Tea quality detection method, system and equipment based on big data and medium

Abstract

The invention provides a tea quality detection method, system and device based on big data and a medium, and relates to the technical field of tea quality detection.The method comprises the steps that a shot image of tea is obtained; based on the shot image of the tea leaves, using a variational auto-encoder to generate tea leaf images of the tea leaves under illumination of different colors and similarity of the tea leaf images under illumination of different colors; an illumination graph structure is constructed, the illumination graph structure comprises multiple color illumination tea nodes and multiple edges among the multiple color illumination tea nodes, and the node feature of each color illumination tea node comprises an illumination image of the tea under the color illumination; the feature of the edge between the tea nodes is the similarity of the tea images under illumination of different colors; and processing the illumination graph structure based on the first graph convolutional network to determine whether the tea quality detection is qualified. The method can quickly and accurately perform quality detection on the tea.

Description

Technical Field

[0001] The present invention relates to the technical field of tea quality detection, and in particular to a tea quality detection method, system, equipment and medium based on big data. Background Art

[0002] As the global tea market continues to expand, consumers have higher and higher requirements for tea quality. The tea quality testing methods commonly used in the market currently include sensory evaluation, laboratory analysis and instrument measurement. Sensory evaluation relies on the experience and judgment of experts and is easily affected by personal preferences; although laboratory analysis is more accurate, it is complicated and time-consuming to operate; and existing instrument measurement equipment is mostly focused on the determination of basic ingredients, and there is little research on the changes in the appearance and color of tea and its internal structural characteristics. These traditional methods have problems such as strong subjectivity, low efficiency, and insufficient accuracy, and it is difficult to meet the needs of tea quality standardization in modern industrial production and international trade.

[0003] Therefore, how to quickly and accurately detect the quality of tea is a problem that needs to be solved urgently. Summary of the invention

[0004] The main technical problem solved by the invention is how to quickly and accurately detect the quality of tea leaves.

[0005] According to a first aspect, the present invention provides a tea quality detection method based on big data, comprising: acquiring a photographed image of tea leaves; generating tea images of tea leaves under different color illuminations and similarities of tea images under different color illuminations using a variational autoencoder based on the photographed image of the tea leaves; constructing a lighting graph structure, the lighting graph structure comprising tea leaves nodes illuminated by multiple colors and multiple edges between tea leaves nodes illuminated by multiple colors, the node feature of each tea leaf node illuminated by a color comprising an illumination image of the tea leaves under this color illumination, and the feature of the edges between the tea leaves nodes being the similarities of tea leaves images under different color illuminations; processing the lighting graph structure based on a first graph convolutional network to determine whether the tea quality detection is qualified.

[0006] In a possible implementation, the method further includes: Based on the photographed image of the tea leaves, a deep neural network model is used to determine multiple vibration frequencies of the tea leaves; based on the multiple vibration frequencies of the tea leaves, a vibration device is controlled to vibrate, and a vibration video of the tea leaves at each vibration frequency is obtained; based on the frequency information determination model, the vibration video of the tea leaves at each vibration frequency is processed to determine the vibration information of the tea leaves at each vibration frequency and the similarity of the vibration information of the tea leaves at different vibration frequencies; a tea leaf vibration graph structure is constructed, the tea leaf vibration graph structure includes tea leaf nodes of multiple frequencies and multiple edges between the tea leaf nodes of multiple frequencies, the node features of the tea leaf nodes of each frequency include the vibration information of the tea leaves at each vibration frequency, and the features of the edges between the tea leaf nodes of different frequencies are the similarity of the vibration information of the tea leaves at different vibration frequencies; based on the second graph convolutional network, the tea leaf vibration graph structure is processed to determine whether the tea leaf vibration detection is qualified.

[0007] In a possible implementation, the frequency information determination model is a long-short term neural network model, the input of the frequency information determination model is the vibration video of the tea leaves at each vibration frequency, the output of the frequency information determination model is the vibration information of the tea leaves at each vibration frequency, and the similarity of the vibration information of the tea leaves at different vibration frequencies, and the vibration information of the tea leaves at each vibration frequency includes the average amplitude of the tea leaves and the amplitude of each piece of tea leaves.

[0008] In a possible implementation, the input of the variational autoencoder is a photographed image of the tea leaves, and the output of the variational autoencoder is images of the tea leaves under different colors of light and similarities of images of the tea leaves under different colors of light.

[0009] According to a second aspect, the present invention provides a tea quality detection system based on big data, comprising: An acquisition module is used to acquire the photographed image of tea leaves; A generation module, used for generating tea images under different colors of light and similarities of tea images under different colors of light by using a variational autoencoder based on the photographed images of the tea leaves; A construction module is used to construct a light map structure, wherein the light map structure includes tea leaves nodes illuminated by multiple colors and multiple edges between the tea leaves nodes illuminated by multiple colors, the node feature of each tea leaf node illuminated by the color includes the illumination image of the tea leaves under the illumination of the color, and the feature of the edge between the tea leaves nodes is the similarity of the tea leaves images under illumination of different colors; The detection module is used to process the illumination map structure based on the first graph convolutional network to determine whether the tea quality test is qualified.

[0010] In a possible implementation, the system is further used for: Based on the photographed image of the tea leaves, a deep neural network model is used to determine multiple vibration frequencies of the tea leaves; based on the multiple vibration frequencies of the tea leaves, a vibration device is controlled to vibrate, and a vibration video of the tea leaves at each vibration frequency is obtained; based on the frequency information determination model, the vibration video of the tea leaves at each vibration frequency is processed to determine the vibration information of the tea leaves at each vibration frequency and the similarity of the vibration information of the tea leaves at different vibration frequencies; a tea leaf vibration graph structure is constructed, the tea leaf vibration graph structure includes tea leaf nodes of multiple frequencies and multiple edges between the tea leaf nodes of multiple frequencies, the node features of the tea leaf nodes of each frequency include the vibration information of the tea leaves at each vibration frequency, and the features of the edges between the tea leaf nodes of different frequencies are the similarity of the vibration information of the tea leaves at different vibration frequencies; based on the second graph convolutional network, the tea leaf vibration graph structure is processed to determine whether the tea leaf vibration detection is qualified.

[0011] In a possible implementation, the frequency information determination model is a long-short term neural network model, the input of the frequency information determination model is the vibration video of the tea leaves at each vibration frequency, the output of the frequency information determination model is the vibration information of the tea leaves at each vibration frequency, and the similarity of the vibration information of the tea leaves at different vibration frequencies, and the vibration information of the tea leaves at each vibration frequency includes the average amplitude of the tea leaves and the amplitude of each piece of tea leaves.

[0012] In a possible implementation, the input of the variational autoencoder is a photographed image of the tea leaves, and the output of the variational autoencoder is images of the tea leaves under different colors of light and similarities of images of the tea leaves under different colors of light.

[0013] According to a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor; a memory; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement a method as described above, the method comprising: obtaining a photographed image of tea leaves; generating tea leaves images under different color illuminations and similarities of tea leaves images under different color illuminations using a variational autoencoder based on the photographed image of the tea leaves; constructing an illumination map structure, the illumination map structure comprising tea leaves nodes illuminated by multiple colors and multiple edges between tea leaves nodes illuminated by multiple colors, the node features of each tea leaves node illuminated by a color comprising an illumination image of the tea leaves under this color illumination, and the features of the edges between the tea leaves nodes being the similarities of tea leaves images under different color illuminations; processing the illumination map structure based on a first graph convolutional network to determine whether the tea quality inspection is qualified.

[0014] According to the fourth aspect, the present embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned big data-based tea quality detection method, the method comprising: obtaining a photographed image of tea leaves; generating tea images of tea leaves under different color illuminations and similarities of tea images under different color illuminations using a variational autoencoder based on the photographed image of the tea leaves; constructing a lighting map structure, the lighting map structure comprising tea leaves nodes illuminated by multiple colors and multiple edges between tea leaves nodes illuminated by multiple colors, the node feature of each tea leaf node illuminated by the color illumination comprising the illumination image of the tea leaves under this color illumination, and the feature of the edges between the tea leaves nodes being the similarities of tea leaves images under different color illuminations; processing the lighting map structure based on a first graph convolutional network to determine whether the tea quality detection is qualified.

[0015] The present invention provides a tea quality detection method and system based on big data, the method comprising acquiring a photographed image of tea; generating tea images of tea under different color illuminations and similarities of tea images under different color illuminations using a variational autoencoder based on the photographed image of tea; constructing a light map structure, the light map structure comprising tea nodes illuminated by multiple colors and multiple edges between the tea nodes illuminated by multiple colors, the node feature of each tea node illuminated by the color comprising the illumination image of the tea under the illumination of the color, and the feature of the edge between the tea nodes being the similarities of tea images under illumination of different colors; processing the light map structure based on a first graph convolutional network to determine whether the tea quality detection is qualified, the method being capable of quickly and accurately performing quality detection on tea. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of an application scenario of a tea quality detection method based on big data provided by an embodiment of the present invention; Figure 2 A schematic diagram of a process of tea quality detection method based on big data provided by an embodiment of the present invention; Figure 3 A schematic diagram of a process for determining whether a tea leaf vibration test is qualified provided by an embodiment of the present invention; Figure 4 A schematic diagram of a tea quality detection system based on big data provided by an embodiment of the present invention; Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention; Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present invention better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present invention are not shown or described in the specification, this is to avoid the core part of the present invention being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0018] Figure 2 A schematic diagram of an application scenario of a tea quality detection method based on big data provided in an embodiment of the present invention. Figure 2 The application scenario of the tea quality detection method based on big data may include a server 11, a network 12, a terminal 13 and a storage device 14.

[0019] In some embodiments, the server 11 may be a single server or a server group. The server 11 may access information and / or data stored in the terminal 13 or the storage device 14 through the network 12. In some embodiments, the server 11 may be used to execute Figure 2 The tea quality detection method based on big data is shown in .

[0020] The network 12 may facilitate the exchange of information and / or data. In some embodiments, the network 12 may be any form of wired or wireless network, or any combination thereof.

[0021] Terminal 13 may refer to one or more terminal devices used by a user. In some embodiments, terminal 13 may include one or more combinations of a mobile device, a tablet computer, a laptop computer, and the like.

[0022] The storage device 14 may store data and / or instructions. For example, the storage device 14 may store data instructions of a tea quality detection method based on big data.

[0023] In an embodiment of the present invention, there is provided Figure 1 A tea quality detection method based on big data is shown, and the tea quality detection method based on big data includes steps S1 to S4: Step S1, obtaining a photographed image of tea leaves.

[0024] The photographed image of tea leaves refers to a two-dimensional visual representation of a tea sample captured by a camera or video device. The photographed image of tea leaves is the basic data for subsequent processing.

[0025] Step S2, based on the photographed image of the tea leaves, using a variational autoencoder to generate images of the tea leaves under different colors of illumination and similarities of the images of the tea leaves under different colors of illumination.

[0026] The input of the variational autoencoder is the photographed image of the tea leaves, and the output of the variational autoencoder is the images of the tea leaves under different colors of light and the similarities of the images of the tea leaves under different colors of light.

[0027] Variational Autoencoder (VAE) is a deep generative model that generates new samples by learning the probability distribution of data and can compress the input data.

[0028] Different lighting conditions can reveal the surface characteristics of tea leaves, and similarity reflects the consistency of tea leaves under various lighting conditions, which is very important for evaluating tea quality. The surface characteristics of tea leaves include glossiness, color change information, etc. By analyzing images of tea leaves under different lighting conditions, the quality of tea leaves can be more comprehensively evaluated and possible problems can be discovered.

[0029] The variational autoencoder encodes the input image into a low-dimensional representation in a latent space, and then samples different points from this latent space, corresponding to different color illumination conditions. Finally, these sampled points are decoded to generate the corresponding tea images under different color illuminations. The variational autoencoder not only generates tea images under different color illuminations, but also calculates the similarity between these images.

[0030] Step S3, constructing a lighting graph structure, wherein the lighting graph structure includes tea leaves nodes illuminated by multiple colors and multiple edges between the tea leaves nodes illuminated by multiple colors, the node feature of each tea leaves node illuminated by a color includes the lighting image of the tea leaves under this color lighting, and the feature of the edges between the tea leaves nodes is the similarity of the tea leaves images under different colors lighting.

[0031] The illumination graph structure is a graph data structure, where nodes represent tea images under specific illumination conditions, and edges represent the relationship (i.e. similarity) between nodes. This structure can be used to capture the association between tea images under different illumination conditions.

[0032] As an example, if there are three different lighting conditions (red light, blue light, green light), then the lighting graph structure will contain three nodes, one for each lighting condition and the edges between the nodes represent the similarity between these images.

[0033] As an example, an image of green tea under red light can be used as a tea leaf node, and images of green tea under blue light and green light can also be used as two other tea leaf nodes.

[0034] Step S4: Process the illumination map structure based on the first graph convolutional network to determine whether the tea quality test is qualified.

[0035] The input of the first graph convolutional network is the illumination map structure, and the output of the first graph convolutional network is whether the tea quality test is qualified or not.

[0036] A graph convolutional network is a neural network specifically designed to process graph structure data. It can perform feature extraction and classification tasks by aggregating information from neighboring nodes. In this step, the first graph convolutional network is used to analyze the illumination graph structure and determine the results of tea quality detection.

[0037] The graph structure can effectively organize and express complex associations, making it easier to conduct comprehensive analysis using graph convolutional networks. By constructing a light graph structure, we can systematically display and analyze the performance of tea leaves under different light conditions and their relationships, which helps to discover potential quality issues.

[0038] Different colors of light can reveal different gloss properties on the surface of tea leaves. For example, some types of tea may show higher reflectivity or gloss under certain wavelengths of light, which may be a sign of high-quality tea. Color performance under different lighting conditions can help identify characteristics such as color uniformity and color depth of tea leaves, which are important factors in evaluating the appearance quality of tea leaves. Some tea varieties may show different light transmittance under specific color lighting, which is related to the thickness and cell structure of the leaves. By analyzing these characteristics, we can indirectly understand the internal structure of tea leaves. Different colors of light help to observe the distribution of pigments in tea leaves, which is very important for judging the processing technology and preservation status of tea leaves. Pigments include chlorophyll, thearubigins, etc. High-resolution images can more clearly show the texture characteristics of the tea surface, such as wrinkles and fluff, under different lighting conditions. These subtle details often reflect the freshness and processing sophistication of the tea leaves. Different lighting conditions can also help to detect minor damage or defects on the surface of tea leaves, such as spots and mildew, which directly affect the quality of tea leaves.

[0039] Constructing a light map structure can systematically present the characteristics of tea under different lighting conditions, and the graph convolutional network improves the speed and accuracy of detection.

[0040] In some embodiments, Figure 3 A schematic diagram of a process for determining whether a tea leaf vibration test is qualified is provided in an embodiment of the present invention, wherein the process for determining whether a tea leaf vibration test is qualified includes steps S21 to S25: Step S21, determining multiple vibration frequencies of the tea leaves using a deep neural network model based on the photographed image of the tea leaves.

[0041] The input of the deep neural network model is the photographed image of the tea leaves, and the output of the deep neural network model is multiple vibration frequencies of the tea leaves.

[0042] In some embodiments, the deep neural network model includes a tea information determination layer, a target frequency determination layer, and multiple vibration frequency determination layers. The tea information determination layer, the target frequency determination layer, and the multiple vibration frequency determination layers all include deep neural networks. The input of the tea information determination layer is the photographed image of the tea leaves, the output of the tea information determination layer is the color information, size information, and number of tea leaves of each tea leaf, the input of the target frequency determination layer is the color information, size information, and number of tea leaves of each tea leaf, the output of the target frequency determination layer is the optimal vibration frequency of the tea leaves, the input of the multiple vibration frequency determination layers is the optimal vibration frequency of the tea leaves, and the output of the multiple vibration frequency determination layers is multiple vibration frequencies of the tea leaves.

[0043] Step S22, controlling the vibration device to vibrate based on the multiple vibration frequencies of the tea leaves, and obtaining a vibration video of the tea leaves at each vibration frequency.

[0044] Start the vibration device and record the vibration process of tea leaves at various frequencies to obtain a vibration video of tea leaves at each vibration frequency. For example, the vibration video is a high-definition video showing the movement of tea leaves at a vibration frequency of 50 Hz, in which the displacement and shape change of each tea leaf can be clearly seen.

[0045] Step S23, based on the frequency information determination model, the vibration video of the tea leaves at each vibration frequency is processed to determine the vibration information of the tea leaves at each vibration frequency and the similarity of the vibration information of the tea leaves at different vibration frequencies.

[0046] The frequency information determination model is a long-term and short-term neural network model, the input of the frequency information determination model is the vibration video of the tea leaves at each vibration frequency, the output of the frequency information determination model is the vibration information of the tea leaves at each vibration frequency and the similarity of the vibration information of the tea leaves at different vibration frequencies, and the vibration information of the tea leaves at each vibration frequency includes the average amplitude of the tea leaves and the amplitude of each piece of tea leaves.

[0047] The long-term and short-term neural network model includes a long-term and short-term neural network (LSTM, Long Short-Term Memory). The long-term and short-term neural network model can process the relationship in the vibration video time series of the tea leaves at each vibration frequency in a continuous time period, and can output the characteristics of the vibration video of the tea leaves at each vibration frequency that comprehensively considers each time point, so that the output characteristics are more accurate and comprehensive.

[0048] The average amplitude is the average amplitude of all tea leaves at the same vibration frequency. For example, the average amplitude is 22 mm.

[0049] The amplitude of each tea leaf is specific to the maximum displacement of each tea leaf at a certain vibration frequency. For example, the maximum displacement of a tea leaf is 15 mm.

[0050] Step S24, constructing a tea vibration graph structure, wherein the tea vibration graph structure includes tea nodes of multiple frequencies and multiple edges between tea nodes of multiple frequencies, the node feature of each frequency tea node includes the vibration information of the tea at each vibration frequency, and the edge feature between tea nodes of different frequencies is the similarity of the vibration information of the tea at different vibration frequencies.

[0051] The tea vibration graph structure is a graph structure in which nodes represent tea vibration information at different vibration frequencies, and edges represent the similarity between these information. The tea vibration graph structure is used to capture the association between the dynamic behavior of tea under different vibration conditions. As an example, if there are three vibration frequencies (40 Hz, 60 Hz, 80 Hz), then the tea vibration graph structure will contain three nodes, each corresponding to the vibration information at one vibration frequency. The edges between the nodes represent the similarity between these information. Each node in the tea vibration graph structure represents the tea vibration information at a specific vibration frequency. The tea vibration graph structure can systematically organize and present tea vibration information and their interrelationships, facilitate further analysis, and promote comprehensive evaluation. By constructing the tea vibration graph structure, the dynamic characteristics of tea at different vibration frequencies can be fully displayed, which helps to identify potential quality problems.

[0052] Step S25, processing the tea vibration graph structure based on the second graph convolutional network to determine whether the tea vibration detection is qualified.

[0053] The input of the second graph convolutional network is the tea vibration graph structure, and the output of the second graph convolutional network is whether the tea vibration test is qualified or unqualified. The response of tea leaves at different vibration frequencies reflects the elastic properties of its internal structure. High-quality tea leaves usually have specific elastic moduli and damping coefficients, and these physical properties affect the vibration mode of tea leaves at different frequencies. Tea leaves with higher compactness may show smaller amplitudes at low-frequency vibrations and greater energy dissipation at high-frequency vibrations. These relationships can be effectively represented by the tea vibration graph structure and processed by the second graph convolutional network to determine whether the tea vibration test is qualified.

[0054] Based on the same inventive concept, Figure 4 A schematic diagram of a tea quality detection system based on big data provided by an embodiment of the present invention, the tea quality detection system based on big data includes: An acquisition module 41 is used to acquire a photographed image of tea leaves; A generating module 42, configured to generate tea images of tea leaves under different colors of light and similarities of tea images under different colors of light based on the photographed images of the tea leaves by using a variational autoencoder; A construction module 43 is used to construct a light map structure, wherein the light map structure includes tea leaves nodes illuminated by multiple colors and multiple edges between the tea leaves nodes illuminated by multiple colors, the node feature of each tea leaf node illuminated by the color includes the illumination image of the tea leaves under the illumination of the color, and the feature of the edge between the tea leaves nodes is the similarity of the tea leaves images under illumination of different colors; The detection module 44 is used to process the illumination map structure based on the first graph convolutional network to determine whether the tea quality test is qualified.

[0055] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, such as Figure 5 As shown, including: It includes: a processor 51; a memory 52; and a computer program; wherein the computer program is stored in the memory 52 and is configured to be executed by the processor 51 to implement the tea quality detection method based on big data as provided above, the method including: obtaining a photographed image of tea leaves; using a variational autoencoder based on the photographed image of the tea leaves to generate tea images under different color illuminations and similarities of tea images under different color illuminations; constructing a lighting graph structure, the lighting graph structure including tea nodes illuminated by multiple colors and multiple edges between tea nodes illuminated by multiple colors, the node feature of each tea node illuminated by a color includes the illumination image of the tea leaves under this color illumination, and the feature of the edge between the tea nodes is the similarity of the tea images under different color illuminations; processing the lighting graph structure based on a first graph convolutional network to determine whether the tea quality detection is qualified.

[0056] Based on the same inventive concept, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by the processor 51, implements the aforementioned tea quality detection method based on big data, the method comprising: obtaining a photographed image of tea leaves; using a variational autoencoder based on the photographed image of tea leaves to generate tea images of tea leaves under different color illuminations and similarities of tea images under different color illuminations; constructing a lighting map structure, the lighting map structure comprising tea leaves nodes illuminated by multiple colors and multiple edges between tea leaves nodes illuminated by multiple colors, the node feature of each tea leaf node illuminated by a color comprising the illumination image of the tea leaves under this color illumination, and the feature of the edges between the tea leaves nodes being the similarities of tea leaves images under different color illuminations; processing the lighting map structure based on a first graph convolutional network to determine whether the tea quality detection is qualified.

[0057] The tea quality detection method based on big data provided in the embodiment of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptops, ultra-mobile personal computers (ultra-mobile personal computers, UMPCs), handheld computers, netbooks, personal digital assistants (personal digital assistants, PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (augmented reality, AR) \ virtual reality (virtual reality, VR) devices, smart home devices, car computers and other electronic devices, and the embodiment of the present application does not impose any restrictions on this.

[0058] Taking the mobile phone 100 as an example of the electronic device, Figure 6 A schematic structural diagram of the mobile phone 100 is shown.

[0059] like Figure 6 As shown, the mobile phone 100 may include a processing module 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0060] The processing module 110 may include one or more processing units, for example, the processing module 110 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0061] The controller can be the nerve center and command center of the mobile phone 100, and is the decision maker that commands the various components of the mobile phone 100 to work in coordination according to the instructions. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0062] The application processor may be installed with an operating system of the mobile phone 100, which is used to manage the hardware and software resources of the mobile phone 100. For example, it may manage and configure memory, determine the priority of system resource supply and demand, manage file systems, manage drivers, etc. The operating system may also be used to provide an operating interface for users to interact with the system. Various types of software may be installed in the operating system, such as drivers, applications (applications, apps), etc. Exemplarily, the operating system of the mobile phone 100 may be an Android system, a Linux system, etc.

[0063] A memory may also be provided in the processing module 110 for storing instructions and data. In some embodiments, the memory in the processing module 110 is a cache memory. The memory may store instructions or data that have just been used or are cyclically used by the processing module 110. If the processing module 110 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processing module 110, and thus improves the efficiency of the system.

[0064] In some embodiments, the processing module 110 may include one or more interfaces. The interface may include an inter-integrated circuit (I2C) interface, an inter-integrated circuits sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0065] The processing module 110 can be used to: obtain a photographed image of tea leaves; generate tea leaves images under different color illuminations and similarities of tea leaves images under different color illuminations using a variational autoencoder based on the photographed image of the tea leaves; construct a light map structure, the light map structure including tea leaves nodes under multiple color illuminations and multiple edges between tea leaves nodes under multiple color illuminations, the node feature of each tea leaves node under each color illumination including the illumination image of the tea leaves under this color illumination, and the feature of the edges between the tea leaves nodes is the similarity of tea leaves images under different color illuminations; and process the light map structure based on a first graph convolutional network to determine whether the tea quality inspection is qualified.

[0066] The charging management module 140 is used to receive charging input from a charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive charging input from a wired charger through the USB interface 130. In some wireless charging embodiments, the charging management module 140 may receive wireless charging input through a wireless charging coil of the mobile phone 100. While the charging management module 140 is charging the battery 142, it may also power the electronic device through the power management module 141.

[0067] The power management module 141 is used to connect the battery 142, the charging management module 140 and the processing module 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, and supplies power to the processing module 110, the internal memory 121, the external memory, the display screen 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle number, battery health status (leakage, impedance), etc. In some other embodiments, the power management module 141 can also be set in the processing module 110. In other embodiments, the power management module 141 and the charging management module 140 can also be set in the same device.

[0068] The wireless communication function of the mobile phone 100 can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor and the baseband processor.

[0069] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in mobile phone 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve the utilization of antennas. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0070] The mobile communication module 150 can provide solutions for wireless communications including 2G / 3G / 4G / 5G applied to the mobile phone 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be arranged in the processing module 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be arranged in the same device as at least some of the modules of the processing module 110.

[0071] The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low-frequency baseband signal to be sent into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to a speaker 170A, a receiver 170B, etc.), or displays an image or video through a display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processing module 110 and be set in the same device as the mobile communication module 150 or other functional modules.

[0072] The wireless communication module 160 can provide wireless communication solutions including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc., which are applied to the mobile phone 100. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, modulates the frequency of the electromagnetic wave signal and performs filtering, and sends the processed signal to the processing module 110. The wireless communication module 160 can also receive the signal to be sent from the processing module 110, modulate the frequency of the signal, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2.

[0073] In some embodiments, the antenna 1 of the mobile phone 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the mobile phone 100 can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technology. The GNSS may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a Beidou navigation satellite system (BDS), a quasi-zenith satellite system (QZSS) and / or a satellite based augmentation system (SBAS).

[0074] The mobile phone 100 implements the display function through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, which is connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processing module 110 may include one or more GPUs, which execute program instructions to generate or change display information.

[0075] The display screen 194 is used to display images, videos, etc. The display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the mobile phone 100 may include 1 or N display screens 194, where N is a positive integer greater than 1.

[0076] The mobile phone 100 can realize the shooting function through the ISP, the camera 193, the video codec, the GPU, the display screen 194 and the application processor. In some embodiments, the mobile phone 100 can realize the video communication function through the ISP, the camera 193, the video codec, the GPU and the application processor.

[0077] ISP is used to process the data fed back by camera 193. For example, when taking a photo, the shutter is opened, and the light is transmitted to the camera photosensitive element through the lens. The light signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to ISP for processing and converts it into an image visible to the naked eye. ISP can also perform algorithm optimization on the noise, brightness, and skin color of the image. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, ISP can be set in camera 193.

[0078] The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then passes the electrical signal to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV or other format. In some embodiments, the mobile phone 100 may include 1 or N cameras 193, where N is a positive integer greater than 1.

[0079] The digital signal processor is used to process digital signals, and can process not only digital image signals but also other digital signals. For example, when the mobile phone 100 is selecting a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy.

[0080] Video codecs are used to compress or decompress digital videos. Mobile phone 100 may support one or more video codecs. Thus, mobile phone 100 may play or record videos in various coding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

[0081] NPU is a neural network (NN) computing processor. By drawing on the structure of biological neural networks, such as the transmission mode between neurons in the human brain, it can quickly process input information and can also continuously self-learn. Through NPU, the intelligent cognition of the mobile phone 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, etc.

[0082] The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the mobile phone 100. The external memory card communicates with the processing module 110 through the external memory interface 120 to implement a data storage function, such as storing music, video and other files in the external memory card.

[0083] The internal memory 121 can be used to store computer executable program codes, which include instructions. The processing module 110 executes various functional applications and data processing of the mobile phone 100 by running the instructions stored in the internal memory 121. The internal memory 121 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area can store data created during the use of the mobile phone 100 (such as audio data, a phone book, etc.), etc. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0084] The mobile phone 100 can implement audio functions such as music playing and recording through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor.

[0085] The audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signals. The audio module 170 can also be used to encode and decode audio signals. In some embodiments, the audio module 170 can be arranged in the processing module 110, or some functional modules of the audio module 170 can be arranged in the processing module 110.

[0086] The speaker 170A, also called a "speaker", is used to convert an audio electrical signal into a sound signal. The mobile phone 100 can listen to music or listen to a hands-free call through the speaker 170A.

[0087] The receiver 170B, also called a "earpiece", is used to convert audio electrical signals into sound signals. When the mobile phone 100 receives a call or voice message, the voice can be received by placing the receiver 170B close to the ear.

[0088] Microphone 170C, also called "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can speak by putting their mouth close to the microphone 170C to input the sound signal into the microphone 170C. The mobile phone 100 can be provided with at least one microphone 170C. In other embodiments, the mobile phone 100 can be provided with two microphones 170C, which can not only collect sound signals but also realize noise reduction function. In other embodiments, the mobile phone 100 can also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify the sound source, realize directional recording function, etc.

[0089] The earphone interface 170D is used to connect a wired earphone and can be a USB interface 130 or a 3.5 mm open mobile terminal platform (OMTP) standard interface or a cellular telecommunications industry association of the USA (CTIA) standard interface.

[0090] The key 190 includes a power key, a volume key, etc. The key 190 can be a mechanical key or a touch key. The mobile phone 100 can receive key input and generate key signal input related to the user settings and function control of the mobile phone 100.

[0091] Motor 191 can generate vibration prompts. Motor 191 can be used for incoming call vibration prompts, and can also be used for touch vibration feedback. For example, touch operations acting on different applications (such as taking pictures, audio playback, etc.) can correspond to different vibration feedback effects. For touch operations acting on different areas of the display screen 194, motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminders, receiving messages, alarm clocks, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

[0092] Indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, messages, missed calls, notifications, etc.

[0093] The SIM card interface 195 is used to connect the SIM card. The SIM card can be connected to and separated from the mobile phone 100 by inserting it into the SIM card interface 195 or pulling it out from the SIM card interface 195. The mobile phone 100 can support 1 or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 can also be compatible with external memory cards. The mobile phone 100 interacts with the network through the SIM card to realize functions such as calls and data communications. In some embodiments, the mobile phone 100 uses an eSIM, i.e., an embedded SIM card. The eSIM card can be embedded in the mobile phone 100 and cannot be separated from the mobile phone 100.

[0094] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0095] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0096] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0097] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0098] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A tea quality detection method based on big data, characterized in that: include: Acquire a photographed image of tea leaves; Based on the photographed images of the tea leaves, a variational autoencoder is used to generate tea leaves images under different colors of light and similarities of tea leaves images under different colors of light; Constructing a lighting graph structure, wherein the lighting graph structure includes tea leaves nodes illuminated by multiple colors and multiple edges between the tea leaves nodes illuminated by multiple colors, wherein the node feature of each tea leaves node illuminated by the color includes a lighting image of the tea leaves under the lighting of the color, and the feature of the edges between the tea leaves nodes is the similarity of the tea leaves images under lighting of different colors; The illumination map structure is processed based on the first graph convolutional network to determine whether the tea quality test is qualified.

2. The tea quality detection method based on big data according to claim 1, characterized in that: The method further comprises: Determining multiple vibration frequencies of the tea leaves using a deep neural network model based on the photographed image of the tea leaves; Controlling the vibration device to vibrate based on the multiple vibration frequencies of the tea leaves, and obtaining a vibration video of the tea leaves at each vibration frequency; Based on the frequency information determination model, the vibration video of the tea leaves at each vibration frequency is processed to determine the vibration information of the tea leaves at each vibration frequency and the similarity of the vibration information of the tea leaves at different vibration frequencies; Constructing a tea vibration graph structure, the tea vibration graph structure comprising tea nodes of multiple frequencies and multiple edges between the tea nodes of multiple frequencies, the node feature of each tea node of each frequency comprising vibration information of the tea at each vibration frequency, and the edge feature between tea nodes of different frequencies being the similarity of vibration information of the tea at different vibration frequencies; The tea vibration graph structure is processed based on the second graph convolutional network to determine whether the tea vibration detection is qualified.

3. The tea quality detection method based on big data according to claim 2, characterized in that: The frequency information determination model is a long-term and short-term neural network model, the input of the frequency information determination model is the vibration video of the tea leaves at each vibration frequency, the output of the frequency information determination model is the vibration information of the tea leaves at each vibration frequency and the similarity of the vibration information of the tea leaves at different vibration frequencies, and the vibration information of the tea leaves at each vibration frequency includes the average amplitude of the tea leaves and the amplitude of each piece of tea leaves.

4. The tea quality detection method based on big data according to claim 1, characterized in that: The input of the variational autoencoder is the photographed image of the tea leaves, and the output of the variational autoencoder is the images of the tea leaves under different colors of light and the similarities of the images of the tea leaves under different colors of light.

5. A tea quality detection system based on big data, characterized in that: include: An acquisition module is used to acquire the photographed image of tea leaves; A generation module, used for generating tea images under different colors of light and similarities of tea images under different colors of light by using a variational autoencoder based on the photographed images of the tea leaves; A construction module is used to construct a light map structure, wherein the light map structure includes tea leaves nodes illuminated by multiple colors and multiple edges between the tea leaves nodes illuminated by multiple colors, the node feature of each tea leaf node illuminated by the color includes the illumination image of the tea leaves under the illumination of the color, and the feature of the edge between the tea leaves nodes is the similarity of the tea leaves images under illumination of different colors; The detection module is used to process the illumination map structure based on the first graph convolutional network to determine whether the tea quality test is qualified.

6. The tea quality detection system based on big data according to claim 5, characterized in that: The system is also used to: Determining multiple vibration frequencies of the tea leaves using a deep neural network model based on the photographed image of the tea leaves; Controlling the vibration device to vibrate based on the multiple vibration frequencies of the tea leaves, and obtaining a vibration video of the tea leaves at each vibration frequency; Based on the frequency information determination model, the vibration video of the tea leaves at each vibration frequency is processed to determine the vibration information of the tea leaves at each vibration frequency and the similarity of the vibration information of the tea leaves at different vibration frequencies; Constructing a tea vibration graph structure, the tea vibration graph structure comprising tea nodes of multiple frequencies and multiple edges between the tea nodes of multiple frequencies, the node feature of each tea node of each frequency comprising vibration information of the tea at each vibration frequency, and the edge feature between tea nodes of different frequencies being the similarity of vibration information of the tea at different vibration frequencies; The tea vibration graph structure is processed based on the second graph convolutional network to determine whether the tea vibration detection is qualified.

7. The tea quality detection system based on big data according to claim 6, characterized in that: The frequency information determination model is a long-term and short-term neural network model, the input of the frequency information determination model is the vibration video of the tea leaves at each vibration frequency, the output of the frequency information determination model is the vibration information of the tea leaves at each vibration frequency and the similarity of the vibration information of the tea leaves at different vibration frequencies, and the vibration information of the tea leaves at each vibration frequency includes the average amplitude of the tea leaves and the amplitude of each piece of tea leaves.

8. The tea quality detection system based on big data according to claim 5, characterized in that: The input of the variational autoencoder is the photographed image of the tea leaves, and the output of the variational autoencoder is the images of the tea leaves under different colors of light and the similarities of the images of the tea leaves under different colors of light.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the tea quality detection method based on big data as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the big data-based tea quality detection method as described in any one of claims 1 to 4 is implemented.

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