Method for judging effectiveness of can sterilization procedure and sterilization kettle data processing system

Abstract

The invention relates to a method for judging effectiveness of a can sterilization procedure and a sterilization kettle data processing system. The method for judging the effectiveness of the can sterilization procedure comprises the following steps: obtaining a product temperature record in a sterilization kettle through a temperature sensor; calculating a real-time sterilization value of the product according to the product temperature record; calculating sterilization prediction time by using the real-time sterilization value; reckoning a theoretical sterilization value according to the sterilization prediction time; and comparing the real-time sterilization value with the theoretical sterilization value to judge the effectiveness of the sterilization program. Compared with the prior art, the method for judging the effectiveness of the can sterilization procedure, provided by the invention, has the advantage that the effectiveness of the can sterilization procedure can be judged by the sterilization kettle.

Description

Technical Field

[0001] The present invention relates to the technical field of food manufacturing, and more specifically to a method for determining the effectiveness of a canned food sterilization program and a sterilization autoclave data processing system. Background Art

[0002] Retorts are devices that sterilize items using high temperature and high pressure of water vapor. However, existing retorts are generally controlled by programmable logic controllers (PLCs), which are not computer interfaces and do not have computer operating systems, so related applications cannot be used. For example, Excel spreadsheets cannot be used to calculate, count, or output reports; engineering calculation software cannot be used to perform complex calculations; and data cannot be connected to the Internet to transmit and interact with the Internet. Therefore, when handling carbon inventory, existing retorts need to rely on a large amount of manpower to handle meter reading, input, data analysis, and report output. In addition, the existing information management system for retorts can only capture operating information for the equipment itself, and its construction cost is high, which is difficult for enterprises or units with smaller capital to build. Furthermore, there is no energy consumption information available for existing retort equipment, and it cannot be used to determine the effectiveness of the sterilization process. Summary of the invention

[0003] The purpose of the present invention is to provide a method for determining the effectiveness of a canned food sterilization program and a data processing system for a sterilization autoclave, so as to solve the technical problems that the prior art sterilization autoclave cannot be used to determine the effectiveness of the sterilization program and has poor carbon inventory efficiency.

[0004] According to one embodiment of the present application, the present application provides a method for determining the effectiveness of a canned food sterilization program, including: obtaining a product temperature record in a sterilization kettle through a temperature sensor; calculating a real-time sterilization value of the product based on the product temperature record; calculating a predicted sterilization time using the real-time sterilization value; back-calculating a theoretical sterilization value based on the predicted sterilization time; and comparing the real-time sterilization value with the theoretical sterilization value to determine the effectiveness of the sterilization program.

[0005] According to another embodiment of the present application, the present application provides a sterilization kettle data processing system including a parameter monitoring unit, a sterilization value real-time calculation unit, a sterilization prediction unit, an energy consumption monitoring unit, a carbon emission cloud computing unit, and a computer control and strategy providing unit. The parameter monitoring unit is configured to obtain a product temperature record in the sterilization kettle through a temperature sensor. The sterilization value real-time calculation unit is connected to the parameter monitoring unit and is configured to calculate the real-time sterilization value of the product according to the product temperature record. The sterilization prediction unit is connected to the sterilization value real-time calculation unit and is configured to calculate the sterilization prediction time using the real-time sterilization value. The energy consumption monitoring unit is configured to monitor multiple energy consumption meters of the sterilization kettle to obtain energy consumption information of multiple energy consumption meters. The carbon emission cloud computing unit is configured to receive energy consumption information of multiple energy consumption meters and calculate carbon emission information based on the energy consumption information. The computer control and strategy providing unit is connected to the energy consumption monitoring unit and is configured to analyze the carbon emission information calculated by the carbon emission cloud computing unit through at least one computer to generate at least one energy-saving and carbon reduction strategy. The real-time sterilization value and the predicted sterilization time are uploaded to the cloud database of the carbon emission cloud computing unit, and the energy consumption information of multiple energy consumption meters obtained by the energy consumption monitoring unit is uploaded to the carbon emission cloud computing unit through the computer control and strategy providing unit.

[0006] Compared with the prior art, the present invention has at least the following technical effects:

[0007] The method for determining the effectiveness of the canned food sterilization program provided in this application can enable the sterilizer to determine the effectiveness of the canned food sterilization program. The data processing system for the sterilizer provided in this application can calculate carbon emission information and generate energy-saving and carbon reduction strategies for equipment owners to evaluate and select, and can save a lot of manpower, avoid human operation errors and improve work efficiency. Cloud computing can also shorten computing time, save database maintenance expenses and reduce system construction costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The following will briefly describe the drawings necessary for describing the embodiments of the present invention or the prior art to facilitate the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, without the need for creative work, drawings of other embodiments can still be obtained based on the illustrations in these drawings.

[0009] Figure 1 An architectural diagram showing a retort data processing system according to some embodiments of the present invention.

[0010] Figure 2 A graph showing the relationship between a sterilization temperature curve and a sterilization value calculation curve according to some embodiments of the present invention.

[0011] Figure 3A graph showing the relationship between the sterilization temperature curve and the logarithmic temperature-time regression line of the heating section according to some embodiments of the present invention.

[0012] Figure 4 A diagram showing the relationship between parameters of the Ball's formula method according to some embodiments of the present invention.

[0013] Explanation of symbols:

[0014] 1: Sterilizer data processing system; 10: Energy consumption monitoring unit; 11: First energy consumption monitoring unit; 12: Second energy consumption monitoring unit; 20: Digital energy consumption meter; 21: Air compressor power consumption digital meter; 22: Sterilizer power consumption digital meter; 23: Boiler power consumption digital meter; 24: Cooling system power consumption digital meter; 30: Non-digital energy consumption meter; 31: Natural gas meter; 40: Carbon emission cloud computing unit; 41: Process automation module; 42: Cloud computing module; 43: Cloud database; 44: Web crawler database acquisition module; 50: Computer control and Strategy providing unit; 51: real-time energy consumption information module; 52: cumulative energy consumption information module; 53: carbon emission information module; 54: sterilization kettle sensor information module; 55: ground carbon emission database; 56: strategy providing module; 60: parameter monitoring unit; 61: temperature sensor; 70: sterilization value real-time calculation unit; 80: sterilization prediction unit; 241: fan power consumption digital meter; 242: pump power consumption digital meter; 421: first calculation module; 422: second calculation module; a: slope; b: intercept; CUT: non-regression zone heating time; F 0 : sterilization value; f h : heat penetration rate; IT: initial product temperature; log(g): logarithmic value; T: temperature value in the regression area of ​​the heating section; Time: time; TR: sterilization set temperature; U: cumulative thermal lethality; x: unknown number; y: unknown number; Y: heating section temperature time expression. DETAILED DESCRIPTION

[0015] In order to better understand the spirit of the present application, it is further described below in conjunction with some preferred embodiments of the present application.

[0016] Various embodiments of the present application are discussed in detail below. Although specific implementations are discussed, it should be understood that these embodiments are only for illustrative purposes. Those skilled in the relevant art will recognize that other components and configurations may be used without departing from the spirit and scope of the present application.

[0017] See also Figure 1, which is a diagram showing the architecture of a retort data processing system 1 according to some embodiments of the present invention. The retort data processing system 1 of the present invention comprises a parameter monitoring unit 60, a sterilization value real-time calculation unit 70, a sterilization time prediction unit 80, an energy consumption monitoring unit 10, a carbon emission cloud computing unit 40, and a computer control and strategy providing unit 50.

[0018] The parameter monitoring unit 60 is configured to obtain product temperature records in the sterilizer through the temperature sensor 61. In some embodiments, the "sterilizer" may also be referred to as "sterilizer", "autoclave" or "sterilizer". In some embodiments, the product temperature records may include product initial temperature, sterilization temperature, heating time, sterilization temperature curve and coldest point temperature curve.

[0019] The sterilization value real-time calculation unit 70 is connected to the parameter monitoring unit 60 and is configured to calculate the real-time sterilization value (F 0 ). Real-time sterilization value (F 0 ) is expressed in minutes as the sterilization condition of the heat treatment condition, and its total thermal lethal effect is equivalent to the killing ability of bacteria or spores with a Z value of 18°F when reaching 250°F (121.1°C). The Z value is the temperature at which the D value of a specific microorganism is reduced by 90%. The D value is the time required for the content of a specific microorganism to be reduced by 90% under a specific temperature condition. In some embodiments, the real-time sterilization value (F 0 ).

[0020] The sterilization prediction unit 80 is connected to the sterilization value real-time calculation unit 70, and is configured to calculate the sterilization prediction time (PT) using the real-time sterilization value. In some embodiments, the sterilization prediction time (PT) can be calculated using the Ball's formula method. The Ball's formula method can calculate the sterilization efficacy generated in the temperature rise stage through the sterilization temperature curve to deduce the sterilization prediction time. Therefore, in some embodiments, the "sterilization prediction time" can also be called the "theoretical sterilization time".

[0021] In some embodiments, the parameters for calculating the predicted sterilization time (PT) may further include: product initial temperature (IT), sterilization set temperature (TR), initial temperature difference (I), heating time (L), non-regression zone heating time (CUT), time equivalent to 121.1°C heat killing for 1 minute (F i ), heat penetration rate (f h ), cumulative thermal lethality (U), pseudo initial temperature (PIT@0.58L), hysteresis factor of heating time (j h), theoretical heating time (B) and logarithmic value (log(g)). In some embodiments, the calculation formula for calculating the predicted sterilization time (PT) may include:

[0022] CUT = 0.58L;

[0023] F i =1 / (10^((TR-250) / 18));

[0024] U=F 0 *F i ;

[0025] f h =-1 / a

[0026] I = TR-IT;

[0027] PIT@0.58L=250-(10^(the value of the pseudo initial temperature into the regression equation)*I);

[0028] j h =(TR-PIT@0.58L) / I;

[0029] Y=log((TR-T) / (TR-IT))=aTime+b;

[0030] log(g)=(0.71*f h / U-1) / (f h / U(<=1));

[0031] B=f h (log(j h )*I-log(g)); and

[0032] PT = BL * 0.42;

[0033] Where a is the slope, b is the intercept, T is the temperature value in the regression area of ​​the heating section, and Y is the temperature-time expression of the heating section.

[0034] Figure 2 A graph showing the relationship between a sterilization temperature curve and a sterilization value calculation curve according to some embodiments of the present invention. Figure 3 The relationship between the sterilization temperature curve and the logarithmic temperature time regression line of the heating section according to some embodiments of the present invention is shown. Figure 2 and Figure 3 Taking the above formulas as an example, the calculation method of sterilization prediction time (PT) is described in detail as follows:

[0035] First, the regression region is set. The regression region is when the temperature rise time (L) in the tank is greater than the temperature rise time (CUT) in the non-regression region, CUT = 0.58L.

[0036] Sterilization set temperature (TR) = 248°F.

[0037] like Figure 3 As shown, the effective regression range is greater than CUT and less than (TR)-5℉.

[0038] F i It is calculated as the time equivalent to 1 minute of thermal killing at 121.1℃ at any other lethal temperature. i =1 / (10^((TR-250) / 18)).

[0039] U is the cumulative thermal lethal dose, which is calculated as the cumulative value of the thermal lethal dose received by the temperature sensing point of the product in the can at each lethal temperature during the actual sterilization process.

[0040] U=F 0 *F i , F 0 can be Figure 2 In the heating curve, the F value of the heating curve is calculated by the cumulative value of Lithality = 10^((T-250) / 18) 0 value.

[0041] Heat penetration rate f h The value of Figure 3 In the regression area of ​​the heating section, the temperature value T is substituted into log((TR-T) / (TR-IT)) to obtain the regression line. By plotting and calculating the trend line, an equation such as aTime+b can be obtained. The slope of the equation is a, the intercept is b, and f h =-1 / a, so by drawing a graph we can get f h and U, and calculate log(g), as shown in the following table.

[0042] Ball's formula method parameter relationship table

[0043]

[0044]

[0045] Figure 4 A diagram showing the relationship between parameters of the Ball's formula method according to some embodiments of the present invention. Figure 4 The parameter relationship diagram is obtained based on the above parameter relationship table.

[0046] In the process of calculating log(g), when f h When U≦1, the calculation formula is log(g)=(0.71*(f h / U)-1) / (f h / U). when f h When / U>1, the calculation formula is log(g)=0.042808*(f h / U)^5-0.35709*(f h / U)^4+1.1929*(f h / U)^3-2.1296*(f h / U)^2+2.4847*(f h / U)-0.28274.

[0047] Theoretical heating time B = f h (log(j h )*I-log(g)), hysteresis factor j of heating time h = (TR-PIT@0.58L) / I. Import the pseudo initial temperature PIT@0.58L=250-(10^(the pseudo initial temperature is substituted into the regression equation)*I), the sterilization setting temperature TR and the initial temperature difference I=TR-IT h In the calculation formula, the obtained j h And log(g) into calculation formula B to get the theoretical heating time.

[0048] The theoretical heating time B and the temperature rise time L are introduced into the calculation formula PT=BL*0.42 to obtain the predicted sterilization time (PT).

[0049] See also Figure 1 Through the configuration of the parameter monitoring unit 60, the sterilization value real-time calculation unit 70 and the sterilization prediction unit 80, the present invention can further provide a method for determining the effectiveness of a canned food sterilization program, which includes:

[0050] The temperature record of the product is obtained in the sterilizer through the temperature sensor 61. The operation description of this step can be the same as the configuration description of the parameter monitoring unit 60. In some embodiments, the temperature sensor 61 can be used to perform a heat penetration test and a heat distribution test on the product in the sterilizer.

[0051] According to the product temperature record, the real-time sterilization value of the product is calculated. The operation description of this step can be the same as the configuration description of the real-time sterilization value calculation unit 70.

[0052] The sterilization prediction time is calculated using the real-time sterilization value. The operation description of this step may be the same as the configuration description of the sterilization prediction unit 80.

[0053] The theoretical sterilization value can be calculated by back-calculating the sterilization prediction time. In some embodiments, the cumulative sterilization value (F 0). For example, log(g) can be calculated using Ball's formula method and predicted sterilization time (PT), and then f can be deduced from the log(g) value. h / U, and convert it to U, through U / F i In some embodiments, the Ball's formula method can be used to calculate the sterilization effect produced during the temperature rise phase through the sterilization temperature curve to deduce the theoretical sterilization value.

[0054] The real-time sterilization value is compared with the theoretical sterilization value to determine the effectiveness of the sterilization procedure. In some embodiments, a high sterilization value has a higher sterilization effect, and the final sterilization value of the product is higher than the theoretical sterilization value, which is theoretically effective sterilization. Therefore, when the real-time sterilization value is higher than the theoretical sterilization value, the sterilization operation can be determined as "effective sterilization". When the real-time sterilization value is lower than the theoretical sterilization value, the sterilization operation can be determined as "ineffective sterilization".

[0055] In some embodiments, whether the sterilization state of the sterilizer is abnormal can be determined by comparing the real-time sterilization value with the theoretical sterilization value.

[0056] See also Figure 1 The energy consumption monitoring unit 10 can be configured to monitor multiple energy consumption meters (including, for example, a digital energy consumption meter 20 and a non-digital energy consumption meter 30) of the sterilizer to obtain energy consumption information of the multiple energy consumption meters (for example, a digital energy consumption meter 20 and a non-digital energy consumption meter 30). In some embodiments, "energy consumption information" can also be referred to as "energy consumption information" or "energy loss information".

[0057] In some embodiments, Figure 1 As shown, the digital energy consumption meter 20 may include but is not limited to a digital power meter 21 for air compressor power consumption, a digital power meter 22 for sterilizer power consumption, a digital power meter 23 for boiler power consumption, or a digital power meter 24 for cooling system power consumption. In some embodiments, the digital power meter 24 for cooling system power consumption may include but is not limited to a digital power meter 241 for fan power consumption or a digital power meter 242 for pump power consumption. In some embodiments, the non-digital energy consumption meter 30 may include but is not limited to a natural gas meter 31.

[0058] In some embodiments, Figure 1As shown, the energy consumption monitoring unit 10 may include a first energy consumption monitoring unit 11 and a second energy consumption monitoring unit 12. The first energy consumption monitoring unit 11 may be configured to monitor a digital energy consumption meter 20 (e.g., an air compressor power consumption digital meter 21, a sterilizer power consumption digital meter 22, a boiler power consumption digital meter 23, and a cooling system power consumption digital meter 24 (including, for example, a fan power consumption digital meter 241 and a pump power consumption digital meter 242)) to obtain energy consumption information (e.g., power consumption information) of the digital energy consumption meter 20 (e.g., an air compressor power consumption digital meter 21, a sterilizer power consumption digital meter 22, a boiler power consumption digital meter 23, and a cooling system power consumption digital meter 24 (including, for example, a fan power consumption digital meter 241 and a pump power consumption digital meter 242)). The second energy consumption monitoring unit 12 may be configured to monitor a non-digital energy consumption meter 30 (eg, a natural gas meter 31 ) to obtain energy consumption information (eg, natural gas usage information) of the non-digital energy consumption meter 30 (eg, a natural gas meter 31 ).

[0059] In some embodiments, the second energy consumption monitoring unit 12 may include but is not limited to an optical character recognition device. The optical character recognition device may be configured to recognize and digitize energy consumption information (e.g., natural gas usage information) of a non-digital energy consumption meter 30 (e.g., a natural gas meter 31). In some embodiments, the recognition step of the optical character recognition device may include: capturing images of different readings of the non-digital energy consumption meter 30 (e.g., a natural gas meter 31); performing image pre-processing; and performing optical character recognition / verification (OCR / OCV). In some embodiments, image pre-processing may include but is not limited to brightness adjustment, color plane extraction, color threshold adjustment, pattern capture, or mask range setting. In some embodiments, image pre-processing may include, but is not limited to, statistical or linear processing in linear or logarithmic mode through color models (including, for example, RGB, HSL, HSV or HIS), image point line angle area analysis measurement, display of light intensity in three-dimensional (3D) coordinates, coordinate system setting, image flipping or rotation, image correction for real units, correction of uneven lighting intensity, image superposition, image calculation, color classification, color segmentation, color matching, color positioning, checking color template matching, object tracking, improving contrast and brightness according to a lookup table, filtering, grayscale pattern modification and reconstruction, frequency filtering, grayscale threshold adjustment, watershed cutting, performing numerical and logical processing, image conversion, image quantization, detecting texture defects, binary image pattern modification and reconstruction, conditional filtering, binary image inversion, particle analysis, shape matching, center point search, edge detection, finding straight edges, finding round edges, finding maximum length, measuring length, template matching, contour analysis, graphic defects, feature detection or particle classification.

[0060] In some embodiments, the energy consumption monitoring unit 10 (including, for example, the first energy consumption monitoring unit 11 and the second energy consumption monitoring unit 12) can provide a microservice monitoring system for users to discover abnormalities of monitoring targets and provide improvement strategies. In some embodiments, the monitoring targets may include but are not limited to user human-machine interfaces, physical devices, or databases.

[0061] The carbon emission cloud computing unit 40 may be configured to receive energy consumption information (e.g., electricity consumption information and natural gas usage information) from a plurality of energy consumption meters (e.g., digital energy consumption meters 20 and non-digital energy consumption meters 30), and calculate carbon emission information based on the energy consumption information (e.g., electricity consumption information and natural gas usage information). In some embodiments, carbon emission information may include, but is not limited to, direct emission equivalents, indirect emission equivalents, total emission equivalents, direct emission share data, indirect emission share data, or total emission share data. In some embodiments, direct emission equivalents may include fixed emission equivalents, mobile emission equivalents, or fugitive emission equivalents. In some embodiments, direct emission share data may include fixed emission share data, mobile emission share data, or fugitive emission share data. The computer control and strategy providing unit 50 may be configured to analyze carbon emission information through at least one computer to generate at least one energy-saving and carbon reduction strategy. In some embodiments, at least one computer may include, but is not limited to, a personal computer or a server computer.

[0062] In some embodiments, Figure 1 As shown, the carbon emission cloud computing unit 40 may include a process automation module 41, a cloud computing module 42, a cloud database 43, and a web crawler database acquisition module 44. The computer control and strategy providing unit 50 may include a real-time energy consumption information module 51, a cumulative energy consumption information module 52, a carbon emission information module 53, a sterilizing autoclave sensor information module 54, a local carbon emission database 55, and a strategy providing module 56.

[0063] The real-time energy consumption information module 51 can be connected to the energy consumption monitoring unit 10 (including, for example, the first energy consumption monitoring unit 11 and the second energy consumption monitoring unit 12), and can be configured to receive the power consumption information obtained by the first energy consumption monitoring unit 11 and the natural gas usage information obtained by the second energy consumption monitoring unit 12. In some embodiments, the power consumption information and the natural gas usage information can be recorded in the database software of at least one computer.

[0064] The accumulated energy consumption information module 52 can be connected to the real-time energy consumption information module 51 and can be configured to accumulate and process the power consumption information and the natural gas usage information. Figure 1As shown, the accumulated energy consumption information module 52 can be connected to the Internet to upload the accumulated power consumption information and natural gas usage information to the carbon emission cloud computing unit 40 (eg, the process automation module 41 ).

[0065] In some embodiments, the process automation module 41 may be configured to receive the power consumption information and natural gas usage information uploaded by the cumulative energy consumption information module 52. In some embodiments, the process automation module 41 may be operated by robotic process automation (RPA) software. In some embodiments, the RPA software may include but is not limited to UiPath, Pega, WorkFusion Express, Kantu Ui Vision, or Autolt.

[0066] The cloud computing module 42 of the carbon emission cloud computing unit 40 can be connected to the process automation module 41 and can be configured to calculate the carbon emission information based on the power consumption information and the natural gas usage information. Figure 1 As shown, the cloud computing module 42 may include a first calculation module 421 and a second calculation module 422. The first calculation module 421 may be configured to calculate the carbon emission information of electricity consumption according to the electricity consumption information. The second calculation module 422 may be configured to calculate the carbon emission information of natural gas use according to the natural gas usage information.

[0067] In some embodiments, the cloud computing (e.g., carbon emission calculation) of the cloud computing module 42 (including, for example, the first computing module 421 and the second computing module 422) can be linked to an online carbon emission database. Since the online carbon emission database is maintained and updated in real time by a professional unit, it is not necessary for the equipment owner to build, maintain and update the database by himself. In some embodiments, the process automation module 41 can connect the power consumption information and the natural gas usage information to the online carbon emission database to convert the power consumption information and the natural gas usage information into the power consumption carbon emission information and the natural gas usage carbon emission information, respectively.

[0068] The carbon emission information module 53 of the computer control and strategy providing unit 50 may be configured to receive and record the electricity consumption carbon emission information returned by the first calculation module 421 and the natural gas consumption carbon emission information returned by the second calculation module 422 .

[0069] The sterilizing kettle sensor information module 54 of the computer control and strategy providing unit 50 can be configured to record the temperature sensor information, pressure sensor information and flow sensor information of the sterilizing kettle. In some embodiments, the temperature sensor information may include the temperature information inside the kettle, the pressure sensor information may include the pressure information inside the kettle, and the flow sensor information may include the hot water flow information, the cold water flow information, the air flow information or the steam flow information.

[0070] The local carbon emission database 55 of the computer control and strategy providing unit 50 can be connected to the carbon emission information module 53 and the sterilizing kettle sensor information module 54. And the local carbon emission database 55 can be configured to store the carbon emission information of electricity consumption, the carbon emission information of natural gas use, and the temperature sensor information, pressure sensor information and flow sensor information of the sterilizing kettle through the database software. In some embodiments, Figure 1 As shown, the local carbon emission database 55 can be connected to the Internet to upload electricity consumption carbon emission information, natural gas usage carbon emission information, and sterilizer temperature sensor information, pressure sensor information, and flow sensor information to the carbon emission cloud computing unit 40 (e.g., cloud database 43) for backup or to provide other applications.

[0071] In some embodiments, the real-time sterilization value (F 0 ), predicted sterilization time (PT) and theoretical sterilization value can be uploaded to the cloud database 43 for use in machine learning big data.

[0072] In some embodiments, the cloud database 43 may be configured to store electricity consumption carbon emission information, natural gas use carbon emission information, and sterilizer temperature sensor information, pressure sensor information, and flow sensor information. In some embodiments, the process automation module 41 may include automatic storage of electricity consumption information and natural gas use information in the ground-end carbon emission database 55, and input the total electricity consumption and total natural gas use into the electricity calculation usage field of the first calculation module 421 of the cloud computing module 42 and the natural gas use field of the second calculation module 422, respectively, to calculate the electricity consumption carbon emission information and natural gas use carbon emission information.

[0073] The web crawler database acquisition module 44 can be configured to collect data from multiple databases on the network for use in system operation. In some embodiments, the web crawler database acquisition module 44 can be connected to the cloud database 43 to store useful data in the cloud database 43.

[0074] The strategy providing module 56 of the computer control and strategy providing unit 50 can be connected to the ground carbon emission database 55, and can be configured to generate at least one energy-saving and carbon reduction strategy based on the carbon emission information of electricity consumption, the carbon emission information of natural gas use, and the temperature sensor information, pressure sensor information and flow sensor information of the sterilizer.

[0075] The data processing system 1 of the present invention obtains the energy consumption information (including, for example, power consumption information and natural gas usage information) of the sterilizer through the energy consumption monitoring unit 10 (including, for example, the first energy consumption monitoring unit 11 and the second energy consumption monitoring unit 12), and then transmits the energy consumption information (including, for example, power consumption information and natural gas usage information) to the carbon emission cloud computing unit 40 (for example, the process automation module 41 and the cloud computing module 42), and then calculates the carbon emission information (including, for example, carbon emission information of power consumption and carbon emission information of natural gas usage), and finally, after the computer control and strategy providing unit 50 (for example, the carbon emission information module 53, the sterilizer sensor information module 54 and the strategy providing module 56) analyzes the carbon emission information and the sensor information of the sterilizer, an energy-saving and carbon reduction strategy can be generated for the equipment owner to evaluate and select. Since the above-mentioned information acquisition, information transmission, cloud computing and strategy generation operations are all achieved through at least one computer control, a lot of manpower can be saved, human operation errors can be avoided and operation efficiency can be improved. In addition, cloud computing (including, for example, linking to an online carbon emission database) can shorten computing time, save database maintenance expenses, and reduce system construction costs.

[0076] On the other hand, the method for determining the effectiveness of the canned food sterilization program of the present invention can be used to determine the effectiveness of the sterilization program by calculating the real-time sterilization value, predicted sterilization time and theoretical sterilization value of the product, and comparing the real-time sterilization value with the theoretical sterilization value.

[0077] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It is understood that these terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning consistent with the background or context of the relevant technology and this disclosure, and should not be interpreted in an idealized or overly formal manner unless specifically defined herein.

[0078] The technical content and technical features of the present invention have been disclosed as above, but those skilled in the art may still make various substitutions and modifications based on the teachings and disclosures of the present invention without departing from the spirit of the present invention. Therefore, the protection scope of the present invention should not be limited to the contents disclosed in the embodiments, but should include various substitutions and modifications that do not depart from the present invention, and are covered by the claims of this patent application.

Claims

1. A method for determining the effectiveness of a canned food sterilization procedure, comprising: Obtain product temperature records in the autoclave through temperature sensors; Calculate the real-time sterilization value (F0) of the product according to the product temperature record; Calculate the predicted sterilization time (PT) using the real-time sterilization value (F0); The theoretical sterilization value is calculated by back-calculating the predicted sterilization time (PT); as well as The real-time sterilization value (F0) is compared with the theoretical sterilization value to determine the effectiveness of the sterilization procedure.

2. The method for determining the effectiveness of a canned food sterilization procedure as claimed in claim 1, wherein the product temperature record comprises the product initial temperature and the sterilization temperature curve.

3. The method for determining the effectiveness of a can sterilization procedure as claimed in claim 1, further comprising: The real-time sterilization value is calculated by the accumulation method.

4. The method for determining the effectiveness of a can sterilization procedure as claimed in claim 1, further comprising: The predicted sterilization time and the theoretical sterilization value are calculated using Ball's formula method.

5. The method for determining the effectiveness of a canned food sterilization procedure as claimed in claim 4, wherein the Ball's formula method calculates the sterilization efficacy produced in the temperature rise stage through a sterilization temperature curve to deduce the sterilization prediction time and the theoretical sterilization value.

6. The method for determining the effectiveness of a can sterilization program as claimed in claim 1, wherein the parameters for calculating the sterilization prediction time (PT) further include: Product initial temperature (IT), sterilization set temperature (TR), initial temperature difference (I), heating time (L), non-regression zone heating time (CUT), equivalent to 121.1℃ heat killing time for 1 minute (F i ), heat penetration rate (f h ), cumulative thermal lethality (U), pseudo initial temperature (PIT@0.58L), hysteresis factor of heating time (j h ), theoretical heating time (B) and logarithmic value (log(g)).

7. The method for determining the effectiveness of a canned food sterilization program according to claim 6, wherein the formula for calculating the predicted sterilization time (PT) comprises: CUT = 0.58L; F i =1 / (10^((TR-250) / 18)); U=F0*F i ; f h =-1 / a; I = TR-IT; PIT@0.58L=250-(10^(the value of the pseudo initial temperature into the regression equation)*I); j h =(TR-PIT@0.58L) / I; Y=log((TR-T) / (TR-IT))=aTime+b; log(g)=(0.71*f h / U-1) / (f h / U(<=1)); B = f h (log(j h ) * I - log(g)); and PT = BL * 0.42; Where a is the slope, b is the intercept, T is the temperature value in the regression area of ​​the heating section, and Y is the temperature-time expression of the heating section.

8. The method for determining the effectiveness of a can sterilization procedure as claimed in claim 1, further comprising: By comparing the real-time sterilization value with the theoretical sterilization value, it is determined whether the sterilization state of the sterilization kettle is abnormal.

9. The method for determining the effectiveness of a can sterilization procedure as claimed in claim 1, further comprising: A heat penetration test is performed on the product in the sterilizer through the temperature sensor.

10. The method for determining the effectiveness of a can sterilization procedure as claimed in claim 1, further comprising: A heat distribution test is performed on the product in the sterilizer through the temperature sensor.

11. The method for determining the effectiveness of a can sterilization procedure as claimed in claim 1, further comprising: The real-time sterilization value (F0), the predicted sterilization time (PT) and the theoretical sterilization value are uploaded to a cloud database.

12. A retort data processing system, comprising: A parameter monitoring unit configured to obtain product temperature records in the autoclave through a temperature sensor; a sterilization value real-time calculation unit, which is connected to the parameter monitoring unit and is configured to calculate a real-time sterilization value of the product according to the product temperature record; a sterilization prediction unit connected to the sterilization value real-time calculation unit and configured to calculate a sterilization prediction time using the real-time sterilization value; An energy consumption monitoring unit configured to monitor a plurality of energy consumption meters of the sterilizing kettle to obtain energy consumption information of the plurality of energy consumption meters; a carbon emission cloud computing unit, configured to receive the energy consumption information of the plurality of energy consumption meters, and calculate carbon emission information according to the energy consumption information; as well as A computer control and strategy providing unit, connected to the energy consumption monitoring unit and configured to analyze the carbon emission information calculated by the carbon emission cloud computing unit through at least one computer to generate at least one energy-saving and carbon-reduction strategy; The real-time sterilization value and the predicted sterilization time are uploaded to the cloud database of the carbon emission cloud computing unit, and the energy consumption information of the multiple energy consumption meters obtained by the energy consumption monitoring unit is uploaded to the carbon emission cloud computing unit through the computer control and strategy providing unit.

13. The retort data processing system according to claim 12, wherein the plurality of energy consumption meters include digital energy consumption meters, and the energy consumption monitoring unit includes: The first energy consumption monitoring unit is configured to monitor the digital energy consumption meter to obtain energy consumption information of the digital energy consumption meter.

14. The sterilizer data processing system according to claim 13, wherein the digital energy consumption meter comprises an air compressor power consumption digital meter, a sterilizer power consumption digital meter, a boiler power consumption digital meter or a cooling system power consumption digital meter.

15. The data processing system for the retort according to claim 14, wherein the cooling system power consumption digital meter comprises a fan power consumption digital meter or a pump power consumption digital meter.

16. The retort data processing system of claim 13, wherein the plurality of energy consumption meters further include non-digital energy consumption meters, and the energy consumption monitoring unit further includes: The second energy consumption monitoring unit is configured to monitor the non-digital energy consumption meter to obtain energy consumption information of the non-digital energy consumption meter.

17. The retort data processing system of claim 16, wherein the second energy consumption monitoring unit comprises an optical character recognition device, wherein the optical character recognition device is configured to recognize and digitize the energy consumption information of the non-digital energy consumption meter.

18. The retort data processing system according to claim 17, wherein the recognition step of the optical character recognition device comprises: Capturing different reading value images of the non-digital energy consumption meter; performing image pre-processing; and perform optical character recognition and verification.

19. The retort data processing system of claim 18, wherein the image pre-processing comprises brightness adjustment, color plane extraction, color threshold adjustment, shape capture or mask range setting.

20. The retort data processing system of claim 18, wherein the image pre-processing comprises statistical or linear processing in a linear or logarithmic mode through a color model, image point line angle area analysis measurement, display of light intensity in coordinates, coordinate system setting, image flipping or rotation, image correction for real units, correction of uneven light intensity, image superposition, image calculation, color classification, color segmentation, color matching, color positioning, checking color template matching, object tracking, contrast and brightness enhancement according to a lookup table, filtering, grayscale pattern modification and reconstruction, frequency filtering, grayscale threshold adjustment, watershed cutting, performing numerical and logical processing, image conversion, image quantization, texture defect detection, binary image pattern modification and reconstruction, conditional filtering, binary image inversion, particle analysis, shape matching, center point search, edge detection, straight edge search, round edge search, maximum length search, length measurement, template matching, contour analysis, graphic defect, feature detection or particle classification.

21. The retort data processing system of claim 16, wherein the non-digital energy consumption meter comprises a natural gas meter.

22. The retort data processing system according to claim 12, wherein the energy consumption information includes power consumption information and natural gas usage information, and the computer control and strategy providing unit includes: A real-time energy consumption information module, which is connected to the energy consumption monitoring unit and configured to receive the power consumption information and the natural gas usage information; and The accumulated energy consumption information module is connected to the real-time energy consumption information module and is configured to accumulate and process the electricity consumption information and the natural gas usage information, and upload the accumulated and processed electricity consumption information and the natural gas usage information to the carbon emission cloud computing unit.

23. The retort data processing system according to claim 22, wherein the carbon emission cloud computing unit comprises: a process automation module configured to receive the power consumption information and the natural gas usage information uploaded by the cumulative energy consumption information module; and A cloud computing module is connected to the process automation module and is configured to calculate the carbon emission information based on the power consumption information and the natural gas usage information.

24. The retort data processing system according to claim 23, wherein the cloud computing module comprises: A first calculation module, configured to calculate power consumption carbon emission information based on the power consumption information; and The second calculation module is configured to calculate the carbon emission information of natural gas usage based on the natural gas usage information.

25. The retort data processing system according to claim 24, wherein the computer control and strategy providing unit further comprises: The carbon emission information module is configured to receive and record the electricity consumption carbon emission information returned by the first calculation module and the natural gas consumption carbon emission information returned by the second calculation module.

26. The retort data processing system according to claim 25, wherein the computer control and strategy providing unit further comprises: A sterilizing kettle sensor information module, which is configured to record the temperature sensor information, pressure sensor information and flow sensor information of the sterilizing kettle; and A ground-side carbon emission database is connected to the carbon emission information module and the sterilizer sensor information module, and is configured to store the carbon emission information of electricity consumption, the carbon emission information of natural gas usage, and the temperature sensor information, pressure sensor information, and flow sensor information of the sterilizer through database software, and upload the carbon emission information of electricity consumption, the carbon emission information of natural gas usage, and the temperature sensor information, pressure sensor information, and flow sensor information of the sterilizer to the carbon emission cloud computing unit.

27. The sterilization autoclave data processing system as described in claim 26, wherein the process automation module includes automatically storing the power consumption information and the natural gas usage information in the ground carbon emission database, and inputting the total power consumption and the total natural gas usage into the power calculation usage field and the natural gas usage field of the cloud computing module respectively to calculate the power consumption carbon emission information and the natural gas usage carbon emission information.

28. The retort data processing system as claimed in claim 26, wherein the cloud database further stores the carbon emission information of electricity consumption, the carbon emission information of natural gas use, and the temperature sensor information, pressure sensor information and flow sensor information of the retort.

29. The sterilization autoclave data processing system as claimed in claim 26, wherein the temperature sensor information includes temperature information inside the autoclave, the pressure sensor information includes pressure information inside the autoclave, and the flow sensor information includes hot water flow information, cold water flow information, air flow information or steam flow information.

30. The retort data processing system according to claim 26, wherein the computer control and strategy providing unit further comprises: A strategy providing module is connected to the local carbon emission database and is configured to generate at least one energy-saving and carbon-reduction strategy based on the carbon emission information of electricity consumption, the carbon emission information of natural gas use, and the temperature sensor information, pressure sensor information, and flow sensor information of the sterilizer.

31. The retort data processing system of claim 23, wherein the cloud computing module is linked to an online carbon emission database.

32. The autoclave data processing system of claim 12, wherein the carbon emission information includes direct emission equivalents, indirect emission equivalents, total emission equivalents, direct emission percentage data, indirect emission percentage data, or total emission percentage data.

33. The autoclave data processing system of claim 32, wherein the direct emission equivalent comprises fixed emission equivalent, mobile emission equivalent or fugitive emission equivalent, and the direct emission proportion data comprises fixed emission proportion data, mobile emission proportion data or fugitive emission proportion data.

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