A standardized processing and quality stable control method for sausage stuffing

Abstract

This invention relates to the field of standardized meat filling preparation technology, and discloses a method for standardized preparation and quality stability control of braised meat filling. The method includes: obtaining the fat content and moisture content parameters of the raw meat to be processed, and obtaining the salt concentration parameter of the sauce; classifying the raw meat to be processed into grades based on the fat content parameters, and determining a set of formula benchmark parameters or a set of formula compensation parameters through target quality indicators; mixing the raw meat to be processed and the sauce in a specific ratio according to the set of formula benchmark parameters or the set of formula compensation parameters, and determining the amount of moisture to be added; during the mixing process, collecting core temperature data of the filling and vacuum data of the mixing tank, and adjusting the operating power of the mixing tank. This application reduces the product quality instability caused by fluctuations in the quality of raw meat and improves the standardization of the braised meat filling production process.

Description

Technical Field

[0001] This invention relates to the field of standardized meat filling preparation technology, and more specifically, to a method for standardized preparation and quality stability control of braised meat filling. Background Technology

[0002] As a common type of steamed bun, the quality of its filling directly affects the product's taste, flavor, and consumer experience. In the industrial production of braised pork buns, the filling is typically made by mixing raw meat, sauce, and a certain proportion of water. The fat and moisture content of the raw meat, as well as the salinity level of the sauce, significantly influence the final filling's flavor profile, texture, and water retention. Therefore, consistently controlling the filling formula and processing parameters during production is a crucial technical issue for ensuring the consistent quality of braised pork buns.

[0003] In existing technologies, the formulation of meat fillings for steamed buns largely relies on manual experience. Production staff typically perform simple grading of raw meat based on experience, then mix and add ingredients according to a fixed formula. However, due to significant fluctuations in fat and moisture content between different batches of raw meat, using a fixed formula can easily lead to an imbalance in the oil-water ratio of the filling, resulting in a dry texture, excessive oil content, or unstable flavor. Furthermore, current production processes rely heavily on manual testing or experience-based estimation for controlling the salt content of the sauce, lacking precise data-driven control methods, which further exacerbates product quality fluctuations. On the other hand, the operating status of the mixing equipment, vacuum level, and temperature changes of the filling also significantly affect the filling structure during mixing. For example, if the temperature rises too quickly during mixing, fat may prematurely separate, affecting the emulsified structure of the filling; unstable vacuum control can also affect the internal bubble structure and textural uniformity of the filling. However, the monitoring and control of stirring process parameters in existing technologies are relatively simple, usually relying on fixed power operation or simple temperature monitoring, lacking a dynamic adjustment mechanism based on multi-parameter collaborative analysis, thus making it difficult to achieve stable and precise processing control.

[0004] Therefore, it is necessary to design a standardized preparation and quality stability control method for braised meat filling to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes a standardized preparation and quality stability control method for braised meat filling, aiming to solve the problem of lacking precise data control means and making it difficult to achieve stable and refined processing control.

[0006] This invention proposes a standardized preparation and quality stability control method for braised meat filling, comprising: Obtain meat quality parameters of the raw meat to be processed, including fat content parameters and moisture content parameters; and obtain the salt concentration parameters of the sauce. The raw meat to be processed is graded according to the fat content parameter to determine the fat content grade; based on the fat content grade and through target quality indicators, a set of formula benchmark parameters for the raw meat to be processed is determined; the formula benchmark parameters are compared with the meat quality parameters to determine the quality deviation parameters; the quality deviation parameters are compared with the quality deviation threshold to determine whether formula compensation is needed; when it is determined that formula compensation is needed, a compensation coefficient is determined according to the quality deviation parameters, and formula compensation is performed on the set of formula benchmark parameters to generate a set of formula compensation parameters. According to the formula baseline parameter set or the formula compensation parameter set, the raw meat and sauce to be processed are added and mixed in proportion, and the amount of water added is determined. During the feeding and mixing process, the core temperature data of the filling and the vacuum degree data of the mixing cylinder are collected; the core temperature data and vacuum degree data are compared with the corresponding threshold data, and the operating power of the mixing cylinder is adjusted according to the comparison results.

[0007] Furthermore, the raw meat to be processed is graded according to the fat content parameter. Determining the fat content grade includes: A numerical space for fat content is defined, and the numerical space for fat content is divided into several continuous grade intervals; the grade intervals corresponding to the fat content parameters are determined to identify the fat content grade.

[0008] Furthermore, when comparing the formula baseline parameters with the meat quality parameters to determine the quality deviation parameters, the following steps are included: Obtain the target fat content and target moisture content values ​​from the formula baseline parameters; The fat content parameter is compared with the target fat content value to determine fat deviation information; the moisture content parameter is compared with the target moisture content value to determine moisture deviation information; weights and priorities are assigned to the fat deviation information and moisture deviation information; the weighted and prioritized fat deviation information and moisture deviation information are integrated to generate the quality deviation parameter.

[0009] Furthermore, when determining the compensation coefficient based on the quality deviation parameter and performing formula compensation on the formula reference parameter set, the process includes: Construct a multidimensional compensation relationship library; match initial compensation coefficients from the multidimensional compensation relationship library according to the deviation direction and deviation magnitude of the quality deviation parameters; perform quality balance verification on the initial compensation coefficients based on the target value of total feed amount; and use the adjustment parameters after the quality balance verification as the formula compensation parameter set.

[0010] Furthermore, when constructing a multidimensional compensation relationship database, the following are included: The input dimensions are fat deviation, moisture deviation, and salinity deviation; the output dimensions are lean meat ratio adjustment, sauce addition adjustment, and water addition adjustment. The input and output dimension data of the finished products in the historical production batches when the quality was qualified are used as basic association samples. The basic association samples are classified and stored according to the combination features of the input dimensions to form an initial mapping relationship. Collect the final inspection data of the current batch of finished products. When the quality index of the finished product deviates from the target value, generate a correction instruction. Adjust the output dimension data in the initial mapping relationship according to the correction instruction to generate the multidimensional compensation relationship library.

[0011] Furthermore, when determining the amount of water to add, the following should be considered: The amount of water to be added is determined based on the moisture deviation information, and water is added to the mixing tank according to the amount of water to be added.

[0012] Furthermore, when comparing the core temperature data and vacuum degree data with the corresponding threshold data, and adjusting the operating power of the mixing cylinder based on the comparison results, the process includes: The upward trend of the core temperature data within a unit of time is obtained as a temperature rise rate indicator; the temperature rise rate indicator is compared with a safe temperature rise rate threshold; when the temperature rise rate indicator is greater than the safe temperature rise rate threshold, the stirring frequency is reduced.

[0013] Furthermore, when comparing the core temperature data and vacuum level data with the corresponding threshold data, and adjusting the operating power of the mixing cylinder based on the comparison results, the method further includes: Frequency domain analysis is performed on the load current signal of the stirring motor to extract the current harmonic components and obtain the abrupt change amplitude of the current harmonic components; when the abrupt change amplitude is greater than the disturbance threshold, it is determined that a change in material density has occurred; the operating frequency of the vacuum pump is adjusted according to the change in material density.

[0014] Furthermore, the process of obtaining the meat quality parameters of the raw meat to be processed includes: The surface of the raw meat to be processed is scanned by a spectrometer to obtain spectral data; specific wavelength points of the characteristic absorption peaks of fat molecules and water molecules in the spectral data are identified; the light intensity signal of the specific wavelength point is extracted and compared with the reference light intensity signal in the standard spectral library; based on the comparison results, the fat content parameter and the water content parameter are determined.

[0015] Furthermore, when obtaining the salt concentration parameter of the sauce, the following are included: The conductivity response signal of the sauce is collected by a conductivity sensor; the real-time temperature value of the sauce is obtained, and a correction coefficient is obtained according to a temperature compensation reference table; the conductivity response signal is normalized according to the correction coefficient, and the salt concentration parameter is determined.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: By quantitatively identifying the quality parameters of raw meat and combining them with the analysis of formula benchmark parameters and quality deviation parameters, dynamic compensation and precise adjustment of the filling formula are achieved, thereby reducing product quality instability caused by fluctuations in raw meat quality and improving the standardization of the meat filling production process. By obtaining the fat content and moisture content parameters of the raw meat and combining them with the salt concentration parameters of the sauce, the raw meat is classified into fat content grades, and a formula benchmark parameter set is determined based on the target quality indicators. This allows the filling formula to be matched and adjusted according to different raw material qualities, avoiding problems such as oil-water imbalance and flavor instability caused by fluctuations in raw material quality in traditional fixed formulas, thereby improving the consistency of product quality. By comparing the formula benchmark parameters with the meat quality parameters, quality deviation parameters are determined, and formula compensation is determined based on the quality deviation threshold. When compensation is required, the formula benchmark parameters are dynamically corrected by calculating the compensation coefficient, generating a formula compensation parameter set. This forms an adaptive adjustment mechanism for the formula based on raw material quality differences, reducing reliance on manual experience and improving the efficiency of formula adjustment. During the filling mixing process, the core temperature data of the filling and the vacuum degree data of the mixing cylinder are collected in real time and compared with the corresponding thresholds. Based on the comparison results, the operating power of the mixing equipment is dynamically adjusted to keep the temperature change and vacuum environment within a reasonable range during the mixing process. This helps to maintain the stability of the fat emulsion structure of the filling and avoids the problems of oil precipitation or texture deterioration caused by excessive temperature or abnormal vacuum. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating the standardized preparation and quality stability control method for braised meat filling provided in this embodiment of the invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] In some embodiments of this application, see Figure 1 As shown, a standardized preparation and quality stability control method for braised meat filling is proposed, including: S100: Obtain the meat quality parameters of the raw meat to be processed, including fat content and moisture content parameters; and obtain the salt concentration parameters of the sauce. S200: Based on the fat content parameter, the raw meat to be processed is graded to determine the fat content grade; based on the fat content grade and through the target quality indicators, the formula baseline parameter set of the raw meat to be processed is determined; the formula baseline parameters are compared with the meat quality parameters to determine the quality deviation parameters; the quality deviation parameters are compared with the quality deviation threshold to determine whether formula compensation is needed; when it is determined that formula compensation is needed, the compensation coefficient is determined based on the quality deviation parameters, and formula compensation is performed on the formula baseline parameter set to generate a formula compensation parameter set; S300: Based on the formula baseline parameter set or formula compensation parameter set, the raw meat and sauce to be processed are added and mixed in proportion, and the amount of water added is determined; S400: During the feeding and mixing process, the core temperature data of the filling and the vacuum degree data of the mixing cylinder are collected; the core temperature data and vacuum degree data are compared with the corresponding threshold data respectively, and the operating power of the mixing cylinder is adjusted according to the comparison results.

[0020] Specifically, in step S100, the meat quality parameters of the raw meat to be processed are obtained. The raw meat sample can be rapidly tested using a spectrometer, near-infrared analyzer, or other meat quality testing devices to obtain key parameter information characterizing the quality status of the raw meat. These meat quality parameters include at least fat content and moisture content. The fat content parameter reflects the proportion of fat tissue in the raw meat, directly affecting the oil content, aroma release, and smoothness of the filling. The moisture content parameter reflects the internal water content of the raw meat, affecting the water retention and final texture of the filling. Simultaneously, the salt concentration parameter of the sauce is also obtained. This can be achieved by detecting the sauce using a conductivity detector or a salinity sensor to obtain a salt concentration parameter characterizing the salinity level of the sauce. By simultaneously obtaining the raw meat quality parameters and the sauce salt concentration parameters, a data foundation is provided for determining the formula baseline parameters and formula compensation.

[0021] Specifically, in step S200, the raw meat to be processed is graded according to its fat content parameter. Specifically, a fat content grading standard can be pre-established, dividing the fat content range into several continuous grade intervals, such as low-fat, medium-fat, and high-fat grades. The grade interval into which the current raw meat's fat content parameter falls is then determined, thus establishing the fat content grade. After determining the fat content grade, a corresponding set of formula benchmark parameters is determined by combining preset target quality indicators, such as target taste indicators, fat ratio indicators, and moisture content indicators. The formula benchmark parameter set may include the basic ratio of raw meat to sauce, the allowable range of added moisture, and other auxiliary ingredient ratio parameters. Then, the formula benchmark parameters are compared with the obtained meat quality parameters to analyze the difference between the current raw material quality and the standard formula conditions, thereby determining the quality deviation parameters. For example, the deviation between the fat content and the target fat content, and the deviation between the moisture content and the target moisture content, can be calculated and integrated to form a comprehensive quality deviation parameter. Furthermore, the quality deviation parameter is compared with a preset quality deviation threshold to determine whether the current raw material quality deviates from the standard range. The quality deviation threshold can be determined based on historical statistical data. When the quality deviation parameter is less than or equal to the quality deviation threshold, the current raw material quality is considered to meet the baseline formula requirements and no adjustment is needed. When the quality deviation parameter is greater than the quality deviation threshold, formula compensation is required. In cases requiring formula compensation, the corresponding compensation coefficient is determined based on the quality deviation parameter, and the original formula baseline parameter set is modified to generate a formula compensation parameter set. This ensures that the fat ratio, moisture ratio, and overall flavor structure of the final filling are closer to the target quality indicators.

[0022] Specifically, in step S300, actual production material feeding is carried out according to the formula baseline parameter set or formula compensation parameter set. Specifically, the raw meat to be processed is mixed with the sauce in a determined ratio in the production equipment, and the amount of additional water to be added is determined according to the formula parameters. By precisely controlling the amount of water added, the overall moisture content of the filling can be maintained within a suitable range, thereby ensuring the softness, water retention, and taste stability of the filling.

[0023] Specifically, in step S400, the processing status is monitored and controlled in real time during the material feeding and mixing process. Specifically, temperature sensors and vacuum sensors are installed inside the mixing equipment to collect real-time core temperature data of the filling and vacuum data inside the mixing cylinder. The core temperature data reflects the temperature rise of the filling due to mechanical friction during mixing, while the vacuum data reflects the air content and negative pressure state in the mixing environment. Subsequently, the core temperature data and vacuum data are compared with corresponding thresholds, and the operating power of the mixing cylinder is automatically adjusted based on the comparison results, such as reducing the mixing speed or adjusting the vacuum pump's operating status. This keeps the mixing process within a suitable range of process conditions, avoiding problems such as fat precipitation due to excessively high temperatures or uneven filling structure due to insufficient vacuum.

[0024] Understandably, grading raw materials based on quality parameters such as fat and moisture content, and dynamically compensating for quality deviations in the formula, allows the filling formula to adapt to changes in the quality of different batches of raw materials, thereby improving the consistency of the meat filling quality. By monitoring the core temperature of the filling and the vacuum level of the mixing tank in real time during the mixing process, and automatically adjusting the equipment's operating power based on the monitoring results, the structural damage to the filling caused by temperature rises or changes in the vacuum environment is avoided, thus improving the product's taste stability.

[0025] In some embodiments of this application, the process of grading the raw meat to be processed according to the fat content parameter and determining the fat content grade includes: Define a numerical space for fat content and divide it into several continuous grade intervals; determine the grade interval corresponding to the fat content parameter to identify the fat content grade.

[0026] Specifically, based on the distribution range of fat content in raw meat during actual production, a pre-defined fat content value space is established. This space covers the entire range of possible fat content in raw meat, such as a continuous range from the lowest to the highest fat content value. Building upon this, based on production experience data, historical production sample data, and the quality requirements of the target product, the fat content value space is further subdivided into several continuous and non-overlapping grade intervals, each corresponding to a fat content grade. For example, it can be divided into low-fat, medium-fat, and high-fat grade intervals according to fat content from low to high, or further subdivided into multiple grade intervals based on the need for refined production control. The fat content parameter obtained in this step is compared with each grade interval in the fat content value space to determine the specific interval range in which the fat content parameter falls, and the grade label corresponding to that interval is used as the fat content grade of that batch of raw meat. This enables standardized classification of the fat content of raw meat, allowing for accurate identification and differentiation of raw meat with different fat content levels.

[0027] Understandably, by constructing a numerical space of fat content and dividing it into multiple continuous grade intervals, the fat content of raw meat can be standardized and graded. This can transform the originally continuously changing fat content data into clear grade information, enabling raw meat of different qualities to be quickly identified and classified.

[0028] In some embodiments of this application, when comparing the formula baseline parameters with meat quality parameters to determine the quality deviation parameters, the following steps are included: Obtain the target fat content and target moisture content values ​​from the formula baseline parameters; The fat content parameter is compared with the target fat content value to determine the fat deviation information; the moisture content parameter is compared with the target moisture content value to determine the moisture deviation information; weights and priorities are assigned to the fat deviation information and the moisture deviation information; the weighted and prioritized fat deviation information and moisture deviation information are integrated to generate the quality deviation parameter.

[0029] Specifically, target fat content and target moisture content values ​​corresponding to the target product quality are obtained from the formula baseline parameter set. The target fat content value characterizes the required fat proportion level in the filling system under ideal production conditions, while the target moisture content value characterizes the required moisture balance state of the filling system. These target parameters are typically pre-set based on historical production data, product quality control standards, and sensory quality evaluation results, and serve as a reference benchmark for subsequent quality deviation calculations. The fat content parameter obtained in the previous steps is compared with the target fat content value to determine fat deviation information. Specifically, the degree of deviation of the current raw meat fat content from the target fat content can be reflected by calculating the difference or deviation ratio between the two, for example, indicating whether the fat content is too high or too low, and the magnitude of the deviation. Similarly, the moisture content parameter is compared with the target moisture content value to determine moisture deviation information. By calculating the difference or deviation ratio between the two, the deviation of the current raw meat moisture content from the target moisture content is determined. After obtaining the fat and moisture deviation information, to improve the accuracy of deviation assessment, weights and priorities can be assigned to the fat and moisture deviation information according to the different degrees of influence of various quality factors on the final product quality. Specifically, the weights of fat content deviation on the product's oil flavor, smoothness, and aroma release, as well as the weights of moisture content deviation on the filling's water retention, structural stability, and overall texture, can be determined based on historical production experience, quality evaluation data, or statistical analysis results. For example, when fat content has a more significant impact on product quality, a higher weight can be assigned to fat deviation information, and a relatively lower weight to moisture deviation information; conversely, adjustments can be made according to specific product processing requirements. After weighting, the fat and moisture deviation information, after priority allocation, are integrated to generate a comprehensive quality deviation parameter. Specifically, weighted summation, comprehensive scoring, or other data fusion methods can be used to transform the two types of deviation information into a unified quality deviation index, reflecting the overall degree of deviation between the current raw meat quality and the target formulation conditions.

[0030] Understandably, by calculating the deviations in fat content and moisture content separately, assigning weights and priorities based on their impact on product quality, and then integrating various deviation information to generate a comprehensive quality deviation parameter, the overall difference between the raw meat quality and the target formula is reflected.

[0031] In some embodiments of this application, when determining the compensation coefficient based on the quality deviation parameter and performing formula compensation on the formula reference parameter set, the following steps are included: Construct a multidimensional compensation relationship library; match initial compensation coefficients from the multidimensional compensation relationship library according to the deviation direction and deviation magnitude of the quality deviation parameters; perform quality balance verification on the initial compensation coefficients based on the target value of total feed amount; and use the adjustment parameters after quality balance verification as the formula compensation parameter set.

[0032] In some embodiments of this application, constructing a multidimensional compensation relationship library includes: The input dimensions are fat deviation, moisture deviation, and salinity deviation; the output dimensions are lean meat ratio adjustment, sauce addition adjustment, and water addition adjustment. The input and output dimension data of the finished products in the historical production batches when the quality was qualified are used as basic association samples. The basic association samples are classified and stored according to the combination features of the input dimensions to form an initial mapping relationship. Collect the final inspection data of the current batch of finished products. When the quality indicators of the finished products deviate from the target value, generate a correction instruction. Adjust the output dimension data in the initial mapping relationship according to the correction instruction to generate a multi-dimensional compensation relationship library.

[0033] Specifically, a data model, namely a multidimensional compensation relationship library, is first established to describe the relationship between raw material quality deviations and formula adjustments. This multidimensional compensation relationship library is used to reflect the formula adjustment strategies to be adopted under different raw material quality deviation conditions, so that the final product quality can be stabilized within the target range.

[0034] Specifically, when constructing the multidimensional compensation relation library, the input and output dimensions are first determined. The input dimensions characterize the quality deviation of raw materials, and in this embodiment, can include three dimensions: fat deviation, moisture deviation, and salinity deviation. Fat deviation represents the difference between the actual and target fat content; moisture deviation represents the difference between the actual and target moisture content; and salinity deviation reflects the deviation between the actual salt concentration and target salinity of the sauce. Through these multiple input dimensions, the overall quality deviation of raw materials and auxiliary materials can be comprehensively reflected. The corresponding output dimensions are then determined. In this embodiment, the output dimensions can include lean meat ratio adjustment, sauce addition adjustment, and water addition adjustment. The lean meat ratio adjustment corrects the ratio of lean to fat in the raw meat to ensure the final fat ratio meets product quality requirements; the sauce addition adjustment adjusts the sauce proportion to maintain product flavor and salinity stability; and the water addition adjustment regulates the overall moisture content of the filling, ensuring stable texture and water retention. After determining the input and output dimensions, basic correlation samples can be constructed using historical production batch data. Specifically, production batches whose finished product quality inspection results meet the target quality requirements during historical production processes are selected, and the corresponding input dimension data and the actual output dimension adjustment data used for these batches are extracted and used as basic correlation samples. Then, based on the combined characteristics of the input dimension data, such as fat deviation range, moisture deviation range, and salinity deviation range, these basic correlation samples are classified, organized, and stored in a structured manner, thereby forming an initial mapping relationship between input deviation states and formula adjustment amounts.

[0035] Specifically, during production, the initial mapping relationship can be continuously optimized. After completing the current batch, a final quality inspection is performed on the finished product to obtain its actual quality indicators. When the inspection results indicate that the finished product's quality indicators deviate from the preset target value, a corresponding correction instruction can be generated. This correction instruction characterizes the direction and magnitude of the need to adjust the formula adjustment strategy. Subsequently, the output dimension data in the initial mapping relationship is adjusted and updated according to the correction instruction, enabling the mapping relationship to more accurately reflect the optimal formula adjustment scheme under different quality deviation conditions. By continuously introducing new production data and making corrections and updates, a dynamically updated multidimensional compensation relationship library is ultimately formed. After completing the construction of the multidimensional compensation relationship library, when calculating the quality deviation parameter according to the previous steps, the deviation direction and magnitude corresponding to the quality deviation parameter can be analyzed first. Then, the corresponding input dimension combination is matched in the multidimensional compensation relationship library to obtain the corresponding initial compensation coefficient. This initial compensation coefficient can be used to determine the adjustment ratio of various raw materials. However, in the actual feeding process, it is also necessary to ensure the overall material mass conservation; therefore, it is also necessary to perform a mass balance verification of the initial compensation coefficient based on the total feeding target value. Specifically, the total amount of various materials can be adjusted to ensure that the total amount of material fed remains unchanged or meets the preset feeding range requirements. After the initial compensation coefficient has been verified by mass balance and corrected as necessary, the final adjustment parameters can be used as the formula compensation parameter set to guide the adjustment of raw material ratios in the actual production feeding process.

[0036] Understandably, by constructing a compensation relation library that includes multi-dimensional input factors such as fat deviation, moisture deviation, and salinity deviation, and linking it with formula adjustment parameters such as lean meat ratio adjustment, sauce addition adjustment, and water addition adjustment, a systematic mapping relationship between raw material quality deviations and formula adjustment strategies can be established. This transforms the formula compensation process from experience-based judgment to data-driven decision-making. By continuously revising and optimizing the relation library using historical production data and final product inspection results, the accuracy of the compensation model can be continuously improved, enabling the filling formula to adapt to fluctuations in raw material quality.

[0037] In some embodiments of this application, determining the amount of water to be added includes: The amount of water to add is determined based on the moisture deviation information, and water is added to the mixing tank according to the amount of water to add.

[0038] Specifically, in the preceding steps, the actual moisture content of the raw meat was obtained by detecting and analyzing meat quality parameters. This moisture content was then compared with the target moisture content value in the formula's baseline parameters to obtain moisture deviation information. Moisture deviation information characterizes the degree of deviation of the current raw meat moisture content from the target moisture content. This information can be obtained, for example, by calculating the difference or deviation ratio between the actual and target moisture contents. After obtaining the moisture deviation information, the amount of water to be added is determined based on this information. Specifically, when the actual moisture content of the raw meat is lower than the target moisture content, it indicates that the raw meat is too dry. In this case, the amount of water added needs to be appropriately increased to compensate for the impact of insufficient moisture content on the texture and water retention of the filling. When the actual moisture content of the raw meat is close to the target moisture content, the basic water addition amount in the original formula can remain unchanged. When the actual moisture content of the raw meat is higher than the target moisture content, the amount of additional water added can be appropriately reduced to avoid excessive moisture content in the filling, which could lead to a loose structure or affect the stability of the taste. Furthermore, in the actual calculation process, the amount of raw meat added in the current batch, the target filling moisture content, and the moisture deviation range can be combined to quantify the amount of water added using a preset calculation model or proportional coefficient, thereby obtaining the appropriate moisture addition parameters for the current production batch. After determining the amount of water to add, the calculated amount is input into the mixing equipment as a production control parameter, and the corresponding amount of water is added to the mixing tank according to this parameter. In the actual production process, the amount of water added can be precisely controlled by a metering pump, flow meter, or quantitative water addition device, so that the water can be evenly added to the mixture of raw meat and sauce. At the same time, during the mixing process, the mechanical action of the equipment ensures that the water is fully mixed with the meat tissue and seasonings, thereby forming a filling system with a stable structure.

[0039] Understandably, by dynamically determining the amount of water to add based on the deviation between the moisture content of the raw meat and the target moisture content, and by precisely adding water according to the calculation results during the mixing process, the impact of the moisture difference between different batches of raw meat on the quality of the filling is compensated.

[0040] In some embodiments of this application, when comparing core temperature data and vacuum level data with corresponding threshold data, and adjusting the operating power of the mixing cylinder based on the comparison results, the following steps are included: The upward trend of core temperature data per unit time is obtained as an indicator of temperature rise rate; the temperature rise rate indicator is compared with the safe temperature rise rate threshold; when the temperature rise rate indicator is greater than the safe temperature rise rate threshold, the stirring frequency is reduced.

[0041] In some embodiments of this application, when comparing the core temperature data and vacuum degree data with corresponding threshold data, and adjusting the operating power of the mixing cylinder based on the comparison results, the method further includes: Frequency domain analysis is performed on the load current signal of the stirring motor to extract the current harmonic components and obtain the abrupt change amplitude of the current harmonic components; when the abrupt change amplitude is greater than the disturbance threshold, it is determined that a change in material density has occurred; the operating frequency of the vacuum pump is adjusted according to the change in material density.

[0042] Specifically, during the filling mixing process, a temperature sensor can be installed inside the mixing tank to collect real-time core temperature data of the filling, and the collected temperature data is recorded in a time series. Based on the continuously collected temperature data, the change in core temperature data per unit time is obtained. By calculating the difference between temperature values ​​at adjacent time points and combining it with the time interval, the upward trend of the core temperature is obtained, thus forming a temperature rise rate index. This temperature rise rate index is used to reflect the heat accumulation generated during the mixing process due to mechanical friction, material shearing, and equipment operation. The temperature rise rate index is compared with a pre-set safe temperature rise rate threshold. The safe temperature rise rate threshold can be set based on historical production experience data and product quality control requirements to limit the maximum allowable temperature rise rate range during the filling mixing process. When the detected temperature rise rate is less than or equal to the safe temperature rise rate threshold, it indicates that the temperature change during the mixing process is within a safe range, and the mixing equipment can maintain its original operating state. However, when the detected temperature rise rate is greater than the safe temperature rise rate threshold, it indicates that the temperature rises too quickly during mixing, which may cause premature fat precipitation or damage to the emulsified structure of the filling. In this case, the operating frequency of the mixing motor can be automatically reduced to reduce mechanical shear strength and frictional heat generation, allowing the temperature rise rate to gradually return to a safe range. The safe temperature rise rate threshold can be determined based on historical statistical data.

[0043] Specifically, during the adjustment of the mixing cylinder's operating power, the load current signal of the stirring motor can be analyzed to assist in judging changes in the material's state. Specifically, the load current signal of the stirring motor during operation can be collected in real time using a current sensor, and frequency domain analysis can be performed on this signal. For example, signal processing methods such as Fast Fourier Transform can be used to convert the original current signal into a frequency domain signal, thereby extracting the harmonic components in the current signal. Subsequently, the trend analysis of the current harmonic components is performed to obtain their abrupt change amplitude within a certain time range. When the abrupt change amplitude of the current harmonic components is detected to be greater than a preset disturbance threshold, it can be determined that the material density inside the mixing cylinder has changed. The disturbance threshold can be determined based on historical statistical data. For example, during the mixing process, changes in water absorption, fat emulsification, or raw material mixing uniformity may lead to changes in the overall material density or flow resistance, thereby causing changes in the motor's load characteristics, which are reflected in changes in the current harmonic components. After determining that a change in material density has occurred, the vacuum environment of the mixing equipment can be adjusted according to this change. Specifically, the negative pressure inside the mixing cylinder can be changed by adjusting the operating frequency of the vacuum pump, thereby optimizing the mixing state of the material. For example, when an increase in material density is detected, which leads to an increase in stirring resistance, the operating frequency of the vacuum pump can be appropriately increased to improve the vacuum level, thereby reducing the air content inside the material and improving the material flow state; while when the material density decreases, the operating frequency of the vacuum pump can be appropriately reduced to keep the mixing process stable.

[0044] Understandably, by monitoring the rate of temperature rise in the core area during the mixing process in real time, and dynamically adjusting the stirring frequency based on the comparison between the rate of temperature rise and the safety threshold, problems such as fat precipitation or damage to the filling structure caused by excessively rapid temperature rise during mixing are prevented. By performing frequency domain analysis on the load current signal of the stirring motor and identifying abrupt changes in current harmonics, changes in material density can be determined in real time, and the operating frequency of the vacuum pump can be adjusted accordingly to maintain a stable vacuum environment inside the mixing cylinder.

[0045] In some embodiments of this application, obtaining the meat quality parameters of the raw meat to be processed includes: The surface of the raw meat to be processed is scanned by a spectrometer to obtain spectral data; specific wavelengths of the characteristic absorption peaks of fat molecules and water molecules in the spectral data are identified; the light intensity signal of the specific wavelength is extracted and compared with the reference light intensity signal in the standard spectral library; based on the comparison results, the fat content parameter and the moisture content parameter are determined.

[0046] In some embodiments of this application, obtaining the salt concentration parameter of the sauce includes: The conductivity response signal of the sauce is collected by a conductivity sensor; the real-time temperature value of the sauce is obtained, and a correction coefficient is obtained according to the temperature compensation reference table; the conductivity response signal is normalized according to the correction coefficient, and the salt concentration parameter is determined.

[0047] Specifically, before production, the raw meat to be processed is placed in a spectral detection area. A spectrometer within the detection device scans the surface of the raw meat to obtain spectral data reflecting the characteristics of its internal chemical composition. Spectral data typically consists of light reflection or absorption intensity information at different wavelengths; variations in this data reflect differences in the content of substances such as fat and water molecules within the meat tissue. After acquiring the spectral data, feature analysis is performed to identify characteristic absorption peaks associated with fat and water molecules. Since different molecular structures have stable absorption characteristics within specific wavelength ranges, specific wavelength points corresponding to the characteristic absorption peaks of fat and water molecules in the spectral curve can be identified using a pre-established molecular spectral feature model. For example, fat molecules have specific absorption peak positions in the near-infrared band, while water molecules also exhibit significant absorption characteristics in another wavelength range. Subsequently, the corresponding light intensity signal is extracted from the specific wavelength point and compared with reference light intensity signals in a pre-established standard spectral library. The standard spectral library can be established through calibration experiments on meat samples with different fat and water contents, and it stores multiple sets of spectral feature data corresponding to known contents. By matching or regressing the collected light intensity signal with the reference signal in the standard spectral library, the fat content and moisture content parameters of the current raw meat sample can be calculated, thereby obtaining the raw meat quality parameters.

[0048] Specifically, conductivity sensors are installed in sauce storage tanks or delivery pipelines to collect the sauce's conductivity response signal in real time. Since dissolved salt ions in the sauce significantly affect the solution's conductivity, the conductivity response signal can reflect the salt concentration level to some extent. However, the sauce's conductivity is also affected by temperature changes; therefore, while acquiring the conductivity response signal, a temperature sensor is also needed to obtain the sauce's real-time temperature. Subsequently, a correction coefficient corresponding to the current temperature is found using a pre-established temperature compensation lookup table, and this correction coefficient is used to perform temperature compensation processing on the collected conductivity response signal. By normalizing the compensated conductivity response signal and comparing it with a standard salinity calibration curve, the sauce's salt concentration parameter can be finally determined.

[0049] Understandably, by employing spectroscopic detection technology to scan raw meat and identify the characteristic absorption peaks of fat and water molecules, fat and moisture content parameters can be quickly obtained without damaging the meat's structure, thus improving the efficiency and accuracy of raw material quality testing. Furthermore, by utilizing a conductivity sensor combined with a temperature compensation algorithm to detect the salt concentration in the sauce, the impact of temperature changes on the measurement results is reduced, making the salinity parameters more stable and reliable.

[0050] In summary, by quantitatively identifying the quality parameters of raw meat and analyzing the formula baseline parameters and quality deviation parameters, dynamic compensation and precise adjustment of the filling formula were achieved. This reduced product quality instability caused by fluctuations in raw meat quality and improved the standardization of the meat filling production process. By obtaining the fat and moisture content parameters of the raw meat and combining them with the salt concentration parameters of the sauce, the fat content of the raw meat was classified into grades. Based on the target quality indicators, a formula baseline parameter set was determined, allowing the filling formula to be matched and adjusted according to different raw material qualities. This avoids problems such as oil-water imbalance and flavor instability caused by fluctuations in raw material quality in traditional fixed formulas, thereby improving the consistency of product quality. By comparing the formula baseline parameters with the meat quality parameters, quality deviation parameters were determined. Based on the quality deviation threshold, it was determined whether formula compensation was needed. When compensation was required, the formula baseline parameters were dynamically corrected by calculating the compensation coefficient, generating a formula compensation parameter set. This formed an adaptive adjustment mechanism for the formula based on raw material quality differences, reducing reliance on manual experience and improving the efficiency of formula adjustment. During the filling mixing process, the core temperature data of the filling and the vacuum degree data of the mixing cylinder are collected in real time and compared with the corresponding thresholds. Based on the comparison results, the operating power of the mixing equipment is dynamically adjusted to keep the temperature change and vacuum environment within a reasonable range during the mixing process. This helps to maintain the stability of the fat emulsion structure of the filling and avoids the problems of oil precipitation or texture deterioration caused by excessive temperature or abnormal vacuum.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A standardized preparation and quality stability control method for braised meat filling, characterized in that, include: Obtain meat quality parameters of the raw meat to be processed, including fat content parameters and moisture content parameters; and obtain the salt concentration parameters of the sauce. The raw meat to be processed is graded according to the fat content parameter to determine the fat content grade; based on the fat content grade and through the target quality index, the formula benchmark parameter set of the raw meat to be processed is determined; the formula benchmark parameters are compared with the meat quality parameters to determine the quality deviation parameters; the quality deviation parameters are compared with the quality deviation threshold to determine whether formula compensation is needed. When it is determined that formula compensation is required, the compensation coefficient is determined based on the quality deviation parameter, and the formula benchmark parameter set is compensated to generate a formula compensation parameter set. According to the formula baseline parameter set or the formula compensation parameter set, the raw meat and sauce to be processed are added and mixed in proportion, and the amount of water added is determined. During the feeding and mixing process, the core temperature data of the filling and the vacuum degree data of the mixing cylinder are collected; the core temperature data and vacuum degree data are compared with the corresponding threshold data, and the operating power of the mixing cylinder is adjusted according to the comparison results.

2. The method for standardized preparation and quality stability control of braised meat filling according to claim 1, characterized in that, The raw meat to be processed is graded according to the fat content parameter. Determining the fat content grade includes: A numerical space for fat content is defined, and the numerical space for fat content is divided into several continuous grade intervals; the grade intervals corresponding to the fat content parameters are determined to identify the fat content grade.

3. The method for standardized preparation and quality stability control of braised meat filling according to claim 2, characterized in that, When comparing the formula baseline parameters with the meat quality parameters to determine the quality deviation parameters, the following steps are included: Obtain the target fat content and target moisture content values ​​from the formula baseline parameters; The fat content parameter is compared with the target fat content value to determine fat deviation information; the moisture content parameter is compared with the target moisture content value to determine moisture deviation information; weights and priorities are assigned to the fat deviation information and moisture deviation information; the weighted and prioritized fat deviation information and moisture deviation information are integrated to generate the quality deviation parameter.

4. The standardized preparation and quality stability control method for braised meat filling according to claim 3, characterized in that, When determining the compensation coefficient based on the quality deviation parameter and performing formula compensation on the formula reference parameter set, the process includes: Construct a multidimensional compensation relationship library; match initial compensation coefficients from the multidimensional compensation relationship library according to the deviation direction and deviation magnitude of the quality deviation parameters; perform quality balance verification on the initial compensation coefficients based on the target value of total feed amount; and use the adjustment parameters after the quality balance verification as the formula compensation parameter set.

5. The standardized preparation and quality stability control method for braised meat filling according to claim 4, characterized in that, When constructing a multidimensional compensation relation library, the following is included: The input dimensions are fat deviation, moisture deviation, and salinity deviation; the output dimensions are lean meat ratio adjustment, sauce addition adjustment, and water addition adjustment. The input and output dimension data of the finished products in the historical production batches when the quality was qualified are used as basic association samples. The basic association samples are classified and stored according to the combination features of the input dimensions to form an initial mapping relationship. Collect the final inspection data of the current batch of finished products. When the quality index of the finished product deviates from the target value, generate a correction instruction. Adjust the output dimension data in the initial mapping relationship according to the correction instruction to generate the multidimensional compensation relationship library.

6. The standardized preparation and quality stability control method for braised meat filling according to claim 5, characterized in that, When determining the amount of water to add, the following should be included: The amount of water to be added is determined based on the moisture deviation information, and water is added to the mixing tank according to the amount of water to be added.

7. The method for standardized preparation and quality stability control of braised meat filling according to claim 1, characterized in that, When comparing the core temperature data and vacuum level data with corresponding threshold data, and adjusting the operating power of the mixing cylinder based on the comparison results, the following steps are included: The upward trend of the core temperature data within a unit of time is obtained as a temperature rise rate indicator; the temperature rise rate indicator is compared with a safe temperature rise rate threshold; when the temperature rise rate indicator is greater than the safe temperature rise rate threshold, the stirring frequency is reduced.

8. The method for standardized preparation and quality stability control of braised meat filling according to claim 7, characterized in that, When comparing the core temperature data and vacuum level data with corresponding threshold data, and adjusting the operating power of the mixing cylinder based on the comparison results, the method further includes: Frequency domain analysis is performed on the load current signal of the stirring motor to extract the current harmonic components and obtain the abrupt change amplitude of the current harmonic components; when the abrupt change amplitude is greater than the disturbance threshold, it is determined that a change in material density has occurred; the operating frequency of the vacuum pump is adjusted according to the change in material density.

9. The method for standardized preparation and quality stability control of braised meat filling according to claim 1, characterized in that, The process of obtaining the meat quality parameters of the raw meat to be processed includes: The surface of the raw meat to be processed is scanned by a spectrometer to obtain spectral data; specific wavelength points of the characteristic absorption peaks of fat molecules and water molecules in the spectral data are identified; the light intensity signal of the specific wavelength point is extracted and compared with the reference light intensity signal in the standard spectral library; based on the comparison results, the fat content parameter and the water content parameter are determined.

10. The method for standardized preparation and quality stability control of braised meat filling according to claim 2, characterized in that, When obtaining the salt concentration parameter of a sauce, the following should be included: The conductivity response signal of the sauce is collected by a conductivity sensor; the real-time temperature value of the sauce is obtained, and a correction coefficient is obtained according to a temperature compensation reference table; the conductivity response signal is normalized according to the correction coefficient, and the salt concentration parameter is determined.

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