Digital visual meat food safety production monitoring management system

Abstract

The invention relates to the technical field of meat food safety production monitoring and management, in particular to a digital visual meat food safety production monitoring and management system. Comprising a data acquisition and binding unit used for acquiring environment time sequence data and static physicochemical data associated with a unique identity identification code of each batch of meat products; the environmental stress modeling unit is used for calculating to obtain a comprehensive environmental stress factor; the microorganism growth prediction unit is used for calculating to obtain the total quantity of predicted microorganisms; the dynamic risk assessment unit is used for calculating to obtain a dynamic safety risk index; and the risk early warning and decision support unit is used for generating a risk level signal, and solving based on a preset critical risk threshold to obtain a predictive remaining shelf life so as to support scheduling decision. According to the system, a solid data foundation is laid for dynamic, continuous and prospective microbial risk assessment of the whole production chain, and remarkable technical progress is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of meat food safety production monitoring and management, in particular to a digital visual meat food safety production monitoring and management system. BACKGROUND

[0002] During the production and circulation of meat food, the growth of microorganisms is a key factor affecting its quality and safety in the processes of slaughtering, cutting, warehousing and logistics; traditional food safety monitoring methods mainly rely on sampling detection at key control points, such as measuring the total number of colonies by microbial culture method; Such traditional methods have significant limitations: long detection period, results have lagging nature, cannot provide real-time risk warning and decision support for the production process; sampling belongs to discrete static monitoring, which is difficult to capture and evaluate the dynamic and cumulative effects of environmental factors on microbial growth in the entire production chain; for example, a short interruption of cold chain may lead to rapid proliferation of microorganisms, and this cumulative risk may not be accurately reflected in subsequent single-point sampling; Therefore, the prior art urgently needs a monitoring and management method that can dynamically, continuously and prospectively evaluate the risks of meat food production throughout the chain, to solve the technical problem that traditional discrete and instantaneous detection indicators cannot effectively evaluate the dynamic and cumulative risks of food, from passive response to active prediction, and improve the scientificity and forward-looking nature of food safety management.

[0003] The above information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0004] To solve the above technical problems, the present application discloses a digital visual meat food safety production monitoring and management system, in particular, the technical solution of the present application comprises: A data acquisition and binding unit is used to obtain environment time series data and static physicochemical data associated with the unique identity code of each batch of meat products; An environmental stress modeling unit is used to calculate the comprehensive environmental stress factor based on the environment time series data and the static physicochemical data through a preset mapping function; A microbial growth prediction unit is used to combine the comprehensive environmental stress factor, the initial number of colonies contained in the static physicochemical data, and the preset maximum specific growth rate of the target strain, and calculate the predicted total amount of microorganisms by solving the microbial growth prediction model; A dynamic risk assessment unit is used to calculate the dynamic safety risk index based on the predicted total amount of microorganisms and the comprehensive environmental stress factor; The risk early warning and decision support unit is configured to compare the dynamic safety risk index with preset grading early warning thresholds, generate a risk level signal, and solve a predictive remaining shelf life based on a preset critical risk threshold to support scheduling decisions.

[0005] Preferably, the environmental time-series data is temperature and humidity collected in real time by Internet of Things sensors deployed at key control points of slaughtering, cutting, warehousing, and logistics; and the static physicochemical data is initial bacterial colony count, initial pH value, and initial water activity detected upon entry into the factory.

[0006] Preferably, the comprehensive environmental stress factor is obtained by multiplying a plurality of independent stress sub-factors corresponding to temperature, humidity, initial pH value, and initial water activity; wherein the function form and internal parameters of each independent stress sub-factor are obtained by regression analysis or machine learning training on historical batch real environmental data and corresponding microbial detection results.

[0007] Preferably, the microbial growth prediction model accumulatively calculates the environmental impact in the production process by introducing an integral form; and the predicted total amount of microorganisms is obtained by multiplying the initial bacterial colony count and the exponential function value of the accumulative calculation result.

[0008] Preferably, the dynamic risk assessment unit first calculates the instantaneous growth rate of microorganisms based on the predicted total amount of microorganisms, the maximum specific growth rate of the target strain, and the comprehensive environmental stress factor.

[0009] Preferably, the dynamic risk assessment unit further performs the following steps to calculate the dynamic safety risk index: determines the inventory risk term based on the predicted total amount of microorganisms and a preset national food safety standard total plate count limit; determines the incremental risk term based on the instantaneous growth rate of microorganisms, a preset characteristic time constant, and the national food safety standard total plate count limit; performs weighted summation on the inventory risk term and the incremental risk term in combination with a preset weight factor to generate the dynamic safety risk index.

[0010] Preferably, the risk early warning and decision support unit generates the risk level signal in the following process: generates a normal signal if the dynamic safety risk index is less than or equal to a preset first early warning threshold; generates an attention signal if the dynamic safety risk index is greater than the first early warning threshold and less than or equal to a preset second early warning threshold; generates an early warning signal if the dynamic safety risk index is greater than the second early warning threshold.

[0011] Preferably, when the generated risk level signal is a pre-warning signal, the risk pre-warning and decision support unit is further configured to backtrack historical values of the comprehensive environmental stress factor, locate a time period when the comprehensive environmental stress factor is abnormally high, and trace a key production link causing the risk accumulation.

[0012] Compared with the prior art, the present application has the following beneficial effects: 1. The system creates a unique identity code for each batch of products, integrates the real-time environmental time series data such as temperature and humidity collected by Internet of Things sensors, and the initial static physicochemical data such as the number of colonies and pH value detected at the time of entering the factory, and constructs a complete digital file throughout the production process. This overcomes the defects of data isolation and inability to trace in traditional methods, integrates discrete data points into continuous process records, lays a solid data foundation for dynamic, continuous and forward-looking microbial risk assessment of the entire production chain, and realizes significant technological progress.

[0013] 2. The environmental stress modeling unit of the system converts multiple independent environmental parameters such as temperature, humidity, initial pH value and water activity into a comprehensive environmental stress factor through a mapping function generated by training historical data and microbial detection results. This design scientifically quantifies the synergistic effect of multiple factors on microbial growth, surpasses the limitations of traditional single variable monitoring, and endows the model with self-adaptive and self-optimizing capabilities, enabling it to accurately reflect the real environmental cumulative impact under specific processes and provide reliable input for subsequent accurate prediction.

[0014] 3. The dynamic risk assessment unit of the system generates a dynamic safety risk index by weighting and summing the stock risk, the comparison of the current predicted total number of colonies with the national standard limit, and the incremental risk based on the deterioration trend of the instantaneous growth rate. This evaluation paradigm that comprehensively considers the current situation and trend can provide early warning for batches with high risk potential that have acceptable current microbial levels but rapid growth, and significantly advance the window period of risk management. It fundamentally solves the pain points of traditional sampling inspection that cannot capture the process history impact and risk dynamic evolution trend, and realizes the transition from passive response to active prediction.

[0015] 4. The system converts the risk assessment results into specific management instructions. The risk pre-warning and decision support unit can not only generate graded warning signals based on the comparison of the dynamic safety risk index with the preset threshold to achieve fine management, but also automatically backtrack the historical values of the comprehensive environmental stress factor to accurately locate the key production link causing the risk accumulation when a high-level warning is triggered. This function provides a powerful diagnostic tool, provides direct data basis for process optimization, realizes the upgrade from treating problem products to eliminating problem sources, and provides scientific decision support for supply chain management through predictive remaining shelf life. BRIEF DESCRIPTION OF DRAWINGS

[0016] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a system structure diagram of the present invention. Detailed Implementation

[0017] 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. Example 1:

[0018] Please see Figure 1 A digital visualization-based meat quality food safety production monitoring and management system, comprising: The data acquisition and binding unit is used to acquire environmental time-series data and static physicochemical data associated with the unique identification code of each batch of meat products; The environmental stress modeling unit is used to calculate the comprehensive environmental stress factor based on environmental time-series data and static physicochemical data through a preset mapping function. The microbial growth prediction unit is used to combine comprehensive environmental stress factors, the initial colony count contained in static physicochemical data, and the preset maximum specific growth rate of the target species to calculate the predicted total number of microorganisms by solving the microbial growth prediction model. The dynamic risk assessment unit is used to calculate the dynamic safety risk index based on the predicted total microbial population and comprehensive environmental stress factors. The risk warning and decision support unit is used to compare and analyze the dynamic safety risk index with the preset graded warning threshold, generate risk level signals, and calculate the predictive remaining shelf life based on the preset critical risk threshold to support scheduling decisions. This embodiment provides a digital visualization meat food safety production monitoring and management system. The system aims to solve the problem that traditional discrete and instantaneous detection indicators cannot effectively assess the dynamic cumulative risks of food throughout the entire production chain. By constructing a digital twin model, the system can predict the microbial growth status in real time and continuously, and conduct forward-looking risk assessment and decision support. The complete technical loop of this system is accomplished through the collaborative work of the following units: The data acquisition and binding unit establishes a comprehensive, time-series digital file for each independent production batch; in this embodiment, when a batch of meat enters the production process, the system assigns it a unique identification code (…). This unit collects real-time data on the batch through IoT sensors deployed at key control points such as slaughtering, processing, warehousing, and logistics. Environmental time-series data that are uniquely correlated mainly includes temperature. With humidity ; meanwhile, the static physicochemical data of the batch, mainly the initial total number of colonies, initial pH value, and initial water activity, are obtained through the interface or detected at the time of factory entry In this model, to simplify the calculation, the initial pH value and water activity are regarded as batch-inherent properties that remain constant throughout the monitoring period. In future model iterations, they can also be treated as dynamic variables that change with microbial growth. All these dynamic and static data are bound to the batch Next, after data cleaning and normalization, the data cleaning step includes effective range verification of sensor data, for example, the temperature range is limited to -20°C to 50°C, and the outlier or missing value outside the range is smoothed by methods such as adjacent value interpolation or moving average filling to ensure the robustness of the model, forming the data basis required for subsequent modeling; The environmental stress modeling unit converts multiple independent environmental parameters into a single index that can comprehensively reflect their combined impact on microbial growth. To further illustrate, this embodiment introduces a comprehensive environmental stress factor To quantify the comprehensive impact of environmental parameters, this unit uses a pre-set mapping function to calculate , which is calculated as follows: ; wherein : the comprehensive environmental stress factor, dimensionless, calculated by this unit based on the input parameters; : the independent stress sub-factor functions corresponding to temperature, humidity, pH value, and water activity, respectively; their function forms and internal undetermined parameters are obtained by regression analysis or machine learning training on a large number of historical batches of real environmental data and corresponding microbial detection results; : real-time environmental parameters, is the real-time temperature, is the real-time humidity, provided by the data acquisition and binding unit; : batch-inherent static physicochemical parameters, is the initial pH value, is the initial water activity, provided by the data acquisition and binding unit; Based on the recognized theoretical model in the field of food microbiology, the independent effects of each environmental factor on the microbial growth rate can be multiplied and superimposed. This embodiment models this theoretical model, thereby combining the real-time changing dynamic parameters with the batch-inherent static parameters ​​) is performed, and the output value can more accurately reflect the comprehensive promotion or inhibition effect of the real environment on microbial growth; The microbial growth prediction unit establishes a microbial growth prediction model that can reflect the cumulative effect based on the quantification results of the environmental impact; the model is an improvement of the classic exponential growth model, and the continuous influence of the process history is embodied by introducing an integral form; in order to calculate the predicted total amount of microorganisms at any time , the unit adopts the following formula: ; Among them, : the predicted total amount of microorganisms at the moment, in units of CFU / g, calculated by the unit; : the initial total number of colonies, in units of CFU / g, provided by the data acquisition and binding unit; : the maximum specific growth rate of the target strain under ideal conditions, in units of , which is obtained from a public database according to the type of meat product, as a key parameter representing the growth rate of the entire microbial population; in actual application, this parameter can be selected as the growth rate of the target dominant spoilage bacteria, or an equivalent rate calibrated according to historical data to represent the overall proliferation trend of the entire microbial population; : the comprehensive environmental stress factor at the historical moment , provided by the environmental stress modeling unit; The internal logic is that the system performs real-time numerical integration on all historical moments to continuously update the predicted value of ; this design enables the impact of any temporary environmental fluctuations in the entire production chain to be accurately accumulated into the final result, solving the problem that traditional detection methods cannot evaluate historical impact; The dynamic risk assessment unit realizes forward-looking risk warning and comprehensive assessment of the safety state of the product, considering not only the current total amount of microorganisms but also the growth trend; for this purpose, the embodiment defines a dynamic safety risk index ; the technical concept of the embodiment is that a rapidly deteriorating product has a higher instantaneous risk than a slowly deteriorating product; the calculation formula is as follows: ; Before calculating , the unit needs to calculate the instantaneous growth rate of microorganisms , which can be directly derived from the calculation formula of :​ ; wherein, : dynamic safety risk index, dimensionless, calculated by this unit; : current total amount of microorganisms, calculated by the microorganism growth prediction unit; : instantaneous growth rate of microorganisms, calculated by this unit according to and ; : statutory limit of total amount of colonies, preset according to national food safety standards; : weight factor, dimensionless, preset according to expert experience, the value of which is selected to balance the contribution of stock risk and incremental risk in the final risk assessment, for example, when monitoring meat with rapid spoilage characteristics, the proportion of weight factor representing incremental risk can be appropriately increased; : characteristic time constant, preset value (e.g. 1 hour) for dimension unification; By weighting and summing the stock risk reflecting the current state ( ) and the incremental risk, i.e. the growth rate term, reflecting the future trend, potential safety risks can be identified earlier and more accurately; Risk warning and decision support unit, which converts risk assessment results into specific and executable management instructions; this unit compares and analyzes the value of risk index with preset graded warning thresholds, generates risk signals of regular, attention or warning levels, and pushes them to management personnel; graded warning thresholds, such as attention threshold and warning threshold , are determined based on statistical analysis of historical safety event database; specifically, by analyzing the correlation between the trajectory of historical batches and whether the final value exceeds the limit, using methods such as receiver operating characteristic curve analysis, the value of that can achieve a preset balance point between specificity and sensitivity is selected as the threshold; In addition, in order to provide proactive decision support, this unit also sets a critical risk threshold , and solves the time in equation , so as to calculate the predictive remaining shelf life of the batch; in solving the equation, the system defaults to using standard, preset warehouse or logistics environment parameters, such as constant refrigeration temperature 4°C and humidity 75% as the environmental data input for the future time period, to ensure the consistency and comparability of the prediction; the value can be used to guide the supply chain to implement a dynamic first-in-first-out strategy, preferentially scheduling the shortest batch, to achieve fine inventory management; The embodiment, through the cooperative work of the above units, builds a complete technical closed loop from data acquisition to decision support; it overcomes the limitations of traditional food safety monitoring relying on discrete, static detection indicators, and realizes dynamic, continuous, and cumulative assessment of microbial risks of meat food in the whole production chain through the establishment of a digital twin model; this not only improves the timeliness and accuracy of risk early warning, but also provides precise data decision basis for supply chain management through functions such as remaining shelf life prediction, improving the scientificity and forward-looking nature of food safety management. Embodiment 2:

[0019] The environmental time series data is the temperature and humidity collected in real time by Internet of Things sensors deployed at key control points of slaughtering, cutting, warehousing, and logistics; the static physicochemical data is the initial number of colonies, initial pH value, and initial water activity detected when entering the factory; The embodiment is a specific implementation of the data acquisition and binding unit based on embodiment 1; in order to ensure the accuracy and relevance of the model input, the embodiment optimizes and limits the source and type of data; The environmental time series data is specifically defined as the temperature ( ) and humidity ( ) collected in real time by Internet of Things sensors deployed at key control points of slaughtering, cutting, warehousing, and logistics; The static physicochemical data is specifically defined as the initial number of colonies ( ), initial pH value ( ), and initial water activity ( ) detected when the meat batch enters the factory; By limiting the data source to these core physical and chemical parameters most directly related to the growth of meat microorganisms, the scheme ensures high fidelity and strong relevance of the input data; compared to using generalized environmental data, this targeted data acquisition strategy greatly improves the accuracy of subsequent environmental stress modeling and microbial growth prediction, laying a solid data foundation for the reliability of the entire system. Embodiment 3:

[0020] The comprehensive environmental stress factor is obtained by multiplying a plurality of independent stress sub-factors corresponding to temperature, humidity, initial pH value and initial water activity respectively; wherein, the function form and internal parameters of each independent stress sub-factor are obtained by regression analysis or machine learning training on the real environmental data and corresponding microbial detection results of historical batches; This embodiment is based on embodiment 1, and a preferred method for calculating the comprehensive environmental stress factor (F) in the environmental stress modeling unit is as follows: In this embodiment, It is explicitly designed to be obtained by multiplying four independent stress sub-factors corresponding to temperature, humidity, initial pH value and initial water activity respectively; each independent stress sub-factor, for example, a bell-shaped function describing the effect of temperature , can adopt a quadratic polynomial model, such as: ; Wherein, and are the minimum and maximum temperature thresholds for microbial growth, respectively, and their function form and internal parameters are not fixed empirical values in advance, but are obtained by analyzing a plurality of historical batch data sets, each of which contains a set of historical environmental parameters, for example, under constant temperature and the corresponding measured microbial final value ; By mathematical methods such as least squares regression analysis, the undetermined parameters in each sub-factor function are calibrated to minimize the error between the predicted results of the model and ; The product form used in this embodiment is an effective and widely accepted model, which assumes that the effects of each environmental factor are relatively independent; in some scenarios with higher accuracy requirements, cross terms such as can also be introduced into the model to more accurately depict the interaction between certain factors; In this preferred method, the product form can achieve a good balance between computational efficiency and model accuracy, and is consistent with the basic principles of microbiology, and can more realistically reflect the synergistic effect of multiple environmental factors, and is more accurate than a simple linear superposition model; by machine learning training to determine the sub-factor function, the model has the ability of self-adaptation and self-optimization, and can learn the unique influence law under a specific production process and environment from historical data, thereby generating a highly customized and more accurate , which improves the accuracy of the entire prediction system; Other independent stress sub-factors can also be modeled using specific function forms; for example, the water activity stress sub-factor can adopt a monotonically increasing function, such as when is lower than the minimum water activity for microbial growth​ hour higher than Time can be represented as pH stress factor A bell-shaped function similar to temperature can also be used for modeling; the form and parameters of these functions are determined by regression analysis or machine learning training on historical data to achieve model adaptation and self-optimization. Furthermore, the model exhibits good physical boundary behavior; when the ambient temperature... At the lowest growth temperature or highest temperature hour, The value is 0, at which point the instantaneous growth rate is 0, and the total number of microorganisms stops growing, which is consistent with common biological knowledge; if the temperature exceeds this range... When the value becomes negative, the model will predict a decrease in the total amount of microorganisms, which can be used to simulate the lethal effects of low or high temperatures on microorganisms. When the system performs predictive remaining shelf life calculations, it will handle negative growth cases specially, such as counting them as an extension or indefinite extension of shelf life, to ensure the rationality of the output results. Example 4:

[0021] The microbial growth prediction model incorporates an integral form to cumulatively calculate the environmental impact of the production process; the predicted total microbial count is obtained by multiplying the initial colony count by the exponential function value of the cumulative calculation result. This embodiment is based on Embodiment 1, and elaborates on the specific microbial growth prediction model used in the microbial growth prediction unit; This model incorporates an integral form to cumulatively calculate the environmental impact at all historical moments during the production process; as shown in the aforementioned formula. As shown, the final predicted total microbial count ( ) is determined by the initial colony count ( This is obtained by multiplying the value of the exponential function, which represents the cumulative effect of the entire process, by the value of the exponential function. The introduction of an integral form is a key innovation of this scheme; it enables the precise quantification of the irreversible accumulation of the impact of adverse environmental factors in the process history, ensuring that any short-term but drastic environmental fluctuations, such as the impact of cold chain interruptions, can be faithfully recorded and accumulated, and will not be ignored when the environment returns to normal. This mechanism solves the pain point that traditional sampling inspection cannot capture the impact of the process history, making the prediction of the final product quality more reliable and in line with the facts. It should be noted that this model is an exponential growth model, which can accurately characterize the dynamics of microorganisms during the exponential growth phase, and is crucial for early risk warning. This model does not include parameters describing the growth plateau phase because, in food safety management, when microorganisms approach the plateau phase, the product has usually already exceeded the limits and lost its edible value. If the study needs to cover the entire growth curve, the stress factors in this model can also be included. Thought and Classics or The models are combined. Example 5:

[0022] The dynamic risk assessment unit calculates the instantaneous growth rate of microorganisms based on the predicted total microbial population, the maximum specific growth rate of the target species, and comprehensive environmental stress factors. The dynamic risk assessment unit further performs the following steps to calculate the dynamic security risk index: Based on the predicted total microbial count and the preset national food safety standard total bacterial count limit, existing risk items are identified; Incremental risk items are determined based on the instantaneous growth rate of microorganisms, the preset characteristic time constant, and the total colony limit of national food safety standards. By combining preset weighting factors, the existing risk items and the incremental risk items are weighted and summed to generate a dynamic safety risk index; This embodiment, based on Embodiment 1, explains how the dynamic risk assessment unit calculates the dynamic security risk index (…). This is a more forward-looking risk assessment process; The dynamic risk assessment unit is based on the predicted total microbial population ( ), the maximum specific growth rate of the target bacterial species ( ) and comprehensive environmental stress factors ( ), through formula The instantaneous growth rate of the microorganisms was calculated; This unit further performs the following steps to calculate... : Identifying existing risk items: based on predicted total microbial count ( ) and the preset national food safety standard total bacterial count limit ( ), calculate the ratio and multiply by its weighting factor This item reflects how close the product's current pollution level is to the safety limit. Identifying incremental risk items: based on the instantaneous growth rate of microorganisms ( ), preset characteristic time constant ( )and ,calculate and multiply by its weighting factor This item reflects the deterioration trend or speed of the product's safety status; Weighted summation: combining preset weight factors and The existing and new risk items are weighted and summed to generate the final dynamic safety risk index. ; This comprehensive assessment paradigm, which considers both existing and new risks, not only evaluates the current state of pollution but, more importantly, assesses its rate of deterioration. This enables the system to provide early warnings for high-risk potential batches that currently have acceptable levels of microorganisms but exhibit extremely rapid growth trends, thereby advancing the window of opportunity for risk management and achieving a shift from passive response to proactive prediction. Example 6:

[0023] The process by which the risk warning and decision support unit generates risk level signals is as follows: If the dynamic safety risk index is less than or equal to the preset first warning threshold, a regular signal is generated; If the dynamic safety risk index is greater than the first warning threshold and less than or equal to the preset second warning threshold, a concern signal is generated. If the dynamic safety risk index is greater than the second warning threshold, a warning signal will be generated; When the generated risk level signal is an early warning signal, the risk early warning and decision support unit is also used to trace back the historical values ​​of the comprehensive environmental stress factor, locate the period when the comprehensive environmental stress factor is abnormally high, and trace the key production links that cause the risk accumulation. Based on Example 1, this embodiment further refines the functions of the risk warning and decision support unit, demonstrating how it transforms risk indices into specific management actions. The process by which this unit generates risk level signals is as follows: If the dynamic security risk index ( () less than or equal to the preset first warning threshold If so, a normal signal is generated, indicating that the state is normal; like Greater than And less than or equal to the preset second warning threshold If so, a warning signal will be generated to alert management personnel. like Greater than If so, a warning signal will be generated, triggering a high-level alarm; When the generated risk level signal is a warning signal, this unit is also activated to perform a key tracing function: it automatically traces back the comprehensive environmental stress factors (…). Historical values ​​of ) through analysis The time series curve of the risk score is drawn, and the time interval with the largest contribution to the risk score and the corresponding production link are accurately positioned.

[0024] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A digital visualization-based meat quality food safety production monitoring and management system, characterized in that, include: The data acquisition and binding unit is used to acquire environmental time-series data and static physicochemical data associated with the unique identification code of each batch of meat products; The environmental stress modeling unit is used to calculate the comprehensive environmental stress factor based on environmental time-series data and static physicochemical data through a preset mapping function. The microbial growth prediction unit is used to combine comprehensive environmental stress factors, the initial colony count contained in static physicochemical data, and the preset maximum specific growth rate of the target species to calculate the predicted total number of microorganisms by solving the microbial growth prediction model. The dynamic risk assessment unit is used to calculate the dynamic safety risk index based on the predicted total microbial population and comprehensive environmental stress factors. The risk warning and decision support unit is used to compare and analyze the dynamic safety risk index with the preset graded warning thresholds, generate risk level signals, and calculate the predictive remaining shelf life based on the preset critical risk thresholds to support scheduling decisions.

2. The digital visualization meat quality food safety production monitoring and management system according to claim 1, characterized in that, The environmental time-series data consists of temperature and humidity collected in real time by IoT sensors deployed at key control points in slaughtering, processing, storage, and logistics; the static physicochemical data consists of the initial bacterial count, initial pH value, and initial water activity detected upon entry into the factory.

3. The digital visualization meat quality food safety production monitoring and management system according to claim 1, characterized in that, The comprehensive environmental stress factor is obtained by multiplying multiple independent stress sub-factors corresponding to temperature, humidity, initial pH value and initial water activity respectively; the functional form and internal parameters of each independent stress sub-factor are obtained by regression analysis or machine learning training on historical batches of real environmental data and corresponding microbial detection results.

4. The digital visualization meat quality food safety production monitoring and management system according to claim 1, characterized in that, The microbial growth prediction model incorporates an integral form to cumulatively calculate the environmental impact of the production process; the predicted total microbial count is obtained by multiplying the initial colony count by the exponential function value of the cumulative calculation result.

5. The digital visualization meat quality food safety production monitoring and management system according to claim 1, characterized in that, The dynamic risk assessment unit first calculates the instantaneous growth rate of microorganisms based on the predicted total microbial population, the maximum specific growth rate of the target species, and comprehensive environmental stress factors.

6. The digital visualization meat quality food safety production monitoring and management system according to claim 5, characterized in that, The dynamic risk assessment unit further performs the following steps to calculate the dynamic security risk index: Based on the predicted total microbial count and the preset national food safety standard total bacterial count limit, existing risk items are identified; Incremental risk items are determined based on the instantaneous growth rate of microorganisms, the preset characteristic time constant, and the total colony limit of national food safety standards. By combining preset weighting factors, the existing risk items and the incremental risk items are weighted and summed to generate a dynamic safety risk index.

7. The digital visualization meat quality food safety production monitoring and management system according to claim 1, characterized in that, The process by which the risk warning and decision support unit generates risk level signals is as follows: If the dynamic safety risk index is less than or equal to the preset first warning threshold, a regular signal is generated; If the dynamic safety risk index is greater than the first warning threshold and less than or equal to the preset second warning threshold, a concern signal is generated. If the dynamic safety risk index is greater than the second warning threshold, a warning signal will be generated.

8. The digital visualization meat quality food safety production monitoring and management system according to claim 1, characterized in that, When the generated risk level signal is an early warning signal, the risk early warning and decision support unit is also used to trace back the historical values ​​of the comprehensive environmental stress factor and locate the period when the comprehensive environmental stress factor is abnormally high, so as to trace the key production links that cause the risk accumulation.

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