CN116627191A - Intelligent temperature adjusting method and system for processing puffed pet food - Google Patents
Intelligent temperature adjusting method and system for processing puffed pet food Download PDFInfo
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- CN116627191A CN116627191A CN202310505230.2A CN202310505230A CN116627191A CN 116627191 A CN116627191 A CN 116627191A CN 202310505230 A CN202310505230 A CN 202310505230A CN 116627191 A CN116627191 A CN 116627191A
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- 238000012545 processing Methods 0.000 title claims abstract description 232
- 235000013305 food Nutrition 0.000 title claims abstract description 125
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000004458 analytical method Methods 0.000 claims abstract description 20
- 235000021067 refined food Nutrition 0.000 claims abstract description 16
- 238000003754 machining Methods 0.000 claims description 17
- 230000008569 process Effects 0.000 claims description 15
- 238000012549 training Methods 0.000 claims description 14
- 238000012360 testing method Methods 0.000 claims description 13
- 239000011159 matrix material Substances 0.000 claims description 11
- 239000013598 vector Substances 0.000 claims description 11
- 238000012098 association analyses Methods 0.000 claims description 6
- 238000007621 cluster analysis Methods 0.000 claims description 5
- 238000013527 convolutional neural network Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 4
- 238000000513 principal component analysis Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 3
- 235000013372 meat Nutrition 0.000 abstract description 4
- 230000008859 change Effects 0.000 description 8
- 230000001007 puffing effect Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 238000009529 body temperature measurement Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 230000000630 rising effect Effects 0.000 description 2
- 229920002472 Starch Polymers 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 235000012041 food component Nutrition 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 235000019629 palatability Nutrition 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000012847 principal component analysis method Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 235000019698 starch Nutrition 0.000 description 1
- 239000008107 starch Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
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- A—HUMAN NECESSITIES
- A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
- A23N—MACHINES OR APPARATUS FOR TREATING HARVESTED FRUIT, VEGETABLES OR FLOWER BULBS IN BULK, NOT OTHERWISE PROVIDED FOR; PEELING VEGETABLES OR FRUIT IN BULK; APPARATUS FOR PREPARING ANIMAL FEEDING- STUFFS
- A23N17/00—Apparatus specially adapted for preparing animal feeding-stuffs
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- Automation & Control Theory (AREA)
- Life Sciences & Earth Sciences (AREA)
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- Food Science & Technology (AREA)
- Polymers & Plastics (AREA)
- Fodder In General (AREA)
Abstract
The present disclosure provides an intelligent temperature regulation method and system for pet puffed food processing, the method comprising: performing temperature control node identification based on the processing flow information to obtain a multi-stage processing temperature control node; performing temperature control characteristic analysis based on the multi-stage processing temperature control nodes to obtain multi-stage node temperature control characteristics; obtaining attribute information of a target processed pet puffed food based on the target processed pet puffed food; constructing an intelligent temperature regulation model; based on the intelligent temperature regulation model and the multi-stage node temperature control characteristics, carrying out temperature regulation analysis on the target processed food information to obtain a target temperature regulation scheme; the processing temperature adjustment of the target processed pet puffed food is performed based on the target temperature adjustment scheme, so that the technical problem that fresh meat quality in the pet puffed food is low due to the fact that the processing temperature adjustment is not accurate enough in the prior art is solved.
Description
Technical Field
The disclosure relates to the technical field of pet puffed food processing, in particular to an intelligent temperature adjusting method and system for pet puffed food processing.
Background
Temperature regulation is one of the important parameters in the process of puffed food processing. The temperature and the moisture content during processing are controlled by adjusting the steam addition amount in the prior art of puffed food processing. However, the expander has a strict upper limit on the moisture content of the mixed feed, and once the mixed feed moisture content exceeds the expander equipment upper limit (typically 15% -35%), curing and expansion of the feed cannot be achieved. Therefore, the prior art is not accurate enough for processing temperature adjustment, and is a difficult problem which is difficult to overcome for a long time in the technical field of pet food processing.
Disclosure of Invention
The disclosure provides an intelligent temperature adjusting method and system for processing pet puffed food, which are used for solving the technical problem that fresh meat quality in the pet puffed food is low due to the fact that the processing temperature is not accurately adjusted in the prior art.
According to a first aspect of the present disclosure, there is provided an intelligent temperature regulation method for pet puffed food processing, comprising: based on the big data, processing flow information of the puffed pet food is obtained; performing temperature control node identification based on the processing flow information to obtain a multi-stage processing temperature control node; performing temperature control characteristic analysis based on the multi-stage processing temperature control nodes to obtain multi-stage node temperature control characteristics; obtaining attribute information of a target processed pet puffed food based on the target processed pet puffed food; constructing an intelligent temperature regulation model; based on the intelligent temperature regulation model and the multi-stage node temperature control characteristics, carrying out temperature regulation analysis on the target processed food information to obtain a target temperature regulation scheme; and adjusting the processing temperature of the target processed pet puffed food based on the target temperature adjustment scheme.
According to a second aspect of the present disclosure, there is provided an intelligent temperature regulation system for pet food processing comprising: the first obtaining module is used for obtaining the processing flow information of the puffed pet food based on the big data; the second obtaining module is used for carrying out temperature control node identification based on the processing flow information to obtain a multi-stage processing temperature control node; the third obtaining module is used for carrying out temperature control characteristic analysis based on the multi-stage processing temperature control node to obtain multi-stage node temperature control characteristics; a fourth obtaining module, configured to obtain attribute information of a target processed pet puffed food based on the target processed pet puffed food; the first construction module is used for constructing an intelligent temperature regulation model; the fifth obtaining module is used for carrying out temperature adjustment analysis on the target processed food information based on the intelligent temperature adjustment model and the multi-level node temperature control characteristics to obtain a target temperature adjustment scheme; a first implementation module for performing a process temperature adjustment of the target processed pet food puffed based on the target temperature adjustment profile.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the intelligent temperature regulation method overcomes the problems of inaccurate temperature control and low fresh meat quality in the prior art, the intelligent temperature regulation directly influences the gelatinization degree of starch in food and the content of various heat-sensitive nutritional components, and the puffing degree and hardness of food particles are also influenced to a certain extent. These effects can be manifested in the palatability of the food, affecting the pet's choice and preference for the food.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
For a clearer description of the present disclosure or of the prior art, the drawings that are required to be used in the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are merely illustrative and that other drawings may be obtained, without inventive effort, by a person skilled in the art from the drawings provided.
Fig. 1 is a schematic flow chart of an intelligent temperature regulation method for pet puffed food processing according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of obtaining a multi-stage processing temperature control node in an intelligent temperature regulation method for pet puffed food processing according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of an intelligent temperature regulation system for pet puffed food processing according to an embodiment of the present disclosure.
Reference numerals illustrate: a first obtaining module 11, a second obtaining module 12, a third obtaining module 13, a fourth obtaining module 14, a first constructing module 15, a fifth obtaining module 16, a first implementing module 17.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In order to solve the technical problem of increasing the content proportion of fresh meat in the pet puffed food by adjusting the temperature, the inventor of the present disclosure obtains the intelligent temperature adjusting method and system for processing the pet puffed food through creative labor:
example 1
As shown in fig. 1, the present application provides an intelligent temperature regulation method for pet puffed food processing, the method comprising:
step 100, obtaining processing flow information of the puffed pet food based on big data;
specifically, the processing flow information of the pet puffed food is a processing flow aiming at a main raw material of a dry material. The processing flow information of the pet puffed food can comprise pretreatment, mixing, puffing and the like, so that the pet puffed food can be effectively processed. Optionally, the processing flow information of the pet puffed food can be specifically obtained through browsing or downloading of books and periodicals related to the pet puffed food, forums related to the pet puffed food, television broadcasting, and the like.
Step 200, performing temperature control node identification based on the processing flow information to obtain a multi-stage processing temperature control node;
specifically, the temperature control node mark is used for marking nodes which influence components, properties and the like in the target processed pet puffed food according to the control processing temperature. The multi-stage processing temperature control node is used for controlling the temperature in the multi-stage processing process so as to change the environmental temperature. According to different processing flow stages of the puffed pet food, for example, rated temperatures of different processes in a mixing stage and a puffing stage are different, so that the temperatures of the different stages are subjected to node division, and nodes with different temperatures are identified to form processing temperature control nodes with different grades, so that temperature intervals required by different processing flows can be accurately controlled and divided.
Step 300, performing temperature control characteristic analysis based on the multi-stage processing temperature control nodes to obtain multi-stage node temperature control characteristics;
in particular, the temperature control feature may be a temperature rise or fall, a rate of temperature change, requiring the ambient temperature to be maintained or changed by controlling the temperature. The multi-stage node temperature control characteristic is a node conforming to the temperature control characteristic in the multi-stage processing process. And (3) carrying out temperature characteristic analysis on the temperature control nodes in the multistage processing, extracting and obtaining temperature change characteristics of the temperature nodes in different stages, for example, the temperature change characteristics in the mixing stage are temperature rising characteristics, the temperature change characteristics in the puffing stage are temperature further rising, and the overall temperature change trend in the multistage processing process can be displayed along with the time.
Step S400, obtaining attribute information of target processed pet puffed food based on the target processed pet puffed food;
specifically, the target processed pet puffed food is a finished product of the pet puffed food. The attribute information of the target processed pet puffed food can be weight information or quality information of the finished pet puffed food and the like. Wherein, the information of the target processed food can be obtained by weighing and the like in the prior art.
S500, constructing an intelligent temperature regulation model;
specifically, the intelligent temperature regulation model is a neural network model, and based on big data, historical processing temperature regulation information is obtained in the processing process of the puffed pet food. And according to the historical processing temperature regulation information and the processing temperature information of the target processed food, setting a proportion for training and testing to obtain an intelligent temperature regulation model.
Step 600, based on the intelligent temperature regulation model and the multi-stage node temperature control characteristics, carrying out temperature regulation analysis on the target processed food information to obtain a target temperature regulation scheme;
specifically, according to the neural network model of the intelligent temperature regulation model and the multi-stage node temperature control characteristics, the temperature regulation is carried out on the attribute information in the target processed food information through the control variable, and different influence results of the attribute information in the target processed food information on the temperature control regulation are obtained. Illustratively, if the weight in the target processed food information is heavier, the longer the processing time is required, thereby changing the time of the temperature adjustment node. Correspondingly, if the weight in the target processed food information is lighter, the required processing time is shorter, so that the time of the temperature regulation node is changed, and different target temperature regulation schemes are obtained.
And step S700, adjusting the processing temperature of the target processed pet puffed food based on the target temperature adjustment scheme.
Specifically, the target temperature adjustment scheme is obtained based on the intelligent temperature adjustment model and the multi-stage node temperature control characteristics, and the processing temperature of the pet puffed food is adjusted.
As shown in fig. 2, step S200 in the method provided in the embodiment of the present application includes:
s210: obtaining a plurality of food processing nodes based on the processing flow information;
s220: performing temperature control influence analysis based on the plurality of food processing nodes to obtain a plurality of temperature control influence parameters;
s230: identifying the plurality of food processing nodes based on the plurality of temperature control influence parameters to obtain the multi-stage processing temperature control nodes;
specifically, the food processing node is a node for changing the processing environment of the target processed pet puffed food, namely a multi-stage processing flow node. The temperature control influence parameter is the influence on components, properties and the like in the target processed food in a set processing temperature. For example, the temperature control influence parameter may include a moisture content parameter of the target processed food. And adjusting the processing temperature at the food processing node based on the temperature control influence parameter to enable components, properties and the like in the target processed food to accord with target processing parameters. And identifying the processing temperatures of the plurality of food processing nodes to obtain the multi-stage processing temperature control node.
Step S220 in the method provided by the embodiment of the present application includes:
s221: obtaining unqualified processing quality records of the puffed pet food based on a preset historical time zone;
s222: performing association analysis on the unqualified processing quality records based on the plurality of food processing nodes to obtain first characteristic association of the plurality of nodes;
s223: performing temperature control association analysis of the plurality of food processing nodes based on the unqualified processing quality records to obtain a plurality of node second feature association;
s224: and calculating the association characteristic duty ratio based on the second feature association degrees of the plurality of nodes and the first feature association degrees of the plurality of nodes to obtain the plurality of temperature control influence degree parameters.
Specifically, the preset historical time zone is a historical processing time zone based on the puffed pet food. And extracting unqualified processing quality records of the puffed pet food in a preset historical time zone.
The first characteristic association degree of the plurality of nodes is the corresponding total disqualified processing times of each food node. The second characteristic association degree of the plurality of nodes is the unqualified processing times caused by temperature control at each food processing node. And calculating the association characteristic duty ratio as the ratio of the second characteristic association degree of the plurality of nodes to the first characteristic association degree of the plurality of nodes.
And extracting the unqualified processing times corresponding to each food processing node in the unqualified processing quality record in the puffed pet food, and the unqualified processing times caused by temperature control at each food processing node. And calculating the ratio of the unqualified machining times caused by temperature control of each node to the unqualified machining times, and obtaining the duty ratio of the unqualified machining caused by temperature control in the unqualified machining times, namely the temperature control influence degree parameter. And obtaining a plurality of temperature control influence degree parameters based on the temperature control influence degree parameters of the multi-stage nodes.
The step S300 in the method provided by the embodiment of the present application includes:
s310: obtaining processing data records of the puffed pet food;
s320: performing principal component analysis based on the machining data record to obtain a standard machining data record;
s330: performing cluster analysis on the standard processing data records based on the multi-stage processing temperature control nodes to obtain a plurality of node processing records;
s340: traversing the plurality of node processing records to extract temperature control factors and obtaining a plurality of node temperature control factor combinations;
s350: and traversing the plurality of node temperature control factor combinations to perform normalization processing, and generating the multi-stage node temperature control characteristics.
In particular, the processing data record of the pet food puffed may be a composition and performance change record of the pet food puffed. The standard processing data record is a processing change record of the main component of the pet puffed food. And extracting the processing data record of the main component according to the processing data record.
And carrying out cluster analysis on main components of the pet puffed food based on the multi-stage processing temperature control nodes, wherein similar components in each main component of the pet puffed food are classified into the same type of record, so as to finish cluster analysis and obtain a plurality of cluster results. Each clustering result comprises a processing temperature control node and standard processing data, so that a plurality of node processing records can be obtained.
And the temperature control factor is a temperature control index of the processing records of the plurality of nodes. And extracting temperature control indexes in the processing records of the plurality of nodes, and generating the multi-stage node temperature control characteristics if the obtained temperature control factors of the plurality of nodes are combined to remove singular data.
Step S320 in the method provided by the embodiment of the present application includes:
s321: according to the processing data record, a first characteristic processing data record is obtained;
s322: performing decentralization treatment on the first characteristic machining data record to obtain a second characteristic machining data record;
s323: obtaining a first feature processing covariance matrix according to the second feature processing data record;
s324: obtaining a first feature processing value and a first feature processing vector according to the first feature processing covariance matrix;
s325: and obtaining the standard processing data record according to the first characteristic processing value and the first characteristic processing vector.
And carrying out numerical processing on the extracted feature processing data, constructing a feature processing data matrix, and obtaining the first feature processing data. And then carrying out centering treatment on each feature processing data in the first feature processing data, firstly solving the average value of each feature in the first feature processing data, then subtracting the average value of each feature from each feature for all samples, and then obtaining a new feature value, wherein the second feature processing data is formed by the new feature processing data, and the second feature processing data is a data matrix. And calculating the second feature processing data through a covariance formula to obtain a first covariance matrix of the second feature processing data, and then calculating a feature processing value and a feature processing vector of the first covariance matrix through matrix calculation, wherein each feature value corresponds to one feature processing vector. And selecting the maximum first K feature processing values and the feature processing vectors corresponding to the first feature processing values from the obtained first feature processing vectors, and projecting original features in the first feature processing data onto the selected feature vectors to obtain the first feature processing data after dimension reduction. And performing dimension reduction processing on the feature processing data in the database by using a principal component analysis method, and removing redundant data on the premise of guaranteeing the information quantity, so that the sample quantity of the feature processing data in the database is reduced, the information quantity loss after dimension reduction is minimum, and the operation speed of the training model on the data is accelerated.
The step S500 in the method provided by the embodiment of the present application includes:
s510: based on the big data, obtaining a pet puffed food processing temperature regulation record;
s520: performing data division of a preset proportion based on the pet puffed food processing temperature regulation record to obtain a training data sequence and a testing data sequence;
s530: based on a convolutional neural network, training and testing are carried out according to the training data sequence and the testing data sequence, and the intelligent temperature regulation model is obtained.
Optionally, the pet puffed food processing temperature adjustment record can be obtained from browsing or downloading books, newspapers, forums, and the like. Wherein the pet food processing temperature regulation record may comprise historical temperature regulation records of a plurality of nodes.
And dividing the processing temperature regulation record of the pet puffed food and the processing temperature regulation record of the preset target processed pet puffed food into data with preset proportion. A training data sequence and a test data sequence are obtained. Illustratively, the scaling may be at 6:4 or 7: 3.
Based on a convolutional neural network, training and testing are carried out according to the training data sequence and the testing data sequence, and the convolutional neural network comprises the following steps: inputting a pet puffed food processing temperature regulation record and a preset target processing pet puffed food processing temperature regulation record, extracting final pet puffed food processing temperature regulation parameters, enabling the temperature control of the target processing pet puffed food at the multi-stage nodes to be more accurate, further enabling the unqualified record of the target processing pet puffed food at the multi-stage nodes to be reduced, and obtaining the intelligent temperature regulation model.
Step S800 in the method provided by the embodiment of the present application includes:
s810: obtaining real-time temperature information of target processed pet puffed food, wherein the real-time temperature information has a processing node identifier;
s820: acquiring a node temperature control interval based on the processing node identification and the multi-stage node temperature control characteristic;
s830: judging whether the real-time temperature information meets the node temperature control interval or not;
s840: and when the real-time temperature information does not meet the node temperature control interval, acquiring a node temperature control early warning instruction.
Alternatively, the real-time temperature information of the target processed pet puffed food can be obtained based on the measurement mode of the temperature measurement device in the prior art.
The node temperature control interval is based on multi-stage node temperature control characteristics, and the temperature control interval is marked for the processing node. And judging whether the real-time temperature information meets the node temperature control interval according to the temperature measurement equipment. And if the real-time temperature information is not in the node temperature control interval, acquiring a node temperature control early warning instruction.
Example two
Based on the same inventive concept as the intelligent temperature regulation method for pet puffed food processing in the foregoing embodiments, as shown in fig. 3, the present application further provides an intelligent temperature regulation system for pet puffed food processing, the system comprising:
a first obtaining module 11, configured to obtain processing flow information of the puffed pet food based on the big data;
a second obtaining module 12, configured to perform temperature control node identification based on the processing flow information, to obtain a multi-stage processing temperature control node;
a third obtaining module 13, configured to perform temperature control feature analysis based on the multi-stage processing temperature control node, to obtain a multi-stage node temperature control feature;
a fourth obtaining module 14 for obtaining attribute information of the target processed pet puffed food based on the target processed pet puffed food;
a first construction module 15 for constructing an intelligent temperature regulation model;
a fifth obtaining module 16, configured to perform temperature adjustment analysis on the target processed food information based on the intelligent temperature adjustment model and the multi-level node temperature control feature, to obtain a target temperature adjustment scheme;
a first implementation module 17 for performing a process temperature adjustment of the target processed pet food puffed based on the target temperature adjustment profile.
Further, the system further comprises:
a sixth obtaining module, configured to obtain a plurality of food processing nodes based on the processing flow information;
a seventh obtaining module, configured to perform temperature control influence analysis based on the plurality of food processing nodes, to obtain a plurality of temperature control influence parameters;
and an eighth obtaining module, configured to identify the plurality of food processing nodes based on the plurality of temperature control influence parameters, to obtain the multi-stage processing temperature control node.
Further, the system further comprises:
a ninth obtaining module, configured to obtain a record of unqualified processing quality of the puffed pet food based on a preset historical time zone;
a tenth obtaining module, configured to perform association analysis on the unqualified processing quality records based on the plurality of food processing nodes, to obtain a plurality of node first feature association degrees;
an eleventh obtaining module, configured to perform temperature control association analysis of the plurality of food processing nodes based on the unqualified processing quality record, to obtain a plurality of node second feature associations;
and a twelfth obtaining module, configured to perform associated feature duty ratio calculation based on the second feature association degrees of the plurality of nodes and the first feature association degrees of the plurality of nodes, and obtain the plurality of temperature control influence parameters.
Further, the system further comprises:
a thirteenth obtaining module for obtaining a processing data record of the pet puffed food;
a fourteenth obtaining module for performing principal component analysis based on the processing data record to obtain a standard processing data record;
a fifteenth obtaining module, configured to perform cluster analysis on the standard processing data record based on the multi-stage processing temperature control node, to obtain a plurality of node processing records;
a sixteenth obtaining module, configured to traverse the plurality of node processing records to perform temperature control factor extraction, and obtain a plurality of node temperature control factor sets;
and a seventeenth obtaining module, configured to traverse the plurality of node temperature control factor sets to perform normalization processing, and generate the multi-level node temperature control feature.
Further, the system further comprises:
an eighteenth obtaining module, configured to obtain a first feature machining data record according to the machining data record;
a nineteenth obtaining module, configured to perform a decentralization process on the first feature processing data record, to obtain a second feature processing data record;
the twentieth acquisition module is used for acquiring a first characteristic processing covariance matrix according to the second characteristic processing data record;
a twenty-first obtaining module, configured to obtain a first feature processing value and a first feature processing vector according to the first feature processing covariance matrix;
and a twenty-second obtaining module, configured to obtain the standard machining data record according to the first feature machining value and the first feature machining vector.
Further, the system further comprises:
a twenty-third obtaining module, configured to obtain a pet puffed food processing temperature adjustment record based on the big data;
a twenty-fourth obtaining module, configured to divide data according to a preset proportion based on the pet puffed food processing temperature adjustment record, and obtain a training data sequence and a test data sequence;
and a twenty-fifth obtaining module, configured to perform training and testing according to the training data sequence and the testing data sequence based on a convolutional neural network, to obtain the intelligent temperature regulation model.
Further, the system further comprises:
a twenty-sixth obtaining module, configured to obtain real-time temperature information of a target processed pet puffed food, where the real-time temperature information has a processing node identifier;
a twenty-seventh obtaining module, configured to obtain a node temperature control interval based on the processing node identifier and the multi-level node temperature control feature;
the first judging module is used for judging whether the real-time temperature information meets the node temperature control interval or not;
and a twenty-eighth obtaining module, configured to obtain a node temperature control early warning instruction when the real-time temperature information does not meet the node temperature control interval.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (8)
1. An intelligent temperature regulation method for pet puffed food processing, the method comprising:
based on the big data, processing flow information of the puffed pet food is obtained;
performing temperature control node identification based on the processing flow information to obtain a multi-stage processing temperature control node;
performing temperature control characteristic analysis based on the multi-stage processing temperature control nodes to obtain multi-stage node temperature control characteristics;
obtaining attribute information of a target processed pet puffed food based on the target processed pet puffed food;
constructing an intelligent temperature regulation model;
based on the intelligent temperature regulation model and the multi-stage node temperature control characteristics, carrying out temperature regulation analysis on the target processed food information to obtain a target temperature regulation scheme;
and adjusting the processing temperature of the target processed pet puffed food based on the target temperature adjustment scheme.
2. The method of claim 1, wherein performing temperature control node identification based on the process flow information to obtain a multi-stage process temperature control node comprises:
obtaining a plurality of food processing nodes based on the processing flow information;
performing temperature control influence analysis based on the plurality of food processing nodes to obtain a plurality of temperature control influence parameters;
and identifying the plurality of food processing nodes based on the plurality of temperature control influence parameters to obtain the multi-stage processing temperature control node.
3. The method of claim 2, wherein performing a temperature control influence analysis based on the plurality of food processing nodes to obtain a plurality of temperature control influence parameters comprises:
obtaining unqualified processing quality records of the puffed pet food based on a preset historical time zone;
performing association analysis on the unqualified processing quality records based on the plurality of food processing nodes to obtain first characteristic association of the plurality of nodes;
performing temperature control association analysis of the plurality of food processing nodes based on the unqualified processing quality records to obtain a plurality of node second feature association;
and calculating the association characteristic duty ratio based on the second feature association degrees of the plurality of nodes and the first feature association degrees of the plurality of nodes to obtain the plurality of temperature control influence degree parameters.
4. The method of claim 1, wherein performing a temperature control feature analysis based on the multi-stage process temperature control node to obtain a multi-stage node temperature control feature comprises:
obtaining processing data records of the puffed pet food;
performing principal component analysis based on the machining data record to obtain a standard machining data record;
performing cluster analysis on the standard processing data records based on the multi-stage processing temperature control nodes to obtain a plurality of node processing records;
traversing the plurality of node processing records to extract temperature control factors and obtaining a plurality of node temperature control factor combinations;
and traversing the plurality of node temperature control factor combinations to perform normalization processing, and generating the multi-stage node temperature control characteristics.
5. The method of claim 4, wherein performing a principal component analysis based on the process data record to obtain a standard process data record, comprising:
according to the processing data record, a first characteristic processing data record is obtained;
performing decentralization treatment on the first characteristic machining data record to obtain a second characteristic machining data record;
obtaining a first feature processing covariance matrix according to the second feature processing data record;
obtaining a first feature processing value and a first feature processing vector according to the first feature processing covariance matrix;
and obtaining the standard processing data record according to the first characteristic processing value and the first characteristic processing vector.
6. The method of claim 1, wherein constructing an intelligent temperature regulation model comprises:
based on the big data, obtaining a pet puffed food processing temperature regulation record;
performing data division of a preset proportion based on the pet puffed food processing temperature regulation record to obtain a training data sequence and a testing data sequence;
based on a convolutional neural network, training and testing are carried out according to the training data sequence and the testing data sequence, and the intelligent temperature regulation model is obtained.
7. The method of claim 1, wherein the method comprises:
obtaining real-time temperature information of target processed pet puffed food, wherein the real-time temperature information has a processing node identifier;
acquiring a node temperature control interval based on the processing node identification and the multi-stage node temperature control characteristic;
judging whether the real-time temperature information meets the node temperature control interval or not;
and when the real-time temperature information does not meet the node temperature control interval, acquiring a node temperature control early warning instruction.
8. An intelligent temperature regulation system for pet food processing, characterized in that the system is adapted to perform the method of any one of claims 1 to 7, the system comprising:
the first obtaining module is used for obtaining the processing flow information of the puffed pet food based on the big data;
the second obtaining module is used for carrying out temperature control node identification based on the processing flow information to obtain a multi-stage processing temperature control node;
the third obtaining module is used for carrying out temperature control characteristic analysis based on the multi-stage processing temperature control node to obtain multi-stage node temperature control characteristics;
a fourth obtaining module, configured to obtain attribute information of a target processed pet puffed food based on the target processed pet puffed food;
the first construction module is used for constructing an intelligent temperature regulation model;
the fifth obtaining module is used for carrying out temperature adjustment analysis on the target processed food information based on the intelligent temperature adjustment model and the multi-level node temperature control characteristics to obtain a target temperature adjustment scheme;
a first implementation module for performing a process temperature adjustment of the target processed pet food puffed based on the target temperature adjustment profile.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117270473A (en) * | 2023-10-12 | 2023-12-22 | 嘉兴美旺机械制造有限公司 | Energy-saving control method and system for equipment on burning machine |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117270473A (en) * | 2023-10-12 | 2023-12-22 | 嘉兴美旺机械制造有限公司 | Energy-saving control method and system for equipment on burning machine |
| CN117270473B (en) * | 2023-10-12 | 2024-03-22 | 嘉兴美旺机械制造有限公司 | Energy-saving control method and system for equipment on burning machine |
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