An intelligent temperature adjusting method and system for pet puffing food processing

Abstract

The present disclosure provides an intelligent temperature regulation method and system for pet puffed food processing, which comprises the following steps: identifying temperature control nodes based on processing flow information to obtain multi-stage processing temperature control nodes; analyzing temperature control characteristics based on the multi-stage processing temperature control nodes to obtain multi-stage node temperature control characteristics; obtaining attribute information of a target pet puffed food based on the target pet puffed food; constructing an intelligent temperature regulation model; performing temperature regulation analysis on the target food information based on the intelligent temperature regulation model and the multi-stage node temperature control characteristics to obtain a target temperature regulation scheme; and performing processing temperature regulation of the target pet puffed food based on the target temperature regulation scheme, so as to solve the technical problem that the quality of fresh meat in pet puffed food is not high due to the insufficient accuracy of the processing temperature regulation in the prior art.

Description

Technical Field

[0001] This disclosure relates to the field of pet puffed food processing technology, specifically to an intelligent temperature control method and system for pet puffed food processing. Background Technology

[0002] Temperature control is one of the crucial parameters in the processing of extruded foods. Current extrusion processing technologies control temperature and moisture content by adjusting the amount of steam added. However, extruders have strict upper limits on the moisture content of the mixed feed; if the moisture content exceeds the extruder's capacity (typically 15%-35%), the food cannot be properly cooked and extruded. Therefore, the lack of precision in temperature control in current technologies remains a long-standing challenge in the field of pet food processing. Summary of the Invention

[0003] This disclosure provides an intelligent temperature control method and system for processing pet puffed food, which solves the technical problem that the quality of fresh meat in pet puffed food is not high due to the lack of precision in temperature control in the prior art.

[0004] According to a first aspect of this disclosure, an intelligent temperature control method for processing pet puffed food is provided, comprising: obtaining processing flow information of pet puffed food based on big data; identifying temperature control nodes based on the processing flow information to obtain multi-level processing temperature control nodes; performing temperature control feature analysis based on the multi-level processing temperature control nodes to obtain multi-level node temperature control features; obtaining attribute information of the target processed pet puffed food based on the target processed pet puffed food; constructing an intelligent temperature control model; performing temperature control analysis on the target processed food information based on the intelligent temperature control model and the multi-level node temperature control features to obtain a target temperature control scheme; and adjusting the processing temperature of the target processed pet puffed food based on the target temperature control scheme.

[0005] According to a second aspect of this disclosure, an intelligent temperature control system for processing pet puffed food is provided, comprising: a first acquisition module for acquiring processing flow information of pet puffed food based on big data; a second acquisition module for identifying temperature control nodes based on the processing flow information to acquire multi-level processing temperature control nodes; a third acquisition module for performing temperature control feature analysis based on the multi-level processing temperature control nodes to acquire multi-level node temperature control features; a fourth acquisition module for acquiring attribute information of the target processed pet puffed food; a first construction module for constructing an intelligent temperature control model; a fifth acquisition module for performing temperature control analysis on the target processed food information based on the intelligent temperature control model and the multi-level node temperature control features to acquire a target temperature control scheme; and a first implementation module for adjusting the processing temperature of the target processed pet puffed food based on the target temperature control scheme.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] This invention employs intelligent temperature regulation, overcoming the problems of inaccurate temperature control and low-quality fresh meat in existing technologies. Intelligent temperature regulation directly affects the gelatinization degree of starch in food and the content of various heat-sensitive nutrients, while also influencing the puffing degree and hardness of food particles. These effects will manifest in the palatability of the food, influencing pets' food choices and preferences.

[0008] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0010] Figure 1 A schematic flowchart illustrating an intelligent temperature control method for processing pet puffed food, provided as an embodiment of this disclosure;

[0011] Figure 2 This is a schematic diagram of the process for obtaining multi-level processing temperature control nodes in an intelligent temperature regulation method for processing pet puffed food according to an embodiment of this disclosure.

[0012] Figure 3This is a schematic diagram of the structure of an intelligent temperature control system for processing pet puffed food according to an embodiment of this disclosure.

[0013] Explanation of reference numerals in the attached drawings: First obtaining module 11, Second obtaining module 12, Third obtaining module 13, Fourth obtaining module 14, First construction module 15, Fifth obtaining module 16, First implementation module 17. Detailed Implementation

[0014] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0015] To address the technical problem of increasing the proportion of fresh meat in pet puffed food solely through temperature adjustment, the inventors of this disclosure, through creative effort, have developed an intelligent temperature control method and system for pet puffed food processing.

[0016] Example 1

[0017] like Figure 1 As shown, this application provides an intelligent temperature control method for processing pet puffed food, the method comprising:

[0018] Step S100: Based on big data, obtain information on the processing flow of pet puffed food;

[0019] Specifically, the processing flow information for pet puffed food refers to the processing flow for products using dry ingredients as the main raw material. This processing flow information may include pretreatment, mixing, and puffing, thereby achieving effective processing of the pet puffed food. Optionally, the processing flow information for pet puffed food can be obtained by browsing or downloading from pet puffed food-related books, magazines, forums, and television broadcasts.

[0020] Step S200: Based on the processing flow information, identify the temperature control nodes to obtain multi-level processing temperature control nodes;

[0021] Specifically, the temperature control node identifier is used to identify nodes that affect the composition and properties of the target processed pet puffed food based on the controlled processing temperature. The multi-level processing temperature control node is a node that controls the temperature during multi-level processing to change the ambient temperature. Since the rated temperatures for different processes in the pet puffed food processing stages, such as the mixing and puffing stages, are different, the temperatures at different stages are divided into nodes, and these nodes are identified to form different levels of processing temperature control nodes. This allows for precise control and division of the temperature ranges required for different processing stages.

[0022] Step S300: Perform temperature control feature analysis based on the multi-level processing temperature control nodes to obtain the temperature control features of the multi-level nodes;

[0023] Specifically, the temperature control characteristics can be temperature rise or fall, the rate of temperature change, and the need to maintain or change the ambient temperature by controlling the temperature. The multi-level node temperature control characteristics are nodes that meet the temperature control characteristics in the multi-level processing process. Temperature feature analysis is performed on the multi-level processing temperature control nodes to extract the temperature change characteristics of different levels of temperature nodes. For example, the temperature change characteristic of the mixing stage is a temperature rise characteristic, and the temperature change characteristic of the expansion stage is a further temperature rise. Over time, the overall temperature change trend of the multi-level processing process can be displayed.

[0024] Step S400: Based on the target processed pet puffed food, obtain the attribute information of the target processed pet puffed food;

[0025] Specifically, the target processed pet puffed food is a finished pet puffed food product. The attribute information of the target processed pet puffed food can be the weight or quality information of the finished pet puffed food product, etc. The information of the target processed food can be obtained through methods such as weighing in the prior art.

[0026] Step S500: Construct an intelligent temperature regulation model;

[0027] Specifically, the intelligent temperature regulation model is a neural network model based on big data, obtaining historical processing temperature regulation information during the processing of pet puffed food. Based on this historical processing temperature regulation information and the processing temperature information of the target processed food, a set ratio is used for training and testing to obtain the intelligent temperature regulation model.

[0028] Step S600: Based on the intelligent temperature regulation model and the multi-level node temperature control characteristics, perform temperature regulation analysis on the target processed food information to obtain the target temperature regulation scheme;

[0029] Specifically, based on the neural network model of the intelligent temperature regulation model and the multi-level node temperature control characteristics, the temperature is adjusted by controlling variables on the attribute information in the target processed food information, thereby obtaining different effects of the attribute information in the target processed food information on temperature control. For example, if the weight of the target processed food information is heavier, the required processing time is longer, thus changing the time of the temperature regulation node. Conversely, if the weight of the target processed food information is lighter, the required processing time is shorter, thus changing the time of the temperature regulation node, resulting in different target temperature regulation schemes.

[0030] Step S700: Adjust the processing temperature of the target processed pet puffed food according to the target temperature adjustment scheme.

[0031] Specifically, based on the intelligent temperature regulation model and the multi-level node temperature control characteristics, the target temperature regulation scheme is obtained to regulate the processing temperature of the pet puffed food.

[0032] like Figure 2 As shown, step S200 in the method provided in this application embodiment includes:

[0033] S210: Based on the processing flow information, obtain multiple food processing nodes;

[0034] S220: Perform temperature control impact analysis based on the multiple food processing nodes to obtain multiple temperature control impact parameters;

[0035] S230: Based on the multiple temperature control influence parameters, the multiple food processing nodes are identified to obtain the multi-level processing temperature control nodes;

[0036] Specifically, the food processing node is a node that modifies the processing environment of the target processed pet puffed food, i.e., a multi-level processing flow node. The temperature control influence parameter refers to the impact on the components and properties of the target processed food at a set processing temperature. For example, the temperature control influence parameter may include the moisture content parameter of the target processed food. Based on the temperature control influence parameter, the processing temperature is adjusted at the food processing node to ensure that the components and properties of the target processed food conform to the target processing parameters. The processing temperatures of the multiple food processing nodes are identified to obtain the multi-level processing temperature control node.

[0037] Step S220 in the method provided in this application embodiment includes:

[0038] S221: Based on a preset historical time zone, obtain records of substandard processing quality of pet puffed food;

[0039] S222: Based on the multiple food processing nodes, perform correlation analysis on the non-conforming processing quality records to obtain the first feature correlation degree of multiple nodes;

[0040] S223: Based on the non-conforming processing quality records, perform temperature control correlation analysis on the multiple food processing nodes to obtain the second feature correlation of the multiple nodes;

[0041] S224: Calculate the proportion of associated features based on the correlation degree of the second feature of the multiple nodes and the correlation degree of the first feature of the multiple nodes to obtain the multiple temperature control influence parameters.

[0042] Specifically, the preset historical time zone is based on the historical processing time zone of pet puffed food. Records of substandard processing quality of pet puffed food within the preset historical time zone are extracted.

[0043] The first feature correlation of the multiple nodes represents the total number of non-conforming processes corresponding to each food processing node. The second feature correlation of the multiple nodes represents the number of non-conforming processes caused by temperature control at each food processing node. The correlation feature ratio is calculated as the ratio of the second feature correlation of the multiple nodes to the first feature correlation of the multiple nodes.

[0044] This study extracts the number of non-conforming processes at each processing node from the non-conforming processing quality records of pet puffed food, as well as the number of non-conforming processes caused by temperature control at each processing node. The ratio of the number of non-conforming processes caused by temperature control to the total number of non-conforming processes at each node is calculated to obtain the proportion of non-conforming processes caused by temperature control in the total number of non-conforming processes, i.e., the temperature control influence parameter. Based on the temperature control influence parameter at multiple node levels, multiple temperature control influence parameters are obtained.

[0045] Step S300 in the method provided in this application embodiment includes:

[0046] S310: Obtain processing data records for pet puffed food;

[0047] S320: Perform principal component analysis based on the processing data record to obtain a standard processing data record;

[0048] S330: Based on the multi-level processing temperature control nodes, perform cluster analysis on the standard processing data records to obtain multiple node processing records;

[0049] S340: Traverse the processing records of the multiple nodes to extract the temperature control factor and obtain the combined temperature control factor of the multiple nodes;

[0050] S350: Traverse the multiple node temperature control factors and perform normalization processing to generate the multi-level node temperature control features.

[0051] Specifically, the processing data record for the pet puffed food can be a record of changes in the composition and properties of the pet puffed food. The standard processing data record is a record of changes in the processing of the main components of the pet puffed food. Based on the processing data record, the main component processing data record is extracted.

[0052] Cluster analysis is performed on the principal components of pet puffed food based on the multi-level processing temperature control nodes. Similar components among the principal components of the pet puffed food are grouped into the same category, completing the cluster analysis and obtaining multiple clustering results. Each clustering result includes a processing temperature control node and a standard processing data set, thus obtaining multiple node processing records.

[0053] The temperature control factor is the temperature control index of the processing records of the multiple nodes. The temperature control index is extracted from the processing records of the multiple nodes, and the obtained multiple node temperature control factors are combined and outlier data is removed to generate the multi-level node temperature control feature.

[0054] Step S320 in the method provided in this application embodiment includes:

[0055] S321: Obtain the first feature processing data record based on the processing data record;

[0056] S322: Decentralize the first feature processing data record to obtain the second feature processing data record;

[0057] S323: Obtain the first feature processing covariance matrix based on the second feature processing data record;

[0058] S324: Based on the first feature processing covariance matrix, obtain the first feature processing value and the first feature processing vector;

[0059] S325: Obtain the standard processing data record based on the first feature processing value and the first feature processing vector.

[0060] The extracted feature processing data is numerically processed, and a feature processing data matrix is ​​constructed to obtain the first feature processing data. Then, each feature processing data in the first feature processing data is centered. First, the average value of each feature in the first feature processing data is calculated. Then, for all samples, each feature is subtracted from its own mean to obtain new feature values. These new feature processing data constitute the second feature processing data, which is a data matrix. The second feature processing data is calculated using the covariance formula to obtain the first covariance matrix of the second feature processing data. Then, through matrix operations, the feature processing values ​​and feature processing vectors of the first covariance matrix are calculated, with each feature value corresponding to a feature processing vector. From the calculated first feature processing vectors, the top K largest feature processing values ​​and their corresponding feature processing vectors are selected. The original features in the first feature processing data are projected onto the selected feature vectors to obtain the dimensionality-reduced first feature processing data. Principal component analysis is used to reduce the dimensionality of the feature processing data in the database. While ensuring the amount of information, redundant data is eliminated, which reduces the sample size of the feature processing data in the database and minimizes the loss of information after dimensionality reduction, thereby speeding up the data processing speed of the training model.

[0061] Step S500 in the method provided in this application embodiment includes:

[0062] S510: Based on big data, obtain the temperature regulation records of pet puffed food processing;

[0063] S520: Based on the temperature regulation records of the pet puffed food processing, perform a preset ratio of data division to obtain training data sequences and test data sequences;

[0064] S530: Based on a convolutional neural network, the intelligent temperature regulation model is obtained by training and testing according to the training data sequence and the test data sequence.

[0065] Optionally, the temperature control records for pet puffed food processing can be obtained by browsing or downloading from books, newspapers, forums, etc. These records may include historical temperature control records from multiple nodes.

[0066] The data is divided into training data sequences and test data sequences by a preset ratio based on the temperature control records for processing pet puffed food and the temperature control records for processing target pet puffed food. For example, the ratio can be 6:4 or 7:3.

[0067] Based on a convolutional neural network, training and testing are performed according to the training data sequence and the test data sequence. The convolutional neural network includes the following steps: inputting the temperature regulation records of pet puffed food processing and the preset temperature regulation records of target processed pet puffed food processing, extracting the final temperature regulation parameters of pet puffed food processing, making the temperature control of target processed pet puffed food more precise at multiple nodes, thereby reducing the number of unqualified records of target processed pet puffed food at multiple nodes, and obtaining the intelligent temperature regulation model.

[0068] Step S800 in the method provided in this application embodiment includes:

[0069] S810: Obtain real-time temperature information of the target processed pet puffed food, wherein the real-time temperature information has a processing node identifier;

[0070] S820: Based on the processing node identifier and the multi-level node temperature control characteristics, obtain the node temperature control range;

[0071] S830: Determine whether the real-time temperature information meets the node temperature control range;

[0072] S840: When the real-time temperature information does not meet the node temperature control range, a node temperature control early warning command is obtained.

[0073] Optionally, the real-time temperature information of the target processed pet puffed food can be obtained by measuring the temperature using existing temperature measuring equipment.

[0074] The node temperature control range is based on multi-level node temperature control characteristics, identifying temperature control ranges for each processing node. The system uses temperature measurement equipment to determine whether the real-time temperature information meets the node temperature control range. If the real-time temperature information is outside the node temperature control range, a node temperature control early warning command is issued.

[0075] Example 2

[0076] Based on the same inventive concept as the intelligent temperature control method for processing pet puffed food in the foregoing embodiments, such as Figure 3 As shown, this application also provides an intelligent temperature control system for processing pet puffed food, the system comprising:

[0077] The first acquisition module 11 is used to obtain processing information of pet puffed food based on big data;

[0078] The second acquisition module 12 is used to identify temperature control nodes based on the processing flow information and obtain multi-level processing temperature control nodes.

[0079] The third acquisition module 13 is used to perform temperature control feature analysis based on the multi-level processing temperature control nodes to obtain the multi-level node temperature control features.

[0080] The fourth obtaining module 14 is used to obtain the attribute information of the target processed pet puffed food based on the target processed pet puffed food;

[0081] The first building module 15 is used to build an intelligent temperature regulation model;

[0082] The fifth acquisition module 16 is used to perform temperature regulation analysis on the target processed food information based on the intelligent temperature regulation model and the multi-level node temperature control characteristics, and obtain the target temperature regulation scheme;

[0083] The first implementation module 17 is used to adjust the processing temperature of the target processed pet puffed food based on the target temperature adjustment scheme.

[0084] Furthermore, the system also includes:

[0085] The sixth acquisition module is used to acquire multiple food processing nodes based on the processing flow information;

[0086] The seventh module is used to perform temperature control influence analysis based on the multiple food processing nodes and obtain multiple temperature control influence parameters.

[0087] The eighth module is used to identify the multiple food processing nodes based on the multiple temperature control influence parameters, and obtain the multi-level processing temperature control nodes.

[0088] Furthermore, the system also includes:

[0089] The ninth module is used to obtain records of substandard processing quality of pet puffed food based on a preset historical time zone.

[0090] The tenth module is used to perform correlation analysis on the non-conforming processing quality records based on the multiple food processing nodes, and obtain the first feature correlation degree of multiple nodes;

[0091] The eleventh module is used to perform temperature control correlation analysis on the multiple food processing nodes based on the non-conforming processing quality records, and obtain the second feature correlation of the multiple nodes;

[0092] The twelfth module is used to calculate the proportion of associated features based on the second feature correlation degree of the multiple nodes and the first feature correlation degree of the multiple nodes, and to obtain the multiple temperature control influence parameters.

[0093] Furthermore, the system also includes:

[0094] The thirteenth module is used to obtain processing data records for pet puffed food;

[0095] The fourteenth module is used to perform principal component analysis based on the processing data records to obtain standard processing data records;

[0096] The fifteenth module is used to perform cluster analysis on the standard processing data records based on the multi-level processing temperature control nodes to obtain multiple node processing records.

[0097] The sixteenth module is used to traverse the processing records of the multiple nodes to extract temperature control factors and obtain a set of temperature control factors for multiple nodes.

[0098] The seventeenth module is used to traverse the multiple node temperature control factor sets for normalization processing to generate the multi-level node temperature control features.

[0099] Furthermore, the system also includes:

[0100] The eighteenth obtaining module is used to obtain the first feature processing data record based on the processing data record;

[0101] The nineteenth obtaining module is used to perform decentralized processing on the first feature processing data record to obtain the second feature processing data record;

[0102] The twentieth obtaining module is used to obtain the first feature processing covariance matrix based on the second feature processing data record;

[0103] The twenty-first obtaining module is used to obtain the first feature processing value and the first feature processing vector based on the first feature processing covariance matrix;

[0104] The twenty-second obtaining module is used to obtain the standard processing data record based on the first feature processing value and the first feature processing vector.

[0105] Furthermore, the system also includes:

[0106] The twenty-third module is used to obtain the temperature regulation records of pet puffed food processing based on big data.

[0107] The twenty-fourth acquisition module is used to divide the data according to a preset ratio based on the pet puffed food processing temperature regulation record, and obtain training data sequence and test data sequence;

[0108] The twenty-fifth module is used to train and test the intelligent temperature regulation model based on the training data sequence and the test data sequence using a convolutional neural network.

[0109] Furthermore, the system also includes:

[0110] The twenty-sixth acquisition module is used to acquire real-time temperature information of the target processed pet puffed food, wherein the real-time temperature information has a processing node identifier;

[0111] The twenty-seventh obtaining module is used to obtain the node temperature control range based on the processing node identifier and the multi-level node temperature control characteristics;

[0112] The first judgment module is used to determine whether the real-time temperature information meets the node temperature control range;

[0113] The twenty-eighth module is used to obtain a node temperature control early warning command when the real-time temperature information does not meet the node temperature control range.

[0114] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0115] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A smart temperature control method for processing pet puffed food, characterized in that, The method includes: Based on big data, information on the processing flow of pet puffed food was obtained; Based on the processing flow information, temperature control nodes are identified to obtain multi-level processing temperature control nodes; Temperature control feature analysis is performed based on the multi-level processing temperature control nodes to obtain the multi-level node temperature control features; Based on the target processed pet puffed food, obtain the attribute information of the target processed pet puffed food; Construct an intelligent temperature regulation model; Based on the intelligent temperature regulation model and the multi-level node temperature control characteristics, temperature regulation analysis is performed on the target processed food information to obtain the target temperature regulation scheme. The processing temperature of the target processed pet puffed food is adjusted based on the target temperature adjustment scheme. Specifically, temperature control nodes are identified based on the processing flow information to obtain multi-level processing temperature control nodes, including: Based on the processing flow information, multiple food processing nodes are obtained; Based on the multiple food processing nodes, a temperature control influence analysis was performed to obtain multiple temperature control influence parameters. Based on the multiple temperature control influence parameters, the multiple food processing nodes are identified to obtain the multi-level processing temperature control nodes; Among them, temperature control influence analysis is performed based on the multiple food processing nodes to obtain multiple temperature control influence parameters, including: Based on a preset historical time zone, obtain records of substandard processing quality of pet puffed food; Based on the multiple food processing nodes, a correlation analysis is performed on the non-conforming processing quality records to obtain the first feature correlation degree of multiple nodes; Based on the non-conforming processing quality records, perform temperature control correlation analysis on the multiple food processing nodes to obtain the second feature correlation of multiple nodes; The correlation ratio of the correlation features is calculated based on the correlation degree of the second feature of the multiple nodes and the correlation degree of the first feature of the multiple nodes to obtain the multiple temperature control influence parameters; Specifically, temperature control feature analysis is performed based on the multi-level processing temperature control nodes to obtain the multi-level node temperature control features, including: Obtain processing data records for pet puffed food; Principal component analysis was performed based on the processing data records to obtain standard processing data records; Based on the multi-level processing temperature control nodes, cluster analysis is performed on the standard processing data records to obtain multiple node processing records; The temperature control factors are extracted by traversing the processing records of the multiple nodes to obtain the combined temperature control factors of the multiple nodes; The multiple node temperature control factors are traversed and normalized to generate the multi-level node temperature control features. Principal component analysis is performed based on the processing data records to obtain standard processing data records, including: Based on the processing data record, obtain the first feature processing data record; The first feature processing data record is decentralized to obtain the second feature processing data record; Based on the second feature processing data record, obtain the first feature processing covariance matrix; Based on the first feature processing covariance matrix, the first feature processing value and the first feature processing vector are obtained; The standard processing data record is obtained based on the first feature processing value and the first feature processing vector; The construction of an intelligent temperature regulation model includes: Based on big data, records of temperature regulation during the processing of pet puffed food were obtained; Based on the temperature regulation records of the pet puffed food processing, the data is divided according to a preset ratio to obtain training data sequences and test data sequences. Based on a convolutional neural network, the intelligent temperature regulation model is obtained by training and testing according to the training data sequence and the test data sequence. Obtain real-time temperature information of the target processed pet puffed food, wherein the real-time temperature information has a processing node identifier; Based on the processing node identifier and the multi-level node temperature control characteristics, the node temperature control range is obtained; Determine whether the real-time temperature information meets the node temperature control range; When the real-time temperature information does not meet the node temperature control range, a node temperature control early warning command is obtained.

2. An intelligent temperature control system for processing pet puffed food, characterized in that, The system is used to perform the method of claim 1, the system comprising: The first acquisition module is used to obtain processing information of pet puffed food based on big data; The second acquisition module is used to identify temperature control nodes based on the processing flow information and obtain multi-level processing temperature control nodes. The third acquisition module is used to perform temperature control feature analysis based on the multi-level processing temperature control nodes to obtain the multi-level node temperature control features. The fourth module is used to obtain attribute information of the target processed pet puffed food based on the target processed pet puffed food; The first building module is used to construct the intelligent temperature regulation model; The fifth acquisition module is used to perform temperature regulation analysis on the target processed food information based on the intelligent temperature regulation model and the multi-level node temperature control characteristics, and to obtain the target temperature regulation scheme. The first implementation module is used to adjust the processing temperature of the target processed pet puffed food based on the target temperature adjustment scheme.

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