Composite probiotic production safety monitoring method and system

Abstract

The invention relates to the technical field of mixing or stirring treatment, in particular to a method and a system for monitoring production safety of complex probiotics. The method comprises the following steps: stirring mono-and diglycerol fatty acid esters and medium-chain triglyceride or sunflower seed oil in a complex probiotic formula for multiple times; and calculating a torque cycle coefficient, a stability coefficient and a generalization degree of each cycle of the fan blade of the stirrer so as to represent fluctuation, stability and generalization ability in the stirring process. And different clusters are formed through clustering analysis of generalization degrees of all weeks. And if the torque cycle coefficient of the current cycle exceeds the range of the cluster to which the torque cycle coefficient belongs, the heating temperature is automatically adjusted to ensure the stability and quality of the stirring process. The problem that the production efficiency is unstable in the production process of the compound probiotics is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of mixing or stirring treatment. More specifically, the present invention relates to a method and system for monitoring the production safety of composite probiotics. Background Art

[0002] The existing compound probiotic production process mainly relies on traditional monitoring and control methods, which are mostly based on physical sensors and simple control systems, and mainly monitor some basic physical parameters such as temperature, pressure and flow. These traditional methods can ensure the stability of the production process to a certain extent, but they are usually unable to cope with the complexity and dynamic changes in the reaction process. For example, in a multi-component reaction process, factors such as the reaction state, reaction speed, and raw material conversion rate of the raw materials may fluctuate to varying degrees at every moment.

[0003] In addition, traditional monitoring systems rely on simplified mathematical models or empirical formulas for process prediction and control, which often have limitations when faced with complex and variable reaction conditions. They cannot fully consider all variables in the reaction process, such as stirring efficiency, reaction uniformity, and material distribution, which directly affect the adequacy of the reaction and the final quality of the product. Due to the lack of comprehensive and accurate modeling of the reaction process, traditional control systems are often slow to respond when adjusting key parameters such as stirring speed, reaction temperature, and pressure in real time, and it is difficult to fine-tune the production process based on real-time data, resulting in low stirring efficiency and uneven reaction, which ultimately affects the quality and yield of compound probiotics.

[0004] However, the existing technical solutions only realize safety monitoring of the production process based on traditional network models, and fail to fully consider the limitations of traditional models. Traditional network models are usually based on fixed rules and simpler feature extraction methods, which are difficult to cope with the variable factors and complex situations that may arise in the production process, such as dynamic changes in the production environment, fluctuations in process parameters, and potential abnormal situations. When faced with different types of safety hazards in the production process of compound probiotics, traditional models cannot achieve efficient and accurate monitoring, which leads to the problem of unstable production efficiency in the production process of compound probiotics. Summary of the invention

[0005] In order to solve the problem of unstable production efficiency in the production process of composite probiotics raised in the above background technology, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for monitoring the production safety of composite probiotics, comprising: stirring mono- and di-glycerol fatty acid esters in a composite probiotic formula with medium-chain triglycerides or sunflower oil for multiple times to obtain a torque sequence of a mixer blade rotating one circle during each stirring; calculating the torque sequence of the mixer blade rotating one circle; Torque cycle coefficient The torque cycle coefficient represents the rotation of the mixer blade. The degree of fluctuation of the mixer blades Weekly Stability Factor , , where , , Respectively Week, Week, The torque cycle coefficient of the cycle, is the total number of revolutions of the mixer blades; calculate the number of revolutions of the mixer blades Zhou's generalization , , where To obtain the minimum function, To obtain the maximum value function, To complete During the first mixing process, the mixer blades The set of torque cycle coefficients during rotation, is the hyperbolic tangent function, , Complete the Stir, The first stirring The stability coefficient of the mixer blades, is the total number of stirrings completed; the generalization degree of each cycle is clustered to obtain multiple clusters; if the torque cycle coefficient of the current cycle exceeds the range of the cluster to which it belongs, the heating temperature is adjusted until the torque cycle coefficient of the current cycle is within the range of the cluster to which it belongs.

[0007] The above technical solution can achieve accurate monitoring and control of the stirring process by systematically analyzing the torque data of the mixer blades rotating weekly during the production process of compound probiotics, calculating the torque cycle coefficient, stability coefficient and generalization degree, and using the ordered sample clustering method to identify abnormal fluctuations in the stirring process. If the torque cycle coefficient of the current week exceeds the cluster range, the abnormality is quickly corrected by adjusting the heating temperature to keep the stirring process stable and ensure the consistency of the production quality of compound probiotics. At the same time, this method significantly improves the dynamic adaptability of the production process by analyzing the historical data of multiple stirrings, provides data support for optimizing the production process, and improves production efficiency and the stability of the finished product.

[0008] Further, the mixer blades rotate Torque cycle coefficient , , where For the The number of torques in the torque sequence of the week, For the The number of torques in the torque sequence of the week, For the The torque sequence of the first week is Mutual information coefficient of the torque series of Zhou.

[0009] The above technical solution calculates the torque cycle coefficient of each cycle of the mixer blade rotation by introducing the mutual information coefficient, which can more accurately quantify the correlation between each cycle and the adjacent cycle torque sequence, thereby reflecting the degree of fluctuation in the mixing process. The mutual information coefficient can effectively capture the similarities or differences between different cycles, making the calculation of the torque cycle coefficient more detailed and reliable. This method not only enhances the ability to monitor the dynamic changes of the mixing process, but also improves the early identification and intervention capabilities of potential abnormal fluctuations, thereby further ensuring the stability of the production process and the accuracy of quality control.

[0010] Further, the mixer blades rotate Torque cycle coefficient , , where For the The number of torques in the torque sequence of the week, For the The number of torques in the torque sequence of the week, For the The torque sequence of the first week is The similarity of the torque series of the week.

[0011] The above technical solution calculates the similarity between each cycle and the adjacent cycle torque sequence through the dynamic time warping algorithm, which is used to determine the torque cycle coefficient of each cycle of the mixer blade rotation, and can more accurately capture the subtle changes and nonlinear correspondence between different cycles. The dynamic time warping algorithm has the ability to adapt to the differences in time series lengths and non-stationary characteristics, making the calculation of similarity more in line with the complex dynamic characteristics of the actual mixing process. By combining the proportional relationship between similarity and the number of sequences, the description accuracy of the torque cycle coefficient is further improved, which helps to more keenly identify abnormal fluctuations and optimize the control of mixing parameters, thereby achieving more accurate monitoring and quality assurance of the production process.

[0012] Furthermore, it also includes eliminating the torque sequence whose stability coefficient is greater than a set threshold.

[0013] The above technical solution can effectively filter out abnormal data in the mixing process by eliminating torque sequences with stability coefficients greater than the set threshold, avoiding the influence of these abnormal values ​​on the torque cycle coefficient, generalization degree and clustering results, thereby improving the accuracy and reliability of data analysis. This will not only improve the accuracy of monitoring the mixing process, but also further optimize the parameter control strategy, help maintain the stability of the production process, reduce the risk of quality fluctuations caused by abnormal fluctuations, and ultimately ensure the stability and consistency of compound probiotic products.

[0014] Furthermore, the similarity is obtained by a dynamic time warping algorithm.

[0015] Furthermore, a torque sensor is used to collect the torque sequence of the mixer blades during each rotation of the mixer blades during the mixing of mono- and di-glycerol fatty acid esters and medium-chain triglycerides or sunflower oil in the composite probiotic formula.

[0016] The above technical solution uses a torque sensor to collect the torque sequence of the mixer blades every rotation, which can accurately reflect the dynamic mechanical properties of mono- and di-glycerol fatty acid esters and medium-chain triglycerides or sunflower oil in the compound probiotic formula during the mixing process in real time, providing high-precision and continuous basic data support for subsequent data analysis and process control. This high-frequency and high-precision data collection method helps to comprehensively monitor the changes in the force of the fan blades during the mixing process, identify abnormal fluctuations in a timely manner, and provide a reliable basis for optimizing mixing process parameters and improving production quality, thereby improving the stability and efficiency of compound probiotic production.

[0017] Furthermore, the torque sensor is a strain type torque sensor or a rotational torque sensor.

[0018] Furthermore, it also includes performing data cleaning and data normalization processing on the torque sequence.

[0019] Furthermore, the clustering is ordered sample clustering.

[0020] In a second aspect, the present invention provides a composite probiotic production safety monitoring system, comprising a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, any one of the composite probiotic production safety monitoring methods described above is implemented.

[0021] The beneficial effects of the present invention are: The present invention effectively reflects the mechanical fluctuation and stability of the mixer blades in each rotation by real-time acquisition of the torque sequence during the stirring process, combined with a variety of advanced calculation models, such as torque cycle coefficient, stability coefficient, generalization degree, etc. By dynamically adjusting the heating temperature to ensure that the torque cycle coefficient is within a specified range, the present invention can achieve real-time optimization and abnormality detection of the production process, avoiding the risks of production fluctuations and quality fluctuations. In particular, by introducing data cleaning, normalization processing and dynamic time warping algorithms for accurate calculation of similarities, the quality and accuracy of monitoring data are improved, and the adaptability to parameter fluctuations under different production environments is enhanced, thereby improving the production efficiency of compound probiotics and the consistency of products. The present invention also provides intelligent and automated monitoring and control means for the production process through big data cluster analysis, ensuring the stability of the production process and the high quality of the final product. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart schematically illustrating a method for monitoring the production safety of composite probiotics according to an embodiment of the present invention; Figure 2 It is a structural block diagram schematically showing a composite probiotic production safety monitoring system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] An embodiment of a method for monitoring the production safety of a composite probiotic.

[0024] like Figure 1 As shown, a flow chart of a composite probiotic production safety monitoring method according to an embodiment of the present invention comprises the following steps: S1: The mono- and di-glycerol fatty acid esters in the compound probiotic formula are mixed with medium-chain triglycerides or sunflower oil for multiple times to obtain a torque sequence per rotation of the mixer blade.

[0025] In one embodiment, during the mixing of mono- and di-glycerol fatty acid esters and medium-chain triglycerides or sunflower oil in a composite probiotic formula, torque is one of the important parameters for measuring the operating state of the mixer, and its change directly reflects the rheological properties of the material during the mixing process and the workload of the mixer. Through multiple mixing, a torque sequence can be obtained each time the mixer blade rotates one circle. This sequence provides detailed torque fluctuation information and can reveal various dynamic changes during the mixing process. In order to ensure the accuracy and reliability of the collected data, high-precision torque sensors, such as strain-type torque sensors or rotational torque sensors, are selected during the implementation process. This type of sensor has very high sensitivity and accuracy, can work stably in a complex dynamic mixing environment, monitor the changes in torque in real time, and convert it into a digital signal; The strain-type torque sensor can calculate the torque by measuring the strain change caused by the torque. It has very high accuracy and strong anti-interference ability, and is very suitable for the strict requirements on accuracy in high-load mixing processes. The rotational torque sensor can realize continuous dynamic monitoring by directly measuring the torque of the rotating shaft, and is suitable for the detection of high-speed rotation and high-frequency changes.

[0026] However, in the actual collection process, data quality may be affected by many factors, such as environmental noise, sensor error, mechanical vibration, etc. These factors may cause abnormal values ​​or inconsistencies in the collected torque data. Therefore, in the data processing stage, the original torque series must be strictly cleaned and normalized. The purpose of data cleaning is to eliminate noise, remove invalid or abnormal data points, and ensure that only data representing the normal working state of the mixer is retained. Data cleaning can not only eliminate meaningless fluctuations caused by instantaneous faults or interference, but also effectively prevent data deviations caused by extreme values ​​or errors, thereby improving the representativeness and accuracy of the data.

[0027] Since the absolute value of torque may vary in different mixing cycles, normalization adjusts the torque data of different mixing processes to a unified scale. This process helps to eliminate data deviations caused by different mixing conditions, such as different materials, mixing speeds, etc., and ensures data comparability and consistency.

[0028] S2: Calculate the torque cycle coefficient, stability coefficient and generalization degree of the mixer blade at each rotation cycle.

[0029] In one embodiment, the number of rotations of the mixer blades is calculated. Torque cycle coefficient , , where For the The number of torques in the torque sequence of the week, For the The number of torques in the torque sequence of the week, For the The torque sequence of the first week is Mutual information coefficient of the torque series of Zhou.

[0030] By calculating the torque cycle coefficient of each rotation cycle of the mixer blade and incorporating the mutual information coefficient between the torque sequence of each cycle and its adjacent cycle into the calculation formula, the correlation and information sharing between the two cycles can be quantified more comprehensively. By combining the ratio of the number of torque sequences, the calculation results not only reflect the correlation between the sequences, but also dynamically adapt to the changes in the length of the torque data, thereby improving the accuracy of the description of the fluctuation characteristics during the mixing process. This method enhances the ability to identify abnormal fluctuations, provides a solid data foundation for the refined monitoring and parameter optimization of the mixing process, and further improves the quality and efficiency of the production of compound probiotics.

[0031] Calculate the number of rotations of the mixer blades Weekly Stability Factor , , where , , Respectively Week, Week, The torque cycle coefficient of the cycle, is the total number of revolutions of the mixer blades, Rotate the mixer blades Torque cycle coefficient of the week; By calculating the stability coefficient of each rotation of the mixer blade, the stability of the mixing process can be dynamically quantified. By weighing the current fluctuations and historical cumulative characteristics, this method not only improves the sensitivity to the stable state of the mixing process, but also enhances the adaptability to long-term trends, which helps to quickly identify potential abnormal fluctuations. As a result, the present invention further optimizes the ability to accurately monitor and control the mixing process, improves production efficiency and quality consistency of composite probiotic products.

[0032] Calculate the number of rotations of the mixer blades Zhou's generalization , , where To obtain the minimum function, To obtain the maximum value function, To complete During the first mixing process, the mixer blades The set of torque cycle coefficients during rotation, is the hyperbolic tangent function, , Complete the Stir, The first stirring The stability coefficient of the mixer blades, is the total number of stirrings completed; By calculating the generalization degree of each cycle of the mixer blade rotation, combining the ratio of the minimum and maximum values ​​of the torque cycle coefficient to complete the dynamic change trend of the stability coefficient during multiple mixing, and using the hyperbolic tangent function for smoothing, it is possible to fully quantify the degree of influence of different mixing times on the current mixing process. This method effectively integrates the historical data and current data characteristics of multiple rounds of mixing, can capture short-term fluctuation characteristics, and reflect long-term stability trends, and improves the overall evaluation ability of the mixing state, thus providing an important basis for the optimization and regulation of the mixing process, and significantly improving the production quality and the intelligent level of process control.

[0033] In another embodiment, the number of rotations of the blender blades is calculated. Torque cycle coefficient , , where For the The number of torques in the torque sequence of the week, For the The number of torques in the torque sequence of the week, For the The torque sequence of the first week is The similarity of the torque sequences of the weeks is obtained by a dynamic time warping algorithm.

[0034] By introducing the dynamic time warping algorithm to calculate the similarity of the torque sequence, and combining the ratio of the number of sequences to determine the torque cycle coefficient of each rotation of the mixer blade, it can effectively deal with the problems of torque sequence length difference and nonlinear changes, and significantly improve the ability to accurately characterize the changes in mechanical properties during the mixing process. The dynamic time warping algorithm can capture the complex dynamic correspondence between torque sequences between different cycles, making the calculation results more consistent with the changing characteristics of the actual mixing process, thereby enhancing the detection sensitivity of abnormal fluctuations, optimizing the monitoring accuracy and parameter control strategy of the mixing process, and providing reliable data support for improving the quality and efficiency of compound probiotic production.

[0035] S3: Clustering the generalization degree of each cycle to obtain multiple clusters; if the torque cycle coefficient of the current cycle exceeds the range of the cluster to which it belongs, adjusting the heating temperature until the torque cycle coefficient returns to the range of the cluster to which it belongs.

[0036] In one embodiment, the clustering is an ordered sample clustering. The ordered sample clustering analysis is performed on the generalization degree of each week, and the torque cycle coefficients of different weeks are divided into multiple clusters using a clustering algorithm, such as , and , if the torque cycle coefficient of the current cycle exceeds the range of the cluster to which it belongs, the rheological properties and mechanical response of the material during the mixing process are changed by adjusting the heating temperature, such as increasing the heating temperature, until the torque cycle coefficient returns to the range of the cluster to which it belongs. This method can effectively avoid process loss of control caused by abnormal torque fluctuations through real-time monitoring and dynamic regulation, thereby significantly improving the stability of the mixing process and the consistency of the final product quality. This method can effectively avoid process loss of control caused by abnormal torque fluctuations through real-time monitoring and dynamic regulation, thereby significantly improving the stability of the mixing process and the consistency of the final product quality.

[0037] The scheme of the present invention can monitor the operating state of the mixer in the production process of composite probiotics in real time by introducing a big data processing method based on torque sequence analysis, effectively identify the law of torque fluctuations and accurately calculate the stability and generalization degree. By dynamically calculating and clustering the torque cycle coefficients of each week, and combining the dynamic time warping algorithm to obtain similarity, it is possible to automatically identify abnormal fluctuations in the stirring process and adjust the heating temperature in time, thereby ensuring the stability and efficiency of the stirring process. Through data cleaning and normalization processing, abnormal data that does not meet the stability requirements are eliminated, making the analysis results more accurate. This method can significantly improve the level of intelligent monitoring of the production process, reduce human intervention, improve the consistency of product quality, while improving production efficiency and reducing energy consumption.

[0038] An embodiment of a composite probiotic production safety monitoring system: like Figure 2 As shown, a structural block diagram of a composite probiotic production safety monitoring system according to an embodiment of the present invention includes a processor and a memory.

[0039] The present invention also provides a composite probiotic production safety monitoring system. Figure 2 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned composite probiotic production safety monitoring method according to the present invention is implemented.

[0040] The composite probiotic production safety monitoring system also includes other components well known to those skilled in the art, such as a communication interface, and the configuration and functions thereof are known in the art, so they will not be described in detail here.

[0041] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions stored or otherwise maintained by such a computer-readable medium.

[0042] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly defined.

[0043] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for monitoring the production safety of composite probiotics, characterized in that: include: The mono- and di-glycerol fatty acid esters in the composite probiotic formula are stirred with medium-chain triglycerides or sunflower oil for multiple times to obtain a torque sequence of the mixer blades per rotation of one circle during each stirring; Calculate the number of rotations of the mixer blades Torque cycle coefficient The torque cycle coefficient represents the rotation of the mixer blade. Weekly volatility; Calculate the number of rotations of the mixer blades Weekly Stability Factor , , where , , Respectively Week, Week, The torque cycle coefficient of the cycle, The total number of revolutions of the mixer blades; Calculate the number of rotations of the mixer blades Zhou's generalization , , where To obtain the minimum function, To obtain the maximum value function, To complete During the first mixing process, the mixer blades The set of torque cycle coefficients during rotation, is the hyperbolic tangent function, , Complete the first Stir, The first stirring The stability coefficient of the mixer blades, is the total number of stirrings completed; The generalization degree of each cycle is clustered to obtain multiple clusters; if the torque cycle coefficient of the current cycle exceeds the range of the cluster to which it belongs, the heating temperature is adjusted until the torque cycle coefficient of the current cycle is within the range of the cluster to which it belongs.

2. A composite probiotic production safety monitoring method according to claim 1, characterized in that: The mixer blades rotate Torque cycle coefficient , , where For the The number of torques in the torque sequence of the week, For the The number of torques in the torque sequence of the week, For the The torque sequence of the first week is Mutual information coefficient of the torque series of Zhou.

3. A composite probiotic production safety monitoring method according to claim 1, characterized in that: The mixer blades rotate Torque cycle coefficient , , where For the The number of torques in the torque sequence of the week, For the The number of torques in the torque sequence of the week, For the The torque sequence of the first week is The similarity of the torque series of the week.

4. A composite probiotic production safety monitoring method according to claim 3, characterized in that: The method also includes eliminating torque sequences whose stability coefficient is greater than a set threshold.

5. The method for monitoring the production safety of composite probiotics according to claim 3, characterized in that: The similarity is obtained by a dynamic time warping algorithm.

6. The method for monitoring the production safety of composite probiotics according to claim 1, characterized in that: The torque sensor is used to collect the torque sequence of the mixer blade during each rotation of the mixer during the mixing of mono- and di-glycerol fatty acid esters and medium-chain triglycerides or sunflower oil in the compound probiotic formula.

7. A composite probiotic production safety monitoring method according to claim 6, characterized in that: The torque sensor is a strain gauge torque sensor or a rotation torque sensor.

8. The method for monitoring the production safety of composite probiotics according to claim 1, characterized in that: The method also includes performing data cleaning and data normalization processing on the torque sequence.

9. The method for monitoring the production safety of composite probiotics according to claim 1, characterized in that: The clustering is ordered sample clustering.

10. A composite probiotic production safety monitoring system, characterized in that: It comprises a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a composite probiotic production safety monitoring method according to any one of claims 1 to 9 is implemented.

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