Edible Vegetable Oil Production Process Traceability Data Processing System

Abstract

This invention relates to the field of food safety monitoring and digital supply chain management technology, specifically a traceability data processing system for the entire production process of edible vegetable oil. The system includes: a traceability data management center for obtaining current quality status predictions and aging rate constants; a risk collision analysis unit for performing safety threshold comparison analysis on the collected current quality status predictions, conducting compliance judgment processing on the edible vegetable oil to be judged, and obtaining a safety retention signal or a risk interception signal; when a risk interception signal is generated, a dynamic decision execution unit for generating a freeze command and sending it to the warehouse management system; and when a safety retention signal is generated, the dynamic decision execution unit for obtaining dynamic shelf-life data and mapping the dynamic shelf-life data to the terminal display port. This invention solves the technical problem that traditional static traceability cannot perceive environmental thermal history, significantly improving the predictability and accuracy of quality control throughout the entire lifecycle.

Description

Technical Field

[0001] This invention relates to the field of food safety monitoring and digital supply chain management technology, specifically a data processing system for the entire process of edible vegetable oil production traceability. Background Technology

[0002] In the context of digital supervision of the modern food supply chain, the entire life cycle of edible vegetable oil covers multiple discrete links such as raw material acquisition, processing and production, and warehousing and logistics. The data generated by these links has multi-source heterogeneous characteristics, and the quality of oil is significantly affected by dynamic physical field factors such as environmental thermal history and light, resulting in complex correlations between the time and spatial dimensions of the data.

[0003] Currently, existing traceability data processing technologies generally adopt a static recording mode based on relational databases, that is, by scanning barcodes to associate fixed production batch information and factory inspection reports. Although this method can achieve physical node path tracking, it has significant lag and logical gaps at the data computing level. The existing computing architecture lacks the ability to model the nonlinear evolution relationship between environmental flow data and physicochemical indicators, and cannot perform deep coupling and dynamic extrapolation of heterogeneous data. As a result, the system can only reflect historical status and cannot detect potential quality deterioration caused by fluctuations in the logistics environment. It is difficult to achieve risk warning and dynamic decision-making based on data calculation before physicochemical indicators exceed the standard. Therefore, how to perform quality evolution dynamic calculation based on the deep integration of multi-source heterogeneous data to achieve accurate status prediction and proactive risk interception throughout the entire process has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a data processing system for tracing the entire production process of edible vegetable oils. Specifically, the technical solution of this invention includes:

[0005] The system includes a data management center for tracing origins, a multi-source data acquisition unit, a dynamic evolution modeling unit, a risk collision analysis unit, and a dynamic decision execution unit.

[0006] The traceability data management center is used to retrieve the circulation information of edible vegetable oil throughout its entire life cycle, and send the circulation information to the multi-source data acquisition unit for heterogeneous data extraction and analysis to obtain raw material fingerprint data, process stress data and environmental circulation data. The raw material fingerprint data, process stress data and environmental circulation data are then sent to the dynamic evolution modeling unit for quality evolution coupling calculation to obtain the current quality status prediction value and aging rate constant.

[0007] The risk collision analysis unit is used to perform safety threshold comparison analysis on the collected current quality status prediction value, and to perform compliance judgment processing on the edible vegetable oil to be judged, so as to obtain a safety retention signal or a risk interception signal.

[0008] When a risk interception signal is generated, the dynamic decision execution unit is used to block the supply chain of the target batch of products, generate a freeze command and send it to the warehouse management system.

[0009] When a safe retention signal is generated, the dynamic decision execution unit uses the aging rate constant to quantitatively calculate and analyze the remaining safe window period to obtain dynamic shelf life data. It then determines whether the dynamic shelf life data meets the minimum sales cycle requirement. If it does not meet the requirement, a freeze command is generated; if it does meet the requirement, the dynamic shelf life data is mapped to the terminal display port.

[0010] Optionally, the heterogeneous data extraction and analysis process is as follows:

[0011] Obtain the production batch code of edible vegetable oil, trace the origin of raw materials and the initial physicochemical test report based on the production batch code, and set the initial acid value and moisture content as the raw material fingerprint data;

[0012] Key node parameters in the production process are obtained, and the pressing and leaching temperature, the concentration of refining and deacidifying alkali solution, and the time-temperature integral of the deodorization section are set as process stress data.

[0013] Acquire continuous time-series data during warehousing and transportation, and set temperature fluctuation curves, light intensity exposure duration, and oxygen permeability of packaging materials as environmental flow data during the logistics process.

[0014] Optionally, the dynamic evolution coupling calculation process is as follows:

[0015] The temperature fluctuation curves in the environmental flow data are obtained, and the thermodynamic equivalent transformation analysis of the temperature fluctuation curves is performed using the Arrhenius equation. The duration of different temperature ranges is converted into the equivalent thermal aging time at the standard reference temperature.

[0016] Based on the time-temperature integral of the deodorization section in the process stress data, the consumption ratio of the oxidation induction period is calculated by calling the preset oxidation induction period loss model. Combined with the equivalent thermal aging time, the lipid oxidation reaction rate constant under the current environmental conditions is calculated using the reaction rate equation, and the lipid oxidation reaction rate constant is set as the aging rate constant.

[0017] Optionally, the process for obtaining the current quality status prediction value is as follows:

[0018] The initial peroxide value in the raw material fingerprint data is obtained as the starting point for the reaction kinetics model;

[0019] Based on the aging rate constant and equivalent thermal aging time, a nonlinear fitting calculation was performed using a free radical chain reaction growth model to simulate the cumulative growth trend of peroxide value over time. The calculated peroxide value simulation data at the current moment was set as the predicted value of the current quality status.

[0020] Optionally, the security threshold comparison and analysis process is as follows:

[0021] Retrieve the upper limit threshold for peroxide value from the preset safety standard database, and set the upper limit threshold as the safety critical value;

[0022] Compare the current quality status prediction value with the safety threshold value:

[0023] If the current quality status prediction value is less than the safety threshold, a safety retention signal is generated.

[0024] If the current quality status prediction value is greater than or equal to the safety threshold, a risk interception signal is generated.

[0025] Optionally, the quantitative measurement and analysis process is as follows:

[0026] In response to a security retention signal, the security threshold value is obtained;

[0027] Using the current quality status prediction value as the starting state, and using the reaction kinetic model in the kinetic evolution modeling unit, with the real-time aging rate constant under the current storage environment as the evolution parameter, the time span required for the peroxide value to reach the safety critical value is deduced, and this time span is set as the remaining shelf life.

[0028] The remaining shelf life is added to the current date to calculate the recommended best-before date, and this recommended best-before date is set as dynamic shelf life data.

[0029] Optionally, the system further includes an anomaly pattern recognition unit, which is used for:

[0030] Obtain the benchmark aging rate model for similar products, and calculate the deviation between the aging rate constant of the current batch of products and the benchmark aging rate model.

[0031] The deviation is compared with the preset abnormal fluctuation threshold. If the deviation exceeds the preset abnormal fluctuation threshold, it is determined that there is a process defect or adulteration risk, and a process warning signal is generated and sent to the production control center.

[0032] Optionally, the process stress data may also include trans fatty acid formation factors;

[0033] The dynamic evolution modeling unit is also used to establish a correlation model of temperature-time-trans fatty acid production. Based on the thermal history data of the refining process, it calculates the cumulative production of trans fatty acids and incorporates the cumulative production of trans fatty acids as an auxiliary judgment indicator into the discrimination logic of the risk collision analysis unit.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. This invention solves the technical problem of traditional static traceability's inability to perceive environmental thermal history by constructing a dynamic evolution modeling unit. Based on the Arrhenius equation and the oxidation-induced period loss model, the system converts temperature fluctuations and light exposure data during the logistics process into equivalent thermal aging time, enabling real-time dynamic extrapolation of the physicochemical quality of edible vegetable oils. This mechanism can accurately capture potential quality deterioration caused by changes in the environmental physical field, significantly improving the predictability and accuracy of quality control throughout the entire life cycle.

[0036] 2. This invention constructs a deep fusion and decoupling mechanism for multi-source heterogeneous data, breaking through the logical discontinuity caused by data discreteness in existing technologies; by abstracting complex full life cycle information into three core elements—raw material fingerprints, process stress, and environmental flow—the system successfully establishes an accurate mapping between the physical world and the digital model; this data structuring not only ensures the physicochemical meaning of the input parameters, but also realizes unified calculation of data from different dimensions, providing a solid data foundation for subsequent quality evolution analysis;

[0037] 3. This invention realizes dynamic decision-making and risk interception based on the current quality status, changing the rigid mode of the traditional fixed shelf life; the system uses the aging rate constant to quantify the remaining safety window period and generate dynamic shelf life data; under the premise of ensuring food safety, it automatically generates interception instructions for high-risk products that have undergone high-temperature transportation and establishes the best consumption date for well-stored products, effectively balancing the contradiction between supply chain risk interruption and reducing food waste.

[0038] 4. This invention introduces an abnormal pattern recognition and benchmark model comparison mechanism, which improves the ability to identify process defects and potential adulteration. By calculating the deviation between the current batch aging rate and the historical benchmark model, the system can deeply identify problematic products that are temporarily qualified in terms of physicochemical indicators but have poor internal stability. This monitoring method based on statistical distribution overcomes the limitations of single-point detection and realizes in-depth auditing of the consistency of production processes and reverse verification of the authenticity of raw materials. Attached Figure Description

[0039] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0040] Figure 1This is a structural diagram of the system of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0042] Example 1:

[0043] Please see Figure 1 The edible vegetable oil production process traceability data processing system includes a traceability data management center, a multi-source data acquisition unit, a dynamic evolution modeling unit, a risk collision analysis unit, and a dynamic decision execution unit.

[0044] The traceability data management center is used to retrieve the circulation information of edible vegetable oil throughout its entire life cycle and send the circulation information to the multi-source data acquisition unit for heterogeneous data extraction and analysis to obtain raw material fingerprint data, process stress data and environmental circulation data. The raw material fingerprint data, process stress data and environmental circulation data are then sent to the dynamic evolution modeling unit for quality evolution coupling calculation to obtain the current quality status prediction value and aging rate constant.

[0045] The risk collision analysis unit is used to compare and analyze the current quality status prediction value collected with the safety threshold, and to perform compliance judgment processing on the edible vegetable oil to be judged, so as to obtain the safety retention signal or risk interception signal.

[0046] When a risk interception signal is generated, the dynamic decision execution unit is used to block the supply chain of the target batch of products, generate a freeze command and send it to the warehouse management system; when a safety retention signal is generated, the dynamic decision execution unit is used to quantitatively calculate and analyze the remaining safety window period using the aging rate constant, obtain dynamic shelf life data, and determine whether the dynamic shelf life data meets the minimum sales cycle requirement. If it does not meet the requirement, a freeze command is generated; if it does meet the requirement, the dynamic shelf life data is mapped to the terminal display port.

[0047] This embodiment details the architecture and operational logic of a full-process traceability data processing system for edible vegetable oil production, aiming to address the problem that existing traceability systems cannot detect environmental thermal history leading to deterioration of intrinsic physicochemical quality. The traceability data management center acts as a data hub, retrieving full lifecycle flow information, including production batch records and logistics trajectories, via API interfaces. A multi-source data acquisition unit decouples this flow information, extracting three core elements that determine oil quality: raw material fingerprint data, process stress data, and environmental flow data. A kinetic evolution modeling unit constructs a virtual simulation mapping, using the principles of physicochemical reaction kinetics and the aforementioned three types of data to perform quality evolution coupling calculations, determining the predicted current quality state value representing the accumulated peroxide value within the edible oil at the current moment. and the aging rate constant, which characterizes the rate of lipid oxidation. ;

[0048] Based on this, the risk collision analysis unit performs compliance checks, and The system compares the value with preset safety standards. If the value is below the threshold, a safety retention signal is generated; otherwise, a risk interception signal is generated. The dynamic decision-making execution unit executes control measures based on the signals: in response to the risk interception signal, a freeze command is generated to lock warehouse outbound permissions; in response to the safety retention signal, [the system utilizes...]. Calculate the remaining safe time window, generate dynamic shelf-life data, and push it to consumer terminals;

[0049] This embodiment introduces the material change process of edible oil into the data traceability system by constructing a closed-loop processing system that includes a dynamic evolution modeling unit. To verify the technical effect of this system, two batches, namely batch A and batch B, of the same brand of first-grade soybean oil were selected for comparative testing. Batch A was connected to this system for full-process data processing, while batch B adopted the traditional traceability method.

[0050] In the simulated transportation experiment, two batches of products were subjected to the same environmental heat history, namely 40°C for 48 hours; the system calculated the aging rate constant based on the environmental circulation data of batch A. From the benchmark / Rise to / Current quality status prediction value Although not exceeding the standard, it was close to the critical value. The system determined that the remaining shelf life was insufficient to support terminal sales, and thus generated a risk interception signal. Batch B, however, was allowed to enter the warehouse normally because its physicochemical indicators were temporarily qualified. Subsequent follow-up testing showed that the peroxide value of batch B exceeded the national standard limit of 10 on the 45th day after entering the warehouse. / This resulted in a food safety incident during the shelf life, but batch A was successfully intercepted, demonstrating the significant advantages of this system in risk warning.

[0051] Example 2:

[0052] The heterogeneous data extraction and analysis process is as follows: obtain the production batch code of edible vegetable oil, trace the source of raw materials and initial physicochemical test reports based on the production batch code, and set the initial acid value and moisture content as raw material fingerprint data;

[0053] Key node parameters in the production process are obtained, and the pressing and leaching temperature, the concentration of refining and deacidifying alkali solution, and the time-temperature integral of the deodorization section are set as process stress data.

[0054] Acquire continuous time-series data during warehousing and transportation, and set temperature fluctuation curves, light intensity exposure duration, and oxygen permeability of packaging materials as environmental flow data during the logistics process.

[0055] This embodiment specifies the steps for heterogeneous data extraction and analysis to ensure the accuracy of the parameters input to the model in a physicochemical sense. The multi-source data acquisition unit obtains the production batch code and traces it back to the raw material origin and initial physicochemical test report to extract the initial acid value. Moisture content and initial peroxide value Set it as the raw material fingerprint data, where, and This determines the potential for lipid hydrolysis, and This serves as the starting point for subsequent kinetic evolution; the system extracts key node parameters from the production control system, including the pressing and leaching temperatures that affect phospholipid dissolution. The concentration of refining and deacidifying alkali solution that affects soap residue And the deodorization time-temperature integral related to the retention rate of natural antioxidants. The system is set as process stress data; it connects to vehicle and warehouse IoT devices to acquire continuous time series data and analyzes temperature fluctuation curves during the logistics process. Light intensity and exposure time and the oxygen permeability of packaging materials Set as environmental flow data;

[0056] This embodiment extracts key physicochemical variables precisely, abstracting the complex production logistics process into a set of parameters that can be input into a mathematical model. In particular, in the refining process control scenario, the introduction of time-temperature integral and packaging oxygen permeability enables the model to comprehensively consider the cumulative contribution of thermal history and oxygen permeation to the oxidation reaction, significantly improving the accuracy of subsequent quality prediction.

[0057] Example 3:

[0058] The dynamic evolution coupling calculation process is as follows:

[0059] The temperature fluctuation curves in the environmental flow data are obtained, and the thermodynamic equivalent transformation analysis of the temperature fluctuation curves is performed using the Arrhenius equation. The duration of different temperature ranges is converted into the equivalent thermal aging time at the standard reference temperature.

[0060] Based on the time-temperature integral of the deodorization section in the process stress data, the consumption ratio of the oxidation induction period is calculated by calling the preset oxidation induction period loss model. Combined with the equivalent thermal aging time, the lipid oxidation reaction rate constant under the current environmental conditions is calculated using the reaction rate equation, and the lipid oxidation reaction rate constant is set as the aging rate constant.

[0061] This embodiment details the kinetic evolution coupled calculation process, which is the core step in converting environmental data into chemical reaction rates; the kinetic evolution modeling unit acquires the temperature fluctuation curve. The equivalent thermal aging time was calculated using the integral form of the Arrhenius equation. The formula is as follows:

[0062]

[0063] in, Equivalent thermal aging time , Total duration of temperature recording , The activation energy for lipid oxidation is 85000. / , The ideal gas constant is 8.314. / ( ), Real-time temperature fluctuation curve , Standard reference temperature 298 , The base is the natural number;

[0064] To address the discretized data processing requirements of computer systems, the above integral formula is converted into Riemann sum form for numerical computation:

[0065]

[0066] in, This represents the total number of samples collected from temperature monitoring points. The sampling interval is... For the first Real-time temperature values ​​at each sampling point;

[0067] The system is based on the deodorization time-temperature integral from process stress data. The pre-defined oxidation induction period loss model is used to calculate the consumption ratio during the oxidation induction period. The specific structure of this model is based on the thermal degradation kinetics of antioxidants, and its calculation formula is as follows:

[0068]

[0069] in, This is a dimensionless value representing the consumption ratio. , With a sensitivity coefficient of 0.8, Deodorization heat integral ( ), Critical heat integral threshold (5000) ), The nonlinear exponent is 1.5;

[0070] Combining light intensity and exposure duration from environmental circulation data and oxygen permeability of packaging materials Calculate the standard reference state aging rate constant after environmental stress correction. Given that the equivalent thermal aging time has been used in subsequent calculations; Temperature fluctuations have been normalized. The rate constant here only needs to be corrected for non-thermal factors and no longer includes the Arrhenius exponent term for temperature. The calculation formula is as follows:

[0071]

[0072] in, The corrected standard reference state aging rate constant ( ), The fundamental reaction rate constant at the standard reference temperature ( );

[0073] The average light intensity characterizing the logistics process ; The oxygen permeability of the packaging material, in units of... / ( ² )

[0074] The photocatalytic coefficient is denoted as , and its value is . This is used to map the dimensions of light and oxygen permeability into reaction rate increments.

[0075] This embodiment utilizes integral transformation to normalize complex non-constant temperature logistics processes into equivalent processes under standard conditions. In scenarios where cold chain interruptions or drastic fluctuations in ambient temperature transportation occur, this method solves the problem of difficulty in quantifying oxidation rates under varying temperature conditions. At the same time, it clearly provides an oxidation-induced period loss model and a mathematical structure for photo-oxidation coupling correction, quantifying the weakening effect of processing technology on antioxidant capacity and the catalytic effect of environmental photo-oxidation, making the calculated aging rate constant more consistent with the true chemical activity of oil products.

[0076] Example 4:

[0077] The process for obtaining the current quality status prediction value is as follows:

[0078] The initial peroxide value in the raw material fingerprint data is obtained as the starting point for the reaction kinetics model;

[0079] Based on the aging rate constant and equivalent thermal aging time, a nonlinear fitting calculation was performed using a free radical chain reaction growth model to simulate the cumulative growth trend of peroxide value over time. The calculated peroxide value simulation data at the current moment was set as the predicted value of the current quality status.

[0080] This embodiment illustrates the process of obtaining the current quality status prediction value, namely, using a kinetic model to simulate the growth of the lipid oxidation chain reaction; the system obtains the initial peroxide value from the raw material fingerprint data. This is used as the starting point for the reaction kinetics model; the standard reference state aging rate constant calculated based on the preceding steps is used. and equivalent thermal aging time Nonlinear fitting calculations were performed using a free radical chain reaction growth model; the model structure includes linear terms characterizing single-molecule decomposition, i.e. And the autocatalytic nonlinear term characterizing branched chain reactions, i.e. The time square term reflects the accelerating effect of oxidation product accumulation on the reaction rate, and the specific calculation formula is as follows:

[0081]

[0082] in, The current quality status prediction value ( / ), Initial peroxide value ( / ), The corrected standard reference state aging rate constant is the one calculated in Example 3. This is the equivalent thermal aging time. The base is the natural number;

[0083] Among them, the autocatalytic coefficient It is not a fixed constant, but rather based on the initial acid value in the raw material fingerprint data. and moisture content Real-time corrections are made to reflect the promoting effect of free fatty acids and water on the oxidation reaction. The calculation formula is as follows:

[0084]

[0085] in, This is the corrected autocatalytic coefficient. The base coefficient, in this embodiment, is taken as a value of , The hydrolysis promoting factor has a value of [value missing]. , Initial acid value, in units of , The value represents the moisture content, expressed as a percentage (%). It should be noted that when substituting values ​​into the above formula for calculation... and All values ​​are taken and dimensionless calculations are performed. For example, when the moisture content is 0.5%, Take 0.5; acid value is 2 / hour, Take 2;

[0086] The system will calculate Output to the risk collision analysis unit;

[0087] This embodiment establishes a nonlinear growth model that includes an autocatalytic term and introduces raw material fingerprint data to dynamically correct the model parameters. It not only considers the primary oxidation reaction but also simulates the explosive growth in the later stage of oxidation caused by the deterioration of the raw material background. In long-term storage scenarios, the system can accurately predict high-risk oil products that are currently qualified but are on the eve of an oxidation outbreak due to the high acid value of the raw materials, thus achieving more forward-looking quality control.

[0088] Example 5:

[0089] The safety threshold comparison and analysis process is as follows:

[0090] Retrieve the upper limit threshold for peroxide value from the preset safety standard database, and set the upper limit threshold as the safety critical value;

[0091] Compare the current quality status prediction value with the safety threshold value:

[0092] If the current quality status prediction value is less than the safety threshold, a safety retention signal is generated.

[0093] If the current quality status prediction value is greater than or equal to the safety threshold, a risk interception signal is generated.

[0094] This embodiment details the safety threshold comparison and analysis process, which is the logical judgment step for the system to make automated decisions. Based on the product type, the system retrieves the upper limit threshold for peroxide value from the corresponding national standard in the preset safety standard database and sets it as the safety critical value. The calculated current quality status prediction value and Perform numerical comparison; respond to This indicates that the current physicochemical indicators of the oil are not exceeding the standards, and the system generates a safe retention signal, allowing the product to continue circulating in the supply chain; in response to This indicates that the oil has undergone severe oxidation and deterioration, and the system generates a risk interception signal, triggering subsequent blocking mechanisms;

[0095] This embodiment transforms complex chemical prediction results into binary control signals; in large-scale logistics distribution scenarios, it realizes automated identification of food safety risks, avoids the subjectivity and lag of manual judgment, and ensures that unqualified products cannot enter the end market.

[0096] Example 6:

[0097] The quantitative calculation and analysis process is as follows:

[0098] In response to a security retention signal, the security threshold value is obtained;

[0099] Using the current quality status prediction value as the starting state, and using the reaction kinetic model in the kinetic evolution modeling unit, with the real-time aging rate constant under the current storage environment as the evolution parameter, the time span required for the peroxide value to reach the safety critical value is deduced, and this time span is set as the remaining shelf life.

[0100] The remaining shelf life is added to the current date to calculate the recommended best-before date, and this recommended best-before date is set as dynamic shelf life data.

[0101] This embodiment provides a detailed explanation of the quantitative calculation and analysis process, specifically how to calculate the dynamic shelf life; and how the system acquires the safety threshold in response to the safety retention signal. and current quality status prediction value Using the inverse operation of the reaction kinetics model, the real-time aging rate constant under the current storage environment is calculated. As an evolution parameter, estimate the remaining shelf life required for the peroxide value to increase from its current value to the critical value. It should be noted that although a complex model containing quadratic terms was used in the preceding quality traceability, a linearized approximation method based on the current instantaneous reaction rate was adopted here to meet the real-time requirements of the terminal calculation when predicting the remaining shelf life. The calculation formula is as follows:

[0102]

[0103] The denominator term represents the value based on the current time. The instantaneous oxidation reaction rate, This is the safety threshold value. This is a predicted value for the current quality status. Remaining shelf life The coefficient 48 is the product of the derivative coefficient 2 of the time square term and the conversion coefficient 24 of the hour to day, and is used to unify the units of time. The aging rate constant at the current storage temperature. , The base is the natural number; The method of obtaining it is as follows: inheriting the standard reference state aging rate constant, which includes antioxidant loss and photo-oxidation history effects, calculated in Example 3. Combined with real-time temperature of the storage environment Thermodynamic extrapolation is performed, and the calculation formula is as follows:

[0104]

[0105] This calculation ensures that the shelf life forecast accurately reflects the quality damage accumulated in the preceding processing and transportation stages of the oil product; the calculated... With current system date By performing cumulative calculations, a recommended optimal consumption date can be obtained. and will Set up dynamic shelf-life data to be pushed to the terminal for display;

[0106] This embodiment completely changes the rigid model of fixed shelf life in the traditional sense. In the scenario of supermarket shelf management, the system can give a shorter recommended consumption period for oil products that have undergone high-temperature transportation, or confirm the safety of well-stored oil products. While protecting the health of consumers, it can minimize food waste caused by blindly discarding food.

[0107] Example 7:

[0108] The system also includes an anomaly pattern recognition unit, which is used for:

[0109] Obtain the benchmark aging rate model for similar products, and calculate the deviation between the aging rate constant of the current batch of products and the benchmark aging rate model.

[0110] The deviation is compared with the preset abnormal fluctuation threshold. If the deviation exceeds the preset abnormal fluctuation threshold, it is determined that there is a process defect or adulteration risk, and a process warning signal is generated and sent to the production control center.

[0111] This embodiment introduces an anomaly pattern recognition unit to detect potential process defects or adulteration. The system acquires a baseline aging rate model of similar products under standard storage conditions. This model is not a single static value, but a statistical distribution model built based on historical data. The construction method is as follows: retrieve aging rate constant data of all qualified batches from the production line in the past 12 months with a sample size greater than 1000, remove outliers, and retain values ​​falling within the specified range. Data within the specified range is selected, and data points outside this range are removed. For the initial calculation of the mean, To initially calculate the standard deviation, a normal distribution is then fitted to obtain the corrected mean. and standard deviation , the mean Set as baseline value ;

[0112] Calculate the aging rate constant of the current batch of products. Mean parameters of the baseline aging rate model deviation The formula is ;Will Abnormal fluctuation thresholds set based on model distribution characteristics Comparative analysis was conducted; among them, The logic of the setting makes full use of the statistical distribution information of the model, and calculates as For example, in this embodiment, the value calculated based on historical data is 0.15, which is 15%; in response to ,For example Significantly higher than The system detects an anomaly, which may mean that new oil with high polar components has been added to the raw materials or that the deodorization temperature during the refining process is too high, causing the natural antioxidants to be completely lost. The system generates a process warning signal and sends it to the production control center, prompting a process check.

[0113] This embodiment utilizes dynamic parameters to conduct in-depth monitoring of product consistency and discloses the statistical construction method of the benchmark model and a clear outlier removal algorithm. In the supply chain quality audit scenario, monitoring based on the rate constant can better reflect the inherent stability of the product, effectively identify problematic products that are temporarily qualified but have extremely poor stability, and play an auxiliary role in process feedback and anti-adulteration.

[0114] Example 8:

[0115] Process stress data also includes trans fatty acid formation factors;

[0116] The kinetic evolution modeling unit is also used to establish a correlation model of temperature-time-trans fatty acid production. Based on the thermal history data of the refining process, it calculates the cumulative production of trans fatty acids and incorporates the cumulative production of trans fatty acids as an auxiliary judgment indicator into the discrimination logic of the risk collision analysis unit.

[0117] This embodiment further considers trans fatty acids as a key safety indicator, enhancing the system's risk assessment dimensions; the kinetic evolution modeling unit establishes a correlation model between temperature, time, and trans fatty acid production; and it utilizes historical thermal data from the refining process, particularly deodorization temperature. and time Calculate the cumulative amount of trans fatty acids produced. The calculation formula is as follows:

[0118]

[0119] in, The cumulative amount of trans fatty acids produced ( / 100g), It is the frequency factor of the isomerization reaction. , Deodorization process holding time , The activation energy for the cis-trans isomerization reaction is 125000. / , The ideal gas constant is 8.314. / ( ), The processing temperature for the deodorization process , The system will use the natural base. As an auxiliary judgment indicator, it is included in the risk collision analysis unit. If the set threshold is exceeded, the system will issue a risk warning even if the peroxide value is within acceptable limits.

[0120] This embodiment solves the double-edged sword problem in the high-temperature deodorization process; in the scenario of refining process optimization, by quantitatively calculating the cumulative amount of trans fatty acids, the system can comprehensively evaluate the overall impact of thermal processing on oil safety and prevent the hidden danger of excessive trans fatty acids due to the one-sided pursuit of low peroxide value.

[0121] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A data processing system for the entire process of edible vegetable oil production traceability, characterized in that, It includes a traceability data management center, a multi-source data acquisition unit, a dynamic evolution modeling unit, a risk collision analysis unit, and a dynamic decision execution unit; The traceability data management center is used to retrieve the circulation information of edible vegetable oil throughout its entire life cycle, and send the circulation information to the multi-source data acquisition unit for heterogeneous data extraction and analysis to obtain raw material fingerprint data, process stress data and environmental circulation data. The raw material fingerprint data, process stress data and environmental circulation data are then sent to the dynamic evolution modeling unit for quality evolution coupling calculation to obtain the current quality status prediction value and aging rate constant. The risk collision analysis unit is used to perform safety threshold comparison analysis on the collected current quality status prediction value, and to perform compliance judgment processing on the edible vegetable oil to be judged, so as to obtain a safety retention signal or a risk interception signal. When a risk interception signal is generated, the dynamic decision execution unit is used to block the supply chain of the target batch of products, generate a freeze command and send it to the warehouse management system. When a safe retention signal is generated, the dynamic decision execution unit uses the aging rate constant to quantitatively calculate and analyze the remaining safe window period to obtain dynamic shelf life data. It then determines whether the dynamic shelf life data meets the minimum sales cycle requirement. If it does not meet the requirement, a freeze command is generated. If it does meet the requirement, the dynamic shelf life data is mapped to the terminal display port. The heterogeneous data extraction and analysis process is as follows: Obtain the production batch code of edible vegetable oil, trace the origin of raw materials and the initial physicochemical test report based on the production batch code, and set the initial acid value and moisture content as the raw material fingerprint data; Key node parameters in the production process are obtained, and the pressing and leaching temperature, the concentration of refining and deacidifying alkali solution, and the time-temperature integral of the deodorization section are set as process stress data. Acquire continuous time-series data during warehousing and transportation, and set temperature fluctuation curves, light intensity exposure duration, and oxygen permeability of packaging materials as environmental flow data during the logistics process.

2. The edible vegetable oil production process traceability data processing system according to claim 1, characterized in that, The coupled calculation process of the dynamic evolution modeling unit is as follows: The temperature fluctuation curves in the environmental flow data are obtained, and the thermodynamic equivalent transformation analysis of the temperature fluctuation curves is performed using the Arrhenius equation. The duration of different temperature ranges is converted into the equivalent thermal aging time at the standard reference temperature. Based on the time-temperature integral of the deodorization section in the process stress data, the consumption ratio of the oxidation induction period is calculated by calling the preset oxidation induction period loss model. Combined with the equivalent thermal aging time, the lipid oxidation reaction rate constant under the current environmental conditions is calculated using the reaction rate equation, and the lipid oxidation reaction rate constant is set as the aging rate constant.

3. The edible vegetable oil production process traceability data processing system according to claim 2, characterized in that, The process for obtaining the current quality status prediction value is as follows: The initial peroxide value in the raw material fingerprint data is obtained as the starting point for the reaction kinetics model; Based on the aging rate constant and equivalent thermal aging time, a nonlinear fitting calculation was performed using a free radical chain reaction growth model to simulate the cumulative growth trend of peroxide value over time. The calculated peroxide value simulation data at the current moment was set as the predicted value of the current quality status.

4. The edible vegetable oil production process traceability data processing system according to claim 1, characterized in that, The security threshold comparison and analysis process is as follows: Retrieve the upper limit threshold for peroxide value from the preset safety standard database, and set the upper limit threshold as the safety critical value; Compare the current quality status prediction value with the safety threshold value: If the current quality status prediction value is less than the safety threshold, a safety retention signal is generated. If the current quality status prediction value is greater than or equal to the safety threshold, a risk interception signal is generated.

5. The edible vegetable oil production process traceability data processing system according to claim 4, characterized in that, The quantitative measurement and analysis process is as follows: In response to a security retention signal, the security threshold value is obtained; Using the current quality status prediction value as the starting state, and using the reaction kinetic model in the kinetic evolution modeling unit, with the real-time aging rate constant under the current storage environment as the evolution parameter, the time span required for the peroxide value to reach the safety critical value is deduced, and this time span is set as the remaining shelf life. The remaining shelf life is added to the current date to calculate the recommended best-before date, and this recommended best-before date is set as dynamic shelf life data.

6. The edible vegetable oil production process traceability data processing system according to claim 1, characterized in that, The system further includes an anomaly pattern recognition unit, which is used for: Obtain the benchmark aging rate model for similar products, and calculate the deviation between the aging rate constant of the current batch of products and the benchmark aging rate model. The deviation is compared with the preset abnormal fluctuation threshold. If the deviation exceeds the preset abnormal fluctuation threshold, it is determined that there is a process defect or adulteration risk, and a process early warning signal is generated and sent to the production control center.

7. The edible vegetable oil production process traceability data processing system according to claim 1, characterized in that, The process stress data also includes trans fatty acid formation factors; The dynamic evolution modeling unit is also used to establish a correlation model of temperature-time-trans fatty acid production. Based on the thermal history data of the refining process, it calculates the cumulative production of trans fatty acids and incorporates the cumulative production of trans fatty acids as an auxiliary judgment indicator into the discrimination logic of the risk collision analysis unit. If the cumulative amount generated exceeds the set threshold, the system will issue a risk warning even if the peroxide value is within acceptable limits.

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