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

Abstract

The invention discloses an intelligent spicy flavor blending method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a current pungency degree value and a current chromaticity parameter of a to-be-blended spicy sample, setting a target pungency degree value and a target chromaticity parameter, determining a pungency degree deviation according to the target pungency degree value and the current pungency degree value, and determining the pungency degree deviation according to the target chromaticity parameter. Calculating a first blending dosage of capsicum oleoresin based on the pungency degree deviation, determining a chromaticity deviation according to the target chromaticity parameter and the current chromaticity parameter, calculating a second blending dosage of capsicum red based on the chromaticity deviation and the influence of capsicum red on chromaticity, and blending based on the first blending dosage and the second blending dosage. Detecting the actual pungency degree value and the actual chromaticity parameter of the blended sample, if the actual pungency degree value and the actual chromaticity parameter do not reach the target value, taking the actual pungency degree value and the actual chromaticity parameter as a to-be-blended spicy sample, and repeating the steps until the target value is reached, so that the quantitative relationship between the dosage of the capsicum oleoresin and the pungency degree is defined, and accurate blending of the spicy flavor is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of food flavor preparation, and in particular to an intelligent preparation method, device, electronic equipment and storage medium for spicy flavor. Background Art

[0002] In the production and development of spicy foods, flavor evaluation (especially spiciness and color) is a key factor influencing product quality. Traditional flavor evaluation relies on manual sensory evaluation, which is subject to high subjectivity, large individual differences, and poor repeatability. Flavor blending, on the other hand, relies primarily on experience, making standardization and precision difficult to achieve, resulting in unstable product quality.

[0003] Although there are single methods for spiciness detection or color analysis in related technologies, there is a lack of a comprehensive evaluation system for multimodal perception, which cannot directly guide the intelligent formulation of key raw materials 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 dosage of capsicum oleoresin and capsanthin, and realize precise control and standardized production of spicy flavor.

[0005] In a first aspect, an embodiment of the present invention provides a method for intelligently preparing spicy food, the method comprising: 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; Determining a spiciness deviation according to the target spiciness value and the current spiciness value, and calculating a first blending amount of capsicum oleoresin based on the spiciness deviation; Determining a chromaticity deviation according to the target chromaticity parameter and the current chromaticity parameter, and calculating a second blending amount of capsanthin based on the chromaticity deviation and an effect of capsanthin on chromaticity; Based on the first and second mixing amounts, mixing is performed, and the actual spiciness value and actual color parameters of the mixed 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 spicy sample to be mixed, and the above steps are repeated for mixing until the target spiciness value and target color parameters are reached.

[0006] In some embodiments, calculating the first blending amount of capsicum oleoresin based on the spiciness deviation comprises: A spiciness deviation calculation model is established in advance, wherein the expression of the spiciness deviation calculation model is:

[0007] Where, The change in the spicy value brought by capsicum oleoresin, For the quality of capsicum oleoresin, is the weight of chili oil, 102.8 is the conversion coefficient between capsaicinoids 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 blending amount of capsicum oleoresin.

[0008] In some embodiments, calculating the second blending amount of capsanthin based on the chromaticity deviation and the effect of capsanthin on chromaticity comprises: A chromaticity model is pre-established, wherein a mapping relationship is constructed in the chromaticity model based on pre-trained capsanthin dosage and chromaticity parameters, wherein the mapping relationship is obtained by collecting chromaticity parameters at different capsanthin dosages and training using a machine learning algorithm; The chromaticity deviation is input into the chromaticity model to obtain a second blending amount of capsanthin.

[0009] In some embodiments, the method further comprises: If the deviation between the actual colorimetric parameters of the prepared sample and the target colorimetric parameters exceeds the preset range, the amount of capsanthin is corrected based on the colorimetric deviation value, and the expression is: +

[0010] Where, is the chromaticity correction coefficient, is the actual chromaticity parameter, is the target chromaticity parameter, is the amount of capsanthin before correction, This is the revised dosage of capsanthin.

[0011] In some embodiments, obtaining the current spiciness value and current color parameter of the spicy sample to be prepared includes: Get the current spiciness value based on electronic mouthpiece detection; The current chromaticity parameters are obtained through chromaticity detection based on AI visual learning.

[0012] In some embodiments, the colorimetric detection based on AI visual learning includes: Image acquisition, image preprocessing, feature extraction and chromaticity classification are performed, wherein the feature extraction is used to extract RGB or HSV space features of color, and the chromaticity classification is used to map the features into standardized chromaticity parameters.

[0013] In some embodiments, inputting the spiciness deviation into the spiciness deviation calculation model to obtain a first blending amount of capsicum oleoresin comprises: The calculation formula for obtaining the first blending amount of the capsicum oleoresin is determined according to the pungency deviation calculation model:

[0014] Where, For the spiciness deviation, , is the target spiciness value, is the current spiciness value; The first blending amount of capsicum oleoresin is calculated according to the spiciness deviation and the calculation variant.

[0015] In a second aspect, an embodiment of the present invention provides an intelligent spicy flavor mixing device, the intelligent spicy flavor mixing device comprising: An acquisition module is used to obtain the current spiciness value and current color parameters of the spicy sample to be prepared; A setting module is used to set the target spiciness value and target color parameters; a first calculation module, configured to determine a spiciness deviation according to the target spiciness value and the current spiciness value, and calculate a first blending amount of capsicum oleoresin based on the spiciness deviation; a second calculation module, configured to determine a chromaticity deviation according to the target chromaticity parameter and the current chromaticity parameter, and calculate a second blending amount of capsanthin based on the chromaticity deviation and an effect of capsanthin on chromaticity; The blending module is used to blend based on the first blending amount and the second blending amount, detect the actual spiciness value and actual color parameters of the blended sample, and if the target spiciness value and target color parameters are not reached, use the actual spiciness value and actual color parameters as the spicy sample to be blended, and repeat the above steps to blend until the target spiciness value and target color parameters are reached.

[0016] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory stores program code that can be run on the processor, and when the program code is executed by the processor, the intelligent blending method of spicy flavor as described in any embodiment of the first aspect is implemented.

[0017] In a fourth aspect, an embodiment of the present application provides 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 blending method of spicy flavor as described in any embodiment of the first aspect.

[0018] An embodiment of the present invention provides an intelligent blending method, device, electronic device and storage medium for spicy flavor, including obtaining a current spiciness value and a current chromaticity parameter of a spicy sample to be blended, setting a target spiciness value and a target chromaticity parameter, determining a spiciness deviation according to the target spiciness value and the current spiciness value, and calculating a first blending amount of capsicum oleoresin based on the spiciness deviation, determining a chromaticity deviation according to the target chromaticity parameter and the current chromaticity parameter, and calculating a second blending amount of capsicum oleoresin based on the chromaticity deviation and the influence of capsicum oleoresin on chromaticity, blending based on the first blending amount and the second blending amount, detecting the actual spiciness value and the actual chromaticity parameter of the blended sample, and if the target value is not reached, using the actual spiciness value and the actual chromaticity parameter as the spicy sample to be blended, repeating the above steps for blending until the target value is reached, clarifying the quantitative relationship between the amount of capsicum oleoresin and the spiciness, and achieving accurate blending of the spicy flavor.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Hereinafter, the present invention will be described in more detail based on embodiments with reference to the accompanying drawings.

[0021] Figure 1 A schematic flow chart of an exemplary intelligent blending method for spicy flavors proposed in one embodiment of the present invention is shown; Figure 2 A schematic diagram of a specific implementation process of intelligent blending of spicy flavors in an exemplary embodiment of the present invention is shown; Figure 3 A schematic block diagram of an exemplary chromaticity detection structure proposed in one embodiment of the present invention is shown; Figure 4 The following is a structural block diagram of an intelligent spicy flavor mixing device proposed in one embodiment of the present invention; Figure 5 A structural block diagram of an electronic device for executing the intelligent spicy flavor preparation method according to an embodiment of the present application is shown; Figure 6 A computer-readable storage medium proposed in an embodiment of the present application for storing or carrying a method for intelligently blending spicy flavor according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0023] While existing technologies offer single methods for spiciness detection or color analysis, they lack a comprehensive, multimodal perception-based evaluation and blending system. Furthermore, the correlation model between spiciness and ingredient dosage is unclear, making it difficult to directly guide the intelligent blending of key ingredients like capsicum oleoresin. Therefore, a technical solution is needed that can digitize multimodal perception and enable intelligent blending based on evaluation results.

[0024] To address the above issues, the applicant has proposed a method, device, electronic device, and storage medium for intelligently preparing spicy flavors, which can achieve the following beneficial effects: 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 to achieve precise blending of the spicy flavor.

[0025] 2. This application also uses multimodal perception (electronic mouthpiece + machine vision) to achieve digital evaluation of spiciness and color, improving the objectivity and consistency of the evaluation.

[0026] 3. This application is applicable to the standardized control of spicy products in industrial production, reducing dependence on manual experience, and improving production efficiency and product quality stability.

[0027] Among them, the intelligent blending method of spicy flavor is described in detail in the subsequent embodiments.

[0028] The following describes the application scenarios of the intelligent spicy flavor preparation method provided by the embodiment of the present invention: See also Figure 1 , Figure 1 This is a flow chart of a method for intelligently preparing spicy flavors provided in an embodiment of the present invention. In this embodiment, the method for intelligently preparing spicy flavors can be applied to, for example, Figure 4 The spicy flavor intelligent mixing device 300 neutralizes Figure 5 In the electronic device 200 shown in FIG. Figure 1 The process shown is described in detail, and the intelligent preparation method of spicy flavor may include S110 to S150.

[0029] S110: Obtain the current spiciness value and current color parameter of the spicy sample to be prepared.

[0030] In some embodiments, S110 includes: S111 to S112.

[0031] S111: Obtaining a current spiciness value based on electronic mouthpiece detection.

[0032] In an embodiment of the present application, a preliminary evaluation of the spicy flavor is achieved through multimodal perception before mixing, wherein an electronic mouthpiece is used as a sensing component to detect the current spiciness value of the spicy sample.

[0033] S112: Obtain current chromaticity parameters through chromaticity detection based on AI visual learning.

[0034] In the embodiment of the present application, based on the AI ​​visual learning function, the color of the spicy sample is imaged and analyzed, and the chromaticity parameters are output.

[0035] In some embodiments, further comprising: A multimodal digital evaluation dataset is constructed based on the current spiciness value and the current chromaticity parameters.

[0036] Based on a multimodal digital evaluation dataset, a comprehensive digital evaluation of the flavor of spicy samples was performed.

[0037] In this embodiment, the spiciness and color of the sample are comprehensively confirmed in detail to ensure the accuracy of the preliminary identification and evaluation.

[0038] The colorimetric detection based on AI visual learning in S112 specifically includes: Image acquisition, image preprocessing, feature extraction and color classification are performed, wherein feature extraction is used to extract RGB or HSV space features of color, and color classification is used to map features into standardized color parameters.

[0039] In this embodiment, the AI ​​visual learning function includes image preprocessing, feature extraction and chromaticity classification, wherein image preprocessing is used to eliminate noise interference in the captured image, feature extraction is used to extract RGB or HSV spatial features of color, and chromaticity classification is used to map features into standardized chromaticity parameters.

[0040] Specifically, the present application uses the data obtained by the above-mentioned AI visual image processing and electronic mouthpiece processing as a data set, performs training, and then comprehensively evaluates the spicy samples to obtain the current spiciness value of the spicy sample to be prepared obtained by the present application. and the current chromaticity parameters .

[0041] Specifically, the spiciness detection of the present application adopts an electronic tongue and throat 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 in units of "degrees (°)" is output.

[0042] See Figure 3The schematic block diagram of the colorimetric detection structure shown in the figure, the image acquisition of this application: an industrial camera is used to capture the surface image of spicy samples (such as chili oil, spicy sauce) under a standard light source.

[0043] AI visual learning: Images are trained using a convolutional neural network (CNN) to learn the color characteristics corresponding to different levels of chili red, and output standardized chromaticity parameters from 0 to 100 (higher values ​​indicate brighter red).

[0044] After constructing the data set, the spiciness value and color parameters are normalized to construct a multimodal dataset containing timestamps and sample numbers.

[0045] Subsequently, an evaluation model was established, which specifically adopted a weighted scoring method, set the spiciness weight to 0.6 and the color weight to 0.4, and calculated the comprehensive score (out of 100 points) to achieve digital evaluation.

[0046] In an embodiment of the present application, in the process of obtaining the current spiciness value and the current chromaticity parameters, the perception detection of spiciness is realized through an electronic mouthpiece, the chromaticity of the spicy food is analyzed by using the machine vision learning function, and the chromaticity parameters are output. The spiciness value and chromaticity parameters obtained by detection are used to construct a multimodal digital evaluation data set, and based on the multimodal digital evaluation data set, a comprehensive digital evaluation of the flavor of the spicy sample is performed to obtain accurate preliminary detection results, so that the detection results are accurate and facilitate subsequent intelligent adjustment of the spiciness value and chromaticity parameters.

[0047] S120: Setting a target spiciness value and a target chromaticity parameter.

[0048] In the embodiment of the present application, the target spiciness value is set and target chromaticity parameters .

[0049] S130: Determine a spiciness deviation according to the target spiciness value and the current spiciness value, and calculate a first blending amount of capsicum oleoresin based on the spiciness deviation.

[0050] Calculating a first blending amount of capsicum oleoresin based on the spiciness deviation in S130 includes: S131: Pre-establish a spiciness deviation calculation model, wherein the expression of the spiciness deviation calculation model is:

[0051] Where, The change in the spicy value brought by capsicum oleoresin, For the quality of capsicum oleoresin, is the weight of chili oil, 102.8 is the conversion coefficient between capsaicinoids and spiciness based on the Scoville index, and 6.6% is the capsaicin content in chili oleoresin; S132: Inputting the spiciness deviation into a spiciness deviation calculation model to obtain a first blending amount of capsicum oleoresin.

[0052] S132 includes: The calculation formula for obtaining the first blending amount of capsicum oleoresin is determined based on the spiciness deviation calculation model:

[0053] Where, For the spiciness deviation, , is the target spiciness value, is the current spiciness value; The first blending amount of capsicum oleoresin is calculated according to the spiciness deviation and the calculation variant.

[0054] S140: determining a chromaticity deviation according to the target chromaticity parameter and the current chromaticity parameter, and calculating a second blending amount of capsanthin based on the chromaticity deviation and the influence of capsanthin on the chromaticity.

[0055] In S140, the second blending amount of capsicum is calculated based on the chromaticity deviation and the influence of capsicum on the chromaticity, including: Pre-establishing a colorimetric model, wherein a mapping relationship is constructed in the colorimetric model based on pre-trained capsanthin dosage and colorimetric parameters, wherein the mapping relationship is obtained by collecting colorimetric parameters at different capsanthin dosages and training using a machine learning algorithm; The chromaticity deviation is input into the chromaticity model to obtain the second blending amount of capsicum red.

[0056] In the embodiments of S130 and S140: For example, the current spicy degree L0 of the chili oil to be prepared is detected to be 30°, and the current color parameter =50; set a goal =50°, =80.

[0057] The amount of capsicum oleoresin (first blending amount) is calculated as: For example, spiciness deviation =50-30=20°.

[0058] It is known that the total weight of chili oil is m2=10kg. According to the formula L=(m1×6.6%×102.8) / m2, we can get m1=(20×10) / (6.6%×102.8)≈2.9kg.

[0059] The amount of capsicum red (second blending amount) is calculated as: Based on the pre-trained colorimetric model (it is known that the colorimetric parameters increase by an average of 5 for every 0.1 kg increase in the amount of capsicum red), the colorimetric deviation ΔC = 80-50 = 30, and the recommended amount of capsicum red m3 = 30 / 5×0.1 = 0.6 kg.

[0060] S150: Mixing is performed based on the first mixing amount and the second mixing amount, and the actual spiciness value and actual color parameters of the mixed 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 spicy sample to be mixed, and the above steps are repeated for mixing until the target spiciness value and target color parameters are reached.

[0061] In the present application, after adding capsicum oleoresin and capsanthin in the recommended amount, retesting was performed: Figure 2 The following is a schematic diagram of the specific implementation process of the intelligent blending of spicy flavor: If the actual spiciness Lact = 48°, with a deviation of 2°, then the additional m1' = (2×10) / (6.6%×102.8) ≈ 0.29 kg.

[0062] If the actual chromaticity C real = 75 and the deviation is 5, then add chili red m3' = 5 / 5×0.1 = 0.1 kg until the target value is reached.

[0063] Among them, the expression of the spiciness deviation calculation model is:

[0064] Where, The spiciness value brought by capsicum oleoresin, For the quality of capsicum oleoresin, is the weight of chili oil, 102.8 is the conversion coefficient of capsaicinoids based on the Scoville index and spiciness, and 6.6% is the content of capsaicin in chili oil resin.

[0065] It should be noted that this application modifies the spiciness of the product system by adjusting the content of chili oleoresin. The conversion coefficient of 102.8, representing the Scoville HSU (SHU) to spiciness (°), was calibrated through extensive experimentation and creative effort. This formula ignores the base spiciness of the original chili oil and only calculates the spiciness change caused by the addition of chili oleoresin. This formula is particularly suitable for secondary blending scenarios using chili oil as a base.

[0066] In some embodiments, the intelligent preparation method of spicy flavor further includes: If the deviation between the actual colorimetric parameters of the sample after blending and the target colorimetric parameters exceeds the preset range, the amount of capsicum red is corrected based on the colorimetric deviation value, and the expression is: +

[0067] Where, is the chromaticity correction coefficient, is the actual chromaticity parameter, is the target chromaticity parameter, is the amount of capsanthin before correction, This is the revised dosage of capsanthin.

[0068] In summary, the intelligent spicy flavor blending method provided in the embodiments of this application uses an electronic mouthpiece to detect spiciness, combines AI visual learning capabilities to analyze the colorimetry of spicy foods, and constructs a multimodal perception digital evaluation model. Furthermore, based on the evaluation results and a preset spiciness calculation scheme, it intelligently recommends the dosage of capsicum oleoresin and capsanthin, achieving precise control and standardized production of spicy flavors. 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.

[0069] See also Figure 4 , Figure 4 This is a block diagram of a device for intelligently blending spicy food provided by the present invention. The device includes: an acquisition module 310, a setting module 320, a first calculation module 330, a second calculation module 340, and a blending module 350, wherein: An acquisition module 310 is used to obtain the current spiciness value and current color parameter of the spicy sample to be prepared; Setting module 320, for setting target spiciness value and target chromaticity parameter; A first calculation module 330 is configured to determine a spiciness deviation according to a target spiciness value and a current spiciness value, and calculate a first blending amount of capsicum oleoresin based on the spiciness deviation; A second calculation module 340 is configured to determine a chromaticity deviation based on the target chromaticity parameter and the current chromaticity parameter, and calculate a second blending amount of capsanthin based on the chromaticity deviation and the effect of capsanthin on the chromaticity; The blending module 350 is used to blend based on the first blending amount and the second blending amount, and detect the actual spiciness value and actual color parameters of the blended sample. If the target spiciness value and target color parameters are not reached, the actual spiciness value and actual color parameters are used as the spicy sample to be blended, and the above steps are repeated for blending until the target spiciness value and target color parameters are reached.

[0070] It should be noted that the device embodiment of the present invention corresponds to the aforementioned method embodiment. The specific principles in the device embodiment can be found in the contents of the aforementioned method embodiment, which will not be repeated here.

[0071] In several embodiments provided in this embodiment, the coupling between modules may be electrical, mechanical or other forms of coupling.

[0072] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0073] See also Figure 5 , Figure 5 The present invention provides a structural block diagram of an electronic device 200 that can execute the above-mentioned intelligent spicy flavor preparation method. The electronic device 200 can be a smart phone, tablet computer, computer, portable computer or other device.

[0074] The electronic device 200 further includes a processor 202 and a memory 204 . The memory 204 stores a program that can execute the contents of the aforementioned embodiments, and the processor 202 can execute the program stored in the memory 204 .

[0075] The processor 202 may include one or more cores for processing data and a message matrix unit. The processor 202 utilizes various interfaces and circuits to connect various components within the electronic device 200. It executes instructions, programs, code sets, or instruction sets stored in the memory 204, and accesses data stored in the memory 204 to perform various functions and process data within the electronic device 200. Optionally, the processor 202 may be implemented in hardware using at least one of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 202 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem (decoder). The CPU primarily handles the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem handles wireless communications. It is understood that the modem (decoder) may also be implemented independently of the processor using a separate communications chip.

[0076] Memory 204 may include random access memory (RAM) or read-only memory (ROM). Memory 204 may 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. 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 a random number), instructions for implementing the various method embodiments described below, and the like. The data storage area may also store data (e.g., random numbers) generated by the terminal during use.

[0077] The electronic device 200 may also include a network module and a screen. The network module is used to receive and transmit electromagnetic waves, converting them into electrical signals, thereby communicating with a communications network or other devices, such as an audio playback device. The network module may include various existing circuit components for performing these functions, such as an antenna, a radio frequency transceiver, a digital signal processor, an encryption / decryption chip, a subscriber identity module (SIM) card, memory, and the like. The network module can communicate with various networks such as the Internet, an intranet, or a wireless network, or with other devices via a wireless network. These wireless networks may include cellular telephone networks, wireless local area networks, or metropolitan area networks. The screen can display interface content and facilitate data exchange.

[0078] Please refer to Figure 6 , Figure 6 The computer-readable storage medium 400 stores program code 410, which can be called by a processor to execute the method described in the above method embodiment.

[0079] 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. Alternatively, the computer-readable storage medium includes a non-transitory computer-readable storage medium. Computer-readable storage medium 400 has storage space for program code 410 for executing any of the method steps described above. This program code 410 can be read from or written to one or more computer program products. Program code 410 may be compressed, for example, in a suitable format.

[0080] The present application also provides a computer program product or computer program, which 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 spicy flavor blending method described in the various optional implementations described above.

[0081] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not drive the essence of the corresponding technical solutions away from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent method for preparing spicy flavor, characterized in that: The method comprises: 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; Determining a spiciness deviation according to the target spiciness value and the current spiciness value, and calculating a first blending amount of capsicum oleoresin based on the spiciness deviation; Determining a chromaticity deviation according to the target chromaticity parameter and the current chromaticity parameter, and calculating a second blending amount of capsanthin based on the chromaticity deviation and an effect of capsanthin on chromaticity; Based on the first and second mixing amounts, mixing is performed, and the actual spiciness value and actual color parameters of the mixed 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 spicy sample to be mixed, and the above steps are repeated for mixing until the target spiciness value and target color parameters are reached.

2. The intelligent preparation method of spicy flavor according to claim 1, characterized in that: The first blending amount of capsicum oleoresin is calculated based on the pungency deviation, comprising: A spiciness deviation calculation model is established in advance, wherein the expression of the spiciness deviation calculation model is: Where, The spiciness value brought by capsicum oleoresin, For the quality of capsicum oleoresin, is the weight of chili oil, 102.8 is the conversion coefficient between capsaicinoids 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 blending amount of capsicum oleoresin.

3. The intelligent preparation method of spicy flavor according to claim 1, characterized in that: The calculating of the second blending amount of capsanthin based on the chromaticity deviation and the influence of capsanthin on chromaticity comprises: A chromaticity model is pre-established, wherein a mapping relationship is constructed in the chromaticity model based on pre-trained capsanthin dosage and chromaticity parameters, wherein the mapping relationship is obtained by collecting chromaticity parameters at different capsanthin dosages and training using a machine learning algorithm; The chromaticity deviation is input into the chromaticity model to obtain a second blending amount of capsanthin.

4. The intelligent preparation method of spicy flavor according to claim 1, characterized in that: The method further comprises: If the deviation between the actual colorimetric parameters of the prepared sample and the target colorimetric parameters exceeds the preset range, the amount of capsanthin is corrected based on the colorimetric deviation value, and the expression is: + Where, is the chromaticity correction coefficient, is the actual chromaticity parameter, is the target chromaticity parameter, is the amount of capsanthin before correction, This is the revised dosage of capsanthin.

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

6. The intelligent method for preparing spicy flavor according to claim 5, characterized in that: The colorimetric detection based on AI visual learning includes: Image acquisition, image preprocessing, feature extraction and chromaticity classification are performed, wherein the feature extraction is used to extract RGB or HSV space features of color, and the chromaticity classification is used to map the features into standardized chromaticity parameters.

7. The intelligent method for preparing spicy flavor according to claim 2, characterized in that: The step of inputting the spiciness deviation into the spiciness deviation calculation model to obtain a first blending amount of capsicum oleoresin comprises: The calculation formula for obtaining the first blending amount of the capsicum oleoresin is determined according to the pungency deviation calculation model: Where, For the spiciness deviation, , is the target spiciness value, is the current spiciness value; The first blending amount of capsicum oleoresin is calculated according to the spiciness deviation and the calculation variant.

8. An intelligent spicy flavor mixing device, characterized in that: The device comprises: An acquisition module is used to obtain the current spiciness value and current color parameters of the spicy sample to be prepared; A setting module is used to set the target spiciness value and target color parameters; a first calculation module, configured to determine a spiciness deviation according to the target spiciness value and the current spiciness value, and calculate a first blending amount of capsicum oleoresin based on the spiciness deviation; a second calculation module, configured to determine a chromaticity deviation according to the target chromaticity parameter and the current chromaticity parameter, and calculate a second blending amount of capsanthin based on the chromaticity deviation and an effect of capsanthin on chromaticity; The blending module is used to blend based on the first blending amount and the second blending amount, detect the actual spiciness value and actual color parameters of the blended sample, and if the target spiciness value and target color parameters are not reached, use the actual spiciness value and actual color parameters as the spicy sample to be blended, and repeat the above steps to blend until the target spiciness value and target color parameters are reached.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein the memory stores a program code that can be run on the processor, and when the program code is executed by the processor, the intelligent spicy flavor preparation method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program codes, which can be called by one or more processors to execute the intelligent spicy flavor preparation method according to any one of claims 1 to 7.

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