Vinegar fermentation detection method and system based on component analysis technology

Abstract

A vinegar fermentation detection method and system based on component analysis technology, relating to the field of vinegar detection. The vinegar fermentation detection method comprises: acquiring raw material information and basic environment information of target vinegar; using a raw material quality determination result and an environmental temperature as indexes to perform retrieval in a vinegar fermentation detection library to obtain a first index impact factor; matching a first detection period on the basis of the first index impact factor; performing component analysis on Q vinegar samples of the target vinegar to obtain Q vinegar sample feature sequence sets; performing bidirectional time series analysis to obtain a first fermentation detection result; using a stability identifier to identify the stability, to obtain a first stability factor; determining whether the first stability factor meets a preset stability factor; and if yes, using the first fermentation detection result as a target fermentation detection result. The problem of poor reliability of vinegar detection results is solved, and the vinegar production quality is improved.

Description

Vinegar fermentation detection method and system based on component analysis technology Technical Field

[0001] The present invention relates to the field of vinegar detection, and in particular to a vinegar fermentation detection method and system based on component analysis technology. Background Art

[0002] In the modern food industry, vinegar, as an important condiment, places particular importance on quality control and fermentation testing during its production process. Vinegar fermentation is a complex biochemical process influenced by numerous factors, including raw material quality, ambient temperature, and other fundamental environmental factors. Traditionally, quality control relies on manual experience and intuition, lacking systematic testing methods and standardized procedures. This results in poorly reliable test results, leading to significant quality fluctuations and difficulty ensuring batch-to-batch consistency and stability. Furthermore, traditional fermentation process monitoring methods are relatively backward, unable to accurately and timely monitor changes in key parameters during fermentation, making it difficult to provide early warnings and timely intervention for potential issues.

[0003] Summary of the Invention

[0004] The embodiments of the present application provide a vinegar fermentation detection method and system based on component analysis technology, which solves the technical problem of poor reliability of vinegar detection results in the prior art.

[0005] In view of the above problems, the embodiments of the present application provide a vinegar fermentation detection method and system based on component analysis technology.

[0006] A first aspect of the embodiments of the present application provides a method for detecting vinegar fermentation based on component analysis technology, the method comprising:

[0007] Collecting raw material information and basic environmental information of the target vinegar, wherein the basic environmental information includes the ambient temperature, and the raw material information includes the raw material quality measurement result;

[0008] Using the raw material quality measurement result and the ambient temperature as indexes, searching in a vinegar fermentation detection library to obtain a first indicator influencing factor;

[0009] Matching a first detection cycle based on the first indicator influencing factor;

[0010] performing component analysis on Q vinegar samples of the target vinegar according to a preset feature indicator set in a time sequence according to the first detection period to obtain Q vinegar sample feature sequence sets, wherein each vinegar sample feature sequence set includes M vinegar feature sequences, and each vinegar feature sequence corresponds to a feature indicator in the preset feature indicator set;

[0011] Performing a binary time series analysis on the Q vinegar sample feature sequence sets to obtain a first fermentation detection result of the target vinegar;

[0012] Using a stability identifier to identify the stability of the Q vinegar sample feature sequence sets to obtain a first stability factor;

[0013] Determine whether the first stability factor meets a preset stability factor. If so, use the first fermentation detection result as a target fermentation detection result.

[0014] A second aspect of the present invention provides a vinegar fermentation detection system based on component analysis technology, the system comprising:

[0015] An information collection module, the information collection module is used to collect raw material information and basic environmental information of the target vinegar, wherein the basic environmental information includes ambient temperature, and the raw material information includes raw material quality measurement results;

[0016] A retrieval module is configured to search a vinegar fermentation detection library using the raw material quality measurement result and the ambient temperature as indexes to obtain a first indicator influencing factor;

[0017] a matching module, configured to match a first detection period based on the first indicator influencing factor;

[0018] a component analysis module, configured to perform component analysis on Q vinegar samples of the target vinegar according to a preset feature indicator set in a time sequence according to the first detection period, to obtain Q vinegar sample feature sequence sets, wherein each vinegar sample feature sequence set includes M vinegar feature sequences, each vinegar feature sequence corresponding to one feature indicator in the preset feature indicator set;

[0019] A time series analysis module, configured to perform a two-item time series analysis on the Q vinegar sample feature sequence sets to obtain a first fermentation detection result of the target vinegar;

[0020] an identification module, the identification module being configured to identify the stability of the Q vinegar sample feature sequence sets using a stability identifier to obtain a first stability factor;

[0021] A judgment module is used to judge whether the first stability factor meets a preset stability factor, and if so, use the first fermentation detection result as a target fermentation detection result.

[0022] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0023] By collecting raw material information and basic environmental information of the target vinegar, the raw material quality measurement results and ambient temperature are obtained. Based on the raw material quality measurement results and ambient temperature, a vinegar fermentation detection library is searched to obtain a first indicator influencing factor and match it to a first detection cycle. Following this cycle, component analysis is performed on Q samples of the target vinegar in sequence to obtain a vinegar sample feature sequence set. These sample feature sequence sets are then subjected to a binary time series analysis to obtain the first fermentation detection result of the target vinegar. A stability identifier is then used to identify the stability of the sample feature sequence set to obtain a first stability factor. Finally, a determination is made as to whether the first stability factor meets a preset stability factor. If so, the first fermentation detection result is used as the target fermentation detection result. This method solves the technical problem of poor reliability of vinegar detection results in the prior art and achieves the technical effect of improving the quality of vinegar production. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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.

[0025] FIG1 is a schematic flow chart of a vinegar fermentation detection method based on component analysis technology provided in an embodiment of the present application;

[0026] FIG2 is a schematic diagram of the structure of a vinegar fermentation detection system based on component analysis technology provided in an embodiment of the present application.

[0027] Description of the accompanying drawings: information collection module 11, retrieval module 12, matching module 13, component analysis module 14, time series analysis module 15, recognition module 16, judgment module 17. DETAILED DESCRIPTION

[0028] The embodiments of the present application solve the technical problem of poor reliability of vinegar detection results in the prior art by providing a vinegar fermentation detection method and system based on component analysis technology.

[0029] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0030] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0031] Example 1

[0032] As shown in FIG1 , the embodiment of the present application provides a vinegar fermentation detection method based on component analysis technology, wherein the method comprises:

[0033] Collecting raw material information and basic environmental information of the target vinegar, wherein the basic environmental information includes the ambient temperature, and the raw material information includes the raw material quality measurement result;

[0034] During vinegar production, raw material information and basic environmental data are key factors influencing the quality of the final product. To ensure the quality stability of vinegar and the controllability of the fermentation process, accurate and comprehensive collection of this basic information is essential. Raw material quality measurements include chemical composition analysis (such as sugar, protein, and fat), microbial contamination, moisture content, and impurity levels. Fluctuations in ambient temperature can affect fermentation rates, microbial population balance, and metabolite production, significantly impacting the flavor and quality of vinegar.

[0035] Using the raw material quality measurement result and the ambient temperature as indexes, searching in a vinegar fermentation detection library to obtain a first indicator influencing factor;

[0036] The Vinegar Fermentation Detection Database integrates a wealth of historical data, fermentation experience, and scientific knowledge. It simulates and analyzes fermentation processes under various raw materials and environmental conditions, and provides corresponding influencing factors. By searching the database using raw material quality measurements and ambient temperature, the database is retrieved to obtain the primary influencing factor. This primary influencing factor quantifies the degree to which raw materials and environmental factors influence the fermentation process.

[0037] Further, the method comprises:

[0038] Acquire multiple sample raw material quality measurement results, multiple sample environmental temperatures, and multiple sample index influencing factors to construct multiple sample particles in the vinegar fermentation detection library;

[0039] Inputting the raw material quality measurement result and the ambient temperature into the vinegar fermentation detection library to obtain target particles;

[0040] Based on the position of the target particle in the vinegar fermentation detection library, matching the k sample particles closest to the target particle;

[0041] An analysis is performed based on the k sample particles to generate the first indicator influencing factor.

[0042] To construct the vinegar fermentation detection library and generate the first indicator influencing factor, the system collects raw material quality measurement results from multiple samples, including data on chemical composition, purity, and impurity content. The ambient temperature corresponding to each sample is also recorded. The sample indicator influencing factors are then calculated using this collected data. These raw material quality measurement results, ambient temperature, and indicator influencing factors are combined to form multiple sample particles within the vinegar fermentation detection library. Each sample particle represents a specific fermentation condition and a corresponding set of influencing factors. When new raw material quality measurement results and ambient temperature data are input into the vinegar fermentation detection library, a target particle is generated, representing a specific state under the current production conditions. Next, the system searches the existing sample particles to identify the k sample particles closest to the target particle. These k sample particles provide information on historical fermentation conditions and influencing factors that are most similar to the target particle. Finally, the first indicator influencing factor is generated through a weighted average of the indicator influencing factors of these k matching sample particles.

[0043] Furthermore, the method further comprises:

[0044] The framework of the vinegar fermentation detection library is constructed by using the raw material quality measurement result as the x-axis of the vinegar fermentation detection library and the ambient temperature as the y-axis of the vinegar fermentation detection library;

[0045] Inputting the mass measurement results of the plurality of sample raw materials and the ambient temperatures of the plurality of samples into the framework of the vinegar fermentation detection library to obtain a plurality of initial sample particles;

[0046] The plurality of sample index influencing factors are used to perform data identification on the plurality of initial sample particles to obtain a plurality of sample particles.

[0047] To effectively organize and manage data during the construction of a vinegar fermentation testing library, a two-dimensional framework can be created, with raw material quality measurement results and ambient temperature as the x-axis and y-axis, respectively. Each point (x, y) in the coordinate system represents a specific combination of raw material quality measurement results and ambient temperature. Multiple sample raw material quality measurement results and ambient temperature are input into the framework of the vinegar fermentation testing library. Each sample data point forms an initial sample particle, whose position in the coordinate system is determined by its raw material quality measurement results and ambient temperature. For each initial sample particle, the data is labeled using its corresponding sample indicator influencing factors. For example, additional dimensions (such as color, size, or labels) are added to the coordinate system to represent the values ​​of these influencing factors. After labeling, the initial sample particles are transformed into sample particles with richer information content. These particles not only represent the raw materials and environmental conditions, but also contain the corresponding fermentation performance data.

[0048] Furthermore, the method further comprises:

[0049] Retrieving identification information of the k sample particles to obtain k sample index influencing factors;

[0050] respectively calculating the inverse of the ratio of the distances from the k sample particles to the target particle to the sum of the distances from the k sample particles to the target particle to obtain k distance coefficients;

[0051] The k distance coefficients are used to perform weighted calculation on the k sample index influence factors, thereby obtaining the first index influence factor.

[0052] The identification information of the k sample particles previously identified as being closest to the target particle is retrieved from the vinegar fermentation detection library. This identification information includes the index impact factor for each sample particle, thereby obtaining the k sample index impact factors. For each sample particle, the ratio of its distance to the target particle to the sum of the distances from all k sample particles to the target particle is calculated. The inverse of this ratio is then taken to obtain the distance coefficient. Sample particles with larger distance coefficients are closer to the target particle and thus receive a greater weight in subsequent calculations. Finally, each sample index impact factor is assigned a different weight based on its corresponding distance coefficient. The k distance coefficients obtained are then used to perform a weighted calculation on the k sample index impact factors. The result of this weighted calculation is the weighted average of all sample index impact factors, which is the first index impact factor.

[0053] Matching a first detection cycle based on the first indicator influencing factor;

[0054] During the vinegar fermentation process, the first indicator impact factor is a quantitative indicator that comprehensively assesses the current fermentation conditions. Based on this impact factor, a corresponding first testing cycle can be assigned to ensure timely monitoring and adjustments to the fermentation process. Generally speaking, a high first indicator impact factor indicates that the current fermentation conditions may have a significant impact on the quality of the final product. For example, when the quality of the base raw materials is poor, the first indicator impact factor will be high, necessitating more frequent testing. Conversely, when the impact factor is low, the testing frequency can be appropriately reduced. Specifically, several testing cycle thresholds are set, each corresponding to a different testing frequency. For example, high, medium, and low thresholds can be set, corresponding to different testing frequencies such as daily, every other day, and weekly. The calculated first indicator impact factor is compared with the set testing cycle thresholds to determine the appropriate testing cycle for the current fermentation conditions. If the impact factor is above the high threshold, the most frequent testing cycle (e.g., daily testing) is selected; if the impact factor is below the low threshold, the least frequent testing cycle (e.g., weekly testing) is selected; and if the impact factor is between the two thresholds, the medium frequent testing cycle (e.g., every other day testing) is selected.

[0055] performing component analysis on Q vinegar samples of the target vinegar according to a preset feature indicator set in a time sequence according to the first detection period to obtain Q vinegar sample feature sequence sets, wherein each vinegar sample feature sequence set includes M vinegar feature sequences, and each vinegar feature sequence corresponds to a feature indicator in the preset feature indicator set;

[0056] During the vinegar fermentation process, the target vinegar will undergo regular compositional analysis according to a pre-determined first testing cycle. Specifically, a pre-determined feature index set is required. This set includes multiple key feature indexes for evaluating vinegar samples, including moisture, acidity, saccharifying enzyme activity, yeast cell count, germination rate, reducing sugars, and alcohol content. During the first testing cycle, Q samples of the target vinegar are collected sequentially in time, each representing the state of the vinegar at a specific point in time. Each collected vinegar sample is then subjected to compositional analysis, using methods such as mass spectrometry to measure the various feature indexes within the sample. After analyzing each sample, the obtained feature index data is organized into feature sequences. Each vinegar sample feature sequence set contains M vinegar feature sequences, each corresponding to a specific feature index within the pre-determined feature index set. Therefore, for each of the Q vinegar samples, Q feature sequence sets are ultimately obtained, each reflecting the performance of the corresponding sample on multiple feature indexes.

[0057] Performing a binary time series analysis on the Q vinegar sample feature sequence sets to obtain a first fermentation detection result of the target vinegar;

[0058] After obtaining the Q vinegar sample feature sequence sets, we next perform a dual-item time series analysis on these feature sequence sets to determine the first fermentation test results of the target vinegar. This dual-item time series analysis involves two aspects: one is to analyze the fermentation uniformity of multiple vinegar samples over the same time period; the other is to analyze the overall fermentation level of the samples at the last moment. The results of these two analyses are combined to obtain the first fermentation test results of the target vinegar.

[0059] Furthermore, performing a binary time series analysis on the Q vinegar sample feature sequence sets to obtain a first fermentation detection result of the target vinegar, the method further includes:

[0060] Performing a simultaneous sequence deviation analysis on the Q vinegar sample feature sequence sets to obtain Q vinegar simultaneous sequence deviations;

[0061] Extracting the vinegar sample features at the last moment of the Q vinegar sample feature sequence sets, and performing central tendency analysis on the extracted Q vinegar sample feature index values ​​to obtain a target vinegar sample feature index value;

[0062] The sequential deviations of the Q vinegar samples and the characteristic index value of the target vinegar sample are used as the first fermentation detection result.

[0063] Performing a simultaneous sequence deviation analysis on the Q vinegar sample feature sequence sets means comparing the differences between the feature index values ​​of different samples at the same time point (i.e., simultaneous sequence). By calculating the standard deviation of the feature index values ​​of each sample at each moment, the degree of dispersion of different samples at the same fermentation stage can be quantified. The vinegar sample features at the last moment (i.e., at or near the end of fermentation) are extracted from the Q vinegar sample feature sequence sets. Central tendency analysis is performed on the Q vinegar sample feature index values ​​at the last moment, such as calculating the mean, median, etc., to obtain a target vinegar sample feature index value that reflects the typical characteristic performance of all samples at the end of fermentation. The aforementioned simultaneous sequence deviation of the Q vinegar samples and the target vinegar sample feature index value are combined to form the first fermentation detection result.

[0064] Furthermore, the method further comprises:

[0065] Performing simultaneous data extraction on the Q vinegar sample feature sequence sets to obtain P simultaneous sample data sets, wherein each simultaneous sample data set includes M vinegar feature index values;

[0066] Traversing the P simultaneous sample data sets to calculate the deviations of vinegar characteristic indicators, obtaining P indicator deviation sets, wherein each indicator deviation set includes the deviations of the M vinegar characteristic indicators in each simultaneous sample data set;

[0067] Performing mean processing on the P indicator deviation sets respectively to obtain P simultaneous sequence deviations;

[0068] The P simultaneous sequence deviations are weightedly calculated to obtain Q simultaneous sequence deviations of vinegar.

[0069] Data is extracted from Q vinegar sample feature sequence sets at the same time series point to form P simultaneous sample data sets. P represents different time points or stages in the time series. Each simultaneous sample data set contains M vinegar feature index values ​​for all Q samples at that time point. The P simultaneous sample data sets are traversed, and the deviation degree of each M vinegar feature index in each data set is calculated. This can be obtained by calculating the average value of each feature index value relative to the average value of that feature index across all samples, for example, using statistics such as standard deviation or absolute deviation. This results in P indicator deviation degree sets, each containing the deviation degrees of the M vinegar feature indexes at the corresponding time point. The deviation degrees in each indicator deviation degree set are averaged, that is, the average of the deviation degrees of all feature indexes in each set is calculated to obtain a single simultaneous deviation degree value representing the overall deviation level of all feature indexes at that time point. This results in P simultaneous deviation degrees, each corresponding to a specific time point or stage in the time series. These P simultaneous deviation degrees are weighted to obtain the final Q vinegar simultaneous deviation degrees.

[0070] Furthermore, the method further comprises:

[0071] Calculating the mean of the Q vinegar sample characteristic index values ​​to obtain the vinegar sample characteristic index mean;

[0072] Using the mean value of the vinegar sample characteristic index as an index, searching among the Q vinegar sample characteristic index values ​​according to a preset concentration step size to obtain a first vinegar sample characteristic index concentration value;

[0073] Determine whether the concentration of the vinegar sample characteristic index mean is greater than the concentration of the first vinegar sample characteristic index concentration value, and if so, update the vinegar sample characteristic index mean to the first vinegar sample index characteristic concentration value according to a certain probability;

[0074] The first vinegar sample index feature concentration value is used as an index to continue searching until a preset number of searches is met, and the first vinegar sample index feature concentration value corresponding to the maximum concentration value in the search process is used as the target vinegar sample feature index value.

[0075] During the vinegar fermentation process, an iterative search and concentration comparison method can be used to identify a representative target vinegar sample characteristic index value. Specifically, the mean of Q vinegar sample characteristic index values ​​is calculated to obtain the mean. Using the mean vinegar sample characteristic index value as an index, a search is performed within the Q vinegar sample characteristic index values ​​using a preset concentration step size. The concentration step size is a customizable distance moved during the search, i.e., the difference from the mean vinegar sample characteristic index value. The search objective is to find a more concentrated value, namely, the first vinegar sample characteristic index concentration value, which has a higher concentration in the raw data than the current mean. The concentration of the mean vinegar sample characteristic index value is then compared with the concentration of the first vinegar sample characteristic index concentration value. The concentration is calculated as the ratio of the number of indicator values ​​to the area of ​​an area constructed with the mean vinegar sample characteristic index value as the center and the preset concentration step size as the radius. This reflects the density of indicator values ​​clustered around the mean vinegar sample characteristic index value. A higher concentration indicates a more representative indicator value. If the concentration of the first vinegar sample characteristic index concentration value is higher, the vinegar sample characteristic index mean is updated to this more concentrated value according to a certain probability. The updated first vinegar sample characteristic index concentration value is used as the new index, and the search continues to find the next more concentrated value. This process is repeated, updating the index value with each iteration and comparing the concentration of the newly found concentration value with the current index value, until the preset number of searches is met. After the iterative search is completed, the first vinegar sample characteristic index concentration value corresponding to the maximum concentration value during the search process is used as the target vinegar sample characteristic index value.

[0076] Using a stability identifier to identify the stability of the Q vinegar sample feature sequence sets to obtain a first stability factor;

[0077] During the vinegar fermentation process, a stability identifier can be used to assess the stability of different sample feature sequence sets. Based on a neural network model, a stability identifier tailored to the characteristics of vinegar fermentation data can be constructed. Q vinegar sample feature sequence sets are fed into the stability identifier. The stability identifier analyzes the data in each feature sequence set, searching for stable patterns, trends, or cyclical changes. Based on the analysis results, it generates a first stability factor. This first stability factor is used to assess the stability of the vinegar fermentation process.

[0078] Determine whether the first stability factor meets a preset stability factor. If so, use the first fermentation detection result as a target fermentation detection result.

[0079] Before analyzing the fermentation process, a stability factor needs to be pre-set to reflect the acceptable level of stability during the vinegar fermentation process. The calculated first stability factor is compared with the preset stability factor standard. If the first stability factor meets or exceeds the preset stability factor standard, it indicates that the stability of the vinegar sample feature sequence set is within an acceptable range, and the current fermentation process is considered stable. If the first stability factor is lower than the preset standard, it may mean that there are unstable factors or potential problems in the fermentation process. If the first stability factor meets the preset stability factor, the first fermentation test result obtained previously can be used as the final target fermentation test result.

[0080] In summary, the embodiments of the present application have at least the following technical effects:

[0081] By collecting raw material information and basic environmental information of the target vinegar, the raw material quality measurement results and ambient temperature are obtained. Based on the raw material quality measurement results and ambient temperature, a vinegar fermentation detection library is searched to obtain a first indicator influencing factor and match it to a first detection cycle. Following this cycle, component analysis is performed on Q samples of the target vinegar in sequence to obtain a vinegar sample feature sequence set. These sample feature sequence sets are then subjected to a binary time series analysis to obtain the first fermentation detection result of the target vinegar. A stability identifier is then used to identify the stability of the sample feature sequence set to obtain a first stability factor. Finally, a determination is made as to whether the first stability factor meets a preset stability factor. If so, the first fermentation detection result is used as the target fermentation detection result. This method solves the technical problem of poor reliability of vinegar detection results in the prior art and achieves the technical effect of improving the quality of vinegar production.

[0082] Example 2

[0083] Based on the same inventive concept as the vinegar fermentation detection method based on component analysis technology in the aforementioned embodiment, as shown in Figure 2, this application provides a vinegar fermentation detection system based on component analysis technology. The system and method embodiments in the embodiments of this application are based on the same inventive concept. The system includes:

[0084] An information collection module 11 is used to collect raw material information and basic environmental information of the target vinegar, wherein the basic environmental information includes the ambient temperature, and the raw material information includes the raw material quality measurement result;

[0085] A retrieval module 12 is configured to search a vinegar fermentation detection library using the raw material quality measurement result and the ambient temperature as indexes to obtain a first indicator influencing factor;

[0086] A matching module 13, configured to match a first detection period based on the first indicator influencing factor;

[0087] a component analysis module 14 configured to perform component analysis on Q vinegar samples of the target vinegar according to a preset feature indicator set in a time sequence according to the first detection period to obtain Q vinegar sample feature sequence sets, wherein each vinegar sample feature sequence set includes M vinegar feature sequences, and each vinegar feature sequence corresponds to a feature indicator in the preset feature indicator set;

[0088] A time series analysis module 15 is configured to perform a two-item time series analysis on the Q vinegar sample feature sequence sets to obtain a first fermentation detection result of the target vinegar;

[0089] an identification module 16 for identifying the stability of the Q vinegar sample feature sequence sets using a stability identifier to obtain a first stability factor;

[0090] The judgment module 17 is used to judge whether the first stability factor meets the preset stability factor. If so, the first fermentation detection result is used as the target fermentation detection result.

[0091] Furthermore, the retrieval module 12 is configured to execute the following method:

[0092] Acquire multiple sample raw material quality measurement results, multiple sample environmental temperatures, and multiple sample index influencing factors to construct multiple sample particles in the vinegar fermentation detection library;

[0093] Inputting the raw material quality measurement result and the ambient temperature into the vinegar fermentation detection library to obtain target particles;

[0094] Based on the position of the target particle in the vinegar fermentation detection library, matching the k sample particles closest to the target particle;

[0095] An analysis is performed based on the k sample particles to generate the first indicator influencing factor.

[0096] Furthermore, the retrieval module 12 is configured to execute the following method:

[0097] The framework of the vinegar fermentation detection library is constructed by using the raw material quality measurement result as the x-axis of the vinegar fermentation detection library and the ambient temperature as the y-axis of the vinegar fermentation detection library;

[0098] Inputting the mass measurement results of the plurality of sample raw materials and the ambient temperatures of the plurality of samples into the framework of the vinegar fermentation detection library to obtain a plurality of initial sample particles;

[0099] The plurality of sample index influencing factors are used to perform data identification on the plurality of initial sample particles to obtain a plurality of sample particles.

[0100] Furthermore, the retrieval module 12 is configured to execute the following method:

[0101] Retrieving identification information of the k sample particles to obtain k sample index influencing factors;

[0102] respectively calculating the inverse of the ratio of the distances from the k sample particles to the target particle to the sum of the distances from the k sample particles to the target particle to obtain k distance coefficients;

[0103] The k distance coefficients are used to perform weighted calculation on the k sample index influence factors, thereby obtaining the first index influence factor.

[0104] Furthermore, the timing analysis module 15 is configured to perform the following method:

[0105] Performing a simultaneous sequence deviation analysis on the Q vinegar sample feature sequence sets to obtain Q vinegar simultaneous sequence deviations;

[0106] Extracting the vinegar sample features at the last moment of the Q vinegar sample feature sequence sets, and performing central tendency analysis on the extracted Q vinegar sample feature index values ​​to obtain a target vinegar sample feature index value;

[0107] The sequential deviations of the Q vinegar samples and the characteristic index value of the target vinegar sample are used as the first fermentation detection result.

[0108] Furthermore, the timing analysis module 15 is configured to perform the following method:

[0109] Performing simultaneous data extraction on the Q vinegar sample feature sequence sets to obtain P simultaneous sample data sets, wherein each simultaneous sample data set includes M vinegar feature index values;

[0110] Traversing the P simultaneous sample data sets to calculate the deviations of vinegar characteristic indicators, obtaining P indicator deviation sets, wherein each indicator deviation set includes the deviations of the M vinegar characteristic indicators in each simultaneous sample data set;

[0111] Performing mean processing on the P indicator deviation sets respectively to obtain P simultaneous sequence deviations;

[0112] The P simultaneous sequence deviations are weightedly calculated to obtain Q simultaneous sequence deviations of vinegar.

[0113] Furthermore, the timing analysis module 15 is configured to perform the following method:

[0114] Calculating the mean of the Q vinegar sample characteristic index values ​​to obtain the vinegar sample characteristic index mean;

[0115] Using the mean value of the vinegar sample characteristic index as an index, searching among the Q vinegar sample characteristic index values ​​according to a preset concentration step size to obtain a first vinegar sample characteristic index concentration value;

[0116] Determine whether the concentration of the vinegar sample characteristic index mean is greater than the concentration of the first vinegar sample characteristic index concentration value, and if so, update the vinegar sample characteristic index mean to the first vinegar sample index characteristic concentration value according to a certain probability;

[0117] The first vinegar sample index feature concentration value is used as an index to continue searching until a preset number of searches is met, and the first vinegar sample index feature concentration value corresponding to the maximum concentration value in the search process is used as the target vinegar sample feature index value.

[0118] It should be noted that the above-mentioned order of the embodiments of the present application is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. The above description is of specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0120] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A vinegar fermentation detection method based on component analysis technology, characterized in that: The method comprises: Collecting raw material information and basic environmental information of the target vinegar, wherein the basic environmental information includes the ambient temperature, and the raw material information includes the raw material quality measurement result; Using the raw material quality measurement result and the ambient temperature as indexes, searching in a vinegar fermentation detection library to obtain a first indicator influencing factor; Matching a first detection cycle based on the first indicator influencing factor; performing component analysis on Q vinegar samples of the target vinegar according to a preset feature indicator set in a time sequence according to the first detection period to obtain Q vinegar sample feature sequence sets, wherein each vinegar sample feature sequence set includes M vinegar feature sequences, and each vinegar feature sequence corresponds to a feature indicator in the preset feature indicator set; Performing a binary time series analysis on the Q vinegar sample feature sequence sets to obtain a first fermentation detection result of the target vinegar; Using a stability identifier to identify the stability of the Q vinegar sample feature sequence sets to obtain a first stability factor; Determine whether the first stability factor meets a preset stability factor. If so, use the first fermentation detection result as a target fermentation detection result.

2. The method according to claim 1, wherein The method further comprises: Acquire multiple sample raw material quality measurement results, multiple sample environmental temperatures, and multiple sample index influencing factors to construct multiple sample particles in the vinegar fermentation detection library; The raw material quality determination results and the ambient temperature are input into the vinegar fermentation detection library Get the target particles; Based on the position of the target particle in the vinegar fermentation detection library, matching the k sample particles closest to the target particle; An analysis is performed based on the k sample particles to generate the first indicator influencing factor.

3. The method according to claim 2, wherein The method further comprises: The framework of the vinegar fermentation detection library is constructed by using the raw material quality measurement result as the x-axis of the vinegar fermentation detection library and the ambient temperature as the y-axis of the vinegar fermentation detection library; Inputting the mass measurement results of the plurality of sample raw materials and the ambient temperatures of the plurality of samples into the framework of the vinegar fermentation detection library to obtain a plurality of initial sample particles; The plurality of sample index influencing factors are used to perform data identification on the plurality of initial sample particles to obtain a plurality of sample particles.

4. The method according to claim 2, wherein The method further comprises: Retrieving identification information of the k sample particles to obtain k sample index influencing factors; respectively calculating the inverse of the ratio of the distances from the k sample particles to the target particle to the sum of the distances from the k sample particles to the target particle to obtain k distance coefficients; The k distance coefficients are used to perform weighted calculation on the k sample index influence factors, thereby obtaining the first index influence factor.

5. The method according to claim 1, wherein Performing a binary time series analysis on the Q vinegar sample feature sequence sets to obtain a first fermentation detection result of the target vinegar, the method further comprising: Performing a simultaneous sequence deviation analysis on the Q vinegar sample feature sequence sets to obtain Q vinegar simultaneous sequence deviations; Extracting the vinegar sample features at the last moment of the Q vinegar sample feature sequence sets, and performing central tendency analysis on the extracted Q vinegar sample feature index values ​​to obtain a target vinegar sample feature index value; The sequential deviations of the Q vinegar samples and the characteristic index value of the target vinegar sample are used as the first fermentation detection result.

6. The method according to claim 5, wherein The method further comprises: Performing simultaneous data extraction on the Q vinegar sample feature sequence sets to obtain P simultaneous sample data sets, wherein each simultaneous sample data set includes M vinegar feature index values; Traversing the P simultaneous sample data sets to calculate the deviations of vinegar characteristic indicators, obtaining P indicator deviation sets, wherein each indicator deviation set includes the deviations of the M vinegar characteristic indicators in each simultaneous sample data set; Performing mean processing on the P indicator deviation sets respectively to obtain P simultaneous sequence deviations; The P simultaneous sequence deviations are weightedly calculated to obtain Q simultaneous sequence deviations of vinegar.

7. The method according to claim 5, wherein The method further comprises: Calculating the mean of the Q vinegar sample characteristic index values ​​to obtain the vinegar sample characteristic index mean; Using the mean value of the vinegar sample characteristic index as an index, searching among the Q vinegar sample characteristic index values ​​according to a preset concentration step size to obtain a first vinegar sample characteristic index concentration value; Determine whether the concentration of the vinegar sample characteristic index mean is greater than the concentration of the first vinegar sample characteristic index concentration value, and if so, update the vinegar sample characteristic index mean to the first vinegar sample index characteristic concentration value according to a certain probability; The first vinegar sample index feature concentration value is used as an index to continue searching until a preset number of searches is met, and the first vinegar sample index feature concentration value corresponding to the maximum concentration value in the search process is used as the target vinegar sample feature index value.

8. A vinegar fermentation detection system based on component analysis technology, characterized in that: For implementing the vinegar fermentation detection method based on component analysis technology according to any one of claims 1 to 7, the system comprises: An information collection module, the information collection module is used to collect raw material information and basic environmental information of the target vinegar, wherein the basic environmental information includes ambient temperature, and the raw material information includes raw material quality measurement results; A retrieval module is configured to search a vinegar fermentation detection library using the raw material quality measurement result and the ambient temperature as indexes to obtain a first indicator influencing factor; a matching module, configured to match a first detection period based on the first indicator influencing factor; a component analysis module, configured to perform component analysis on Q vinegar samples of the target vinegar according to a preset feature indicator set in a time sequence according to the first detection period, to obtain Q vinegar sample feature sequence sets, wherein each vinegar sample feature sequence set includes M vinegar feature sequences, each vinegar feature sequence corresponding to one feature indicator in the preset feature indicator set; A time series analysis module is used to analyze the characteristics of the Q vinegar samples. The sequence data set was subjected to a double-item time series analysis to obtain the first fermentation test results of the target vinegar; an identification module, the identification module being configured to identify the stability of the Q vinegar sample feature sequence sets using a stability identifier to obtain a first stability factor; A judgment module is used to judge whether the first stability factor meets a preset stability factor, and if so, use the first fermentation detection result as a target fermentation detection result.

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