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

Abstract

The present application relates to the technical field of cold chain logistics, and more particularly to a cold chain logistics data traceability method based on big data analysis, which comprises the following steps: a production end generates a traceability code for a single batch of goods; a collection end acquires transportation data; the collection end divides the monitoring category of the single batch of goods based on the transportation aggregation characteristic value and monitors the goods based on the monitoring category; an intermediate end generates an abnormal report based on the monitoring category and the monitoring data, and the intermediate end transmits the abnormal report to a selling end. According to the accumulation and aggregation of the goods, the monitoring parameters are adjusted in a targeted manner, the monitoring accuracy of the goods is improved, and the reliability of the logistics data is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cold chain logistics, and in particular to a cold chain logistics data traceability method based on big data analysis. BACKGROUND

[0002] Cold chain logistics plays a crucial role in ensuring the quality and safety of perishable goods such as food and medicine. With the improvement of people's living standards and the increasing attention to food safety and drug effectiveness, the demand for cold chain logistics is showing a rapid growth trend. However, the current cold chain logistics industry is facing many challenges.

[0003] In the process of cold chain logistics, a large amount of data is involved. The data is scattered in different links and devices, and lacks effective integration and management, making data traceability and analysis difficult.

[0004] The existing cold chain logistics monitoring strategy is usually unified, without considering the actual state of the goods in the transportation carrier.

[0005] In the transportation process of fresh food such as meat, the requirements for temperature and humidity are very strict, and the temperature is slightly high and easy to deteriorate. Ice blocks are usually added in the transportation vehicle to provide a low-temperature environment.

[0006] Chinese patent application publication No. CN112215623A discloses a cold chain logistics information traceability system for agricultural products, and the detailed design steps are as follows: the design mainly includes five levels of presentation layer, application layer, data layer, intermediate processing layer and hardware layer. The presentation layer provides cold chain logistics information traceability query services for consumers through portal websites, mobile phone message push and barcode query, etc. The application layer carries out safety management and information collection of fruit and vegetable agricultural products. The data layer stores important data. The intermediate processing layer collects data through various interfaces to realize data transmission and sharing in each circulation link. Fruit and vegetable agricultural products access and information integration on the hardware layer during the circulation process, complete data conversion and interaction; it can be seen that the above technical solution has the following problems: the accumulation of goods affects the temperature change speed, the monitoring parameters cannot be adjusted, the monitoring accuracy of the goods is affected, and the reliability of the logistics data is affected. SUMMARY

[0007] Therefore, the present application provides a cold chain logistics data traceability method based on big data analysis to overcome the problem that the accumulation of goods affects the temperature change speed in the prior art, the monitoring parameters cannot be adjusted, the monitoring accuracy of the goods is affected, and the reliability of the logistics data is affected.

[0008] To achieve the above object, the application provides a cold chain logistics data traceability method based on big data analysis, comprising:

[0009] S1, the production end generates a traceability code for a single batch of goods;

[0010] S2, the acquisition end obtains transportation data, which includes transportation aggregation characteristic values;

[0011] S3, the acquisition end divides the monitoring category of the single batch of goods based on the transportation aggregation characteristic values, and monitors the goods based on the monitoring category, comprising:

[0012] When the monitoring category of the single batch of goods is determined to be the strong monitoring category, the acquisition frequency of the corrected monitoring data is determined based on the transportation aggregation characteristic values;

[0013] S4, the intermediate end generates an abnormal report based on the monitoring category and the monitoring data, and the intermediate end transmits the abnormal report to the selling end, and the monitoring data includes the temperature and humidity in the transportation carrier.

[0014] Further, the acquisition process of the transportation aggregation characteristic values comprises:

[0015] The ratio of the volume of the smallest circumscribed cuboid of the goods in the transportation carrier to the actual volume of the goods is calculated to obtain the transportation aggregation characteristic values;

[0016] The process of dividing the monitoring category of the single batch of goods based on the transportation aggregation characteristic values and monitoring the goods based on the monitoring category comprises:

[0017] If the transportation aggregation characteristic values are less than or equal to the preset transportation aggregation characteristic values, the monitoring category of the single batch of goods is divided into the weak monitoring category, and the current monitoring parameters are continuously used to complete the monitoring of the goods;

[0018] If the transportation aggregation characteristic values are greater than the preset transportation aggregation characteristic values, the monitoring category of the single batch of goods is divided into the strong monitoring category, and the acquisition frequency of the corrected monitoring data is determined based on the transportation aggregation characteristic values.

[0019] Further, the acquisition frequency of the corrected monitoring data is determined based on the transportation aggregation characteristic values, wherein:

[0020] The increase range of the acquisition frequency of the monitoring data is positively correlated with the transportation aggregation characteristic values.

[0021] Further, when the acquisition frequency of the monitoring data is corrected based on the transportation aggregation characteristic values, the alarm threshold value used to generate the abnormal report is corrected based on the refrigeration reference value, wherein,

[0022] The ratio of the mass of the ice block to the mass of the goods is determined as the refrigeration reference value;

[0023] The increase range of the alarm threshold is positively correlated with the cooling reference value.

[0024] Further, when the adjustment for the alarm threshold is completed, it is determined whether the monitoring for the single batch of goods is qualified based on the transportation stability characterization value, including:

[0025] The vibration with the acquired vibration amplitude greater than the preset vibration amplitude is marked as an abnormal vibration;

[0026] The ratio of the number of abnormal vibrations in the preset detection duration to the preset detection duration is calculated to obtain the transportation stability characterization value;

[0027] If the transportation stability characterization value is less than or equal to the preset transportation stability characterization value, it is determined that the monitoring for the single batch of goods is qualified, and the current monitoring parameter is continuously used to complete the monitoring of the goods;

[0028] If the transportation stability characterization value is greater than the preset transportation stability characterization value, it is determined that the monitoring for the single batch of goods is abnormal, and the monitoring parameter for the single batch of goods is corrected based on the stability difference amount.

[0029] Further, the process of correcting the monitoring parameter for the single batch of goods based on the stability difference amount includes:

[0030] The variance of the time interval of each abnormal vibration is solved to obtain the stability difference amount;

[0031] If the stability difference amount is less than or equal to the preset stability difference amount, the driving speed of the transportation carrier is adjusted to a corresponding value based on the stability difference amount;

[0032] If the stability difference amount is greater than the preset stability difference amount, the acquisition frequency of the monitoring data is adjusted to a corresponding value based on the distribution density of each sensor.

[0033] Further, the driving speed of the transportation carrier is adjusted to a corresponding value based on the stability difference amount, wherein,

[0034] The increase range of the driving speed is negatively correlated with the stability difference amount.

[0035] Further, the acquisition frequency of the monitoring data is adjusted to a corresponding value based on the distribution density of each sensor, wherein,

[0036] The increase range of the acquisition frequency is negatively correlated with the distribution density of each sensor.

[0037] Further, when the adjustment of the acquisition frequency of the monitoring data based on the distribution density of each sensor is completed, a candidate route for the transportation of the single batch of goods is obtained, and the candidate route is replaced.

[0038] Further, the process of generating an abnormal report by the intermediate end based on the monitoring category and the monitoring data includes:

[0039] If the monitoring category of the single batch of goods is a weak monitoring category, an abnormal report is generated based on the alarm threshold;

[0040] If the monitoring category of the single batch of goods is a strong monitoring category, data is fitted, and an abnormal report is generated based on the alarm threshold.

[0041] Beneficial effects:

[0042] Compared with the prior art, the beneficial effects of the present application are that the production end generates a traceability code for a single batch of goods; the collection end acquires transportation data; the collection end divides the monitoring category for a single batch of goods based on the transportation aggregation characteristic value and monitors the goods based on the monitoring category; the intermediate end generates an abnormal report based on the monitoring category and the monitoring data, and the intermediate end transmits the abnormal report to the selling end. In the cold chain logistics process, the temperature and humidity of the goods are important factors affecting the quality of the goods. By analyzing the monitoring category and the monitoring data, it is determined whether the goods have abnormal conditions, and an abnormal report is generated, which facilitates timely understanding of the transportation state of the goods and the taking of corresponding measures. According to the stacking and aggregation of the goods, the monitoring parameters cannot be adjusted specifically, which improves the monitoring accuracy of the goods and further improves the reliability of the logistics data.

[0043] Further, the transportation aggregation characteristic value represents the stacking and aggregation of the goods in the transportation carrier, the more aggregated the goods are stacked, the slower the melting speed is, and the closer the transportation aggregation characteristic value is to 1, indicating that the goods are more aggregated. The monitoring category for a single batch of goods is divided based on the transportation aggregation characteristic value, and the goods are monitored based on the monitoring category. Different transportation aggregation characteristic values reflect different aggregation states of the goods, and different monitoring strategies are needed. By dividing the monitoring category, 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 transportation aggregation characteristic value is less than or equal to the preset transportation aggregation characteristic value, the goods are more aggregated, and the melting speed is slower; when the transportation aggregation characteristic value is greater than the preset transportation aggregation characteristic value, the goods are less aggregated, and the melting speed is faster, and the acquisition frequency of the monitoring data needs to be increased to discover problems in time. According to the actual aggregation of the goods, the monitoring strategy is adjusted to avoid excessive monitoring or insufficient monitoring, improve the monitoring efficiency, and further improve the reliability of the logistics data.

[0044] Further, based on the transportation aggregation characteristic value, the acquisition frequency of the corrected monitoring data is determined. In the case of loose cargo accumulation, the temperature and humidity of the cargo change rapidly. If the acquisition frequency of the monitoring data is low, some important temperature and humidity changes will be missed, and problems cannot be found in time. According to the transportation aggregation characteristic value, the acquisition frequency of the monitoring data is increased. The larger the transportation aggregation characteristic value, the larger the heat dissipation area of the cargo, the faster the temperature changes, and the more frequently the monitoring data needs to be acquired to timely grasp the temperature and humidity changes of the cargo. The timeliness and accuracy of the monitoring data are improved, and the temperature and humidity changes of the cargo can be found more timely. The reliability of the logistics data is further improved.

[0045] Further, based on the cooling reference value, the alarm threshold for generating an abnormal report is corrected. The cooling reference value reflects the proportion of the mass of the ice block to the mass of the cargo. The larger the cooling reference value, the stronger the refrigeration capacity of the ice block, and the slower the temperature change of the cargo. The alarm threshold needs to be increased accordingly to avoid frequent alarms. The reliability of the logistics data is further improved.

[0046] Further, based on the transportation stability characteristic value, whether the monitoring of a single batch of cargo is qualified or not is determined. The transportation stability characteristic value reflects the vibration condition of the cargo during transportation. When the vehicle vibrates, the ice block will be shaken and collide due to external force. The energy of this mechanical movement will be partially converted into the internal energy of the ice block. The increase in internal energy leads to the intensification of thermal motion of ice block molecules, thereby increasing the temperature inside the ice block and accelerating the melting speed. When the transportation stability characteristic value is less than or equal to the preset transportation stability characteristic value, the vibration condition of the cargo during transportation is within the normal range, and the monitoring is qualified. When the transportation stability characteristic value is greater than the preset transportation stability characteristic value, the cargo has been subjected to excessive vibration during transportation. Further, the specific processing method is determined in combination with the stability difference. The stability difference reflects whether the vibration is regular. If the stability difference is less than or equal to the preset stability difference, the vibration has a certain regularity. This situation is caused by normal jolting during normal transportation. At this time, the driving speed of the transportation carrier is adjusted to reduce the transportation time. When the stability difference is greater than the preset stability difference, the stability difference is large. At this time, irregular vibration is caused by encountering bumpy roads during vehicle driving, and the ice block collides in the carriage, causing the internal energy to increase in a short time, thereby accelerating the melting. At this time, the route is changed, and the acquisition frequency of the monitoring data is increased to find problems in time and increase the accuracy of the data. According to the regularity and stability of the vibration, different processing measures are taken to improve the stability and safety of the cargo transportation. The reliability of the logistics data is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The step flowchart of the cold chain logistics data traceability method based on big data analysis of the embodiments of the present application;

[0048] Figure 2 A logic decision diagram for dividing the monitoring category for a single batch of goods based on the transport aggregation characteristic value of the embodiment of the present application;

[0049] Figure 3 A logic decision diagram for determining whether the monitoring for a single batch of goods is qualified based on the transport stability characteristic value of the embodiment of the present application;

[0050] Figure 4 A logic decision diagram for correcting the monitoring parameter for a single batch of goods based on the stability difference amount of the embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the objects and advantages of the present application clearer, the present application will be further described below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0052] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. It should be understood by those skilled in the art that the embodiments are only used to explain the technical principles of the present application and are not used to limit the protection scope of the present application.

[0053] It should be noted that, in the description of the present application, the terms of direction or position relationship such as "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or position relationship shown in the drawings, which 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, and therefore cannot be understood as a limitation on the present application.

[0054] In addition, it should also be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connection" should be understood in a broad sense, for example, can be fixedly connected, can be detachably connected, or integrally connected; can be mechanically connected, can be electrically connected; can be directly connected, can be indirectly connected through an intermediate medium, or can be the communication inside two elements. Those skilled in the art can understand the specific meaning of the above terms in the present application according to the specific circumstances.

[0055] Please refer to Figure 1 , Figure 2 , Figure 3 and Figure 4As shown, they are respectively a step flow chart of a cold chain logistics data traceability method based on big data analysis of the embodiment of the application, a logic decision chart for dividing the monitoring category of a single batch of goods based on the transportation aggregation characteristic value, a logic decision chart for determining whether the monitoring of a single batch of goods is qualified based on the transportation stability characteristic value, and a logic decision chart for correcting the monitoring parameters of a single batch of goods based on the stability difference amount; the cold chain logistics data traceability method based on big data analysis of the embodiment of the application comprises:

[0056] S1, the production end generates a traceability code for a single batch of goods;

[0057] S2, the collection end acquires transportation data, including a transportation aggregation characteristic value;

[0058] S3, the collection end divides the monitoring category of a single batch of goods based on the transportation aggregation characteristic value, and monitors the goods based on the monitoring category, comprising:

[0059] when it is determined that the monitoring category of a single batch of goods is divided into a strong monitoring category, the acquisition frequency of the corrected monitoring data is determined based on the transportation aggregation characteristic value;

[0060] S4, the intermediate end generates an abnormal report based on the monitoring category and the monitoring data, and the intermediate end transmits the abnormal report to the selling end, and the monitoring data includes the temperature and humidity in the transportation carrier.

[0061] Specifically, the temperature and humidity in the transportation carrier can be acquired by arranging temperature sensors and humidity sensors.

[0062] Specifically, the acquisition process of the transportation aggregation characteristic value comprises:

[0063] the ratio of the volume of the smallest circumscribed cuboid of the goods in the transportation carrier to the actual volume of the goods is calculated to obtain the transportation aggregation characteristic value;

[0064] the process of dividing the monitoring category of a single batch of goods based on the transportation aggregation characteristic value and monitoring the goods based on the monitoring category comprises:

[0065] if the transportation aggregation characteristic value is less than or equal to a preset transportation aggregation characteristic value, the monitoring category of a single batch of goods is divided into a weak monitoring category, and the current monitoring parameters are continuously used to complete the monitoring of the goods;

[0066] if the transportation aggregation characteristic value is greater than the preset transportation aggregation characteristic value, the monitoring category of a single batch of goods is divided into a strong monitoring category, and the acquisition frequency of the corrected monitoring data is determined based on the transportation aggregation characteristic value.

[0067] Specifically, the specific manner of obtaining the volume of the minimum circumscribed cuboid of the goods in the transportation carrier and the actual volume of the goods is not limited, and the laser radar sensor can be controlled to emit a laser beam and measure the time of reflected light to scan the distribution of the goods in the transportation carrier to create a three-dimensional point cloud map of the environment. In this embodiment, after the goods are loaded, the laser radar sensor is started to scan and obtain three-dimensional point cloud data of the surface of the goods. The three-dimensional point cloud data is processed to calculate the volume of the minimum circumscribed cuboid occupied by the goods and the actual volume of the goods.

[0068] Specifically, the preset transportation aggregation characteristic value is selected in the interval [1.5, 1.8], and those skilled in the art can determine the value of the preset transportation aggregation characteristic value according to the specific use scene. It can be understood that the division of the aggregation of the goods can be realized. In this embodiment, preferably, the preset transportation aggregation characteristic value is 1.8.

[0069] Specifically, the transportation aggregation characteristic value represents the aggregation of the goods stacked in the transportation carrier. The more aggregated the goods are, the slower the melting speed is, and the closer the transportation aggregation characteristic value is to 1, indicating that the goods are more aggregated.

[0070] Specifically, the monitoring category for a single batch of goods is divided based on the transportation aggregation characteristic value, and the goods are monitored based on the monitoring category. Different transportation aggregation characteristic values reflect different aggregation states of the goods, and different monitoring strategies need to be used. By dividing the monitoring category, 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 transportation aggregation characteristic value is less than or equal to the preset transportation aggregation characteristic value, the goods are more aggregated, and the melting speed is slower. When the transportation aggregation characteristic value is greater than the preset transportation aggregation characteristic value, the goods are more loose, and the melting speed is faster, so the acquisition frequency of the monitoring data needs to be increased to discover problems in time. According to the actual aggregation of the goods, the monitoring strategy is adjusted to avoid excessive monitoring or insufficient monitoring, improve the monitoring efficiency, and further improve the reliability of the logistics data.

[0071] Specifically, the acquisition frequency of the corrected monitoring data is determined based on the transportation aggregation characteristic value, wherein:

[0072] The increase amplitude of the acquisition frequency of the monitoring data is positively correlated with the transportation aggregation characteristic value.

[0073] In this embodiment, optionally,

[0074] The transportation aggregation characteristic value is compared with the first preset aggregation comparison threshold and the second preset aggregation comparison threshold.

[0075] 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 will be adjusted to 1.11 times the initial acquisition frequency.

[0076] 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 will be adjusted to 1.19 times the initial acquisition frequency.

[0077] If the transport aggregation characterization value is greater than the second preset aggregation comparison threshold, the acquisition frequency of the monitoring data will be adjusted to 1.25 times the initial acquisition frequency.

[0078] The first preset aggregation comparison threshold is set to 1.2J0, and the second preset aggregation comparison threshold is set to 1.3J0, where J0 is the preset transport aggregation characterization value.

[0079] Specifically, the frequency of data acquisition is the frequency at which each sensor acquires monitoring data.

[0080] Specifically, the frequency of monitoring data acquisition is adjusted based on the transport clustering characteristic value. When goods are loosely stacked, their temperature and humidity change rapidly. If the monitoring data acquisition frequency is low, some important temperature and humidity changes will be missed, leading to delayed problem detection. The frequency of monitoring data acquisition is increased based on the transport clustering characteristic value. A larger transport clustering characteristic value indicates a larger heat dissipation area of ​​the goods, resulting in faster temperature changes and requiring more frequent monitoring data acquisition to promptly grasp the temperature and humidity changes. This improves the timeliness and accuracy of the monitoring data, enabling more timely detection of temperature and humidity changes in the goods. Furthermore, it enhances the reliability of logistics data.

[0081] Specifically, when adjusting the acquisition frequency of monitoring data based on transportation aggregation characterization values, the alarm thresholds used to generate anomaly reports are adjusted based on cooling reference values.

[0082] The increase in the alarm threshold is positively correlated with the cooling reference value.

[0083] The ratio of the mass of ice to the mass of goods is determined as the reference value for refrigeration.

[0084] In this embodiment, optionally,

[0085] The cooling reference value is compared with the first preset cooling reference value and the second preset cooling reference value;

[0086] If the cooling reference value is less than or equal to the first preset cooling reference value, the alarm threshold will be adjusted to 1.08 times the initial alarm threshold.

[0087] 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 will be adjusted to 1.13 times the initial alarm threshold.

[0088] If the cooling reference value is greater than the second preset cooling reference value, the alarm threshold will be adjusted to 1.29 times the initial alarm threshold.

[0089] The first preset cooling reference value is 0.1, and the second preset cooling reference value is 0.3.

[0090] Specifically, the alarm thresholds used to generate anomaly reports are adjusted based on the cooling supply reference value. The cooling supply reference value reflects the ratio of ice mass to cargo mass. A higher cooling supply reference value indicates stronger cooling capacity of the ice and slower temperature change of the cargo, requiring a corresponding increase in the alarm threshold to avoid frequent alarms. This further improves the reliability of logistics data.

[0091] Specifically, when adjusting the alarm threshold, the monitoring of a single shipment is determined based on the transportation stability characterization value, including:

[0092] Vibrations with amplitudes greater than the preset amplitude are marked as abnormal vibrations.

[0093] The ratio of the number of abnormal vibrations within the preset detection time to the preset detection time is calculated to obtain the transportation stability characterization value;

[0094] If the transport stability characterization value is less than or equal to the preset transport stability characterization value, then the monitoring of a single batch of goods is deemed qualified, and the current monitoring parameters will continue to be used to complete the monitoring of the goods.

[0095] If the transport stability characterization value is greater than the preset transport stability characterization value, then an anomaly is identified for the monitoring of a single batch of goods, and the monitoring parameters for the single batch of goods are corrected based on the stability difference.

[0096] Specifically, the preset transportation 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 transportation stability characterization value according to the specific application scenario. The preset transportation stability characterization value is derived from a large number of experiments and statistical data of actual transportation. It can be understood that it can be divided into the states of impact on goods during actual transportation. In this embodiment, preferably, the preset transportation stability characterization value is set within the range of 7 times / hour.

[0097] Specifically, the process of adjusting monitoring parameters for a single batch of goods based on stable variance includes:

[0098] The variance of the time intervals for each abnormal vibration is calculated to obtain the stability difference.

[0099] If the stable difference is less than or equal to the preset stable difference, the driving speed of the transport carrier is adjusted to the corresponding value based on the stable difference;

[0100] If the stable difference is greater than the preset stable difference, the acquisition frequency of the monitoring data is adjusted to the corresponding value based on the distribution density of each sensor.

[0101] Specifically, the preset stable difference is selected within the range [0.47, 0.68], and the preset stable difference is obtained by analyzing and statistically processing a large amount of vibration data. Those skilled in the art can determine the preset stable difference according to the specific transportation situation. It can be understood that it is only necessary to be able to divide the situation where the vibration is regular or not. In this embodiment, preferably, the preset stable difference is taken as 0.52.

[0102] Specifically, it is determined whether the monitoring of a single batch of goods is qualified based on the transport stability characterization value. The transport stability characterization value reflects the vibration situation of the goods during transportation. When the vehicle vibrates, the ice cubes will be subjected to external forces and generate晃动 and collisions. Part of the energy of this mechanical movement will be converted into the internal energy of the ice cubes. The increase in internal energy causes the thermal movement of the ice cube molecules to intensify, thereby increasing the temperature inside the ice cubes and accelerating the melting speed. When the transport stability characterization value is less than or equal to the preset transport stability characterization value, the vibration situation of the goods during transportation is within the normal range, and the monitoring is qualified. When the transport stability characterization value is greater than the preset transport stability characterization value, the goods are excessively vibrated during transportation. Further, the specific treatment method is determined in combination with the stable difference. The stable difference reflects whether the vibration is regular. If the stable difference is less than or equal to the preset stable difference, the vibration has a certain pattern. This situation is due to normal bumps caused by normal transportation. At this time, the driving speed of the transport carrier is adjusted to reduce the transportation time; when the stable difference is greater than the preset stable difference, the stable difference is relatively large. At this time, due to encountering bumpy roads during vehicle driving, irregular vibrations occur, and the ice cubes collide in the carriage, resulting in an increase in internal energy in a short time, which further accelerates melting. At this time, the route is changed, and the acquisition frequency of the monitoring data is increased to timely discover problems and increase the accuracy of the data. Different treatment measures are taken according to the pattern and stability of the vibration, which improves the stability and safety of goods transportation. Further improves the reliability of logistics data.

[0103] Specifically, the driving speed of the transport carrier is adjusted to the corresponding value based on the stable difference, where

[0104] The increase amplitude of the driving speed is negatively correlated with the stable difference.

[0105] In this embodiment, optionally,

[0106] The stable difference is compared with the first difference comparison threshold and the second difference comparison threshold;

[0107] If the stable difference is less than or equal to the first difference comparison threshold, the speed of the transport vehicle will be adjusted to 1.28 times the initial speed.

[0108] If the stable difference is less than or equal to the first difference comparison threshold, the speed of the transport vehicle will be adjusted to 1.21 times the initial speed.

[0109] If the stable difference is less than or equal to the first difference comparison threshold, the speed of the transport vehicle will be adjusted to 1.13 times the initial speed.

[0110] The first difference comparison threshold is set to 0.7W0, and the second difference comparison threshold is set to 0.8W0, where W0 is a preset stable difference amount.

[0111] Specifically, the acquisition frequency of monitoring data is adjusted to a corresponding value based on the distribution density of each sensor, wherein,

[0112] The increase in acquisition frequency is negatively correlated with the distribution density of each sensor.

[0113] The distribution density of each sensor is the ratio of the total number of sensors to the volume of the transport vehicle.

[0114] In this embodiment, optionally,

[0115] The distribution density is compared with the first preset distribution comparison value and the second preset distribution comparison value;

[0116] If the distribution density is less than or equal to the first preset distribution comparison value, the acquisition frequency of the monitoring data will be adjusted to 1.22 times the current acquisition frequency.

[0117] 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 will be adjusted to 1.17 times the current acquisition frequency.

[0118] If the distribution density is greater than the second preset distribution comparison value, the acquisition frequency of the monitoring data will be adjusted to 1.11 times the current acquisition frequency.

[0119] The first preset distribution comparison value is 0.5 per cubic meter, and the second preset distribution comparison value is 1 per cubic meter.

[0120] Specifically, when adjusting the acquisition frequency of monitoring data based on the distribution density of each sensor, alternative routes for single batch of cargo transportation are obtained and the alternative routes are changed.

[0121] Specifically, there are no restrictions on the specific methods for obtaining alternative routes and determining the alternative routes to be replaced. The alternative route with the shortest estimated travel time can be selected based on map navigation software, or historical data of the same goods transportation in the past can be analyzed and the alternative route with the shortest travel time can be selected based on the current departure time. This is existing technology and will not be elaborated further.

[0122] Specifically, the process of generating anomaly reports based on monitoring categories and monitoring data at the intermediate end includes:

[0123] If the monitoring category of a single batch of goods is a weak monitoring category, an anomaly report will be generated based on the alarm threshold.

[0124] If the monitoring category of a single batch of goods is a strong monitoring category, then the data is fitted and an anomaly report is generated based on the alarm threshold.

[0125] Specifically, when a single batch of goods is classified as a weak monitoring category, the process of generating an anomaly report based on an alarm threshold includes:

[0126] The intermediate end continuously receives monitoring data sent by the acquisition end, including temperature and humidity inside the transport vehicle;

[0127] Based on preset alarm thresholds, the monitoring data is filtered 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.

[0128] Obtain the anomaly information corresponding to each abnormal data point, including time and specific value;

[0129] Abnormal information is associated with the source code;

[0130] Anomaly information generates an anomaly report according to a preset report template.

[0131] Specifically, when the monitoring category for a single batch of goods is a strong monitoring category, the process of fitting data includes:

[0132] The intermediate end receives monitoring data and preprocesses the data to remove outliers and noise.

[0133] The preprocessed monitoring data were fitted using a linear fitting algorithm to obtain the humidity change curve and temperature change curve of the data.

[0134] When the change curve exceeds the corresponding alarm threshold, obtain the abnormal information of the corresponding occurrence node. The abnormal information includes the time and specific value.

[0135] Abnormal information is associated with the source code;

[0136] Generate an anomaly report, which includes the change curve corresponding to the anomaly information and the anomaly information of the occurrence node.

[0137] The technical solution of the present invention has been described above with reference to 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 can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0138] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cold chain logistics data traceability 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 acquisition end obtains transportation data, which includes transportation aggregation characterization values; The process of obtaining transport aggregation characterization values ​​includes: The ratio of the volume of the smallest circumscribed cuboid of the cargo within the transport vehicle to the actual volume of the cargo is calculated to obtain the transport aggregation characterization value; S3, the data acquisition end classifies 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: If the transport aggregation characterization value is less than or equal to the preset transport aggregation characterization value, the monitoring category of a single batch of goods will be classified as a weak monitoring category, and the current monitoring parameters will continue to be 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 a single batch of goods will be classified as a strong monitoring category, and the frequency of acquiring monitoring data will be determined based on the transport aggregation characterization value; the increase in the frequency of acquiring monitoring data is positively correlated with the transport aggregation characterization value. S4, the middle terminal generates anomaly reports based on the monitoring category and monitoring data, and transmits the anomaly reports to the sales terminal. The monitoring data includes the temperature and humidity inside the transport vehicle.

2. The cold chain logistics data traceability method based on big data analysis according to claim 1, characterized in that, When completing the acquisition frequency of monitoring data based on the transportation aggregation characterization value, the alarm threshold used to generate anomaly reports is corrected based on the cooling reference value. The ratio of the mass of ice to the mass of goods is determined as the reference value for refrigeration. The increase in the alarm threshold is positively correlated with the cooling reference value.

3. The cold chain logistics data traceability method based on big data analysis according to claim 2, characterized in that, When adjusting the alarm thresholds, the monitoring of a single shipment is determined based on the transport stability characterization value, including: Vibrations with amplitudes greater than the preset amplitude are marked as abnormal vibrations. The ratio of the number of abnormal vibrations within the preset detection time to the preset detection time is calculated to obtain the transportation stability characterization value; If the transport stability characterization value is less than or equal to the preset transport stability characterization value, then the monitoring of a single batch of goods is deemed qualified, and the current monitoring parameters will continue 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, then an anomaly is identified for the monitoring of a single batch of goods, and the monitoring parameters for the single batch of goods are corrected based on the stability difference.

4. The cold chain logistics data traceability method based on big data analysis according to claim 3, characterized in that, The process of adjusting monitoring parameters for a single batch of goods based on stable variance includes: The variance of the time intervals for each abnormal vibration is calculated to obtain the stability difference. If the stability difference is less than or equal to the preset stability difference, the speed of the transport vehicle will be adjusted to the corresponding value based on the stability difference. If the stable difference is greater than the preset stable difference, the acquisition frequency of the monitoring data will be adjusted to the corresponding value based on the distribution density of each sensor.

5. The cold chain logistics data traceability method based on big data analysis according to claim 4, characterized in that, The 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 stability difference.

6. The cold chain logistics data traceability method based on big data analysis according to claim 5, characterized in that, The frequency of monitoring data acquisition is adjusted to a corresponding value based on the distribution density of each sensor, whereby... The increase in acquisition frequency is negatively correlated with the distribution density of each sensor.

7. The cold chain logistics data traceability method based on big data analysis according to claim 6, characterized in that, When adjusting the acquisition frequency of monitoring data based on the distribution density of each sensor, alternative routes for single batch of cargo transportation are obtained and the alternative routes are changed.

8. The cold chain logistics data traceability method based on big data analysis according to claim 7, characterized in that, The process of generating anomaly reports based on monitoring categories and monitoring data in the middleware includes: If the monitoring category of a single batch of goods is a weak monitoring category, an anomaly report will be generated based on the alarm threshold. If the monitoring category of a single batch of goods is a strong monitoring category, then the data is fitted and an anomaly report is generated based on the alarm threshold.

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