time image data to form pixel value range parameters. A frost severity calculation formula is constructed based on the frost prediction value of the refrigerated area and the pixel value range parameters. The frost severity calculation formula of the refrigerated area inside the vending machine is: ； Where: is the severity of frost in the refrigerated area inside the vending machine. is the number of pixels of the real-time image data of the refrigerated area inside the vending machine, is the pixel value of the pth pixel in the real-time image data of the refrigerated area inside the vending machine, is the pixel value of the pth pixel in the standard image data when there is no frost in the refrigerated area inside the vending machine, is the maximum pixel value of each pixel in the real-time image data of the refrigerated area inside the vending machine, is the minimum pixel value of each pixel in the real-time image data of the refrigerated area inside the vending machine, is the maximum pixel value of each pixel in the standard image data when there is no frost in the refrigerated area inside the vending machine, is the minimum pixel value of each pixel in the standard image data when there is no frost in the refrigerated area inside the vending machine, Adjustment parameter to prevent the denominator from being zero.

5. According to the Internet of Things-based vending machine remote management system of claim 3, it is characterized in that: The first frost determination subunit is used to extract a frost prediction value for the cold storage area, and compare the frost prediction value for the cold storage area with a preset frost prediction threshold for the cold storage area. If the frost prediction value for the cold storage area is greater than or equal to the preset frost prediction threshold for the cold storage area, frost exists in the cold storage area; if the frost prediction value for the cold storage area is less than the preset frost prediction threshold for the cold storage area, no frost exists in the cold storage area.

6. According to the Internet of Things-based vending machine remote management system of claim 3, it is characterized in that: The second frost determination subunit is used to extract the severity of frost in the refrigerated area inside the vending machine, and compare the severity of frost in the refrigerated area inside the vending machine with a preset frost severity threshold. If the severity of frost in the refrigerated area inside the vending machine is greater than or equal to the preset frost severity threshold, the frost in the refrigerated area inside the vending machine needs to be cleared; if the severity of frost in the refrigerated area inside the vending machine is less than the preset frost severity threshold, the frost in the refrigerated area inside the vending machine does not need to be cleared.

Images