Modular system and process for pomegranate product production based on sweetness and sourness detection

Abstract

The present invention discloses a modular system and process for producing pomegranate products based on sourness and sweetness detection, the system comprising: a pomegranate sweetness measurement module for determining the sweetness of pomegranates by chemical methods; a user interface module for displaying measurement results, modeling calculation results and providing a user input interface; a modeling unit electrically connected to the sweetness measurement module for receiving sweetness data and calculating the sugar content of the pomegranate according to a preset algorithm, and then determining the amount of added white sugar by modeling calculation; the present invention ensures the consistency and high quality of the taste of the pomegranate product by accurately measuring the sweetness of the pomegranate and adjusting the amount of added white sugar in real time. This helps to meet consumers' expectations for sweetness and enhance the market competitiveness of products; the automated sweetness measurement and white sugar addition system reduces the need for manual operation and improves production efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of chemical detection, and more specifically, it particularly relates to a modular system for producing pomegranate products based on sourness and sweetness detection. At the same time, the present invention also relates to a production process of pomegranate products based on sourness and sweetness detection. Background Art

[0002] In the production process of pomegranate products, sweetness is an important factor that determines the taste and quality of the product. Traditional pomegranate sweetness detection mainly relies on manual tasting or simple chemical analysis methods, which have the problems of low accuracy, low efficiency, and inability to monitor in real time. In addition, since the sweetness of pomegranates is affected by many factors such as variety, growth environment, and maturity, a system that can accurately measure and adjust the sweetness of pomegranates in real time is needed to ensure product quality.

[0003] However, there is currently a lack of a modular system for the production of pomegranate products based on sourness and sweetness detection in the market. The system can determine the sweetness of pomegranates by chemical methods and calculate the amount of added sugar according to a preset algorithm to ensure the quality of the product. In addition, although some existing systems can achieve automated production, they can often only operate on a single variety or pomegranates under specific conditions, and cannot adapt to the needs of different varieties and different production environments; therefore, we propose a modular system and process for the production of pomegranate products based on sourness and sweetness detection. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art, and to propose a modular system and process for producing pomegranate products based on sourness and sweetness detection, which ensures the consistency and high quality of the taste of pomegranate products by accurately measuring the sweetness of pomegranates and adjusting the amount of added sugar in real time. This helps to meet consumers' expectations for sweetness and improve the market competitiveness of products.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] Modular system for the production of pomegranate products based on sweetness and sourness detection, including:

[0007] A pomegranate sweetness measurement module is used to measure the sweetness of pomegranates by chemical methods; the sweetness measurement module comprises:

[0008] a sample extraction unit for extracting a representative sample from the pomegranate;

[0009] A chemical analysis unit, which uses chemical analysis methods to determine the sweetness of the sample;

[0010] A sensor for real-time monitoring and transmitting sweetness data to the data processing and modeling module;

[0011] A user interface module, used to display measurement results, modeling calculation results and provide a user input interface;

[0012] A modeling unit, electrically connected to the sweetness measurement module, for receiving sweetness data and calculating the sugar content of the pomegranate according to a preset algorithm, and then determining the amount of added white sugar through modeling calculation;

[0013] The modeling unit comprises:

[0014] Data storage module, used to store historical sweetness data and sugar content records;

[0015] Data analysis module, used to analyze sweetness data trends and perform quality control;

[0016] Model building and optimization module, which is used to build a mathematical model based on sweetness and sugar content data and optimize the model parameters to accurately predict the amount of added sugar;

[0017] The output interface is used to output the processing result to the user interface module.

[0018] Preferably, the user interface module includes:

[0019] Display screen, used to display sweetness measurement results, sugar content calculation results and recommended white sugar addition amount;

[0020] Input devices, such as keyboards or touch screens, for users to enter operating instructions or adjust system settings;

[0021] Communication interface, used to exchange data with equipment or systems in other production links.

[0022] The pomegranate product production process based on sourness and sweetness detection, the process uses the above-mentioned system to calculate and determine the amount of white sugar added to the pomegranate product, and includes the following steps:

[0023] S1. Extract representative samples from pomegranates, with no less than 5 samples from the same batch;

[0024] S2, after the extracted samples are placed for different periods of time, they are sequentially sent to the chemical analysis unit, and the chemical analysis unit sensor detects in real time and transmits the sweetness data to the modeling module;

[0025] S3, the modeling module receives the sweetness data. The model building and optimization module builds a mathematical model based on the sweetness and sugar content data and analyzes the sweetness trend based on historical data and current measurement results;

[0026] S4. All relevant data are displayed to the user through the user interface module, and the user adjusts the system settings or inputs operation instructions through the input device.

[0027] Preferably, in step S2, the sampling method is:

[0028] Five pomegranates were sampled from the same batch and numbered: A, B, C, D, E;

[0029] The pomegranate numbered A is sampled and sent to the chemical analysis unit for real-time sweetness data detection to obtain data T A ;

[0030] The pomegranate numbered B is placed in the pomegranate product production environment, and the pomegranate numbered C is placed in a high humidity simulation box simulating a rainy day. The pomegranate numbered B and the pomegranate numbered C are then left to stand for 3 days, and then sampled and sent to the chemical analysis unit for real-time sweetness data detection to obtain data T. B and T C ;

[0031] The pomegranate numbered D is set in the same manner as the pomegranate numbered B, and the pomegranate numbered E is set in the same manner as the pomegranate numbered C, and then left to stand for 5 days, and then samples are taken respectively and the samples are sent to the chemical analysis unit for real-time sweetness data detection, and data T is obtained. D and T E ;

[0032] Preferably, the sweetness data is calculated by a weighted algorithm, and the calculation formula is:

[0033] T=aT A +b(T B +T C )+c(T D +T E ) / n;

[0034] Where T represents the sweetness data of the batch of pomegranates, a, b, c are weighted constants, and a+b+c=1, and n is the total number of samples.

[0035] Preferably, in step S2, the specific analysis process of the chemical analysis unit is:

[0036] 1) Accurately weigh the reference substance, then add an appropriate amount of solvent to prepare a standard solution;

[0037] 2) Extract juice from pomegranate samples and use it as the sample to be tested after appropriate treatment;

[0038] 3) Calibrate the burette, fill the burette with the standard solution, and record the initial reading;

[0039] 4) Add the sample solution to be tested into the container and drop the indicator;

[0040] 5) Add the standard solution drop by drop until the reaction is complete and record the final reading;

[0041] 6) Calculate the sugar content in the sample by calculating the volume of the standard solution consumed;

[0042] 7) Based on the data collected during the titration, calculate the sugar content in the sample.

[0043] Preferably, the specific process of step S3 is:

[0044] S31, collecting historical sweetness data and corresponding sugar content records, cleaning the data, and removing outliers and noise;

[0045] S32, establishing a model based on the data;

[0046] S33, use historical data sets to train the model and evaluate the performance of the model through cross-validation method;

[0047] S34, deploying the trained model to the production environment, receiving sweetness data in real time and outputting sugar content prediction and recommended white sugar addition amount;

[0048] Among them, the conversion expression from sweetness to sugar content is:

[0049] C = k × S + m;

[0050] In the formula, S is sweetness, C is sugar content, k and m are coefficients obtained by fitting historical data; sugar content C is a linear function of sweetness S, where k is the slope, indicating the amount of increase in sugar content for every unit increase in sweetness; m is the intercept, indicating the sugar content when the sweetness is zero.

[0051] Preferably, the cross-validation method is specifically:

[0052] The entire data set is randomly divided into k subsets, where k is a preset value of 10;

[0053] For K rounds of validation, a different subset is selected as the validation set for each round, and the remaining k-1 subsets are combined as the training set;

[0054] Use the training set to train the model and test the performance of the model on the validation set, recording the performance indicators obtained in each iteration;

[0055] After completing all k iterations, an estimate of the model performance is provided; the performance index is calculated as:

[0056] ;

[0057] In the formula, is the true value, is the predicted value, n is the number of samples in the validation set;

[0058] For cross validation, the final MSE will be the average of all fold MSEs:

[0059] ;

[0060] In the formula, MSE j is the mean square error of the jth fold; the above verification avoids overfitting and provides a reliable estimate of the generalization ability of the model.

[0061] Preferably, step S4 is specifically:

[0062] Use charts and digital dashboards to intuitively display data to users through the display screen of the user interface module;

[0063] Provide input devices to allow users to interact with the system;

[0064] Users adjust system settings through the user interface;

[0065] Input operation instructions to modify the target sweetness value, adjust model parameters, start / stop sweetness measurement, and manually trigger model update;

[0066] Monitor system performance to ensure user interface accuracy and stability.

[0067] Technical effects and advantages of the present invention: Compared with the prior art, the modular system for producing pomegranate products based on sourness and sweetness detection provided by the present invention has the following advantages:

[0068] By accurately measuring the sweetness of pomegranates and adjusting the amount of added sugar in real time, the consistency and high quality of the taste of pomegranate products are ensured. This helps meet consumers' expectations for sweetness and improves the market competitiveness of products. The automated sweetness measurement and sugar addition system reduces the need for manual operation and improves production efficiency. At the same time, the modular design allows for rapid adjustment of the production process to adapt to different varieties of pomegranates or changing production conditions, enhancing the flexibility and responsiveness of the production line.

[0069] The system’s built-in data analysis module can analyze historical sweetness data trends, providing producers with valuable insights to help them make smarter production decisions, such as adjusting planting strategies or improving processing techniques. The intuitive user interface module allows operators to easily monitor the production process, adjust system settings and enter commands, improving the convenience of operation and system usability.

[0070] The cross-validation method is used to optimize the mathematical model to ensure that the model accurately predicts the relationship between sweetness and sugar content, reducing the risk of overfitting and improving the generalization ability of the model. Through real-time monitoring of sweetness data and historical data analysis, the system can promptly detect abnormal situations in the production process, thereby taking preventive measures to ensure the stability of product quality.

[0071] The modular design allows customization according to different production needs. Whether it is changing the pomegranate variety or adjusting the production scale, the system can adapt flexibly, reducing upgrade and maintenance costs. The system tests the impact of different environmental conditions on the sweetness of pomegranates, making production closer to actual consumption scenarios, enhancing the product's environmental adaptability and consumer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a modular system architecture diagram for producing pomegranate products based on sourness and sweetness detection of the present invention;

[0073] Figure 2 The present invention is a process flow chart of the production of pomegranate products based on sourness and sweetness detection. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0075] The present invention provides a modular system for producing pomegranate products based on sourness and sweetness detection, which monitors the sweetness of pomegranates in real time and automatically adjusts the amount of white sugar added according to the measurement results. The real-time feedback mechanism ensures the precise control of sweetness during the production process, avoids the lag and inaccuracy of manual detection, and can accurately calculate the amount of sugar added for different pomegranate products, further ensuring the precise control of sweetness during the production process.

[0076] like Figure 1 As shown, the system includes:

[0077] A pomegranate sweetness measurement module is used to measure the sweetness of pomegranates by chemical methods; the sweetness measurement module comprises:

[0078] a sample extraction unit for extracting a representative sample from the pomegranate;

[0079] A chemical analysis unit, which uses chemical analysis methods to determine the sweetness of the sample;

[0080] A sensor for real-time monitoring and transmitting sweetness data to the data processing and modeling module;

[0081] A user interface module is used to display measurement results, modeling calculation results and provide a user input interface; the user interface module includes:

[0082] Display screen, used to display sweetness measurement results, sugar content calculation results and recommended white sugar addition amount;

[0083] Input devices, such as keyboards or touch screens, for users to enter operating instructions or adjust system settings;

[0084] Communication interface, used to exchange data with other production equipment or systems;

[0085] The modeling unit is electrically connected to the sweetness measuring module, and is used to receive the sweetness data and calculate the sugar content of the pomegranate according to a preset algorithm, and then determine the amount of added white sugar through modeling calculation.

[0086] The modeling unit comprises:

[0087] Data storage module, used to store historical sweetness data and sugar content records;

[0088] Data analysis module, used to analyze sweetness data trends and perform quality control;

[0089] Model building and optimization module, which is used to build a mathematical model based on sweetness and sugar content data and optimize the model parameters to accurately predict the amount of added sugar;

[0090] The output interface is used to output the processing result to the user interface module.

[0091] Based on the above system, this embodiment also proposes a pomegranate product production process based on sourness and sweetness detection. The process uses the above system to calculate and determine the amount of white sugar added to the pomegranate product, such as Figure 2 , including the following steps:

[0092] S1. Extract representative samples from pomegranates, specifically:

[0093] Sample quantity requirements: At least 5 representative samples must be extracted from each batch of pomegranates to ensure that the samples are sufficiently representative and reduce sampling errors; the number of samples can be increased according to actual conditions to improve accuracy, but the minimum number must not be less than 5;

[0094] Sample selection method: When selecting samples, random sampling should be used to avoid human selection or bias; samples should be randomly selected from different packaging boxes, different storage areas or different production batches to ensure that the samples can fully cover the entire batch;

[0095] Sample processing and storage: The extracted samples should be processed and analyzed in time to avoid quality changes caused by long-term storage; if analysis cannot be performed immediately, the samples should be properly stored under appropriate temperature and humidity conditions and the analysis should be completed as soon as possible;

[0096] Recording and tracking: Keep detailed records of the source, processing and analysis results of each sample for subsequent tracking and verification; this helps to quickly locate the cause when problems are discovered and take appropriate improvement measures.

[0097] S2, after the extracted samples are placed for different periods of time, they are sequentially sent to the chemical analysis unit, and the chemical analysis unit sensor detects in real time and transmits the sweetness data to the modeling module; in step S2, the sampling method is:

[0098] Five pomegranate samples were sampled from the same batch and numbered: A, B, C, D, E;

[0099] The pomegranate numbered A is sampled and sent to the chemical analysis unit for real-time sweetness data detection to obtain data T A ;

[0100] The pomegranate numbered B is placed in the pomegranate product production environment, and the pomegranate numbered C is placed in a high humidity simulation box simulating a rainy day. The pomegranate numbered B and the pomegranate numbered C are then left to stand for 3 days, and then sampled and sent to the chemical analysis unit for real-time sweetness data detection to obtain data T. B and T C ;

[0101] The pomegranate numbered D is set in the same manner as the pomegranate numbered B, and the pomegranate numbered E is set in the same manner as the pomegranate numbered C, and then left to stand for 5 days, and then samples are taken respectively and the samples are sent to the chemical analysis unit for real-time sweetness data detection, and data T is obtained. D and T E；

[0102] According to the number of samples A, B, C, D, and E, the sweetness data is calculated by weighted algorithm. The calculation formula is:

[0103] T=aT A +b(T B +T C )+c(T D +T E ) / n;

[0104] Where T represents the sweetness data of the batch of pomegranates, a, b, c are weighted constants, and a+b+c=1, and n is the total number of samples.

[0105] It should be noted that in step S2, the specific analysis process of the chemical analysis unit is as follows:

[0106] 1) Accurately weigh the reference substance, then add an appropriate amount of solvent to prepare a standard solution;

[0107] 2) Extract juice from pomegranate samples and use it as the sample to be tested after appropriate treatment;

[0108] 3) Calibrate the burette, fill the burette with the standard solution, and record the initial reading;

[0109] 4) Add the sample solution to be tested into the container and drop the indicator;

[0110] 5) Add the standard solution drop by drop until the reaction is complete and record the final reading;

[0111] 6) Calculate the sugar content in the sample by calculating the volume of the standard solution consumed;

[0112] 7) Based on the data collected during the titration, calculate the sugar content in the sample.

[0113] The above are exemplary:

[0114] From a batch of pomegranates, 5 representative samples were randomly selected and numbered as A, B, C, D, and E.

[0115] The pomegranate numbered A is sampled and sent to a chemical analysis unit for real-time sweetness data detection, and the data TA=10°Brix is ​​obtained;

[0116] The pomegranate numbered B is placed in a pomegranate product production environment, and the pomegranate numbered C is placed in a high humidity simulation box simulating a rainy day; then, the pomegranates numbered B and C are left to stand for 3 days; thereafter, they are sampled respectively, and the samples are sent to a chemical analysis unit for real-time sweetness data detection, and the data TB=12°Brix and TC=11°Brix are obtained;

[0117] Next, the pomegranate numbered D was set up the same as the pomegranate numbered B, that is, placed in a pomegranate product production environment, and the pomegranate numbered E was set up the same as the pomegranate numbered C, that is, placed in a high humidity simulation box simulating a rainy day; then, the pomegranates numbered D and E were left to stand for 5 days; thereafter, they were sampled separately, and the samples were sent to a chemical analysis unit for real-time detection of sweetness data, and the data TD=13°Brix and TE=14°Brix were obtained.

[0118] Use a weighted algorithm to calculate the sweetness data T of this batch of pomegranates. Assume that the weighted constants a, b, and c we choose are 0.2, 0.3, and 0.5, respectively, and satisfy a+b+c=1. The total number of samples n is 5, and the calculation formula is:

[0119]

[0120] According to the above, the specific analysis process of the chemical analysis unit is as follows:

[0121] Accurately weigh the reference material: First, accurately weigh the reference material to ensure that the weighing process is accurate;

[0122] Prepare standard solution: add the weighed reference substance into an appropriate amount of solvent, fully dissolve and mix evenly to prepare a standard solution of known concentration;

[0123] Extracting the sample to be tested: extracting juice from the pomegranate sample, removing impurities and suspended matter through appropriate filtration or centrifugation to obtain a clear and transparent sample solution to be tested;

[0124] Calibrate the burette: Clean and calibrate the burette to ensure that its scale is clear and accurate; then, fill the burette with the standard solution and record the initial reading;

[0125] Add the sample to be tested and the indicator: add the sample solution to be tested into the container and drop an appropriate amount of indicator;

[0126] Titration process: add the standard solution drop by drop until the reaction is complete, observe the color change of the indicator, and record the final reading;

[0127] Calculate sugar content: By calculating the volume of standard solution consumed, combined with the concentration of the standard solution and the volume of the sample to be tested, the sugar content in the sample can be obtained.

[0128] S3, the modeling module receives the sweetness data. The model building and optimization module establishes a mathematical model based on the sweetness and sugar content data, and analyzes the sweetness trend based on historical data and current measurement results. The specific process of step S3 is as follows:

[0129] S31, collecting historical sweetness data and corresponding sugar content records, cleaning the data, and removing outliers and noise;

[0130] S32, establishing a model based on the data;

[0131] S33, use historical data sets to train the model and evaluate the performance of the model through cross-validation method;

[0132] Furthermore, the cross-validation method is specifically as follows:

[0133] The entire data set is randomly divided into k subsets, where k is a preset value of 10;

[0134] For K rounds of validation, a different subset is selected as the validation set for each round, and the remaining k-1 subsets are combined as the training set;

[0135] Use the training set to train the model and test the performance of the model on the validation set, recording the performance indicators obtained in each iteration;

[0136] After completing all k iterations, an estimate of the model performance is provided; the performance index is calculated as:

[0137] ;

[0138] In the formula, is the true value, is the predicted value, n is the number of samples in the validation set;

[0139] For cross validation, the final MSE will be the average of all fold MSEs:

[0140] ;

[0141] In the formula, MSE j is the mean square error of the jth fold; the above verification avoids overfitting and provides a reliable estimate of the generalization ability of the model.

[0142] S34, deploying the trained model to the production environment, receiving sweetness data in real time and outputting sugar content prediction and recommended white sugar addition amount;

[0143] Among them, the conversion expression from sweetness to sugar content is:

[0144] C = k × S + m;

[0145] In the formula, S is sweetness, C is sugar content, k and m are coefficients obtained by fitting historical data; sugar content C is a linear function of sweetness S, where k is the slope, indicating the amount of increase in sugar content for every unit increase in sweetness; m is the intercept, indicating the sugar content when the sweetness is zero.

[0146] S4. All relevant data are displayed to the user through the user interface module, and the user adjusts the system settings or inputs operation instructions through the input device; step S4 is specifically:

[0147] Use charts and digital dashboards to intuitively display data to users through the display screen of the user interface module;

[0148] Provide input devices to allow users to interact with the system;

[0149] Users adjust system settings through the user interface;

[0150] Input operation instructions to modify the target sweetness value, adjust model parameters, start / stop sweetness measurement, and manually trigger model update;

[0151] Monitor system performance to ensure user interface accuracy and stability.

[0152] Compared with the prior art, the present invention has the following advantages:

[0153] By accurately measuring the sweetness of pomegranates and adjusting the amount of added sugar in real time, the consistency and high quality of the taste of pomegranate products are ensured, which helps meet consumers' expectations for sweetness and improves the market competitiveness of products. The automated sweetness measurement and sugar addition system reduces the need for manual operation and improves production efficiency. At the same time, the modular design allows for rapid adjustment of the production process to adapt to different varieties of pomegranates or changing production conditions, enhancing the flexibility and responsiveness of the production line.

[0154] The system’s built-in data analysis module can analyze historical sweetness data trends, providing producers with valuable insights to help them make smarter production decisions, such as adjusting planting strategies or improving processing techniques. The intuitive user interface module allows operators to easily monitor the production process, adjust system settings and enter commands, improving the convenience of operation and system usability.

[0155] The cross-validation method is used to optimize the mathematical model to ensure that the model accurately predicts the relationship between sweetness and sugar content, reducing the risk of overfitting and improving the generalization ability of the model. Through real-time monitoring of sweetness data and historical data analysis, the system can promptly detect abnormal situations in the production process, thereby taking preventive measures to ensure the stability of product quality.

[0156] The modular design allows customization according to different production needs. Whether it is changing the pomegranate variety or adjusting the production scale, the system can adapt flexibly, reducing upgrade and maintenance costs. The system tests the impact of different environmental conditions on the sweetness of pomegranates, making production closer to actual consumption scenarios, enhancing the product's environmental adaptability and consumer satisfaction.

[0157] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A pomegranate product production process based on sourness and sweetness detection, characterized in that: The steps include: S1. Extract representative samples from pomegranates, with no less than 5 samples from the same batch; S2, after the extracted samples are placed for different periods of time, they are sequentially sent to the chemical analysis unit, and the chemical analysis unit sensor detects in real time and transmits the sweetness data to the modeling module; in step S2, the sampling method is: Five pomegranate samples were sampled from the same batch and numbered: A, B, C, D, E; The pomegranate numbered A is sampled and sent to the chemical analysis unit for real-time sweetness data detection to obtain data T A ; The pomegranate numbered B is placed in the pomegranate product production environment, and the pomegranate numbered C is placed in a high humidity simulation box simulating a rainy day. The pomegranate numbered B and the pomegranate numbered C are then left to stand for 3 days, and then sampled and sent to the chemical analysis unit for real-time sweetness data detection to obtain data T. B and T C ; The pomegranate numbered D is set in the same manner as the pomegranate numbered B, and the pomegranate numbered E is set in the same manner as the pomegranate numbered C, and then left to stand for 5 days, and then samples are taken respectively and the samples are sent to the chemical analysis unit for real-time sweetness data detection, and data T is obtained. D and T E , according to the number: A, B, C, D, E5 samples, the sweetness data is calculated by weighted algorithm, the calculation formula is: T = aT A +b(T B +T C )+c(T D +T E ) / n; In the formula, T represents the sweetness data of the batch of pomegranates, a, b, c are weighted constants, and a+b+c=1, and n is the total number of samples; In step S2, the specific analysis process of the chemical analysis unit is as follows: Accurately weigh the reference substance, then add an appropriate amount of solvent to prepare a standard solution; Extract juice from the pomegranate sample and use it as the sample to be tested after appropriate treatment; To calibrate the burette, fill the burette with the standard solution and record the initial reading; Add the sample solution to be tested into the container and drop the indicator; Add the standard solution drop by drop until the reaction is complete and record the final reading; By calculating the volume of the consumed standard solution, the sugar content in the sample can be obtained; Based on the data collected during the titration, the sugar content in the sample was calculated; S3, receiving sweetness data model construction and optimization module to establish a mathematical model based on sweetness and sugar content data, and analyze the sweetness trend based on historical data and current measurement results; the specific process of step S3 is: S31, collecting historical sweetness data and corresponding sugar content records, cleaning the data, and removing outliers and noise; S32, establishing a model based on the data; S33, use historical data sets to train the model and evaluate the performance of the model through cross-validation method; S34, deploying the trained model to the production environment, receiving sweetness data in real time and outputting sugar content prediction and recommended white sugar addition amount; Among them, the conversion expression from sweetness to sugar content is: C = k × S + m; In the formula, S is sweetness, C is sugar content, k and m are coefficients obtained by fitting historical data; sugar content C is a linear function of sweetness S, where k is the slope, indicating the amount of increase in sugar content for each unit increase in sweetness; m is the intercept, indicating the sugar content when the sweetness is zero; The cross validation method is specifically as follows: The entire data set is randomly divided into k subsets, where k is a preset value of 10; For K rounds of validation, a different subset is selected as the validation set for each round, and the remaining k-1 subsets are combined as the training set; Use the training set to train the model and test the performance of the model on the validation set, recording the performance indicators obtained in each iteration; After all k iterations are completed, the average value of each performance indicator is calculated; these average values ​​provide an estimate of the model performance; among them, the performance indicator is calculated as: ; In the formula, is the true value, is the predicted value, n is the number of samples in the validation set; For cross validation, the final MSE will be the average of all fold MSEs: ; In the formula, MSE j is the mean square error of the jth fold; the above verification avoids overfitting and provides a reliable estimate of the generalization ability of the model; S4. All relevant data are displayed to the user, and the user adjusts system settings or enters operating instructions through input devices.

2. The pomegranate product production process based on sourness and sweetness detection according to claim 1, characterized in that: Step S4 is specifically as follows: Use charts and digital dashboards to intuitively display data to users through the display screen of the user interface module; Provide input devices to allow users to interact with the system; Users adjust system settings through the user interface; Input operation instructions to modify the target sweetness value, adjust model parameters, start / stop sweetness measurement, and manually trigger model update; Monitor system performance to ensure user interface accuracy and stability.

3. A modular system for producing pomegranate products based on sourness and sweetness detection, the system being used to implement any of the methods described in claims 1-2, characterized in that: include: Pomegranate sweetness measurement module, used to determine the sweetness of pomegranates by chemical methods; The sweetness measurement module comprises: a sample extraction unit for extracting a representative sample from the pomegranate; A chemical analysis unit, which uses chemical analysis methods to determine the sweetness of the sample; A sensor for real-time monitoring and transmitting sweetness data to the data processing and modeling module; A user interface module, used to display measurement results, modeling calculation results and provide a user input interface; A modeling unit, electrically connected to the sweetness measurement module, for receiving sweetness data and calculating the sugar content of the pomegranate according to a preset algorithm, and then determining the amount of added white sugar through modeling calculation; The modeling unit comprises: Data storage module, used to store historical sweetness data and sugar content records; Data analysis module, used to analyze sweetness data trends and perform quality control; Model building and optimization module, which is used to build a mathematical model based on sweetness and sugar content data and optimize the model parameters to accurately predict the amount of added sugar; The output interface is used to output the processing result to the user interface module.

4. The modular system for producing pomegranate products based on sourness and sweetness detection according to claim 3, characterized in that: The user interface module comprises: Display screen, used to display sweetness measurement results, sugar content calculation results and recommended white sugar addition amount; Input devices, including keyboards or touch screens, for users to enter operating instructions or adjust system settings; Communication interface, used to exchange data with equipment or systems in other production links.

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