A food quality and safety traceability method and system

Abstract

The invention discloses a food quality and safety traceability method and system, which specifically relate to the technical field of food safety traceability, and are used to solve the existing problem that food quality and safety traceability cannot be performed efficiently and accurately; the node structure, operation process complexity and dependency of a food supply chain are analyzed through entropy weight fuzzy comprehensive evaluation, and the potential impact of supply chain complexity on food quality and safety is evaluated; when a food quality and safety problem occurs, based on the evaluation result, it is determined whether to give priority to tracing the process of transporting food to a point of sale; if priority tracing is determined, the transport batch is marked as a target transport batch; the delayed reaction of a target batch of food after a change in environmental conditions is analyzed through a time-delay differential equation, and the potential risk of its delayed change over time is evaluated; the cumulative risk of temperature and humidity fluctuations during transportation and storage on food quality deterioration is analyzed through a GARCH model; and finally, the above evaluation results are combined to optimize the traceability path of the target transport batch.

Description

Technical Field

[0001] The present invention relates to the technical field of food safety traceability, and more specifically, to a food quality safety traceability method and system thereof. Background Art

[0002] Food quality and safety traceability refers to the technology and management means of recording and tracking each link of the food supply chain to ensure that the source of the problem can be quickly located when a food safety problem occurs. It involves tracking and managing the entire process of food production, processing, transportation, storage and sales, ensuring data transparency and traceability at each link. When food has quality or safety problems, it can quickly identify the point of occurrence, the responsible party and the batches involved, helping the relevant parties take measures to prevent the problem from further expanding.

[0003] At present, the existing food quality and safety traceability methods are usually carried out according to a predetermined process to track all links of food from production to sales. However, in the process of food transportation to the point of sale, the traditional traceability method has some shortcomings. In the prior art, since food quality and safety traceability is usually carried out according to a fixed predetermined process, there is a lack of dynamic analysis of food quality and safety traceability, and it is impossible to efficiently and accurately trace food quality and safety, which will lead to low traceability efficiency, unreasonable resource allocation, and inaccurate traceability.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a food quality and safety traceability method and system thereof to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A food quality and safety traceability method comprises the following steps:

[0008] S1: Analyze the node structure, operation process complexity and dependency in the food supply chain through entropy weight fuzzy comprehensive evaluation, and evaluate the potential impact of supply chain complexity on food quality and safety;

[0009] S2: When food quality and safety traceability is required, based on the potential impact of supply chain complexity on food quality and safety, determine whether to prioritize traceability from food transportation to the point of sale;

[0010] S3: When tracing the food from the food transport to the point of sale is prioritized, the transport batches involved in the food quality and safety incidents will be marked as target transport batches;

[0011] S4: Analyze the delayed response of food to changes in environmental conditions through time-delay differential equations, and evaluate the potential risk of food quality changes over time in the target transport batches; analyze the impact of temperature and humidity fluctuations on food during transportation and storage through the GARCH model, and evaluate the cumulative risk of food quality deterioration caused by environmental condition fluctuations in the target transport batches;

[0012] S5: Based on a comprehensive analysis of the potential risks of food quality changes with time lags and the cumulative risks of food quality deterioration due to fluctuations in environmental conditions, the traceability route for the target transport batches is optimized.

[0013] In a preferred embodiment, S1 specifically includes:

[0014] S101: Collect information on the number, location and operation process of nodes in the food supply chain;

[0015] S102: Use the entropy weight method to analyze the operational complexity, process importance and connection of each node in the supply chain, and determine the weight value of each node;

[0016] S103: Based on the weight values ​​of the nodes, the complexity of the entire supply chain structure is analyzed using the fuzzy comprehensive evaluation method to quantify the complexity index of the supply chain;

[0017] S104: Evaluate the potential impact of operational process complexity in the supply chain on food quality and safety based on the operational processes of each node and the connections between nodes;

[0018] S105: Integrate the evaluation results of supply chain structure complexity, node weights and operational process complexity to derive the overall potential impact of supply chain complexity on food quality and safety:

[0019] Calculate the potential complexity index of the supply chain, and its expression is: ;in, is the potential complexity index of the supply chain, It is The weight of the node, is the combined process complexity between two nodes, is an indicator of the overall complexity of the supply chain. is the number of nodes.

[0020] In a preferred embodiment, S2 is specifically:

[0021] When food quality and safety traceability is required, the supply chain potential complexity index is compared with its corresponding preset threshold:

[0022] when When , it is determined that the food transportation to the point of sale process should be traced first; when When the food is transported to the point of sale, it is determined that the traceability process is not prioritized;

[0023] in, The preset threshold value corresponding to the potential complexity index of the supply chain.

[0024] In a preferred embodiment, S3 specifically includes:

[0025] Determine the transport batches involved based on the time, location and relevant supply chain information of the food quality and safety issue;

[0026] By comparing the transport records, environmental conditions and quality monitoring data of the transport batches, confirm whether there is any correlation between the transport batches and the food quality and safety incidents;

[0027] When it is determined that the transport batch is associated, the transport batch is marked as a target transport batch.

[0028] In a preferred embodiment, the delayed reaction of food after the environmental conditions change is analyzed by a time-delay differential equation to evaluate the potential risk of the food quality of the target transport batch changing with time, specifically including:

[0029] Obtain the change data of environmental conditions of the target transport batch during transportation and the corresponding time information;

[0030] Based on food characteristics, a time-delay differential equation model of food quality to environmental conditions is established, and the delayed response behavior of food quality is described by the time-delay differential equation model:

[0031] A time-delay differential equation model is established to describe the delayed changes in food quality: ;in, Represents the quality of food at a certain moment. represents the rate of change of food quality over time, represents the environmental conditions at a previous moment, is the lag time, is the environmental sensitivity coefficient, Represents time;

[0032] Determine the key parameters in the time-delay differential equation using experimental data and historical data, including the time-delay time and the environmental sensitivity coefficient;

[0033] The time-delay differential equation is solved by numerical calculation method to obtain the prediction results of food quality changing with time; the numerical calculation method includes finite difference method and Runge-Kutta method;

[0034] Analyze the lagged change trend of food quality based on the prediction results of food quality changes over time, and evaluate the potential risk of lagged changes in food quality of the target transport batch over time:

[0035] Based on the integral calculation of the rate of change of food quality over time, the lagging quality risk index is calculated, and its expression is: ;in, is the lagging quality risk index, is the forecast start time, is the forecast end time.

[0036] In a preferred embodiment, the GARCH model is used to analyze the impact of temperature and humidity fluctuations on food during transportation and storage, and to evaluate the cumulative risk of environmental condition fluctuations in the target transportation batch on food quality deterioration, specifically including:

[0037] Obtain temperature and humidity data of the target transport batch and identify abnormal change points by analyzing data fluctuations and mutations;

[0038] The collected temperature and humidity data are used to construct a time series model to preliminarily describe its fluctuation characteristics and trends;

[0039] The GARCH model is used to model the temperature and humidity time series and accurately quantify the intensity and frequency of its fluctuations;

[0040] Based on the sensitivity parameters of food to temperature and humidity fluctuations, the cumulative risk index of food quality deterioration is calculated:

[0041] By combining the temperature and humidity fluctuation data and the sensitivity parameters of food, the cumulative risk index of food quality deterioration is calculated, and its expression is: ;in, is the cumulative risk index of food quality deterioration, and are the sensitivity parameters of food to temperature and humidity fluctuations, and are the conditional variances of temperature and humidity, respectively.

[0042] In a preferred embodiment, S5 is specifically:

[0043] Normalize the lagged quality risk index and the cumulative risk index of food quality deterioration;

[0044] The normalized lagged quality risk index and the cumulative risk index of food quality deterioration are assigned weight coefficients respectively, and the traceability path ranking coefficients are calculated, where the weight coefficients are all greater than 0;

[0045] The larger the traceability path ranking coefficient is, the higher the priority of the target batch for traceability analysis should be.

[0046] On the other hand, the present invention provides a food quality and safety traceability system, including a supply complexity assessment module, a traceability priority judgment module, a target batch marking module, a risk assessment and analysis module, and a traceability path optimization module;

[0047] Supply Complexity Assessment Module: Analyzes the node structure, operational process complexity and dependency relationships in the food supply chain through entropy weight fuzzy comprehensive assessment, and assesses the potential impact of supply chain complexity on food quality and safety;

[0048] Traceability priority judgment module: When food quality and safety traceability is required, based on the potential impact of supply chain complexity on food quality and safety, it is determined whether to prioritize the traceability of the food transportation to the point of sale;

[0049] Target batch marking module: When tracing the food transportation to the point of sale is prioritized, the transport batches involved in food quality and safety issues will be marked as target transport batches;

[0050] Risk assessment and analysis module: Analyze the delayed response of food to changes in environmental conditions through time-delay differential equations, and evaluate the potential risk of food quality changes in the target transport batch over time; analyze the impact of temperature and humidity fluctuations on food during transportation and storage through the GARCH model, and evaluate the cumulative risk of food quality deterioration caused by environmental condition fluctuations in the target transport batch;

[0051] Traceability route optimization module: Based on a comprehensive analysis of the potential risks of food quality changes with time lag and the cumulative risk of food quality deterioration due to fluctuations in environmental conditions, the traceability route of the target transport batch is optimized.

[0052] The technical effects and advantages of a food quality and safety traceability method and system of the present invention are as follows:

[0053] 1. Through entropy weight fuzzy comprehensive evaluation, the complexity of the supply chain is deeply analyzed, and the cumulative impact of food quality deterioration caused by the delayed reaction of food after environmental conditions change and temperature and humidity fluctuations is comprehensively evaluated, achieving accurate traceability path optimization, and being able to dynamically evaluate the operational complexity and dependencies of each node in the supply chain, thereby identifying potential high-risk links in the supply chain. By analyzing the potential impact of supply chain complexity on food quality and safety, it is possible to intelligently determine whether to prioritize the process of tracing the food from transportation to the point of sale, thus avoiding the problems of unreasonable resource allocation and inefficient traceability.

[0054] 2. Through the time-delay differential equation model and the GARCH model, the potential risks of food quality changes with time lag and the cumulative risks of food quality deterioration due to fluctuations in environmental conditions are accurately quantified. Through comprehensive analysis of the results, the batch traceability path ranking coefficient is calculated, and based on this, the traceability path of the target transport batches is dynamically optimized, giving priority to high-risk batches to ensure the rapid location and resolution of food safety issues. This not only improves the efficiency and accuracy of traceability, but also effectively shortens the response time, reduces the losses caused by food quality problems, significantly improves the overall level of supply chain management, and ensures continuous monitoring and safety of food quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of a food quality and safety traceability method of the present invention;

[0056] Figure 2 The present invention is a structural schematic diagram of a food quality and safety traceability system. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Example 1

[0059] Figure 1 The present invention provides a food quality and safety traceability method, which comprises the following steps:

[0060] S1: The node structure, operational process complexity and dependency relationships in the food supply chain are analyzed through entropy weight fuzzy comprehensive evaluation to assess the potential impact of supply chain complexity on food quality and safety.

[0061] S2: When food quality and safety traceability is required, determine whether to prioritize traceability of the process from food transportation to the point of sale based on the potential impact of supply chain complexity on food quality and safety.

[0062] S3: When tracing the food transportation to the point of sale is prioritized, the transport batches involved in food quality and safety incidents will be marked as target transport batches.

[0063] S4: The delayed response of food to changes in environmental conditions is analyzed through time-delay differential equations, and the potential risk of food quality changes in the target transport batches over time is evaluated. The impact of temperature and humidity fluctuations on food during transportation and storage is analyzed through the GARCH model, and the cumulative risk of food quality deterioration caused by environmental condition fluctuations in the target transport batches is evaluated.

[0064] S5: Based on a comprehensive analysis of the potential risks of food quality changes with time lags and the cumulative risks of food quality deterioration due to fluctuations in environmental conditions, the traceability route for the target transport batches is optimized.

[0065] S1 specifically includes:

[0066] S101: Collect information on the number, location, and operation process of nodes in the food supply chain, and ensure that the data covers the operational details of each key node:

[0067] Collect information about each node in the food supply chain, including the number, geographic location, and operation process of each node. The number of nodes represents the number of key nodes involved in the entire supply chain, and the location of the nodes is the physical location of these nodes in the supply chain. The operation process information covers data on the processing capacity, working mode, and process duration of each node.

[0068] S102: Use the entropy weight method to analyze the operational complexity, process importance and connection of each node in the supply chain, and determine the weight value of each node:

[0069] After collecting the node information, the entropy weight method is used to determine the operational complexity, process importance, and connection of each node in the supply chain.

[0070] The complexity of a node is determined by the complexity of its operation process and multi-dimensional factors such as resource usage. The information complexity of a node is measured by its entropy value, which is expressed as:

[0071] ;in, Indicates The entropy value of the node, Indicates The node in The proportion of complexity indicators, Indicates the number of complexity indicators involved in the evaluation, is the standardization coefficient (used to ensure that the entropy value is within a reasonable range, usually set to ).

[0072] The weight of the node is calculated according to the entropy value of the node, and its expression is: ;in, It is The weight of each node reflects the relative importance of the node in the entire supply chain; is the number of nodes.

[0073] S103: Based on the weight values ​​of the nodes, the complexity of the entire supply chain structure is analyzed using the fuzzy comprehensive evaluation method to quantify the complexity indicators of the supply chain:

[0074] The complexity of the supply chain is made up of multiple factors, including the strength of connections between nodes, processing time, and the collaborative relationships between nodes.

[0075] In order to quantify the complexity of the supply chain, a membership matrix is ​​established:

[0076] ;in, is the membership matrix, Indicates The node in The membership matrix is ​​used to represent the membership of each node under different complexity factors.

[0077] By combining the node weights with the membership matrix, the total complexity index of the supply chain is calculated: ;in, is an indicator of the overall complexity of the supply chain. is the weight vector.

[0078] in, The weight of each node is Specifically, is the weight of all nodes The collection of .

[0079] S104: Based on the operation process of each node and the connection between nodes, evaluate the potential impact of the complexity of the operation process in the supply chain on food quality and safety:

[0080] Get the operation process complexity of the node. The operation process complexity of the node includes the number of operation steps, resource usage, and processing time of each node.

[0081] Get the connection strength between nodes. The connection strength between nodes represents the degree of mutual dependence between nodes.

[0082] The comprehensive process complexity between two nodes can be expressed by the following formula: ;in, is the combined process complexity between two nodes, i.e., node With Node The process complexity between This is to distinguish nodes ); It is The complexity of the operation process of each node; Is a node With Node The strength of the connection between them.

[0083] S105: Integrate the evaluation results of supply chain structure complexity, node weights and operational process complexity to derive the overall potential impact of supply chain complexity on food quality and safety:

[0084] Each node in the supply chain has a weight that reflects its importance in the supply chain. In addition, the operational process complexity represents the complexity of each node and its relationship with other nodes. The total complexity of the supply chain reflects the overall complexity of the supply chain.

[0085] Calculate the potential complexity index of the supply chain, and its expression is: ;in, The potential supply chain complexity index reflects the overall potential impact of supply chain complexity on food quality and safety.

[0086] The larger the potential complexity index of the supply chain, the greater the potential impact of supply chain complexity on food quality and safety, and the greater the impact of supply chain complexity on food quality and safety.

[0087] S2 is specifically:

[0088] When food quality and safety traceability is required, that is, when a food quality and safety situation occurs, the supply chain potential complexity index is compared with its corresponding preset threshold:

[0089] when When the food is transported to the point of sale, the priority is to trace the source. At this time, the supply chain is more complex and there is a greater food safety risk.

[0090] when When the food is transported to the point of sale, it is determined that the food is not traced first, and the traceability is carried out according to the originally preset traceability sequence.

[0091] in, The preset thresholds corresponding to the supply chain potential complexity index are determined through historical data, risk analysis of different food categories and supply chain characteristics.

[0092] This determination is based on the potential impact of supply chain complexity on food quality and safety. The complexity of the supply chain usually increases the risk of food quality problems, especially in the case of multiple nodes and complex operating procedures. High complexity means strong dependencies between nodes, cumbersome operating procedures, and difficulty in management, which can easily lead to information opacity or delays, thereby increasing the probability of food safety incidents. Therefore, when the potential complexity index of the supply chain is higher than the preset threshold, priority is given to tracing the process of food transportation to the point of sale, which can quickly identify and solve problems in high-risk links and reduce the possibility of further expansion. If the potential complexity index is lower than the threshold, it means that the complexity of this link has little impact on food safety. At this time, traceability can be carried out in the order originally set, and no special priority treatment is required. Such a determination can help decision makers allocate resources more effectively and focus on the high-risk links that are most likely to cause problems.

[0093] S3 specifically includes:

[0094] Determine the transport batches involved based on the time, place and relevant supply chain information of the food quality and safety issue.

[0095] By comparing the transportation records, environmental conditions and quality monitoring data of the transport batches, confirm whether there is any connection between the transport batch and the food quality and safety incident.

[0096] When it is determined that the transport batches are associated, the transport batch will be marked as the target transport batch so that it can be tracked in the subsequent traceability process.

[0097] Record and organize all key data of the target transport batch (such as batch number, time period, transport route, temperature and humidity records, etc.) to ensure the completeness of the traceability data.

[0098] The delayed response of food to changes in environmental conditions is analyzed through time-delay differential equations to assess the potential risks of food quality changes over time in the target transport batch, including:

[0099] Obtain the changing data of the environmental conditions of the target transport batch during the transportation process and the corresponding time information.

[0100] Among them, the data on changes in environmental conditions refers to external factors that affect food quality, such as temperature, humidity, and pressure, which change over time during transportation. Each environmental data needs to correspond to a precise time point in order to perform dynamic analysis over time. The frequency of collecting time data should be high enough to ensure complete coverage of all key changes in the transportation process.

[0101] Based on food characteristics, a time-delay differential equation model of food quality to environmental conditions is established, and the delayed response behavior of food quality is described by the time-delay differential equation model:

[0102] There is a certain lag in the response of different foods to environmental conditions, that is, food quality does not change immediately with environmental changes, but there is a certain delay.

[0103] A time-delay differential equation model is established, which can reflect the changing pattern of food quality with time lag.

[0104] The delayed change of food quality is described by the time-delay differential equation, and the formula is as follows: ;in, Represents the quality of food at a certain moment. represents the rate of change of food quality over time, Indicates the environmental conditions at a previous moment (this moment is delayed by a period of time from the current moment). is the lag time (indicating the delay in the food's response to environmental changes), is the environmental sensitivity coefficient (reflecting the degree of influence of environmental conditions on changes in food quality), Represents time.

[0105] The time-delay differential equation describes the delayed effect of changes in environmental conditions on food quality, and adjusts the accuracy of the model by using lag time and environmental sensitivity coefficients.

[0106] The key parameters in the time-delay differential equation are determined using experimental data and historical data. The key parameters include the time-delay time and the environmental sensitivity coefficient:

[0107] In order to make the delay differential equation more practical and accurate, the key parameters in the model need to be determined through experiments and historical data.

[0108] Lag time indicates the time delay of the change in environmental conditions on food quality. For example, after a drastic change in temperature, it may take several hours or even longer for some foods to have a significant impact on their quality. The lag time is determined by simulating the reaction speed of food quality changes under different environmental conditions in the laboratory.

[0109] The environmental sensitivity coefficient represents the sensitivity of food to changes in environmental conditions. Different types of food have different sensitivities to environmental changes. Through historical data analysis, the deterioration rate of food under different temperature, humidity or pressure changes is determined, and the environmental sensitivity coefficient is estimated through statistical and regression analysis methods. For more sensitive foods, the environmental sensitivity coefficient should be larger, and vice versa.

[0110] The numerical calculation method is used to solve the time-delay differential equation to obtain the prediction results of food quality changes over time:

[0111] Since time-delay differential equations are usually difficult to solve analytically, numerical methods are used to solve them. Commonly used numerical methods include the finite difference method and the Runge-Kutta method, which can effectively handle differential equations with lag terms.

[0112] Numerical calculation methods can be used to obtain prediction results of food quality at different time points, indicating the dynamic changes of food quality over time. Numerical calculation can generate food quality data at a series of time points to determine the trend of quality changes over time.

[0113] Analyze the lagged change trend of food quality based on the prediction results of food quality changes over time, and evaluate the potential risk of lagged changes in food quality of the target transport batch over time:

[0114] Based on the integral calculation of the rate of change of food quality over time, the lagging quality risk index is calculated, and its expression is: ;in, is the lagging quality risk index, is the forecast start time, is the forecast end time.

[0115] The larger the lagged quality risk index, the higher the potential risk of time-lagged changes in food quality of the target transport batch, which means that food quality is more affected by environmental changes during transportation and may be at greater risk of deterioration.

[0116] The GARCH model is used to analyze the impact of temperature and humidity fluctuations on food during transportation and storage, and to evaluate the cumulative risk of food quality deterioration caused by environmental condition fluctuations in the target transportation batch, including:

[0117] Obtain temperature and humidity data for the target transport batch and identify abnormal change points by analyzing data fluctuations and mutations:

[0118] Obtain temperature and humidity data collected during the transportation and storage of the target shipment. This data is usually obtained by relying on temperature and humidity sensors, which can monitor and record changes in environmental conditions in real time. For each time point, the data should include temperature and humidity values.

[0119] A preliminary analysis of the fluctuations in temperature and humidity is required. Fluctuations in temperature and humidity data refer to slight changes within the normal range, while mutations refer to drastic changes beyond the normal range.

[0120] Calculating the change in temperature and humidity between consecutive time points can effectively identify abnormal fluctuations and mutation points of temperature and humidity. If the change in temperature or humidity between consecutive time points exceeds the preset threshold, the moment is marked as an abnormal change point.

[0121] The collected temperature and humidity data are used to construct a time series model to preliminarily describe its fluctuation characteristics and trends.

[0122] The GARCH model is used to model the temperature and humidity time series and accurately quantify the intensity and frequency of its fluctuations:

[0123] In order to more accurately quantify the intensity and frequency of temperature and humidity fluctuations, the generalized autoregressive conditional heteroskedasticity model (GARCH model) is used for analysis. This model can handle the changes in volatility in time series well, especially when the fluctuations have "clustering" characteristics, that is, a period of large fluctuations is often accompanied by more fluctuations.

[0124] The GARCH model of temperature can be expressed as: ; The GARCH model of humidity can be expressed as: .

[0125] in, and are the temperature and humidity time series respectively; and are the means of the temperature and humidity time series, respectively; and are the fluctuation terms in the temperature and humidity time series, respectively; and are the conditional variances of temperature and humidity, respectively, indicating the intensity of fluctuations; is the standard normally distributed random disturbance term in the temperature time series; Standard normally distributed random disturbance term in the humidity time series.

[0126] is the constant term in the temperature GARCH model, which controls the base level of temperature fluctuations; it defines the intensity of fluctuations when there is no historical fluctuation.

[0127] It is the regression coefficient in the temperature GARCH model, which indicates the impact of past temperature fluctuations on current fluctuations. The larger the coefficient, the more significant the impact of past fluctuations on current fluctuations.

[0128] is the regression coefficient of the temperature conditional variance, reflecting the persistent impact of the previous conditional variance on the current variance. It is used to describe the persistence and clustering of temperature fluctuations.

[0129] is a constant term in the humidity GARCH model, defining the base level of humidity fluctuations. In the absence of historical fluctuations, it controls the initial intensity of humidity fluctuations.

[0130] is the regression coefficient in the humidity GARCH model, indicating the influence of past humidity fluctuations on current humidity fluctuations. Indicates the significant influence of historical humidity fluctuations on current fluctuations.

[0131] is the regression coefficient of the humidity conditional variance, which indicates the persistent impact of the past humidity conditional variance on the current humidity conditional variance. It is used to capture the clustering of humidity fluctuations.

[0132] Based on the sensitivity parameters of food to temperature and humidity fluctuations, the cumulative risk index of food quality deterioration is calculated:

[0133] Different foods have different sensitivities to temperature and humidity fluctuations, so it is necessary to combine the sensitivity parameters of food characteristics to fluctuations.

[0134] By combining the temperature and humidity fluctuation data and the sensitivity parameters of food, the cumulative risk index of food quality deterioration is calculated, and its expression is: ;in, is the cumulative risk index of food quality deterioration, and are the sensitivity parameters of food to temperature and humidity fluctuations, and are the conditional variances of temperature and humidity, respectively.

[0135] The larger the cumulative risk index of food quality deterioration, the higher the cumulative risk of food quality deterioration caused by fluctuations in environmental conditions of the target transport batch. This means that the greater the impact of environmental changes on food during transportation and storage, the greater the possibility of quality deterioration.

[0136] Among them, the sensitivity parameters of food to temperature and humidity fluctuations can be obtained through laboratory experiments and historical data analysis. In the experiment, the food is placed under different temperature and humidity conditions to measure its quality changes, such as the rate of change of quality indicators such as color, texture, and microbial growth rate. By performing statistical regression analysis on these data, the degree of influence of different temperature and humidity conditions on food quality is determined, thereby obtaining the sensitivity parameters of food to temperature and humidity fluctuations. These parameters reflect the quality change response of food under different environmental fluctuations.

[0137] S5 is as follows:

[0138] In order to make a unified comparison and comprehensive analysis, the lagged quality risk index and the cumulative risk index of food quality deterioration are normalized. The purpose of normalization is to convert risk indices of different magnitudes into the same standard range to facilitate subsequent weighted summation.

[0139] The normalized lagged quality risk index and the cumulative risk index of food quality deterioration are assigned weight coefficients respectively, and the traceability path ranking coefficient is calculated; for example, its expression can be: ;in, represents the sorting coefficient of the traceability path, is the normalized lagged quality risk index, is the normalized cumulative risk index of food quality deterioration, where and are the weight coefficients of the lagging quality risk index and the cumulative risk index of food quality deterioration, respectively, and and Both are greater than 0.

[0140] The larger the traceability path ranking coefficient is, the higher the potential risk of the target batch is, and the higher the priority for traceability analysis of the target batch should be.

[0141] The specific optimization process is as follows:

[0142] Calculate the traceability path sorting coefficients of all target transport batches.

[0143] The traceability path sorting coefficients are prioritized from large to small, and the target transport batch with the largest traceability path sorting coefficient is ranked first.

[0144] Optimize the traceability path based on the sorting results to ensure that resources are concentrated on high-risk batches first, thereby improving traceability efficiency.

[0145] Among them, the weight coefficients of the hysteresis quality risk index and the cumulative risk index of food quality deterioration need to be adjusted based on historical data and specific application scenarios. Usually, the actual impact of the two types of risks on food quality and safety can be observed through retrospective analysis and experimental data, and the optimal weights can be determined using statistical methods (such as regression analysis). If the hysteresis effect has a greater impact on a certain type of food, a higher weight can be set. On the contrary, if environmental fluctuations have a significant impact on food quality, then , ensuring that the final weighted result reflects the actual risk.

[0146] Through comprehensive analysis of the lagged quality risk index and the cumulative risk index of food quality deterioration, the batch traceability path ranking coefficient is generated using a nonlinear weighted summation formula, realizing priority traceability of different batches. Through normalization processing and nonlinear weight summation, the problem of magnitude differences of different risk indicators is solved, ensuring that each risk factor can be properly considered in the traceability decision. It can significantly improve the traceability efficiency, especially in the priority processing of high-risk batches, and reduce the response time of food safety incidents.

[0147] Example 2

[0148] The difference between Example 2 of the present invention and Example 1 is that this example introduces a food quality and safety traceability system.

[0149] Figure 2 A structural schematic diagram of a food quality and safety traceability system of the present invention is given, which includes a supply complexity assessment module, a traceability priority judgment module, a target batch marking module, a risk assessment and analysis module, and a traceability path optimization module.

[0150] Supply complexity assessment module: Analyze the node structure, operational process complexity and dependency relationships in the food supply chain through entropy weight fuzzy comprehensive evaluation, and evaluate the potential impact of supply chain complexity on food quality and safety.

[0151] Traceability priority judgment module: When food quality and safety traceability is required, based on the potential impact of supply chain complexity on food quality and safety, determine whether to prioritize traceability of the food transportation to the point of sale.

[0152] Target batch marking module: When tracing the food transportation to the point of sale is prioritized, the transport batches involved in food quality and safety issues will be marked as target transport batches.

[0153] Risk assessment and analysis module: Analyze the delayed response of food to changes in environmental conditions through time-delay differential equations, and evaluate the potential risk of food quality changes in the target transportation batch over time; analyze the impact of temperature and humidity fluctuations on food during transportation and storage through the GARCH model, and evaluate the cumulative risk of food quality deterioration due to fluctuations in environmental conditions of the target transportation batch.

[0154] Traceability route optimization module: Based on a comprehensive analysis of the potential risks of food quality changes with time lag and the cumulative risk of food quality deterioration due to fluctuations in environmental conditions, the traceability route of the target transport batch is optimized.

[0155] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0156] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0157] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0158] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0159] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0160] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0161] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0162] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0163] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0164] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A food quality and safety traceability method, characterized in that: The steps include: S1: Analyze the node structure, operation process complexity and dependency in the food supply chain through entropy weight fuzzy comprehensive evaluation, and evaluate the potential impact of supply chain complexity on food quality and safety; S2: When food quality and safety traceability is required, based on the potential impact of supply chain complexity on food quality and safety, determine whether to prioritize traceability from food transportation to the point of sale; S3: When tracing the food from the food transport to the point of sale is prioritized, the transport batches involved in the food quality and safety incidents will be marked as target transport batches; S4: Analyze the delayed response of food to changes in environmental conditions through time-delay differential equations, and evaluate the potential risk of food quality changes over time in the target transport batches; analyze the impact of temperature and humidity fluctuations on food during transportation and storage through the GARCH model, and evaluate the cumulative risk of food quality deterioration caused by environmental condition fluctuations in the target transport batches; S5: Based on the comprehensive analysis of the potential risk of food quality changes with time lag and the cumulative risk of food quality deterioration caused by environmental condition fluctuations, the traceability route of the target transport batch is optimized; S1 specifically includes: S101: Collect information on the number, location and operation process of nodes in the food supply chain; S102: Use the entropy weight method to analyze the operational complexity, process importance and connection of each node in the supply chain, and determine the weight value of each node; S103: Based on the weight values ​​of the nodes, the complexity of the entire supply chain structure is analyzed using the fuzzy comprehensive evaluation method to quantify the complexity index of the supply chain; S104: Evaluate the potential impact of operational process complexity in the supply chain on food quality and safety based on the operational processes of each node and the connections between nodes; S105: Integrate the evaluation results of supply chain structure complexity, node weights and operational process complexity to derive the overall potential impact of supply chain complexity on food quality and safety: Calculate the potential complexity index of the supply chain, and its expression is: ;in, is the potential complexity index of the supply chain, It is The weight of the node, is the combined process complexity between two nodes, is an indicator of the overall complexity of the supply chain. is the number of nodes; The delayed response of food to changes in environmental conditions is analyzed through time-delay differential equations to assess the potential risks of food quality changes over time in the target transport batch, including: Obtain the change data of environmental conditions of the target transport batch during transportation and the corresponding time information; Based on food characteristics, a time-delay differential equation model of food quality to environmental conditions is established, and the delayed response behavior of food quality is described by the time-delay differential equation model: A time-delay differential equation model is established to describe the delayed changes in food quality: ;in, Represents the quality of food at a certain moment. represents the rate of change of food quality over time, represents the environmental conditions at a previous moment, is the lag time, is the environmental sensitivity coefficient, Represents time; Determine the key parameters in the time-delay differential equation using experimental data and historical data, including the time-delay time and the environmental sensitivity coefficient; The time-delay differential equation is solved by numerical calculation method to obtain the prediction results of food quality changing with time; the numerical calculation method includes finite difference method and Runge-Kutta method; Analyze the lagged change trend of food quality based on the prediction results of food quality changes over time, and evaluate the potential risk of lagged changes in food quality of the target transport batch over time: Based on the integral calculation of the rate of change of food quality over time, the lagging quality risk index is calculated, and its expression is: ;in, is the lagging quality risk index, is the forecast start time, is the predicted end time; The GARCH model is used to analyze the impact of temperature and humidity fluctuations on food during transportation and storage, and to evaluate the cumulative risk of food quality deterioration caused by environmental condition fluctuations in the target transportation batch, including: Obtain temperature and humidity data of the target transport batch and identify abnormal change points by analyzing data fluctuations and mutations; The collected temperature and humidity data are used to construct a time series model to preliminarily describe its fluctuation characteristics and trends; The GARCH model is used to model the temperature and humidity time series and accurately quantify the intensity and frequency of its fluctuations; Based on the sensitivity parameters of food to temperature and humidity fluctuations, the cumulative risk index of food quality deterioration is calculated: By combining the temperature and humidity fluctuation data and the sensitivity parameters of food, the cumulative risk index of food quality deterioration is calculated, and its expression is: ;in, is the cumulative risk index of food quality deterioration, and are the sensitivity parameters of food to temperature and humidity fluctuations, and are the conditional variances of temperature and humidity, respectively.

2. A food quality and safety traceability method according to claim 1, characterized in that: S2 is specifically: When food quality and safety traceability is required, the supply chain potential complexity index is compared with its corresponding preset threshold: when When , it is determined that the food transportation to the point of sale process should be traced first; when When the food is transported to the point of sale, it is determined that the traceability process is not prioritized; in, The preset threshold value corresponding to the potential complexity index of the supply chain.

3. A food quality and safety traceability method according to claim 2, characterized in that: S3 specifically includes: Determine the transport batches involved based on the time, location and relevant supply chain information of the food quality and safety issue; By comparing the transport records, environmental conditions and quality monitoring data of the transport batches, confirm whether there is any correlation between the transport batches and the food quality and safety incidents; When it is determined that the transport batch is associated, the transport batch is marked as a target transport batch.

4. A food quality and safety traceability method according to claim 3, characterized in that: S5 is as follows: Normalize the lagged quality risk index and the cumulative risk index of food quality deterioration; The normalized lagged quality risk index and the cumulative risk index of food quality deterioration are assigned weight coefficients respectively, and the traceability path ranking coefficients are calculated, where the weight coefficients are all greater than 0; The larger the traceability path ranking coefficient is, the higher the priority of the target batch for traceability analysis should be.

5. A food quality and safety traceability system, used to implement a food quality and safety traceability method according to any one of claims 1 to 4, characterized in that: It includes supply complex assessment module, traceability priority judgment module, target batch marking module, risk assessment and analysis module and traceability path optimization module; Supply Complexity Assessment Module: Analyzes the node structure, operational process complexity and dependency relationships in the food supply chain through entropy weight fuzzy comprehensive assessment, and assesses the potential impact of supply chain complexity on food quality and safety; Traceability priority judgment module: When food quality and safety traceability is required, based on the potential impact of supply chain complexity on food quality and safety, it is determined whether to prioritize the traceability of the food transportation to the point of sale; Target batch marking module: When tracing the food transportation to the point of sale is prioritized, the transport batches involved in food quality and safety issues will be marked as target transport batches; Risk assessment and analysis module: Analyze the delayed response of food to changes in environmental conditions through time-delay differential equations, and evaluate the potential risk of food quality changes in the target transport batch over time; analyze the impact of temperature and humidity fluctuations on food during transportation and storage through the GARCH model, and evaluate the cumulative risk of food quality deterioration caused by environmental condition fluctuations in the target transport batch; Traceability route optimization module: Based on a comprehensive analysis of the potential risks of food quality changes with time lag and the cumulative risk of food quality deterioration due to fluctuations in environmental conditions, the traceability route of the target transport batch is optimized.

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