A method for tracing safety information of Bazhen Cake food production chain

Abstract

The present invention relates to the field of data processing technology based on enterprise management, and specifically to a method for tracing safety information of the production chain of Bazhen Cake, comprising: collecting a plurality of dimensional data and the collection time; obtaining the dimensional proportion according to the dimensional data; obtaining the initial process dimensional degree according to the dimensional proportion; obtaining the actual abnormality degree according to the initial process dimensional degree; obtaining process relevance according to the initial process dimensional degree and the actual abnormality degree; obtaining a transformation slope according to the process relevance; obtaining the process transformation degree according to the transformation slope; obtaining an evaluation degree according to the process transformation degree; obtaining the type of dimensionality reduction data of each production batch according to the evaluation degree and the collection time; and tracing information according to the type of dimensionality reduction data. The present invention improves the accuracy of obtaining traceability data and better manages the food safety of Bazhen Cake.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing based on enterprise management, and in particular to a method for tracing safety information of a food production chain of Bazhen Cake. Background Art

[0002] With the rapid development of technologies like big data, the Internet of Things, and blockchain, food companies are establishing their own food traceability systems. For companies producing Bazhen Cake, establishing traceability along the food production chain has become an effective method for managing food safety. Traditional methods rely on establishing traceability codes to effectively manage food safety.

[0003] However, in the actual process of constructing the traceability code, since different production batches of Eight Treasures Cake will have certain differences in production process environment and operation, the data of different production batches of Eight Treasures Cake in different production processes may also be different, making the traceability data obtained according to the assigned traceability code inaccurate, which interferes with the food safety management of Eight Treasures Cake. Summary of the Invention

[0004] The present invention provides a method for tracing the safety information of the food production chain of Eight Treasures Cake to solve the existing problem that different production batches of Eight Treasures Cake may have certain differences in production process environments and operations, which may cause the data of different production batches of Eight Treasures Cake in different production processes to also be different, so that the traceability data obtained according to the assigned traceability code is inaccurate, which interferes with the food safety management of the Eight Treasures Cake.

[0005] The present invention provides a method for tracing safety information of the production chain of Bazhen Cake food by adopting the following technical solutions:

[0006] The following steps are involved:

[0007] Collect data of several dimensions and the collection time for each production batch of Bazhen Cake under each production process;

[0008] Based on the proportion of dimensional data in different production processes for production batches, the dimensional proportion of each dimensional data is obtained; based on the overall cumulative degree of the dimensional proportion of different dimensional data under the same production process, the initial process dimensionality degree of each production process is obtained; based on the difference in the change of the initial process dimensionality degree of the production process between different production batches, the actual abnormality degree of each production process is obtained; based on the initial process dimensionality degree and the actual abnormality degree, the process correlation of each production process is obtained. The process correlation is used to describe the difference in security information contained in different production processes;

[0009] Constructing several transformation slopes based on the process correlations of different production processes; obtaining the degree of process transformation for each production batch based on the changes in the transformation slopes between different production processes; and obtaining the degree of judgment for each dimension data based on the degree of process transformation, which is used to describe the probability that the dimension data needs to be retained;

[0010] According to the evaluation degree and the collection time, several types of reduced dimension data are selected from several dimensional data under different production processes; and information is traced according to the types of reduced dimension data.

[0011] Preferably, the method of obtaining the dimension proportion of each dimension data according to the proportion of dimension data in different production processes of the production batch includes the following specific methods:

[0012] Record any production process in any production batch as the target production process, and any dimension data of the target production process;

[0013]

[0014] Where, Indicates the proportion of dimension data of production batches in the target production process; Indicates the numerical value of the dimensional data of the production batch under the target production process; Indicates the number of types of production processes; Indicates that the production batch is The numerical size of dimensional data under a production process.

[0015] Preferably, the initial process dimension degree of each production process is obtained according to the overall cumulative degree of the dimension proportion of different dimensional data under the same production process, including the specific method of:

[0016]

[0017] Where, Indicates the degree of initial process dimension of any production process in any production batch; Indicates the type of dimensional data included in the production process of a production batch; Indicates that the production batch is the first The dimension proportion of the dimensional data.

[0018] Preferably, the actual abnormality degree of each production process is obtained according to the difference in the degree of change of the initial process dimension of the production process between different production batches, including the specific method of:

[0019] Any production batch is recorded as the first target production batch. For any production process in the first target production batch, a production process before the production process in the first target production batch is recorded as the predecessor process of the production process in the first target production batch; each production batch before the first target production batch is recorded as the predecessor batch of the first target production batch;

[0020]

[0021] Where, Indicates the actual abnormality level of the production process in the first target production batch; Indicates the initial process dimension degree of the preceding process of the production process in the first target production batch; Indicates the initial process dimension degree of the production process in the first target production batch; Indicates the initial process dimension degree of the production process in the predecessor batch of the first target production batch; Indicates the number of production batches before the predecessor batch of the first target production batch; Indicates the batch before the first target production batch. The degree of initial process dimension of the production process in each production batch; Represents the preset hyperparameters; Indicates taking the absolute value.

[0022] Preferably, the process relevance of each production process is obtained according to the initial process dimension degree and the actual abnormality degree, including the specific method of:

[0023]

[0024] Where, Indicates the process relevance of any production process in any production batch; Indicates the actual abnormality level of the production process in a production batch; Indicates the degree of initial process dimension of the production process in a production batch.

[0025] Preferably, the method of constructing a plurality of transformation slopes according to the process correlation of different production processes includes:

[0026] A two-dimensional process coordinate system is constructed based on the process correlation of all production processes in all production batches. The horizontal axis represents the production process of each production batch, and the vertical axis represents the size of the process correlation of the production process. In the two-dimensional process coordinate system, the slope of the process correlation of each production process in each production batch is obtained, and recorded as the transformation slope of each production process in each production batch.

[0027] Preferably, the process conversion degree of each production batch is obtained according to the change of the conversion slope between different production processes, including the specific method of:

[0028]

[0029] Where, Indicates the degree of process change for any production batch; Indicates the number of types of production processes; Indicates the number of The slope of the transformation of the production process; Indicates the number of The slope of the transformation of the production process; Indicates the number of production batches; Indicates the Production batch No. The slope of the transformation of the production process; Indicates the Production batch No. The slope of the transformation of the production process; Indicates taking the absolute value.

[0030] Preferably, the specific method of obtaining the evaluation degree of each dimension data according to the process transformation degree includes:

[0031] For any dimension data of any production batch under any production process, obtain the variance contribution of the dimension data of the production batch under the production process;

[0032]

[0033] Where, Indicates the degree of evaluation of the dimensional data of the production batch under the production process; Indicates the variance contribution of the dimensional data under the production process in the production batch; Indicates the degree of process change for this production batch.

[0034] Preferably, the method of selecting a plurality of dimensionality-reduced data types from a plurality of dimensional data under different production processes according to the evaluation degree and the collection time includes the following specific methods:

[0035] A preset evaluation degree threshold and a comparison coefficient are recorded as T1 and T2 respectively; for any production batch, in the evaluation degree of all dimensional data in all production processes of the production batch, the evaluation degree is accumulated in the order of collection time from early to late, until the accumulated evaluation degree is greater than T1 T2 stops accumulation and records the type of accumulated dimensional data as the type of reduced dimensional data of the production batch.

[0036] Preferably, the information tracing according to the type of dimension-reduced data includes the following specific methods:

[0037] For any production batch, perform K-means clustering on all the Bazhen cakes in the production batch according to the type of dimension reduction data of the production batch to obtain several clusters; for any cluster, take the mean of all dimensional data of all Bazhen cakes in the cluster as the traceability code of each Bazhen cake in the cluster, take the mean square deviation of all dimensional data of all Bazhen cakes in the cluster as the error fluctuation range, construct a traceability matrix based on the traceability code and error fluctuation range, and record it as the Bazhen cake traceability matrix, use the ZXing tool to convert the Bazhen cake traceability matrix into a QR code, and record it as the QR code of each Bazhen cake in the production batch;

[0038] Obtain the QR code of each Bazhen Cake in each production batch and complete information traceability by scanning the QR code.

[0039] The beneficial effects of the technical solution of the present invention are: obtaining the dimension proportion degree according to the dimensional data, obtaining the process correlation according to the dimension proportion degree, obtaining the process transformation degree according to the process correlation, obtaining the evaluation degree according to the process transformation degree, obtaining the type of dimensionality reduction dimension data according to the evaluation degree, and performing information tracing according to the type of dimensionality reduction dimension data; compared with the existing technology, it is impossible to accurately trace information according to the differences between dimensional data in different production processes; the dimension proportion degree of the present invention better reflects the reference value of the production process to the overall production process, the process correlation better reflects the influence of the production process on the clustering results, and the evaluation degree better reflects the dimensional data belonging to the main dimension, thereby improving the accuracy of obtaining traceability data and better managing the food safety of Bazhen Cake. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 The present invention provides a flowchart of the steps of a method for tracing safety information of the production chain of Bazhen Cake food. DETAILED DESCRIPTION

[0042] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a method for tracing food safety information along the production chain of Bazhen Cake, as proposed by the present invention. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0044] The following describes in detail a method for tracing safety information of the production chain of Bazhen Cake provided by the present invention in conjunction with the accompanying drawings.

[0045] See also Figure 1 , which shows a flowchart of a method for tracing safety information of a food production chain of Bazhen Cake provided by one embodiment of the present invention, the method comprising the following steps:

[0046] Step S001: Collect several dimensional data and collection time of each production batch of Bazhen Cake under each production process.

[0047] It should be noted that, in the actual process of constructing the traceability code, due to the differences in production process environments and operations of different production batches of Bazhen Cake, the data of different production batches of Bazhen Cake in different production processes may also be different, making the traceability data obtained based on the assigned traceability code inaccurate, which interferes with the food safety management of Bazhen Cake. To this end, this embodiment proposes a method for tracing the safety information of the Bazhen Cake food production chain.

[0048] Specifically, in order to implement the method for tracing the safety information of the food production chain of Eight Treasures Cake proposed in this embodiment, it is first necessary to collect the dimensional data of the Eight Treasures Cake. The specific process is: in the Eight Treasures Cake production database, obtain the weight data, temperature data, humidity data and processing time data of all Eight Treasures Cakes in each production batch in the past week under each production process, and record the collection time at the same time, and set the production code of each Eight Treasures Cake, and record the four data of weight data, temperature data, humidity data and processing time data as a kind of dimensional data in turn; obtain all dimensional data of each production batch under each production process; wherein each dimensional data under each production process corresponds to only one dimensional data, each production batch contains exactly the same type of production process, each production batch contains multiple Eight Treasures Cakes, the dimensional data of each Eight Treasures Cake in each production batch is exactly the same, and each Eight Treasures Cake corresponds to a unique production code.

[0049] At this point, all dimensional data and collection time of each production batch of Bazhen Cake under each production process are obtained through the above method.

[0050] Step S002: According to the proportion of dimensional data of production batches under different production processes, the dimensional proportion of each dimensional data is obtained; according to the overall cumulative degree of the dimensional proportion of different dimensional data under the same production process, the initial process dimensional degree of each production process is obtained; according to the difference in the change of the initial process dimensional degree of the production process between different production batches, the actual abnormality degree of each production process is obtained; according to the initial process dimensional degree and the actual abnormality degree, the process correlation of each production process is obtained.

[0051] It should be noted that for any production process in any production batch, since this production process contains data of multiple dimensions, each production process has a high data dimension; if the production process is directly clustered, the high data dimension will lead to a large error in the classification result, so it is necessary to reduce the dimension of the dimensional data under each production process. For the dimensional data of the production batch, since the production process of Bazhen Cake is composed of several production processes connected in sequence, the dimensional data of the previous production process of the same production batch will affect the dimensional data of the next production process to a certain extent. Therefore, there is a certain correlation between the dimensional data of different production processes in the same production batch; therefore, this embodiment obtains the process correlation of different processes by analyzing the correlation for subsequent analysis and processing.

[0052] Specifically, taking any dimension data of any production batch under any production process as an example, based on the dimension data of the production batch under all production processes, the dimension proportion of the production batch under the production process is obtained; wherein the calculation method of the dimension proportion of the production batch under the production process is:

[0053]

[0054] Where, Indicates the proportion of this dimension data in this production batch under this production process; Indicates the numerical value of this dimension data for this production batch under this production process; Indicates the number of types of production processes; Indicates that the production batch is in The numerical value of this dimension data under a production process. The greater the proportion of this dimension data under a production batch under a production process, the more relevant this dimension data is for the entire production process. Obtain the proportion of all dimension data under a production batch under a production process; obtain the proportion of all dimension data under each production process for a production batch.

[0055] Furthermore, the initial process dimension degree of the production process in the production batch is obtained according to the dimension proportion of all dimension data of the production batch under the production process; wherein the calculation method of the initial process dimension degree of the production process in the production batch is:

[0056]

[0057] Where, Indicates the degree of initial process dimension of this production process in this production batch; Indicates the type of dimensional data included in this production process in this production batch; Indicates that this production batch is the first batch under this production process. The dimensional proportion of the dimensional data of the production process in the production batch is greater, indicating that the overall reference degree of all dimensional data in the production process is higher. Obtain the initial process dimensional proportion of all production processes in the production batch.

[0058] Furthermore, taking any production process in the production batch as an example, a production process in the production batch that precedes the production process is recorded as the predecessor process of the production process in the production batch; each production batch before the production batch is recorded as the predecessor batch of the production batch; according to the dimension proportion of the dimension data of the production batch under all production processes, the predecessor process and the predecessor batch, the actual abnormality degree of the production process in the production batch is obtained; wherein the calculation method of the actual abnormality degree of the production process in the production batch is:

[0059]

[0060] Where, Indicates the actual abnormality level of this production process in this production batch; Indicates the initial process dimension degree of the preceding process of this production process in this production batch; Indicates the degree of initial process dimension of this production process in this production batch; Indicates the degree of initial process dimension of this production process in the previous batch of this production batch; Indicates the number of production batches before the predecessor batch of this production batch; Indicates the batch before the previous batch of this production batch. The degree of initial process dimension of this production process in a production batch; Represents hyperparameters, which are preset in this example , used to prevent the denominator from being 0; It represents the absolute value; if the actual abnormality of the production process in the production batch is greater, it means that there is a possibility that the production process is abnormal in the production batch, which reflects that the production process has a greater impact on the classification of the production batch.

[0061] Furthermore, the process relevance of the production process in the production batch is obtained according to the actual abnormality degree and the initial process dimension degree; wherein the process relevance of the production process in the production batch is calculated as follows:

[0062]

[0063] Where, Indicates the process relevance of this production process in this production batch; Indicates the actual abnormality level of this production process in this production batch; Indicates the degree of initial process dimension for this production process within this production batch. A greater process relevance indicates a higher correlation between this production process and the preceding and following production processes, reflecting a greater impact on the clustering results. Obtain the process relevance of all production processes within this production batch; obtain the process relevance of all production processes within this production batch; obtain the process relevance of all production processes within each production batch.

[0064] At this point, the process correlation of all production processes of each production batch is obtained through the above method.

[0065] Step S003: construct several transformation slopes according to the process correlation of different production processes; obtain the process transformation degree of each production batch according to the change of the transformation slope between different production processes; and obtain the evaluation degree of each dimension data according to the process transformation degree.

[0066] It should be noted that since the process correlations of all production processes in each production batch have more or less numerical differences, in order to better determine the relationship between production processes in different production batches, it is necessary to obtain the degree of transformation between production processes in the same production batch by analyzing the differences in process correlations; then, dimensionality reduction is performed according to the degree of transformation to obtain the dimensionality reduction result, and accurate clustering is finally achieved based on the dimensionality reduction result.

[0067] Specifically, a two-dimensional process coordinate system is constructed based on the process relevance of all production processes within all production batches. The horizontal axis represents the production process of each production batch, and the vertical axis represents the degree of process relevance of the production processes. In this two-dimensional process coordinate system, the slope of the process relevance of each production process within each production batch is obtained, and this is recorded as the transformation slope of each production process within each production batch. The process of obtaining the slope is well known and will not be described in this embodiment.

[0068] Furthermore, taking any production batch as an example, the degree of transformation between the production processes within the production batch is obtained according to the transformation slopes of all production processes within the production batch, which is recorded as the process transformation degree of the production batch; wherein the calculation method of the process transformation degree of the production batch is:

[0069]

[0070] Where, Indicates the degree of process change of the production batch; Indicates the number of types of production processes; Indicates the number of The slope of the transformation of the production process; Indicates the number of The slope of the transformation of the production process; Indicates the number of production batches; Indicates the Production batch No. The slope of the transformation of the production process; Indicates the Production batch No. The slope of the transformation of the production process; It means taking the absolute value; if the process change degree of the production batch is greater, it means that the numerical change degree between the production processes within the production batch is greater, reflecting that the production process of this production batch is more meaningful than the production processes of other production batches.

[0071] Furthermore, taking any dimension data of the production batch under any production process as an example, the variance contribution of the dimension data of the production batch under the production process is obtained, and the evaluation degree of the dimension data of the production batch under the production process is obtained based on the variance contribution and the degree of process transformation; wherein the acquisition of the variance contribution is a well-known technology and is not described in this embodiment. In addition, the calculation method of the evaluation degree of the dimension data of the production batch under the production process is:

[0072]

[0073] Where, Indicates the evaluation degree of this dimension data of this production batch under this production process; Indicates the variance contribution of the dimension data under the production process in the production batch; Indicates the degree of process change for a production batch. The higher the degree of judgment for a particular dimension under a particular production process, the more important that dimension is, and the more important it is to retain. Obtain the judgment degree for each dimension under each production process for each production batch.

[0074] At this point, the above method is used to obtain the evaluation degree of each dimension data of each production batch under each production process.

[0075] Step S004: Filter out a number of reduced dimension data types from a number of dimensional data under different production processes according to the evaluation degree and the collection time; and perform information tracing according to the reduced dimension data types.

[0076] Specifically, a judgment degree threshold T1 and a comparison coefficient T2 are preset, wherein this embodiment is described by taking T1=0.6 and T2=0.8 as an example, and this embodiment does not make specific limitations, wherein T1 and T2 can be determined according to the specific implementation situation; taking any production batch as an example, in the judgment degree of all dimensional data under all production processes of this production batch, the judgment degree of all dimensional data in each production process is accumulated in sequence according to the collection time of the production process from early to late, until the accumulated judgment degree is greater than T1 At T2, the accumulation is stopped, and the types of the accumulated dimensional data are recorded as the types of the reduced dimensional data of the production batch. It should be noted that the types of the reduced dimensional data of each production batch may include all types of dimensional data.

[0077] Furthermore, K-means clustering is performed on all eight-treasure cakes in the production batch according to the type of dimension-reduced dimensional data of the production batch to obtain several clusters; taking any cluster as an example, the mean of all dimensional data of all eight-treasure cakes in the cluster is used as the traceability code of each eight-treasure cake in the cluster, and the mean square deviation of all dimensional data of all eight-treasure cakes in the cluster is used as the error fluctuation range. A traceability matrix is constructed based on the traceability code and the error fluctuation range, and recorded as the eight-treasure cake traceability matrix; the eight-treasure cake traceability matrix is converted into a QR code using the ZXing tool, and recorded as the QR code of each eight-treasure cake in the production batch; the QR code of each eight-treasure cake in all production batches is obtained, and information tracing is completed by scanning the QR code. K-means clustering is a well-known technology and requires a preset number of classifications K. This embodiment uses K=8 as an example for description, and this embodiment does not specifically limit it. K can be determined according to the specific implementation situation. Using the ZXing tool to obtain the QR code is well-known content and is not described in this embodiment. It should be noted that the process of obtaining the traceability matrix from the traceability code and the error fluctuation interval is disclosed in CN116485418B, which will not be described in detail in this embodiment.

[0078] At this point, this embodiment is completed.

[0079] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for tracing safety information of the production chain of Bazhen Cake, characterized in that: The method comprises the following steps: Collect data of several dimensions and the collection time for each production batch of Bazhen Cake under each production process; Based on the proportion of dimensional data in different production processes for production batches, the dimensional proportion of each dimensional data is obtained; based on the overall cumulative degree of the dimensional proportion of different dimensional data under the same production process, the initial process dimensionality degree of each production process is obtained; based on the difference in the change of the initial process dimensionality degree of the production process between different production batches, the actual abnormality degree of each production process is obtained; based on the initial process dimensionality degree and the actual abnormality degree, the process correlation of each production process is obtained. The process correlation is used to describe the difference in security information contained in different production processes; Constructing several transformation slopes based on the process correlations of different production processes; obtaining the degree of process transformation for each production batch based on the changes in the transformation slopes between different production processes; and obtaining the degree of judgment for each dimension data based on the degree of process transformation, which is used to describe the probability that the dimension data needs to be retained; According to the evaluation degree and collection time, several dimensionality reduction data types are selected from several dimensional data under different production processes; information is traced according to the dimensionality reduction data types; The specific method for obtaining the dimension proportion of each dimension data according to the proportion of dimension data in different production processes of production batches is as follows: Record any production process in any production batch as the target production process, and any dimension data of the target production process; Where, Indicates the proportion of dimension data of production batches in the target production process; Indicates the numerical value of the dimensional data of the production batch under the target production process; Indicates the number of types of production processes; Indicates that the production batch is The numerical value of the dimensional data under the production process; The actual abnormality degree of each production process is obtained based on the difference in the degree of change of the initial process dimension of the production process between different production batches, including the specific method of: Any production batch is recorded as the first target production batch. For any production process in the first target production batch, a production process before the production process in the first target production batch is recorded as the predecessor process of the production process in the first target production batch; each production batch before the first target production batch is recorded as the predecessor batch of the first target production batch; Where, Indicates the actual abnormality level of the production process in the first target production batch; Indicates the initial process dimension degree of the preceding process of the production process in the first target production batch; Indicates the initial process dimension degree of the production process in the first target production batch; Indicates the initial process dimension degree of the production process in the predecessor batch of the first target production batch; Indicates the number of production batches before the predecessor batch of the first target production batch; Indicates the batch before the first target production batch. The degree of initial process dimension of the production process in each production batch; Represents the preset hyperparameters; Indicates taking the absolute value; The specific method for obtaining the process relevance of each production process based on the initial process dimension degree and the actual abnormality degree is as follows: Where, Indicates the process relevance of any production process in any production batch; Indicates the actual abnormality level of the production process in a production batch; Indicates the degree of initial process dimension of the production process in a production batch.

2. A method for tracing safety information of the production chain of Bazhen Cake according to claim 1, characterized in that: The initial process dimension degree of each production process is obtained based on the overall cumulative degree of the dimension proportion of different dimensional data under the same production process, including the specific method as follows: Where, Indicates the degree of initial process dimension of any production process in any production batch; Indicates the type of dimensional data included in the production process of a production batch; Indicates that the production batch is the first The dimension proportion of the dimensional data.

3. The method for tracing safety information of the production chain of Bazhen Cake according to claim 1, characterized in that: The specific method of constructing several transformation slopes according to the process correlation of different production processes is as follows: A two-dimensional process coordinate system is constructed based on the process relevance of all production processes within all production batches. The horizontal axis represents the production process of each production batch, and the vertical axis represents the degree of process relevance of the production process. In the two-dimensional coordinate system of the process, the slope of the process correlation of each production process in each production batch is obtained, and recorded as the transformation slope of each production process in each production batch.

4. The method for tracing safety information of the production chain of Bazhen Cake according to claim 1, characterized in that: The specific method for obtaining the process conversion degree of each production batch according to the change of the conversion slope between different production processes is as follows: Where, Indicates the degree of process change for any production batch; Indicates the number of types of production processes; Indicates the number of The slope of the transformation of the production process; Indicates the number of The slope of the transformation of the production process; Indicates the number of production batches; Indicates the Production batch No. The slope of the transformation of the production process; Indicates the Production batch No. The slope of the transformation of the production process; Indicates taking the absolute value.

5. The method for tracing safety information of the production chain of Bazhen Cake according to claim 1, characterized in that: The specific method for obtaining the evaluation degree of each dimension data according to the process transformation degree is as follows: For any dimension data of any production batch under any production process, obtain the variance contribution of the dimension data of the production batch under the production process; Where, Indicates the degree of evaluation of the dimensional data of the production batch under the production process; Indicates the variance contribution of the dimensional data under the production process in the production batch; Indicates the degree of process change for this production batch.

6. A method for tracing safety information of the production chain of Bazhen Cake according to claim 1, characterized in that: The specific method of screening out several types of dimensionality-reduced data from several dimensional data under different production processes according to the evaluation degree and the collection time includes: A preset evaluation degree threshold and a comparison coefficient are recorded as T1 and T2 respectively; for any production batch, in the evaluation degree of all dimensional data in all production processes of the production batch, the evaluation degree is accumulated in the order of collection time from early to late, until the accumulated evaluation degree is greater than T1 T2 stops accumulation and records the type of accumulated dimensional data as the type of reduced dimensional data of the production batch.

7. The method for tracing safety information of the production chain of Bazhen Cake according to claim 1, characterized in that: The specific method of tracing information based on the type of dimensionality-reduced data is as follows: For any production batch, perform K-means clustering on all the Bazhen cakes in the production batch according to the type of dimension reduction data of the production batch to obtain several clusters; for any cluster, take the mean of all dimensional data of all Bazhen cakes in the cluster as the traceability code of each Bazhen cake in the cluster, take the mean square deviation of all dimensional data of all Bazhen cakes in the cluster as the error fluctuation range, construct a traceability matrix based on the traceability code and error fluctuation range, and record it as the Bazhen cake traceability matrix, use the ZXing tool to convert the Bazhen cake traceability matrix into a QR code, and record it as the QR code of each Bazhen cake in the production batch; Obtain the QR code of each Bazhen Cake in each production batch and complete information traceability by scanning the QR code.

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