Intelligent preparation method, device, electronic equipment and storage medium for spicy flavor

Abstract

This invention discloses an intelligent blending method, device, electronic device, and storage medium for spicy flavor. The method includes: acquiring the current spiciness value and current color parameter of the spicy sample to be blended; setting a target spiciness value and target color parameter; determining the spiciness deviation based on the target spiciness value and the current spiciness value; calculating a first blending amount of chili oleoresin based on the spiciness deviation; determining the color deviation based on the target color parameter and the current color parameter; calculating a second blending amount of paprika based on the color deviation and the influence of paprika on color; blending based on the first and second blending amounts; detecting the actual spiciness value and actual color parameter of the blended sample; if the target value is not reached, using the actual spiciness value and actual color parameter as the spicy sample to be blended, and repeating the above steps until the target value is reached. This method clarifies the quantitative relationship between the amount of chili oleoresin and the spiciness, achieving precise blending of spicy flavor.

Description

Technical Field

[0001] This invention relates to the field of food flavor blending technology, specifically to an intelligent blending method, apparatus, electronic device, and storage medium for spicy flavor. Background Technology

[0002] In the production and research and development of spicy food, flavor evaluation (especially spiciness and color) is a key factor affecting product quality. Traditional flavor evaluation relies on human sensory assessment, which has problems such as strong subjectivity, large individual differences, and poor repeatability; while flavor blending mainly relies on experience, making it difficult to achieve standardization and precision, resulting in unstable product quality.

[0003] While there are single methods for detecting spiciness or analyzing color in related technologies, there is a lack of a comprehensive evaluation system based on multimodal perception, which cannot directly guide the intelligent blending of key ingredients such as chili oil. Summary of the Invention

[0004] In view of the above problems, the present invention provides an intelligent blending method, device, electronic device and storage medium for spicy flavor, which can intelligently recommend the amount of capsicum oleoresin and capsicum red, so as to achieve precise control and standardized production of spicy flavor.

[0005] In a first aspect, embodiments of the present invention provide an intelligent preparation method for a spicy flavor, the intelligent preparation method for a spicy flavor comprising:

[0006] Obtain the current spiciness value and current color parameters of the spicy sample to be prepared;

[0007] Set the target spiciness value and target color parameters;

[0008] The spiciness deviation is determined based on the target spiciness value and the current spiciness value, and the first dosage of chili oil resin is calculated based on the spiciness deviation.

[0009] The color deviation is determined based on the target color parameter and the current color parameter, and the second amount of paprika is calculated based on the color deviation and the effect of paprika on color.

[0010] The mixture is prepared based on the first and second mixing amounts. The actual spiciness value and actual color parameters of the prepared sample are detected. If the target spiciness value and target color parameters are not reached, the actual spiciness value and actual color parameters are taken as the sample to be prepared. The above steps are repeated until the target spiciness value and target color parameters are reached.

[0011] In some embodiments, calculating the first dosage of capsicum oleoresin based on the spiciness deviation includes:

[0012] A spiciness deviation calculation model is pre-established, wherein the expression of the spiciness deviation calculation model is:

[0013]

[0014] In the formula, The change in spiciness caused by capsicum oleoresin. For the quality of chili oil resin, The value represents the weight of the chili oil, 102.8 is the conversion coefficient between capsaicin and spiciness based on the Scoville index, and 6.6% is the capsaicin content in chili oleoresin.

[0015] The spiciness deviation is input into the spiciness deviation calculation model to obtain the first dosage of chili oil resin.

[0016] In some embodiments, calculating the second amount of capsanthin based on the color deviation and the effect of capsanthin on color includes:

[0017] A colorimetric model is pre-established, wherein a mapping relationship is constructed between the amount of paprika oleoresin and the colorimetric parameters based on pre-trained data. The mapping relationship is obtained by collecting colorimetric parameters under different amounts of paprika oleoresin and training them using a machine learning algorithm.

[0018] The chromaticity deviation is input into the chromaticity model to obtain the second amount of paprika red used in the blend.

[0019] In some embodiments, the method further includes:

[0020] If the deviation between the actual chromaticity parameters of the prepared sample and the target chromaticity parameters exceeds a preset range, the amount of paprika oleoresin is adjusted based on the chromaticity deviation value, and the expression is as follows:

[0021] +

[0022] In the formula, This is the color correction factor. These are the actual chromaticity parameters. For target chromaticity parameters, The amount of paprika used before the correction. This is the revised amount of paprika oleoresin.

[0023] In some embodiments, obtaining the current spiciness value and current color parameter of the spicy sample to be prepared includes:

[0024] The current spiciness value is obtained based on electronic throat and tongue detection;

[0025] The current chromaticity parameters are obtained through chromaticity detection based on AI visual learning.

[0026] In some embodiments, the AI-based visual learning-based colorimetric detection includes:

[0027] The process includes image acquisition, image preprocessing, feature extraction, and color classification. The feature extraction is used to extract RGB or HSV space features of colors, and the color classification is used to map the features to standardized color parameters.

[0028] In some embodiments, inputting the spiciness deviation into the spiciness deviation calculation model to obtain the first dosage of capsicum oleoresin includes:

[0029] Based on the aforementioned spiciness deviation calculation model, a variation of the calculation method for determining the first formulation amount of the chili oleoresin is determined:

[0030]

[0031] In the formula, Due to spiciness deviation, , For the target spiciness value, This is the current spiciness level.

[0032] The first amount of chili oleoresin used in the formulation is calculated based on the spiciness deviation and the calculation variation.

[0033] Secondly, embodiments of the present invention provide an intelligent preparation device for a spicy flavor, the intelligent preparation device for a spicy flavor comprising:

[0034] The acquisition module is used to acquire the current spiciness value and current color parameters of the spicy sample to be prepared;

[0035] The setting module is used to set the target spiciness value and target color parameters;

[0036] The first calculation module is used to determine the spiciness deviation based on the target spiciness value and the current spiciness value, and to calculate the first dosage of chili oil resin based on the spiciness deviation.

[0037] The second calculation module is used to determine the color deviation based on the target color parameter and the current color parameter, and to calculate the second amount of paprika based on the color deviation and the influence of paprika on the color.

[0038] The mixing module is used to mix the sample based on the first mixing amount and the second mixing amount, detect the actual spiciness value and actual color parameter of the mixed sample, and if the target spiciness value and target color parameter are not reached, the actual spiciness value and actual color parameter are taken as the spicy sample to be mixed, and the above steps are repeated until the target spiciness value and target color parameter are reached.

[0039] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory stores program code that can run on the processor, and when the program code is executed by the processor, it implements the intelligent blending method for spicy flavor as described in any embodiment of the first aspect.

[0040] Fourthly, embodiments of this application provide a computer storage medium storing one or more programs, which can be executed by an electronic device as described in the third aspect to implement the intelligent preparation method for spicy flavor as described in any embodiment of the first aspect.

[0041] This invention provides an intelligent blending method, apparatus, electronic device, and storage medium for spicy flavor. The method includes: acquiring the current spiciness value and current color parameter of the spicy sample to be blended; setting a target spiciness value and target color parameter; determining the spiciness deviation based on the target spiciness value and the current spiciness value; calculating a first blending amount of chili oleoresin based on the spiciness deviation; determining the color deviation based on the target color parameter and the current color parameter; calculating a second blending amount of paprika based on the color deviation and the influence of paprika on color; blending based on the first and second blending amounts; detecting the actual spiciness value and actual color parameter of the blended sample; if the target value is not reached, using the actual spiciness value and actual color parameter as the spicy sample to be blended, and repeating the above steps until the target value is reached. This clarifies the quantitative relationship between the amount of chili oleoresin and the spiciness, achieving precise blending of spicy flavor.

[0042] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0043] The invention will now be described in more detail with reference to embodiments and the accompanying drawings.

[0044] Figure 1 A schematic flowchart of an exemplary intelligent preparation method for spicy flavor is shown in one embodiment of the present invention.

[0045] Figure 2 The illustration shows a schematic diagram of the intelligent blending process for spicy flavor in an exemplary embodiment of the present invention.

[0046] Figure 3 A schematic block diagram of an exemplary colorimetric detection structure proposed in one embodiment of the present invention is shown;

[0047] Figure 4The diagram shows a structural block diagram of a smart mixing device for spicy flavor according to an embodiment of the present invention.

[0048] Figure 5 A structural block diagram of an electronic device for performing a smart blending method for spicy flavor according to an embodiment of this application is shown.

[0049] Figure 6 This application illustrates a computer-readable storage medium for storing or carrying a smart blending method for spicy flavor according to an embodiment of this application. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0051] While existing technologies employ single methods for spiciness detection or colorimetric analysis, they lack a comprehensive evaluation and blending system based on multimodal perception. Furthermore, the correlation model between spiciness and ingredient quantities is not clearly defined, failing to directly guide the intelligent blending of key ingredients such as chili oleoresin. Therefore, a technical solution is needed that combines multimodal perception for digitization and enables intelligent blending based on evaluation results.

[0052] To address the aforementioned issues, the applicant proposes an intelligent preparation method, device, electronic equipment, and storage medium for spicy flavor, which can achieve the following beneficial effects:

[0053] 1. This application clarifies the quantitative relationship between the amount of capsicum oleoresin used and the spiciness, and combines machine vision to guide the amount of capsicum red used, thereby achieving precise blending of numbing and spicy flavor.

[0054] 2. This application also employs multimodal perception (electronic tongue + machine vision) to achieve digital evaluation of spiciness and color, thereby improving the objectivity and consistency of the evaluation.

[0055] 3. This application is applicable to the standardized control of spicy products in industrial production, reducing reliance on manual experience and improving production efficiency and product quality stability.

[0056] The intelligent blending method for spicy flavor will be described in detail in subsequent embodiments.

[0057] The following describes the application scenarios of the intelligent blending method for spicy flavor provided in the embodiments of the present invention:

[0058] Please see Figure 1 , Figure 1This is a schematic flowchart of a smart preparation method for spicy flavor provided in an embodiment of the present invention. In this embodiment, the smart preparation method for spicy flavor can be applied to, for example... Figure 4 The shown is a smart blending device 300 for neutralizing spicy flavor. Figure 5 In the electronic device 200 shown, the following is specifically for... Figure 1 The process shown is explained in detail, and the intelligent preparation method for spicy flavor can include S110 to S150.

[0059] S110: Obtain the current spiciness value and current color parameters of the spicy sample to be prepared.

[0060] In some implementations, S110 includes S111 to S112.

[0061] S111: The current spiciness value is obtained based on electronic throat and tongue detection.

[0062] In this embodiment of the application, a preliminary evaluation of the spicy flavor is achieved through multimodal sensing before blending. Specifically, an electronic gag is used as a sensing component to detect the current spiciness value of the spicy sample.

[0063] S112: The current chromaticity parameters are obtained through chromaticity detection based on AI visual learning.

[0064] In this embodiment of the application, based on the AI ​​visual learning function, the color of the spicy sample is acquired and analyzed, and the color parameters are output.

[0065] In some implementations, it also includes:

[0066] A multimodal digital evaluation dataset is constructed based on the current spiciness value and the current color parameter.

[0067] Based on a multimodal digital evaluation dataset, a comprehensive digital evaluation of the flavor of spicy samples is conducted.

[0068] In this embodiment, the spiciness and color of the sample are comprehensively verified in detail to ensure the accuracy of the initial identification and evaluation.

[0069] S112's AI-based visual learning-based colorimetric detection specifically includes:

[0070] Image acquisition, image preprocessing, feature extraction, and color classification are performed. Feature extraction is used to extract RGB or HSV space features of color, and color classification is used to map the features to standardized color parameters.

[0071] In this embodiment, the AI ​​visual learning function includes image preprocessing, feature extraction, and color classification. Image preprocessing is used to eliminate noise interference in the acquired image, feature extraction is used to extract RGB or HSV space features of color, and color classification is used to map the features to standardized color parameters.

[0072] Specifically, this application uses the data obtained from the aforementioned AI visual image processing and electronic gag processing as a dataset, trains the data, and then comprehensively evaluates the spicy samples to obtain the current spiciness value of the spicy samples to be prepared subsequently obtained by this application. and current chromaticity parameters .

[0073] Specifically, the spiciness detection in this application uses an electronic tongue sensor (such as a taste sensor based on electrochemical principles) to convert the spiciness signal into an electrical signal. After signal amplification and A / D conversion, the spiciness value is output in "degrees (°)".

[0074] See Figure 3 The schematic block diagram of the colorimetric detection structure shown in this application illustrates the image acquisition process: using an industrial camera to capture surface images of spicy samples (such as chili oil or spicy sauce) under a standard light source.

[0075] AI vision learning: The image is trained by a convolutional neural network (CNN) to learn the color features corresponding to different capsicum red contents and output a standardized chromaticity parameter of 0-100 (the higher the value, the brighter the color).

[0076] After constructing the dataset, the spiciness value and color parameter were normalized to build a multimodal dataset containing timestamps and sample numbers.

[0077] The evaluation model was subsequently established, which specifically adopted a weighted scoring method, setting the spiciness weight at 0.6 and the color weight at 0.4, and calculating a comprehensive score (out of 100) to achieve digital evaluation.

[0078] In this embodiment, during the acquisition of the current spiciness value and current color parameters, the spiciness is perceived and detected through an electronic tongue. The color of the spicy food is analyzed using machine vision learning function, and color parameters are output. A multimodal digital evaluation dataset is constructed using the detected spiciness value and color parameters. Based on this multimodal digital evaluation dataset, the flavor of the spicy sample is comprehensively digitally evaluated to obtain accurate preliminary detection results. This ensures accurate detection results and facilitates subsequent intelligent adjustment of the spiciness value and color parameters.

[0079] S120: Set the target spiciness value and target color parameters.

[0080] In this embodiment of the application, a target spiciness value is set. and target chromaticity parameters .

[0081] S130: Determine the spiciness deviation based on the target spiciness value and the current spiciness value, and calculate the first dosage of chili oleoresin based on the spiciness deviation.

[0082] The first formulation amount of capsicum resin calculated based on spiciness deviation in S130 includes:

[0083] S131: A spiciness deviation calculation model is established in advance, wherein the expression of the spiciness deviation calculation model is:

[0084]

[0085] In the formula, The change in spiciness caused by capsicum oleoresin. For the quality of chili oil resin, The value represents the weight of the chili oil, 102.8 is the conversion coefficient between capsaicin and spiciness based on the Scoville index, and 6.6% is the capsaicin content in chili oleoresin.

[0086] S132: Input the spiciness deviation into the spiciness deviation calculation model to obtain the first blending amount of chili oleoresin.

[0087] S132 includes:

[0088] Based on the spiciness deviation calculation model, a variation of the calculation method for determining the first blending amount of capsicum oleoresin is determined:

[0089]

[0090] In the formula, Due to spiciness deviation, , For the target spiciness value, This is the current spiciness level.

[0091] Based on the spiciness deviation and calculation variation, calculate the first blending amount of chili oleoresin.

[0092] S140: Determine the chromaticity deviation based on the target chromaticity parameter and the current chromaticity parameter, and calculate the second blending amount of paprika based on the chromaticity deviation and the influence of paprika on chromaticity.

[0093] S140 calculates the second blending amount of capsanthin based on chromaticity deviation and the effect of capsanthin on chromaticity, including:

[0094] A colorimetric model is pre-established, in which a mapping relationship is constructed between the amount of paprika oleoresin and the colorimetric parameters based on pre-trained data. The mapping relationship is obtained by collecting colorimetric parameters under different amounts of paprika oleoresin and training them using a machine learning algorithm.

[0095] Input the colorimetric deviation into the colorimetric model to obtain the second amount of paprika red used in the blend.

[0096] In embodiments S130 and S140:

[0097] For example, the current spiciness of the chili oil to be prepared is detected as L0=30°, and the current color parameter is... =50; Set target =50°, =80.

[0098] The amount of capsicum oleoresin used (first batch dosage) is calculated as follows:

[0099] For example, spiciness deviation =50-30=20°.

[0100] Given that the total weight of the chili oil is m2 = 10 kg, according to the formula L = (m1 × 6.6% × 102.8) / m2, we get m1 = (20 × 10) / (6.6% × 102.8) ≈ 2.9 kg.

[0101] The amount of paprika (second blending dosage) is calculated as follows:

[0102] Based on the pre-trained chromaticity model (it is known that for every 0.1kg increase in paprika oleoresin dosage, the chromaticity parameter increases by an average of 5), the chromaticity deviation ΔC = 80 - 50 = 30, and the recommended paprika oleoresin dosage m3 = 30 / 5 × 0.1 = 0.6kg.

[0103] S150: Based on the first and second mixing amounts, mix the ingredients and test the actual spiciness value and actual color parameters of the mixed sample. If the target spiciness value and target color parameters are not reached, use the actual spiciness value and actual color parameters as the sample to be mixed, and repeat the above steps until the target spiciness value and target color parameters are reached.

[0104] In this embodiment of the application, after adding capsicum oleoresin and capsicum red according to the recommended dosage, the test was repeated: see exemplary reference. Figure 2 The diagram below illustrates the specific implementation process of intelligent blending of spicy and numbing flavors:

[0105] If the actual spiciness Lactual = 48°, with a deviation of 2°, then the supplementary amount m1' = (2 × 10) / (6.6% × 102.8) ≈ 0.29 kg.

[0106] If the actual color Cactual = 75 and the deviation is 5, then add m3' = 5 / 5 × 0.1 = 0.1 kg of paprika oleoresin until the target value is reached.

[0107] The expression for the spiciness deviation calculation model is as follows:

[0108]

[0109] In the formula, The change in spiciness caused by capsicum oleoresin. For the quality of chili oil resin, 102.8 represents the weight of the chili oil, 102.8 represents the conversion coefficient between capsaicin and spiciness based on the Scoville index, and 6.6% represents the capsaicin content in chili oleoresin.

[0110] It should be noted that this application alters the spiciness of the product system by adjusting the content of chili oleoresin. The formula 102.8, representing the conversion coefficient between the Scoville Heat Unit (SHU) and spiciness (°), was obtained through extensive experimental and creative calibration. This formula ignores the base spiciness of the original chili oil and only calculates the change in spiciness brought about by the addition of chili oleoresin, making it particularly suitable for secondary blending scenarios using chili oil as a base.

[0111] In some implementations, the intelligent blending method for spicy flavor also includes:

[0112] If the deviation between the actual colorimetric parameters of the prepared sample and the target colorimetric parameters exceeds the preset range, the amount of paprika oleoresin will be adjusted based on the colorimetric deviation value, and the expression is as follows:

[0113] +

[0114] In the formula, This is the color correction factor. These are the actual chromaticity parameters. For target chromaticity parameters, The amount of paprika used before the correction. This is the revised amount of paprika oleoresin.

[0115] In summary, the intelligent blending method for spicy flavor provided in this application utilizes an electronic tongue to detect and perceive spiciness, combines AI visual learning to analyze the color of spicy foods, and constructs a multimodal perception-based digital evaluation model. Simultaneously, based on the evaluation results and a preset spiciness calculation scheme, it intelligently recommends the dosage of capsicum oleoresin and capsicum red, achieving precise control and standardized production of the spicy flavor. This application can improve the objectivity of spicy flavor evaluation and the accuracy of blending, providing strong support for quality control of spicy products in the food industry.

[0116] Please see Figure 4 , Figure 4 The present invention provides a structural block diagram of a smart mixing device for spicy flavoring. The device includes: an acquisition module 310, a setting module 320, a first calculation module 330, a second calculation module 340, and a mixing module 350, wherein:

[0117] The acquisition module 310 is used to acquire the current spiciness value and current color parameters of the spicy sample to be prepared;

[0118] The setting module 320 is used to set the target spiciness value and target color parameters;

[0119] The first calculation module 330 is used to determine the spiciness deviation based on the target spiciness value and the current spiciness value, and to calculate the first amount of chili oil resin to be added based on the spiciness deviation.

[0120] The second calculation module 340 is used to determine the color deviation based on the target color parameter and the current color parameter, and to calculate the second amount of paprika based on the color deviation and the influence of paprika on the color.

[0121] The mixing module 350 is used to mix the sample based on the first mixing amount and the second mixing amount, detect the actual spiciness value and actual color parameter of the mixed sample, and if the target spiciness value and target color parameter are not reached, the actual spiciness value and actual color parameter are taken as the spicy sample to be mixed, and the above steps are repeated until the target spiciness value and target color parameter are reached.

[0122] It should be noted that the device embodiments in this invention correspond to the aforementioned method embodiments. The specific principles in the device embodiments can be found in the content of the aforementioned method embodiments, and will not be repeated here.

[0123] In the several embodiments provided in this example, the coupling between modules can be electrical, mechanical, or other forms of coupling.

[0124] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0125] Please see Figure 5 , Figure 5 The present application provides a structural block diagram of an electronic device 200 that can perform the above-described intelligent preparation method for spicy flavor. The electronic device 200 may be a smartphone, tablet computer, computer, or portable computer.

[0126] The electronic device 200 also includes a processor 202 and a memory 204. The memory 204 stores programs that can execute the contents of the foregoing embodiments, and the processor 202 can execute the programs stored in the memory 204.

[0127] The processor 202 may include one or more cores for data processing and message matrix units. The processor 202 connects to various parts of the electronic device 200 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 204, and by calling data stored in the memory 204. Optionally, the processor 202 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 202 may integrate one or more of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem / decoder. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem is used for wireless communication. It is understood that the modem / decoder may also not be integrated into the processor and may be implemented separately through a communication chip.

[0128] Memory 204 may include random access memory (RAM) or read-only memory (ROM). Memory 204 can be used to store instructions, programs, code, code sets, or instruction sets. Memory 204 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (e.g., instructions for a user to obtain random numbers), instructions for implementing the various method embodiments described below, etc. The data storage area may also store data (e.g., random numbers) created by the terminal during use.

[0129] Electronic device 200 may also include a network module and a screen. The network module is used to receive and transmit electromagnetic waves, converting electromagnetic waves into electrical signals, thereby enabling communication with communication networks or other devices, such as audio playback devices. The network module may include various existing circuit elements used to perform these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, SIM cards, memory, etc. The network module can communicate with various networks such as the Internet, corporate intranets, and wireless networks, or communicate with other devices via wireless networks. The aforementioned wireless networks may include cellular telephone networks, wireless local area networks, or metropolitan area networks. The screen can display interface content and facilitate data interaction.

[0130] Please refer to Figure 6 , Figure 6 This diagram illustrates a structural block diagram of a computer-readable storage medium according to an embodiment of this application. The computer-readable storage medium 400 stores program code 410, which can be called by a processor to execute the methods described in the above method embodiments.

[0131] The computer-readable storage medium 400 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium 400 has storage space for program code 410 that performs any of the method steps described above. This program code 410 can be read from or written to one or more computer program products. The program code 410 may be compressed, for example, in a suitable form.

[0132] This application also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the intelligent blending method for spicy flavor described in the various optional implementations above.

[0133] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart preparation method for a spicy flavor, characterized in that, The method includes: Obtain the current spiciness value and current color parameters of the spicy sample to be prepared; Set the target spiciness value and target color parameters; The spiciness deviation is determined based on the target spiciness value and the current spiciness value, and the first dosage of chili oleoresin is calculated based on the spiciness deviation, including: A spiciness deviation calculation model is pre-established, wherein the expression of the spiciness deviation calculation model is: In the formula, The change in spiciness caused by capsicum oleoresin. For the quality of chili oil resin, The value represents the weight of the chili oil, 102.8 is the conversion coefficient between capsaicin and spiciness based on the Scoville index, and 6.6% is the capsaicin content in chili oleoresin. The spiciness deviation is input into the spiciness deviation calculation model to obtain the first dosage of chili oil resin, including: Based on the aforementioned spiciness deviation calculation model, a variation of the calculation formula for determining the first blending amount of the chili oleoresin is determined: In the formula, Due to spiciness deviation, , For the target spiciness value, This is the current spiciness level. Based on the spiciness deviation and the calculation variation, calculate the first blending amount of chili oil resin; The color deviation is determined based on the target color parameter and the current color parameter, and the second amount of paprika is calculated based on the color deviation and the effect of paprika on color. The mixture is prepared based on the first and second mixing amounts. The actual spiciness value and actual color parameters of the prepared sample are detected. If the target spiciness value and target color parameters are not reached, the actual spiciness value and actual color parameters are used as the sample to be prepared, and the above steps are repeated until the target spiciness value and target color parameters are reached. If the deviation between the actual color parameters of the prepared sample and the target color parameters exceeds a preset range, the amount of paprika is adjusted based on the color deviation value, and the expression is as follows: + In the formula, This is the color correction factor. These are the actual chromaticity parameters. For target chromaticity parameters, The amount of paprika used before the correction. This is the revised amount of paprika oleoresin.

2. The intelligent blending method for spicy flavor according to claim 1, characterized in that, The calculation of the second blending amount of paprika based on the color deviation and the influence of paprika on color includes: A colorimetric model is pre-established, wherein a mapping relationship is constructed between the amount of paprika oleoresin and the colorimetric parameters based on pre-trained data. The mapping relationship is obtained by collecting colorimetric parameters under different amounts of paprika oleoresin and training them using a machine learning algorithm. The chromaticity deviation is input into the chromaticity model to obtain the second amount of paprika red used in the blend.

3. The intelligent preparation method for spicy flavor according to claim 1, characterized in that, The process of obtaining the current spiciness value and current color parameters of the spicy sample to be prepared includes: The current spiciness value is obtained based on electronic throat and tongue detection; The current chromaticity parameters are obtained through chromaticity detection based on AI visual learning.

4. The intelligent blending method for spicy flavor according to claim 3, characterized in that, The AI-based visual learning-based colorimetric detection includes: The process includes image acquisition, image preprocessing, feature extraction, and color classification. The feature extraction is used to extract RGB or HSV space features of colors, and the color classification is used to map the features to standardized color parameters.

5. A smart mixing device for spicy flavor, characterized in that, The device includes: The acquisition module is used to acquire the current spiciness value and current color parameters of the spicy sample to be prepared; The setting module is used to set the target spiciness value and target color parameters; The first calculation module is used to determine the spiciness deviation based on the target spiciness value and the current spiciness value, and to calculate the first dosage of chili oil resin based on the spiciness deviation, including: A spiciness deviation calculation model is pre-established, wherein the expression of the spiciness deviation calculation model is: In the formula, The change in spiciness caused by capsicum oleoresin. For the quality of chili oil resin, The value represents the weight of the chili oil, 102.8 is the conversion coefficient between capsaicin and spiciness based on the Scoville index, and 6.6% is the capsaicin content in chili oleoresin. The spiciness deviation is input into the spiciness deviation calculation model to obtain the first dosage of chili oil resin, including: Based on the aforementioned spiciness deviation calculation model, a variation of the calculation formula for determining the first blending amount of the chili oleoresin is determined: In the formula, Due to spiciness deviation, , For the target spiciness value, This is the current spiciness level. Based on the spiciness deviation and the calculation variation, calculate the first blending amount of chili oil resin; The second calculation module is used to determine the color deviation based on the target color parameter and the current color parameter, and to calculate the second amount of paprika based on the color deviation and the influence of paprika on the color. The mixing module is used to mix the sample based on the first and second mixing amounts, detect the actual spiciness value and actual color parameters of the mixed sample, and if the target spiciness value and target color parameters are not reached, the actual spiciness value and actual color parameters are used as the sample to be mixed, and the above steps are repeated until the target spiciness value and target color parameters are reached; if the deviation between the actual color parameters of the mixed sample and the target color parameters exceeds a preset range, the amount of paprika is adjusted based on the color deviation value, the expression of which is: + In the formula, This is the color correction factor. These are the actual chromaticity parameters. For target chromaticity parameters, The amount of paprika used before the correction. This is the revised amount of paprika oleoresin.

6. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores program code that can run on the processor. When the program code is executed by the processor, it implements the intelligent preparation method for spicy flavor as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be invoked by one or more processors to execute the intelligent preparation method for spicy flavor as described in any one of claims 1-4.

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