Cold-chain logistics data tracing method based on big data analysis

Abstract

The invention relates to the technical field of cold-chain logistics, in particular to a cold-chain logistics data traceability method based on big data analysis, and the method comprises the steps: a production end generates a traceability code for a single batch of goods; the acquisition end acquires transportation data; the acquisition end divides monitoring categories for a single batch of goods based on the transportation aggregation characterization value, and monitors the goods based on the monitoring categories; the middle end generates an abnormal report based on the monitoring category and the monitoring data, and the middle end transmits the abnormal report to the selling end. According to the stacking and gathering condition of the goods, the monitoring parameters are adjusted in a targeted mode, the monitoring precision of the goods is improved, and then the reliability of logistics data is improved.

Description

Technical Field

[0001] The present invention relates to the field of cold chain logistics technology, and in particular to a cold chain logistics data tracing method based on big data analysis. Background Art

[0002] Cold chain logistics plays a vital role in ensuring the quality and safety of perishable goods such as food and medicine. With improving living standards and increasing awareness of food safety and drug efficacy, demand for cold chain logistics is rapidly growing. However, the industry currently faces numerous challenges.

[0003] Cold chain logistics involves a large amount of data, which is scattered across different links and devices. Lack of effective integration and management makes data tracing and analysis difficult.

[0004] Existing cold chain logistics monitoring strategies are often unified and do not take into account the actual status of the goods in the transport carrier.

[0005] During the transportation of fresh food such as meat, the temperature and humidity requirements are very strict. If the temperature is slightly higher, it will easily deteriorate. Usually, ice cubes are added to the transport vehicle to provide a low temperature environment.

[0006] Chinese patent application publication number CN112215623A discloses a cold chain logistics information traceability system for agricultural products. The detailed design steps are as follows: The system is divided into five layers: presentation layer, application layer, data layer, intermediate processing layer, and hardware layer. The presentation layer provides consumers with cold chain logistics information traceability services for fruits and vegetables through portal websites, mobile phone text message push notifications, and barcode queries. The application layer manages the safety of fruits and vegetables and collects information. The data layer stores important data. The intermediate processing layer collects data through various interfaces, enabling data transmission and sharing across various distribution links. During the distribution process, fruits and vegetables access and exchange information at the hardware layer, completing data conversion and interaction. This technical solution suffers from the following issues: It fails to consider the impact of the accumulation of goods on their temperature change rate, making it impossible to adjust monitoring parameters in a targeted manner. This impacts the accuracy of goods monitoring and, in turn, the reliability of logistics data. Summary of the Invention

[0007] To this end, the present invention provides a cold chain logistics data traceability method based on big data analysis, which is used to overcome the problem in the prior art that the stacking and aggregation of goods will affect the temperature change rate, and the monitoring parameters cannot be adjusted in a targeted manner, which affects the monitoring accuracy of the goods and thus affects the reliability of logistics data.

[0008] To achieve the above objectives, the present invention provides a cold chain logistics data traceability method based on big data analysis, comprising: S1, the production end generates a traceability code for a single batch of goods; S2, the collection end obtains transportation data, which includes transportation aggregation representation values; S3: The collection end divides the monitoring categories for a single batch of goods based on the transport aggregation characterization value and monitors the goods based on the monitoring categories, including: When the monitoring category of a single batch of goods is determined to be classified as a strong monitoring category, the acquisition frequency of the revised monitoring data is determined based on the transport aggregation characterization value; S4, the middle end generates an abnormality report based on the monitoring category and monitoring data, and transmits the abnormality report to the sales end. The monitoring data includes the temperature and humidity in the transport carrier.

[0009] Furthermore, the process of obtaining the transport aggregation characterization value includes: Calculate the ratio of the volume of the smallest circumscribed cuboid of the cargo in the transport carrier to the actual volume of the cargo to obtain the transport aggregation representation value; The process of classifying a single batch of goods into monitoring categories based on transport aggregation characterization values ​​and monitoring the goods based on the monitoring categories includes: If the transport aggregation characterization value is less than or equal to the preset transport aggregation characterization value, the monitoring category of the single batch of goods is classified as a weak monitoring category, and the current monitoring parameters are continuously used to complete the monitoring of the goods; If the transport aggregation characterization value is greater than the preset transport aggregation characterization value, the monitoring category of the single batch of goods is classified as a strong monitoring category, and the acquisition frequency of the revised monitoring data is determined based on the transport aggregation characterization value.

[0010] Furthermore, the acquisition frequency of the revised monitoring data is determined based on the transport aggregation characterization value, wherein: The increase in the frequency of monitoring data acquisition is positively correlated with the transport aggregation characterization value.

[0011] Furthermore, when the acquisition frequency of the monitoring data is corrected based on the transport aggregation characterization value, the alarm threshold for generating an abnormality report is corrected based on the cooling reference value, wherein, The ratio of the mass of ice to the mass of goods is determined as the reference value for cooling; The increase in the alarm threshold is positively correlated with the cooling reference value.

[0012] Furthermore, when the adjustment for the alarm threshold is completed, determining whether the monitoring of a single batch of goods is qualified based on the transport stability characterization value includes: Marking the vibration amplitude obtained that is greater than the preset vibration amplitude as abnormal vibration; Calculate the ratio of the number of abnormal vibrations within the preset detection time to the preset detection time to obtain a transport stability characterization value; If the transport stability characterization value is less than or equal to the preset transport stability characterization value, the monitoring of the single batch of goods is determined to be qualified, and the current monitoring parameters are continued to be used to complete the monitoring of the goods; If the transport stability characterization value is greater than the preset transport stability characterization value, it is determined that the monitoring of the single batch of goods is abnormal, and the monitoring parameters for the single batch of goods are corrected based on the stability difference amount.

[0013] Furthermore, the process of correcting the monitoring parameters for a single batch of goods based on the stable difference includes: Solve the variance of the time interval of each abnormal vibration to obtain the stable difference; If the stable difference is less than or equal to the preset stable difference, adjusting the travel speed of the transport vehicle to a corresponding value based on the stable difference; If the stable difference is greater than the preset stable difference, the acquisition frequency of the monitoring data is adjusted to a corresponding value based on the distribution density of each sensor.

[0014] Furthermore, the travel speed of the transport vehicle is adjusted to a corresponding value based on the stable difference, wherein: The increase in driving speed is negatively correlated with the amount of stable difference.

[0015] Furthermore, the acquisition frequency of monitoring data is adjusted to a corresponding value based on the distribution density of each sensor, wherein, The increase in acquisition frequency is negatively correlated with the distribution density of each sensor.

[0016] Furthermore, when the adjustment of the acquisition frequency of the monitoring data based on the distribution density of each sensor is completed, an alternative route for the transportation of a single batch of goods is obtained and the alternative route is replaced.

[0017] Furthermore, the middle end generates anomaly reports based on monitoring categories and monitoring data, including: If the monitoring category of a single batch of goods is weak monitoring category, an exception report will be generated based on the alarm threshold; If the monitoring category of a single batch of goods is a strong monitoring category, the data is fitted and an exception report is generated based on the alarm threshold.

[0018] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are that the production end generates a traceability code for a single batch of goods; the collection end obtains transportation data; the collection end divides the monitoring category for a single batch of goods based on the transportation aggregation characterization value, and monitors the goods based on the monitoring category; the intermediate end generates an abnormality report based on the monitoring category and monitoring data, and the intermediate end transmits the abnormality report to the sales end. In the cold chain logistics process, parameters such as the temperature and humidity of the goods are important factors affecting the quality of the goods. By analyzing the monitoring category and monitoring data, it is determined whether the goods have abnormal conditions, and an abnormality report is generated, which facilitates timely understanding of the transportation status of the goods and taking corresponding measures. According to the accumulation and aggregation of the goods, the monitoring parameters cannot be adjusted in a targeted manner, which improves the monitoring accuracy of the goods and thereby improves the reliability of the logistics data.

[0019] Furthermore, the transport aggregation characterization value characterizes the aggregation of goods stacked in the transport carrier. The more aggregated the goods are, the slower the melting speed is. The closer the transport aggregation characterization value is to 1, the more aggregated the goods are. Based on the transport aggregation characterization value, the monitoring category for a single batch of goods is divided, and the goods are monitored based on the monitoring category. Different transport aggregation characterization values ​​reflect different aggregation states of goods, and different monitoring strategies need to be adopted. By dividing the monitoring categories, the monitoring frequency can be adjusted according to the actual situation of the goods to ensure the quality and safety of the goods during transportation. When the transport aggregation characterization value is less than or equal to the preset transport aggregation characterization value, the goods are more aggregated and the melting speed is slower; when the transport aggregation characterization value is greater than the preset transport aggregation characterization value, the goods are loosely stacked and the melting speed is faster, and the frequency of obtaining monitoring data needs to be increased to detect problems in a timely manner. Adjusting the monitoring strategy according to the actual aggregation situation of the goods avoids excessive or insufficient monitoring, improves monitoring efficiency, and further improves the reliability of logistics data.

[0020] Furthermore, the frequency of monitoring data acquisition is determined and corrected based on the transport aggregation characterization value. When goods are loosely stacked, the temperature and humidity of the goods change rapidly. If the monitoring data acquisition frequency is low, some important temperature and humidity changes will be missed, resulting in the inability to detect problems in a timely manner. The frequency of monitoring data acquisition is increased according to the transport aggregation characterization value. The larger the transport aggregation characterization value, the larger the heat dissipation area of ​​the goods and the faster the temperature changes. More frequent monitoring data acquisition is required to keep abreast of changes in the temperature and humidity of the goods. This improves the timeliness and accuracy of monitoring data, making it possible to detect changes in the temperature and humidity of the goods more timely. This further improves the reliability of logistics data.

[0021] Furthermore, the alarm threshold used to generate abnormality reports is modified based on the cooling reference value. The cooling reference value reflects the ratio of the mass of the ice to the mass of the cargo. A larger cooling reference value indicates a stronger cooling capacity of the ice and slower temperature changes in the cargo. Therefore, the alarm threshold needs to be raised accordingly to avoid frequent alarms. This further improves the reliability of logistics data.

[0022] Furthermore, the pass / fail status of a single batch of cargo is determined based on the transport stability characterization value. This value reflects the vibration of the cargo during transportation. When the vehicle vibrates, the ice cubes are subjected to external forces, causing them to shake and collide. The energy from this mechanical motion is partially converted into the ice's internal energy. This increase in internal energy intensifies the thermal motion of the ice molecules, raising the internal temperature and accelerating melting. When the transport stability characterization value is less than or equal to the preset transport stability characterization value, the cargo's vibration during transportation is within the normal range and the monitoring passes. When the transport stability characterization value is greater than the preset transport stability characterization value, the cargo has experienced excessive vibration during transportation. The specific handling method is further determined based on the stability difference value. The stability difference value reflects whether there is a pattern in the vibration. If the stability difference value is less than or equal to the preset stability difference value, the vibration exhibits a certain pattern, which is due to normal bumps caused by normal transportation. In this case, the speed of the transport vehicle can be adjusted to reduce transportation time. When the stability difference value is greater than the preset stability difference value, the stability difference value is large, indicating that the vehicle encounters bumpy roads, causing irregular vibrations. The ice cubes collide within the vehicle compartment, causing a short-term increase in internal energy and accelerating melting. At this point, we reroute and increase the frequency of monitoring data acquisition to promptly identify problems and improve data accuracy. We then implement different treatment measures based on the vibration patterns and stability, improving the stability and safety of cargo transportation and further enhancing the reliability of logistics data. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flowchart of the steps of a cold chain logistics data tracing method based on big data analysis according to an embodiment of the present invention; Figure 2 This is a logic decision diagram for classifying monitoring categories for a single batch of goods based on transport aggregation characterization values ​​according to an embodiment of the present invention; Figure 3 This is a logic decision diagram for determining whether monitoring of a single batch of goods is qualified based on the transport stability characterization value according to an embodiment of the present invention; Figure 4 This is a logic decision diagram for correcting monitoring parameters for a single batch of goods based on stable difference amounts according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0025] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0026] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0027] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0028] See also Figure 1 、 Figure 2 、 Figure 3 as well as Figure 4 As shown, they are respectively a flowchart of a cold chain logistics data traceability method based on big data analysis according to an embodiment of the present invention, a logical decision diagram for classifying monitoring categories for a single batch of goods based on transport aggregation characterization values, a logical decision diagram for determining whether monitoring for a single batch of goods is qualified based on transport stability characterization values, and a logical decision diagram for correcting monitoring parameters for a single batch of goods based on stable difference values. A cold chain logistics data traceability method based on big data analysis according to an embodiment of the present invention comprises: S1, the production end generates a traceability code for a single batch of goods; S2, the collection end obtains transportation data, which includes transportation aggregation representation values; S3: The collection end divides the monitoring categories for a single batch of goods based on the transport aggregation characterization value and monitors the goods based on the monitoring categories, including: When the monitoring category of a single batch of goods is determined to be classified as a strong monitoring category, the acquisition frequency of the revised monitoring data is determined based on the transport aggregation characterization value; S4, the middle end generates an abnormality report based on the monitoring category and monitoring data, and transmits the abnormality report to the sales end. The monitoring data includes the temperature and humidity in the transport carrier.

[0029] Specifically, the temperature and humidity inside the transport carrier can be obtained by deploying temperature sensors and humidity sensors.

[0030] Specifically, the process of obtaining the transport aggregation characterization value includes: Calculate the ratio of the volume of the smallest circumscribed cuboid of the cargo in the transport carrier to the actual volume of the cargo to obtain the transport aggregation representation value; The process of classifying a single batch of goods into monitoring categories based on transport aggregation characterization values ​​and monitoring the goods based on the monitoring categories includes: If the transport aggregation characterization value is less than or equal to the preset transport aggregation characterization value, the monitoring category of the single batch of goods is classified as a weak monitoring category, and the current monitoring parameters are continuously used to complete the monitoring of the goods; If the transport aggregation characterization value is greater than the preset transport aggregation characterization value, the monitoring category of the single batch of goods is classified as a strong monitoring category, and the acquisition frequency of the revised monitoring data is determined based on the transport aggregation characterization value.

[0031] Specifically, the specific method for obtaining the volume of the minimum circumscribed cuboid of the cargo within the transport vehicle and the actual volume of the cargo is not limited. A laser radar sensor installed within the transport vehicle can be controlled to emit a laser beam and measure the timing of reflected light, thereby scanning the distribution of the cargo within the transport vehicle to create a three-dimensional point cloud map of the environment. In this embodiment, after the cargo is loaded, the laser radar sensor is activated to scan and obtain three-dimensional point cloud data of the cargo surface. This three-dimensional point cloud data is then processed to calculate the volume of the minimum circumscribed cuboid occupied by the cargo and the actual volume of the cargo.

[0032] Specifically, the preset transport aggregation characterization value is selected within the interval [1.5, 1.8]. Those skilled in the art can determine the value of the preset transport aggregation characterization value based on the specific usage scenario. It can be understood that the division of the aggregation situation of the goods can be achieved. In this embodiment, preferably, the preset transport aggregation characterization value is 1.8.

[0033] Specifically, the transport aggregation characterization value represents the aggregation of goods stacked in the transport carrier. The more aggregated the goods are, the slower the melting speed is. The closer the transport aggregation characterization value is to 1, the more aggregated the goods are.

[0034] Specifically, the monitoring categories for a single batch of goods are divided based on the transport aggregation characterization value, and the goods are monitored based on the monitoring categories. Different transport aggregation characterization values ​​reflect different aggregation states of the goods, and different monitoring strategies need to be adopted. By dividing the monitoring categories, the monitoring frequency can be adjusted according to the actual situation of the goods to ensure the quality and safety of the goods during transportation. When the transport aggregation characterization value is less than or equal to the preset transport aggregation characterization value, the goods are piled up more and melted more slowly; when the transport aggregation characterization value is greater than the preset transport aggregation characterization value, the goods are piled up more loosely and melted more quickly, and the frequency of obtaining monitoring data needs to be increased to detect problems in a timely manner. Adjusting the monitoring strategy according to the actual aggregation situation of the goods avoids excessive or insufficient monitoring, improves monitoring efficiency, and further improves the reliability of logistics data.

[0035] Specifically, the acquisition frequency of the revised monitoring data is determined based on the transport aggregation characterization value, where: The increase in the frequency of monitoring data acquisition is positively correlated with the transport aggregation characterization value.

[0036] In this embodiment, optionally, Comparing the transport aggregation characterization value with a first preset aggregation comparison threshold and a second preset aggregation comparison threshold; If the transport aggregation characterization value is less than or equal to the first preset aggregation comparison threshold, the acquisition frequency of the monitoring data is adjusted to 1.11 times the initial acquisition frequency; If the transport aggregation characterization value is less than or equal to the second preset aggregation comparison threshold and greater than the first preset aggregation comparison threshold, the acquisition frequency of the monitoring data is adjusted to 1.19 times the initial acquisition frequency; If the transport aggregation characterization value is greater than the second preset aggregation comparison threshold, the acquisition frequency of the monitoring data is adjusted to 1.25 times the initial acquisition frequency; The first preset aggregation comparison threshold is 1.2J0, and the second preset aggregation comparison threshold is 1.3J0, where J0 is the preset transport aggregation characterization value.

[0037] Specifically, the frequency of acquiring monitoring data is the frequency at which each sensor acquires monitoring data.

[0038] Specifically, the frequency of monitoring data acquisition is modified based on the transport aggregation characterization value. When cargo is loosely stacked, its temperature and humidity change rapidly. If the monitoring data acquisition frequency is low, some important temperature and humidity changes may be missed, resulting in the inability to detect problems in a timely manner. The frequency of monitoring data acquisition is increased based on the transport aggregation characterization value. The larger the transport aggregation characterization value, the larger the heat dissipation area of ​​the cargo and the faster the temperature changes. More frequent monitoring data acquisition is required to keep abreast of changes in the cargo's temperature and humidity. This improves the timeliness and accuracy of monitoring data, enabling more timely detection of changes in cargo's temperature and humidity. This further enhances the reliability of logistics data.

[0039] Specifically, when the acquisition frequency of the monitoring data is corrected based on the transport aggregation characterization value, the alarm threshold for generating the abnormality report is corrected based on the cooling reference value, wherein, The increase in the alarm threshold is positively correlated with the cooling reference value.

[0040] The ratio of the mass of ice to the mass of goods is determined as the reference value for cooling.

[0041] In this embodiment, optionally, comparing the cooling reference value with the first preset cooling reference value and the second preset cooling reference value; If the cooling reference value is less than or equal to the first preset cooling reference value, the alarm threshold is adjusted to 1.08 times the initial alarm threshold; If the cooling reference value is less than or equal to the second preset cooling reference value and greater than the first preset cooling reference value, the alarm threshold is adjusted to 1.13 times the initial alarm threshold; If the cooling reference value is greater than the second preset cooling reference value, the alarm threshold is adjusted to 1.29 times the initial alarm threshold; The first preset cooling reference value is 0.1, and the second preset cooling reference value is 0.3.

[0042] Specifically, the alarm threshold used to generate abnormality reports is modified based on the cooling reference value. The cooling reference value reflects the ratio of the mass of the ice to the mass of the cargo. A larger cooling reference value indicates a stronger cooling capacity of the ice and slower temperature changes in the cargo. Consequently, the alarm threshold needs to be raised accordingly to avoid frequent alarms. This further improves the reliability of logistics data.

[0043] Specifically, when the adjustment for the alarm threshold is completed, whether the monitoring of a single batch of goods is qualified is determined based on the transport stability characterization value, including: Marking the vibration amplitude obtained that is greater than the preset vibration amplitude as abnormal vibration; Calculate the ratio of the number of abnormal vibrations within the preset detection time to the preset detection time to obtain a transport stability characterization value; If the transport stability characterization value is less than or equal to the preset transport stability characterization value, the monitoring of the single batch of goods is determined to be qualified, and the current monitoring parameters are continued to be used to complete the monitoring of the goods; If the transport stability characterization value is greater than the preset transport stability characterization value, it is determined that the monitoring of the single batch of goods is abnormal, and the monitoring parameters for the single batch of goods are corrected based on the stability difference amount.

[0044] Specifically, the preset transport stability characterization value is selected within the range of [3 times / hour, 7 times / hour]. Those skilled in the art can determine the value of the preset transport stability characterization value based on the specific usage scenario. The preset transport stability characterization value is derived from a large number of experiments and actual transport data statistics. It is understandable that the status of the impact on the cargo during the actual transportation process can be divided. In this embodiment, preferably, the preset transport stability characterization value is selected within the range of 7 times / hour.

[0045] Specifically, the process of correcting the monitoring parameters for a single batch of goods based on the stable difference includes: Solve the variance of the time interval of each abnormal vibration to obtain the stable difference; If the stable difference is less than or equal to the preset stable difference, adjusting the travel speed of the transport vehicle to a corresponding value based on the stable difference; If the stable difference is greater than the preset stable difference, the acquisition frequency of the monitoring data is adjusted to a corresponding value based on the distribution density of each sensor.

[0046] Specifically, the preset stability difference is selected within the range of [0.47, 0.68]. This preset stability difference is derived through analysis and statistical analysis of a large amount of vibration data. Those skilled in the art can determine the preset stability difference based on specific transportation conditions. It is understood that it is sufficient to determine whether the vibration pattern is regular. In this embodiment, the preset stability difference is preferably 0.52.

[0047] Specifically, the pass / fail status of a single shipment is determined based on the transport stability characterization value. This value reflects the vibration experienced during transport. When the vehicle vibrates, ice cubes are subjected to external forces, causing them to shake and collide. The energy from this mechanical motion is partially converted into the ice's internal energy. This increased internal energy intensifies the thermal motion of the ice molecules, raising the internal temperature and accelerating melting. If the transport stability characterization value is less than or equal to the preset transport stability characterization value, the vibration of the cargo during transport is within the normal range and the monitoring passes. If the transport stability characterization value is greater than the preset transport stability characterization value, the cargo has experienced excessive vibration during transport. The specific handling method is further determined based on the stability difference value, which reflects whether there is a pattern in the vibration. If the stability difference value is less than or equal to the preset stability difference value, the vibration exhibits a certain pattern, which is due to normal bumps caused by normal transport. In this case, the vehicle's speed can be adjusted to reduce transport time. If the stability difference value is greater than the preset stability difference value, the stability difference value is large, indicating irregular vibration caused by bumpy roads during travel. The ice cubes collide within the vehicle compartment, causing a short-term increase in internal energy and accelerating melting. At this point, we reroute and increase the frequency of monitoring data acquisition to promptly identify problems and improve data accuracy. We then implement different treatment measures based on the vibration patterns and stability, improving the stability and safety of cargo transportation and further enhancing the reliability of logistics data.

[0048] Specifically, the driving speed of the transport vehicle is adjusted to a corresponding value based on the stable difference amount, wherein, The increase in driving speed is negatively correlated with the amount of stable difference.

[0049] In this embodiment, optionally, Comparing the stable difference amount with a first difference comparison threshold and a second difference comparison threshold; If the stable difference is less than or equal to the first difference comparison threshold, the driving speed of the transport vehicle is adjusted to 1.28 times the initial driving speed; If the stable difference is less than or equal to the first difference comparison threshold, the driving speed of the transport vehicle is adjusted to 1.21 times the initial driving speed; If the stable difference is less than or equal to the first difference comparison threshold, the driving speed of the transport vehicle is adjusted to 1.13 times the initial driving speed; The first difference comparison threshold is 0.7W0, and the second difference comparison threshold is 0.8W0, where W0 is the preset stable difference amount.

[0050] Specifically, the acquisition frequency of monitoring data is adjusted to a corresponding value based on the distribution density of each sensor, wherein, The increase in acquisition frequency is negatively correlated with the distribution density of each sensor.

[0051] The distribution density of each sensor is the ratio of the total number of sensors to the volume of the transport carrier.

[0052] In this embodiment, optionally, Comparing the distribution density with a first preset distribution comparison value and a second preset distribution comparison value; If the distribution density is less than or equal to the first preset distribution comparison value, the acquisition frequency of the monitoring data is adjusted to 1.22 times the current acquisition frequency; If the distribution density is less than or equal to the second preset distribution comparison value and greater than the first preset distribution comparison value, the acquisition frequency of the monitoring data is adjusted to 1.17 times the current acquisition frequency; If the distribution density is greater than the second preset distribution comparison value, the acquisition frequency of the monitoring data is adjusted to 1.11 times the current acquisition frequency; The first preset distribution comparison value is 0.5 per cubic meter, and the second preset distribution comparison value is 1 per cubic meter.

[0053] Specifically, when the frequency of acquiring monitoring data based on the distribution density of each sensor is adjusted, an alternative route for transporting a single batch of goods is obtained and the alternative route is replaced.

[0054] Specifically, there is no limitation on the specific method of obtaining alternative routes and determining the replacement alternative routes. The alternative route with the shortest estimated travel time can be selected based on the map navigation software, or historical data of previous transportation of the same goods can be analyzed, and the alternative route with the shortest travel time can be screened out based on the current departure time. This is an existing technology and will not be repeated here.

[0055] Specifically, the process of generating anomaly reports based on monitoring categories and monitoring data on the middle end includes: If the monitoring category of a single batch of goods is weak monitoring category, an exception report will be generated based on the alarm threshold; If the monitoring category of a single batch of goods is a strong monitoring category, the data is fitted and an exception report is generated based on the alarm threshold.

[0056] Specifically, when the monitoring category of a single batch of goods is weak monitoring, the process of generating an exception report based on the alarm threshold includes: The middle end continuously receives monitoring data sent by the collection end, including the temperature and humidity inside the transport carrier; According to the preset alarm thresholds, the monitoring data is screened to obtain data points that exceed the alarm thresholds; and abnormal data points are marked. The alarm thresholds include temperature alarm thresholds and humidity alarm thresholds; Obtain the abnormal information corresponding to each abnormal data point, including time and specific value; Abnormal information associated with the traceability code; Exception information generates exception reports according to the preset report template.

[0057] Specifically, when the monitoring category of a single batch of goods is the strong monitoring category, the data fitting process includes: The middle end receives monitoring data and preprocesses the data to remove outliers and noise in the monitoring data; The pre-processed monitoring data are fitted separately by linear fitting algorithm to obtain the humidity change curve and temperature change curve of the data; When the change curve exceeds the corresponding alarm threshold, obtain the abnormal information of the corresponding node, including time and specific value; Abnormal information associated with the traceability code; Generate an exception report, which includes the change curve corresponding to the exception information and the exception information of the node where the exception occurred.

[0058] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0059] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A cold chain logistics data tracing method based on big data analysis, characterized in that: include: S1, the production end generates a traceability code for a single batch of goods; S2, the collection end obtains transportation data, which includes transportation aggregation representation values; S3: The collection end divides the monitoring categories for a single batch of goods based on the transport aggregation characterization value and monitors the goods based on the monitoring categories, including: When the monitoring category of a single batch of goods is determined to be classified as a strong monitoring category, the acquisition frequency of the revised monitoring data is determined based on the transport aggregation characterization value; S4, the middle end generates an abnormality report based on the monitoring category and monitoring data, and transmits the abnormality report to the sales end. The monitoring data includes the temperature and humidity in the transport carrier.

2. The cold chain logistics data tracing method based on big data analysis according to claim 1 is characterized in that: The process of obtaining the transport aggregation characterization value includes: Calculate the ratio of the volume of the smallest circumscribed cuboid of the cargo in the transport carrier to the actual volume of the cargo to obtain the transport aggregation representation value; The process of classifying a single batch of goods into monitoring categories based on transport aggregation characterization values ​​and monitoring the goods based on the monitoring categories includes: If the transport aggregation characterization value is less than or equal to the preset transport aggregation characterization value, the monitoring category of the single batch of goods is classified as a weak monitoring category, and the current monitoring parameters are continuously used to complete the monitoring of the goods; If the transport aggregation characterization value is greater than the preset transport aggregation characterization value, the monitoring category of the single batch of goods is classified as a strong monitoring category, and the acquisition frequency of the revised monitoring data is determined based on the transport aggregation characterization value.

3. The cold chain logistics data tracing method based on big data analysis according to claim 2 is characterized in that: The acquisition frequency of the revised monitoring data is determined based on the transport aggregation characterization value, where: The increase in the frequency of monitoring data acquisition is positively correlated with the transport aggregation characterization value.

4. The cold chain logistics data tracing method based on big data analysis according to claim 3 is characterized in that: When the acquisition frequency of the monitoring data is corrected based on the transport aggregation characterization value, the alarm threshold for generating an abnormality report is corrected based on the cooling reference value, wherein, The ratio of the mass of ice to the mass of goods is determined as the reference value for cooling; The increase in the alarm threshold is positively correlated with the cooling reference value.

5. The cold chain logistics data tracing method based on big data analysis according to claim 4 is characterized in that: When the adjustment for the alarm threshold is completed, the qualification of the monitoring for a single batch of goods is determined based on the transport stability characterization value, including: Marking the vibration amplitude obtained that is greater than the preset vibration amplitude as abnormal vibration; Calculate the ratio of the number of abnormal vibrations within the preset detection time to the preset detection time to obtain a transport stability characterization value; If the transport stability characterization value is less than or equal to the preset transport stability characterization value, the monitoring of the single batch of goods is determined to be qualified, and the current monitoring parameters are continued to be used to complete the monitoring of the goods; If the transport stability characterization value is greater than the preset transport stability characterization value, it is determined that the monitoring of the single batch of goods is abnormal, and the monitoring parameters for the single batch of goods are corrected based on the stability difference amount.

6. The cold chain logistics data tracing method based on big data analysis according to claim 5 is characterized in that: The process of correcting monitoring parameters for a single batch of goods based on the stable difference includes: Solve the variance of the time interval of each abnormal vibration to obtain the stable difference; If the stable difference is less than or equal to the preset stable difference, adjusting the travel speed of the transport vehicle to a corresponding value based on the stable difference; If the stable difference is greater than the preset stable difference, the acquisition frequency of the monitoring data is adjusted to a corresponding value based on the distribution density of each sensor.

7. The cold chain logistics data tracing method based on big data analysis according to claim 6 is characterized in that: The driving speed of the transport vehicle is adjusted to a corresponding value based on the stable difference, wherein: The increase in driving speed is negatively correlated with the amount of stable difference.

8. The cold chain logistics data tracing method based on big data analysis according to claim 7 is characterized in that: The acquisition frequency of monitoring data is adjusted to the corresponding value based on the distribution density of each sensor, where: The increase in acquisition frequency is negatively correlated with the distribution density of each sensor.

9. The cold chain logistics data tracing method based on big data analysis according to claim 8 is characterized in that: When the adjustment of the acquisition frequency of the monitoring data based on the distribution density of each sensor is completed, an alternative route for the transportation of a single batch of goods is obtained and the alternative route is replaced.

10. The cold chain logistics data tracing method based on big data analysis according to claim 9 is characterized in that: The middle end generates anomaly reports based on monitoring categories and monitoring data, including: If the monitoring category of a single batch of goods is weak monitoring category, an exception report will be generated based on the alarm threshold; If the monitoring category of a single batch of goods is a strong monitoring category, the data is fitted and an exception report is generated based on the alarm threshold.

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