Production quality control method and equipment for vegetable protein beverage and medium

Abstract

The embodiment of the invention discloses a production quality control method and device for a vegetable protein beverage and a medium, and relates to the technical field of vegetable protein beverages, and the method comprises the steps: obtaining production line parameters of a current beverage production line, and carrying out allergen cross contact risk analysis based on the production line parameters, determining at least one target allergen quality inspection node in the current beverage production line; collecting allergen quality inspection data of each target allergen quality inspection node in real time to determine a real-time beverage quality index parameter corresponding to each target allergen quality inspection node; when preset conditions are met, according to the real-time beverage quality index parameters and pre-obtained real-time production line parameters, tracing analysis is conducted on allergen standard exceeding reasons, standard exceeding evaluation data of target allergen quality inspection nodes are determined, and the allergen quality inspection nodes are determined through a pre-constructed allergen component control knowledge base according to the standard exceeding evaluation data. And a corresponding production control strategy is determined, and production quality optimization is carried out through the production control strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of plant protein beverages, and in particular to a production quality control method, equipment and medium for plant protein beverages. Background Art

[0002] Plant protein beverages are milky beverages made from plant nuts, pulp and soybeans (such as soybeans, peanuts, almonds, walnuts, coconuts, etc.), which are processed and blended, and then sterilized or aseptically packaged. Plant protein beverages are typical beverage products based on allergen raw materials. In plant protein beverage processing companies, different types of beverages are produced in the same factory. There is a risk of cross-contact of allergens in the production process of different types of plant protein beverages. For example, when the production line of allergen raw materials and the production line of non-allergen raw materials are similar, the allergen raw materials will fall into the production line of non-allergenic raw materials, increasing the risk of cross-contact of allergens. Since some production processes are common in the processing processes of plant protein beverages with different types of raw materials, the common process flow is produced on the same production line. For example, almond raw materials and oat raw materials are produced using the same grinding equipment and blending tank, which has the characteristics of flexible production lines, high daily production capacity of single products, and low equipment investment. However, there will also be problems of cross-contact of allergens due to residual allergen raw materials in the equipment. Therefore, in the processing of plant protein beverages, in addition to the situation where the production lines are similar, there will also be situations where some processes share the same production line. In the production scenario where different types of plant protein beverages are produced in the same factory, the risk of cross-contact of allergens is increased. Even non-allergenic raw materials will be affected by cross-contact of allergens during processing, and there is a risk of allergenic ingredients being mistakenly introduced into other types of food and beverages during the processing process.

[0003] In the traditional beverage production process, after the beverage is made, the beverage is uniformly tested for quality, and only the final products that do not meet the quality standards are eliminated. In the complex scenario of producing multiple types of plant protein beverages in the same factory, it is difficult to detect allergen cross-contact problems in a timely manner using traditional methods. Once allergens are found to be excessive in the final testing link, a large number of products may have been contaminated before, resulting in the inability to intercept products containing allergens in the production process in a timely manner, increasing the number of unqualified products. In addition, the particularity of allergens exceeding the standard is not taken into account. Unlike other quality issues, allergens exceeding the standard found in the previous process can eliminate or reduce the activity of allergens without affecting product stability and taste.

[0004] Therefore, in the production scenario where different types of plant protein beverages are produced in the same factory, due to the impact of cross-contact of allergens during the processing, unified quality inspection of the beverages after they are produced increases the number of unqualified products and affects the product quality of beverage products with non-allergen raw materials. Summary of the invention

[0005] One or more embodiments of the present specification provide a method, device and medium for controlling the production quality of plant protein beverages, which are used to solve the following technical problems: in the production scenario where different types of plant protein beverages are produced in the same factory, due to the influence of cross-contact of allergens during the processing process, uniform quality inspection is performed on the beverages after the beverages are produced, which increases the number of unqualified products and affects the product quality of beverage products made from non-allergen raw materials.

[0006] One or more embodiments of this specification adopt the following technical solutions: One or more embodiments of the present specification provide a production quality control method for a plant protein beverage, the method comprising: obtaining production line parameters of a current beverage production line, and performing allergen cross-contact risk analysis on the current beverage production line based on the production line parameters, and determining at least one target allergen quality inspection node in the current beverage production line; collecting allergen quality inspection data of each target allergen quality inspection node in real time, and determining a real-time beverage quality index parameter corresponding to each target allergen quality inspection node through the allergen quality inspection data; when the real-time beverage quality index parameter meets a preset condition, tracing the cause of allergen exceeding the standard according to the real-time beverage quality index parameter and the real-time production line parameters obtained in advance, and determining the exceeding standard assessment data of the target allergen quality inspection node, wherein the exceeding standard assessment data includes at least one allergen exceeding standard cause and the allergen exceeding standard predicted quantity corresponding to each exceeding standard cause; determining a corresponding production control strategy according to the exceeding standard assessment data of the target allergen quality inspection node through a pre-constructed allergen component control knowledge base, and optimizing production quality through the production control strategy, wherein the production control strategy includes a preventive node control strategy and a remedial node control strategy.

[0007] One or more embodiments of the present specification provide a production quality control device for plant protein beverages, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above method.

[0008] One or more embodiments of the present specification provide a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to execute the above method.

[0009] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects: through the above technical solution, production line parameters are obtained and allergen cross-contact risk analysis is performed, at least one target allergen quality inspection node is determined based on the risk analysis, and risk production nodes are determined based on the quantified cross-contact risk index. By setting clear preset conditions, production nodes with actual risks are screened out, avoiding excessive attention to the entire production line or missing high-risk areas, and providing precise targets for subsequent quality control and preventive measures; when the real-time beverage quality index parameters meet the preset conditions, the traceability analysis is performed in combination with the real-time beverage quality index parameters and the real-time production line parameters, so as to accurately find out the specific reasons for the excessive allergens. The excessive assessment data not only clarifies the reasons for the excessive allergens, but also gives the predicted amount of excessive allergens corresponding to each reason. By obtaining the real-time production line parameters, the previous production node of the target allergen quality inspection node is Real-time production risk identification is carried out at the production point, taking into account the three main sources of allergen risks: cross-contact of production lines, cross-contact of nodes, and introduction of allergens during the production process. In the production scenario where different types of plant protein beverages are produced in the same factory, not only the risk of cross-contact of allergens between different production lines and different batches at the same production node is paid attention to, but also the risk that the processing technology itself may cause allergen components from non-allergen raw materials, thus achieving full coverage of various potential allergen risks in the production process. The preventive node control strategy in the production control strategy takes measures in advance for potential factors that may cause allergens to exceed the standard based on the risk analysis of the production process. The remedial node control strategy provides effective solutions for products that have already exceeded the standard for allergens, which can save beverages with problems and reduce the economic losses caused by non-conformity, while ensuring that the quality and taste of the beverages meet the standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings: Figure 1 A schematic diagram of a production quality control method for a plant protein beverage provided in an embodiment of this specification; Figure 2 This is a schematic diagram of the structure of a production quality control device for a plant protein beverage provided in an embodiment of this specification. DETAILED DESCRIPTION

[0011] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0012] The embodiment of this specification provides a method for controlling the production quality of a plant protein beverage. It should be noted that the execution subject in the embodiment of this specification can be a server or any device with data processing capabilities. Figure 1 A schematic diagram of a production quality control method for a plant protein beverage provided in an embodiment of this specification, such as Figure 1 As shown, it mainly includes the following steps: Step S101, obtaining production line parameters of a current beverage production line, and performing allergen cross-contact risk analysis on the current beverage production line based on the production line parameters to determine at least one target allergen quality inspection node in the current beverage production line.

[0013] In one embodiment of the present specification, when the current beverage production line is put into operation, the production line parameters of the current beverage production line are obtained, where the production line parameters include a production log corresponding to the current beverage production line, in which the production record of each production node is stored; in addition, in addition to the production log, the node position of each production node in the current beverage production line is also included.

[0014] In traditional beverage production lines or other types of product production lines, a quality inspection process is set up at the finished product production stage or after each production node. Setting a quality inspection process at the finished product production stage makes it difficult to detect allergen cross-contact problems in a timely manner in the complex scenario of producing multiple types of plant protein beverages in the same factory. Once the allergen is found to be excessive in the final inspection link, a large number of products may have been contaminated in the previous stage, resulting in the inability to intercept products containing allergens in the production process in a timely manner, increasing the number of unqualified products. Setting a quality inspection process after each production node increases the workload of quality inspection. Each production node is balanced. Not all production nodes have the same risk of allergen cross-contact. Testing production nodes that do not have the risk of allergen cross-contact increases additional production losses. Therefore, in the complex scenario of a factory producing multiple types of plant protein beverages, target allergen quality inspection nodes should be set according to the actual situation of each beverage production line. The risk of allergen cross-contact in different production lines and production nodes is different. By setting target allergen quality inspection nodes, we can focus on the key links where allergen cross-contact is most likely to occur for testing.

[0015] In one embodiment of the present specification, the previous batch production information corresponding to each production node in the current beverage production line is obtained through the production log in the production line parameters, wherein the previous batch production information includes the previous batch beverage raw material information and the previous batch production time. In the beverage production process, the production log is the data recording the production activities, and records in detail the information of each link from raw material input to product output. The enterprise resource planning (ERP) system or a special production management software can be used to record and manage the production log. According to the layout and process of the production line, the identification of each production node is determined. The production node here can be a specific production equipment (such as a mixing tank, a sterilizer, a filling machine, etc.). In the production log, each production node corresponds to a corresponding record identification for accurate identification and query. According to the identification of the production node, the corresponding previous batch production record is searched in the production log, that is, the production record of the last production line. Through the index information such as the time sequence or the batch number, the previous production record of each production node is located, and the previous batch of beverage raw material information is extracted from these records, including the type, source, batch number, dosage and other detailed information of the raw material, and the previous batch production time is obtained at the same time, and the production time here includes the specific date and time.

[0016] Obtain the node location information of each production node in the production line parameters in the production park. In modern production parks, geographic information system (GIS) technology or production management system is usually used to record and manage the location information of production equipment and production nodes. For example, in the production management system, each production node has corresponding coordinate information (X, Y), indicating its specific location in the production park. Based on the node location information, determine the allergen flow assessment area. It should be noted that the scope of the assessment area is determined based on the understanding of the allergen propagation characteristics. When the production line of the allergen raw material and the production line of the non-allergen raw material are close, the allergen raw material falls into the non-allergen raw material production line. Generally, the propagation distance of the allergen in the direction of air flow with good ventilation conditions is the longest. Therefore, the maximum propagation distance of the allergen can be taken as the assessment radius R1 of the allergen flow assessment area through empirical data or test data. Set a circular area with a radius of R1 with the production node as the center as the allergen flow assessment area. According to the distribution of production lines in the production park, determine the adjacent production nodes of the adjacent production lines located in the allergen flow assessment area. And the node distance between the adjacent production nodes and the node position of the production nodes in the current beverage production line is determined. When determining the node distance between the adjacent production nodes, the Euclidean distance formula can be used. In general, the smaller the node distance, the greater the risk of allergen raw materials falling into the non-allergenic raw material production line. If there are no adjacent production nodes of other production lines in the allergen flow assessment area, it means that there is almost no risk of allergen raw materials falling into this production node. According to the previous batch production information corresponding to each production node and the node distance, the risk production node is determined in the current beverage production line, and the target allergen quality inspection node is determined by the quality inspection node located after the risk production node.

[0017] Through the above technical solution, by obtaining the previous batch production information and node location information, the risk of allergen cross-contamination in the production process can be comprehensively and accurately evaluated, targeting the specific conditions of each production node, including the potential risks related to the previously used raw materials and their locations; the quality inspection node after the risk production node is used as the target allergen quality inspection node, avoiding comprehensive and unfocused quality inspection of the entire production line. The targeted quality inspection node determination method can concentrate limited quality inspection resources on the links most likely to have problems, thereby improving quality inspection efficiency; the method that comprehensively considers production history (previous batch production information) and spatial layout (node ​​location information) can more effectively control the cross-contamination of allergens between different production lines. By determining and monitoring the risk production nodes and target allergen quality inspection nodes, the situation of allergen exceeding the standard can be discovered and handled in a timely manner to ensure that the product meets the allergen safety standards.

[0018] According to the previous batch production information corresponding to each production node and the node distance, the risk production node is determined in the current beverage production line, specifically including: evaluating the cross-contact risk of adjacent production lines of the production node through the node distance and the area radius corresponding to the allergen flow assessment area, and determining the corresponding production line cross-contact risk index of the production node; determining the node cross-contact risk coefficient of the production node according to the raw material attributes of the previous batch beverage raw material information corresponding to each production node, wherein the raw material attributes include allergen raw materials and non-allergen raw materials; using the previous batch production time corresponding to each production node, determining the current node processing time corresponding to the production node, and determining the corresponding node cross-contact risk quantification parameter of the production node based on the current node processing time and the preset allergen component processing time threshold corresponding to the production node; determining the corresponding node cross-contact risk index of the production node based on the node cross-contact risk coefficient of the production node and the corresponding node cross-contact risk quantification parameter of the production node; generating a cross-contact risk index according to the production line cross-contact risk index and the node cross-contact risk index, and determining the risk production node whose cross-contact risk index meets the preset conditions in the current beverage production line.

[0019] In one embodiment of the present specification, the smaller the node distance between a production node and an adjacent production node, the greater the risk that the allergen raw material falls into the non-allergenic raw material production line. Therefore, the adjacent production line cross-contact risk of the production node is evaluated in a 1-ratio manner through the ratio of the node distance and the area radius corresponding to the allergen flow assessment area, and the 1-ratio is determined as the production line cross-contact risk index corresponding to this production node. That is to say, the production line cross-contact risk index is used to represent the impact of the adjacent production line on this production node in the current production line. Assuming that the adjacent production line produces with allergen raw materials, the higher the production line cross-contact risk index, the more likely the allergen raw materials in the adjacent production line are to have a cross-contact impact on this production node in the current production line.

[0020] In addition, the raw material information of the previous batch of beverages corresponding to each production node includes raw material attributes, which include allergen raw materials and non-allergen raw materials. The node cross-contact risk coefficient of this production node is determined by the raw material attributes of this production node in the previous batch production process. When the raw material attribute is an allergen raw material, it means that allergen residues will be produced in the previous batch production process, resulting in the node cross-contact risk before and after this node, so the corresponding node cross-contact risk coefficient is 1; when the raw material attribute is a non-allergen raw material, since the raw material is a non-allergen raw material, the previous batch production process has no effect on the current production node, so the corresponding node cross-contact risk coefficient is 0.

[0021] The processing time of the current node corresponding to each production node is determined by the previous batch production time corresponding to the production node, that is, the time interval between the previous batch production time of this production node in the previous batch production process and this production node in the current production process. Generally speaking, if the allergen raw materials were processed by equipment A in the previous batch production process, in order to avoid obvious cross-contact of equipment A in the subsequent processing of non-allergen raw materials, equipment A is usually cleaned for a long time during the production change. The longer the equipment cleaning operation, the better the corresponding cleaning effect, that is, the less allergen raw materials are left.

[0022] Obtain the minimum time threshold corresponding to the equipment cleaning operation. The minimum time threshold here is the minimum value under the condition of ensuring the cleaning effect, and determine the allergen component processing time threshold corresponding to the production node. Obtain the current cumulative production time of the production node in the current batch production process, calculate the difference between the current node processing time and the current cumulative production time, and the difference here can represent the equipment cleaning operation time corresponding to the production change after the previous batch production process; then, calculate the ratio of the difference and the allergen component processing time threshold, and determine the corresponding node cross-contact risk quantification parameter of the production node by 1-ratio. It should be noted that the smaller the current node processing time here, the larger the corresponding node cross-contact risk quantification parameter, and the higher the corresponding node cross-contact risk, indicating that the cleaning time for production change is short and the risk of allergen residue is higher. The corresponding node cross-contact risk index of the production node is determined by multiplying the node cross-contact risk coefficient of the production node and the corresponding node cross-contact risk quantification parameter of the production node.

[0023] The cross-contact risk index is generated based on the sum of the production line cross-contact risk index and the node cross-contact risk index, and the risk production node whose cross-contact risk index meets the preset conditions is determined in the current beverage production line. It should be noted that the preset condition here is the risk production node whose cross-contact risk index is greater than 0. After the risk production node is determined, the target allergen quality inspection node is determined based on the quality inspection node located after the risk production node.

[0024] Through the above technical scheme, the cross-contact risk index is generated by integrating the production line cross-contact risk index and the node cross-contact risk index, which can comprehensively consider the risk of cross-contact of allergens from adjacent production lines and different batches of the same production line during the production process, and considers both the possibility of cross-contamination of allergens due to distance factors between adjacent production lines and the risk of residual allergens due to incomplete cleaning of the same equipment in different batches of production; based on the quantified cross-contact risk index, the risky production nodes are determined, and by setting clear preset conditions, the production nodes with actual risks are screened out, avoiding excessive attention to the entire production line or missing high-risk areas, and providing precise targets for subsequent quality control and preventive measures.

[0025] Step S102, collecting allergen quality inspection data of each target allergen quality inspection node in real time, so as to determine the real-time beverage quality index parameters corresponding to each target allergen quality inspection node through the allergen quality inspection data.

[0026] In the prior art, there are many ways to detect the quality of allergens, for example, through immunochromatographic test strips, enzyme-linked immunosorbent assay (ELISA) kits, etc., but immunochromatographic test strips are based on the specific reaction of antigen-antibody, and the specific antibody is fixed on the detection area of ​​the test strip. When the sample containing the allergen is dripped onto the test strip, the allergen (antigen) in the sample will bind to the antibody, and the presence of the allergen will be indicated by the color development of the marker (such as colloidal gold); in addition, ELISA is based on the specific binding of antigen-antibody and amplifies the detection signal through enzyme-labeled secondary antibodies. Although ELISA kits have high sensitivity and accuracy, the operation is relatively complicated and requires multiple steps of incubation, washing and sample addition. The entire detection process usually takes several hours (about 2-4 hours), and professional equipment such as an enzyme reader is required to read the results. Therefore, traditional allergen quality detection methods are not compatible in terms of allergen content and real-time performance.

[0027] In one embodiment of the present specification, the allergen quality inspection data of each target allergen quality inspection node is collected by near infrared spectroscopy technology. The near infrared spectrum mainly reflects the vibration information of hydrogen-containing groups (such as CH, NH, OH). Allergen proteins and other ingredients in plant protein beverages contain these groups. Different allergen components have specific absorption peaks in the near infrared region. Therefore, the near infrared spectroscopy technology can achieve the effect of obtaining the allergen content in real time.

[0028] The real-time beverage quality index parameters corresponding to each target allergen quality inspection node are determined through the allergen quality inspection data, specifically including: obtaining the allergen quality inspection data, wherein the allergen quality inspection data is beverage spectral data; performing data cleaning and preprocessing operations on the beverage spectral data to determine spectral processing data; and determining the corresponding real-time beverage quality index parameters according to the spectral processing data through a pre-constructed regression prediction model, wherein the real-time beverage quality index parameters include allergen quality inspection content.

[0029] In one embodiment of the present specification, allergen quality inspection data is obtained, and the allergen quality inspection data is beverage spectral data; data cleaning and preprocessing operations are performed on the beverage spectral data to determine spectral processing data. Data cleaning is performed on the beverage spectral data to remove outliers and noise. Outliers may be data points that significantly deviate from the normal spectral range due to spectrometer failure or other factors. Noise can be removed by smoothing filtering and other methods, such as using a moving average method to improve data quality. In order to convert beverage spectral data into allergen quality inspection content, a regression prediction model needs to be built in advance.

[0030] First, a large number of plant protein beverage samples with known allergen content are collected. The allergen content of the samples can be accurately determined by enzyme-linked immunosorbent assay, covering different raw material sources, production batches and possible allergen concentration ranges to ensure that the calibration model has good representativeness and versatility. Use a near-infrared spectrometer to perform spectral scanning on each sample to obtain near-infrared spectral data with a wavelength range of usually 780-2500nm. During this process, the conditions for spectral acquisition (such as optical path, temperature, number of scans, etc.) should be kept consistent to reduce the impact of external factors on the spectrum. The collected raw spectral data is cleaned to remove outliers and noise to improve the quality of the data. In order to eliminate the influence of factors such as baseline drift and scattering effects on the spectrum, it is usually necessary to preprocess the spectrum. Common preprocessing methods include standardization (such as mean centering, standard deviation standardization), normalization (such as maximum-minimum normalization), derivative processing (first-order derivative or second-order derivative) and multivariate scattering correction (MSC), etc. The above preprocessing methods can enhance the correlation between spectral features and allergen content and improve the accuracy of the model.

[0031] A partial least squares (PLS) regression model was established, and the collected sample data was divided into a training set and a validation set. The training set is used to build the PLS model, usually accounting for 70%-80% of the total number of samples, and the validation set is used to evaluate the performance of the model, accounting for 20%-30% of the total number of samples. In this process, it is ensured that the sample distribution of the training set and the validation set can represent the characteristics of the entire data set, for example, the distribution of allergen content should have a similar range and proportion. The core of PLS ​​regression is to explain the relationship between spectral data (X variable) and allergen content (Y variable) by extracting principal components. In the training set, the covariance matrix of the spectral data matrix X is calculated, and the eigenvalue decomposition is performed on it to obtain a set of principal components. These principal components are linear combinations of the original spectral variables, which can maximize the explanation of the covariance between X and Y. In order to ensure the selection of an appropriate number of principal components, it can usually be determined by methods such as cross-validation to avoid overfitting or underfitting. The spectral data in the training set and the corresponding allergen content data are used to fit the PLS model by the least squares method. That is, a regression equation between the principal component and the allergen content is established to determine the coefficients of the model. In this process, the model will learn the intrinsic relationship between spectral features and allergen content, so that it can predict allergen content based on new spectral data. Use the validation set to evaluate the established PLS model. Common evaluation indicators include root mean square error (RMSE), coefficient of determination (R²), and relative analytical error (RPD). RMSE reflects the average error between the model's predicted value and the true value, R² measures the model's fit to the data, and RPD is used to evaluate the model's predictive ability. Based on the evaluation results, the model is optimized, such as adjusting the number of principal components, trying different spectral preprocessing methods, or increasing the number of samples, until satisfactory model performance is obtained.

[0032] In a small data processing unit next to the production line, the established PLS calibration model is embedded in the chemometrics software. When the near-infrared spectrometer acquires new beverage spectral data, the data is first preprocessed in the same way as in the model training phase, and then the preprocessed spectral data is input into the PLS model. The model predicts the allergen content in the beverage based on the learned relationship to determine the allergen quality inspection content. As the production process progresses and sample data accumulates, the model is verified and updated regularly. Factors such as production raw materials, process parameters, or spectrometer performance may change, resulting in a decrease in the accuracy of the model. By continuously adding new sample data, re-evaluating and optimizing the model, it can be ensured that the model maintains good predictive performance over a long period of time and accurately converts spectral data into allergen content values.

[0033] Through the above technical solution, near-infrared spectroscopy technology can be used to collect allergen quality inspection data for each target allergen quality inspection node in real time. Compared with traditional detection methods such as ELISA kits that take several hours to obtain results, near-infrared spectroscopy technology can obtain data instantly, which makes it possible to grasp the allergen content in real time during the production process; the collected beverage spectral data is cleaned and preprocessed to remove noise and interference information and improve data quality; and then the pre-built regression prediction model is used to accurately determine the real-time beverage quality index parameters based on the processed spectral data, including the allergen quality inspection content. A series of operations from data collection to processing ensure the accuracy of the test results.

[0034] Step S103, when the real-time beverage quality index parameter meets the preset conditions, the cause of the allergen exceeding the standard is traced and analyzed based on the real-time beverage quality index parameter and the real-time production line parameters obtained in advance, and the exceeding standard assessment data of the target allergen quality inspection node is determined.

[0035] The excess assessment data includes at least one allergen excess cause and the predicted excess amount of allergens corresponding to each excess cause; In one embodiment of the present specification, if the allergen quality inspection content in the real-time beverage quality index parameter of the target allergen quality inspection node is a non-zero value, it means that the target allergen quality inspection node of the current beverage production line has an allergen exceeding the standard during the processing of non-allergen raw materials. In this case, if the allergen quality inspection content is higher than the preset allergen content threshold, it means that the production node corresponding to the target allergen quality inspection node has a large allergen introduction problem. It should be noted that the preset allergen content threshold here can be obtained through experimental data or set according to empirical data, which is used to indicate the maximum allergen content that can be eliminated or reduced in activity through subsequent production processes, that is, exceeding this allergen content threshold, it cannot be eliminated or reduced in activity through subsequent production processes. Therefore, when the allergen quality inspection content is greater than 0 and lower than the preset allergen content threshold, the cause of the allergen exceeding the standard is traced and analyzed according to the real-time beverage quality index parameter and the pre-acquired real-time production line parameters, and the exceeding standard evaluation data of the target allergen quality inspection node is determined, and the exceeding standard evaluation data includes at least one allergen exceeding standard cause and the predicted amount of allergen exceeding standard corresponding to each exceeding standard cause.

[0036] Through the above technical scheme, by real-time monitoring of the allergen quality inspection content in the real-time beverage quality index parameters of the target allergen quality inspection node, once the value is non-zero and higher than the preset allergen content threshold, it can be quickly determined that there is a large allergen introduction problem at the production node, and abnormalities can be discovered in time during the production process to prevent problematic products from further flowing into subsequent production links, thereby reducing potential losses. On the beverage production line, when it is detected that the allergen content of a certain target allergen quality inspection node is higher than the preset allergen content threshold, the relevant production process can be stopped immediately to prevent more unqualified products from being produced; the preset allergen content threshold is used as a measurement standard, which can not only determine whether it exceeds the standard, but also distinguish the severity of the allergen introduction. When the allergen quality inspection content is greater than 0 but lower than the threshold, it indicates that the allergen exceeding the standard problem at this time can be controlled through the subsequent quality control process.

[0037] According to the real-time beverage quality index parameters and the real-time production line parameters acquired in advance, the causes of allergen exceeding the standard are traced and analyzed to determine the exceeding standard assessment data of the target allergen quality inspection node, specifically including: obtaining the real-time production line parameters, wherein the real-time production line parameters include the real-time processing technology data of each production node; according to the real-time production line parameters of the current beverage production line, real-time production risk identification is performed on the previous production node of the target allergen quality inspection node to determine the production risk index set corresponding to the previous production node, wherein the production risk index set includes the production line cross-contact risk index, the node cross-contact risk index and the production process introduction risk index; through the production risk index set and the real-time beverage quality index parameters, weights are assigned to each cause of allergen exceeding the standard to determine the allergen exceeding standard prediction quantity corresponding to each cause of allergen exceeding the standard, wherein the cause of allergen exceeding the standard includes production line cross-contact, node cross-contact and production process introduction.

[0038] In one embodiment of the present specification, real-time production line parameters are obtained, and the real-time production line parameters include real-time processing technology data of each production node, such as heat treatment process parameters, chemical treatment process parameters, and enzyme treatment process parameters. In addition to the real-time processing technology data, the real-time production line parameters also include the real-time production log of each production node and the adjacent production line production nodes of each production node in the allergen flow assessment area. According to the real-time production line parameters of the current beverage production line, the previous production node of the target allergen quality inspection node is identified in real time, and the production risk indicator set corresponding to the previous production node is determined. The production risk indicator set includes the production line cross-contact risk indicator, the node cross-contact risk indicator, and the production process introduction risk indicator; it should be noted that the previous production node of the target allergen quality inspection node refers to the output node of the quality inspection object of the quality inspection node.

[0039] In addition to the cross-contact of allergens between adjacent production lines and the cross-contact of allergens in the production process of adjacent batches at the same production node, in the processing process of certain production nodes, even non-allergen raw materials will have allergen components during the processing process, so the introduction of risk indicators in the production process is used to measure this risk. For example, heat treatment processes such as high-temperature sterilization and baking may change the protein structure in non-allergen raw materials and produce new allergen epitopes; in addition, when adding chemical reagents for processing, the chemical properties of the raw materials may be changed, resulting in the production of allergens. For example, in the process of adjusting the pH value of the beverage, some proteins may undergo structural changes due to changes in pH and generate allergenic components; in addition, enzymes are used in the processing of plant protein beverages to improve the functional properties of proteins, but if the type of enzyme or the processing conditions are inappropriate, allergens may be produced. For example, using certain proteases to hydrolyze plant proteins may produce some peptides with allergenic activity. Through the production risk indicator set and real-time beverage quality indicator parameters, weights are assigned to each cause of allergen exceeding the standard to determine the predicted amount of allergen exceeding the standard corresponding to each cause of allergen exceeding the standard. The causes of allergen exceeding the standard include cross-contact of production lines, cross-contact of nodes and introduction into the production process.

[0040] Through the above technical solution, by obtaining real-time production line parameters, real-time production risk identification is performed on the previous production node of the target allergen quality inspection node, and the three main allergen risk sources of production line cross-contact, node cross-contact and production process introduction are fully considered. Not only the risk of allergen cross-contact between different production lines and different batches at the same production node is paid attention to, but also the risk that the processing technology itself may cause non-allergen raw materials to produce allergen components. All-round coverage of various potential allergen risks in the production process is achieved; based on real-time processing technology data, real-time production logs and adjacent production line production nodes and other information, the risk status of specific production nodes can be accurately located; a set of production risk indicators is determined, and different types of risks are converted into specific risk indicators, such as production line cross-contact risk indicators, node cross-contact risk indicators and production process introduction risk indicators, so that the risk level can be quantified, which is convenient for intuitive comparison and analysis of different risks. After clarifying the cause of each allergen exceeding the standard and its corresponding predicted quantity of exceeding the standard, prevention and control strategies can be formulated in a targeted manner according to the severity and possibility of the risk.

[0041] In one embodiment of the present specification, according to the real-time production line parameters of the current beverage production line, the previous production node of the target allergen quality inspection node is identified in real time, and the production risk index set corresponding to the previous production node is determined, which specifically includes: according to the beverage raw material information in the current beverage production line, the process parameter standard value corresponding to each production node is matched, so as to determine the overflow ratio of the processing parameter corresponding to the production node through the real-time processing technology data and the corresponding process parameter standard value of the previous production node of the target allergen quality inspection node, so as to determine the corresponding production process introduction risk index. First, the ideal process parameter standard values ​​of different plant protein beverage raw materials at each production node are collected, which can come from the standard specifications of the beverage industry, the long-term accumulated production experience of the enterprise, the research results of scientific research institutions, and the raw material processing guide provided by the supplier. For example, for soy protein beverages, the appropriate grinding speed and temperature in the grinding link, the standard addition amount and pH range of various additives in the mixing link, the appropriate temperature and time in the sterilization link, etc. are collected, and a database is constructed through the collected data. Through the sensors and data acquisition equipment installed on the production line, the processing data of the previous production node of the target allergen quality inspection node can be obtained in real time. The equipment may include temperature sensors, pressure sensors, flow meters, component analyzers, etc. The matching of the processing data collected in real time with the corresponding process parameter standard values ​​in the database can be achieved through data processing software. According to the production node and raw material information, the corresponding standard value is found in the database and associated with the real-time data. Processing parameter overflow includes positive overflow (real-time data is greater than the standard value) and negative overflow (real-time data is less than the standard value). For different process parameters, the impact of overflow may be different. For example, in the sterilization link, the temperature lower than the standard value may lead to incomplete sterilization, while in the addition of chemical additives, the addition amount higher than the standard value may cause abnormal chemical reactions and increase the risk of allergens. Therefore, here, a certain process parameter value is determined by positive overflow or negative overflow to determine which overflow will affect the production of allergens. That is to say, the risk of allergen introduction in the processing process can be measured by the overflow ratio. According to the nature of the process parameters, determine the appropriate calculation method to calculate the overflow ratio. For positive overflow, overflow ratio = (real-time data - standard value) / standard value × 100%; for negative overflow, overflow ratio = (standard value - real-time data) / standard value × 100%. For example, the standard addition amount of citric acid in the preparation of a protein beverage is 0.5% (mass fraction), and the real-time detection of the addition amount is 0.6%, then the positive overflow ratio = (0.6% - 0.5%) / 0.5% × 100% = 20%. The processing parameter overflow ratio is determined as the corresponding production process introduction risk indicator.

[0042] In addition, the first production timestamp of the previous allergen raw material corresponding to the previous production node of the target allergen quality inspection node is determined through the real-time production log of each production node in the real-time production line parameters to determine the allergen processing time of the production node, and the corresponding real-time node cross-contact risk index is determined according to the allergen processing time and the allergen component processing time threshold corresponding to the previous allergen raw material. The real-time production log records the detailed production activity information of each production node. First, the production log needs to be parsed to extract the records related to the previous production node of the target allergen quality inspection node. In the extracted related records, the production record of the previous use of the allergen raw material is determined by searching and identifying the raw material information. For example, there may be such records as "[timestamp 1]-Use peanut raw material to start production" in the log. By identifying "peanut raw material", it is determined that this is the previous production activity using the allergen raw material, and the corresponding timestamp, i.e., the first production timestamp, is recorded. After determining the first production timestamp, the current production time is obtained, which can be achieved through the clock or timestamp recording function in the production system. The current time represents the time point when the current production process advances to the previous production node of the target allergen quality inspection node. Get the current cumulative production time of the production node in the current batch production process. According to the current time and the first production timestamp, the time difference is the sum of the allergen processing time and the working time of the current production line in processing the current raw material. Therefore, the time difference is subtracted from the current cumulative production time to get the allergen processing time. Calculate the difference between the current node processing time and the current cumulative production time. The difference here can represent the equipment cleaning operation time corresponding to the production change after the previous batch production process. For example: Assume that the last time the allergen raw material (such as peanut raw material) was used in the blending tank to start production at 9 am on November 10, 2024, that is, the first production timestamp is 2024-11-10 09:00:00. When the current production process advances to the production node before the target allergen quality inspection node (still this blending tank), the time is 2 pm on November 10, 2024, that is, 2024-11-10 14:00:00. In the current batch production process, from the start of the current batch production to this moment (2024-11-10 14:00:00), the cumulative production time of the blending tank in processing the current non-allergenic raw materials is 3 hours. Calculate the time difference between the current time and the first production timestamp, that is, the time difference from 2024-11-10 09:00:00 to 2024-11-10 14:00:00 is 5 hours. This 5 hours is actually the total time from the last production using peanut raw materials to the present, including the processing time of peanut allergens and the working time of the current production line in processing the current non-allergenic raw materials. It is known that the current cumulative production time is 3 hours, and the time difference of 5 hours obtained above is subtracted from the current cumulative production time of 3 hours, that is, hours.Therefore, the allergen processing time for this production node is 2 hours.

[0043] The threshold for the processing time of allergen components is determined based on past production experience, relevant experimental research, and understanding of the characteristics of allergens. Different allergen raw materials require different processing times to effectively reduce or eliminate the risk of allergen residues due to their different physical and chemical properties. For example, for peanut allergens, after multiple experiments and actual production verification, it was found that using a specific cleaning process, at least 3 hours of processing time is required to reduce allergen residues to a safe level. Then 3 hours is the threshold for the processing time of peanut allergen components. With the improvement of production processes, the introduction of new equipment, or the in-depth study of allergens, the threshold for the processing time of allergen components may need to be updated. When more efficient cleaning equipment or detergents are used, the threshold for the processing time of peanut allergens may be shortened to 2 hours. This threshold data needs to be updated in a timely manner to ensure the accuracy of risk assessment. The reference difference is obtained by subtracting the threshold for the processing time of allergen components from the processing time of allergens, and the ratio of this difference to the threshold for the processing time of allergen components is calculated. The corresponding real-time node cross-contact risk index of the production node is determined by the 1-ratio method. The higher the real-time node cross-contact risk index, the higher the node cross-contact risk.

[0044] In addition, the adjacent production line production nodes of each production node in the real-time production line parameters in the pre-set allergen flow assessment area are determined, and the current production time interval corresponding to the production node is determined through the real-time production log of the production node, so as to determine the startup state of the adjacent production line production node in the current production time interval, and the node cross-contact risk coefficient corresponding to the production node is determined based on the startup state. For example, a circular area with a radius of 5 meters and each production node as the center is used as the allergen flow assessment area. In the allergen flow assessment area of ​​node X, there is an adjacent production line (set as production line B) producing almond protein beverages. The mixing tank production node of this production line (set as node Y) is in the assessment area, so node Y is the adjacent production line production node of node X. By checking the real-time production log of node X, it is determined that the current production time interval corresponding to node X is 10:0-11:00 on December 15, 2024. The real-time production log of production line B is obtained again to determine that node Y is in the startup production state in this time interval. Since node Y is in the startup state during the current production time interval, it means that there is a possibility that allergens cross-contact node X through air transmission, equipment connection, etc.; if the production node of the adjacent production line is not started, the risk is relatively low, and the coefficient can be set to a value close to 0. According to the preset rules, when the production node of the adjacent production line is started during the current production time interval, the node cross-contact risk coefficient is set to 0.8, 0.9 or 1; if it is not started, it is set to 0.2, 0.1 or 0. You can choose to set the rules according to your needs. Here, 0.8 and 0.2 are used as examples for explanation. The node cross-contact risk coefficient corresponding to node X is 0.8.

[0045] The quantitative parameter of the risk of cross-contact of the production line corresponding to the production node is determined by the node distance between the production node of the adjacent production line and the production node obtained in advance and the area radius corresponding to the allergen flow assessment area. Continuing with the above example, the node distance between node X and node Y is 3 meters, and the radius of the allergen flow assessment area is 5 meters. Then the ratio of the node distance to the area radius is 3 / 5=0.6. The larger this data is, the larger the node distance is under the same startup state. Since the larger the node distance is, the lower the corresponding production line cross risk is, the quantitative parameter of the risk production line cross contact risk is determined by the 1-ratio method, that is, 1-0.6=0.4. According to the product of the quantitative parameter of the production line cross contact risk and the cross contact risk coefficient of the node, 0.4*0.8=0.32 is obtained, and the real-time production line cross contact risk index corresponding to node X is determined, so as to determine the real-time production line cross contact risk index; through this index, the risk degree of the production node affected by the cross contact of the adjacent production line can be intuitively evaluated. That is to say, the smaller the node distance, the smaller the ratio of the corresponding node distance to the area radius, the larger the corresponding risk production line cross-contact risk quantification parameter, and the higher the risk of falling into the risk; in this case, if the production node of the adjacent production line is in the startup state, the node cross-contact risk coefficient is larger than that in the non-startup state, and the corresponding real-time production line cross-contact risk index is larger.

[0046] The production process introduction risk index, the real-time node cross-contact risk index and the real-time production line cross-contact risk index obtained through the above process are used to determine the production risk index set corresponding to the previous production node.

[0047] In one embodiment of the present specification, a weight is assigned to each allergen exceeding the standard cause through the production risk index set and the real-time beverage quality index parameter to determine the allergen exceeding standard prediction amount corresponding to each allergen exceeding the standard cause, specifically including: establishing a mapping relationship between each allergen exceeding the standard cause and the corresponding risk index. The corresponding mapping relationship in the first embodiment here is that the risk index corresponding to the production line cross-contact is the real-time production line cross-contact risk index, the risk index corresponding to the node cross-contact is the real-time node cross-contact risk index, and the risk index corresponding to the production process introduction is the production process introduction risk index.

[0048] In addition to the above methods, the second embodiment can also be used to achieve: collect detailed data on allergen excess in the historical production process, including different types of allergen excess reasons (production line cross-contact, node cross-contact, production process introduction, etc.) and corresponding production risk indicators (production line cross-contact risk indicator, node cross-contact risk indicator, production process introduction risk indicator), conduct in-depth analysis of the above data, determine the intrinsic connection between each allergen excess reason and each risk indicator, and use statistical analysis methods, such as correlation analysis, to quantify the relationship between the allergen excess reason and each risk indicator. For example, through data analysis, it is found that there is a positive correlation between the allergen excess caused by production line cross-contact and the production line cross-contact risk indicator, and roughly presents a certain proportional relationship. Based on the above analysis results, a mapping relationship model between each allergen excess reason and the corresponding risk indicator is established. Establish a functional relationship, such as the predicted amount of allergen excess caused by production line cross-contact = f (production line cross-contact risk indicator). This function can be linear (such as y = kx + b) or nonlinear, and the specific form depends on the analysis results of the actual data.

[0049] According to the production risk indicator set, multiple risk indicators in the production risk indicator set are processed to determine the risk ratio of each risk indicator in the production risk indicator set. Since the risk indicators in the above embodiments are all numerical values ​​greater than 0 and less than 1, the cross-contact risk indicator value of the production line is set to x1, the cross-contact risk indicator value of the node is x2, and the risk indicator value introduced in the production process is x3. The sum of the cross-contact risk indicator value of the production line, the cross-contact risk indicator value of the node, and the risk indicator value introduced in the production process is x3. The cross-contact risk indicator value of the production line is x1, the cross-contact risk indicator value of the node is x2, and the sum of the cross-contact risk indicator value of the production line, the cross-contact risk indicator value of the node is x2, and the risk indicator value introduced in the production process is x3. The corresponding ratio of the cross-contact risk indicator of the production line is x1 / (x1+ x2+ x3), and so on. The risk ratio of each risk indicator in the production risk indicator set is obtained. If the risk indicators in the above embodiments are adopted in other ways, then in this step, it is necessary to normalize the risk indicators before calculating the risk ratio.

[0050] The predicted excess amount corresponding to each risk indicator is determined by the risk ratio of each risk indicator and the real-time beverage quality indicator parameter, and the total amount of allergen content exceeding the standard in the real-time beverage quality indicator is allocated. First, the total amount of allergen content exceeding the standard is determined by performing a difference calculation between the real-time beverage quality indicator parameter and the preset standard of the allergen allowable amount, and the product of the risk ratio of each risk indicator and the total amount of allergen content exceeding the standard is calculated to obtain the predicted excess amount corresponding to each risk indicator. And based on the mapping relationship and the predicted excess amount corresponding to each risk indicator, the predicted excess amount of allergens corresponding to the cause of each allergen exceeding the standard is determined. In the case of the above-mentioned embodiment one, according to the corresponding relationship between the cause of allergen exceeding the standard and the risk indicator in the mapping relationship, the predicted excess amount corresponding to each risk indicator is determined as the predicted excess amount of allergens corresponding to the cause of each allergen exceeding the standard.

[0051] In the case corresponding to the above-mentioned second embodiment, the predicted amount of allergen excess corresponding to each allergen excess cause is determined according to the mapping relationship between the cause of each allergen excess and the corresponding risk indicator established previously, as well as the predicted excess amount corresponding to each risk indicator. For example, it is known that the predicted excess amount corresponding to the production line cross-contact risk indicator is Q1. Through the mapping relationship "predicted amount of allergen excess caused by production line cross-contact = f (production line cross-contact risk indicator)", Q1 is substituted into the function f to obtain the predicted amount of allergen excess caused by production line cross-contact P1. Similarly, according to the predicted excess amount Q2 corresponding to the node cross-contact risk indicator and the corresponding mapping relationship, the predicted amount of allergen excess P2 caused by node cross-contact is obtained; according to the predicted excess amount Q3 corresponding to the risk indicator of introduction of the production process and the corresponding mapping relationship, the predicted amount of allergen excess P3 caused by the introduction of the production process is obtained.

[0052] Through the above technical scheme, by establishing a mapping relationship between the cause of each allergen exceeding the standard and the corresponding risk indicator, determining the connection between different risk factors and the cause of exceeding the standard, and calculating the risk ratio of each risk indicator in the production risk indicator set, it is possible to quantify the contribution of different risk factors to the overall risk; by calculating the total amount of allergen content exceeding the standard and allocating it to each risk indicator according to the risk ratio, the predicted exceeding amount is more in line with the risk situation in actual production; combining the mapping relationship and the predicted exceeding amount to determine the predicted exceeding amount of allergens corresponding to the cause of each allergen exceeding the standard, further refining the accuracy of the prediction; after clarifying the predicted exceeding amount corresponding to the cause of each allergen exceeding the standard, it is convenient to formulate more targeted production control strategies.

[0053] Step S104, through the pre-constructed allergen component control knowledge base, according to the over-standard assessment data of the target allergen quality inspection node, the corresponding production control strategy is determined to optimize the production quality through the production control strategy.

[0054] By using the pre-constructed allergen component control knowledge base, according to the exceeding standard assessment data of the target allergen quality inspection node, the corresponding production control strategy is determined, specifically including: determining the allergen exceeding standard cause in which the allergen exceeding standard predicted amount in the exceeding standard assessment data is higher than the preset exceeding standard threshold as the target control exceeding standard cause; determining the corresponding target control object, wherein the target control object includes the production node corresponding to the target allergen quality inspection node and the next production node corresponding to the target allergen quality inspection node; through the target control exceeding standard cause, using the allergen component control knowledge base, determining the preventive node control strategy corresponding to the next production process corresponding to the production node in the target control object, wherein the preventive node control strategy includes any one or more of equipment cleaning operation, equipment isolation operation and process parameter adjustment operation; according to the allergen exceeding standard predicted amount corresponding to the target control exceeding standard cause and the real-time beverage quality index parameter, using the allergen component control knowledge base, determining the remedial node control strategy corresponding to the current production process corresponding to the next production node in the target control object, wherein the remedial node control strategy includes any one or more of physical treatment and chemical treatment.

[0055] In one embodiment of the present specification, the predicted amount of allergen exceeding the standard corresponding to each allergen exceeding the standard reason is extracted from the exceeding standard evaluation data of the target allergen quality inspection node, and the predicted amount is compared with the preset exceeding standard threshold one by one. The preset exceeding standard threshold can be obtained by processing the standard value set according to the product quality standard, previous production experience or relevant regulatory requirements. For example, 60% of the standard value can be set. If the standard value is that the content of allergen protein in each milliliter of beverage does not exceed 10 micrograms, the allergen exceeding standard threshold is set to 6 micrograms. Since there may be multiple reasons for cross-contact, even if the predicted amount generated by each reason is less than 10 micrograms, the final cumulative exceeding standard amount after the superposition of multiple allergen exceeding reasons may be greater than 10 micrograms. Therefore, the preset exceeding standard threshold should be set to a value lower than the standard value. The predicted amount is compared with the preset exceeding standard threshold one by one to determine the cause of the target control exceeding the standard. Here, there is at least one cause of the target control exceeding the standard. For example, in the production of plant protein beverages, if the allergen exceeding the standard threshold is set at no more than 6 micrograms of allergen protein per milliliter of beverage, when the predicted amount of allergen exceeding the standard caused by cross-contact of the production line is 8 micrograms per milliliter, the cross-contact of the production line will be identified as the cause of the target control exceeding the standard.

[0056] By recording the production process and associating the quality inspection nodes, the production node corresponding to the target allergen quality inspection node can be determined through the process mapping table in the production management system. Similarly, according to the production process sequence, the next production node corresponding to the target allergen quality inspection node can be determined through the logical relationship of the production process. For example, if the target allergen quality inspection node is in the middle of the processing link, then the next production node may be the production node of the subsequent sterilization and filling links.

[0057] Pre-build the knowledge base for allergen ingredient control. Collect internal and external data of the enterprise. The internal data of the enterprise includes the historical production data of the enterprise, including production logs, equipment operation records, raw material procurement records, etc., and extract data about production processes, equipment usage, raw material batch information, and quality problems that have occurred in the past. For example, by analyzing the production log, understand the impact of changes in raw material suppliers in different seasons on product allergen risks. Obtain the internal quality inspection reports of the enterprise, including raw material inspection reports, semi-finished products and finished products, and obtain information about allergen test results, exceeding standards, and corresponding production links. For example, in the finished product quality inspection report, if it is found that a certain allergen in a batch of beverages exceeds the standard, combined with the production records, it can be traced back to the link where the problem may occur in the production process of the batch of products. In addition, it can also be shared and communicated through expert experience. For example, front-line operators may feedback that during the equipment cleaning process, some parts are prone to residual allergens and special attention needs to be paid to cleaning. External data of the enterprise includes the latest research results, technology development trends, and typical case analysis on allergen control released by industry research institutions. As pointed out in the research report, by optimizing the temperature and time parameters in the production process, the activity of potential allergens in certain plant protein beverages can be effectively reduced. It can also include papers and documents on food allergen detection, control, prevention, etc. In addition, since there are standard regulations for food, it is also necessary to clarify the definition of allergens, detection methods, limit requirements, and labeling regulations.

[0058] Determine the storage architecture of the allergen component control knowledge base, including the allergen basic information base, production environment knowledge base, control strategy base, and case knowledge base. The allergen basic information base records various common allergens in detail, such as peanuts, nuts, wheat, soybeans, etc., including the chemical composition, molecular structure, sensitization mechanism, etc. of each allergen, as well as plant raw materials, additives, and auxiliary materials involved in the production process. The production link knowledge base includes production process and node information and equipment information. It sorts out the complete production process and divides it into different production nodes, such as raw material reception, storage, pretreatment, preparation, processing (such as grinding, sterilization, fermentation, etc.), packaging, etc., and clarifies the specific operation content of each production node, the equipment used, and the connection relationship between the previous and next production nodes. For the equipment used in each production node, record the model, specification, function, scope of application, and cleaning and maintenance methods of the equipment. For example, the material, capacity, stirring speed adjustment range of the preparation tank, and the recommended cleaning steps and detergent types after each use. In the control strategy library, for different production nodes and possible allergen risks, corresponding preventive measures are formulated, including equipment cleaning operation specifications (such as cleaning frequency, detergent concentration and usage method), equipment isolation measures (such as isolation device material, installation location), process parameter adjustment strategy (such as appropriate temperature, pressure, time range), etc. When allergens exceed the standard, corresponding remedial measures are provided. It is divided into physical treatment methods (such as filtration, centrifugation, adsorption, etc., including the equipment model and operating parameters used) and chemical treatment methods (such as the type, dosage and reaction conditions of added chemical reagents). For example, when a small amount of allergen particles exceeding the standard are detected in the beverage, a microfiltration membrane with a pore size of 0.2 microns can be used for filtration, and the filtration pressure can be controlled at 0.3-0.5MPa. The case knowledge base can include historical cases of exceeding the standard and successful prevention and control cases. It should be noted that a regular review mechanism should be established to conduct a comprehensive review of the knowledge base at regular intervals. With the emergence of new allergen detection technologies, the content of allergen detection methods in the knowledge base needs to be updated in a timely manner. When new cases of allergen exceeding the standard or successful prevention and control cases occur within the enterprise, new research results are released in the industry, and regulatory standards change, the knowledge base should be updated in a timely manner to ensure its availability.

[0059] The query mechanism is established by using the allergen component control knowledge base. It should be noted that the knowledge base here is a database containing a variety of information, such as the associated data table of "allergen type-exceeding cause-preventive strategy". After the target control exceeding cause (such as cross-contact of production line) and production node are determined, the knowledge base is queried with this information as index. According to the records in the knowledge base, the appropriate preventive node control strategy is selected. For example, if the knowledge base has suggestions for equipment cleaning operations in the next production process corresponding to the production node for allergen problems caused by cross-contact of production line, such as the use of specific cleaning agents, cleaning time and cleaning process, these equipment cleaning operations will be used as part of the preventive strategy. At the same time, it may also be combined with relevant suggestions for equipment isolation operations (such as setting the specifications and position of isolation baffles) and process parameter adjustment operations (such as adjusting parameters such as temperature, pressure, time, etc.) to form a complete preventive node control strategy. In other words, the preventive node control strategy here aims to reduce the impact of allergen component residues on subsequent production processes.

[0060] According to the predicted amount of allergens exceeding the standard and the real-time beverage quality index parameters corresponding to the reason for the target control exceeding the standard, the allergen component control knowledge base is used again for query. The real-time beverage quality index parameters may include information such as the allergen content in the beverage, the pH value of the beverage, and the temperature. The knowledge base may store such associated information as "degree of allergen exceeding the standard-real-time beverage parameters-remedial strategy". By querying the knowledge base, the remedial node control strategy applicable to the current production process corresponding to the next production node is determined. For example, if the predicted amount of allergen exceeding the standard is high, and the real-time beverage quality index parameters show that the pH value of the beverage is within a certain range, the knowledge base may recommend the use of chemical treatment methods, such as adding specific enzymes to decompose allergen proteins. Or if the allergens in the beverage are granular and the predicted amount of exceeding the standard is relatively low, it may be recommended to use physical treatment methods, such as filtering with a microfiltration device, and at the same time give the parameters such as the pore size and filtration speed of the microfiltration device to determine the specific remedial node control strategy. In other words, the remedial node control strategy here is for products that have already exceeded the standard of allergen components, and in the subsequent production process, the steps of eliminating allergen components or reducing active proteins are added to achieve the remediation of products exceeding the standard.

[0061] Based on the association between allergen type, cause of exceeding the standard, and preventive strategy, after determining the cause of target control exceeding the standard (such as cross-contact of production lines) and production nodes, the preventive node control strategy can be accurately obtained from the knowledge base. For example, for cross-contact of production lines, clear equipment cleaning operation suggestions such as the use of specific cleaning agents, cleaning time and process, as well as equipment isolation, process parameter adjustment and other measures are given. Effective preventive measures can be taken in advance for specific risks to reduce the risk of allergen residues and ensure that subsequent production is not affected. Combined with the predicted amount of allergen exceeding the standard corresponding to the cause of target control exceeding the standard and the real-time beverage quality indicator parameters (such as pH, temperature, allergen content, etc.), the remedial node control strategy can be accurately matched. For example, when the predicted amount of allergen exceeding the standard is high and the pH is within a specific range, it is recommended to add specific enzymes. Decompose allergen proteins; when the allergens are granular and the predicted amount of exceeding the standard is low, it is recommended to filter with a microfiltration device with a specific pore size and filtration speed to achieve precise remediation for different exceeding conditions; preventive node control strategies can avoid potential allergen exceeding standards in advance, and by taking appropriate measures such as equipment cleaning, isolation and process parameter adjustment, reduce the probability of allergen-related quality problems in subsequent production, reduce production interruptions and product scrapping due to problems, and improve production efficiency; for allergen exceeding standards, remedial node control strategies provide targeted solutions, and adding steps to eliminate or reduce the activity of allergens in the subsequent production process can remedy the exceeding products and reduce the economic losses caused by unqualified beverages, while ensuring that the quality and taste of the beverages meet the standards.

[0062] Through the above technical solution, production line parameters are obtained and allergen cross-contact risk analysis is performed. Based on the risk analysis, at least one target allergen quality inspection node is determined. Based on the quantified cross-contact risk index, risky production nodes are determined. By setting clear preset conditions, production nodes with actual risks are screened out, avoiding excessive attention to the entire production line or missing high-risk areas, and providing precise targets for subsequent quality control and preventive measures. When the real-time beverage quality index parameters meet the preset conditions, traceability analysis is performed in combination with the real-time beverage quality index parameters and the real-time production line parameters, which can accurately identify the specific reasons for the excessive allergens. The excessive assessment data not only clarifies the reasons for the excessive allergens, but also gives the predicted amount of allergens exceeding the standard corresponding to each reason. By obtaining the real-time production line parameters, real-time production risk identification is performed on the production node before the target allergen quality inspection node, and a comprehensive assessment is conducted. The three main sources of allergen risks, namely cross-contact of production lines, cross-contact of nodes and introduction of allergens during the production process, are taken into account. In the production scenario where different types of plant protein beverages are produced in the same factory, not only the risk of cross-contact of allergens between different production lines and between different batches at the same production node is taken into account, but also the risk that the processing technology itself may cause allergen components from non-allergen raw materials is taken into account, thus achieving full coverage of various potential allergen risks in the production process. The preventive node control strategy in the production control strategy takes measures in advance for potential factors that may cause allergens to exceed the standard based on the risk analysis of the production process. The remedial node control strategy provides effective solutions for products that have already exceeded the standard for allergens, which can save beverages that have problems and reduce the economic losses caused by non-conformity, while ensuring that the quality and taste of the beverages meet the standards.

[0063] The present specification also provides a production quality control device for a plant protein beverage, such as Figure 2 As shown, the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above method.

[0064] The embodiments of the present specification also provide a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to execute the above method.

[0065] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0066] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded 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 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.

[0067] The devices and media provided in the embodiments of this specification correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0068] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0070] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0072] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0073] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0074] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0075] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0076] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for controlling the production quality of a plant protein beverage, characterized in that: The method comprises: Acquire production line parameters of a current beverage production line, perform allergen cross-contact risk analysis on the current beverage production line based on the production line parameters, and determine at least one target allergen quality inspection node in the current beverage production line; Collecting allergen quality inspection data of each target allergen quality inspection node in real time, so as to determine the real-time beverage quality index parameter corresponding to each target allergen quality inspection node through the allergen quality inspection data; When the real-time beverage quality index parameter meets the preset conditions, the cause of the allergen exceeding the standard is traced and analyzed according to the real-time beverage quality index parameter and the real-time production line parameters obtained in advance, and the exceeding standard assessment data of the target allergen quality inspection node is determined, wherein the exceeding standard assessment data includes at least one allergen exceeding standard cause and the predicted amount of allergen exceeding standard corresponding to each exceeding standard cause; Through the pre-constructed allergen component control knowledge base, the corresponding production control strategy is determined according to the exceeding standard assessment data of the target allergen quality inspection node, so as to optimize the production quality through the production control strategy, wherein the production control strategy includes a preventive node control strategy and a remedial node control strategy.

2. The production quality control method of a plant protein beverage according to claim 1, characterized in that: Based on the production line parameters, an allergen cross-contact risk analysis is performed on the current beverage production line to determine at least one target allergen quality inspection node in the current beverage production line, specifically including: Obtaining the previous batch production information corresponding to each production node in the current beverage production line through the production log in the production line parameters, wherein the previous batch production information includes the previous batch beverage raw material information and the previous batch production time; Obtaining node location information of each of the production nodes in the production line parameters within the production park, so as to determine an allergen flow assessment area based on the node location information, so as to determine a node distance between an adjacent production node of an adjacent production line located within the allergen flow assessment area and a production node in the current beverage production line; According to the previous batch production information corresponding to each production node and the node distance, a risky production node is determined in the current beverage production line, and the target allergen quality inspection node is determined by a quality inspection node located after the risky production node.

3. The production quality control method of a plant protein beverage according to claim 2, characterized in that: Determining risky production nodes in the current beverage production line according to the previous batch production information corresponding to each production node and the node distance specifically includes: The cross-contact risk of adjacent production lines of the production node is evaluated by the node distance and the area radius corresponding to the allergen flow assessment area, and the corresponding production line cross-contact risk index of the production node is determined; Determining a node cross-contact risk coefficient of each production node according to the raw material attributes of the previous batch of beverage raw material information corresponding to each production node, wherein the raw material attributes include allergen raw materials and non-allergen raw materials; Using the previous batch production time corresponding to each production node, determine the current node processing time corresponding to the production node, and determine the node cross-contact risk quantification parameter corresponding to the production node based on the current node processing time and a preset allergen component processing time threshold corresponding to the production node; Determining a node cross-contact risk index corresponding to the production node based on a node cross-contact risk coefficient of the production node and a node cross-contact risk quantification parameter corresponding to the production node; A cross-contact risk index is generated according to the production line cross-contact risk index and the node cross-contact risk index, and a risk production node whose cross-contact risk index meets a preset condition is determined in the current beverage production line.

4. The production quality control method of a plant protein beverage according to claim 1, characterized in that: Determine the real-time beverage quality index parameters corresponding to each target allergen quality inspection node through the allergen quality inspection data, specifically including: Acquire the allergen quality inspection data, wherein the allergen quality inspection data is beverage spectrum data; Performing data cleaning and preprocessing operations on the beverage spectrum data to determine spectrum processing data; The corresponding real-time beverage quality index parameters are determined based on the spectral processing data through a pre-constructed regression prediction model, wherein the real-time beverage quality index parameters include the allergen quality inspection content.

5. The production quality control method of a plant protein beverage according to claim 1, characterized in that: According to the real-time beverage quality index parameters and the pre-acquired real-time production line parameters, the causes of allergen exceeding the standard are traced and analyzed to determine the exceeding standard assessment data of the target allergen quality inspection node, specifically including: Acquiring the real-time production line parameters, wherein the real-time production line parameters include real-time processing technology data of each production node; According to the real-time production line parameters of the current beverage production line, real-time production risk identification is performed on the previous production node of the target allergen quality inspection node, and a production risk indicator set corresponding to the previous production node is determined, wherein the production risk indicator set includes a production line cross-contact risk indicator, a node cross-contact risk indicator, and a production process introduction risk indicator; A weight is assigned to each cause of allergen exceeding the standard through the production risk indicator set and the real-time beverage quality indicator parameters to determine the predicted amount of allergen exceeding the standard corresponding to each cause of allergen exceeding the standard, wherein the causes of allergen exceeding the standard include production line cross-contact, node cross-contact and introduction into the production process.

6. The production quality control method of a plant protein beverage according to claim 5, characterized in that: According to the real-time production line parameters of the current beverage production line, real-time production risk identification is performed on the previous production node of the target allergen quality inspection node, and a set of production risk indicators corresponding to the previous production node is determined, specifically including: According to the beverage raw material information in the current beverage production line, the process parameter standard value corresponding to each production node is matched, so as to determine the overflow ratio of the process parameter corresponding to the production node through the real-time processing technology data of the previous production node of the target allergen quality inspection node and the corresponding process parameter standard value, so as to determine the corresponding production process introduction risk index; Determine the first production timestamp of the previous allergen raw material corresponding to the previous production node of the target allergen quality inspection node through the real-time production log of each production node in the real-time production line parameters, so as to determine the allergen processing time of the production node, and determine the corresponding real-time node cross-contact risk index according to the allergen processing time and the allergen component processing time threshold corresponding to the previous allergen raw material; Determine the adjacent production line production nodes of each production node in the real-time production line parameters within the preset allergen flow assessment area, determine the current production time interval corresponding to the production node through the real-time production log of the production node, and determine the startup state of the adjacent production line production node within the current production time interval; In the startup state, determining a node cross-contact risk coefficient corresponding to the production node, and determining a production line cross-contact risk quantification parameter corresponding to the production node through a pre-acquired node distance between an adjacent production line production node and the production node and an area radius corresponding to the allergen flow assessment area, and determining a real-time production line cross-contact risk index according to the production line cross-contact risk quantification parameter and the node cross-contact risk coefficient; Based on the production process introduction risk index, the real-time node cross-contact risk index and the real-time production line cross-contact risk index, a production risk index set corresponding to the previous production node is determined.

7. The production quality control method of a plant protein beverage according to claim 5, characterized in that: By using the production risk index set and the real-time beverage quality index parameter, a weight is assigned to each allergen exceeding the limit reason to determine the allergen exceeding limit prediction amount corresponding to each allergen exceeding the limit reason, specifically including: Establish a mapping relationship between the cause of each allergen exceeding the limit and the corresponding risk indicator; According to the production risk indicator set, multiple risk indicators in the production risk indicator set are processed to determine the risk proportion of each risk indicator in the production risk indicator set; Determining the predicted excess amount corresponding to each risk indicator through the risk proportion of each risk indicator and the real-time beverage quality indicator parameter; Based on the mapping relationship and the predicted excess amount corresponding to each risk indicator, the predicted excess amount of allergen corresponding to the cause of excess of each allergen is determined.

8. The method for controlling the production quality of a plant protein beverage according to claim 1, characterized in that: Through the pre-built allergen component control knowledge base, according to the over-standard assessment data of the target allergen quality inspection node, the corresponding production control strategy is determined, specifically including: Determine the cause of allergen excess in the excess assessment data where the predicted amount of allergen excess is higher than a preset excess threshold as the cause of target control excess; Determine a corresponding target control object, wherein the target control object includes a production node corresponding to the target allergen quality inspection node and a next production node corresponding to the target allergen quality inspection node; Based on the cause of the target control exceeding the limit, the preventive node control strategy corresponding to the next production process corresponding to the production node in the target control object is determined by using the allergen component control knowledge base, wherein the preventive node control strategy includes any one or more of equipment cleaning operation, equipment isolation operation and process parameter adjustment operation; According to the predicted amount of allergen excess corresponding to the cause of the target control excess and the real-time beverage quality index parameter, the allergen component control knowledge base is used to determine the remedial node control strategy corresponding to the current production process corresponding to the next production node in the target control object, wherein the remedial node control strategy includes any one or more of physical treatment and chemical treatment.

9. A production quality control device for a plant protein beverage, characterized in that: The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to execute the method according to any one of claims 1 to 8.

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