Hand coffee making process data acquisition and digitalization method based on 3D depth camera and electronic scale

Abstract

The invention provides a hand coffee making process data acquisition and digitalization method based on a 3D depth camera and an electronic scale. The method specifically comprises the steps of S1, device building; s2, data acquisition; s3, data processing; s4, data integration and storage and the like. According to the invention, accurate recording and quantitative analysis of the hand coffee making process are realized, accurate data support is provided for restoring the hand coffee making process by hand coffee making equipment, and the hand coffee making skills are converted from traditional experience description into specific digital parameters, such as the moving track of a hand coffee pot, the water flow speed, the water yield of each stage and the like; the hand coffee making process can be accurately copied and inherited, the problem that the hand coffee making process cannot be scientifically and accurately recorded and restored in the prior art is solved, and the requirements of the coffee industry for accurate inheritance of hand coffee making skills and intelligent development of hand coffee making equipment are met.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition and digitization, and in particular to a method for acquiring and digitizing data during a hand-brewed coffee process based on a 3D depth camera and an electronic scale. Background Art

[0002] Pour-over coffee, a coffee-making method with a unique flavor and cultural connotations, has gained increasing popularity in recent years. Learning and mastering the art of pour-over coffee is crucial for both coffee enthusiasts and professional baristas. Masters of pour-over coffee, with their unique techniques and consistent quality, have become a model for many to emulate. However, documenting and learning pour-over coffee techniques currently presents numerous challenges.

[0003] Traditional recording methods rely primarily on manual observation and empirical analysis, which are highly subjective and difficult to accurately quantify. For example, a barista verbally describes their pour-over technique, including the speed of the pour-over pot and the contact time between the water and the coffee grounds. However, for pour-over equipment, these descriptions alone make it difficult to accurately reconstruct the process.

[0004] Recording with a standard camera also has limitations. Standard cameras can only record two-dimensional images and cannot capture depth information during the pour-over process. This makes it difficult to accurately capture the spatial position and trajectory of the pour-over pot. For example, when recording the pour-over pot pouring water in a circle, a standard camera cannot accurately measure the actual distance change between the pour-over pot and the coffee grounds, nor can it accurately calculate the speed and trajectory of the pour-over pot in space. This makes it impossible to fully and accurately recreate the pour-over process.

[0005] Furthermore, existing methods for measuring water output and flow rate during each stage of pour-over coffee are equally inadequate. Common methods rely on manual estimation or the use of simple measuring instruments, but these methods are unable to accurately and accurately record the dynamic changes in water output and flow rate in real time. For example, throughout the pour-over process, water flow rate fluctuates continuously as the water level in the pour-over pot drops and as the pour-over technique changes. Manual estimation or simple measuring instruments struggle to capture these subtle yet crucial changes, hindering the scientific analysis and accurate reconstruction of the entire pour-over process.

[0006] In summary, the existing technical means of recording and analyzing the hand-brew coffee process have problems such as inaccurate recording, difficulty in quantification, and difficulty in comprehensive restoration, which cannot meet the coffee industry's demand for scientific and precise reproduction of hand-brew coffee techniques. Summary of the Invention

[0007] In response to the shortcomings of the existing technology, the present invention proposes a method for collecting and digitizing hand-brew coffee process data based on a 3D depth camera and an electronic scale, which solves the problems in the existing technology of inaccurate recording and analysis of the hand-brew coffee process, difficulty in quantification, and inability to fully restore.

[0008] To implement the above technical solution, the present invention provides a method for collecting and digitizing data during a hand-brew coffee process based on a 3D depth camera and an electronic scale, which specifically includes the following steps:

[0009] S1. Device Construction

[0010] S11. Install the 3D depth camera at an appropriate height to ensure that the camera's field of view fully covers the entire range of motion of the hand-pour kettle, from picking it up to prepare for pouring water to putting it back after pouring water.

[0011] S12, connecting the 3D depth camera to a data processing device via a wired network or a high-speed wireless transmission module to transmit the collected image and depth data in real time;

[0012] S13. Place a high-precision electronic scale under the pour-over coffee filter cup, ensuring that the coffee flowing out of the filter cup can drip directly onto the load-bearing platform of the electronic scale;

[0013] S14, establishing a connection between the electronic scale and the data processing device to ensure that the weight data measured by the electronic scale can be transmitted to the data processing device in real time;

[0014] S2. Data Collection

[0015] S21, start the 3D depth camera and the electronic scale to enter the data collection state;

[0016] S22: The operator picks up the pour-over kettle and starts filling it with water. The 3D depth camera quickly captures the position information of the pour-over kettle in space at a set frame rate. Each frame contains not only a two-dimensional appearance image of the pour-over kettle, but also its depth data in three-dimensional space.

[0017] S23, an electronic scale measures the weight change of the coffee liquid in the filter cup in real time, records the weight data at regular time intervals, and transmits the data to a data processing device;

[0018] S3. Data Processing

[0019] S31, for the data collected by the 3D depth camera, first perform image preprocessing to remove noise interference in the image;

[0020] S32: Identify the outline of the hand-poured teapot using an edge detection algorithm, and then use a stereo matching algorithm to match the feature points in the left and right views to obtain the precise three-dimensional coordinates of the hand-poured teapot;

[0021] S33, using a trajectory fitting algorithm to fit a smooth moving trajectory curve according to the three-dimensional coordinates of the hand brewing pot at different time points;

[0022] S34, arranging the weight data collected by the electronic scale in chronological order according to the timestamp;

[0023] S35. Calculate the average water flow rate in different time periods using the formula "water flow rate = water output difference / time interval";

[0024] S4. Data integration and storage

[0025] S41. Integrate the movement trajectory data, water flow velocity data, and water output data of the pour-over coffee pot obtained through algorithm processing, and associate these data one by one with time to form a complete pour-over coffee process data file.

[0026] S42. The integrated data is stored in the data processing device and uploaded to the cloud platform so that the hand brewing device can call these data to restore the hand brewing process.

[0027] Preferably, in step S11, after the 3D depth camera is installed at a suitable height, the camera needs to be horizontally calibrated to ensure that the captured image is parallel to the operating table.

[0028] Preferably, in step S11, the 3D depth camera is installed at a height of 80 to 150 cm from the hand brewing operation table.

[0029] Preferably, in step S22, the distance information of the hand brewing pot relative to the camera in the X, Y, and Z directions is obtained through a depth sensor inside the camera.

[0030] Preferably, in step 33, a polynomial fitting algorithm is used in the process of fitting the movement trajectory curve, and the order of the polynomial is adjusted so that the fitting curve reflects the actual movement trajectory of the hand brewing kettle as accurately as possible.

[0031] Preferably, in step 33, a filtering algorithm is combined with the movement trajectory curve fitting process to smooth the coordinate data.

[0032] Preferably, in step 35, the instantaneous water flow velocity is calculated using the differential concept, and the average water flow velocity in a very short time interval is approximated as the instantaneous water flow velocity at that moment.

[0033] Preferably, in step 41, the position information of the hand brewing kettle at a certain moment, the water flow speed corresponding to the moment, and the water output from the start of water filling to the moment are integrated into one data record.

[0034] The beneficial effects of the method for collecting and digitizing data of a hand-brew coffee process based on a 3D depth camera and an electronic scale provided by the present invention are:

[0035] (1) The present invention uses a 3D depth camera to record the trajectory of hand-brewed coffee. Unlike ordinary cameras, a 3D depth camera can obtain depth information during the hand-brew process and accurately capture the position changes of the hand-brew pot in space. For example, during hand-brewed coffee, the 3D depth camera can monitor the three-dimensional coordinate changes of the hand-brew pot from the starting position to each water injection point in real time. It can not only record the movement of the hand-brew pot on the horizontal plane, but also accurately perceive its height changes in the vertical direction, which is crucial for restoring the actual movement trajectory of the hand-brew pot.

[0036] (2) While recording the trajectory of hand-brewed coffee, the present invention uses an electronic scale to record the water output at each stage. The electronic scale has a high-precision weight sensing function and can accurately measure the weight change of the coffee liquid when it flows out in real time. By combining the weight change data with time information, the water output at different stages can be accurately calculated. For example, in the early, middle and late stages of water injection, the electronic scale can record the increase in the weight of the coffee liquid in the corresponding time period, thereby deriving the water output at each stage. Moreover, the data from the electronic scale can be quickly transmitted in the form of a digital signal, which is convenient for subsequent integration and analysis with the data obtained by the 3D depth camera.

[0037] (3) The present invention utilizes an algorithm to perform in-depth processing on the collected data. The spatial position data of the hand-brewed kettle obtained by the 3D depth camera can be used to accurately obtain the movement trajectory of the hand-brewed kettle through a specific trajectory calculation algorithm. For example, the three-dimensional coordinates of the hand-brewed kettle at different time points are fitted using a spatial coordinate fitting algorithm to generate a continuous and accurate motion trajectory curve, which intuitively shows the movement path of the hand-brewed kettle during the entire hand-brewed process. For the water output data obtained by the electronic scale, the water flow velocity can be calculated by combining the time information and using a speed calculation algorithm. For example, based on the change in water output within a certain time period, the average water flow velocity within the time period and the instantaneous water flow velocity at different moments are calculated by a formula.

[0038] (4) The present invention achieves accurate recording and quantitative analysis of the hand-brew coffee process, providing accurate data support for hand-brew equipment to restore the hand-brew process. By converting the traditional empirical description of hand-brew skills into specific digital parameters, such as the movement trajectory of the hand-brew pot, water flow rate, and water output at each stage, the hand-brew coffee process can be accurately replicated and passed on. This solves the problem of the inability to scientifically and accurately record and restore the hand-brew coffee process in the existing technology, and meets the coffee industry's demand for accurate inheritance of hand-brew skills and intelligent development of hand-brew equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of the present invention.

[0040] Figure 2 It is a structural diagram of the device built in the present invention. DETAILED DESCRIPTION

[0041] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary persons in this field without creative work are within the scope of protection of the present invention.

[0042] Embodiment: A method for collecting and digitizing data during a hand-brew coffee process based on a 3D depth camera and an electronic scale.

[0043] Reference Figure 1 and Figure 2 As shown, a method for collecting and digitizing data during a hand-brew coffee process based on a 3D depth camera and an electronic scale specifically includes the following steps:

[0044] (1) Device construction:

[0045] like Figure 2 As shown, first, set up a 3D depth camera. Install the 3D depth camera at an appropriate height, for example, 80 to 150 cm from the hand-pour operation table, to ensure that the camera's field of view can fully cover the entire range of motion of the hand-pour pot from picking it up to prepare for water filling to putting it back after water filling. The camera needs to be horizontally calibrated to ensure that the image it captures is parallel to the operation table to avoid measurement errors caused by angle deviation. At the same time, the 3D depth camera is connected to the data processing equipment, which can be achieved through a wired network or a high-speed wireless transmission module to transmit the collected images and depth data in real time.

[0046] Next, place the electronic scale. A high-precision electronic scale should be placed beneath the pour-over coffee filter, ensuring that the coffee drips directly onto the scale's load-bearing platform. The electronic scale also needs to be connected to the data processing equipment using a Bluetooth interface to ensure that the weight data measured by the scale is transmitted to the data processing equipment in real time.

[0047] (2) Data Collection:

[0048] Before preparing to perform hand-brew coffee operations, start the 3D depth camera and electronic scale to enter the data acquisition state. When the operator picks up the hand-brew pot and starts to add water, the 3D depth camera quickly captures the position information of the hand-brew pot in space at a set frame rate (60 frames per second or higher). Each frame image not only contains a two-dimensional appearance image of the hand-brew pot, but also carries its depth data in three-dimensional space. For example, through the depth sensor inside the camera, the distance information of the hand-brew pot relative to the camera in the X, Y, and Z directions can be obtained. At the same time, the electronic scale measures the change in the weight of the coffee liquid in the filter cup in real time, records the weight data at a certain time interval, and transmits the data to the data processing equipment.

[0049] This invention uses a 3D depth camera to record the trajectory of hand-poured coffee. Unlike ordinary cameras, a 3D depth camera can capture depth information during the brewing process, accurately capturing the changes in the pot's position in space. For example, during brewing, the 3D depth camera can monitor the three-dimensional coordinate changes of the pot from its starting position to each pouring point in real time. This not only records the pot's horizontal movement but also accurately perceives its vertical height changes, which is crucial for reconstructing the pot's actual motion trajectory.

[0050] While recording the trajectory of hand-brewed coffee, the present invention uses an electronic scale to record the water output at each stage. The electronic scale has a high-precision weight sensing function and can accurately measure the change in weight of the coffee liquid as it flows out in real time. By combining the weight change data with time information, the water output at different stages can be accurately calculated. For example, in the early, middle, and late stages of water injection, the electronic scale can record the increase in the weight of the coffee liquid in the corresponding time period, thereby deriving the water output at each stage. Moreover, the data from the electronic scale can be quickly transmitted in the form of a digital signal, which is convenient for subsequent integration and analysis with the data obtained by the 3D depth camera.

[0051] (3) Data processing:

[0052] After receiving data from the 3D depth camera and electronic scale, the data processing equipment applies a specific algorithm to analyze and process the data. For the data captured by the 3D depth camera, image preprocessing is first performed to remove noise interference from the image. An edge detection algorithm is used to identify the outline of the hand-poured teapot. A stereo matching algorithm is then used to match the feature points in the left and right views to obtain the teapot's precise three-dimensional coordinates. Next, a trajectory fitting algorithm is used to fit a smooth movement trajectory curve based on the teapot's three-dimensional coordinates at different time points. For example, a polynomial fitting algorithm can be used to adjust the order of the polynomial so that the fitted curve reflects the teapot's actual motion trajectory as accurately as possible. During the fitting process, a filtering algorithm can also be used to smooth the coordinate data to further improve the trajectory accuracy.

[0053] For weight data collected by the electronic scale, the data is first arranged in chronological order based on the timestamp. Then, the water output is calculated using a formula: the water output during a given time period is equal to the weight measured by the electronic scale at the end of that time period minus the weight at the beginning. To calculate the water flow rate, the difference in water output between adjacent time points and the time interval are used, and the average water flow rate for each time period is calculated using the formula "water flow rate = water flow rate difference / time interval." For calculating the instantaneous water flow rate, differential calculations can be used, approximating the instantaneous water flow rate at that moment by taking the average water flow rate within a very short time interval.

[0054] The present invention utilizes an algorithm to perform in-depth processing on the collected data. For the spatial position data of the hand-poured kettle obtained by the 3D depth camera, the movement trajectory of the hand-poured kettle can be accurately obtained through a specific trajectory calculation algorithm. For example, the three-dimensional coordinates of the hand-poured kettle at different time points are fitted using a spatial coordinate fitting algorithm to generate a continuous and accurate motion trajectory curve, which intuitively shows the movement path of the hand-poured kettle during the entire hand-poured process. For the water output data obtained by the electronic scale, the water flow velocity can be calculated by combining the time information and using a speed calculation algorithm. For example, based on the change in water output within a certain time period, the average water flow velocity within the time period and the instantaneous water flow velocity at different moments are calculated by a formula.

[0055] (4) Data integration and storage:

[0056] The algorithm-processed data on the hand-poured coffee pot's movement trajectory, water flow rate, and water output at each stage are integrated. This data is mapped against time to form a complete data file for the hand-poured coffee process. For example, the hand-poured coffee pot's position at a specific moment, the corresponding water flow rate, and the water output from the start of water filling to that moment are combined into a single data record. Finally, this integrated data is stored on the data processing device and uploaded to the cloud platform, allowing subsequent hand-poured coffee machines to access this data and reconstruct the hand-poured coffee process.

[0057] This invention enables precise recording and quantitative analysis of the hand-pour coffee process, providing accurate data support for reproducing the hand-pour coffee process using hand-pour equipment. By converting traditional empirical descriptions of hand-pour coffee techniques into specific digital parameters, such as the movement trajectory of the hand-pour pot, water flow rate, and water output at each stage, the hand-pour coffee process can be accurately replicated and passed down. This addresses the existing technical challenges of scientifically and accurately recording and reproducing the hand-pour coffee process, meeting the coffee industry's demand for precise transmission of hand-pour coffee techniques and the intelligent development of hand-pour equipment.

[0058] The present invention uses a 3D depth camera and an electronic scale to record the trajectory of hand-brewed coffee and the water output and water flow rate at each stage, and performs digital processing, thereby bringing significant beneficial effects in many aspects.

[0059] (1) It plays an important role in improving the stability of coffee quality. Due to the differences in hand-brew techniques among different baristas, the quality of the coffee produced may fluctuate greatly even if the same general process is followed. The present invention realizes the digitization and standardization of the hand-brew process. The hand-brew equipment performs restoration operations based on precise trajectory, water flow rate and water output data, which can ensure that the quality of the coffee produced each time is highly consistent. This is particularly important for coffee chain companies, as it can ensure that they can provide hand-brew coffee products with stable taste and quality in different stores, thereby enhancing brand image and consumer satisfaction. For example, no matter which city the store is in, as long as the same digital parameters are followed, consumers can taste hand-brew coffee with consistent flavor.

[0060] (2) It provides strong data support for coffee research and innovation. Based on these precise data, researchers can deeply analyze the impact of different hand-brew parameters on coffee flavor. For example, they can study how different water flow rates, water output, and changes in the movement trajectory of the hand-brew pot change the extraction degree, aroma, and taste of coffee. Through a large amount of data comparison and experiments, it helps to develop new hand-brew techniques and flavor formulas, and promote the innovation and development of the coffee industry. For example, it was found that a specific combination of water flow rate and hand-brew pot movement trajectory can stimulate the unique flavor of coffee beans, thus bringing a new taste experience to coffee lovers.

[0061] (3) The present invention plays a key role in promoting the intelligent development of coffee equipment. Traditional hand-brew coffee equipment has relatively simple functions, while the digital data of the present invention enables hand-brew equipment to achieve intelligent and precise control. Future hand-brew equipment can have built-in data receiving and processing modules to directly read the stored hand-brew data and accurately control the movement of the hand-brew pot, the speed of the water flow and the amount of water output based on this data, thus achieving a highly automated hand-brew process. This not only improves the technological content and market competitiveness of hand-brew equipment, but also brings new development directions to coffee making.

[0062] The above description is only a preferred embodiment of the present invention, but the present invention should not be limited to the contents disclosed in the embodiment and the drawings. Therefore, any equivalent or modification completed without departing from the spirit disclosed in the present invention shall fall within the scope of protection of the present invention.

Claims

1. A method for collecting and digitizing data during the hand-brew coffee process based on a 3D depth camera and an electronic scale, characterized in that The specific steps include: S1. Device Construction S11. Install the 3D depth camera at an appropriate height to ensure that the camera's field of view fully covers the entire range of motion of the hand-pour kettle, from picking it up to prepare for pouring water to putting it back after pouring water. S12, connecting the 3D depth camera to a data processing device via a wired network or a high-speed wireless transmission module to transmit the collected image and depth data in real time; S13. Place a high-precision electronic scale under the pour-over coffee filter cup, ensuring that the coffee flowing out of the filter cup can drip directly onto the load-bearing platform of the electronic scale; S14, establishing a connection between the electronic scale and the data processing device to ensure that the weight data measured by the electronic scale can be transmitted to the data processing device in real time; S2. Data Collection S21, start the 3D depth camera and the electronic scale to enter the data collection state; S22: The operator picks up the pour-over kettle and starts filling it with water. The 3D depth camera quickly captures the position information of the pour-over kettle in space at a set frame rate. Each frame contains not only a two-dimensional appearance image of the pour-over kettle, but also its depth data in three-dimensional space. S23, an electronic scale measures the weight change of the coffee liquid in the filter cup in real time, records the weight data at regular time intervals, and transmits the data to a data processing device; S3. Data Processing S31, for the data collected by the 3D depth camera, first perform image preprocessing to remove noise interference in the image; S32: Identify the outline of the hand-poured teapot using an edge detection algorithm, and then use a stereo matching algorithm to match the feature points in the left and right views to obtain the precise three-dimensional coordinates of the hand-poured teapot; S33, using a trajectory fitting algorithm to fit a smooth moving trajectory curve according to the three-dimensional coordinates of the hand brewing pot at different time points; S34, arranging the weight data collected by the electronic scale in chronological order according to the timestamp; S35. Calculate the average water flow rate for different time periods using the formula "water flow rate = water output difference / time interval"; S4. Data integration and storage S41. Integrate the movement trajectory data, water flow velocity data, and water output data of the pour-over coffee pot obtained through algorithm processing, and associate these data one by one with time to form a complete pour-over coffee process data file. S42. The integrated data is stored in the data processing device and uploaded to the cloud platform so that the hand brewing device can call these data to restore the hand brewing process.

2. The method for collecting and digitizing data during a hand-brew coffee process based on a 3D depth camera and an electronic scale according to claim 1, wherein: In step S11, after the 3D depth camera is installed at a suitable height, the camera needs to be horizontally calibrated to ensure that the captured image is parallel to the operating table.

3. The method for collecting and digitizing data during a hand-brew coffee process based on a 3D depth camera and an electronic scale according to claim 1, wherein: In step S11, the 3D depth camera is installed at a height of 80 to 150 cm from the hand-washing operation table.

4. The method for collecting and digitizing data during a hand-brew coffee process based on a 3D depth camera and an electronic scale according to claim 1, wherein: In step S22, the distance information of the hand brewing pot relative to the camera in the X, Y, and Z directions is obtained through the depth sensor inside the camera.

5. The method for collecting and digitizing data during the hand-brew coffee process based on a 3D depth camera and an electronic scale according to claim 1, wherein: In step 33, a polynomial fitting algorithm is used in the process of fitting the movement trajectory curve, and the order of the polynomial is adjusted so that the fitting curve reflects the actual movement trajectory of the hand brewing kettle as accurately as possible.

6. The method for collecting and digitizing data during the hand-brew coffee process based on a 3D depth camera and an electronic scale according to claim 1, wherein: In step 33, the coordinate data is smoothed by combining a filtering algorithm during the movement trajectory curve fitting process.

7. The method for collecting and digitizing data during a hand-brew coffee process based on a 3D depth camera and an electronic scale according to claim 1, wherein: In step 35, the instantaneous water flow velocity is calculated using the differential concept, and the average water flow velocity in a very short time interval is approximated as the instantaneous water flow velocity at that moment.

8. The method for collecting and digitizing data during a hand-brew coffee process based on a 3D depth camera and an electronic scale according to claim 1, wherein: In step 41, the position information of the hand brewing kettle at a certain moment, the water flow speed corresponding to the moment, and the water output from the start of water filling to the moment are integrated into one data record.

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