Goat milk product cold-chain logistics system risk evaluation method

Abstract

The invention discloses a goat milk product cold-chain logistics system risk evaluation method. The method comprises the steps of S1, collecting a plurality of key parameters in a goat milk product cold-chain logistics process in real time; s2, historical cold-chain logistics data are collected, risk event labeling is carried out, and a historical data set is generated; s3, preprocessing the real-time data set and the historical data set, and generating a feature vector for risk assessment by applying a feature engineering technology; s4, based on the feature vectors, a support vector machine algorithm is adopted to construct a multi-level risk assessment model through a multi-dimensional feature fusion technology; s5, performing dynamic risk assessment according to the real-time data set and the multi-level risk assessment model; and S6, according to a dynamic risk assessment result, by setting an intelligent threshold value, automatically identifying a potential risk and triggering an early warning mechanism, forming an early warning signal, and according to an early warning level, automatically starting an intervention measure. The method has the advantages of being accurate, real-time and dynamic in response, and the safety and reliability in the cold-chain logistics process are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of risk assessment, and in particular to a risk assessment method for a cold chain logistics system of goat milk products. Background Art

[0002] With the widespread application of cold chain logistics, especially in the food and pharmaceutical industries, the efficiency and safety of cold chain logistics have become important factors in determining product quality and consumer health. As a high-value, highly perishable food, the safety and reliability of the cold chain transportation process of goat milk products are crucial. However, there are many potential risk factors in the cold chain logistics process, such as temperature and humidity fluctuations, equipment failures, and improper transportation route selection, which may lead to product quality degradation or loss, thereby affecting consumer health.

[0003] In the existing technology, the risk assessment of cold chain logistics mostly relies on a single monitoring method and a static risk management model. Traditional methods usually conduct risk assessment and early warning through manual monitoring and simple rule setting, but these methods have obvious shortcomings when facing a complex and dynamic logistics environment. Specifically, the problems in the existing technology include: 1. Lack of multi-dimensional risk assessment: Traditional cold chain logistics risk assessment methods often rely only on single monitoring data, such as temperature or humidity, lack the integration of multi-dimensional features, and cannot fully reflect the various potential risks in the cold chain logistics process.

[0004] 2. Static risk management model: The risk assessment methods in existing technologies usually use static models for assessment, which is difficult to cope with the real-time changing logistics environment and different risk levels. This makes it impossible for the system to flexibly adjust the operation strategy according to the real-time situation, making the risk control less precise.

[0005] 3. Delayed response mechanism: In the traditional risk warning mechanism, manual intervention is often required to initiate adjustment measures, resulting in a long response time and an inability to respond to sudden high-risk events in a timely manner, reducing the reliability of the overall cold chain logistics.

[0006] 4. Lack of dynamic feedback and adaptive adjustment: The risk assessment models in existing technologies usually do not have sufficient adaptive capabilities and cannot automatically adjust risk management strategies according to changes in real-time data in the cold chain logistics process. Therefore, it is often impossible to always maintain an appropriate risk control level at all stages of the cold chain logistics process.

[0007] Therefore, how to provide a risk assessment method for the cold chain logistics system of goat milk products is an urgent problem that technicians in this field need to solve. Summary of the invention

[0008] One purpose of the present invention is to propose a risk assessment method for a cold chain logistics system of goat milk products, build a multi-level risk assessment model based on multidimensional feature fusion technology and support vector machine algorithm, realize dynamic risk assessment in the cold chain logistics process through the combination of real-time data sets and historical data sets, and automatically adjust the operation strategy according to the assessment results. The method can realize multi-level risk monitoring and adaptive adjustment, has the advantages of accurate, real-time and dynamic response, and effectively improves the safety and reliability of the cold chain logistics process.

[0009] A risk assessment method for a cold chain logistics system of goat milk products according to an embodiment of the present invention comprises the following steps: S1. Real-time collection of multiple key parameters in the cold chain logistics process of goat milk products, and the use of data fusion technology to convert various heterogeneous data in key parameters into real-time data sets in a unified format; S2. Collect historical cold chain logistics data, annotate risk events on the historical cold chain logistics data, and generate a historical data set; S3, preprocessing the real-time data set and the historical data set, and applying feature engineering techniques to generate feature vectors for risk assessment; S4, based on feature vectors, uses support vector machine algorithm to build a multi-level risk assessment model through multi-dimensional feature fusion technology; S5. Perform dynamic risk assessment based on real-time data sets and multi-level risk assessment models; S6. Based on the dynamic risk assessment results, by setting intelligent thresholds, potential risks are automatically identified and early warning mechanisms are triggered to form early warning signals. Intervention measures are automatically initiated based on the early warning signals. At the same time, the dynamic risk assessment results and early warning signals are fed back to the operators of the cold chain logistics system.

[0010] Optionally, the S2 specifically includes: S21. Collect historical cold chain logistics data during the cold chain logistics process from multiple data sources, where the data sources include temperature and humidity records of transport vehicles, operating status data of cold chain transport equipment, ambient temperature and humidity data during transportation, various accident records during transportation, and fault logs of transport equipment; S22, preprocessing the collected historical cold chain logistics data; S23. Obtain the risk event types in the cold chain logistics process, use a data-driven automatic labeling method to label historical cold chain logistics data for risk events, and obtain risk event labels. The risk event type labels include high temperature risk, humidity fluctuation risk, equipment failure risk, and transportation delay risk, and associate each piece of historical cold chain logistics data with a corresponding risk event type label; S24. Set the threshold conditions for various risk events and determine the occurrence of risk events by calculating the deviation values ​​of various parameters: ; in, For the moment risk markers, For the moment The temperature value, is the set temperature target value, is the maximum temperature deviation, For the moment The humidity value, is the set humidity target value, is the maximum deviation value of humidity; S25. For other risk events, the equipment status in the historical cold chain logistics data and the time delay in the transportation process are marked, and the equipment failure threshold is set. When the equipment fails, a failure risk mark is generated. When the transportation delay time exceeds the preset threshold, a transportation delay risk mark is generated; S26. By introducing a multi-dimensional weighted and time-period adjusted risk distribution model, the probability of occurrence of risk events is weighted and calculated by combining multiple risk factors: ; in, Indicates Risk events during the period The probability of occurrence, It is with The weight coefficients of the factors are For the The risk event is The impact of historical cold chain logistics data, Whether to trigger the A marker of a risk event. is the total number of historical cold chain logistics data, The number of risk events considered; S27. Each piece of data in the historical data set consists of multiple fields, including a timestamp. , Temperature data , humidity data , Device Status , Transportation status , and the corresponding risk event labels , the historical data set is represented as: ; in, Indicates Timestamp of historical cold chain logistics data, Indicates Temperature data of historical cold chain logistics data, Indicates Humidity data of historical cold chain logistics data, Indicates Equipment status of historical cold chain logistics data, Indicates The transportation status of historical cold chain logistics data, Indicates Risk event labels of historical cold chain logistics data, It is the total number of historical cold chain logistics data.

[0011] Optionally, the S4 specifically includes: S41, based on feature vectors, feature vectors from different data sources are integrated through multi-dimensional feature fusion technology; S42, performing standardization processing on the fused feature vector; S43, inputting the standardized feature vector into a support vector machine model, and mapping the feature vector using a linear kernel function; S44, training the support vector machine model using training set data, wherein the training set data is labeled historical cold chain logistics data, and optimizing the parameters of the support vector machine model by a cross-validation method; S45. Predicting the probability of occurrence of risk events in real-time cold chain logistics data through the trained support vector machine model, wherein the prediction result is a multi-level risk assessment result, including high risk, medium risk and low risk classification; S46. Generate multi-level risk warnings based on the multi-level risk assessment results, including high-risk warnings, medium-risk warnings and low-risk warnings, where each warning level corresponds to a different risk response strategy; S47. The output result of the support vector machine model is the probability of occurrence of various risk events: ; ; in, Represents the feature vector Middle The value of the feature, is the weight coefficient associated with this feature, is the total number of features, Representative The feature vector of the historical data, is the total number of samples in the historical data set, For a given feature vector Time The probability of occurrence of risk events of this type, and Respectively Risk assessment value and standardized coefficient of risk events of this type, The number of risk events considered; S48. Based on the multi-level risk assessment results, automatically generate and adjust the optimization strategy of cold chain logistics operations in real time, including adjusting the transportation route, adjusting the cold chain equipment settings and increasing the frequency of transportation monitoring; S49. Construct a multi-level risk assessment model, which includes multiple risk levels, each level corresponds to a different risk assessment factor, and generates a comprehensive risk assessment result based on the characteristic vectors of each level and the probability of occurrence of various risk events: ; in, For the moment The comprehensive risk assessment results are For the The weight coefficient of the hierarchical risk event, For the The probability of occurrence of such risk, is the number of risk events considered.

[0012] Optionally, the S5 specifically includes: S51. Obtain various real-time parameters in the cold chain logistics process through real-time data sets, and calculate the probability of occurrence of various risk events in combination with multi-level risk assessment models; S52. Based on the multi-level risk assessment model, obtain the risk assessment result at each moment, and assign a risk level to each risk assessment result, wherein the risk level includes high risk, medium risk and low risk; S53. Calculate the dynamic risk assessment value in the cold chain logistics process in real time based on the real-time data set and risk assessment results, and adjust the operations in the transportation process based on the dynamic risk assessment value; S54. When the risk assessment result reaches a high risk or medium risk level, the corresponding operation adjustment strategy is automatically activated; S55. Based on the real-time data set and risk assessment results, the assessment model is continuously updated to assess and feedback the risk level in real time, and the corresponding cold chain operations are adjusted to ensure that the risk control in the cold chain logistics process is always within a reasonable range; S56. The dynamic risk assessment value calculation method is: ; in, For the moment The dynamic risk assessment value of For the The probability of occurrence of risk events of this type, For the The weight coefficient of the risk event, For the Real-time measurement of risk events, For the The standard value of the risk event class, For the The weight coefficient of the risk event class, is the number of risk event categories, is the number of risk events considered.

[0013] Optionally, the S54 specifically includes: S541. Obtain various real-time parameters in the cold chain logistics process through real-time data sets, and determine the current risk assessment results in combination with multi-level risk assessment models; S542. When the current risk assessment result is high risk or medium risk, automatically determine the operation adjustment strategy that needs to be activated; S543. Based on the current risk assessment result, select a corresponding operation adjustment strategy; S544, executing the selected operation adjustment strategy in real time, and feeding back the execution result to the cold chain logistics system; S545. According to the implementation of the operation adjustment strategy, adjust other operation parameters in the cold chain logistics process in real time to control the risk within a reasonable range; S546: The risk level triggering flag of the operation adjustment strategy is: ; in, It is a risk level trigger mark. is the risk assessment level at the current moment, High risk level, It is a medium risk level.

[0014] Optionally, the S6 specifically includes: S61. According to the dynamic risk assessment results, intelligent thresholds are set to judge potential risks in real time, and the early warning mechanism is dynamically adjusted according to the risk assessment results; S62, by obtaining the dynamic risk assessment value in real time, comparing the dynamic risk assessment value with the set intelligent threshold, and judging whether there is a potential risk in the current cold chain logistics process; S63. When the dynamic risk assessment value exceeds the intelligent threshold, the early warning mechanism is automatically triggered to generate an early warning signal, which includes a high risk warning, a medium risk warning and a low risk warning; S64, automatically initiate corresponding intervention measures according to the level of the early warning signal; S65. Feedback the dynamic risk assessment results and early warning signals to the operators of the cold chain logistics system synchronously; S66. The method for generating the warning signal is: ; in, To generate warning signals, is the dynamic risk assessment value, is the threshold for high risk warning, The threshold for medium risk warning.

[0015] Optionally, the S64 specifically includes: S641. Based on the real-time data set, historical data set and dynamic risk assessment results, when the dynamic risk assessment results exceed the set intelligent threshold, automatically generate a warning signal and activate the intervention mechanism; S642. Automatically select the appropriate intervention measures based on the level of the warning signal: According to high-risk warning signals, adjust the transportation route and choose a safer route to avoid possible dangerous areas; According to the medium-risk warning signal, adjust the temperature and humidity settings of cold chain transportation equipment to ensure that the goods are always in an ideal storage environment; According to low-risk early warning signals, increase the monitoring frequency during transportation and track the status of goods in real time; S643. By real-time monitoring of the implementation of the selected intervention measures, the intensity and frequency of the intervention measures are automatically adjusted to ensure that the risk level in the cold chain logistics process is always within a controllable range; S644, the execution feedback result of the intervention measure is transmitted to the system via real-time data; S645. The intervention measures are dynamically adjusted according to the actual needs of different risk levels and time periods in the cold chain logistics process: ; in, For the moment The intensity of intervention measures, For the The probability of occurrence of risk events of this type, For the The weight coefficient of the risk event class, For the The intensity coefficient of intervention measures related to risk events, The number of risk events considered; S646. Continuously optimize operational adjustment strategies in the cold chain logistics process through multiple feedback and revisions of intervention measures.

[0016] The beneficial effects of the present invention are: (1) The present invention combines multi-dimensional feature fusion technology, support vector machine algorithm and dynamic risk assessment method to construct an efficient cold chain logistics system risk assessment model, which can monitor various risk events in the cold chain logistics process in real time and automatically adjust the operation strategy according to the multi-level risk assessment results. This method can dynamically adapt to the ever-changing logistics environment and risk factors, especially can accurately assess the risk level of different cold chain logistics links, thereby effectively improving the safety and stability of the cold chain logistics process.

[0017] (2) The present invention realizes the full monitoring and dynamic adjustment of the cold chain logistics process by combining real-time data sets, historical data sets and multi-level risk assessment models. When high-risk or medium-risk events occur, the method can quickly activate the corresponding operation adjustment strategy, such as adjusting the transportation route, adjusting equipment settings, etc., reducing the impact of potential risks on the logistics process and ensuring the quality and safety of goat milk products during transportation.

[0018] (3) The present invention can automatically identify potential risks and trigger early warning mechanisms through dynamic risk assessment and intelligent threshold setting, generate early warning signals and automatically initiate intervention measures, and can efficiently and in real time handle emergencies in the cold chain logistics process. Through real-time monitoring and adjustment of intervention measures, cold chain logistics operations are continuously optimized, so that risks are always kept within a controllable range, the need for human intervention is reduced, and logistics efficiency and emergency response capabilities are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a general framework diagram of a risk assessment method for a cold chain logistics system of goat milk products proposed in the present invention. DETAILED DESCRIPTION

[0020] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0021] refer to Figure 1 , a risk assessment method for a cold chain logistics system of goat milk products, comprising the following steps: S1. Real-time collection of multiple key parameters in the cold chain logistics process of goat milk products, and the use of data fusion technology to convert various heterogeneous data in key parameters into real-time data sets in a unified format; This embodiment realizes comprehensive monitoring of the cold chain logistics process by collecting the temperature and humidity records of the transport vehicles in the cold chain logistics process, the operating status data of the cold chain transportation equipment, and the ambient temperature and humidity data during the transportation process in real time.

[0022] S2. Collect historical cold chain logistics data, annotate risk events on the historical cold chain logistics data, and generate a historical data set; In this implementation, S2 specifically includes: S21. Collect historical cold chain logistics data during the cold chain logistics process from multiple data sources, where the data sources include temperature and humidity records of transport vehicles, operating status data of cold chain transport equipment, ambient temperature and humidity data during transportation, various accident records during transportation, and fault logs of transport equipment; S22, preprocessing the collected historical cold chain logistics data; S23. Obtain the risk event types in the cold chain logistics process, use a data-driven automatic labeling method to label historical cold chain logistics data for risk events, and obtain risk event labels. The risk event type labels include high temperature risk, humidity fluctuation risk, equipment failure risk, and transportation delay risk, and associate each piece of historical cold chain logistics data with a corresponding risk event type label; S24. Set the threshold conditions for various risk events and determine the occurrence of risk events by calculating the deviation values ​​of various parameters: ; in, For the moment risk markers, For the moment The temperature value, is the set temperature target value, is the maximum temperature deviation, For the moment The humidity value, is the set humidity target value, is the maximum deviation value of humidity; S25. For other risk events, the equipment status in the historical cold chain logistics data and the time delay in the transportation process are marked, and the equipment failure threshold is set. When the equipment fails, a failure risk mark is generated. When the transportation delay time exceeds the preset threshold, a transportation delay risk mark is generated; S26. By introducing a multi-dimensional weighted and time-period adjusted risk distribution model, the probability of occurrence of risk events is weighted and calculated by combining multiple risk factors: ; in, Indicates Risk events during the period The probability of occurrence, It is with The weight coefficients of the factors are For the The risk event is The impact of historical cold chain logistics data, Whether to trigger the A marker of a risk event. is the total number of historical cold chain logistics data, The number of risk events considered; S27. Each piece of data in the historical data set consists of multiple fields, including a timestamp. , Temperature data , humidity data , Device Status , Transportation status , and the corresponding risk event labels , the historical data set is represented as: ; in, Indicates Timestamp of historical cold chain logistics data, Indicates Temperature data of historical cold chain logistics data, Indicates Humidity data of historical cold chain logistics data, Indicates Equipment status of historical cold chain logistics data, Indicates The transportation status of historical cold chain logistics data, Indicates Risk event labels of historical cold chain logistics data, It is the total number of historical cold chain logistics data.

[0023] This implementation method generates a historical data set by collecting historical cold chain logistics data and marking risk events, thereby providing an accurate historical basis for subsequent risk assessment. This process not only provides real and verified historical data support for the cold chain logistics system, but also ensures that risk patterns in different time periods are taken into account in the assessment, which helps to improve the accuracy and reliability of risk prediction.

[0024] S3, preprocessing the real-time data set and the historical data set, and applying feature engineering techniques to generate feature vectors for risk assessment; This implementation method preprocesses the real-time data set and the historical data set, including data cleaning and format unification, and applies feature engineering technology to generate feature vectors for risk assessment. Combined with multi-dimensional data features, it can accurately extract key feature information related to the risk.

[0025] S4, based on feature vectors, uses support vector machine algorithm to build a multi-level risk assessment model through multi-dimensional feature fusion technology; In this implementation, S4 specifically includes: S41, based on feature vectors, feature vectors from different data sources are integrated through multi-dimensional feature fusion technology; S42, performing standardization processing on the fused feature vector; S43, inputting the standardized feature vector into a support vector machine model, and mapping the feature vector using a linear kernel function; S44, training the support vector machine model using training set data, wherein the training set data is labeled historical cold chain logistics data, and optimizing the parameters of the support vector machine model by a cross-validation method; S45. Predicting the probability of occurrence of risk events in real-time cold chain logistics data through the trained support vector machine model, wherein the prediction result is a multi-level risk assessment result, including high risk, medium risk and low risk classification; S46. Generate multi-level risk warnings based on the multi-level risk assessment results, including high-risk warnings, medium-risk warnings and low-risk warnings, where each warning level corresponds to a different risk response strategy; S47. The output result of the support vector machine model is the probability of occurrence of various risk events: ; ; in, Represents the feature vector Middle The value of the feature, is the weight coefficient associated with this feature, is the total number of features, Representative The feature vector of the historical data, is the total number of samples in the historical data set, For a given feature vector Time The probability of occurrence of risk events of this type, and Respectively Risk assessment value and standardized coefficient of risk events of this type, The number of risk events considered; S48. Based on the multi-level risk assessment results, automatically generate and adjust the optimization strategy of cold chain logistics operations in real time, including adjusting the transportation route, adjusting the cold chain equipment settings and increasing the frequency of transportation monitoring; S49. Construct a multi-level risk assessment model, which includes multiple risk levels, each level corresponds to a different risk assessment factor, and each level is based on a feature vector, combined with the probability of occurrence of various risk events and their weight coefficients, to calculate and generate a comprehensive risk assessment result. Specifically, the first level mainly evaluates basic risk factors, such as environmental factors such as temperature and humidity; the second level evaluates mid-level risks such as equipment failure and transportation delays; the third level weightedly calculates the risk results of the first and second levels to comprehensively evaluate the overall risk of the system. The weight coefficient of each risk level is dynamically adjusted according to the characteristics of historical data and real-time data, and the generated comprehensive risk assessment result is used to determine the final risk level and trigger different risk response strategies accordingly: ; in, For the moment The comprehensive risk assessment results are For the The weight coefficient of the hierarchical risk event, For the The probability of occurrence of such risk, is the number of risk events considered.

[0026] This implementation adopts the support vector machine algorithm and multi-dimensional feature fusion technology, and combines feature vectors from different data sources to build a multi-level risk assessment model. This model can comprehensively consider risk factors in multiple dimensions, and achieve high-precision risk prediction and classification through the classification capability of the support vector machine, thereby effectively improving the accuracy and real-time performance of risk assessment in the cold chain logistics system.

[0027] S5. Perform dynamic risk assessment based on real-time data sets and multi-level risk assessment models; In this implementation, S5 specifically includes: S51. Obtain various real-time parameters in the cold chain logistics process through real-time data sets, and calculate the probability of occurrence of various risk events in combination with multi-level risk assessment models; S52. Based on the multi-level risk assessment model, obtain the risk assessment result at each moment, and assign a risk level to each risk assessment result, wherein the risk level includes high risk, medium risk and low risk; S53. Calculate the dynamic risk assessment value in the cold chain logistics process in real time based on the real-time data set and risk assessment results, and adjust the operations in the transportation process based on the dynamic risk assessment value; S54. When the risk assessment result reaches a high risk or medium risk level, the corresponding operation adjustment strategy is automatically activated; S55. Based on the real-time data set and risk assessment results, the assessment model is continuously updated to assess and feedback the risk level in real time, and the corresponding cold chain operations are adjusted to ensure that the risk control in the cold chain logistics process is always within a reasonable range; S56. The dynamic risk assessment value calculation method is: ; in, For the moment The dynamic risk assessment value of For the The probability of occurrence of risk events of this type, For the The weight coefficient of the risk event class, For the Real-time measurement of risk events, For the The standard value of the risk event class, For the The weight coefficient of the risk event class, is the number of risk event categories, is the number of risk events considered.

[0028] The S54 specifically includes: S541. Obtain various real-time parameters in the cold chain logistics process through real-time data sets, and determine the current risk assessment results in combination with multi-level risk assessment models; S542. When the current risk assessment result is high risk or medium risk, automatically determine the operation adjustment strategy that needs to be activated; S543. Based on the current risk assessment result, select a corresponding operation adjustment strategy; S544, executing the selected operation adjustment strategy in real time, and feeding back the execution result to the cold chain logistics system; S545. According to the implementation of the operation adjustment strategy, adjust other operation parameters in the cold chain logistics process in real time to control the risk within a reasonable range; S546: The risk level triggering flag of the operation adjustment strategy is: ; in, It is a risk level trigger mark. is the risk assessment level at the current moment, High risk level, It is a medium risk level.

[0029] This implementation method combines real-time data sets and multi-level risk assessment models to conduct dynamic risk assessment, which can monitor the probability of occurrence of various risk events in the cold chain logistics process in real time and flexibly adjust logistics operations based on the assessment results.

[0030] S6. Based on the dynamic risk assessment results, by setting intelligent thresholds, potential risks are automatically identified and early warning mechanisms are triggered to form early warning signals. Intervention measures are automatically initiated based on the early warning signals. At the same time, the dynamic risk assessment results and early warning signals are fed back to the operators of the cold chain logistics system.

[0031] In this implementation, S6 specifically includes: S61. According to the dynamic risk assessment results, intelligent thresholds are set to judge potential risks in real time, and the early warning mechanism is dynamically adjusted according to the risk assessment results; S62, by obtaining the dynamic risk assessment value in real time, comparing the dynamic risk assessment value with the set intelligent threshold, and judging whether there is a potential risk in the current cold chain logistics process; S63. When the dynamic risk assessment value exceeds the intelligent threshold, the early warning mechanism is automatically triggered to generate an early warning signal, which includes a high risk warning, a medium risk warning and a low risk warning; S64, automatically initiate corresponding intervention measures according to the level of the early warning signal; S65. Feedback the dynamic risk assessment results and early warning signals to the operators of the cold chain logistics system synchronously; S66. The method for generating the warning signal is: ; in, To generate warning signals, is the dynamic risk assessment value, is the threshold for high risk warning, The threshold for medium risk warning.

[0032] The S64 specifically includes: S641. Based on the real-time data set, historical data set and dynamic risk assessment results, when the dynamic risk assessment results exceed the set intelligent threshold, automatically generate a warning signal and activate the intervention mechanism; S642. Automatically select the appropriate intervention measures based on the level of the warning signal: According to high-risk warning signals, adjust the transportation route and choose a safer route to avoid possible dangerous areas; According to the medium-risk warning signal, adjust the temperature and humidity settings of cold chain transportation equipment to ensure that the goods are always in an ideal storage environment; According to low-risk early warning signals, increase the monitoring frequency during transportation and track the status of goods in real time; S643. By real-time monitoring of the implementation of the selected intervention measures, the intensity and frequency of the intervention measures are automatically adjusted to ensure that the risk level in the cold chain logistics process is always within a controllable range; S644, the execution feedback result of the intervention measure is transmitted to the system via real-time data; S645. The intervention measures are dynamically adjusted according to the actual needs of different risk levels and time periods in the cold chain logistics process: ; in, For the moment The intensity of intervention measures, For the The probability of occurrence of risk events of this type, For the The weight coefficient of the risk event class, For the The intensity coefficient of intervention measures related to risk events, The number of risk events considered; S646. Continuously optimize operational adjustment strategies in the cold chain logistics process through multiple feedback and revisions of intervention measures.

[0033] This implementation method sets intelligent thresholds based on dynamic risk assessment results, automatically identifies potential risks and triggers early warning mechanisms. It can monitor risk events that may occur during the cold chain logistics process in real time and automatically initiate corresponding intervention measures based on different early warning levels.

[0034] Example: In order to verify the feasibility of the present invention, the present invention is applied to the daily operation of a medium-sized goat milk product cold chain logistics company. Since cold chain logistics is crucial to the quality and safety of goat milk products, especially the strict control of temperature and humidity, how to effectively monitor and manage potential risks, especially during transportation, has become an important issue facing the company. To this end, the company decided to deploy the cold chain logistics risk assessment method based on dynamic risk assessment and intelligent early warning of the present invention.

[0035] The system first obtains various key parameters in the cold chain logistics process through real-time data sets, including temperature, humidity, transportation time, transportation route, vehicle status and other data. These data are combined with historical data sets and multi-level risk assessment models to calculate the risk assessment results of the cold chain logistics process in real time. When the risk assessment results reach a high risk or medium risk level, the system automatically triggers the early warning mechanism through the set intelligent threshold, generates corresponding early warning signals, and automatically initiates intervention measures according to different early warning levels.

[0036] In actual application, the system ensures the quality of goat milk products by automatically adjusting temperature control equipment and transportation routes. For example, if the system finds that a transport vehicle has temperature fluctuations during transportation and the goods being transported are close to exceeding the safe temperature range, the system will automatically adjust the temperature control equipment according to the preset risk level to ensure that the temperature returns to the ideal state. At the same time, the system adjusts the transportation route according to the risk level and chooses to bypass some areas that may have traffic congestion or bad weather to ensure the safety of goat milk products. In this way, the system greatly reduces the risk of goat milk products being damaged during transportation and improves the reliability of transportation.

[0037] In order to further verify the effectiveness of the present invention, the company compared various indicators in cold chain logistics before and after using the system of the present invention. The data table is shown in Table 1: Table 1 Comparison report before and after cold chain logistics risk control

[0038] According to the data in Table 1, before the system was deployed, the company had about 12 high-risk events per month due to temperature control problems and other reasons. After the system was deployed, the number of high-risk events dropped to 2, significantly reducing the risk of damage to goat milk products during transportation. In addition, the system's response time to early warning signals has also been greatly shortened, from about 30 minutes in the past to 6 minutes, thereby improving the system's real-time response capabilities.

[0039] In actual application, the system has successfully avoided many potential risks that may lead to the decline of goat milk product quality by automatically adjusting the transportation route. For example, during the transportation of a batch of goat milk products, due to road congestion ahead, the system automatically selected an alternative route to avoid temperature fluctuations and quality decline that may be caused by delays. This adjustment successfully avoided quality problems of goat milk products and ensured that the qualified rate of products increased to 98.5%.

[0040] The system also pays special attention to the adjustment of temperature control equipment. By monitoring temperature data in real time, the system can accurately determine whether the temperature control equipment is working properly and automatically adjust the temperature control parameters. After the implementation of the system, the accuracy of the temperature control equipment reaching the set temperature range increased from 85% to 98%. This significantly improved temperature control accuracy ensures that goat milk products will not suffer quality loss due to temperature fluctuations during transportation, and improves the quality pass rate of products.

[0041] By comparing the data before and after, we can see that the deployment of the system has brought significant improvements, especially in reducing high-risk events and improving temperature control accuracy. The data in Table 1 fully proves that the present invention can effectively improve risk control capabilities in cold chain logistics management and ensure the quality and safety of goat milk products.

[0042] During the implementation period, the system's automatic adjustment and intelligent intervention mechanisms demonstrated high stability and reliability. All early warning signals and risk assessment results were fed back to operators through real-time data transmission, ensuring that operators could obtain risk information in a timely manner and make corresponding treatments.

[0043] The implementation of the present invention successfully solves the risk problems of temperature control failure and unreasonable transportation routes in the cold chain logistics process, and improves the intelligence level and risk response capability of the cold chain logistics system. Through the application of this system, not only the risk control in the transportation process of goat milk products is optimized, but also the quality qualification rate and transportation efficiency of the products are effectively improved, which ultimately brings higher production efficiency and less risk loss to the enterprise.

[0044] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A risk assessment method for a cold chain logistics system of goat milk products, characterized in that: The steps include: S1. Real-time collection of multiple key parameters in the cold chain logistics process of goat milk products, and the use of data fusion technology to convert various heterogeneous data in key parameters into real-time data sets in a unified format; S2. Collect historical cold chain logistics data, annotate risk events on the historical cold chain logistics data, and generate a historical data set; S3, preprocessing the real-time data set and the historical data set, and applying feature engineering techniques to generate feature vectors for risk assessment; S4, based on feature vectors, uses support vector machine algorithm to build a multi-level risk assessment model through multi-dimensional feature fusion technology; S5. Perform dynamic risk assessment based on real-time data sets and multi-level risk assessment models; S6. Based on the dynamic risk assessment results, by setting intelligent thresholds, potential risks are automatically identified and early warning mechanisms are triggered to form early warning signals. Intervention measures are automatically initiated based on the early warning signals. At the same time, the dynamic risk assessment results and early warning signals are fed back to the operators of the cold chain logistics system.

2. A risk assessment method for a cold chain logistics system of goat milk products according to claim 1, characterized in that: The S2 specifically includes: S21. Collect historical cold chain logistics data during the cold chain logistics process from multiple data sources, where the data sources include temperature and humidity records of transport vehicles, operating status data of cold chain transport equipment, ambient temperature and humidity data during transportation, various accident records during transportation, and fault logs of transport equipment; S22, preprocessing the collected historical cold chain logistics data; S23. Obtain the risk event types in the cold chain logistics process, use a data-driven automatic labeling method to label historical cold chain logistics data for risk events, and obtain risk event labels. The risk event type labels include high temperature risk, humidity fluctuation risk, equipment failure risk, and transportation delay risk, and associate each piece of historical cold chain logistics data with a corresponding risk event type label; S24. Set the threshold conditions for various risk events and determine the occurrence of risk events by calculating the deviation values ​​of various parameters: ； in, For the moment risk markers, For the moment The temperature value, is the set temperature target value, is the maximum temperature deviation, For the moment The humidity value, is the set humidity target value, is the maximum deviation value of humidity; S25. For other risk events, the equipment status in the historical cold chain logistics data and the time delay during transportation are marked, and the equipment failure threshold is set. When the equipment fails, a failure risk mark is generated. When the transportation delay time exceeds the preset threshold, a transportation delay risk mark is generated; S26. By introducing a multi-dimensional weighted and time-period adjusted risk distribution model, the probability of occurrence of risk events is weighted and calculated by combining multiple risk factors: ； in, Indicates Risk events during the period The probability of occurrence, It is with The weight coefficients of the factors are For the The risk event is The impact of historical cold chain logistics data, Whether to trigger the A marker of a risk event. is the total number of historical cold chain logistics data, The number of risk events considered; S27. Each piece of data in the historical data set consists of multiple fields, including a timestamp. , Temperature data , humidity data , Device Status , Transportation status , and the corresponding risk event labels , the historical data set is represented as: ； in, Indicates Timestamp of historical cold chain logistics data, Indicates Temperature data of historical cold chain logistics data, Indicates Humidity data of historical cold chain logistics data, Indicates Equipment status of historical cold chain logistics data, Indicates The transportation status of historical cold chain logistics data, Indicates Risk event labels of historical cold chain logistics data, It is the total number of historical cold chain logistics data.

3. A risk assessment method for a cold chain logistics system of goat milk products according to claim 1, characterized in that: The S4 specifically includes: S41, based on feature vectors, feature vectors from different data sources are integrated through multi-dimensional feature fusion technology; S42, performing standardization processing on the fused feature vector; S43, inputting the standardized feature vector into a support vector machine model, and mapping the feature vector using a linear kernel function; S44, training the support vector machine model using training set data, wherein the training set data is labeled historical cold chain logistics data, and optimizing the parameters of the support vector machine model by a cross-validation method; S45. Predicting the probability of occurrence of risk events in real-time cold chain logistics data through the trained support vector machine model, wherein the prediction result is a multi-level risk assessment result, including high risk, medium risk and low risk classification; S46. Generate multi-level risk warnings based on the multi-level risk assessment results, including high-risk warnings, medium-risk warnings and low-risk warnings, where each warning level corresponds to a different risk response strategy; S47. The output result of the support vector machine model is the probability of occurrence of various risk events: ； ； in, Represents the feature vector Middle The value of the feature, is the weight coefficient associated with this feature, is the total number of features, Representative The feature vector of the historical data, is the total number of samples in the historical data set, For a given feature vector Time The probability of occurrence of risk events of this type, and Respectively Risk assessment value and standardized coefficient of risk events of this type, The number of risk events considered; S48. Based on the multi-level risk assessment results, automatically generate and adjust the optimization strategy of cold chain logistics operations in real time, including adjusting the transportation route, adjusting the cold chain equipment settings and increasing the frequency of transportation monitoring; S49. Construct a multi-level risk assessment model, which includes multiple risk levels, each level corresponds to a different risk assessment factor, and generates a comprehensive risk assessment result based on the characteristic vectors of each level and the probability of occurrence of various risk events: ； in, For the moment The comprehensive risk assessment results are For the The weight coefficient of the hierarchical risk event, For the The probability of occurrence of such risk, is the number of risk events considered.

4. A risk assessment method for a cold chain logistics system of goat milk products according to claim 1, characterized in that: The S5 specifically includes: S51. Obtain various real-time parameters in the cold chain logistics process through real-time data sets, and calculate the probability of occurrence of various risk events in combination with multi-level risk assessment models; S52. Based on the multi-level risk assessment model, obtain the risk assessment result at each moment, and assign a risk level to each risk assessment result, wherein the risk level includes high risk, medium risk and low risk; S53. Calculate the dynamic risk assessment value in the cold chain logistics process in real time based on the real-time data set and risk assessment results, and adjust the operations in the transportation process based on the dynamic risk assessment value; S54. When the risk assessment result reaches a high risk or medium risk level, the corresponding operation adjustment strategy is automatically activated; S55. Based on the real-time data set and risk assessment results, the assessment model is continuously updated to assess and feedback the risk level in real time, and the corresponding cold chain operations are adjusted to ensure that the risk control in the cold chain logistics process is always within a reasonable range; S56. The dynamic risk assessment value calculation method is: ； in, For the moment The dynamic risk assessment value of For the The probability of occurrence of risk events of this type, For the The weight coefficient of the risk event, For the Real-time measurement of risk events, For the The standard value of the risk event class, For the The weight coefficient of the risk event, is the number of risk event categories, is the number of risk events considered.

5. A risk assessment method for a cold chain logistics system of goat milk products according to claim 4, characterized in that: The S54 specifically includes: S541. Obtain various real-time parameters in the cold chain logistics process through real-time data sets, and determine the current risk assessment results in combination with multi-level risk assessment models; S542. When the current risk assessment result is high risk or medium risk, automatically determine the operation adjustment strategy that needs to be activated; S543. Based on the current risk assessment result, select a corresponding operation adjustment strategy; S544, executing the selected operation adjustment strategy in real time, and feeding back the execution result to the cold chain logistics system; S545. According to the implementation of the operation adjustment strategy, adjust other operation parameters in the cold chain logistics process in real time to control the risk within a reasonable range; S546: The risk level triggering flag of the operation adjustment strategy is: ； in, It is a risk level trigger mark. is the risk assessment level at the current moment, High risk level, It is a medium risk level.

6. A risk assessment method for a cold chain logistics system of goat milk products according to claim 1, characterized in that: The S6 specifically includes: S61. According to the dynamic risk assessment results, intelligent thresholds are set to judge potential risks in real time, and the early warning mechanism is dynamically adjusted according to the risk assessment results; S62, by obtaining the dynamic risk assessment value in real time, comparing the dynamic risk assessment value with the set intelligent threshold, and judging whether there is a potential risk in the current cold chain logistics process; S63. When the dynamic risk assessment value exceeds the intelligent threshold, the early warning mechanism is automatically triggered to generate an early warning signal, which includes a high risk warning, a medium risk warning and a low risk warning; S64, automatically initiate corresponding intervention measures according to the level of the early warning signal; S65. Feedback the dynamic risk assessment results and early warning signals to the operators of the cold chain logistics system synchronously; S66. The method for generating the warning signal is: ； in, To generate warning signals, is the dynamic risk assessment value, is the threshold for high risk warning, The threshold for medium risk warning.

7. A risk assessment method for a cold chain logistics system of goat milk products according to claim 6, characterized in that: The S64 specifically includes: S641. Based on the real-time data set, historical data set and dynamic risk assessment results, when the dynamic risk assessment results exceed the set intelligent threshold, automatically generate a warning signal and activate the intervention mechanism; S642. Automatically select the appropriate intervention measures based on the level of the warning signal: According to high-risk warning signals, adjust the transportation route and choose a safer route to avoid possible dangerous areas; According to the medium-risk warning signal, adjust the temperature and humidity settings of cold chain transportation equipment to ensure that the goods are always in an ideal storage environment; According to low-risk early warning signals, increase the monitoring frequency during transportation and track the status of goods in real time; S643. By real-time monitoring of the implementation of the selected intervention measures, the intensity and frequency of the intervention measures are automatically adjusted to ensure that the risk level in the cold chain logistics process is always within a controllable range; S644, the execution feedback result of the intervention measure is transmitted to the system via real-time data; S645. The intervention measures are dynamically adjusted according to the actual needs of different risk levels and time periods in the cold chain logistics process: ； in, For the moment The intensity of intervention measures, For the The probability of occurrence of risk events of this type, For the The weight coefficient of the risk event, For the The intensity coefficient of intervention measures related to risk events, The number of risk events considered; S646. Continuously optimize operational adjustment strategies in the cold chain logistics process through multiple feedback and revisions of intervention measures.

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