A vacuum sterilization method, system and equipment for freeze-dried pet food

Abstract

The present application relates to the field of vacuum sterilization control technology for pet food, and specifically to a vacuum sterilization processing method, system and equipment for freeze-dried pet food, the method comprising: collecting historical temperature and pressure data at all collection moments within a preset time period before the current sterilization moment of the pre-packaged freeze-dried semi-finished product; according to the distribution of all historical temperature data at each historical abnormal moment and its neighborhood and the discrete degree of all historical pressure data, combined with the respective change trends and average distribution of all historical temperature data and all pressure data at each historical abnormal moment and its neighborhood, determine the historical control error at the current sterilization moment of the pre-packaged freeze-dried semi-finished product to control the sterilization temperature. Based on the historical control error, the present application improves the proportional gain of the PID controller and improves the accuracy of temperature control during vacuum sterilization.

Description

Technical Field

[0001] The present application relates to the technical field of vacuum sterilization control of pet food, and in particular to a vacuum sterilization processing method, system and equipment for freeze-dried pet food. Background Art

[0002] When processing freeze-dried pet food, irradiation sterilization technology is an advanced non-thermal sterilization method that uses ionizing radiation to penetrate all levels of food and directly destroy the DNA structure of microorganisms, thereby achieving the purpose of killing or inhibiting the growth of microorganisms. Irradiation sterilization is a non-thermal sterilization technology. Although irradiation sterilization can effectively extend the shelf life of food and reduce microbial infection, during the irradiation process, the vitamins and fat components in the food are more sensitive to irradiation. High-dose irradiation may cause fat deoxidation, produce free radicals and derivatives, accelerate fatty acid rancidity and affect its digestion and absorption rate. Compared with irradiation sterilization, vacuum drying sterilization technology combines the advantages of thermal sterilization and vacuum drying, effectively reducing oxygen content, slowing down oxidation reactions, and protecting the nutrients in food.

[0003] However, in vacuum drying and sterilization technology, the rate of temperature change during sterilization will affect the structure and material of the semi-finished product. Rapid heating will cause uneven evaporation of water inside the semi-finished product, causing deformation or rupture of the protein structure. At the same time, a higher rate of temperature change will also accelerate the oxidation or decomposition of certain heat-sensitive substances, leading to the loss of nutrients. During the vacuum sterilization process, the vacuum temperature set by the equipment remains unchanged. According to the ideal gas law, the change in gas volume in a closed system is proportional to the temperature. When the temperature rises, the movement of gas molecules accelerates, causing the gas volume to increase. In vacuum drying and sterilization equipment, if the equipment volume is fixed, the increase in temperature will cause the pressure inside the equipment to increase, thereby reducing the vacuum degree inside the equipment, further producing a positive feedback enhancement effect on the heating rate, resulting in a deviation in the heating rate. Summary of the invention

[0004] In a first aspect, an embodiment of the present application provides a vacuum sterilization method for freeze-dried pet food, the method comprising the following steps:

[0005] Collect historical temperature and pressure data of all collection times within a preset time period before the current sterilization time of the prepackaged freeze-dried semi-finished product;

[0006] All historical temperature data of the current sterilization time of the prepackaged freeze-dried semi-finished product are used as input of the anomaly detection algorithm to obtain abnormal temperature data, and the collection time corresponding to the abnormal temperature data is recorded as the historical abnormal time;

[0007] At the current sterilization moment of the prepackaged freeze-dried semi-finished product, according to the distribution of all historical temperature data at each historical abnormal moment and its neighborhood and the discrete degree of all historical pressure data, combined with the number of all historical abnormal moments, determine the first temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0008] At the current sterilization moment of the prepackaged freeze-dried semi-finished product, according to the respective change trends and average distribution of all historical temperature data and all pressure data at each historical abnormal moment and its neighborhood, determine the second temperature and pressure abnormality index at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0009] Based on all the first temperature-pressure abnormality indexes and all the second temperature-pressure abnormality indexes, the historical control error of the pre-packaged freeze-dried semi-finished product at the current sterilization moment is determined to control the vacuum sterilization temperature.

[0010] Preferably, the method for determining the first temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is:

[0011] Analyze the extreme difference of all temperature data at each historical abnormal moment and its neighborhood at the current sterilization moment of prepackaged freeze-dried semi-finished products;

[0012] Analyze the proportion of all historical abnormal moments at the current sterilization moment of the prepackaged freeze-dried semi-finished product in all collection moments, and record it as the abnormal ratio of the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0013] The first temperature and pressure anomaly index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is a fusion result of the abnormal ratio, the range and the discrete degree of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product.

[0014] Preferably, the method for determining the second temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is:

[0015] All historical temperature data of each historical abnormal moment and its neighborhood at the current sterilization moment of the prepackaged freeze-dried semi-finished product constitute a temperature sequence of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product, and fit the temperature sequence to obtain a corresponding temperature fitting straight line;

[0016] At the current sterilization moment of the prepackaged freeze-dried semi-finished product, based on the difference between the change trend of the temperature fitting straight line at each historical abnormal moment and the change trend of the temperature fitting straight line at the next historical abnormal moment, combined with the difference between the temperature sequence at each historical abnormal moment and the temperature sequence at the next historical abnormal moment, determine the first abnormal mean value of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0017] For all historical pressure data of the prepackaged freeze-dried semi-finished product at the current sterilization time, according to the first abnormal mean value acquisition method, obtain the second abnormal mean value of each historical abnormal moment of the prepackaged freeze-dried semi-finished product at the current sterilization time;

[0018] Based on the first abnormal mean and the second abnormal mean, the second temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is determined.

[0019] Preferably, the method for determining the first abnormal mean value of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is:

[0020] Analyze the difference between the slope of the temperature fitting line at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product and the slope of the temperature fitting line at the next historical abnormal moment, and record it as the first difference of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0021] Analyze the distance between the temperature sequence of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product and the temperature sequence of the next historical abnormal moment, and record it as the second difference of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0022] The first abnormal mean value of each historical abnormal moment at the current sterilization moment of the pre-packaged freeze-dried semi-finished product is the mean value of the first difference and the second difference of each historical abnormal moment at the current sterilization moment of the pre-packaged freeze-dried semi-finished product.

[0023] Preferably, the second temperature and pressure anomaly index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is the product of the first anomaly mean and the second anomaly mean of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product.

[0024] Preferably, the method for determining the historical control error of the current sterilization moment of the prepackaged freeze-dried semi-finished product is:

[0025] Determine the local control error of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product according to the first temperature and pressure abnormality index and the second temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the freeze-dried semi-finished product;

[0026] The historical control error of the prepackaged freeze-dried semi-finished product at the current sterilization moment is the average of the local control errors of all historical abnormal moments at the current sterilization moment of the prepackaged freeze-dried semi-finished product.

[0027] Preferably, the local control error of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is the product of the first temperature and pressure abnormality index and the second temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product.

[0028] Preferably, the control of the vacuum sterilization temperature includes:

[0029] The expression of the improved proportional gain of the PID controller is: ,in is the improved proportional gain of the PID controller, To preset the initial proportional gain, The historical control error of the current sterilization moment of the pre-packaged freeze-dried semi-finished product;

[0030] The temperature data of the pre-packaged freeze-dried semi-finished product at the current moment is obtained, the temperature data of the pre-packaged freeze-dried semi-finished product at the current moment and the preset target temperature are used as inputs of the PID controller, the improved proportional gain is used as the adaptive proportional gain in the PID controller, and the temperature control signal of the pre-packaged freeze-dried semi-finished product at the current moment is output to regulate the temperature data of the pre-packaged freeze-dried semi-finished product at the current moment.

[0031] In a second aspect, an embodiment of the present application provides a vacuum sterilization processing system for freeze-dried pet food, the system comprising:

[0032] The temperature and pressure data acquisition module is used to collect the historical temperature and pressure data of all acquisition times within a preset time before the current sterilization time of the prepackaged freeze-dried semi-finished product;

[0033] The temperature and pressure data analysis module is used to use all historical temperature data of the current sterilization time of the pre-packaged freeze-dried semi-finished product as the input of the abnormality detection algorithm to obtain abnormal temperature data, and record the collection time corresponding to the abnormal temperature data as the historical abnormal time;

[0034] At the current sterilization moment of the prepackaged freeze-dried semi-finished product, according to the distribution of all historical temperature data at each historical abnormal moment and its neighborhood and the discrete degree of all historical pressure data, combined with the number of all historical abnormal moments, determine the first temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0035] At the current sterilization moment of the prepackaged freeze-dried semi-finished product, according to the respective change trends and average distribution of all historical temperature data and all pressure data at each historical abnormal moment and its neighborhood, determine the second temperature and pressure abnormality index at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0036] The temperature data control module is used to determine the historical control error of the current sterilization time of the pre-packaged freeze-dried semi-finished product based on all the first temperature and pressure abnormality indexes and all the second temperature and pressure abnormality indexes, so as to control the vacuum sterilization temperature.

[0037] In a third aspect, an embodiment of the present application further provides a vacuum sterilization processing device for freeze-dried pet food, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0038] It can be seen from the above embodiments that the vacuum sterilization method for freeze-dried pet food provided in the embodiments of the present application has at least the following beneficial effects:

[0039] This application determines the first temperature and pressure anomaly index of each historical abnormal moment at the current sterilization moment of the pre-packaged freeze-dried semi-finished product based on the distribution of all historical temperature data at each historical abnormal moment and its neighborhood and the discreteness of all historical pressure data, combined with the number of all historical abnormal moments; uses anomaly detection and time window analysis to analyze the uneven characteristics of the semi-finished product when abnormal values ​​occur during the vacuum sterilization process, and evaluates the temperature and pressure changes of the semi-finished product at the historical abnormal moments to solve the problem of uneven heating of the semi-finished product during the vacuum sterilization process.

[0040] The present application determines the second temperature and pressure anomaly index of each historical abnormal moment at the current sterilization moment of the pre-packaged freeze-dried semi-finished product according to the respective change trends and average distribution of all historical temperature data and all pressure data in each historical abnormal moment and its neighborhood; determines the historical control error of the pre-packaged freeze-dried semi-finished product at the current sterilization moment based on all the first temperature and pressure anomaly indexes and all the second temperature and pressure anomaly indexes; comprehensively evaluates the deviation of the semi-finished product in the entire vacuum sterilization process, thereby improving the proportional gain of the PID controller, achieving precise control of the temperature during the vacuum sterilization process, and solving the deviation problem between the actual heating rate and the set heating rate caused by positive feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 A flowchart of a vacuum sterilization method for freeze-dried pet food provided in one embodiment of the present application;

[0043] Figure 2 A schematic diagram of the first temperature and pressure anomaly index extraction process provided in this application;

[0044] Figure 3 A schematic diagram of the process of obtaining the second temperature and pressure anomaly index provided in this application;

[0045] Figure 4 A schematic diagram of the historical control error extraction process provided for this application;

[0046] Figure 5 A block diagram of a vacuum sterilization processing system for freeze-dried pet food provided in this application. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the vacuum sterilization method, system and equipment for freeze-dried pet food proposed in the present application, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0049] The specific scheme of the vacuum sterilization method, system and equipment for freeze-dried pet food provided by the present application is described in detail below with reference to the accompanying drawings.

[0050] See also Figure 1 , which shows a flow chart of the steps of a vacuum sterilization method for freeze-dried pet food provided by an embodiment of the present application, the method comprising the following steps:

[0051] Step S1: freeze-drying and packaging the pretreated raw materials.

[0052] S101: freeze-drying the freeze-dried raw material.

[0053] Before vacuum sterilization, the freeze-dried raw materials must be pretreated to obtain freeze-dried semi-finished products. Therefore, first select fresh, uncontaminated meat, vegetables and fruits as raw materials. After cleaning, peeling and deboning the raw materials, cut or mix them according to needs. After quick freezing, place the materials in a freeze dryer for processing. Set the pressure in the freeze dryer to N and the temperature of the heating plate to K. Most of the ice in the quick-frozen materials sublimates into water vapor and condenses on the condensing device to be removed. At the same time, some microorganisms in the materials are preliminarily inactivated. Further, increase the temperature to Q so that the remaining ice adheres to the condensing device, which is equivalent to removing the remaining water in the materials to further reduce the living environment of the microorganisms, thereby forming a dry solid state and maintaining the cell structure to obtain a freeze-dried semi-finished product after freeze drying.

[0054] It should be noted that the values ​​of the pressure N, the heating plate temperature K and the temperature Q in the freeze dryer are all set manually. In this embodiment, the value of the pressure N in the freeze dryer is 200 Pa, the value of the heating plate temperature K is 60°C, and the value of the temperature Q is 65°C. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0055] S102: Pre-packaging the freeze-dried semi-finished product after freeze-drying.

[0056] (1) Cooling and demolding: Cool the freeze-dried semi-finished product to room temperature and then demold it;

[0057] (2) Inspection and packaging: The freeze-dried semi-finished products after demoulding are loaded into the packaging area, and the products in the packaging area are inspected. The qualified products are quantitatively packaged to obtain pre-packaged freeze-dried semi-finished products.

[0058] (3) Spread the pre-packaged freeze-dried semi-finished product on a plate, set the temperature of the vacuum drying equipment during the vacuuming process to S, put the pre-packaged freeze-dried semi-finished product into the machine, start the vacuuming, and start heating the plate after the vacuum degree drops to P for vacuum sterilization.

[0059] It should be noted that the value of the vacuum degree P and the value of the temperature S are artificially set. In this embodiment, the value of the initial vacuum degree is 200Pa and the temperature S is 90°C. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0060] Step S2: Data collection is performed on the prepackaged freeze-dried semi-finished product during the vacuum sterilization process. Further, after vacuuming, the prepackaged freeze-dried semi-finished product is placed in a vacuum drying device to start vacuum sterilization. In this embodiment, when the prepackaged freeze-dried semi-finished product is vacuum sterilized, a temperature sensor is installed inside the vacuum drying device to ensure that the temperature change inside the vacuum drying device can be collected, and a capacitor press is installed inside the vacuum drying device to evaluate the vacuum degree change during vacuum sterilization.

[0061] During the vacuum sterilization process, the temperature sensor collects the historical temperature and pressure data of all the collection times within the preset time before the current sterilization time of the prepackaged freeze-dried semi-finished product. The data collection interval of the temperature sensor and the capacitance pressure gauge is set to T, and the collected data is normalized as a whole to eliminate the influence of the dimension.

[0062] It should be noted that the values ​​of the preset time length and the collection interval T are set manually. In this embodiment, the preset time length is 5 minutes, and the collection interval T is 5 seconds. The implementer can set them according to the specific situation. This embodiment does not impose any special restrictions. Among them, if the time before the current sterilization moment is less than the preset time length, the current sterilization moment will not be analyzed.

[0063] Step S3: At the current sterilization moment of the pre-packaged freeze-dried semi-finished product, according to the distribution of all temperature data at each historical abnormal moment and its neighborhood and the discreteness of all pressure data, combined with the number of all historical abnormal moments, determine the first temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the pre-packaged freeze-dried semi-finished product.

[0064] Vacuum usually refers to the pressure level in a vacuum chamber or vacuum system, that is, the density of the gas or the number of gas molecules. In a vacuum chamber, the fewer the number of gas molecules, the higher the vacuum. When sterilizing pre-packaged freeze-dried semi-finished products, since the freeze-dried semi-finished products contain a small amount of water, the water forms vaporized molecules by increasing the temperature. A small number of vaporized small molecules enter the vacuum chamber through the pre-package, increasing the number of gas molecules inside, resulting in a decrease in vacuum and an increase in the pressure inside the vacuum drying equipment. In a vacuum environment, the main modes of heat conduction are convection and radiation. Convection depends on the movement of gas molecules. When the vacuum decreases, the number of gas molecules increases, thereby increasing the rate of temperature change. The increase in temperature affects the increase in pressure, and this effect in turn exacerbates the rate of temperature increase, forming a positive feedback effect of cyclic enhancement.

[0065] Therefore, by analyzing the data in the historical abnormal moments and their neighborhoods, the uniformity of the entire vacuum sterilization process is evaluated, so as to timely discover and adjust the sterilization temperature during the heating process to ensure the quality of the food. Specifically, at the current sterilization moment of the pre-packaged freeze-dried semi-finished product, based on the distribution of all temperature data in each historical abnormal moment and its neighborhood and the discrete degree of all pressure data, combined with the number of all historical abnormal moments, the first temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the pre-packaged freeze-dried semi-finished product is determined, specifically:

[0066] All historical temperature data of the pre-packaged freeze-dried semi-finished product at the current sterilization moment are used as the input of the anomaly detection algorithm to obtain the various abnormal temperature data of the pre-packaged freeze-dried semi-finished product at the current moment, and the collection moment corresponding to the abnormal temperature data is recorded as the historical abnormal moment of the pre-packaged freeze-dried semi-finished product at the current sterilization moment.

[0067] It should be noted that there are many commonly used anomaly detection algorithms. In this embodiment, the LOF anomaly detection algorithm is used to obtain abnormal temperature data in all historical temperature data at the current sterilization time. The implementer may also use other anomaly detection methods such as recurrent neural networks according to specific circumstances. This embodiment does not impose any special restrictions.

[0068] Among them, the LOF anomaly detection algorithm is a well-known technology, and the specific process of detecting anomalies is not repeated here.

[0069] A neighborhood W is set with each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product as the center, wherein the value of the neighborhood W is artificially set. In this embodiment, the value of the neighborhood W is 3, and the implementer can also set it according to the specific situation. This embodiment does not make any special limitation.

[0070] Furthermore, the range of all temperature data at each historical abnormal moment and its neighborhood at the current sterilization moment of the prepackaged freeze-dried semi-finished product is analyzed;

[0071] Analyze the discrete degree of the pressure data of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product and all the pressure data in its neighborhood;

[0072] It should be noted that there are many methods for measuring the degree of discreteness. In this embodiment, the standard deviation of the pressure data at each historical abnormal moment and all the pressure data in its neighborhood at the current sterilization moment of the pre-packaged freeze-dried semi-finished product is calculated to measure the degree of discreteness of all historical pressure data at each historical abnormal moment and its neighborhood. The implementer may also use other methods for measuring the degree of discreteness, such as variance and mean difference, and this embodiment does not impose any special restrictions.

[0073] Analyze the proportion of all historical abnormal moments at the current sterilization moment of the prepackaged freeze-dried semi-finished product in all collection moments, and record it as the abnormal ratio of the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0074] The first temperature and pressure abnormality index of each historical abnormal moment of the prepackaged freeze-dried semi-finished product at the current sterilization time is the result of the fusion of the range of each historical abnormal moment of the prepackaged freeze-dried semi-finished product at the current sterilization time, the discrete degree and the abnormal ratio of the prepackaged freeze-dried semi-finished product at the current sterilization time.

[0075] It should be understood that fusion refers to combining two or more indicators by adding or multiplying them together in order to obtain a comprehensive indicator, thereby more comprehensively and accurately evaluating a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods. The implementer can choose according to the specific situation, and this embodiment does not impose any special restrictions.

[0076] Preferably, in the present embodiment, the first temperature-pressure anomaly index of the pre-packaged freeze-dried semi-finished product at each historical abnormal moment is the product of the range of the pre-packaged freeze-dried semi-finished product at each historical abnormal moment at the current sterilization moment, the degree of discreteness and the abnormal ratio of the pre-packaged freeze-dried semi-finished product at the current sterilization moment; in the actual application process, as other implementation methods, the first temperature-pressure anomaly index of the pre-packaged freeze-dried semi-finished product at each historical abnormal moment is an exponential function value with a natural constant as the base and the sum of the range of the pre-packaged freeze-dried semi-finished product at each historical abnormal moment at the current sterilization moment, the degree of discreteness and the abnormal ratio of the pre-packaged freeze-dried semi-finished product at the current sterilization moment as the independent variable.

[0077] The extreme difference of pre-packaged freeze-dried semi-finished products at each historical abnormal moment and the maximum range of temperature change of pre-packaged freeze-dried semi-finished products indicate the extreme difference of abnormal temperature change of vacuum drying equipment in a short period of time. The larger the extreme difference value, the more drastic the temperature change inside the vacuum drying equipment within the abnormal time window, which means that it has been heated unevenly or too quickly during the vacuum sterilization process. The first temperature and pressure abnormality index of pre-packaged freeze-dried semi-finished products at each historical abnormal moment increases with the increase of the extreme difference of pre-packaged freeze-dried semi-finished products at each historical abnormal moment; the discrete degree of pre-packaged freeze-dried semi-finished products at each historical abnormal moment indicates the pressure at the historical abnormal moment. The fluctuation degree of force change, the larger its value, means that the pressure change is unstable at this moment, which reflects the unevenness of the vacuum sterilization process from the side. The first temperature and pressure abnormality index of the pre-packaged freeze-dried semi-finished product at each historical abnormal moment increases with the increase of the discrete degree of the pre-packaged freeze-dried semi-finished product at each historical abnormal moment; the frequency of all historical abnormal moments of the pre-packaged freeze-dried semi-finished product reflects the frequency of temperature mutations of the pre-packaged freeze-dried semi-finished product in the entire vacuum sterilization process. The larger its value, the more frequent the temperature mutations of the pre-packaged freeze-dried semi-finished product in the entire vacuum sterilization process, indicating that the vacuum sterilization process is uneven.

[0078] The first temperature and pressure anomaly index of the pre-packaged freeze-dried semi-finished product at each historical abnormal moment at the current sterilization moment represents the overall heterogeneity of the pre-packaged freeze-dried semi-finished product during the vacuum sterilization process. The larger the first temperature and pressure anomaly index of the pre-packaged freeze-dried semi-finished product at each historical abnormal moment, the higher the heating unevenness characteristic is caused by the positive feedback effect of the above analysis in the local time period when the selected abnormal value occurs.

[0079] Preferably, the schematic diagram of the first temperature and pressure anomaly index extraction process provided in the present application is as follows: Figure 2 shown.

[0080] Step S4: at the current sterilization moment of the prepackaged freeze-dried semi-finished product, determine the second temperature and pressure anomaly index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product according to all historical temperature data at each historical abnormal moment and its neighborhood and the respective change trends and average distribution of all temperature data.

[0081] The difference in the rate of temperature change between adjacent historical abnormal moments can, to a certain extent, reflect the stability of temperature change. The greater the difference in the rate of temperature change between adjacent historical abnormal moments, the more unstable the temperature change during vacuum sterilization, which may affect the sterilization effect. The rapid change in temperature causes the changes in gas molecules in the vacuum chamber to change rapidly during vacuum sterilization, which leads to a rapid change in the pressure data in the vacuum chamber. The rapid change in pressure data will lead to more drastic changes in temperature.

[0082] Therefore, at the current sterilization moment of the prepackaged freeze-dried semi-finished product, based on all the historical temperature data at each historical abnormal moment and its neighborhood and the respective change trends and average distribution of all the temperature data, the second temperature and pressure abnormality index at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is determined to determine the stability of the temperature change, so as to further adjust the heating parameters to better sterilize the prepackaged freeze-dried semi-finished product, specifically:

[0083] All historical temperature data of each historical abnormal moment and its neighborhood at the current sterilization moment of the prepackaged freeze-dried semi-finished product constitute a temperature sequence of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product, and fit the temperature sequence to obtain a corresponding temperature fitting straight line;

[0084] Similarly, all historical pressure data at each historical abnormal moment and its neighborhood at the current sterilization moment of the prepackaged freeze-dried semi-finished product constitute a pressure sequence at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product, and the pressure sequence is fitted to obtain the corresponding pressure fitting straight line.

[0085] It should be noted that there are many commonly used fitting methods. In this embodiment, the least squares method is used to fit the historical temperature data and the historical pressure data. The implementer also uses other fitting methods such as simple linear regression, and this embodiment does not make any special restrictions.

[0086] Among them, the least square method is a well-known technology, and its specific fitting process is not repeated here.

[0087] Further, the difference between the slope of the temperature fitting line at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product and the slope of the temperature fitting line at the next historical abnormal moment is analyzed, and recorded as the first difference of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0088] Analyze the distance between the temperature sequence of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product and the temperature sequence of the next historical abnormal moment, and record it as the second difference of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0089] The first abnormal mean value of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is the mean value of the first difference and the second difference of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0090] Analyze the difference between the slope of the pressure fitting line at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product and the slope of the pressure fitting line at the next historical abnormal moment, and record it as the third difference of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0091] Analyze the distance between the pressure sequence of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product and the pressure sequence of the next historical abnormal moment, and record it as the fourth difference of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0092] It should be noted that there are many methods for measuring the difference between two sequences. In this embodiment, the Euclidean distance between the temperature sequences and the Euclidean distance between the pressure sequences at adjacent historical abnormal moments are calculated to determine the difference between the sequences. The implementer may also use other methods for measuring the difference, such as the Manhattan distance, which is not particularly limited in this embodiment.

[0093] The calculation process of the Euclidean distance is a well-known technique, and the specific calculation process will not be described in detail.

[0094] The second abnormal mean value of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is the mean value of the third difference and the fourth difference of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0095] The second temperature and pressure anomaly index of each historical abnormal moment of the prepackaged freeze-dried semi-finished product at the current sterilization moment is the product of the first anomaly mean and the second anomaly mean of each historical abnormal moment of the prepackaged freeze-dried semi-finished product at the current sterilization moment.

[0096] The first difference and the third difference reflect the severity of the rate change of temperature and pressure, the second difference and the fourth difference reflect the changes in volatility and consistency of temperature and pressure, the first anomaly mean reflects the degree of temperature change at the historical anomaly moment, and the second anomaly mean reflects the degree of pressure change at the historical anomaly moment. The larger the first anomaly mean, the more drastic the change in temperature data at adjacent historical anomaly moments, which will cause the pressure data in the vacuum chamber to increase, that is, the second anomaly mean will also increase, and the second temperature and pressure anomaly index will also increase; conversely, the smaller the first anomaly mean, the more stable the change in temperature data at adjacent historical anomaly moments, the smaller the second anomaly mean, and the smaller the second temperature and pressure anomaly index will also decrease accordingly.

[0097] Preferably, the schematic diagram of the second temperature and pressure anomaly index acquisition process provided by the present application is as follows: Figure 3 shown.

[0098] Step S5: Based on all the first temperature-pressure abnormality indexes and all the second temperature-pressure abnormality indexes, determine the historical control error of the pre-packaged freeze-dried semi-finished product at the current sterilization moment.

[0099] In order to better evaluate the accuracy and stability of temperature control during the freeze-drying process, the first temperature and pressure abnormality index and the second temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the pre-packaged freeze-dried semi-finished product are combined to determine the historical control error of the current sterilization moment of the pre-packaged freeze-dried semi-finished product, so as to improve the degree of temperature control during the sterilization process, specifically:

[0100] The local control error of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is the product of the first temperature and pressure abnormality index and the second temperature and pressure abnormality index at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product.

[0101] The historical control error of the prepackaged freeze-dried semi-finished product at the current sterilization moment is the average of the local control errors of all historical abnormal moments at the current sterilization moment of the prepackaged freeze-dried semi-finished product.

[0102] The local control error is used to measure the intensity of abnormal temperature changes during the sterilization process. The larger the value of the local control error, the larger the second temperature and pressure abnormality index, the larger the historical control error, and the more necessary it is to control and adjust the temperature during the sterilization process; conversely, the smaller the value of the local control error, the smaller the second temperature and pressure abnormality index, the smaller the historical control error, indicating that the temperature of the sterilization process is appropriate.

[0103] Preferably, the historical control error extraction process diagram provided in this application is as follows: Figure 4 shown.

[0104] Step S6: Based on the historical control error of the current sterilization time of the pre-packaged freeze-dried semi-finished product, the sterilization temperature is controlled in combination with a PID controller.

[0105] In the vacuum sterilization process of step S2, a PID controller is used to control the vacuum sterilization process of the vacuum drying equipment, and the initial proportional gain of the PID controller is set. , integral gain , differential gain .

[0106] It should be noted that the proportional gain , integral gain , differential gain The values ​​of are all set manually. In this embodiment, the proportional gain The value of is 16, the integral gain The value of is 10, the differential gain The value of is 10, and the implementer can also set it according to the specific situation. This embodiment does not make any special restrictions.

[0107] The target temperature for vacuum heating is set according to the pre-packaged freeze-dried semi-finished product. In the PID controller, the PID controller directly responds to the difference between the expected value and the actual output value. The higher the proportional gain is set, the stronger the controller's response to the error and the faster the temperature change rate. However, due to the positive feedback effect generated when the temperature rises, the heating rate will become faster and faster, causing the actual heating rate to exceed the controlled heating rate. Excessive temperature changes will cause changes in the structure of the pre-packaged freeze-dried semi-finished product, resulting in the loss of some nutrients in the pre-packaged freeze-dried semi-finished product. If the proportional gain is reduced in the initial stage to offset the positive feedback effect, the slower heating rate in the initial stage of heating will affect the sterilization efficiency during the vacuum heating process, resulting in poor sterilization effect.

[0108] Therefore, the proportional gain of the PID controller is improved, and the expression of the improved proportional gain of the PID controller is: ,in is the improved proportional gain of the PID controller, To preset the initial proportional gain, It is the historical control error of the current sterilization moment of the pre-packaged freeze-dried semi-finished product.

[0109] The temperature data of the pre-packaged freeze-dried semi-finished product at the current moment and the preset target temperature are used as the input of the PID controller, the improved proportional gain of the PID controller is used as the adaptive proportional gain of the PID controller, and the temperature control signal of the pre-packaged freeze-dried semi-finished product at the current moment is output to regulate the temperature data of the pre-packaged freeze-dried semi-finished product at the current moment.

[0110] Thus, this embodiment can realize that during the vacuum sterilization process, by monitoring the temperature and pressure changes of the pre-packaged freeze-dried semi-finished product, the error of the heating rate caused by positive feedback in the historical vacuum sterilization process can be evaluated, and the negative exponential function can be used to Adjustments are made by adaptively adjusting the proportional function so that when the control error is small, a larger proportional coefficient is used and a faster heating rate is used, and when the control error is large, a smaller proportional coefficient is used to reduce the heating rate to offset the positive feedback effect, so that the vacuum heating process not only ensures the sterilization effect, but also avoids the problem of destruction of the structure and nutrition of the pre-packaged freeze-dried semi-finished products due to an excessively fast heating rate.

[0111] At this point, the proportional gain of the PID controller is adjusted based on the historical control error to complete the temperature control during the vacuum sterilization process.

[0112] Based on the same inventive concept as the above method, the embodiment of the present application also provides a vacuum sterilization processing system for freeze-dried pet food, comprising:

[0113] The temperature and pressure data acquisition module is used to collect the historical temperature and pressure data of all acquisition times within a preset time before the current sterilization time of the prepackaged freeze-dried semi-finished product;

[0114] The temperature and pressure data analysis module is used to use all historical temperature data of the current sterilization time of the pre-packaged freeze-dried semi-finished product as the input of the abnormality detection algorithm to obtain abnormal temperature data, and record the collection time corresponding to the abnormal temperature data as the historical abnormal time;

[0115] At the current sterilization moment of the prepackaged freeze-dried semi-finished product, according to the distribution of all historical temperature data at each historical abnormal moment and its neighborhood and the discrete degree of all historical pressure data, combined with the number of all historical abnormal moments, determine the first temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0116] At the current sterilization moment of the prepackaged freeze-dried semi-finished product, according to the respective change trends and average distribution of all historical temperature data and all pressure data at each historical abnormal moment and its neighborhood, determine the second temperature and pressure abnormality index at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product;

[0117] The temperature data control module is used to determine the historical control error of the current sterilization time of the pre-packaged freeze-dried semi-finished product based on all the first temperature and pressure abnormality indexes and all the second temperature and pressure abnormality indexes, so as to control the vacuum sterilization temperature.

[0118] The present application provides a block diagram of a vacuum sterilization system for freeze-dried pet food, such as Figure 5 shown.

[0119] Based on the same inventive concept as the above method, an embodiment of the present application also provides a vacuum sterilization processing device for freeze-dried pet food, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned vacuum sterilization processing methods for freeze-dried pet food are implemented.

[0120] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0122] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A vacuum sterilization method for freeze-dried pet food, characterized in that: The method comprises the following steps: Collect historical temperature and pressure data of all collection times within a preset time period before the current sterilization time of the prepackaged freeze-dried semi-finished product; All historical temperature data of the current sterilization time of the prepackaged freeze-dried semi-finished product are used as input of the anomaly detection algorithm to obtain abnormal temperature data, and the collection time corresponding to the abnormal temperature data is recorded as the historical abnormal time; At the current sterilization moment of the prepackaged freeze-dried semi-finished product, according to the distribution of all historical temperature data at each historical abnormal moment and its neighborhood and the discrete degree of all historical pressure data, combined with the number of all historical abnormal moments, determine the first temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product; At the current sterilization moment of the prepackaged freeze-dried semi-finished product, according to the respective change trends and average distribution of all historical temperature data and all pressure data at each historical abnormal moment and its neighborhood, determine the second temperature and pressure abnormality index at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product; Based on all the first temperature-pressure abnormality indexes and all the second temperature-pressure abnormality indexes, determining the historical control error of the prepackaged freeze-dried semi-finished product at the current sterilization moment, so as to control the vacuum sterilization temperature; The method for determining the first temperature and pressure abnormality index at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is: Analyze the extreme difference of all temperature data at each historical abnormal moment and its neighborhood at the current sterilization moment of prepackaged freeze-dried semi-finished products; Analyze the proportion of all historical abnormal moments at the current sterilization moment of the prepackaged freeze-dried semi-finished product in all collection moments, and record it as the abnormal ratio of the current sterilization moment of the prepackaged freeze-dried semi-finished product; The first temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is a result of the fusion of the abnormal ratio, the range and the discrete degree of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product; The method for determining the second temperature and pressure abnormality index at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is: All historical temperature data of each historical abnormal moment and its neighborhood at the current sterilization moment of the prepackaged freeze-dried semi-finished product constitute a temperature sequence of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product, and fit the temperature sequence to obtain a corresponding temperature fitting straight line; At the current sterilization moment of the prepackaged freeze-dried semi-finished product, based on the difference between the change trend of the temperature fitting straight line at each historical abnormal moment and the change trend of the temperature fitting straight line at the next historical abnormal moment, combined with the difference between the temperature sequence at each historical abnormal moment and the temperature sequence at the next historical abnormal moment, determine the first abnormal mean value of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product; For all historical pressure data of the prepackaged freeze-dried semi-finished product at the current sterilization time, according to the first abnormal mean value acquisition method, obtain the second abnormal mean value of each historical abnormal moment of the prepackaged freeze-dried semi-finished product at the current sterilization time; Based on the first abnormal mean and the second abnormal mean, the second temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is determined.

2. The vacuum sterilization method for freeze-dried pet food according to claim 1, characterized in that: The method for determining the first abnormal mean value of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is: Analyze the difference between the slope of the temperature fitting line at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product and the slope of the temperature fitting line at the next historical abnormal moment, and record it as the first difference of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product; Analyze the distance between the temperature sequence of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product and the temperature sequence of the next historical abnormal moment, and record it as the second difference of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product; The first abnormal mean value of each historical abnormal moment at the current sterilization moment of the pre-packaged freeze-dried semi-finished product is the mean value of the first difference and the second difference of each historical abnormal moment at the current sterilization moment of the pre-packaged freeze-dried semi-finished product.

3. The vacuum sterilization method for freeze-dried pet food according to claim 1, characterized in that: The second temperature and pressure anomaly index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is the product of the first anomaly mean and the second anomaly mean of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product.

4. The vacuum sterilization method for freeze-dried pet food according to claim 1, characterized in that: The method for determining the historical control error of the current sterilization moment of the prepackaged freeze-dried semi-finished product is as follows: Determine the local control error of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product according to the first temperature and pressure abnormality index and the second temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product; The historical control error of the current sterilization moment of the freeze-dried semi-finished product is the average of the local control errors of all historical abnormal moments at the current sterilization moment of the pre-packaged freeze-dried semi-finished product.

5. The vacuum sterilization method for freeze-dried pet food according to claim 4, characterized in that: The local control error of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product is the product of the first temperature and pressure abnormality index and the second temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product.

6. The vacuum sterilization method for freeze-dried pet food according to claim 1, characterized in that: The vacuum sterilization temperature is controlled, including: The expression of the improved proportional gain of the PID controller is: ,in is the improved proportional gain of the PID controller, To preset the initial proportional gain, The historical control error of the current sterilization moment of the pre-packaged freeze-dried semi-finished product; The temperature data of the pre-packaged freeze-dried semi-finished product at the current moment is obtained, the temperature data of the pre-packaged freeze-dried semi-finished product at the current moment and the preset target temperature are used as inputs of the PID controller, the improved proportional gain is used as the adaptive proportional gain in the PID controller, and the temperature control signal of the pre-packaged freeze-dried semi-finished product at the current moment is output to regulate the temperature data of the pre-packaged freeze-dried semi-finished product at the current moment.

7. A vacuum sterilization system for freeze-dried pet food, using the vacuum sterilization method for freeze-dried pet food according to claim 1, characterized in that: The system comprises: The temperature and pressure data acquisition module is used to collect the historical temperature and pressure data of all acquisition times within a preset time before the current sterilization time of the prepackaged freeze-dried semi-finished product; The temperature and pressure data analysis module is used to use all historical temperature data of the current sterilization time of the pre-packaged freeze-dried semi-finished product as the input of the abnormality detection algorithm to obtain abnormal temperature data, and record the collection time corresponding to the abnormal temperature data as the historical abnormal time; At the current sterilization moment of the prepackaged freeze-dried semi-finished product, according to the distribution of all historical temperature data at each historical abnormal moment and its neighborhood and the discrete degree of all historical pressure data, combined with the number of all historical abnormal moments, determine the first temperature and pressure abnormality index of each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product; At the current sterilization moment of the prepackaged freeze-dried semi-finished product, according to the respective change trends and average distribution of all historical temperature data and all pressure data at each historical abnormal moment and its neighborhood, determine the second temperature and pressure abnormality index at each historical abnormal moment at the current sterilization moment of the prepackaged freeze-dried semi-finished product; The temperature data control module is used to determine the historical control error of the current sterilization time of the pre-packaged freeze-dried semi-finished product based on all the first temperature and pressure abnormality indexes and all the second temperature and pressure abnormality indexes, so as to control the vacuum sterilization temperature.

8. A vacuum sterilization processing device for freeze-dried pet food, 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, the steps of the vacuum sterilization method for freeze-dried pet food as described in any one of claims 1 to 6 are implemented.

Images