Coffee making equipment management method based on Internet of Things

Abstract

The invention relates to a coffee making equipment management method based on the Internet of Things, and belongs to the technical field of coffee equipment management.The coffee making equipment management method comprises the steps that coffee order information is obtained through network platforms, interest labels of users are constructed according to the coffee order information, user portraits are constructed based on the interest labels of the users, and then a time period threshold range is set; and updating the user portrait based on the time period threshold range to obtain the user portrait features within the preset time, thereby performing making control on the coffee making equipment through the Internet of Things platform according to the user portrait features within the preset time, and finally monitoring the quality features of the coffee making equipment in the making process. And making control is carried out according to quality characteristics. By fusing the user portrait and the Internet of Things platform, the coffee making equipment can be adaptively controlled according to the preference condition of the user, so that coffee made by the coffee making equipment conforms to the hobbies and interests of the user, and the coffee drinking experience of the user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of coffee equipment management, and in particular to a coffee making equipment management method based on the Internet of Things. Background Art

[0002] With the development of society, coffee making equipment is becoming more and more popular among consumers, especially vending coffee making equipment is rapidly entering public areas such as shopping malls, convenience stores, and communities. In the new consumer era, the channels for obtaining information are diverse and market trends change rapidly, leading to an increase in the complexity of consumer groups. Enterprises face a large amount of data challenges from multiple fields. How to obtain insights into the target audience and provide support for business decisions has become a research hotspot. User portraits are widely used as a labeling tool to help companies gain a deep understanding of consumer characteristics. User portrait research covers multiple disciplines and has a wide range of application scenarios. There are currently a variety of user portrait models and construction technology methods in the consumer goods field, but because the concept of new consumption has not yet fully matured, there is a lack of sufficient empirical research on user portraits in this field. In view of the complexity and diversity of the new consumer population, it is necessary to conduct more empirical research on user portraits of emerging consumer brands to help companies grasp user needs and behavior directions and provide deeper insights and guidance for the development of consumer brands and market competition. However, due to the different needs of each user, current coffee making equipment does not have a deep integration of user portrait functions, resulting in automatic coffee vending machines, semi-automatic coffee making equipment, ordinary coffee making equipment and other types of coffee making equipment being unable to automatically control the corresponding operating parameters based on user preferences, resulting in the produced coffee not meeting the user's taste requirements, and thus the user's coffee drinking experience is poor. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a coffee making equipment management method based on the Internet of Things.

[0004] To achieve the above object, the technical solution adopted by the present invention is: The first aspect of the present invention provides a coffee making equipment management method based on the Internet of Things, specifically: Obtain coffee order information through various network platforms, construct user interest tags based on the coffee order information, and construct user portraits based on the user interest tags; Setting a time period threshold range, and updating the user portrait based on the time period threshold range to obtain user portrait features within a preset time; Controlling the coffee making equipment through the Internet of Things platform according to the user portrait characteristics within the preset time; The quality characteristics of the coffee making equipment during the making process are monitored, and the making control is performed according to the quality characteristics.

[0005] Furthermore, in the coffee making equipment management method based on the Internet of Things, a user's interest tag is constructed according to the coffee order information, and a user portrait is constructed based on the user's interest tag, specifically: constructing a user first interest tag according to the coffee order information, wherein the order information includes order quantity, order type, and coffee ingredient data; Collect users’ online review data, users’ health status data, and operating parameters of coffee making equipment; Building a second interest tag based on the user's online review data, the user's health status data, and the operating parameters of the coffee making device; A user profile is constructed based on the first interest tag and the second interest tag.

[0006] Furthermore, in the coffee making equipment management method based on the Internet of Things, a time period threshold range is set, and the user portrait is updated based on the time period threshold range to obtain user portrait features within a preset time, specifically: Construct a timestamp, set a time period threshold range based on the timestamp, obtain user profile attributes for each timestamp in the time period threshold range, and introduce a dynamic Bayesian network; The user portrait attribute of each timestamp within the time period threshold range is used as an observation vector in the dynamic Bayesian network, and the changes of the observation vectors within a preset time are observed one by one through the dynamic Bayesian network; Count the number of occurrences of each observation vector, calculate the probability value of each observation vector, set a probability threshold, and when the probability value is greater than the probability threshold, update the attribute value in the user portrait; When the probability value is not greater than the probability threshold, the current user portrait is maintained unchanged, and the user portrait features within a preset time are acquired through updating.

[0007] Furthermore, in the coffee making equipment management method based on the Internet of Things, the coffee making equipment is controlled through the Internet of Things platform according to the user portrait characteristics within the preset time, specifically: Obtaining the current coffee order information, and calculating the degree of fit between the label attribute and the user portrait feature according to the current coffee order information and the user portrait feature within a preset time, and then determining whether the degree of fit between the label attribute and the user portrait feature is greater than a preset degree of fit; When the degree of consistency between the label attribute and the user portrait feature is greater than a preset degree of consistency, and the historical order is a favorable comment, extracting the historical working parameters of the coffee making equipment from the user portrait feature; When the degree of consistency between the label attribute and the user portrait feature is greater than a preset degree of consistency, and the historical order has a bad review, the user portrait attribute with the highest praise rate is obtained from other user portraits, and the historical working parameters of the coffee making equipment are obtained from the user portrait attribute with the highest praise rate; When the degree of fit between the label attribute and the user portrait feature is not greater than the preset degree of fit, the user portrait attribute with the highest favorable comment rate is obtained from other user portraits, and the historical working parameters of the coffee making device are obtained from the user portrait attribute with the highest favorable comment rate; The coffee making equipment is controlled through the Internet of Things platform according to the historical working parameters of the coffee making equipment.

[0008] Furthermore, in the coffee making equipment management method based on the Internet of Things, the quality characteristics of the coffee making equipment during the production process are monitored, specifically: By placing sensors in the coffee making equipment, the real-time coffee component characteristic data of the coffee making equipment is obtained through the sensors, and a number of component data characteristic evaluation indicators are set; By performing noise processing and data cleaning on the real-time coffee component characteristic data of the coffee making device, the pre-processed real-time coffee component characteristic data of the coffee making device is obtained; The real-time coffee component characteristic data of the pre-processed coffee making equipment is evaluated based on the component data characteristic evaluation index to obtain the quality characteristics of the coffee making equipment during the production process.

[0009] Furthermore, in the coffee making equipment management method based on the Internet of Things, production control is performed according to the quality characteristics, specifically: Setting a quality characteristic evaluation index, and determining whether the quality characteristic of the coffee making device during the coffee making process is greater than the quality characteristic evaluation index; When the quality characteristic of the coffee making device during the making process is greater than the quality characteristic evaluation index, the coffee making device having the quality characteristic greater than the quality characteristic evaluation index is controlled by the Internet of Things platform to automatically make coffee; When the quality characteristic of the coffee making device during the making process is greater than the quality characteristic evaluation index, the coffee making device having the quality characteristic greater than the quality characteristic evaluation index is controlled by the Internet of Things platform to stop making coffee; The location information of the coffee making equipment that has stopped production is obtained, and an early warning and a maintenance reminder are issued according to the location information of the coffee making equipment that has stopped production.

[0010] The present invention solves the defects existing in the background technology and has the following beneficial effects: The present invention obtains coffee order information through various network platforms, and constructs user interest tags based on the coffee order information, constructs user portraits based on the user interest tags, and then sets a time period threshold range, and updates the user portrait based on the time period threshold range, obtains user portrait features within a preset time, and then controls the coffee making equipment through the Internet of Things platform based on the user portrait features within the preset time, and finally monitors the quality features of the coffee making equipment during the production process, and controls the production based on the quality features. By integrating user portraits and the Internet of Things platform, the present invention can adaptively control various coffee making equipment according to the user's preferences, so that the coffee made by the coffee making equipment meets the user's interests and hobbies, and improves the user's coffee drinking experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0012] Figure 1 The overall flow chart of the coffee making equipment management method based on the Internet of Things is shown; Figure 2 A system block diagram of a coffee making equipment management system based on the Internet of Things is shown. DETAILED DESCRIPTION

[0013] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0014] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0015] like Figure 1 As shown, the first aspect of the present invention provides a coffee making equipment management method based on the Internet of Things, specifically: S102: Obtain coffee order information through various network platforms, construct user interest tags based on the coffee order information, and construct a user profile based on the user interest tags; S104: Setting a time period threshold range, and updating the user portrait based on the time period threshold range to obtain user portrait features within a preset time; S106: Controlling the coffee making equipment through the Internet of Things platform according to the user portrait characteristics within a preset time; S108: Monitor the quality characteristics of the coffee making equipment during the production process and perform production control based on the quality characteristics.

[0016] It should be noted that the present invention can adaptively control various coffee making devices according to the user's preferences by integrating user portraits and the Internet of Things platform, so that the coffee made by the coffee making device meets the user's interests and hobbies, and improves the user's coffee drinking experience, wherein the coffee making device can be a device for use in scenarios such as a coffee self-service machine and a coffee home machine. Among them, the network platform includes supply and demand service platforms such as WeChat applets, mobile phone food delivery platforms, and computer food delivery platforms.

[0017] Furthermore, in the coffee making equipment management method based on the Internet of Things, user interest tags are constructed based on the coffee order information, and user portraits are constructed based on the user interest tags, specifically: Build the user's first interest tag based on coffee order information, which includes order quantity, order type, and coffee ingredient data; Collect users’ online review data, users’ health status data, and operating parameters of coffee making equipment; Building a second interest tag based on the user's online review data, the user's health status data, and the operating parameters of the coffee making equipment; A user portrait is constructed based on the first interest tag and the second interest tag.

[0018] It should be noted that the user's first interest tag includes data such as order quantity, order type, coffee ingredient data, historical orders, etc. Among them, the user's online review data includes evaluation indicators such as taste (such as taste, delicious, taste, taste), appearance and packaging (such as packaging, cute, design, appearance), freshness (such as date, freshness, shelf life), etc. The user's health status data includes disease information, disease type and other data. The operating parameters of the coffee making equipment include the production temperature, coffee bean particle size, motor speed and other operating parameters.

[0019] Furthermore, in the coffee making equipment management method based on the Internet of Things, a time period threshold range is set, and the user portrait is updated based on the time period threshold range to obtain the user portrait features within the preset time, specifically: Construct a timestamp, set a time period threshold range based on the timestamp, obtain the user profile attributes of each timestamp in the time period threshold range, and introduce a dynamic Bayesian network; The user profile attributes of each timestamp within the time period threshold range are used as observation vectors in the dynamic Bayesian network, and the changes of the observation vectors within the preset time are observed one by one through the dynamic Bayesian network; Count the number of times each observation vector appears, calculate the probability value of each observation vector, set the probability threshold, and update the attribute value in the user portrait when the probability value is greater than the probability threshold; When the probability value is not greater than the probability threshold, the current user portrait is maintained unchanged, and the user portrait features within the preset time are obtained through updating.

[0020] It should be noted that when the probability value is greater than the probability threshold, the attribute value in the user portrait is updated, and when the probability value is not greater than the probability threshold, the current user portrait is maintained unchanged. Through the update, a user portrait feature within a predetermined time is formed, so as to understand the changes in the user's label attributes within the predetermined time, thereby obtaining label attribute data that is closer to the user's needs and forming the user's favorite coffee data.

[0021] Furthermore, in the coffee making equipment management method based on the Internet of Things, the coffee making equipment is controlled through the Internet of Things platform according to the user portrait characteristics within a preset time, specifically: Obtain the current coffee order information, and calculate the degree of fit between the label attribute and the user portrait feature based on the current coffee order information and the user portrait feature within a preset time, and then determine whether the degree of fit between the label attribute and the user portrait feature is greater than the preset fit; When the degree of consistency between the label attribute and the user portrait feature is greater than a preset degree of consistency, and the historical order is a favorable comment, the historical working parameters of the coffee making equipment are extracted from the user portrait feature; When the degree of fit between the label attribute and the user portrait feature is greater than the preset degree of fit, and the historical order has a bad review, the user portrait attribute with the highest positive review rate is obtained from other user portraits, and the historical working parameters of the coffee making equipment are obtained from the user portrait attribute with the highest positive review rate; When the degree of fit between the label attribute and the user portrait feature is not greater than the preset degree of fit, the user portrait attribute with the highest favorable comment rate is obtained from other user portraits, and the historical working parameters of the coffee making equipment are obtained from the user portrait attribute with the highest favorable comment rate; The coffee making equipment is controlled through the Internet of Things platform according to the historical working parameters of the coffee making equipment.

[0022] It should be noted that, when judging whether the degree of fit between the label attribute and the user portrait feature is greater than the preset degree of fit, the Euclidean distance measurement method, the Mahalanobis distance measurement method, and the Manhattan distance measurement method can be used to judge whether the degree of fit (similarity) between the label attribute and the user portrait feature is greater than the preset degree of fit. If it is greater and the comment data in the historical order is favorable, it means that the historical working parameters of the coffee making equipment are in line with the user's taste requirements. When the degree of fit between the label attribute and the user portrait feature is greater than the preset degree of fit, and the historical order is a negative review, it does not meet the user's taste requirements. When the degree of fit between the label attribute and the user portrait feature is not greater than the preset degree of fit, it means that the user has never ordered the coffee product. Then, the user portrait attribute with the highest favorable rate is obtained from other user portraits, and the historical working parameters of the coffee making equipment are obtained from the user portrait attribute with the highest favorable rate to control the coffee making equipment.

[0023] Furthermore, in the coffee making equipment management method based on the Internet of Things, the quality characteristics of the coffee making equipment during the production process are monitored, specifically: By placing sensors in the coffee making equipment, the real-time coffee component characteristic data of the coffee making equipment is obtained through the sensors, and several component data characteristic evaluation (contaminant concentration threshold) indicators are set; By performing noise processing and data cleaning on the real-time coffee component characteristic data of the coffee making equipment, the pre-processed real-time coffee component characteristic data of the coffee making equipment is obtained; Based on the component data characteristic evaluation index, the real-time coffee component characteristic data of the pre-processed coffee making equipment is evaluated to obtain the quality characteristics of the coffee making equipment during the production process.

[0024] It should be noted that the coffee component characteristic data includes the concentration of non-pollutant chemical components, the concentration of pollutant chemical components, the type of pollutant chemical components, the type of non-pollutant chemical components, etc. By deploying sensors to detect whether there are chemical pollutants, the quality characteristics of the coffee making equipment during the production process can be further evaluated.

[0025] Furthermore, in the coffee making equipment management method based on the Internet of Things, production control is performed according to quality characteristics, specifically: Setting a quality characteristic evaluation index, and determining whether the quality characteristic of the coffee making equipment during the making process is greater than the quality characteristic evaluation index; When the quality characteristics of the coffee making equipment during the making process are greater than the quality characteristic evaluation index, the coffee making equipment with the quality characteristics greater than the quality characteristic evaluation index will be controlled by the Internet of Things platform to automatically make coffee; When the quality characteristics of the coffee making equipment during the making process are greater than the quality characteristic evaluation index, the coffee making equipment whose quality characteristics are greater than the quality characteristic evaluation index is controlled by the Internet of Things platform to stop making coffee; The location information of the coffee making equipment that has stopped production is obtained, and an early warning and maintenance reminder are issued according to the location information of the coffee making equipment that has stopped production.

[0026] It should be noted that this method can dynamically control the coffee making equipment according to the quality of the coffee, detect abnormalities in time and optimize the control of the coffee making equipment, avoid too many inferior products of coffee making equipment entering the market, and protect the health of consumers.

[0027] In addition, the method further comprises: Obtaining geographical location information of the self-service coffee machines, and constructing a self-service coffee machine map according to the geographical location information of the self-service coffee machines, and recording the number of times that the quality characteristic of each self-service coffee machine in the self-service coffee machine map is not greater than the evaluation index; Counting the number of times that the quality characteristic of each coffee self-service machine in the coffee self-service machine map is greater than the evaluation index, and calculating the probability value of the quality characteristic of the coffee self-service machine being not greater than the evaluation index within a preset time based on the number of times that the quality characteristic of the coffee self-service machine is not greater than the evaluation index and the number of times that the quality characteristic of the coffee self-service machine is greater than the evaluation index; The coffee vending machine map is represented by quality characteristics based on the probability value that the quality characteristics of the coffee vending machine are not greater than the evaluation index within the preset time, and is displayed according to the preset conditions; Selecting coffee machines whose quality characteristics are not greater than the evaluation index and whose probability values ​​are lower than the preset probability values ​​as the initial recommended coffee machines, and obtaining the geographical location information of the users in the target area; Calculate the distance from the user's location to the location of the initially recommended coffee machine by the navigation software according to the user's location information in the target area and the location information of the initially recommended coffee machine; The initially recommended coffee machine with the shortest distance value is selected as the final self-service coffee machine, and the final self-service coffee machine is used as the preferred self-service coffee machine and displayed in a preset manner.

[0028] It should be noted that this method can select excellent quality coffee self-service machines for users, further provide better choices to users, provide better quality coffee to users, and improve users' coffee drinking experience.

[0029] In addition, the method further comprises: Obtaining fault change feature data of the coffee vending machine through big data, building a coffee vending machine fault prediction model based on a Bayesian network, and inputting the fault change feature data of the coffee vending machine into the coffee vending machine fault prediction model for training; Through training, a coffee machine fault prediction model that meets expectations is obtained, and service performance change characteristic data of the preferred coffee machine within a preset time is obtained; Inputting the service performance change characteristic data of the preferred self-service coffee machine within a preset time into the coffee machine failure prediction model for prediction, obtaining the failure time of the preferred self-service coffee machine, and obtaining the time range for the current user to get coffee; When the failure time of the priority recommended coffee vending machine is within the time range when the current user needs to get coffee, a priority recommended coffee vending machine is reselected until the failure time of the priority recommended coffee vending machine is no longer within the time range when the current user needs to get coffee.

[0030] It should be noted that this method fully considers the failure time of the coffee vending machine, so as to further optimize the priority recommended coffee vending machines, avoid users' wasted trips, and improve users' purchasing experience.

[0031] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0032] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0033] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0034] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0035] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0036] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A coffee making equipment management method based on the Internet of Things, characterized in that: Specifically: Obtain coffee order information through various network platforms, construct user interest tags based on the coffee order information, and construct user portraits based on the user interest tags; Setting a time period threshold range, and updating the user portrait based on the time period threshold range to obtain user portrait features within a preset time; Controlling the coffee making equipment through the Internet of Things platform according to the user portrait characteristics within the preset time; The quality characteristics of the coffee making equipment during the making process are monitored, and the making control is performed according to the quality characteristics.

2. The coffee making equipment management method based on the Internet of Things according to claim 1, characterized in that: Constructing user interest tags according to the coffee order information, and constructing user portraits based on the user interest tags, specifically: constructing a user first interest tag according to the coffee order information, wherein the order information includes order quantity, order type, and coffee ingredient data; Collect users’ online review data, users’ health status data, and operating parameters of coffee making equipment; Building a second interest tag based on the user's online review data, the user's health status data, and the operating parameters of the coffee making device; A user profile is constructed based on the first interest tag and the second interest tag.

3. The coffee making equipment management method based on the Internet of Things according to claim 1, characterized in that: A time period threshold range is set, and the user portrait is updated based on the time period threshold range to obtain user portrait features within a preset time, specifically: Construct a timestamp, set a time period threshold range based on the timestamp, obtain user profile attributes for each timestamp in the time period threshold range, and introduce a dynamic Bayesian network; The user portrait attribute of each timestamp within the time period threshold range is used as an observation vector in the dynamic Bayesian network, and the changes of the observation vectors within a preset time are observed one by one through the dynamic Bayesian network; Count the number of occurrences of each observation vector, calculate the probability value of each observation vector, set a probability threshold, and when the probability value is greater than the probability threshold, update the attribute value in the user portrait; When the probability value is not greater than the probability threshold, the current user portrait is maintained unchanged, and the user portrait features within a preset time are acquired through updating.

4. The coffee making equipment management method based on the Internet of Things according to claim 1, characterized in that: The coffee making equipment is controlled through the Internet of Things platform according to the user portrait characteristics within the preset time, specifically: Obtaining the current coffee order information, and calculating the degree of fit between the label attribute and the user portrait feature according to the current coffee order information and the user portrait feature within a preset time, and then determining whether the degree of fit between the label attribute and the user portrait feature is greater than a preset degree of fit; When the degree of consistency between the label attribute and the user portrait feature is greater than a preset degree of consistency, and the historical order is a favorable comment, extracting the historical working parameters of the coffee making equipment from the user portrait feature; When the degree of consistency between the label attribute and the user portrait feature is greater than a preset degree of consistency, and the historical order has a bad review, the user portrait attribute with the highest praise rate is obtained from other user portraits, and the historical working parameters of the coffee making equipment are obtained from the user portrait attribute with the highest praise rate; When the degree of fit between the label attribute and the user portrait feature is not greater than the preset degree of fit, the user portrait attribute with the highest favorable comment rate is obtained from other user portraits, and the historical working parameters of the coffee making device are obtained from the user portrait attribute with the highest favorable comment rate; The coffee making equipment is controlled through the Internet of Things platform according to the historical working parameters of the coffee making equipment.

5. The coffee making equipment management method based on the Internet of Things according to claim 1, characterized in that: Monitor the quality characteristics of coffee making equipment during the production process, specifically: By placing sensors in the coffee making equipment, the real-time coffee component characteristic data of the coffee making equipment is obtained through the sensors, and a number of component data characteristic evaluation indicators are set; By performing noise processing and data cleaning on the real-time coffee component characteristic data of the coffee making device, the pre-processed real-time coffee component characteristic data of the coffee making device is obtained; The real-time coffee component characteristic data of the pre-processed coffee making equipment is evaluated based on the component data characteristic evaluation index to obtain the quality characteristics of the coffee making equipment during the production process.

6. The coffee making equipment management method based on the Internet of Things according to claim 1, characterized in that: Production control is performed according to the quality characteristics, specifically: Setting a quality characteristic evaluation index, and determining whether the quality characteristic of the coffee making device during the coffee making process is greater than the quality characteristic evaluation index; When the quality characteristic of the coffee making device during the making process is greater than the quality characteristic evaluation index, the coffee making device having the quality characteristic greater than the quality characteristic evaluation index is controlled by the Internet of Things platform to automatically make coffee; When the quality characteristic of the coffee making device during the making process is greater than the quality characteristic evaluation index, the coffee making device having the quality characteristic greater than the quality characteristic evaluation index is controlled by the Internet of Things platform to stop making coffee; The location information of the coffee making equipment that has stopped production is obtained, and an early warning and a maintenance reminder are issued according to the location information of the coffee making equipment that has stopped production.

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