Sesame stability analysis method, system and equipment based on processing technology and medium

Abstract

The invention relates to a data analysis technology, and discloses a sesame stability analysis method and device based on a processing technology, and the method comprises the steps: obtaining sesame roasting process parameters, calculating a sesame roasting processing coefficient according to the process parameters, and obtaining main component content data of a preset group number of sesame samples, calculating the sesame stability index of each group according to the roasting processing coefficient and the sesame main component content data of a preset group number to obtain a sesame stability index set, calculating the standard deviation of the sesame stability index set, and if the standard deviation is greater than a preset standard deviation threshold, determining that the sesame stability index is the sesame stability index. If yes, returning to the step of acquiring the main component content data of the preset group number of sesame samples, and otherwise, calculating the average value of the sesame stability index set to obtain a sesame stability analysis result. The invention further provides a sesame stability analysis device based on the processing technology, electronic equipment and a storage medium. According to the method, the precision of sesame stability analysis can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a sesame stability analysis method, system, equipment and medium based on processing technology. Background Art

[0002] In the food processing industry, for sesame products with rich nutritional value and broad application prospects, such as black sesame paste, black sesame compound beverages, etc., the stability of their raw materials is an important indicator to measure product quality and consumer acceptance.

[0003] However, in the prior art, the analytical methods for the stability of sesame mostly rely on empirical judgment or the determination of a single indicator, such as observing the amount of precipitation, sensory evaluation, etc. These methods have the disadvantages of strong subjectivity, poor repeatability, and inability to accurately quantify, and are difficult to meet the strict requirements of the modern food processing industry for product quality control. Summary of the invention

[0004] The present invention provides a sesame stability analysis method, system, equipment and medium based on processing technology, the main purpose of which is to solve the problem of low accuracy of the existing sesame stability analysis method.

[0005] To achieve the above object, the present invention provides a sesame stability analysis method based on processing technology, comprising:

[0006] Acquiring process parameters for roasting sesame, and calculating a roasting processing coefficient for sesame according to the process parameters;

[0007] Obtain the main component content data of a preset number of sesame samples;

[0008] Calculate the sesame stability index of each group according to the roasting processing coefficient and the content data of the main components of sesame in a preset number of groups to obtain a sesame stability index set;

[0009] Calculating the standard deviation of the sesame stability index set;

[0010] If the standard deviation is greater than a preset standard deviation threshold, returning to the step of obtaining the main component content data of the preset number of groups of sesame samples;

[0011] If the standard deviation is less than or equal to a preset standard deviation threshold, the average value of the sesame stability index set is calculated to obtain a sesame stability analysis result.

[0012] Optionally, calculating the roasting process coefficient of sesame according to the process parameters includes:

[0013] The roasting processing coefficient is calculated using the following formula:

[0014]

[0015] Wherein, Z is the roasting processing coefficient, W is the roasting weight included in the process parameters, T a is the standard roasting time obtained in advance, T is the roasting time included in the process parameters, R a is the standard roasting temperature acquired in advance, R is the roasting temperature included in the process parameters, α is the roasting time weight parameter, and β is the roasting temperature weight parameter.

[0016] Optionally, obtaining the main component content data of a preset number of groups of sesame samples includes:

[0017] The moisture content of sesame samples was determined using the direct drying method;

[0018] The soluble sugar content of sesame samples was determined using Fehling's reagent titration method;

[0019] The crude fat content of sesame samples was determined using the Soxhlet extraction method;

[0020] The crude protein content of sesame samples was determined using the Kjeldahl method.

[0021] Optionally, the step of calculating the sesame stability index of each group according to the roasting process coefficient and the content data of the main components of sesame of a preset number of groups includes:

[0022] Performing outlier processing on the main component content data of sesame to obtain pre-processed component content data;

[0023] The sesame stability index is calculated based on the pre-treatment component content data using the following formula:

[0024]

[0025] Wherein, S is the sesame stability index, H is the crude protein content in the pre-processed component content data, C is the crude fat content in the pre-processed component content data, G is the soluble sugar content in the pre-processed component content data, e is the base of the natural logarithm, P is the moisture content in the pre-processed component content data, ω is a preset moisture content weight parameter, and Z is the roasting processing coefficient. It is the preset weight parameter of roasting processing coefficient.

[0026] Optionally, performing outlier processing on the sesame main component content data to obtain pre-processed component content data includes:

[0027] Identifying abnormal data in the sesame main component content data according to a preset content threshold;

[0028] Calculate the extreme value difference of the abnormal data;

[0029] Determine whether the extreme value difference is greater than a preset difference threshold;

[0030] If the extreme value difference is greater than the difference threshold, the value contained in the abnormal data is confirmed to be an abnormal value, and the abnormal data is deleted;

[0031] If the extreme value difference is less than or equal to the difference threshold, it is confirmed that the value contained in the abnormal data is not an abnormal value, and the value in the abnormal data is replaced by the average value of the abnormal data.

[0032] Optionally, calculating the standard deviation of the sesame stability index set includes:

[0033] Normalizing the sesame stability index set to obtain a normalized sesame stability index set;

[0034] The standard deviation is calculated based on the normalized sesame stability index set using the following formula:

[0035]

[0036] Wherein, σ is the standard deviation, N is the number of samples of the normalized sesame stability index set, and x i is the ith data in the normalized sesame stability index set, x avg is the average value of the normalized sesame stability index set.

[0037] Optionally, the normalizing the sesame stability index set to obtain a normalized sesame stability index set includes:

[0038] The sesame stability index set is normalized using the following formula:

[0039]

[0040] Where X is the normalized sesame stability index set, N is the number of samples in the sesame stability index set, and s i is the ith data in the sesame stability index set, s min is the minimum value in the sesame stability index set, s max is the maximum value in the sesame stability index set, and e is the base of the natural logarithm.

[0041] In order to solve the above problems, the present invention also provides a sesame stability analysis device based on processing technology, the device comprising:

[0042] A data acquisition module, used to acquire the process parameters of sesame roasting, calculate the sesame roasting process coefficient according to the process parameters, and acquire the main component content data of a preset number of sesame samples;

[0043] A data calculation module, used to calculate the sesame stability index of each group according to the roasting processing coefficient and the content data of the main components of sesame of a preset number of groups, obtain a sesame stability index set, and calculate the standard deviation of the sesame stability index set;

[0044] The data judgment module returns to the step of obtaining the main component content data of the preset number of groups of sesame samples if the standard deviation is greater than a preset standard deviation threshold;

[0045] The data confirmation module calculates the average value of the sesame stability index set to obtain a sesame stability analysis result if the standard deviation is less than or equal to a preset standard deviation threshold.

[0046] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0047] at least one processor;

[0048] and, a memory communicatively coupled to the at least one processor;

[0049] Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned sesame stability analysis method based on processing technology.

[0050] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned sesame stability analysis method based on processing technology.

[0051] The embodiment of the present invention obtains the process parameters of sesame roasting, calculates the roasting processing coefficient of sesame according to the process parameters, improves the efficiency of subsequent calculation of sesame stability index, obtains the main component content data of a preset number of sesame samples, calculates the sesame stability index of each group according to the roasting processing coefficient and the main component content data of the preset number of sesame groups, obtains a sesame stability index set, calculates the standard deviation of the sesame stability index set, and if the standard deviation is greater than the preset standard deviation threshold, returns to the step of obtaining the main component content data of the preset number of sesame samples, thereby improving the reliability of the data. If the standard deviation is less than or equal to the preset standard deviation threshold, the average value of the sesame stability index set is calculated to obtain the sesame stability analysis result, thereby improving the effect of stability analysis on sesame. Therefore, the sesame stability analysis method, device, electronic device and computer-readable storage medium based on processing technology proposed by the present invention can solve the problem of low accuracy of the existing sesame stability analysis method. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of a process for analyzing sesame stability based on processing technology provided by one embodiment of the present invention;

[0053] Figure 2 A schematic diagram of a process for outlier processing provided by an embodiment of the present invention;

[0054] Figure 3 A functional module diagram of a sesame stability analysis device based on processing technology provided by one embodiment of the present invention;

[0055] Figure 4 A schematic diagram of the structure of an electronic device for implementing the sesame stability analysis method based on processing technology provided by an embodiment of the present invention.

[0056] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0058] The embodiment of the present application provides a sesame stability analysis method based on processing technology. The execution subject of the sesame stability analysis method based on processing technology includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the sesame stability analysis method based on processing technology can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0059] Reference Figure 1 FIG. 1 is a flow chart of a sesame stability analysis method based on processing technology provided by an embodiment of the present invention. In this embodiment, the sesame stability analysis method based on processing technology includes:

[0060] S1. Obtaining process parameters for roasting sesame, and calculating a roasting processing coefficient of sesame according to the process parameters.

[0061] In the embodiments of the present invention, roasting of sesame is an important process in the sesame processing. Roasting can reduce the moisture in sesame and promote the release of oil inside sesame. Therefore, the process parameters of sesame roasting have a certain influence on the stability of sesame.

[0062] In the embodiment of the present invention, the process parameters include roasting temperature, roasting time and roasting weight.

[0063] In detail, the roasted weight refers to the weight of sesame seeds roasted once.

[0064] In the embodiment of the present invention, the step of calculating the roasting processing coefficient of sesame according to the process parameters includes:

[0065] The roasting processing coefficient is calculated using the following formula:

[0066]

[0067] Wherein, Z is the roasting processing coefficient, W is the roasting weight included in the process parameters, T a is the standard roasting time obtained in advance, T is the roasting time included in the process parameters, R ais the standard roasting temperature acquired in advance, R is the roasting temperature included in the process parameters, α is the roasting time weight parameter, and β is the roasting temperature weight parameter.

[0068] In detail, the pre-acquired standard roasting time may be 20 minutes.

[0069] In detail, the pre-acquired standard roasting temperature may be 220 degrees.

[0070] In the embodiment of the present invention, by obtaining the process parameters of sesame roasting, the sesame roasting process coefficient is calculated according to the process parameters, thereby improving the efficiency of subsequent calculation of the sesame stability index.

[0071] S2. Obtaining the main component content data of a preset number of groups of sesame samples.

[0072] In the embodiment of the present invention, the method of obtaining the main component content data of the preset number of groups of sesame samples is to obtain the preset number of groups of sesame samples and obtain the main component content data of each group of sesame samples. The preset number of groups needs to be set according to actual requirements. Too many groups can improve the accuracy of subsequent analysis, but will reduce the calculation efficiency.

[0073] In the embodiment of the present invention, the main component content data includes water content, soluble sugar content, crude fat content and crude protein content.

[0074] In the embodiment of the present invention, the step of obtaining the main component content data of a preset number of groups of sesame samples includes:

[0075] The moisture content of sesame samples was determined using the direct drying method;

[0076] The soluble sugar content of sesame samples was determined using Fehling's reagent titration method;

[0077] The crude fat content of sesame samples was determined using the Soxhlet extraction method;

[0078] The crude protein content of sesame samples was determined using the Kjeldahl method.

[0079] In detail, the direct drying method is to dry the sesame sample at 101 to 105 degrees to a constant weight, and then calculate the moisture content by weighing the mass difference before and after drying.

[0080] In detail, the Fehling's reagent titration method is to titrate a certain amount of Fehling's reagent with a sesame sample solution under heating conditions to reduce divalent copper to monovalent copper, and use methylene blue as an indicator to determine the soluble sugar content.

[0081] In detail, the Soxhlet extraction method is to remove the crude fat in the sample by reflux extraction with a low boiling point organic solvent (such as ether or petroleum ether), and then calculate the crude fat content based on the weight difference between the sample and the residue.

[0082] In detail, the Kjeldahl method is based on the fact that protein in food is decomposed under catalytic heating conditions, and the generated ammonia combines with sulfuric acid to form ammonium sulfate. The ammonia is freed by alkaline distillation, absorbed by boric acid, and titrated with a standard sulfuric acid or hydrochloric acid solution. The nitrogen content is calculated based on the acid consumption, and then multiplied by the conversion factor to obtain the protein content.

[0083] In the embodiment of the present invention, by obtaining the main component content data of a preset number of groups of sesame samples, the efficiency of subsequent calculation of the sesame stability index is improved.

[0084] S3. Calculate the sesame stability index of each group according to the roasting processing coefficient and the content data of the main components of sesame in a preset number of groups to obtain a sesame stability index set.

[0085] In the embodiment of the present invention, the sesame stability index is a comprehensive index calculated based on relevant measurement data of the sesame sample, and can be used to indicate the quality of sesame.

[0086] In the embodiment of the present invention, the step of calculating the sesame stability index of each group according to the roasting processing coefficient and the content data of the main components of sesame of a preset number of groups includes:

[0087] Performing outlier processing on the main component content data of sesame to obtain pre-processed component content data;

[0088] The sesame stability index is calculated based on the pre-treatment component content data using the following formula:

[0089]

[0090] Wherein, S is the sesame stability index, H is the crude protein content in the pre-processed component content data, C is the crude fat content in the pre-processed component content data, G is the soluble sugar content in the pre-processed component content data, e is the base of the natural logarithm, P is the moisture content in the pre-processed component content data, ω is a preset moisture content weight parameter, and Z is the roasting processing coefficient. It is the preset weight parameter of roasting processing coefficient.

[0091] For details, refer to Figure 2 As shown, a schematic diagram of a process of outlier processing is provided in an embodiment of the present invention, wherein outlier processing is performed on the sesame main component content data to obtain pre-processed component content data, including:

[0092] S21, identifying abnormal data in the sesame main component content data according to a preset content threshold;

[0093] S22, calculating the extreme value difference of the abnormal data;

[0094] S23, determining whether the extreme value difference is greater than a preset difference threshold;

[0095] If the extreme value difference is greater than the difference threshold, executing S24, confirming that the value contained in the abnormal data is an abnormal value, and deleting the abnormal data;

[0096] If the extreme value difference is less than or equal to the difference threshold, execute S25, confirm that the value contained in the abnormal data is not an abnormal value, and replace the value in the abnormal data with the average value of the abnormal data.

[0097] In detail, the extreme value difference of the abnormal data is calculated by subtracting the minimum value of the abnormal data from the maximum value of the abnormal data.

[0098] In detail, the preset moisture content weight parameter is used to control the influence of the moisture content of sesame on the size of the sesame stability index.

[0099] In detail, the preset roasting process coefficient weight parameter is used to control the influence of the roasting process coefficient on the size of the sesame stability index.

[0100] In the embodiment of the present invention, the sesame stability index of each group is calculated according to the roasting processing coefficient and the sesame main component content data of a preset number of groups to obtain a sesame stability index set, thereby improving the efficiency of subsequent calculation of the standard deviation of the sesame stability set.

[0101] S4. Calculate the standard deviation of the sesame stability index set.

[0102] In the embodiment of the present invention, the standard deviation is a statistic that describes the degree of dispersion of the numerical distribution in the data set, and represents the average of the deviations of the numerical values ​​in the data set from the average value.

[0103] In the embodiment of the present invention, the step of calculating the standard deviation of the sesame stability index set includes:

[0104] Normalizing the sesame stability index set to obtain a normalized sesame stability index set;

[0105] The standard deviation is calculated based on the normalized sesame stability index set using the following formula:

[0106]

[0107] Wherein, σ is the standard deviation, N is the number of samples of the normalized sesame stability index set, and x i is the ith data in the normalized sesame stability index set, x avg is the average value of the normalized sesame stability index set.

[0108] In detail, the normalizing the sesame stability index set to obtain a normalized sesame stability index set includes:

[0109] The sesame stability index set is normalized using the following formula:

[0110]

[0111] Where X is the normalized sesame stability index set, N is the number of samples in the sesame stability index set, and s i is the ith data in the sesame stability index set, s min is the minimum value in the sesame stability index set, s max is the maximum value in the sesame stability index set, and e is the base of the natural logarithm.

[0112] In the embodiment of the present invention, by calculating the standard deviation of the sesame stability index set, the efficiency of subsequent determination of whether the sesame stability index set data meets the requirements is improved.

[0113] S5. Determine whether the standard deviation is greater than a preset standard deviation threshold.

[0114] If the standard deviation is greater than a preset standard deviation threshold, S2, the step of obtaining the main component content data of a preset number of groups of sesame samples, is executed again.

[0115] In an embodiment of the present invention, when the standard deviation is greater than a preset standard deviation threshold, it indicates that the data dispersion in the sesame stability index set at this time is too large, and there may be problems in the step of obtaining the main component content data of the sesame sample. Therefore, it is necessary to re-execute the step of obtaining the main component content data of a preset number of groups of sesame samples.

[0116] If the standard deviation is less than or equal to a preset standard deviation threshold, S6 is executed to calculate the average value of the sesame stability index set to obtain a sesame stability analysis result.

[0117] In the embodiment of the present invention, when the standard deviation is less than or equal to the preset standard deviation threshold, it means that the data dispersion degree in the sesame stability index set meets the requirements and can be used for the next step of research. In the embodiment of the present invention, when the standard deviation is less than or equal to the preset standard deviation threshold, the sesame stability analysis result is obtained by calculating the average value of the sesame stability index set, thereby improving the efficiency of stability analysis on sesame.

[0118] like Figure 3 , which is a functional module diagram of a sesame stability analysis device based on processing technology provided by one embodiment of the present invention.

[0119] The sesame stability analysis device 100 based on processing technology of the present invention can be installed in an electronic device. According to the functions to be implemented, the sesame stability analysis device 100 based on processing technology can include a data acquisition module 101, a data calculation module 102, a data judgment module 103 and a data confirmation module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0120] In this embodiment, the functions of each module / unit are as follows:

[0121] The data acquisition module 101 is used to acquire the process parameters of sesame roasting, calculate the sesame roasting process coefficient according to the process parameters, and acquire the main component content data of a preset number of sesame samples;

[0122] The data calculation module 102 is used to calculate the sesame stability index of each group according to the roasting process coefficient and the content data of the main components of sesame in a preset number of groups, obtain a sesame stability index set, and calculate the standard deviation of the sesame stability index set;

[0123] The data judgment module 103 returns to the step of obtaining the main component content data of the preset number of groups of sesame samples if the standard deviation is greater than a preset standard deviation threshold;

[0124] The data confirmation module 104 calculates the average value of the sesame stability index set to obtain a sesame stability analysis result if the standard deviation is less than or equal to a preset standard deviation threshold.

[0125] In detail, each module described in the sesame stability analysis device 100 based on processing technology in the embodiment of the present invention is used in the same manner as described above. Figures 1 to 3 The sesame stability analysis method based on processing technology described in the present invention is the same technical means and can produce the same technical effects, so it will not be repeated here.

[0126] like Figure 4 , which is a schematic diagram of the structure of an electronic device for a sesame stability analysis method based on processing technology provided in an embodiment of the present invention.

[0127] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a sesame stability analysis program based on processing technology.

[0128] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a sesame stability analysis program based on processing technology, etc.), and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0129] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. The memory 11 may be an internal storage unit of an electronic device in some embodiments, such as a mobile hard disk of the electronic device. The memory 11 may also be an external storage device of an electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 may not only be used to store application software and various types of data installed in the electronic device, such as the code of a sesame stability analysis program based on processing technology, but may also be used to temporarily store data that has been output or is to be output.

[0130] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0131] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in this embodiment, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0132] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0133] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0134] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0135] The sesame stability analysis program based on processing technology stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0136] Acquiring process parameters for roasting sesame, and calculating a roasting processing coefficient for sesame according to the process parameters;

[0137] Obtain the main component content data of a preset number of sesame samples;

[0138] Calculate the sesame stability index of each group according to the roasting processing coefficient and the content data of the main components of sesame in a preset number of groups to obtain a sesame stability index set;

[0139] Calculating the standard deviation of the sesame stability index set;

[0140] If the standard deviation is greater than a preset standard deviation threshold, returning to the step of obtaining the main component content data of the preset number of groups of sesame samples;

[0141] If the standard deviation is less than or equal to a preset standard deviation threshold, the average value of the sesame stability index set is calculated to obtain a sesame stability analysis result.

[0142] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0143] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0144] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0145] Acquiring process parameters for roasting sesame, and calculating a roasting processing coefficient for sesame according to the process parameters;

[0146] Obtain the main component content data of a preset number of sesame samples;

[0147] Calculate the sesame stability index of each group according to the roasting processing coefficient and the content data of the main components of sesame in a preset number of groups to obtain a sesame stability index set;

[0148] Calculating the standard deviation of the sesame stability index set;

[0149] If the standard deviation is greater than a preset standard deviation threshold, returning to the step of obtaining the main component content data of the preset number of groups of sesame samples;

[0150] If the standard deviation is less than or equal to a preset standard deviation threshold, the average value of the sesame stability index set is calculated to obtain a sesame stability analysis result.

[0151] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0152] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0154] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0155] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0156] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0157] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A sesame stability analysis method based on processing technology, characterized in that: The method comprises: Acquiring process parameters for roasting sesame, and calculating a roasting processing coefficient for sesame according to the process parameters; Obtain the main component content data of a preset number of sesame samples; Calculate the sesame stability index of each group according to the roasting processing coefficient and the content data of the main components of sesame in a preset number of groups to obtain a sesame stability index set; Calculating the standard deviation of the sesame stability index set; If the standard deviation is greater than a preset standard deviation threshold, returning to the step of obtaining the main component content data of the preset number of groups of sesame samples; If the standard deviation is less than or equal to a preset standard deviation threshold, the average value of the sesame stability index set is calculated to obtain a sesame stability analysis result.

2. The sesame stability analysis method based on processing technology according to claim 1, characterized in that: The step of calculating the roasting processing coefficient of sesame according to the process parameters comprises: The roasting processing coefficient is calculated using the following formula: Wherein, Z is the roasting processing coefficient, W is the roasting weight included in the process parameters, T a is the standard roasting time obtained in advance, T is the roasting time included in the process parameters, R a is the standard roasting temperature acquired in advance, R is the roasting temperature included in the process parameters, α is the roasting time weight parameter, and β is the roasting temperature weight parameter.

3. The sesame stability analysis method based on processing technology as claimed in claim 2, characterized in that: The method of obtaining the main component content data of the preset number of groups of sesame samples includes: The moisture content of sesame samples was determined using the direct drying method; The soluble sugar content of sesame samples was determined using Fehling's reagent titration method; The crude fat content of sesame samples was determined using the Soxhlet extraction method; The crude protein content of sesame samples was determined using the Kjeldahl method.

4. The sesame stability analysis method based on processing technology according to claim 1, characterized in that: The step of calculating the sesame stability index of each group according to the roasting processing coefficient and the content data of the main components of sesame of a preset number of groups includes: Performing outlier processing on the main component content data of sesame to obtain pre-processed component content data; The sesame stability index is calculated based on the pre-treatment component content data using the following formula: Wherein, S is the sesame stability index, H is the crude protein content in the pre-processed component content data, C is the crude fat content in the pre-processed component content data, G is the soluble sugar content in the pre-processed component content data, e is the base of the natural logarithm, P is the moisture content in the pre-processed component content data, ω is a preset moisture content weight parameter, and Z is the roasting processing coefficient. It is the preset weight parameter of roasting processing coefficient.

5. The sesame stability analysis method based on processing technology according to claim 4, characterized in that: The step of performing outlier processing on the sesame main component content data to obtain pre-processed component content data includes: Identifying abnormal data in the sesame main component content data according to a preset content threshold; Calculate the extreme value difference of the abnormal data; Determine whether the extreme value difference is greater than a preset difference threshold; If the extreme value difference is greater than the difference threshold, the value contained in the abnormal data is confirmed to be an abnormal value, and the abnormal data is deleted; If the extreme value difference is less than or equal to the difference threshold, it is confirmed that the value contained in the abnormal data is not an abnormal value, and the value in the abnormal data is replaced by the average value of the abnormal data.

6. The sesame stability analysis method based on processing technology according to claim 1, characterized in that: The calculating the standard deviation of the sesame stability index set includes: Normalizing the sesame stability index set to obtain a normalized sesame stability index set; The standard deviation is calculated based on the normalized sesame stability index set using the following formula: Wherein, σ is the standard deviation, N is the number of samples of the normalized sesame stability index set, and x i is the ith data in the normalized sesame stability index set, x avg is the average value of the normalized sesame stability index set.

7. The sesame stability analysis method based on processing technology according to claim 6, characterized in that: The normalizing process is performed on the sesame stability index set to obtain a normalized sesame stability index set, including: The sesame stability index set is normalized using the following formula: Where X is the normalized sesame stability index set, N is the number of samples in the sesame stability index set, and s i is the ith data in the sesame stability index set, s min is the minimum value in the sesame stability index set, s max is the maximum value in the sesame stability index set, and e is the base of the natural logarithm.

8. A sesame stability analysis device based on processing technology, characterized in that: The device comprises: A data acquisition module, used to acquire the process parameters of sesame roasting, calculate the sesame roasting process coefficient according to the process parameters, and acquire the main component content data of a preset number of sesame samples; A data calculation module, used to calculate the sesame stability index of each group according to the roasting processing coefficient and the content data of the main components of sesame of a preset number of groups, obtain a sesame stability index set, and calculate the standard deviation of the sesame stability index set; The data judgment module returns to the step of obtaining the main component content data of the preset number of groups of sesame samples if the standard deviation is greater than a preset standard deviation threshold; The data confirmation module calculates the average value of the sesame stability index set to obtain a sesame stability analysis result if the standard deviation is less than or equal to a preset standard deviation threshold.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the sesame stability analysis method based on processing technology as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the sesame stability analysis method based on processing technology as described in any one of claims 1 to 7 is implemented.

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