Low-carbon environment-friendly cold-chain logistics monitoring system based on big data analysis

Abstract

The invention discloses a low-carbon environment-friendly cold-chain logistics monitoring system based on big data analysis, and belongs to the technical field of logistics monitoring. Data monitoring and risk assessment of the internal environment are carried out in the whole process of cold-chain logistics transportation, multi-dimensional monitoring analysis can be carried out on the internal local temperature environment and the internal overall temperature environment, and the diversity and comprehensiveness of internal environment monitoring analysis during cold-chain logistics transportation are improved; data monitoring and risk assessment of an external environment are carried out in the whole process of cold-chain logistics transportation, so that external temperature change conditions and influence conditions of goods in different moving links can be obtained; meanwhile, reliable local monitoring data support can be provided for analysis of abnormal influences generated by subsequent external temperature changes and quality management of goods; the method and the device are used for solving the technical problems of one-sidedness of analysis of monitoring data and poor effect of subsequent data expansion and utilization due to single cold-chain logistics monitoring dimension in the existing scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics monitoring, and in particular to a low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis. Background Art

[0002] Cold chain logistics refers to the use of special facilities and measures for temperature-sensitive goods (such as food, medicine, cosmetics, etc.) during transportation and storage to ensure that they maintain appropriate temperature conditions throughout the supply chain to maintain the quality and safety of the goods.

[0003] The existing cold chain logistics monitoring solutions have certain defects in implementation. For example, they only focus on single-dimensional temperature monitoring, comparative analysis and early warning, and fail to implement comprehensive monitoring and data analysis of the entire process of cold chain logistics transportation from different dimensions, which makes the analysis of monitoring data one-sided and the subsequent data expansion and utilization effect poor. Summary of the invention

[0004] The purpose of the present invention is to provide a low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis, which is used to solve the technical problems that the cold chain logistics monitoring dimension in the existing solutions is single, resulting in one-sided analysis of the monitoring data and poor effect of subsequent data expansion and utilization.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis, including: The cold chain logistics monitoring and analysis module is used to implement different-dimensional monitoring and risk assessment of the entire process of cold chain logistics transportation, obtain a risk assessment set including the first risk assessment data and the second risk assessment data, and upload it to the logistics monitoring sharing platform in real time; including: When implementing internal environment data monitoring and risk assessment for the entire process of cold chain logistics transportation, real-time temperature monitoring and statistics are performed on temperature monitoring points at different locations in the cold chain compartment, and the corresponding real-time temperature curve is obtained through the pre-built temperature coordinate system; And, obtaining the real-time temperature values ​​of the temperature monitoring points at different locations and arranging them in descending order, calculating the first temperature difference between the maximum real-time temperature value Wmax and the minimum real-time temperature value Wmin according to the preset monitoring interval period, and obtaining the real-time temperature mean Wp of all locations at the same time, calculating the second temperature difference between the real-time temperature mean Wp and the real-time temperature mean Wp0 of the last monitoring statistics, analyzing the first temperature difference and the second temperature difference to obtain the first risk assessment data consisting of a local temperature normal label or a local temperature abnormal label, an overall temperature normal label or an overall temperature abnormal label; When implementing data monitoring and risk assessment of the external environment throughout the entire process of cold chain logistics transportation, the handling conditions of different goods in the cold chain compartment are monitored and counted, and a handling monitoring instruction is generated when the detection sensor detects that the goods are being handled. According to the handling monitoring instruction, the external real-time temperature of the entire process of cargo handling is monitored and counted, and the numerical value of the external real-time temperature of the entire process of cargo handling is extracted, and the corresponding real-time moving temperature curve is obtained through a pre-constructed temperature coordinate system. The changes in the real-time moving temperature curve are monitored and analyzed to obtain the second risk assessment data consisting of moving normal labels or abnormal traceability labels.

[0006] Preferably, the first temperature difference value and the second temperature difference value are compared and classified with the preset first temperature difference range and the second temperature difference range respectively; If the first temperature difference value belongs to the first temperature difference range and the second temperature difference value belongs to the second temperature difference range, a local temperature normal label and an overall temperature normal label are generated; If the first temperature difference does not belong to the first temperature difference range and the second temperature difference does not belong to the second temperature difference range, a local temperature anomaly label and an overall temperature anomaly label are generated.

[0007] Preferably, when the real-time moving temperature curve is not higher than the preset temperature warning line, a moving normal label is generated; When the real-time moving temperature curve is higher than the preset temperature warning line, an abnormal traceability label is generated, and the corresponding link is marked as an abnormal link according to the abnormal traceability label.

[0008] Preferably, the logistics abnormality verification and control module is used to perform abnormality tracing verification on abnormalities occurring in different aspects of the risk assessment set to determine the corresponding abnormality type and abnormal impact, upload the abnormality tracing verification results to the logistics monitoring sharing platform in real time, and implement dynamic management of cold chain logistics transportation based on the abnormality tracing verification results.

[0009] Preferably, a risk assessment set uploaded in the logistics monitoring sharing platform is obtained and the corresponding first risk assessment data or the second risk assessment data is traversed to obtain the corresponding local temperature anomaly label, overall temperature anomaly label or anomaly traceability label; When tracing back and verifying the abnormal monitored temperatures of temperature monitoring points at different positions in the cold chain compartment based on the local temperature abnormality labels or the overall temperature abnormality labels, the periodic temperature monitoring of the monitoring interval period is canceled based on the local temperature abnormality labels or the overall temperature abnormality labels to start the analysis and evaluation of the real-time temperature.

[0010] Preferably, the first temperature difference and the second temperature difference corresponding to the temperature monitoring points at different positions of K times are obtained and analyzed to obtain the corresponding K first risk assessment data, and the total number of local temperature anomaly labels or the total number of overall temperature anomaly labels in the K first risk assessment data are traversed and counted; If the total number of local temperature anomaly labels or the total number of overall temperature anomaly labels is not less than M, a local temperature anomaly stable label or an overall temperature anomaly stable label is generated; If the total number of local temperature anomaly labels or the total number of overall temperature anomaly labels is less than M and not zero, a local temperature anomaly fluctuation label or an overall temperature anomaly fluctuation label is generated; The local temperature abnormality stable label or the overall temperature abnormality stable label, the local temperature abnormality fluctuation label or the overall temperature abnormality fluctuation label constitute the first abnormality tracing analysis data.

[0011] Preferably, when tracing and verifying the abnormal moving temperature in the cargo handling process according to the abnormal tracing label, the area of ​​the area enclosed by the real-time moving temperature curve and the preset temperature warning line is obtained and marked as the first abnormal value, and the difference between the maximum value of the real-time moving temperature curve and the temperature warning line is obtained and marked as the second abnormal value; The type of goods is obtained, and the corresponding first abnormal threshold and second abnormal threshold are obtained according to the type of goods and the transportation requirement parameters, and the first abnormal value and the second abnormal value are respectively compared and classified with the first abnormal threshold and the second abnormal threshold.

[0012] Preferably, if the first abnormal value is not greater than the first abnormal threshold and the second abnormal value is not greater than the second abnormal threshold, an extreme temperature normal label and a temperature continuous normal label are generated; If the first abnormal value is greater than the first abnormal threshold and the second abnormal value is greater than the second abnormal threshold, an extreme temperature abnormality label and a continuous temperature abnormality label are generated; The extreme temperature normal label or the extreme temperature abnormal label, the temperature continuous normal label or the temperature continuous abnormal label constitute the second abnormality tracing analysis data; The first abnormality tracing analysis data and the second abnormality tracing analysis data constitute the abnormality tracing verification result.

[0013] Preferably, when dynamic management of cold chain logistics transportation is implemented according to the abnormal tracing verification result, an alarm prompt of internal local temperature abnormality or overall temperature abnormality is generated according to the local temperature abnormal fluctuation tag or the overall temperature abnormal fluctuation tag in the first abnormal tracing analysis data, and the maintenance personnel are prompted to handle it immediately; In addition, quality sampling prompts are implemented for goods corresponding to the extreme temperature abnormality label or the continuous temperature abnormality label in the second abnormality traceability analysis data, and a decision is made whether to continue to put them on the market based on the quality sampling results.

[0014] Preferably, when targeted alarm prompts and management are carried out for different abnormal links that occur, the total number of occurrences of all abnormal links is counted, and the abnormal links whose total number of occurrences is greater than the standard total number are marked as high-risk links, and alarm prompts are generated to indicate that high-risk links require immediate targeted behavioral training.

[0015] Compared with the existing solutions, the present invention achieves the following beneficial effects: The present invention implements multi-dimensional monitoring and data analysis of the internal local temperature environment and the internal overall temperature environment through data monitoring and risk assessment of the internal environment throughout the entire process of cold chain logistics transportation, thereby improving the diversity and comprehensiveness of internal environment monitoring and analysis during cold chain logistics transportation; by implementing data monitoring and risk assessment of the external environment throughout the entire process of cold chain logistics transportation, the external temperature changes and impacts of goods in different moving links can be obtained in a timely and efficient manner, and at the same time, reliable local monitoring data support can be provided for the subsequent analysis of abnormal impacts caused by external temperature changes and the quality management of goods, thereby improving the diversity of cold chain logistics monitoring and the reliability of subsequent data traceability analysis.

[0016] The present invention determines the corresponding specific abnormal type and abnormal impact degree by tracing back and verifying the abnormal data monitored and analyzed in different aspects in the early stage. According to the abnormal tracing and verification results, targeted alarm prompts of monitoring data in different dimensions can be realized, so that maintenance personnel and management personnel can timely and efficiently carry out targeted maintenance and training on the transportation behavior of cold chain transportation vehicles and transportation personnel, thereby improving the diversity of cold chain logistics monitoring data utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 This is a module block diagram of a low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis of the present invention.

[0019] Figure 2 This is a flowchart of the present invention for implementing traceability verification on abnormal monitored temperatures at temperature monitoring points at different locations in a cold chain compartment.

[0020] Figure 3 The present invention is a flowchart for implementing traceability verification of abnormal movement temperature during cargo handling. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary operation and maintenance personnel in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1: Figure 1 As shown, the present invention is a low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis, including a cold chain logistics monitoring and analysis module and a logistics monitoring sharing platform; The cold chain logistics monitoring and analysis module is used to implement different-dimensional monitoring and risk assessment of the entire process of cold chain logistics transportation, obtain a risk assessment set including the first risk assessment data and the second risk assessment data, and upload it to the logistics monitoring sharing platform in real time; including: When implementing internal environment data monitoring and risk assessment for the entire process of cold chain logistics transportation, real-time temperature monitoring and statistics are performed on temperature monitoring points at different locations in the cold chain compartment, and the corresponding real-time temperature curve is obtained through the pre-built temperature coordinate system; Among them, the temperature monitoring points at different positions in the cold chain compartment can be determined based on the historical temperature fault location big data of the cold chain transport vehicle, or the specific monitoring locations can be customized according to the actual type of goods transported; in addition, the horizontal axis of the pre-constructed temperature coordinate system is the real-time Beijing time, and the vertical axis is the temperature value with an evenly increasing value, and the temperature difference can be 0.1 degrees Celsius; And, obtain the real-time temperature values ​​of temperature monitoring points at different locations and arrange them in descending order, calculate the first temperature difference between the maximum real-time temperature value Wmax and the minimum real-time temperature value Wmin according to the preset monitoring interval period, the unit of the monitoring interval period is minutes, specifically three minutes, that is, data calculation is performed every three minutes, through the calculation and analysis of the monitoring interval period, while realizing the calculation and analysis of the monitoring data, it can also reduce the pressure and cost of data resource operation, and improve the low-carbon and environmental protection effect of the monitoring data operation, and at the same time obtain the real-time temperature mean Wp of all locations, calculate the second temperature difference between the real-time temperature mean Wp and the real-time temperature mean Wp0 of the last monitoring statistics, and compare and classify the first temperature difference and the second temperature difference with the preset first temperature difference range and the second temperature difference range respectively; wherein, the first temperature difference range and the second temperature difference range are determined according to the design parameters of the cold chain transport vehicle and the condition requirement parameters of the specific goods actually transported; If the first temperature difference value belongs to the first temperature difference range and the second temperature difference value belongs to the second temperature difference range, a local temperature normal label and an overall temperature normal label are generated; If the first temperature difference does not belong to the first temperature difference range and the second temperature difference does not belong to the second temperature difference range, a local temperature anomaly label and an overall temperature anomaly label are generated; The local temperature normal label or the local temperature abnormal label, the overall temperature normal label or the overall temperature abnormal label constitute the first risk assessment data; In the embodiment of the present invention, by implementing data monitoring and risk assessment of the internal environment throughout the entire process of cold chain logistics transportation, multi-dimensional monitoring and data analysis of the internal local temperature environment and the internal overall temperature environment can be implemented, thereby improving the diversity and comprehensiveness of internal environment monitoring and analysis during cold chain logistics transportation.

[0023] When implementing data monitoring and risk assessment of the external environment throughout the entire process of cold chain logistics transportation, the handling conditions of different goods in the cold chain compartment are monitored and counted, and a handling monitoring instruction is generated when the detection sensor detects that the goods are being handled. The detection sensor includes but is not limited to a gravity sensor and an accelerometer, and the external real-time temperature of the entire process of cargo handling is monitored and counted according to the handling monitoring instruction. The monitoring can be achieved by installing a temperature sensor on the surface of the goods or on the inner surface of a storage device for storing the goods. In addition, several detection sensors and temperature sensors can be recycled after delivery, and the external real-time temperature values ​​of the entire process of cargo handling are extracted and the corresponding real-time moving temperature curve is obtained through a pre-constructed temperature coordinate system. Monitor and analyze the changes in the real-time moving temperature curve. When the real-time moving temperature curve is not higher than the preset temperature warning line, a moving normal label is generated. The temperature warning line is determined according to the parameters required by the conditions of the specific goods actually transported. When the real-time moving temperature curve is higher than the preset temperature warning line, an abnormal traceability label is generated, and the corresponding link is marked as an abnormal link according to the abnormal traceability label; Moving the normal tag or the abnormal traceability tag constitutes the second risk assessment data; During the cold chain transportation process, food may spoil in the loading, transshipment, warehousing and delivery links. This is different from the existing technical solutions in that no external temperature monitoring, analysis and evaluation are implemented for the different moving links in the cold chain transportation process, resulting in the inability to timely and accurately trace the specific abnormal source when quality problems occur in the goods transported by the cold chain, and thus the subsequent inability to provide targeted alarm prompts and avoidance. In the embodiment of the present invention, by implementing data monitoring and risk assessment of the external environment for the entire process of cold chain logistics transportation, the external temperature changes and impacts of goods in different moving links can be obtained in a timely and efficient manner, and at the same time, reliable local monitoring data support can be provided for the subsequent analysis of the abnormal impact of external temperature changes and the quality management of goods, thereby improving the diversity of cold chain logistics monitoring and the reliability of subsequent data traceability analysis.

[0024] Embodiment 2: A logistics abnormality verification and control module is used to perform abnormality tracing and verification on abnormalities occurring in different aspects of the risk assessment set to determine the corresponding abnormality type and abnormality impact, upload the abnormality tracing and verification results to the logistics monitoring sharing platform in real time, and implement dynamic management of cold chain logistics transportation based on the abnormality tracing and verification results; including: Obtain the risk assessment set uploaded in the logistics monitoring sharing platform and traverse to obtain the corresponding first risk assessment data or second risk assessment data, and traverse the first risk assessment data or the second risk assessment data to obtain the corresponding local temperature anomaly label, overall temperature anomaly label or anomaly traceability label; like Figure 2 As shown, when the abnormal monitoring temperature of the temperature monitoring points at different positions in the cold chain compartment is traced and verified according to the local temperature abnormality label or the overall temperature abnormality label, the periodic temperature monitoring of the monitoring interval period is canceled according to the local temperature abnormality label or the overall temperature abnormality label to start the analysis and evaluation of the real-time temperature, obtain the first temperature difference and the second temperature difference corresponding to the temperature monitoring points at different positions K times, analyze and obtain the corresponding K first risk assessment data, and traverse and count the total number of local temperature abnormality labels or the total number of overall temperature abnormality labels in the K first risk assessment data; If the total number of local temperature anomaly labels or the total number of overall temperature anomaly labels is not less than M, a local temperature anomaly stable label or an overall temperature anomaly stable label is generated. K and M are both positive integers and M≤K. The specific values ​​of K and M can be determined based on the specific value of the goods actually transported by the cold chain. The higher the specific value of the goods, the smaller the specific values ​​of K and M. If the total number of local temperature anomaly labels or the total number of overall temperature anomaly labels is less than M and not zero, a local temperature anomaly fluctuation label or an overall temperature anomaly fluctuation label is generated; The local temperature abnormal stability label or the overall temperature abnormal stability label, the local temperature abnormal fluctuation label or the overall temperature abnormal fluctuation label constitute the first abnormal tracing analysis data; In the embodiment of the present invention, the specific abnormal type of the abnormal temperature in the previous monitoring and analysis is determined by performing abnormal tracing verification on the abnormal temperature of the local monitoring location or the overall internal abnormal temperature in the previous monitoring and analysis, so that the monitoring temperature alarm prompts corresponding to the monitoring area and the abnormal impact type can be timely and accurately issued, thereby improving the initiative and reliability of internal environment temperature monitoring and analysis alarm.

[0025] like Figure 3As shown, when the abnormal moving temperature in the cargo handling process is traced and verified according to the abnormal traceability label, the area enclosed by the real-time moving temperature curve and the preset temperature warning line is obtained and marked as the first abnormal value, and the difference between the maximum value of the real-time moving temperature curve and the temperature warning line is obtained and marked as the second abnormal value; Obtain the type of goods, and obtain the corresponding first abnormal threshold and second abnormal threshold according to the type of goods and transportation requirement parameters, and compare and classify the first abnormal value and the second abnormal value with the first abnormal threshold and the second abnormal threshold respectively; If the first abnormal value is not greater than the first abnormal threshold and the second abnormal value is not greater than the second abnormal threshold, an extreme temperature normal label and a temperature continuous normal label are generated; If the first abnormal value is greater than the first abnormal threshold and the second abnormal value is greater than the second abnormal threshold, an extreme temperature abnormality label and a continuous temperature abnormality label are generated; The extreme temperature normal label or the extreme temperature abnormal label, the temperature continuous normal label or the temperature continuous abnormal label constitute the second abnormality tracing analysis data; In the embodiment of the present invention, the specific abnormal type of the abnormal temperature monitored and analyzed in the early stage is determined by performing abnormal tracing verification on the abnormal temperature of the environment in which the goods are moved, so that the monitoring temperature alarm prompt corresponding to the abnormal link and abnormal impact type can be carried out in a timely and accurate manner, thereby improving the initiative and reliability of mobile environment temperature monitoring and analysis.

[0026] The first abnormal tracing analysis data and the second abnormal tracing analysis data constitute the abnormal tracing verification result; when dynamic management of cold chain logistics transportation is implemented according to the abnormal tracing verification result, an alarm prompt of internal local temperature abnormality or overall temperature abnormality is generated according to the local temperature abnormal fluctuation tag or the overall temperature abnormal fluctuation tag in the first abnormal tracing analysis data, and the maintenance personnel are prompted to handle it immediately; In addition, according to the extreme temperature abnormality label or the continuous temperature abnormality label in the second abnormal traceability analysis data, quality spot check prompts are implemented for the goods, and the decision on whether to continue to put them on the market is made based on the quality spot check results. At the same time, targeted alarm prompts and management are carried out for different abnormal links. The total number of occurrences of all abnormal links is counted, and the abnormal links with a total number of occurrences greater than the standard total number are marked as high-risk links, and alarm prompts are generated for the high-risk links that require immediate targeted behavioral training; the standard total number is determined based on the work requirement parameters of the corresponding cold chain transportation entity.

[0027] In the embodiment of the present invention, the corresponding specific abnormal type and abnormal impact degree are determined by tracing and verifying the abnormal data monitored and analyzed in different aspects in the early stage. According to the abnormal tracing and verification results, targeted alarm prompts of monitoring data in different dimensions can be realized, so that maintenance personnel and management personnel can timely and efficiently carry out targeted maintenance and training on the transportation behaviors of cold chain transportation vehicles and transportation personnel, thereby improving the diversity of cold chain logistics monitoring data utilization.

[0028] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.

[0029] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0030] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0031] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0032] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the basic features of the present invention.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary operation and maintenance personnel in the field should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis, characterized in that: It includes a cold chain logistics monitoring and analysis module, which is used to implement different-dimensional monitoring and risk assessment of the entire process of cold chain logistics transportation, obtain a risk assessment set including the first risk assessment data and the second risk assessment data, and upload it to the logistics monitoring sharing platform in real time; including: When implementing internal environment data monitoring and risk assessment for the entire process of cold chain logistics transportation, real-time temperature monitoring and statistics are performed on temperature monitoring points at different locations in the cold chain compartment, and the corresponding real-time temperature curve is obtained through the pre-built temperature coordinate system; And, obtaining the real-time temperature values ​​of the temperature monitoring points at different locations and arranging them in descending order, calculating the first temperature difference between the maximum real-time temperature value Wmax and the minimum real-time temperature value Wmin according to the preset monitoring interval period, and obtaining the real-time temperature mean Wp of all locations at the same time, calculating the second temperature difference between the real-time temperature mean Wp and the real-time temperature mean Wp0 of the last monitoring statistics, analyzing the first temperature difference and the second temperature difference to obtain the first risk assessment data consisting of a local temperature normal label or a local temperature abnormal label, an overall temperature normal label or an overall temperature abnormal label; When implementing data monitoring and risk assessment of the external environment throughout the entire process of cold chain logistics transportation, the handling conditions of different goods in the cold chain compartment are monitored and counted, and a handling monitoring instruction is generated when the detection sensor detects that the goods are being handled. According to the handling monitoring instruction, the external real-time temperature of the entire process of cargo handling is monitored and counted, and the numerical value of the external real-time temperature of the entire process of cargo handling is extracted, and the corresponding real-time moving temperature curve is obtained through a pre-constructed temperature coordinate system. The changes in the real-time moving temperature curve are monitored and analyzed to obtain the second risk assessment data consisting of moving normal labels or abnormal traceability labels.

2. According to claim 1, a low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis is characterized in that: Compare and classify the first temperature difference and the second temperature difference with the preset first temperature difference range and the second temperature difference range respectively; If the first temperature difference value belongs to the first temperature difference range and the second temperature difference value belongs to the second temperature difference range, a local temperature normal label and an overall temperature normal label are generated; If the first temperature difference does not belong to the first temperature difference range and the second temperature difference does not belong to the second temperature difference range, a local temperature anomaly label and an overall temperature anomaly label are generated.

3. According to claim 2, a low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis is characterized in that: When the real-time moving temperature curve is not higher than the preset temperature warning line, a moving normal label is generated; When the real-time moving temperature curve is higher than the preset temperature warning line, an abnormal traceability label is generated, and the corresponding link is marked as an abnormal link according to the abnormal traceability label.

4. According to claim 1, a low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis is characterized in that: The logistics abnormality verification and control module is used to implement abnormality tracing and verification on abnormalities occurring in different aspects of the risk assessment set to determine the corresponding abnormality type and abnormal impact, upload the abnormality tracing and verification results to the logistics monitoring sharing platform in real time, and implement dynamic management of cold chain logistics transportation based on the abnormality tracing and verification results.

5. A low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis according to claim 4, characterized in that: Obtain the risk assessment set uploaded in the logistics monitoring sharing platform and traverse to obtain the corresponding first risk assessment data or second risk assessment data, and traverse the first risk assessment data or the second risk assessment data to obtain the corresponding local temperature anomaly label, overall temperature anomaly label or anomaly traceability label; When tracing back and verifying the abnormal monitored temperatures of temperature monitoring points at different positions in the cold chain compartment based on the local temperature abnormality labels or the overall temperature abnormality labels, the periodic temperature monitoring of the monitoring interval period is canceled based on the local temperature abnormality labels or the overall temperature abnormality labels to start the analysis and evaluation of the real-time temperature.

6. A low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis according to claim 5, characterized in that: Obtain the first temperature difference and the second temperature difference corresponding to the temperature monitoring points at different positions K times, analyze and obtain the corresponding K first risk assessment data, and traverse and count the total number of local temperature anomaly labels or the total number of overall temperature anomaly labels in the K first risk assessment data; If the total number of local temperature anomaly labels or the total number of overall temperature anomaly labels is not less than M, a local temperature anomaly stable label or an overall temperature anomaly stable label is generated; If the total number of local temperature anomaly labels or the total number of overall temperature anomaly labels is less than M and not zero, a local temperature anomaly fluctuation label or an overall temperature anomaly fluctuation label is generated; The local temperature abnormality stable label or the overall temperature abnormality stable label, the local temperature abnormality fluctuation label or the overall temperature abnormality fluctuation label constitute the first abnormality tracing analysis data.

7. A low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis according to claim 6, characterized in that: When tracing and verifying the abnormal moving temperature in the cargo handling process according to the abnormal tracing label, the area enclosed by the real-time moving temperature curve and the preset temperature warning line is obtained and marked as the first abnormal value, and the difference between the maximum value of the real-time moving temperature curve and the temperature warning line is obtained and marked as the second abnormal value; The type of goods is obtained, and the corresponding first abnormal threshold and second abnormal threshold are obtained according to the type of goods and the transportation requirement parameters, and the first abnormal value and the second abnormal value are respectively compared and classified with the first abnormal threshold and the second abnormal threshold.

8. A low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis according to claim 7, characterized in that: If the first abnormal value is not greater than the first abnormal threshold and the second abnormal value is not greater than the second abnormal threshold, an extreme temperature normal label and a temperature continuous normal label are generated; If the first abnormal value is greater than the first abnormal threshold and the second abnormal value is greater than the second abnormal threshold, an extreme temperature abnormality label and a continuous temperature abnormality label are generated; The extreme temperature normal label or the extreme temperature abnormal label, the temperature continuous normal label or the temperature continuous abnormal label constitute the second abnormality tracing analysis data; The first abnormality tracing analysis data and the second abnormality tracing analysis data constitute the abnormality tracing verification result.

9. A low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis according to claim 8, characterized in that: When dynamic management of cold chain logistics transportation is implemented according to the abnormal tracing verification results, an alarm prompt of internal local temperature abnormality or overall temperature abnormality is generated according to the local temperature abnormal fluctuation tag or the overall temperature abnormal fluctuation tag in the first abnormal tracing analysis data, and the maintenance personnel are prompted to handle it immediately; In addition, quality sampling prompts are implemented for goods corresponding to the extreme temperature abnormality label or the continuous temperature abnormality label in the second abnormality traceability analysis data, and a decision is made whether to continue to put the goods on the market is made based on the quality sampling results.

10. A low-carbon and environmentally friendly cold chain logistics monitoring system based on big data analysis according to claim 9, characterized in that: When providing targeted alarm prompts and management for different abnormal links, the total number of occurrences of all abnormal links is counted, and abnormal links with a total number of occurrences greater than the standard total number are marked as high-risk links, and alarm prompts are generated to indicate that high-risk links require immediate targeted behavioral training.

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