A method, system and apparatus for temperature monitoring in a food fermentation process

Abstract

This application relates to the field of intelligent sensing system technology, specifically to a method, system, and device for temperature monitoring during food fermentation. The method includes: collecting fermentation temperatures at different monitoring points of the fermentation device at different collection times; determining the temperature gradient and fermentation temperature fluctuation coefficient of the monitoring points at the collection times; collecting the fermentation temperature during normal food fermentation, determining the ideal fermentation temperature, and determining the optimal fermentation temperature for each monitoring point at each collection time; and obtaining the actual fermentation temperature of the monitoring points at each collection time based on the optimal and actual fermentation temperatures, thereby achieving temperature monitoring during food fermentation. This application can improve the accuracy of temperature monitoring during food fermentation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent sensing systems, in particular to a temperature monitoring method, system and device in a food fermentation process. BACKGROUND

[0002] Food fermentation technology utilizes specific microorganisms to decompose and metabolize nutrients such as carbohydrates, proteins, and organic matter in the substrate under controlled conditions to generate corresponding products. During the entire fermentation process, accurate control of temperature in the environmental parameters is crucial. Specifically, excessively high temperature can cause inactivation of bacterial cells or accumulation of by-products, while excessively low temperature can inhibit metabolic activity and prolong the fermentation period, seriously affecting product quality and production efficiency. Therefore, it is necessary to monitor the fermentation temperature in real time and accurately. Generally, a sensor is used to collect the temperature in the fermentation broth, and whether the temperature is in the optimal fermentation temperature range is determined according to the temperature, so as to determine whether temperature adjustment control is needed.

[0003] However, the temperature distribution in the fermentation broth is uneven, and there may even be local cold zones and hot spots. The temperature in the fermentation broth collected by the single-point sensor cannot timely discover the corresponding temperature abnormalities, so that the temperature monitoring result often has a significant response lag when the abnormality is discovered, resulting in a large error in the temperature monitoring result and easily causing overshoot and oscillation of the temperature control system. SUMMARY

[0004] To solve the above technical problems, the purpose of the present application is to provide a temperature monitoring method, system and device in a food fermentation process, and the technical solution adopted is as follows:

[0005] In a first aspect, the present application provides a temperature monitoring method in a food fermentation process, which comprises the following steps:

[0006] Collecting the fermentation temperatures of different monitoring points in the vertical and horizontal directions of the center position of the fermentation device at different collection time points;

[0007] Taking any one collection time point as a target collection time point, determining the derivative of each collection time point according to the change trend of the fermentation temperatures at adjacent collection time points, determining the temperature gradient of each monitoring point at each collection time point according to the difference in the derivatives of all monitoring points in the same horizontal direction and the same vertical direction at adjacent collection time points and the distance between different monitoring points, and determining the fermentation temperature fluctuation coefficient of the monitoring point at the collection time point according to the similarity of the derivative sequence of the monitoring point and all other monitoring points at the collection time point and the difference in the temperature gradient and the symmetry of each monitoring point with respect to the center position of the fermentation device;

[0008] The fermentation temperature of the normal fermentation process of the food is collected, the ideal fermentation temperature of each monitoring point at each moment of fermentation is determined, the optimal value is obtained by combining the fermentation temperature of the monitoring point at the collection moment and the fermentation temperature fluctuation coefficient, and the optimal fermentation temperature of each monitoring point at each collection moment is determined.

[0009] According to the optimal fermentation temperature and the fermentation temperature of the monitoring point at each collection moment, the actual fermentation temperature of the monitoring point at the collection moment is obtained, and temperature monitoring in the fermentation process of the food is realized.

[0010] Further, the determination process of the derivative of the collection moment is as follows:

[0011] The fermentation temperature curve of the monitoring point at the target collection moment is obtained by performing curve fitting on the fermentation temperature of the monitoring point at the target collection moment and all collection moments within the first preset time before the target collection moment.

[0012] According to the fermentation temperature curve, the derivative of the target collection moment and all collection moments within the first preset time before the target collection moment is calculated.

[0013] Further, the calculation formula of the temperature gradient of the monitoring point at each collection moment is as follows:

[0014]

[0015]

[0016]

[0017] wherein, represents the horizontal temperature gradient of the i th monitoring point at the target collection moment; represents the average value of the derivative of all monitoring points in the same horizontal direction as the i th monitoring point at the target collection moment and all collection moments within the first preset time before the target collection moment; represents the average value of the derivative of the i th monitoring point at the target collection moment and all collection moments within the first preset time before the target collection moment; represents the average value of the distance between all monitoring points in the same horizontal direction as the i th monitoring point; represents the average value of the derivative of all monitoring points in the same vertical direction as the i th monitoring point at the target collection moment and all collection moments within the first preset time before the target collection moment; represents the average value of the distance between all monitoring points in the same vertical direction as the i th monitoring point; represents the i th monitoring point; and represents the average value of the derivative of all monitoring points in the same vertical direction as the i th monitoring point at the target collection moment and all collection moments within the first preset time before the target collection moment; represents the average value of the distance between all monitoring points in the same vertical direction as the i th monitoring point; represents the i th monitoring point; and represents the average value of the derivative of all monitoring points in the same vertical direction as the i th monitoring point at the target collection moment and all collection moments within the first preset time before the target collection moment; represents the average value of the distance between all monitoring points in the same vertical direction as the i th monitoring point; represents the i th monitoring point; and Vertical temperature gradient at each monitoring point at the target acquisition time; Indicates the first The mean of the derivatives of all acquisition times at the target acquisition time and within the first preset time before the target acquisition time for the adjacent monitoring points vertically above the target acquisition point. Indicates the distance between two adjacent monitoring points; Indicates the first Temperature gradient at each monitoring point at the time of target acquisition.

[0018] Furthermore, the method for obtaining the fermentation temperature fluctuation coefficient at the monitoring point at the time of data collection is as follows:

[0019] Select and the first All monitoring points that are centrally and axially symmetric about the center of the fermentation unit are selected and compared with the first monitoring point. The sum of the differences in temperature gradients at each monitoring point at the target acquisition time is denoted as the i-th... The relative gradient differences of the monitoring points at the target acquisition time;

[0020] The fermentation temperature fluctuation coefficient is calculated using the following formula:

[0021]

[0022] in, Indicates the first Fermentation temperature fluctuation coefficient at each monitoring point at the target collection time; Indicates the first The sum of the absolute values ​​of the Pearson correlation coefficients of the derivative sequences of the monitoring point and all other monitoring points at the target acquisition time; Indicates the first The relative gradient differences of the monitoring points at the target acquisition time; This indicates the preset parameter tuning coefficients.

[0023] Furthermore, the process for determining the ideal fermentation temperature at each monitoring point during fermentation is as follows:

[0024] Fermentation temperatures at different monitoring points during a first preset number of normal fermentation processes of food are collected at different collection times. Curve fitting is performed on the fermentation temperatures at the same monitoring point during the same normal fermentation process of food at different collection times to obtain the normal fermentation temperature curve of the monitoring point. The average value of the fitted values ​​of the first preset number of normal fermentation temperature curves of the same monitoring point at the same fermentation time is recorded as the ideal fermentation temperature of the same monitoring point at the same fermentation time.

[0025] Furthermore, the specific steps for determining the optimal fermentation temperature for each monitoring point at each sampling time by combining the fermentation temperature and fermentation temperature fluctuation coefficient at the monitoring point at the sampling time are as follows:

[0026] The time elapsed from the start of food fermentation to the target collection time is calculated and recorded as the target time. The first function is used as the objective function of the particle swarm optimization algorithm. The particle swarm optimization algorithm is used to process the fitted values ​​of the fermentation temperature at the same monitoring point at the target collection time and the fermentation temperature of all normal fermentation temperature curves at the fermentation time to the target time, so as to minimize the value of the objective function and obtain the optimal fermentation temperature at the same monitoring point at the target time.

[0027] Furthermore, the expression for the first function is:

[0028]

[0029] in, Indicates the first The first function at each acquisition time; Indicates the first The monitoring point at the 1st The ideal fermentation temperature at each sampling time; Indicates the first The monitoring point at the 1st Fermentation temperature fluctuation coefficient at each sampling time; Represents the normalization function; Indicates the number of monitoring points set; Indicates the first The monitoring point at the 1st The optimal fermentation temperature at each sampling time.

[0030] Furthermore, the step of obtaining the actual fermentation temperature at each sampling time based on the optimal fermentation temperature and fermentation temperature at the monitoring point, thereby achieving temperature monitoring during the food fermentation process, includes the following steps:

[0031] Based on the optimal fermentation temperature of the same monitoring point at all sampling times within the first preset time before the target sampling time at the target sampling time, the predicted value of the optimal fermentation temperature of the monitoring point at the adjacent sampling time after the target sampling time is obtained.

[0032] The average of the fermentation temperature at the monitoring point at the next adjacent collection time and the predicted value of the optimal fermentation temperature is taken as the actual fermentation temperature at the monitoring point at the collection time.

[0033] Secondly, embodiments of this application also provide a temperature monitoring device during food fermentation. The displacement monitoring device includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the methods described above.

[0034] Thirdly, embodiments of this application provide a temperature monitoring system for food fermentation processes, the temperature monitoring system comprising: a fermentation temperature acquisition module, a temperature fluctuation analysis module, an optimal fermentation temperature determination module, and a temperature monitoring module.

[0035] The fermentation temperature acquisition module is used to collect the fermentation temperature at different monitoring points at the center position, vertical direction, and horizontal direction of the fermentation device at different acquisition times;

[0036] The temperature fluctuation analysis module is used to record any acquisition time as the target acquisition time, determine the derivative of each acquisition time based on the trend of fermentation temperature change between adjacent acquisition times, determine the temperature gradient of the monitoring point at each acquisition time based on the difference of derivatives of all monitoring points in the same horizontal and vertical directions at adjacent acquisition times, and the distance between different monitoring points, and determine the fermentation temperature fluctuation coefficient of the monitoring point at the acquisition time based on the similarity of the derivative sequence of the monitoring point with all other monitoring points at the acquisition time, the difference of the temperature gradient, and the symmetry of each monitoring point about the center position of the fermentation device.

[0037] The optimal fermentation temperature determination module is used to collect the fermentation temperature during the normal fermentation process of food, determine the ideal fermentation temperature at each monitoring point at each time of fermentation, and combine the fermentation temperature and fermentation temperature fluctuation coefficient at the monitoring point at the time of collection to obtain the optimal value and determine the optimal fermentation temperature at each monitoring point at each time of collection.

[0038] The temperature monitoring module is used to obtain the actual fermentation temperature of the monitoring point at the time of collection based on the optimal fermentation temperature and fermentation temperature at each collection point, thereby realizing temperature monitoring during the food fermentation process.

[0039] As can be seen from the above embodiments, the temperature monitoring method, system, and equipment provided in this application for food fermentation process have at least the following beneficial effects:

[0040] This application evaluates the gradient changes and local inhomogeneities of temperature distribution at monitoring points in the fermentation broth based on the correlation of fermentation temperature trends at different monitoring points in the vertical and horizontal directions of the fermentation apparatus, as well as the axisymmetric distribution characteristics of temperature gradient changes. It determines the fermentation temperature fluctuation coefficient at each monitoring point at each sampling time. Since microorganisms are highly sensitive to changes in fermentation temperature, the application analyzes the difference between the actual fermentation temperature and the ideal fermentation temperature during food fermentation and calculates the optimal value, determining the optimal fermentation temperature for each monitoring point at each sampling time. Finally, based on the optimal fermentation temperature and actual fermentation temperature at each sampling time, the application obtains the actual fermentation temperature at the monitoring point at the sampling time, achieving temperature monitoring during food fermentation, obtaining accurate fermentation temperature values ​​at each sampling time, eliminating the time lag in the collected fermentation temperature data, and solving the problem of large and delayed temperature anomaly monitoring errors caused by uneven temperature distribution in the fermentation broth. Attached Figure Description

[0041] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart illustrating the steps of a temperature monitoring method during food fermentation, as provided in one embodiment of this application;

[0043] Figure 2 This is a schematic diagram of a temperature monitoring system for a food fermentation process, provided as an embodiment of this application. Detailed Implementation

[0044] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a temperature monitoring method, system, and device for food fermentation according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0045] The following description, in conjunction with the accompanying drawings, details the specific scheme of a temperature monitoring method, system, and equipment for food fermentation provided in this application.

[0046] Please see Figure 1The diagram illustrates a flowchart of a temperature monitoring method during food fermentation according to an embodiment of this application. The method includes the following steps:

[0047] S001: Collect fermentation temperatures at different monitoring points at different times, including the center position, vertical direction, and horizontal direction of the fermentation device.

[0048] A monitoring point is set at the center of the fermentation device. Multiple monitoring points are evenly set in the vertical and horizontal directions of the fermentation device for food fermentation. A temperature sensor is set at each monitoring point, and the fermentation temperature is collected using the temperature sensor at the monitoring point.

[0049] Preferably, in one embodiment of this application, when setting up the monitoring points, five monitoring points are set in the vertical direction and nine monitoring points are evenly set in the horizontal direction. When collecting fermentation temperature data, the fermentation temperature is collected every one minute, for a total of one hour. In practical applications, as other implementation methods, the implementer can decide the number of monitoring points, the sampling frequency of fermentation temperature, and the number of fermentation temperature samples according to the actual situation. This application does not impose any special restrictions.

[0050] The fermentation temperature was normalized. It should be noted that this embodiment uses the Z-Score standard normalization method to calculate the normalized value. In practical applications, implementers may use other existing methods such as the maximum-minimum normalization method or the sigmoid function to calculate the normalized value, which is not limited here.

[0051] Thus, the fermentation temperature at different monitoring points in the vertical and horizontal directions at different collection times was obtained.

[0052] S002: Record any collection time as the target collection time. Based on the trend of fermentation temperature change at adjacent collection times, determine the derivative at each collection time. Based on the difference in derivatives at adjacent collection times for all monitoring points in the same horizontal and vertical directions as the monitoring point, as well as the distance between different monitoring points, determine the temperature gradient at each collection time for the monitoring point. Based on the similarity of the derivative sequence of the monitoring point with all other monitoring points at the collection time and the difference in temperature gradient, determine the fermentation temperature fluctuation coefficient at the collection time for the monitoring point.

[0053] During the normal fermentation process of food, the fermentation temperature exhibits certain trends and characteristics. Firstly, in the initial stage of fermentation, the number of microbial communities is relatively small, and these communities need to adapt to the environment; therefore, heat production is relatively low, and the fermentation temperature fluctuates relatively little. As the microbial population grows, their metabolic activity gradually intensifies, causing the fermentation temperature to rise rapidly. Furthermore, as the microbial community concentration reaches saturation, nutrients in the fermentation broth are gradually consumed, and the fermentation temperature begins to decrease.

[0054] Secondly, fermentation is a metabolic decomposition activity of microorganisms. When the oxygen content is high, the microorganisms are in an aerobic fermentation state, their metabolism is faster, and they produce more heat. Conversely, when the oxygen content is low, the microorganisms undergo anaerobic fermentation, and they produce relatively less heat.

[0055] Vertically, the oxygen content in the fermentation apparatus gradually decreases, becoming closer to an anaerobic fermentation environment towards the bottom. Therefore, the temperature variation in the vertical direction exhibits a certain gradient characteristic, although the gradient difference between adjacent layers is relatively small. Furthermore, horizontally, the temperature is relatively higher closer to the center of the fermentation apparatus. Simultaneously, the horizontal temperature gradient in the fermentation apparatus often exhibits an axisymmetric distribution. Conversely, the presence of localized cold or hot zones can affect the metabolic activity of microorganisms, leading to a significant and abnormal reduction in fermentation heat production. This change increases the temperature gradient difference at the corresponding location and disrupts the temporal trend and symmetry of the fermentation temperature variation.

[0056] Any acquisition time is designated as the target acquisition time. All acquisition times within a first preset time period prior to the target acquisition time are numbered according to their acquisition order. The acquisition time number is used as the independent variable, and the fermentation temperature at the same monitoring point at the acquisition time is used as the dependent variable. Curve fitting is performed to obtain the fermentation temperature curve of the same monitoring point at the target acquisition time. Based on the fermentation temperature curve, the derivatives of all acquisition times within the first preset time period prior to the target acquisition time are calculated. The derivatives of all acquisition times within the first preset time period prior to the target acquisition time are sorted according to their acquisition time order to obtain the derivative sequence of the monitoring point at the target acquisition time.

[0057] In this embodiment, the first preset time is set to 1 hour. It should be understood that if the acquisition time before the target acquisition time is less than the first preset time, the target acquisition time will not be analyzed.

[0058] The temperature gradient of the monitoring point at the target acquisition time is determined based on the differences in the derivatives of all monitoring points in the same horizontal and vertical directions as the monitoring point at the target acquisition time and the adjacent acquisition times before the target acquisition time, as well as the distance between different monitoring points.

[0059]

[0060]

[0061]

[0062] in, Indicates the first The horizontal temperature gradient at each monitoring point at the target acquisition time; Indicates the relationship with the first The mean of the derivatives of all monitoring points in the same horizontal direction at the target acquisition time and all acquisition times within the first preset time before the target acquisition time; Indicates the first The mean of the derivatives of all acquisition times at each monitoring point within the target acquisition time and the first preset time before the target acquisition time; Indicates the relationship with the first The average distance between all monitoring points in the same horizontal direction; Indicates the relationship with the first The mean of the derivatives of all monitoring points in the same vertical direction at the target acquisition time and all acquisition times within the first preset time before the target acquisition time; Indicates the relationship with the first The average distance between all monitoring points in the same vertical direction of a given monitoring point; Indicates the first Vertical temperature gradient at each monitoring point at the target acquisition time; Indicates the first The mean of the derivatives of all acquisition times at the target acquisition time and within the first preset time before the target acquisition time for the adjacent monitoring points vertically above the target acquisition point. Indicates the distance between two adjacent monitoring points; Indicates the first Temperature gradient at each monitoring point at the time of target acquisition.

[0063] Based on the similarity of the derivative sequences of the monitoring point with all other monitoring points at the target acquisition time, the difference in temperature gradient, and the symmetry of each monitoring point about the center position of the fermentation device, the fermentation temperature fluctuation coefficient of the monitoring point at the target acquisition time is determined.

[0064]

[0065] in, Indicates the first Fermentation temperature fluctuation coefficient at each monitoring point at the target collection time; Indicates the first The sum of the absolute values ​​of the Pearson correlation coefficients of the derivative sequences of the monitoring point and all other monitoring points at the target acquisition time; Indicates the first The relative gradient difference of the monitoring points at the target acquisition time is selected from the data of the first monitoring point. All monitoring points that are centrally and axially symmetric about the center of the fermentation unit are compared with the monitoring points of the first monitoring point. The sum of the differences in temperature gradients at each monitoring point at the target acquisition time is denoted as the i-th... The relative gradient differences of the monitoring points at the target acquisition time; This represents the preset parameter tuning coefficient. In this embodiment, the parameter tuning coefficient is set to 0.01.

[0066] The fermentation temperature fluctuation coefficient is used to evaluate the gradient change and local non-uniformity of the temperature distribution at monitoring points in the fermentation broth. The closer the temperature change trends are among all monitoring points in the same horizontal and vertical directions as the monitoring point, and the closer they are among all monitoring points that are centrally and axially symmetrical about the center of the fermentation apparatus, the less likely the fermentation temperature at the monitoring point is to be abnormal. The smaller the fermentation temperature fluctuation coefficient at the corresponding acquisition time, the lower the temperature fluctuation coefficient. Temperature anomalies disrupt this similarity in the corresponding location's temperature trend, resulting in a larger fermentation temperature fluctuation coefficient.

[0067] The same method can be used to obtain the fermentation temperature fluctuation coefficient at any collection time of the monitoring point.

[0068] Thus, the fermentation temperature fluctuation coefficient at any monitoring point at any given time is obtained.

[0069] S003: Collect the fermentation temperature during the normal fermentation process of food, determine the ideal fermentation temperature at each monitoring point at each moment of fermentation, and combine the fermentation temperature and fermentation temperature fluctuation coefficient at the monitoring point at the time of collection to obtain the optimal value, and determine the optimal fermentation temperature at each monitoring point at each time of collection.

[0070] Microorganisms are quite sensitive to changes in fermentation temperature. For example, during beer fermentation, a temperature fluctuation of 0.5 degrees Celsius can have a significant impact on the taste. Therefore, when monitoring the temperature during food fermentation, it is also necessary to consider the difference between the actual fermentation temperature and the ideal fermentation temperature.

[0071] Fermentation temperatures at different monitoring points during a first preset number of normal food fermentation processes are collected at different collection times. Curve fitting is performed on the fermentation temperatures at the same monitoring point during the same normal food fermentation process at different collection times to obtain the normal fermentation temperature curve of the monitoring point. The average value of the fitted values ​​of the first preset number of normal fermentation temperature curves of the same monitoring point at the same fermentation time is recorded as the ideal fermentation temperature of the same monitoring point at the same fermentation time.

[0072] In this embodiment, the first preset quantity is set to 100.

[0073] The time elapsed from the start of food fermentation to the target sampling time is calculated and recorded as the target time. The first function is used as the objective function of the particle swarm optimization (PSO) algorithm. The PSO algorithm is then used to process the fitted values ​​of the fermentation temperature at the same monitoring point at the target sampling time and the fermentation temperatures of all normal fermentation temperature curves at the target time, minimizing the objective function value to obtain the optimal fermentation temperature at the same monitoring point at the target time. The use of the PSO algorithm to find the optimal value is a well-known technique and will not be elaborated further. In this embodiment, the maximum number of iterations for the PSO algorithm system is set to 100, the inertia weight is set to 0.5, and the initial learning factor is set to 1.5.

[0074] The expression for the first function is:

[0075]

[0076] in, Indicates the first The first function at each acquisition time; Indicates the first The monitoring point at the 1st The ideal fermentation temperature at each sampling time; Indicates the first The monitoring point at the 1st Fermentation temperature fluctuation coefficient at each sampling time; Represents the normalization function; Indicates the number of monitoring points set; Indicates the first The monitoring point at the 1st The optimal fermentation temperature at each sampling time.

[0077] Thus, the optimal fermentation temperature at each monitoring point at each collection moment is obtained.

[0078] S004: Based on the optimal fermentation temperature and fermentation temperature of the monitoring point at each collection time, obtain the actual fermentation temperature of the monitoring point at the collection time to realize temperature monitoring during the food fermentation process.

[0079] Monitoring of fermentation temperature during the fermentation process has a certain lag, but the control of fermentation temperature is carried out in real time. The direction of fermentation temperature regulation is to make the actual fermentation temperature as close as possible to the optimal fermentation temperature, and to minimize local temperature anomalies such as cold zones and hot spots in the fermentation broth. The direction of change of the optimal fermentation temperature is to closely resemble the actual temperature changes during operation and to minimize local temperature anomalies. Therefore, it is necessary to determine the predicted value of the optimal fermentation temperature at each sampling time after the sampling time, so as to improve the accuracy of temperature prediction and reduce the monitoring and adjustment errors caused by the lag effect of real-time sampling of fermentation temperature.

[0080] The optimal fermentation temperatures at all sampling times within the target sampling time and a first preset time period prior to the target sampling time at the same monitoring point are arranged in chronological order to obtain the optimal fermentation temperature sequence at the same monitoring point at the target sampling time. A time series prediction algorithm is then used to process the optimal fermentation temperature sequence at the target sampling time to obtain the predicted optimal fermentation temperature at adjacent sampling times after the target sampling time.

[0081] In this embodiment, the ARIMA autoregressive integral moving average model in time series forecasting algorithms is used to predict the optimal fermentation temperature. In practical applications, as other implementation methods, in addition to achieving the purpose of data prediction, implementers can use other existing technologies such as autoregressive model (AR), moving average model (MA), autoregressive moving average model, seasonal autoregressive integral moving average model (SARIMA), and exponential smoothing to achieve data prediction.

[0082] The average of the fermentation temperature at the monitoring point at the next adjacent collection time and the predicted value of the optimal fermentation temperature is taken as the actual fermentation temperature at the monitoring point at the collection time.

[0083] It is understandable that the actual fermentation temperature corresponds to the accurate fermentation temperature value at the time of collection, thus eliminating the influence of time lag in the collected fermentation temperature.

[0084] This enables temperature monitoring during the food fermentation process.

[0085] Based on the same inventive concept as the above method, this application embodiment also provides a temperature monitoring device in the food fermentation process, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described methods for temperature monitoring in the food fermentation process.

[0086] Please see Figure 2 , Figure 2This is a schematic diagram of a temperature monitoring system for a food fermentation process according to one embodiment of this application. In this embodiment, the temperature monitoring system includes units that perform the steps in the embodiment corresponding to a temperature monitoring method for a food fermentation process. See also... Figure 2 The temperature monitoring system includes: a fermentation temperature acquisition module, a temperature fluctuation analysis module, an optimal fermentation temperature determination module, and a temperature monitoring module.

[0087] The fermentation temperature acquisition module is used to collect the fermentation temperature at different monitoring points at the center position, vertical direction, and horizontal direction of the fermentation device at different acquisition times;

[0088] The temperature fluctuation analysis module is used to record any acquisition time as the target acquisition time, determine the derivative of each acquisition time based on the trend of fermentation temperature change between adjacent acquisition times, determine the temperature gradient of the monitoring point at each acquisition time based on the difference of derivatives of all monitoring points in the same horizontal and vertical directions at adjacent acquisition times, and the distance between different monitoring points, and determine the fermentation temperature fluctuation coefficient of the monitoring point at the acquisition time based on the similarity of the derivative sequence of the monitoring point with all other monitoring points at the acquisition time, the difference of the temperature gradient, and the symmetry of each monitoring point about the center position of the fermentation device.

[0089] The optimal fermentation temperature determination module is used to collect the fermentation temperature during the normal fermentation process of food, determine the ideal fermentation temperature at each monitoring point at each time of fermentation, and combine the fermentation temperature and fermentation temperature fluctuation coefficient at the monitoring point at the time of collection to obtain the optimal value and determine the optimal fermentation temperature at each monitoring point at each time of collection.

[0090] The temperature monitoring module is used to obtain the actual fermentation temperature of the monitoring point at the time of collection based on the optimal fermentation temperature and fermentation temperature at each collection point, thereby realizing temperature monitoring during the food fermentation process.

[0091] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0092] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0093] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. A method for monitoring temperature during food fermentation, characterized in that, The method includes the following steps: Fermentation temperatures were collected at different monitoring points at the center, vertical, and horizontal directions of the fermentation device at different times. Record any sampling time as the target sampling time. Based on the trend of fermentation temperature change at adjacent sampling times, determine the derivative at each sampling time. Based on the difference of derivatives at adjacent sampling times for all monitoring points in the same horizontal and vertical directions as the monitoring point, and the distance between different monitoring points, determine the temperature gradient at each sampling time for the monitoring point. Based on the similarity of the derivative sequence of the monitoring point with all other monitoring points at sampling times, the difference in temperature gradient, and the symmetry of each monitoring point about the center position of the fermentation device, determine the fermentation temperature fluctuation coefficient at the sampling time for the monitoring point. The fermentation temperature during the normal fermentation process of food is collected, and the ideal fermentation temperature at each monitoring point at each time of fermentation is determined. The optimal value is obtained by combining the fermentation temperature and the fermentation temperature fluctuation coefficient at the monitoring point at the time of collection. The optimal fermentation temperature at each monitoring point at each time of collection is determined. Based on the optimal fermentation temperature and fermentation temperature at each collection point, the actual fermentation temperature at the collection point is obtained, thereby enabling temperature monitoring during the food fermentation process. The method for obtaining the fermentation temperature fluctuation coefficient at the monitoring point at the time of data collection is as follows: Select and the first All monitoring points that are centrally and axially symmetric about the center of the fermentation unit are selected and compared with the first monitoring point. The sum of the differences in temperature gradients at each monitoring point at the target acquisition time is denoted as the i-th... The relative gradient differences of the monitoring points at the target acquisition time; The formula for calculating the fermentation temperature fluctuation coefficient is as follows: in, Indicates the first Fermentation temperature fluctuation coefficient at each monitoring point at the target collection time; Indicates the first The sum of the absolute values ​​of the Pearson correlation coefficients of the derivative sequences of the monitoring point and all other monitoring points at the target acquisition time; Indicates the first The relative gradient differences of the monitoring points at the target acquisition time; This indicates the preset parameter tuning coefficients; Indicates the first Temperature gradient at each monitoring point at the target acquisition time; The process of combining the fermentation temperature and fermentation temperature fluctuation coefficient at the monitoring point at the time of data collection to determine the optimal value, and determining the optimal fermentation temperature for each monitoring point at each time of data collection, includes the following specific steps: The time elapsed from the start of food fermentation to the target collection time is calculated and recorded as the target time. The first function is used as the objective function of the particle swarm optimization algorithm. The particle swarm optimization algorithm is used to process the fitted values ​​of the fermentation temperature at the same monitoring point at the target collection time and the fermentation temperature of all normal fermentation temperature curves at the fermentation time to the target time, so as to minimize the value of the objective function and obtain the optimal fermentation temperature at the same monitoring point at the target time.

2. The temperature monitoring method during food fermentation as described in claim 1, characterized in that, The process for determining the derivative at the acquisition time is as follows: The fermentation temperature at the monitoring point is obtained by curve fitting at all collection times within the target collection time and the first preset time before the target collection time. Based on the fermentation temperature curve, calculate the derivatives of all sampling times within the first preset time period before the target sampling time and the target sampling time.

3. The temperature monitoring method during food fermentation as described in claim 1, characterized in that, The formula for calculating the temperature gradient at each monitoring point at each data acquisition time is: in, Indicates the first The horizontal temperature gradient at each monitoring point at the target acquisition time; Indicates the relationship with the first The mean of the derivatives of all monitoring points in the same horizontal direction at the target acquisition time and all acquisition times within the first preset time before the target acquisition time; Indicates the first The mean of the derivatives of all acquisition times at each monitoring point within the target acquisition time and the first preset time before the target acquisition time; Indicates the relationship with the first The average distance between all monitoring points in the same horizontal direction; Indicates the relationship with the first The mean of the derivatives of all monitoring points in the same vertical direction at the target acquisition time and all acquisition times within the first preset time before the target acquisition time; Indicates the relationship with the first The average distance between all monitoring points in the same vertical direction of a given monitoring point; Indicates the first Vertical temperature gradient at each monitoring point at the target acquisition time; Indicates the first The mean of the derivatives of all acquisition times at the target acquisition time and within the first preset time before the target acquisition time for the adjacent monitoring points vertically above the target acquisition point. This indicates the distance between two adjacent monitoring points.

4. The temperature monitoring method during food fermentation as described in claim 1, characterized in that, The process for determining the ideal fermentation temperature at each monitoring point during fermentation is as follows: Fermentation temperatures at different monitoring points during a first preset number of normal fermentation processes of food are collected at different collection times. Curve fitting is performed on the fermentation temperatures at the same monitoring point during the same normal fermentation process of food at different collection times to obtain the normal fermentation temperature curve of the monitoring point. The average value of the fitted values ​​of the first preset number of normal fermentation temperature curves of the same monitoring point at the same fermentation time is recorded as the ideal fermentation temperature of the same monitoring point at the same fermentation time.

5. The temperature monitoring method during food fermentation as described in claim 1, characterized in that, The expression for the first function is: in, Indicates the first The first function at each acquisition time; Indicates the first The monitoring point at the 1st The ideal fermentation temperature at each sampling time; Indicates the first The monitoring point at the 1st Fermentation temperature fluctuation coefficient at each sampling time; Represents the normalization function; Indicates the number of monitoring points set; Indicates the first The monitoring point at the 1st The optimal fermentation temperature at each sampling time.

6. The temperature monitoring method during food fermentation as described in claim 1, characterized in that, The process of obtaining the actual fermentation temperature at each sampling time based on the optimal fermentation temperature and the actual fermentation temperature at each sampling point, thereby achieving temperature monitoring during the food fermentation process, includes the following steps: Based on the optimal fermentation temperature of the same monitoring point at all sampling times within the first preset time before the target sampling time at the target sampling time, the predicted value of the optimal fermentation temperature of the monitoring point at the adjacent sampling time after the target sampling time is obtained. The average of the fermentation temperature at the monitoring point at the next adjacent collection time and the predicted value of the optimal fermentation temperature is taken as the actual fermentation temperature at the monitoring point at the collection time.

7. A temperature monitoring device for a food fermentation process, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-6.

8. A temperature monitoring system for a food fermentation process, implementing the method as described in claim 1, characterized in that, The temperature monitoring system includes: The fermentation temperature acquisition module is used to collect the fermentation temperature at different monitoring points at the center position, vertical direction, and horizontal direction of the fermentation device at different acquisition times; The temperature fluctuation analysis module is used to record any acquisition time as the target acquisition time, determine the derivative of each acquisition time based on the trend of fermentation temperature change between adjacent acquisition times, determine the temperature gradient of the monitoring point at each acquisition time based on the difference of derivatives of all monitoring points in the same horizontal and vertical directions at adjacent acquisition times, and the distance between different monitoring points, and determine the fermentation temperature fluctuation coefficient of the monitoring point at the acquisition time based on the similarity of the derivative sequence of the monitoring point with all other monitoring points at the acquisition time, the difference of the temperature gradient, and the symmetry of each monitoring point about the center position of the fermentation device. The optimal fermentation temperature determination module is used to collect the fermentation temperature during the normal fermentation process of food, determine the ideal fermentation temperature at each monitoring point at each time of fermentation, and combine the fermentation temperature and fermentation temperature fluctuation coefficient at the monitoring point at the time of collection to obtain the optimal value and determine the optimal fermentation temperature at each monitoring point at each time of collection. The temperature monitoring module is used to obtain the actual fermentation temperature of the monitoring point at the time of collection based on the optimal fermentation temperature and fermentation temperature at each collection point, thereby realizing temperature monitoring during the food fermentation process.

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