CN112488561B - Moon cake safety quality guaranteeing method and system based on short-time hot baking and ultraviolet rays - Google Patents
Moon cake safety quality guaranteeing method and system based on short-time hot baking and ultraviolet rays Download PDFInfo
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Abstract
The invention relates to a moon cake safety quality guaranteeing method and system based on short-time hot baking and ultraviolet rays, wherein the method comprises the steps of obtaining test data of a test moon cake under the conditions of hot baking and ultraviolet rays sterilization, wherein the test data comprise moon cake moisture content data, hot baking sterilization condition data and moon cake bacterial load data; performing data preprocessing on the test data, and taking the test data corresponding to the data preprocessed as data to be extracted; performing feature engineering on the data to be extracted to obtain a data set to be trained; modeling is carried out according to the training data set, and a moon cake baking and sterilizing model for predicting the baking and sterilizing conditions required by different moon cakes is obtained. The invention can intelligently adjust the heat baking and ultraviolet process configuration conditions of different moon cakes, thereby improving the safety quality of the moon cakes.
Description
Technical Field
The invention relates to the technical field of food quality safety guarantee, in particular to a moon cake safety quality guarantee method and system based on short-time hot baking and ultraviolet rays.
Background
At present, the moon cake market is hot, various flower-type moon cakes such as Bai Rong moon cakes, flowing moon cakes, snowy moon cakes and the like appear, the shelf life of the moon cakes is not long, generally about one month, more than two months, therefore, in the processing and production process of the moon cakes, the process requirements are relatively high, ultraviolet rays are generally adopted for sterilizing the moon cakes in the prior art, then short-time hot baking is externally added, the moisture content of the moon cakes can be reduced by the short-time hot baking technology, so that the possibility of bacteria in the growing food of the moon cakes is reduced, meanwhile, bacteria are sterilized as much as possible through ultraviolet rays, but the requirements for hot baking and ultraviolet sterilization are different in order to ensure the quality and taste of the moon cakes, the temperature and the time of the hot baking and the ultraviolet sterilization are generally manually adjusted, the manual adjustment is generally empirically adjusted, the adjustment error exists, and the quality of each kind of moon cakes is difficult to be ensured to reach the standard, and therefore a more intelligent moon cake quality control method is needed.
Disclosure of Invention
In order to overcome the defects of the prior art, the application provides a moon cake safety quality guaranteeing method and system based on short-time hot baking and ultraviolet rays, which can intelligently adjust the hot baking and ultraviolet ray process configuration conditions of different moon cakes, thereby improving the safety quality of the moon cake.
In a first aspect, the method for guaranteeing the safety quality of the moon cake based on short-time baking and ultraviolet rays provided by the application adopts the following technical scheme:
A moon cake safety quality guaranteeing method based on short-time baking and ultraviolet rays comprises the following steps:
obtaining test data of a test moon cake under the conditions of baking and ultraviolet sterilization, wherein the test data comprises moon cake moisture content data, baking sterilization condition data and moon cake bacterial load data;
performing data preprocessing on the test data, and taking the test data corresponding to the data preprocessed as data to be extracted;
Performing feature engineering on the data to be extracted to obtain a data set to be trained;
Modeling is carried out according to the training data set, and a moon cake baking and sterilizing model for predicting the baking and sterilizing conditions required by different moon cakes is obtained.
By adopting the technical scheme, the data pretreatment and the feature extraction are carried out by acquiring the test data of the test moon cake under the conditions of hot baking and ultraviolet sterilization, so that the data modeling can be carried out, the moon cake hot baking sterilization model for predicting the hot baking sterilization conditions required by different moon cakes is acquired, the hot baking and ultraviolet process configuration conditions of different moon cakes are intelligently regulated, and the safety quality of the moon cake is improved.
Optionally, performing data preprocessing on the test data, and taking the test data corresponding to the data preprocessed as the data to be extracted, where the data preprocessing includes:
Performing data cleaning on the test data to delete abnormal data, and taking the test data after data cleaning as data to be counted;
the moon cake bacterial load data when different heat drying and sterilizing conditions are adopted for each moon cake with the moisture content is obtained through statistics on the moon cake moisture content data, the heat drying and sterilizing condition data and the moon cake bacterial load data after statistics are used as data to be normalized;
Normalizing the data to be normalized to obtain the data of the shelf life of the moon cakes under different baking and sterilizing conditions of each moisture content moon cake, and composing the obtained data of the shelf life of the moon cakes into the data to be extracted.
By adopting the technical scheme, abnormal data is deleted by cleaning the test data, and modeling errors are reduced; the moisture content data of the moon cakes and the heat-baking sterilization condition data and the moon cake bacterial amount data in the data to be counted are used for counting, so that the subsequent rapid feature extraction is facilitated; and data processing is performed through normalization, so that the subsequent data processing and modeling are faster and more efficient.
Optionally, performing feature engineering on the data to be extracted to obtain a data set to be trained, including:
Based on a preset feature extraction algorithm, carrying out feature extraction on data to be extracted to obtain feature information;
and carrying out feature derivation on the feature information in the quality-keeping time dimension, and taking the feature information after feature derivation as a data set to be trained.
By adopting the technical scheme, the feature information is acquired by carrying out feature extraction on the data to be extracted, so that modeling, training and learning are facilitated; and the feature information is subjected to feature derivation in the quality guarantee time dimension, so that the quality guarantee time is considered in the subsequent modeling process, and the applicability and accuracy of the model are improved.
Optionally, modeling training is performed based on the data set to be trained to obtain a moon cake baking sterilization model for predicting the baking sterilization conditions required by different moon cakes, including:
modeling according to the characteristic information of different categories in the training data set to obtain a moon cake moisture and moon cake quality guarantee related sub-model, a heat-baking sterilization and moon cake quality guarantee related sub-model and a fungus amount and moon cake quality guarantee related sub-model;
And carrying out model fusion on the moon cake moisture and the moon cake quality guarantee related sub-model, and the heat-baking sterilization and moon cake quality guarantee related sub-model and the bacterial load and the moon cake quality guarantee related sub-model to obtain a moon cake heat-baking sterilization model.
By adopting the technical scheme, the moon cake hot-baking sterilization model can analyze predicted hot-baking sterilization conditions required by different moon cakes from multiple dimensions by constructing weak relation models of the moon cake moisture and the moon cake quality-related sub-model, the hot-baking sterilization and moon cake quality-related sub-model and the bacterial quantity and the moon cake quality-related sub-model according to the training data set so as to fuse and obtain the strong relation model of the moon cake hot-baking sterilization model.
Optionally, acquiring a target moon cake hot baking and sterilizing configuration request, wherein the target moon cake hot baking and sterilizing configuration request comprises initial moisture content data of the target moon cake;
Inputting the initial moisture content data into the moon cake hot baking sterilization model to obtain the hot baking sterilization configuration conditions of the target moon cake.
By adopting the technical scheme, according to the thermal baking and sterilizing configuration request of a user on the target moon cake, the thermal baking and sterilizing configuration condition of the target moon cake is predicted by utilizing the moon cake thermal baking and sterilizing model, so that the thermal baking and sterilizing configuration condition is replaced by manually adjusting, the thermal baking and ultraviolet process configuration conditions of different moon cakes are intelligently adjusted, and the safety quality of the moon cake is improved.
In a first aspect, the moon cake safety quality assurance system based on short-time baking and ultraviolet rays provided by the application adopts the following technical scheme:
a moon cake safety quality assurance system based on short-time hot baking and ultraviolet rays, the system comprising:
The data acquisition module is used for acquiring test data of the test moon cake under the conditions of hot baking and ultraviolet sterilization, wherein the test data comprises moon cake moisture content data, hot baking sterilization condition data and moon cake bacterial load data;
The preprocessing module is used for preprocessing the data of the test data and taking the corresponding test data after the data preprocessing as the data to be extracted;
The feature extraction module is used for carrying out feature engineering on the data to be extracted to obtain a data set to be trained;
The modeling module is used for modeling according to the training data set and obtaining a moon cake hot baking sterilization model for predicting the hot baking sterilization conditions required by different moon cakes.
By adopting the technical scheme, the data pretreatment and the feature extraction are carried out by acquiring the test data of the test moon cake under the conditions of hot baking and ultraviolet sterilization, so that the data modeling can be carried out, the moon cake hot baking sterilization model for predicting the hot baking sterilization conditions required by different moon cakes is acquired, the hot baking and ultraviolet process configuration conditions of different moon cakes are intelligently regulated, and the safety quality of the moon cake is improved.
Optionally, the preprocessing module includes:
The data cleaning sub-module is used for cleaning the data of the test data to delete abnormal data, and taking the test data after data cleaning as data to be counted;
The counting sub-module is used for counting the moon cake moisture content data, the heat-drying sterilization condition data and the moon cake bacterial amount data in the data to be counted, obtaining the moon cake bacterial amount data when different heat-drying sterilization conditions are adopted for each moisture content moon cake, and taking the counted moon cake bacterial amount data as data to be normalized;
The normalization sub-module is used for carrying out normalization processing on the data to be normalized, obtaining the quality guarantee rate data of the moon cakes with each moisture content under different baking and sterilizing conditions, and forming the obtained quality guarantee rate data of the moon cakes into the data to be extracted.
By adopting the technical scheme, abnormal data are deleted by respectively carrying out data cleaning on the test data, so that modeling errors are reduced; the moisture content data of the moon cakes and the heat-baking sterilization condition data and the moon cake bacterial amount data in the data to be counted are used for counting, so that the subsequent rapid feature extraction is facilitated; and data processing is performed through normalization, so that the subsequent data processing and modeling are faster and more efficient.
Optionally, the feature extraction module includes:
the extraction sub-module is used for carrying out feature extraction on the data to be extracted based on a preset feature extraction algorithm to obtain feature information;
And the feature deriving sub-module is used for carrying out feature derivation on the feature information in the quality-keeping time dimension, and taking the feature information after the feature derivation as a data set to be trained.
By adopting the technical scheme, the feature information is acquired by carrying out feature extraction on the data to be extracted, so that modeling, training and learning are facilitated; and the feature information is subjected to feature derivation in the quality guarantee time dimension, so that the quality guarantee time is considered in the subsequent modeling process, and the applicability and accuracy of the model are improved.
Optionally, the modeling module includes:
The first model construction sub-module is used for modeling according to the characteristic information of different categories in the training data set to obtain a moon cake moisture and moon cake quality guarantee related sub-model, a heat drying sterilization and moon cake quality guarantee related sub-model and a fungus amount and moon cake quality guarantee related sub-model;
and the second model construction submodule is used for carrying out model fusion on the moon cake moisture and the moon cake quality guarantee related submodule, the heat baking sterilization and moon cake quality guarantee related submodule and the fungus amount and the moon cake quality guarantee related submodule to obtain a moon cake heat baking sterilization model.
By adopting the technical scheme, the moon cake hot-baking sterilization model can analyze predicted hot-baking sterilization conditions required by different moon cakes from multiple dimensions by constructing weak relation models of the moon cake moisture and the moon cake quality-related sub-model, the hot-baking sterilization and moon cake quality-related sub-model and the bacterial quantity and the moon cake quality-related sub-model according to the training data set so as to fuse and obtain the strong relation model of the moon cake hot-baking sterilization model.
Optionally, the system further comprises:
the acquisition request module is used for acquiring a target moon cake hot baking and sterilizing configuration request, wherein the target moon cake hot baking and sterilizing configuration request comprises initial moisture content data of a target moon cake;
and the output result module is used for inputting the initial moisture content data into the moon cake baking and sterilizing model to obtain the baking and sterilizing configuration conditions of the target moon cake.
By adopting the technical scheme, according to the thermal baking and sterilizing configuration request of a user on the target moon cake, the thermal baking and sterilizing configuration condition of the target moon cake is predicted by utilizing the moon cake thermal baking and sterilizing model, so that the thermal baking and sterilizing configuration condition is replaced by manually adjusting, the thermal baking and ultraviolet process configuration conditions of different moon cakes are intelligently adjusted, and the safety quality of the moon cake is improved.
Drawings
FIG. 1 is a flow chart of an implementation of a method for guaranteeing the safety quality of a moon cake based on short-time baking and ultraviolet rays in an embodiment of the application;
Fig. 2 is a flowchart showing a method for guaranteeing the safety quality of a moon cake based on short-time baking and ultraviolet rays according to an embodiment of the present application, wherein the method comprises step S20;
fig. 3 is a flowchart showing an implementation of step S30 of the method for guaranteeing the safety quality of a moon cake based on short-time baking and ultraviolet rays according to the embodiment of the present application;
fig. 4 is a flowchart showing a method for guaranteeing the safety quality of a moon cake based on short-time baking and ultraviolet rays according to an embodiment of the present application, wherein the method includes step S40;
FIG. 5 is a flowchart of another implementation of a method for guaranteeing the safety quality of a moon cake based on short-time baking and ultraviolet rays according to an embodiment of the present application;
FIG. 6 is a schematic block diagram of a moon cake safety quality assurance system based on short-time baking and ultraviolet rays according to an embodiment of the present application;
fig. 7 is a functional block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Examples:
In this embodiment, as shown in fig. 1, the application discloses a method for guaranteeing the safety quality of a moon cake based on short-time baking and ultraviolet rays, which comprises the following steps:
S10: obtaining test data of the test moon cake under the conditions of baking and ultraviolet sterilization, wherein the test data comprise moon cake moisture content data, baking sterilization condition data and moon cake bacterial load data.
In this embodiment, the test moon cake refers to a plurality of types of moon cakes which are processed and to be sterilized, such as various kinds of invaginated snowy moon cakes, are taken out from a moon cake processing production line before a moon cake sterilization related model is constructed; the test data are obtained by carrying out related test detection on different types of test moon cakes after different heat drying and ultraviolet irradiation tests; the moisture content data of the moon cakes refer to moisture content data of the moon cakes obtained by detecting different test moon cakes through a direct drying method; the heat-baking sterilization condition data refer to heat-baking temperature and time length data and ultraviolet treatment time length adopted in the heat-baking and ultraviolet irradiation test process for moon cakes of different test groups; the bacterial load data of the moon cake refers to the bacterial colony type and quantity data generated by the moon cake after passing through the heat drying and ultraviolet irradiation tests and changing along with time.
Specifically, after the test moon cake is tested and detected by a manual test method, test data at least comprising moon cake moisture content data, heat baking sterilization condition data and moon cake bacterial load data are obtained, and then the test data are uploaded to a server at a user side and stored in a database in the server. In this embodiment, the client may refer to a PC computer or a notebook computer.
S20: and carrying out data preprocessing on the test data, and taking the corresponding test data after the data preprocessing as the data to be extracted.
In this embodiment, the data to be extracted refers to a data set that is used in the model construction process and needs to be extracted from features.
Specifically, the test data is preprocessed, abnormal data in the test data are cleaned, statistics of different dimensions are carried out, and normalization processing is carried out on the counted data to obtain data to be extracted.
S30: and carrying out feature engineering on the data to be extracted to obtain a data set to be trained.
In this embodiment, the data set to be trained refers to a data set used for subsequent training to obtain a hot-baking sterilization model.
Specifically, feature extraction is performed on the data set of the data to be extracted to extract important features, and the important features are formed into the data set to be trained.
S40: modeling training is carried out based on the data set to be trained, and a moon cake hot baking sterilization model for predicting the hot baking sterilization conditions required by different moon cakes is obtained.
In this embodiment, the moon cake baking and sterilizing model is a model for predicting the baking and ultraviolet sterilizing configuration parameters required by different moon cakes when the finished moon cake needs to be sterilized on a moon cake production line.
Specifically, different sub-models are established according to the data of the moisture content of the moon cake, the data of the heat-drying sterilization condition and the data set to be trained corresponding to the data of the fungus amount of the moon cake, and then the heat-drying sterilization model of the moon cake is obtained by counting the correlation among the sub-models.
In one embodiment, as shown in fig. 2, in step S20, data preprocessing is performed on test data, and test data corresponding to the data preprocessed is used as data to be extracted, including:
S201: and performing data cleaning on the test data to delete the abnormal data, and taking the test data after data cleaning as the data to be counted.
In this embodiment, the data to be counted refers to test data requiring data counting after data cleaning.
Specifically, discrete data existing in the test data are deleted, and the missing data are filled, so that data to be extracted are obtained.
S202: the method comprises the steps of counting moon cake moisture content data, heat-drying sterilization condition data and moon cake bacterial load data in the to-be-counted data, counting to obtain moon cake bacterial load data when different heat-drying sterilization conditions are adopted for moon cakes with each moisture content, and taking the counted moon cake bacterial load data as to-be-normalized data;
Specifically, according to the data to be counted, counting is carried out from three dimensions of moon cake moisture content data, heat-drying sterilization condition data and moon cake bacterial load data, so that the moon cake bacterial load data when different heat-drying sterilization conditions are adopted for each moisture content moon cake is obtained. For example, snowy moon cakes with different moisture contents can be heated for 0min, 1min, 2min and 3min at 170 degrees, and under the condition of 12min, the types and the numbers of bacterial colonies generated during the storage process of the moon cakes are obtained.
S203: normalizing the data to be normalized to obtain the data of the shelf life of the moon cakes under different baking and sterilizing conditions of each moisture content moon cake, and forming the obtained data of the shelf life of the moon cakes into the data to be extracted.
In this embodiment, the data of the shelf life of the moon cake refers to the level of security quality gradually reduced due to colony generation of the moon cake with the increase of the storage time after the moon cake with different moisture is subjected to heat baking and ultraviolet sterilization.
Specifically, the data set in the data to be normalized is normalized, so that the quality guarantee rate data of the moon cakes with different moisture contents under different baking and sterilizing conditions is obtained.
In one embodiment, as shown in fig. 3, in step S30, performing feature engineering on data to be extracted to obtain a data set to be trained, including:
S301: and carrying out feature extraction on the data to be extracted based on a preset feature extraction algorithm to obtain feature information.
In the present embodiment, the feature information refers to a value obtained by each feature in the data set of the data to be extracted by calculation of the feature extraction algorithm.
Specifically, a wavelet analysis method can be adopted to perform feature extraction on a data set of data to be extracted, obtain feature information of moon cakes with different moisture contents, and select the feature information of the moon cakes when the quality of the moon cakes is normal from the feature information. For example, under each moisture content moon cake, the characteristic relation value between the condition of a heat drying and sterilizing system and the quality guarantee rate of the moon cake reaches the standard.
S302: and carrying out feature derivation on the feature information in the quality-keeping time dimension, and taking the feature information after feature derivation as a data set to be trained.
Specifically, for moon cakes with different moisture contents, the quality guarantee time requirements are different, so that characteristic derivation is carried out on characteristic information in the quality guarantee time dimension, and the characteristic information after the characteristic derivation is used as a data set to be trained.
In one embodiment, as shown in fig. 4, in step S40, modeling training is performed based on a data set to be trained to obtain a moon cake baking sterilization model for predicting baking sterilization conditions required for different moon cakes, including:
s401: modeling is carried out according to the characteristic information of different categories in the training data set, and a moon cake moisture and moon cake quality guarantee related sub-model, a heat baking sterilization and moon cake quality guarantee related sub-model and a fungus amount and moon cake quality guarantee related sub-model are obtained.
Specifically, according to the characteristic information, respectively carrying out statistical modeling in the moon cake moisture content data, the baking sterilization condition and the moon cake bacterial amount data to obtain a moon cake moisture and moon cake quality guarantee related sub-model, a baking sterilization and moon cake quality guarantee related sub-model and a bacterial amount and moon cake quality guarantee related sub-model. In the embodiment, the moon cake moisture and moon cake quality guarantee related sub-model, the heat baking sterilization and moon cake quality guarantee related sub-model and the fungus amount and moon cake quality guarantee related sub-model are weak relation models.
The moon cake moisture and moon cake quality guarantee related model is used for predicting the bacterial load of moon cakes with different moisture contents under the same baking sterilization condition, and in the storage process of the moon cakes, the bacterial load generated by the moon cakes can be influenced by the different moisture contents of the moon cakes. The heat-baking sterilization and moon cake quality guarantee related sub-model is used for predicting the bacterial load of the moon cake with the same moisture content under different heat-baking sterilization conditions. The bacterial load and moon cake quality guarantee related sub-model is used for predicting the safe quality guarantee time corresponding to different moon cake bacterial loads.
S402: and carrying out model fusion on the moon cake moisture and the moon cake quality guarantee related sub-model, the heat-baking sterilization and the moon cake quality guarantee related sub-model, and the bacterial load and the moon cake quality guarantee related sub-model to obtain the moon cake heat-baking sterilization model.
Specifically, the moon cake thermal baking sterilization model is obtained by counting the association relation between the moon cake moisture and the moon cake quality guarantee related sub-model, thermal baking sterilization and characteristic information corresponding to the moon cake quality guarantee related sub-model and fusing the bacterial amount and the moon cake quality guarantee related sub-model, and the thermal baking sterilization model is used for predicting thermal baking sterilization conditions required by different moon cakes when the quality guarantee time is reached.
In an embodiment, as shown in fig. 5, after step S40, the method for guaranteeing the safety quality of the moon cake according to the present embodiment further includes:
s50: obtaining a target moon cake hot baking and sterilizing configuration request, wherein the target moon cake hot baking and sterilizing configuration request comprises initial moisture content data of the target moon cake.
In this embodiment, the target moon cake refers to a well-produced moon cake to be baked and sterilized; the sterilization configuration request refers to a request message sent by a user side and related to a specific target moon cake baking sterilization parameter; the initial moisture content data refers to the moisture content of the sink and crust of the target moon cake.
Specifically, the initial moisture content of a plurality of target moon cakes can be obtained by adopting a direct drying method, then a hot baking sterilization configuration request is manually sent to the server through the user side according to the initial moisture content, and the server receives the hot baking sterilization configuration request.
S60: inputting the initial moisture content data into a moon cake hot baking sterilization model to obtain the hot baking sterilization configuration conditions of the target moon cake.
In this embodiment, the heat-baking sterilization configuration conditions refer to a heat-baking temperature, a time period, and an ultraviolet sterilization time period to be performed on the target moon cake.
Specifically, the initial moisture content data is input into a moon cake baking and sterilizing model, the shelf life of the moon cake baking and sterilizing model is set to be 30 days by default, and the baking temperature, the duration and the ultraviolet sterilization duration required by the moon cake with the initial moisture content are predicted. In addition, if the thermal baking and sterilizing configuration request comprises a quality guarantee time request, the thermal baking and sterilizing model of the moon cake predicts the thermal baking temperature and time length and the ultraviolet sterilizing time length required by the moon cake according to the initial moisture content and the quality guarantee time, so that the thermal baking and sterilizing configuration conditions are intelligently adjusted according to the moon cake with different moisture contents and the quality guarantee time requirements.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
In this embodiment, a system for guaranteeing the safety quality of a moon cake based on short-time baking and ultraviolet rays is further provided, and the system for guaranteeing the safety quality of a moon cake based on short-time baking and ultraviolet rays is in one-to-one correspondence with the method for guaranteeing the safety quality of a moon cake based on short-time baking and ultraviolet rays in the embodiment. As shown in fig. 6, the moon cake safety quality assurance system based on short-time hot baking and ultraviolet rays comprises a data acquisition module, a preprocessing module, a feature extraction module and a modeling module. The functional modules are described in detail as follows:
The data acquisition module is used for acquiring test data of the test moon cake under the conditions of hot baking and ultraviolet sterilization, wherein the test data comprise moon cake moisture content data, hot baking sterilization condition data and moon cake bacterial load data;
the preprocessing module is used for preprocessing the data of the test data and taking the corresponding test data after the data preprocessing as the data to be extracted;
the feature extraction module is used for carrying out feature engineering on the data to be extracted to obtain a data set to be trained;
The modeling module is used for modeling according to the training data set and obtaining a moon cake hot baking sterilization model for predicting the hot baking sterilization conditions required by different moon cakes.
Optionally, the preprocessing module includes:
The data cleaning sub-module is used for cleaning the test data to delete abnormal data, and taking the test data after data cleaning as data to be counted;
The counting sub-module is used for counting the moon cake moisture content data, the heat-drying sterilization condition data and the moon cake bacterial amount data in the data to be counted, counting the moon cake bacterial amount data obtained when different heat-drying sterilization conditions are adopted for each moisture content moon cake, and taking the counted moon cake bacterial amount data as data to be normalized;
the normalization sub-module is used for carrying out normalization processing on the data to be normalized, obtaining the quality guarantee rate data of the moon cakes with each moisture content under different baking and sterilizing conditions, and forming the obtained quality guarantee rate data of the moon cakes into the data to be extracted.
Optionally, the feature extraction module includes:
the extraction sub-module is used for carrying out feature extraction on the data to be extracted based on a preset feature extraction algorithm to obtain feature information;
And the feature deriving sub-module is used for carrying out feature derivation on the feature information in the quality-keeping time dimension, and taking the feature information after the feature derivation as a data set to be trained.
Optionally, the modeling module includes:
The first model construction submodule is used for modeling according to different types of characteristic information in the training data set to obtain a moon cake moisture and moon cake quality guarantee related submodule, a heat-baking sterilization and moon cake quality guarantee related submodule and a fungus amount and moon cake quality guarantee related submodule;
And the second model construction submodule is used for carrying out model fusion on the moon cake moisture and the moon cake quality guarantee related submodule, the hot baking sterilization and the moon cake quality guarantee related submodule, and the bacterial load and the moon cake quality guarantee related submodule to obtain the moon cake hot baking sterilization model.
Optionally, the system of this embodiment further includes:
The acquisition request module is used for acquiring a target moon cake hot baking and sterilization configuration request, wherein the target moon cake hot baking and sterilization configuration request comprises initial moisture content data of the target moon cake;
and the output result module is used for inputting the initial moisture content data into a moon cake hot baking sterilization model to obtain the hot baking sterilization configuration conditions of the target moon cake.
The specific limitation of the moon cake safety quality assurance system based on short-time heat baking and ultraviolet rays can be referred to as limitation of the moon cake safety quality assurance method based on short-time heat baking and ultraviolet rays, and the description thereof is omitted herein. The modules in the moon cake safety quality assurance system based on short-time hot baking and ultraviolet rays can be all or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In this embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing test data, characteristic information and a data set to be trained. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by the processor to realize a moon cake safety quality guarantee method based on short-time hot baking and ultraviolet rays.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:
S10: obtaining test data of a test moon cake under the conditions of baking and ultraviolet sterilization, wherein the test data comprise moon cake moisture content data, baking sterilization condition data and moon cake bacterial load data;
S20: performing data preprocessing on the test data, and taking the corresponding test data after the data preprocessing as data to be extracted;
S30: carrying out feature engineering on data to be extracted to obtain a data set to be trained;
s40: modeling is carried out according to the training data set, and a moon cake baking and sterilizing model for predicting the baking and sterilizing conditions required by different moon cakes is obtained.
In this embodiment, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
S10: obtaining test data of a test moon cake under the conditions of baking and ultraviolet sterilization, wherein the test data comprise moon cake moisture content data, baking sterilization condition data and moon cake bacterial load data;
S20: performing data preprocessing on the test data, and taking the corresponding test data after the data preprocessing as data to be extracted;
S30: carrying out feature engineering on data to be extracted to obtain a data set to be trained;
s40: modeling is carried out according to the training data set, and a moon cake baking and sterilizing model for predicting the baking and sterilizing conditions required by different moon cakes is obtained.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (2)
1. A moon cake safety quality guaranteeing method based on short-time hot baking and ultraviolet rays is characterized by comprising the following steps:
obtaining test data of a test moon cake under the conditions of baking and ultraviolet sterilization, wherein the test data comprises moon cake moisture content data, baking sterilization condition data and moon cake bacterial load data;
performing data preprocessing on the test data, and taking the test data corresponding to the data preprocessed as data to be extracted;
Performing data preprocessing on the test data, taking the test data corresponding to the data preprocessed as data to be extracted, wherein the data preprocessing comprises the following steps:
Performing data cleaning on the test data to delete abnormal data, and taking the test data after data cleaning as data to be counted; the moon cake bacterial load data when different heat drying and sterilizing conditions are adopted for each moon cake with the moisture content is obtained through statistics on the moon cake moisture content data, the heat drying and sterilizing condition data and the moon cake bacterial load data after statistics are used as data to be normalized; normalizing the data to be normalized to obtain the data of the shelf life of the moon cakes under different baking and sterilizing conditions of each moisture content moon cake, and composing the obtained data of the shelf life of the moon cakes into the data to be extracted;
Performing feature engineering on the data to be extracted to obtain a data set to be trained;
Performing feature engineering on the data to be extracted to obtain a data set to be trained, including:
Based on a preset feature extraction algorithm, carrying out feature extraction on data to be extracted to obtain feature information; performing feature derivation on the feature information in the quality-keeping time dimension, and taking the feature information after the feature derivation as a data set to be trained;
modeling according to the data set to be trained to obtain moon cake baking and sterilizing models for predicting the baking and sterilizing conditions required by different moon cakes;
Modeling training is carried out according to the data set to be trained to obtain moon cake baking sterilization models for predicting the baking sterilization conditions required by different moon cakes, and the modeling training comprises the following steps:
Modeling according to the characteristic information of different categories in the training data set to obtain a moon cake moisture and moon cake quality guarantee related sub-model, a heat-baking sterilization and moon cake quality guarantee related sub-model and a fungus amount and moon cake quality guarantee related sub-model; performing model fusion on the moon cake moisture and the moon cake quality guarantee related sub-model, and performing thermal baking sterilization and moon cake quality guarantee related sub-model, and performing model fusion on the bacterial load and the moon cake quality guarantee related sub-model to obtain a moon cake thermal baking sterilization model;
acquiring a target moon cake hot baking and sterilizing configuration request, wherein the target moon cake hot baking and sterilizing configuration request comprises initial moisture content data of a target moon cake;
Inputting the initial moisture content data into the moon cake hot baking sterilization model to obtain the hot baking sterilization configuration conditions of the target moon cake.
2. A moon cake safety quality assurance system based on short-time hot baking and ultraviolet rays, which is characterized by comprising:
The data acquisition module is used for acquiring test data of the test moon cake under the conditions of hot baking and ultraviolet sterilization, wherein the test data comprises moon cake moisture content data, hot baking sterilization condition data and moon cake bacterial load data;
The preprocessing module is used for preprocessing the data of the test data and taking the corresponding test data after the data preprocessing as the data to be extracted;
the preprocessing module comprises:
The data cleaning sub-module is used for cleaning the data of the test data to delete abnormal data, and taking the test data after data cleaning as data to be counted;
The counting sub-module is used for counting the moon cake moisture content data, the heat-drying sterilization condition data and the moon cake bacterial amount data in the data to be counted, obtaining the moon cake bacterial amount data when different heat-drying sterilization conditions are adopted for each moisture content moon cake, and taking the counted moon cake bacterial amount data as data to be normalized;
The normalization sub-module is used for carrying out normalization processing on the data to be normalized, obtaining the quality guarantee rate data of the moon cakes with each moisture content under different baking and sterilizing conditions, and forming the obtained quality guarantee rate data of the moon cakes into the data to be extracted;
The feature extraction module is used for carrying out feature engineering on the data to be extracted to obtain a data set to be trained;
The feature extraction module includes:
the extraction sub-module is used for carrying out feature extraction on the data to be extracted based on a preset feature extraction algorithm to obtain feature information;
the feature derivation submodule is used for carrying out feature derivation on the feature information in the quality-keeping time dimension, and taking the feature information after the feature derivation as a data set to be trained;
the modeling module is used for modeling according to the training data set to obtain moon cake hot baking sterilization models for predicting the hot baking sterilization conditions required by different moon cakes;
The modeling module includes:
The first model construction sub-module is used for modeling according to the characteristic information of different categories in the training data set to obtain a moon cake moisture and moon cake quality guarantee related sub-model, a heat drying sterilization and moon cake quality guarantee related sub-model and a fungus amount and moon cake quality guarantee related sub-model;
the second model construction submodule is used for carrying out model fusion on the moon cake moisture and the moon cake quality guarantee related submodule, the heat baking sterilization and moon cake quality guarantee related submodule and the fungus amount and the moon cake quality guarantee related submodule to obtain a moon cake heat baking sterilization model;
the acquisition request module is used for acquiring a target moon cake hot baking and sterilizing configuration request, wherein the target moon cake hot baking and sterilizing configuration request comprises initial moisture content data of a target moon cake;
and the output result module is used for inputting the initial moisture content data into the moon cake baking and sterilizing model to obtain the baking and sterilizing configuration conditions of the target moon cake.
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| CN106957889A (en) * | 2017-03-22 | 2017-07-18 | 南京农业大学 | When prediction ultrasonic assistant based on Mathematical Modeling is pickled in pickling liquid content of microorganisms method |
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