A coffee bean grinder control system and method

Abstract

The invention discloses a control system and method for a coffee bean grinder, belonging to the technical field of coffee bean state monitoring, wherein the system comprises: a user interaction module; a bean bin state module, used to obtain bean bin environment data and coffee bean weight through a sensor; a bean bin early warning module; a model building module, used to obtain storage environment data and coffee bean weight within a period of time before reaching a deterioration threshold, and to construct a regression tree based on coffee bean types, bean bin specification information, storage environment data and coffee bean weight, to obtain a coffee bean deterioration prediction model with coffee bean types, bean bin environment data and coffee bean weight within each time interval as input and predicted deterioration time as output; a deterioration early warning module; and a use suggestion module, used to obtain the daily recommended number of coffee brewing times and the weight of coffee beans used for each coffee brewing based on the current coffee bean weight and the predicted deterioration time of the coffee beans. The invention can detect the state of coffee beans in the bean bin and issue an early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of coffee bean status monitoring, and in particular to a coffee bean grinder control system and method. Background Art

[0002] The shelf life of coffee beans depends on many factors, including storage conditions, type of coffee beans, degree of roasting, and packaging method. Generally speaking, coffee beans can be stored at room temperature for about two years, but the shelf life may be shortened in high humidity, high temperature or low temperature environments. When using a coffee bean grinder, since most bean silos are light-transmitting and the bean silos are poorly sealed when open or closed, the coffee beans stored in the bean silo of the coffee bean grinder are affected by light, temperature, humidity and oxygen, which may accelerate deterioration. After contacting oxygen, coffee beans will gradually undergo oxidation reactions, causing the taste of coffee beans to deteriorate, and the appearance of sourness and bitterness, which seriously affects the drinking experience; coffee beans are prone to dehydration and deterioration in high temperature environments, resulting in a decrease in taste; coffee beans are prone to mold in high humidity environments, resulting in a deterioration in taste or a decrease in taste. It would be very troublesome to open the bean silo to check whether the coffee beans have deteriorated every time you make coffee, and the judgment may be inaccurate.

[0003] Therefore, there is a need for a coffee bean grinder control system that can detect the state of coffee beans. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a coffee bean grinder control system and method, which can detect the state of coffee beans and issue early warning information.

[0005] On the one hand, an embodiment of the present invention provides a control system for a coffee bean grinder, comprising: a user interaction module, for displaying status information of the coffee bean grinder and obtaining operation instructions of the coffee bean grinder; the status information comprises bean bin status warning information and predicted deterioration time of coffee beans; a bean bin status module, for obtaining bean bin environment data and coffee bean weight through sensors; the bean bin environment data comprises gas composition data, light intensity, temperature and humidity; a bean bin warning module, for determining whether the temperature and humidity, light intensity data and oxygen concentration in the bean bin after the bean bin is closed for a period of time exceed threshold values, and if so, issuing bean bin status warning information; a model building module, for obtaining storage environment data and coffee bean weight of coffee bean piles of different types and weights stored in bean bins of different specifications under different storage environments based on a certain time interval, and determining the deterioration degree of the coffee bean piles; obtaining the deterioration degree reached The storage environment data and the weight of the coffee beans in the coffee bean pile within a period of time before the threshold are used to construct a regression tree based on the coffee bean type, bean silo specification information, the storage environment data, the coffee bean weight and the deterioration occurrence time to obtain a coffee bean deterioration prediction model; the storage environment data includes gas composition data, light intensity, temperature and humidity; the coffee bean deterioration prediction model takes the coffee bean type, bean silo specification information, the bean silo environment data and the coffee bean weight in each time interval as input, and takes the predicted deterioration time as output; a deterioration early warning module is used to obtain the coffee bean type, bean silo environment data and coffee bean weight in the current bean silo based on operation instructions and preset time intervals, and obtain the predicted deterioration time of the coffee beans based on the coffee bean deterioration prediction model; a usage suggestion module is used to obtain the recommended daily number of coffee brewing times and the weight of coffee beans used for each coffee brewing based on the current coffee bean weight and the predicted deterioration time of the coffee beans.

[0006] According to some embodiments of the present invention, the model building module includes: a sample data acquisition unit, which is used to set bean silo devices with different specifications, different degrees of airtightness, different temperatures and humidity, and different light intensities, the bean silo devices store coffee beans of different types and weights, obtain the storage environment data and the weight of the coffee beans in the bean silo device based on a preset time interval, and sample a certain number of coffee beans to detect whether they are deteriorated through a coffee bean deterioration detection model unit to obtain the deterioration degree of the coffee beans; the deterioration degree is the ratio of the number of deteriorated coffee beans to the number of sampled coffee beans; the bean silo specifications include shape and silo cover diameter; a coffee bean deterioration detection unit, which is used to obtain an image of a single coffee bean through a camera device on an operating table with a preset color, obtain a coffee bean image by removing the preset color and edge contour recognition, obtain a segmented image by threshold segmentation, and determine whether the coffee beans are deteriorated by whether the number of pixels in the segmented image is greater than a threshold.

[0007] According to some embodiments of the present invention, the model building module includes: a sample data preprocessing unit, which is used to preprocess the collected gas composition data, coffee bean weight data, light intensity and temperature and humidity, and corresponding time points, including data cleaning, denoising and normalization operations, and numbering the coffee bean types and bean silo specification information.

[0008] According to some embodiments of the present invention, the model building module includes: a sample data construction unit, which is used to construct a sample data set based on the coffee bean type, bean silo specification information, gas composition data at several time points, coffee bean weight, light intensity, temperature and humidity, and deterioration time, and the sample data set includes several features that affect the prediction results and the predicted deterioration time; wherein the gas composition data includes nitrogen, oxygen, and odor components collected from normal or deteriorated coffee beans; a feature selection unit, which is used to measure the feature importance of the features that affect the prediction results and the predicted deterioration time based on the feature selection method of random forest, obtain the correlation between each feature and the predicted deterioration time, and obtain a second sample data set based on the correlation, and the second sample data set is used to create a regression tree; a data regression prediction unit, which is used to create a regression tree based on the second sample data set to obtain a regression prediction model.

[0009] According to some embodiments of the present invention, the bean silo status module includes: a bean silo gas sensing unit, used to collect gas composition data of coffee beans in the bean silo through an electronic nose; a bean silo weight weighing unit, used to collect weight data of coffee beans in the bean silo; a bean silo temperature and humidity sensing unit, used to collect temperature and humidity data in the bean silo; and a bean silo light intensity sensing unit, used to collect light intensity data in the bean silo.

[0010] According to some embodiments of the present invention, the bean silo status module includes: a bean silo opening sensing unit, used to obtain a bean silo opening and closing signal through a sensor; a weight data resetting unit, used to reset the weight data of coffee beans in the bean silo after obtaining a bean silo opening or closing signal, or after a certain weight of coffee beans is fed into the silo for grinding according to an operating instruction.

[0011] According to some embodiments of the present invention, the usage suggestion module includes: a historical information analysis unit, used to obtain user usage history records, the user usage history records include usage time periods and corresponding coffee bean usage amounts, the user usage history records are classified according to the usage time periods and the corresponding coffee bean usage amounts, and a number of commonly used time periods and corresponding commonly used coffee bean usage amounts are obtained according to the number of user usage history records in each category; a personalized interaction unit, used to generate personalized customization information according to the current coffee bean weight and the predicted deterioration time of the coffee beans, the personalized customization information includes: the number of times coffee is brewed per day, the time period for brewing coffee and the amount of coffee beans used each time.

[0012] According to some embodiments of the present invention, the user interaction module includes: an early warning prompt unit, which is used to issue corresponding early warning information according to the bean silo status early warning information and the predicted deterioration time of the coffee beans, and the early warning information includes sound prompt information or light prompt information.

[0013] According to some embodiments of the present invention, the system includes: a bean grinding operation module, which is used to feed a certain weight of coffee beans for grinding operation based on operation instructions and a weighing unit arranged in a bean bin; and a coffee brewing module, which is used to brew the coffee powder obtained after the grinding operation is completed.

[0014] A coffee bean grinder control system according to an embodiment of the present invention includes at least the following beneficial effects: the embodiment of the present invention displays the bean bin status warning information and the predicted deterioration time of coffee beans through the user interaction interface, realizes the state monitoring of the bean bin and the prediction of the use time of coffee beans, and avoids the user from being unable to use up the coffee beans in time before the coffee beans deteriorate, and using deteriorated coffee beans affects the taste or wastes the coffee beans. By obtaining the coffee bean type and bean bin specification information, collecting the coffee bean weight, gas composition data, light intensity, temperature and humidity of the coffee beans before and after a period of time, constructing a regression tree, obtaining a coffee bean deterioration prediction model with the coffee bean type, bean bin specification information, bean bin environmental data and coffee bean weight in each time interval as input, and predicted deterioration time as output, obtaining the predicted deterioration time of coffee beans according to the coffee bean deterioration prediction model, and realizing the prediction of the use time of coffee beans; and obtaining the recommended number of coffee brewing times per day and the weight of coffee beans used for each coffee brewing through the current coffee bean weight and the predicted deterioration time of coffee beans, so as to facilitate the user to use up the coffee beans in the bean bin in time to avoid deterioration and waste.

[0015] On the other hand, an embodiment of the present invention provides a method for controlling a coffee bean grinder, comprising the following steps: S100, displaying status information of the coffee bean grinder and obtaining operation instructions of the coffee bean grinder; the status information includes bean bin status warning information and predicted deterioration time of coffee beans; S200, obtaining bean bin environment data and coffee bean weight through sensors; the bean bin environment data includes gas composition data, light intensity, temperature and humidity; S300, determining whether the temperature and humidity, light intensity data and oxygen concentration in the bean bin after the bean bin is closed for a period of time exceed a threshold value, and if so, issuing a bean bin status warning information; S400, obtaining storage environment data of coffee bean piles of different types and weights stored in bean bins of different specifications under different storage environments based on a certain time interval The method comprises the following steps: step S500, obtaining the type of coffee beans, the bean silo environment data and the weight of coffee beans in the current bean silo based on the operation instructions and the preset time interval, and obtaining the predicted deterioration time of the coffee beans based on the coffee bean deterioration prediction model; step S600, obtaining the recommended number of coffee brewing times per day and the weight of coffee beans used for each coffee brewing based on the current weight of coffee beans and the predicted deterioration time of coffee beans.

[0016] A coffee bean grinder control method according to an embodiment of the present invention includes at least the following beneficial effects: the embodiment of the present invention displays the bean bin status warning information and the predicted deterioration time of coffee beans through the user interaction interface, realizes the state monitoring of the bean bin and the prediction of the use time of coffee beans, and avoids the user from not being able to use up the coffee beans in time before the coffee beans deteriorate, and using deteriorated coffee beans affects the taste or wastes the coffee beans. By obtaining the coffee bean type and bean bin specification information, collecting the coffee bean weight, gas composition data, light intensity, temperature and humidity of the coffee beans before and after a period of time, constructing a regression tree, obtaining a coffee bean deterioration prediction model with the coffee bean type, bean bin specification information, bean bin environmental data and coffee bean weight in each time interval as input, and predicted deterioration time as output, obtaining the predicted deterioration time of coffee beans according to the coffee bean deterioration prediction model, and realizing the prediction of the use time of coffee beans; and obtaining the recommended number of coffee brewing times per day and the weight of coffee beans used for each coffee brewing through the current coffee bean weight and the predicted deterioration time of coffee beans, so as to facilitate the user to use up the coffee beans in the bean bin in time to avoid deterioration and waste.

[0017] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0019] Figure 1 A schematic block diagram of modules of a system according to an embodiment of the present invention;

[0020] Figure 2 The figure is a flowchart of a method according to an embodiment of the present invention.

[0021] Reference numerals:

[0022] User interaction module 100, bean silo status module 200, bean silo warning module 300, model building module 400, deterioration warning module 500, usage suggestion module 600. DETAILED DESCRIPTION

[0023] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0024] In the description of the present invention, "several" means one or more, "more" means two or more, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0025] Coffee is a popular and beloved drink. However, coffee beans also have the risk of deterioration. If you do not pay attention to the preservation of coffee beans, it may cause problems such as taste degradation, rancidity, oxidation, etc. 1. Oxygen in the air can cause coffee beans to deteriorate: After coffee beans are exposed to oxygen, they will gradually undergo oxidation reactions, causing the taste of coffee beans to deteriorate, and the appearance of rancidity and bitterness, which seriously affects the drinking experience. Therefore, when storing coffee beans, it is best to store them in an oxygen-free environment, such as vacuum bags or sealed cans. In addition, the shelf life of coffee beans should also be shortened as much as possible, and they should be consumed as soon as possible to ensure the freshness of the taste. 2. High temperature will affect the taste of coffee beans: Coffee beans are prone to dehydration and deterioration in a high temperature environment, resulting in a decrease in taste. Therefore, it should be avoided as much as possible in high temperature places, such as balconies and ovens where direct sunlight is exposed. At the same time, coffee beans should be stored at a relatively stable temperature, and do not move and move them frequently to avoid destroying the integrity of the coffee beans. 3. Humidity can cause coffee beans to mold: Coffee beans are prone to mold in an environment with high humidity, resulting in a deterioration of taste or a decrease in taste. Therefore, when storing coffee beans, you should pay attention to moisture-proof and moisturizing, and store them in a dry place. You can use desiccant, dry biscuits or place them in a dry environment to reduce the impact of humidity. 4. Time will make coffee beans lose their freshness: No matter how good the environment is for coffee beans, time is not a friend of coffee beans. Long-term storage will make coffee beans lose their freshness and taste, affecting their quality and taste.

[0026] If the coffee beans go bad, the following situations may exist: If the coffee beans have gone bad, they may emit an unpleasant odor. Coffee beans are easily dehydrated and deteriorated in a high temperature environment, and their weight will change. Coffee beans are easy to mold in a high humidity environment, in which case the weight of the coffee beans will also change. Therefore, the embodiment of the present invention predicts the deterioration state of the coffee beans by acquiring odor component data and weight data, and because the types of coffee beans are different, the odor component data and weight data when deterioration occurs in the above situations may be different, so the type of coffee beans is used as one of the influencing factors.

[0027] Since different types of coffee beans deteriorate at different rates when stored in environments with different light intensities, temperatures, humidity, and oxygen concentrations, the embodiments of the present invention improve the prediction accuracy of coffee bean deterioration information by collecting light intensity, temperature, humidity, and oxygen concentration data as factors affecting the deterioration rate of coffee beans.

[0028] An electronic nose is an electronic system that uses the response pattern of a gas sensor array to identify odors. It can continuously and in real time monitor the odor conditions at a specific location over a period of hours, days, or even months. The electronic nose is mainly composed of three functional devices: an odor sampling operator, a gas sensor array, and a signal processing system. The main mechanism for the electronic nose to identify odors is that each sensor in the array has a different sensitivity to the gas being measured. For example, gas No. 1 can produce a high response on a certain sensor, but a low response on other sensors. Similarly, a sensor that produces a high response to gas No. 2 is not sensitive to gas No. 1. In the final analysis, the response patterns of the entire sensor array to different gases are different. It is this difference that enables the system to identify odors based on the response pattern of the sensor. The embodiment of the present invention obtains the odor of coffee beans through an electronic nose, and realizes the mixed gas analysis of coffee beans through appropriate analysis methods.

[0029] Reference Figure 1 , an embodiment of the present invention provides a coffee bean grinder control system, comprising:

[0030] The user interaction module 100 is used to display the status information of the coffee bean grinder and obtain the operation instructions of the coffee bean grinder; the status information includes the bean bin status warning information and the predicted deterioration time of the coffee beans.

[0031] The bean silo status module 200 is used to obtain bean silo environmental data and coffee bean weight through sensors; the bean silo environmental data includes gas composition data, light intensity, temperature and humidity.

[0032] The bean silo warning module 300 is used to determine whether the temperature, humidity, light intensity data and oxygen concentration in the bean silo exceed the threshold value after the bean silo is closed for a period of time, and if so, issue a bean silo status warning message.

[0033] The model building module 400 is used to obtain storage environment data and coffee bean weight of coffee bean piles of different types and weights stored in bean silos of different specifications under different storage environments based on a certain time interval, and determine the degree of deterioration of the coffee bean pile; obtain the storage environment data and coffee bean weight of the coffee bean pile within a period of time before reaching the deterioration threshold, and build a regression tree based on the coffee bean type, storage environment data and coffee bean weight to obtain a coffee bean deterioration prediction model with coffee bean type, bean silo environment data and coffee bean weight in each time interval as input and predicted deterioration time as output; the storage environment data includes gas composition data, light intensity, temperature and humidity. The bean silo specification information in this embodiment includes but is not limited to shape and silo cover diameter. The bean silo of a commonly used coffee bean grinder can be a conical bean silo or a bean silo with a cylindrical upper part and a conical lower part. Since the coffee beans stored in bean silos with different shapes and silo cover diameters have different contact areas with the air, the deterioration speed of the coffee beans will be affected. In addition, coffee beans of different weights (equivalent to different volumes) are stored in the bean silo, and the contact areas with the air are also different. The weight of coffee beans in this embodiment is not only used as a feature to detect the degree of deterioration of coffee beans, but is also used to determine the contact area between coffee beans and air in combination with the specifications of the bean bin, thereby determining the deterioration speed;

[0034] The deterioration warning module 500 is used to obtain the type of coffee beans in the current bean bin, bean bin environmental data and coffee bean weight based on the operation instruction and the preset time interval, and obtain the predicted deterioration time of the coffee beans based on the coffee bean deterioration prediction model.

[0035] The usage suggestion module 600 is used to obtain the daily recommended number of coffee brewing times and the weight of coffee beans used for each coffee brewing based on the current weight of coffee beans and the predicted deterioration time of coffee beans.

[0036] It can be understood that the several storage environments of the embodiments of the present invention not only include environments that are the same or similar to the standard environment of the bean silo, where the differences in temperature, humidity, light, and oxygen concentration with those of the bean silo do not exceed the threshold, but can also be non-standard environments where the differences in temperature, humidity, light, and oxygen concentration with those of the bean silo exceed the threshold.

[0037] In some embodiments, the model building module 400 of the embodiment of the present invention includes:

[0038] The sample data acquisition unit is used to set bean silo devices with different specifications, different airtightness, different temperature and humidity, and different light intensity. The bean silo devices store coffee beans of different types and weights. Based on a preset time interval, the storage environment data and the weight of the coffee beans in the bean silo device are obtained, and a certain number of coffee beans are sampled to detect whether they are deteriorated through a coffee bean deterioration detection model unit, so as to obtain the deterioration degree of the coffee beans; the deterioration degree is the ratio of the number of deteriorated coffee beans to the number of sampled coffee beans; the specifications of the bean silo include shape and silo cover diameter.

[0039] The coffee bean deterioration detection unit is used to obtain an image of a single coffee bean through a camera device on an operating table of preset color, obtain the coffee bean image by removing the preset color and edge contour recognition, obtain a segmented image by threshold segmentation, and determine whether the coffee beans are deteriorated by whether the number of pixels in the segmented image is greater than a threshold.

[0040] In some embodiments, the model building module 400 of the embodiment of the present invention includes: a sample data preprocessing unit, which is used to preprocess the collected gas composition data, coffee bean weight data, light intensity and temperature and humidity, and corresponding time points, including data cleaning, denoising and normalization operations, and numbering the coffee bean types and bean silo specification information.

[0041] In some embodiments, the model building module 400 includes:

[0042] The sample data construction unit is used to construct a sample data set based on the coffee bean type, bean silo specification information, gas composition data at several time points, coffee bean weight, light intensity, temperature and humidity, and deterioration time. The sample data set includes several features that affect the prediction results and the predicted deterioration time; wherein the gas composition data includes nitrogen, oxygen, and odor components collected from normal or deteriorated coffee beans.

[0043] The feature selection unit is used to measure the feature importance of the features that affect the prediction results and the predicted metamorphism occurrence time based on the random forest feature selection method, obtain the correlation between each feature and the predicted metamorphism occurrence time, and obtain a second sample data set based on the correlation. The second sample data set is used to create a regression tree.

[0044] The data regression prediction unit is used to construct a regression tree based on the second sample data set to obtain a regression prediction model.

[0045] In some embodiments, the bean silo status module 200 of the embodiment of the present invention includes: a bean silo gas sensing unit, used to collect gas composition data of coffee beans in the bean silo through an electronic nose; a bean silo weight weighing unit, used to collect weight data of coffee beans in the bean silo; a bean silo temperature and humidity sensing unit, used to collect temperature and humidity data in the bean silo; and a bean silo light intensity sensing unit, used to collect light intensity data in the bean silo.

[0046] In some embodiments, the bean hopper status module 200 of the embodiment of the present invention includes: a bean hopper opening sensing unit, which is used to obtain the bean hopper opening and closing signals through a sensor; a weight data resetting unit, which is used to reset the weight data of the coffee beans in the bean hopper after obtaining the bean hopper opening or closing signal, or after a certain weight of coffee beans are fed and ground according to an operating instruction.

[0047] In some embodiments, the usage suggestion module 600 of the embodiment of the present invention includes:

[0048] The historical information analysis unit is used to obtain the user usage history records, which include usage time periods and corresponding coffee bean usage amounts. The user usage history records are classified according to the usage time periods and corresponding coffee bean usage amounts, and a number of commonly used time periods and corresponding commonly used coffee bean usage amounts are obtained according to the number of user usage history records in each category.

[0049] The personalized interactive unit is used to generate personalized information based on the current weight of coffee beans and the predicted deterioration time of coffee beans. The personalized information includes: the number of times coffee is brewed per day, the time period for brewing coffee, and the amount of coffee beans used each time.

[0050] In some embodiments, the user interaction module 100 of the embodiment of the present invention includes: an early warning prompt unit, which is used to issue corresponding early warning information according to the bean silo status early warning information and the predicted deterioration time of the coffee beans, and the early warning information includes sound prompt information or light prompt information.

[0051] In some embodiments, the system of the embodiment of the present invention includes: a bean grinding operation module, which is used to grind a certain weight of coffee beans based on operation instructions and a weighing unit set in the bean bin; and a coffee brewing module, which is used to brew the coffee powder obtained after the grinding operation.

[0052] Corresponding to the above-mentioned embodiments, the present invention also provides a method embodiment. As for the method embodiment, since it basically corresponds to the system embodiment, the relevant parts can be referred to the partial description of the system embodiment.

[0053] Reference Figure 2 The embodiment of the present invention provides a coffee bean grinder control method, comprising the following steps:

[0054] S100, displaying the status information of the coffee bean grinder and obtaining the operation instruction of the coffee bean grinder; the status information includes the bean bin status warning information and the predicted deterioration time of the coffee beans.

[0055] S200, obtaining bean silo environmental data and coffee bean weight through sensors; the bean silo environmental data includes gas composition data, light intensity, temperature and humidity.

[0056] S300: Determine whether the temperature and humidity, light intensity data and oxygen concentration in the bean bin after the bean bin has been closed for a period of time exceed a threshold value, and if so, issue a bean bin status warning message.

[0057] S400, based on a certain time interval, obtaining the storage environment data and the weight of coffee beans of different types and weights stored in bean silos of different specifications under different storage environments, and determining the degree of deterioration of the coffee beans; obtaining the storage environment data and the weight of coffee beans of the coffee beans in a period of time before reaching a threshold value of the degree of deterioration, constructing a regression tree based on the type of coffee beans, bean silo specification information, storage environment data and coffee bean weight, and obtaining a coffee bean deterioration prediction model; the storage environment data includes gas composition data, light intensity, temperature and humidity.

[0058] S500: obtaining the type of coffee beans in the current bean bin, bean bin environmental data, and coffee bean weight based on the operation instruction and the preset time interval, and obtaining the predicted deterioration time of the coffee beans based on the coffee bean deterioration prediction model.

[0059] S600: Obtain a recommended number of coffee brewing times per day and a weight of coffee beans used for each coffee brewing based on the current weight of coffee beans and the predicted deterioration time of coffee beans.

[0060] Although specific embodiments are described herein, those of ordinary skill in the art will recognize that many other modifications or alternative embodiments are also within the scope of the present disclosure. For example, any of the functions and / or processing capabilities described in conjunction with a particular device or component may be performed by any other device or component. In addition, although various exemplary implementations and architectures have been described according to embodiments of the present disclosure, those of ordinary skill in the art will recognize that many other modifications to the exemplary implementations and architectures described herein are also within the scope of the present disclosure.

[0061] It will be appreciated by those skilled in the art that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0062] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions without violating the spirit of the present application. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A coffee bean grinder control system, characterized in that: include: A user interaction module is used to display the status information of the coffee bean grinder and obtain the operation instructions of the coffee bean grinder; The status information includes bean silo status warning information and predicted deterioration time of coffee beans; A bean silo status module is used to obtain bean silo environmental data and coffee bean weight through sensors; the bean silo environmental data includes gas composition data, light intensity, temperature and humidity; The bean silo warning module is used to determine whether the temperature, humidity, light intensity data and oxygen concentration in the bean silo exceed the threshold after the bean silo has been closed for a period of time. If so, a bean silo status warning message is issued; A model building module, for obtaining storage environment data and coffee bean weights of coffee bean piles of different types and weights stored in bean silos of different specifications under different storage environments at certain time intervals, and determining the degree of deterioration of the coffee bean piles; Obtain storage environment data and coffee bean weight of a pile of coffee beans within a period of time before reaching a deterioration threshold, and construct a regression tree based on coffee bean type, bean bin specification information, the storage environment data, coffee bean weight, and deterioration occurrence time to obtain a coffee bean deterioration prediction model; the storage environment data includes gas composition data, light intensity, temperature, and humidity; A spoilage warning module is used to obtain the type of coffee beans in the current bean silo, bean silo environmental data and coffee bean weight based on operation instructions and preset time intervals, and obtain the predicted deterioration time of the coffee beans based on the coffee bean spoilage prediction model; The usage suggestion module is used to obtain the daily recommended number of coffee brewing times and the weight of coffee beans used for each coffee brewing based on the current weight of coffee beans and the predicted deterioration time of coffee beans.

2. The coffee bean grinder control system according to claim 1, characterized in that: The model building module includes: The sample data acquisition unit is used to set bean silo devices of different specifications, different airtightness, different temperature and humidity, and different light intensity. The bean silo devices store coffee beans of different types and weights. Storage environment data and coffee bean weights in the bean silo devices are obtained based on preset time intervals. A certain number of coffee beans are sampled to detect whether they are deteriorated through a coffee bean deterioration detection model unit, and the deterioration degree of the coffee beans is obtained. The deterioration degree is the ratio of the number of deteriorated coffee beans to the number of sampled coffee beans. The specifications of the bean silo include shape and silo cover diameter. The coffee bean deterioration detection unit is used to obtain an image of a single coffee bean through a camera device on an operating table of preset color, obtain the coffee bean image by removing the preset color and edge contour recognition, obtain a segmented image by threshold segmentation, and determine whether the coffee beans are deteriorated by whether the number of pixels in the segmented image is greater than a threshold.

3. The coffee bean grinder control system according to claim 1, characterized in that: The model building module includes: The sample data preprocessing unit is used to preprocess the collected gas composition data, coffee bean weight data, light intensity, temperature and humidity, and corresponding time points, including data cleaning, denoising and normalization operations, and numbering the coffee bean types and bean silo specification information.

4. The coffee bean grinder control system according to claim 1, characterized in that: The model building module includes: A sample data construction unit is used to construct a sample data set based on coffee bean types, bean bin specification information, gas composition data at several time points, coffee bean weight, light intensity, temperature and humidity, and deterioration occurrence time. The sample data set includes several features that affect the prediction results and the predicted deterioration occurrence time; wherein the gas composition data includes nitrogen, oxygen, and odor components collected from normal or deteriorated coffee beans; A feature selection unit is used to measure the feature importance of the features that affect the prediction result and the predicted deterioration time based on the random forest feature selection method, obtain the correlation between each feature and the predicted deterioration time, and obtain a second sample data set according to the correlation, wherein the second sample data set is used to create a regression tree; The data regression prediction unit is used to construct a regression tree based on the second sample data set to obtain a regression prediction model.

5. The coffee bean grinder control system according to claim 1, characterized in that: The bean silo status module includes: The bean silo gas sensing unit is used to collect the gas composition data of coffee beans in the bean silo through an electronic nose; Bean silo weight weighing unit, used to collect coffee bean weight data in the bean silo; The bean silo temperature and humidity sensing unit is used to collect temperature and humidity data in the bean silo; The bean silo light intensity sensing unit is used to collect light intensity data in the bean silo.

6. The coffee bean grinder control system according to claim 1, characterized in that: The bean silo status module includes: A bean bin opening sensing unit is used to obtain bean bin opening and closing signals through a sensor; The weight data resetting unit is used to reset the weight data of the coffee beans in the bean hopper after obtaining the bean hopper opening or closing signal, or after a certain weight of coffee beans are fed and ground according to an operation instruction.

7. The coffee bean grinder control system according to claim 1, characterized in that: The usage suggestion module includes: A history information analysis unit is used to obtain a user usage history record, wherein the user usage history record includes a usage time period and a corresponding amount of coffee beans used, classify the user usage history records according to the usage time period and the corresponding amount of coffee beans used, and obtain a number of commonly used time periods and corresponding commonly used amounts of coffee beans according to the number of user usage history records in each category; The personalized interactive unit is used to generate personalized customization information according to the current weight of coffee beans and the predicted deterioration time of coffee beans, and the personalized customization information includes: the number of times of coffee brewing per day, the time period of coffee brewing and the amount of coffee beans used each time.

8. The coffee bean grinder control system according to claim 1, characterized in that: The user interaction module comprises: The early warning prompt unit is used to issue corresponding early warning information according to the bean bin status early warning information and the predicted deterioration time of the coffee beans, wherein the early warning information includes sound prompt information or light prompt information.

9. The coffee bean grinder control system according to claim 1, characterized in that: The system comprises: A bean grinding operation module is used to grind coffee beans of a certain weight based on an operation instruction and a weighing unit arranged in a bean bin; The coffee brewing module is used to brew the coffee powder obtained after the grinding operation is completed.

10. A coffee bean grinder control method, used in the coffee bean grinder control system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S100, displaying status information of a coffee bean grinder and obtaining operation instructions of the coffee bean grinder; the status information includes warning information of a bean bin status and predicted deterioration time of coffee beans; S200, obtaining bean silo environmental data and coffee bean weight through sensors; the bean silo environmental data includes gas composition data, light intensity, temperature and humidity; S300, determining whether the temperature and humidity, light intensity data and oxygen concentration in the bean silo after the bean silo has been closed for a period of time exceed the threshold value, and if so, issuing a bean silo status warning message; S400, acquiring storage environment data and coffee bean weights of coffee bean piles of different types and weights stored in bean silos of different specifications under different storage environments at certain time intervals, and determining the degree of deterioration of the coffee bean piles; Obtain storage environment data and coffee bean weight of a pile of coffee beans within a period of time before reaching a deterioration threshold, and construct a regression tree based on coffee bean types, bean silo specification information, the storage environment data and the coffee bean weight to obtain a coffee bean deterioration prediction model; the storage environment data includes gas composition data, light intensity, temperature and humidity; S500, obtaining the type of coffee beans in the current bean bin, bean bin environmental data and coffee bean weight based on the operation instruction and the preset time interval, and obtaining the predicted deterioration time of the coffee beans based on the coffee bean deterioration prediction model; S600: Obtain a recommended number of coffee brewing times per day and a weight of coffee beans used for each coffee brewing based on the current weight of coffee beans and the predicted deterioration time of coffee beans.

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