Logistics management system and method based on big data technology

Abstract

The invention relates to the technical field of material management, and discloses a logistics management system based on a big data technology, which comprises a transportation data acquisition module, a transportation data analysis module and an early warning module, and is characterized in that the transportation data acquisition module is used for monitoring and acquiring temperature data, humidity data, gas concentration data and vibration data in a transportation compartment; the transportation data analysis module is used for calculating a temperature contrast data set, a humidity contrast data set, a gas concentration contrast data set and a vibration index according to the corresponding data sets, comparing the data sets with corresponding threshold values, and judging whether abnormity exists or not; according to the invention, the early warning module sends an abnormal signal to the early warning module while judging that there is a fault or an article easy to damage, and the early warning module gives out a corresponding alarm according to the corresponding signal, so that problems existing in the transportation process are found in time from multiple aspects comprehensively, corresponding early warning is carried out, and the freshness and video safety of food, vegetables and fruits in the transportation process are effectively ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of material management, and in particular to a logistics management system and method based on big data technology. Background Art

[0002] Logistics refers to the whole process of planning, implementing and managing the transportation of raw materials, semi-finished products, finished products or related information from the place of production to the place of consumption of goods through transportation, storage and distribution in order to meet customer needs at the lowest cost. This process covers basic functions such as transportation, warehousing, packaging, handling and loading and unloading, circulation processing, distribution and related logistics information processing. Logistics is an indispensable part of the supply chain. It connects multiple links such as production, sales and consumption. Effective logistics management can reduce the operating costs of enterprises, improve service quality and customer satisfaction. Logistics is also one of the important forces promoting economic globalization. By optimizing logistics networks and processes, enterprises can use global resources more efficiently and expand international markets. Logistics management system is a software system used to optimize and manage logistics transportation processes, aiming to improve logistics efficiency, reduce costs and enhance customer service levels.

[0003] During transportation, food or fruits and vegetables need to maintain appropriate temperature and humidity. However, when the refrigeration equipment or temperature sensor fails during transportation, it is difficult to grasp the actual situation in the car in real time, which may cause damage to the transported goods and increase the cost of damage to the goods. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the existing technology, the present invention provides a logistics management system and method based on big data technology, which has the advantages of monitoring the temperature, humidity, gas and vibration data during the transportation process, timely discovering problems in the transportation process from multiple aspects, and issuing corresponding early warnings, thereby effectively ensuring the freshness of food and fruits during transportation.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a logistics management system based on big data technology, including a transportation data collection module, a transportation data analysis module, and an early warning module;

[0008] The transport data acquisition module collects the temperature data WDsj in the transport compartment, and records them by number to form a temperature data set, collects the humidity data SDsj, and records them by number to form a humidity data set, collects the gas concentration data QTsj, and records them by number to form a gas concentration data set, monitors the vibration data ZDsj, and records them by number to form a vibration data set, and the transport data acquisition module sends the above data sets to the transport data analysis module;

[0009] The transport data analysis module calculates the temperature control data group WDdz according to the temperature data set, calculates the humidity control data group SDdz according to the humidity data set, calculates the gas concentration control data group QTdz according to the gas concentration data set, and calculates the vibration index ZDzs according to the vibration data set;

[0010] The transport data analysis module is internally provided with a temperature data comparison threshold, a humidity data comparison threshold, a gas concentration data comparison threshold, and a vibration data comparison threshold. The transport data analysis module compares the temperature data comparison threshold, the humidity data comparison threshold, and the gas concentration data comparison threshold with the temperature control data group WDdz, the humidity control data group SDdz, and the gas concentration control data group QTdz, respectively, to determine whether the current equipment has a fault. The transport data analysis module compares the vibration data comparison threshold with the vibration index ZDzs to determine whether the current road condition and vehicle speed will cause damage to the transported goods, and then determines whether there is a fault or a vulnerable item and sends an abnormal signal to the early warning module;

[0011] The early warning module issues a corresponding alarm according to the corresponding signal.

[0012] Preferably, the expression of the temperature data set is: {WDsj1, WDsj2, WDsj3, . . . , WDsj n}, where WDsj1 represents the first temperature data collected in the temperature data set, WDsj n Represents the last collected temperature data in the temperature data set;

[0013] The expression of the humidity data set is: {SDsj1, SDsj2, SDsj3, . . . , SDsj n}, where SDsj1 represents the humidity data collected for the first time in the humidity data set, SDsj n Represents the humidity data collected last time in the humidity dataset;

[0014] The expression of the gas concentration data set is: {QTsj1, QTsj2, QTsj3, . . . , QTsj n}, where QTsj1 represents the gas concentration data collected for the first time in the gas concentration data set, QTsjn Represents the gas concentration data collected last time in the gas concentration data set. The gas concentration data includes oxygen concentration, carbon dioxide concentration, and ethylene concentration. The numbers are: QTyq, QTeh, and QTyx;

[0015] The expression of the vibration data set is: {ZDsj1, ZDsj2, ZDsj3, . . . , ZDsj n}, where ZDsj1 represents the vibration data collected for the first time in the vibration data set, and ZDsj n Represents the last collected vibration data in the vibration data set;

[0016] In the above expression, n represents that there are a total of n data collected in the data set.

[0017] Preferably, the temperature comparison data set WDdz includes a temperature difference ratio WC and a next test section temperature evaluation WP, and the temperature comparison data set WDdz is calculated as follows:

[0018]

[0019] In the above formula, WDsj i is the i-th temperature data in the temperature data set, WDsj b is the standard temperature during material transportation, α is the weight of temperature data during transportation, WDsj s is the real-time temperature, WDsj s +WD s-1 WDsj is the sum of the real-time temperature and the temperature before the real-time temperature. s-2 +WD s-3 is the sum of the second and third temperatures before the real-time temperature, (WDsj s +WD s-1 )-(WDsj s-2 +WD s-3 ) is the difference between the real-time temperature and the sum of the temperature before the real-time temperature and the sum of the second and third temperatures before the real-time temperature.

[0020] Preferably, the humidity comparison data set SDdz includes a humidity difference ratio SC and a next test section humidity assessment SP, and the calculation formula of the humidity comparison data set SDdz is:

[0021]

[0022] In the above formula, SDsj i is the i-th humidity data in the humidity data set, SDsj b is the standard humidity during material transportation, β is the weight of humidity data during transportation, SDsjs For real-time humidity data, SDsj s +SDs s-1 SDsj is the sum of the real-time humidity and the humidity before the real-time humidity. s-2 +SDs s-3 is the sum of the second and third humidity before the real-time humidity, (SDsj s +SDs s-1 )-(SDsj s-2 +SDs s-3 ) is the difference between the real-time humidity and the sum of the humidity before the real-time humidity and the sum of the second and third humidity before the real-time humidity.

[0023] Preferably, the gas concentration control data set QTdz includes an oxygen concentration control value YQdz, a carbon dioxide concentration control value EYdz, and an ethylene concentration control value YXdz. The calculation formula of the gas concentration control data set QTdz is as follows:

[0024]

[0025] In the above formula, QTyq s is the real-time oxygen concentration value in the gas concentration data set, QTyq b is the standard value of oxygen concentration, QTey s is the real-time carbon dioxide concentration value in the gas concentration dataset, QTey b is the standard value of carbon dioxide concentration, QTyx s is the real-time ethylene concentration value in the gas concentration data set, QTyx b It is the standard value of ethylene concentration.

[0026] Preferably, the calculation formula of the vibration index ZDzs is:

[0027]

[0028] In the above formula, ZDsj s For real-time vibration data in the vibration data set, ZDsj b is the standard vibration data, and γ is the corresponding weight of the vibration value during transportation.

[0029] Preferably, the temperature data comparison threshold includes a temperature difference ratio threshold and a temperature conventional threshold. When the temperature difference ratio WC is greater than the temperature difference ratio threshold or when the temperature evaluation WP of the next test section is greater than the temperature conventional threshold, it means that the temperature is abnormal, and a temperature abnormality signal is sent to the early warning alarm module for alarm;

[0030] The humidity data comparison threshold includes a humidity difference ratio threshold and a humidity conventional threshold. When the humidity difference ratio SC is greater than the humidity difference ratio threshold or the humidity assessment SP of the next test period is greater than the humidity conventional threshold, it means that the humidity is abnormal, and a humidity abnormality signal is sent to the early warning module for alarm;

[0031] The gas concentration data comparison thresholds include an oxygen concentration threshold, a carbon dioxide concentration threshold and an ethylene concentration threshold. When the oxygen concentration control value YQdz, the carbon dioxide concentration control value EYdz and the ethylene concentration control value YXdz in the gas concentration comparison data group QTdz are greater than the corresponding oxygen concentration threshold, the carbon dioxide concentration threshold and the ethylene concentration threshold, it indicates that the transported goods in the vehicle have deteriorated, and a gas abnormality signal is sent to the early warning module for alarm.

[0032] Preferably, when the vibration index ZDzs is greater than the vibration data comparison threshold, it means that the current road condition or vehicle speed produces a large vibration, which may easily cause the transported objects to collide, and the vibration data abnormality is sent to the early warning module for an alarm.

[0033] Preferably, when the early warning module receives abnormal temperature signal, abnormal humidity signal, or abnormal gas signal, it sends a maintenance signal to the driver to remind the next server to perform equipment maintenance inspection. When the early warning module receives abnormal vibration data, it sends a deceleration signal to the driver to remind him to slow down and reduce vibration.

[0034] A logistics management system and method based on big data technology, comprising the following steps:

[0035] Step 1: Collect temperature data, humidity data, gas concentration data and vibration data in the car, and form corresponding data sets after numbering;

[0036] Step 2: Calculate the temperature control data group WDdz, humidity control data group SDdz, gas concentration control data group QTdz, and vibration index ZDzs according to the data set;

[0037] Step 3: judging whether there is an abnormality for the corresponding threshold value according to the calculation result, and sending a corresponding abnormal signal in the case of an abnormality;

[0038] Step 4: Issue corresponding warnings based on corresponding abnormal signals.

[0039] Compared with the prior art, the present invention provides a logistics management system and method based on big data technology, which has the following beneficial effects:

[0040] 1. The present invention monitors the temperature and humidity changes in the transportation environment in real time, and then performs real-time evaluation and prediction of temperature and humidity based on the collected data, so as to promptly discover abnormal humidity and temperature conditions, ensure that the temperature and humidity are always within an appropriate range, prevent the transported goods from mildewing, rotting and other deterioration due to the humid environment, effectively prevent the growth of microorganisms such as bacteria and mold, and thus reduce the risk of contamination of the transported goods.

[0041] 2. The present invention can detect problems in time by monitoring oxygen concentration, carbon dioxide concentration, and ethylene concentration to avoid spoilage, over-ripening, and reduced metabolic rate of transported goods, thereby further extending the shelf life of food, effectively delaying the aging process of fruits, and extending the shelf life of perishable foods such as fruits and vegetables. Monitoring vibration data can prevent severe impact and damage to transported goods during transportation, reduce physical damage to food, and thus ensure food safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the system flow of the present invention;

[0043] Figure 2 It is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1-2 , a logistics management system based on big data technology, including a transportation data collection module, a transportation data analysis module, and an early warning module;

[0046] The transport data collection module collects the temperature data WDsj in the transport compartment and records them by number to form a temperature data set;

[0047] Real-time monitoring of temperature changes in the transportation environment ensures that the temperature is always within the appropriate range, timely detection of temperature anomalies during transportation, and taking measures to adjust the temperature to inhibit the growth of microorganisms and ensure food safety. The expression of the temperature data set is: {WDsj1, WDsj2, WDsj3, ···, WDsj n}, where WDsj1 represents the first temperature data collected in the temperature data set, WDsj n Represents the last collected temperature data in the temperature data set;

[0048] Collect humidity data SDsj, and record them by number to form a humidity data set;

[0049] Real-time humidity monitoring can detect humidity anomalies in time, prevent the transported goods from mildewing, rotting and other deterioration due to the humid environment, and effectively prevent the growth of microorganisms such as bacteria and mold, thereby reducing the risk of transported goods contamination. The expression of the humidity data set is: {SDsj1, SDsj2, SDsj3, ···, SDsj n}, where SDsj1 represents the humidity data collected for the first time in the humidity data set, SDsj n Represents the humidity data collected last time in the humidity dataset;

[0050] Collect gas concentration data QTsj and number them to form a gas concentration data set;

[0051] The expression of the gas concentration data set is: {QTsj1, QTsj2, QTsj3, ···, QTsj n}, where QTsj1 represents the gas concentration data collected for the first time in the gas concentration data set, QTsj n Represents the gas concentration data collected last time in the gas concentration data set. The gas concentration data includes oxygen concentration, carbon dioxide concentration, and ethylene concentration. The numbers are: QTyq, QTeh, and QTyx;

[0052] By monitoring oxygen concentration, the gas composition in the cargo compartment can be adjusted in time to extend the shelf life of the transported goods. Monitoring carbon dioxide concentration helps to detect problems in time to avoid spoilage of transported goods. Ethylene concentration can help control the ripening speed of transported goods to avoid premature decay.

[0053] Monitor the vibration data ZDsj and number them to form a vibration data set;

[0054] Monitoring vibration data can prevent the transported goods from being severely impacted and thus damaged during transportation. The expression of the vibration data set is: {ZDsj1, ZDsj2, ZDsj3, ···, ZDsj n}, where ZDsj1 represents the vibration data collected for the first time in the vibration data set, and ZDsj n Represents the last collected vibration data in the vibration data set;

[0055] The transport data collection module sends the above data set to the transport data analysis module;

[0056] The transport data analysis module calculates the temperature control data set WDdz based on the temperature data set;

[0057] The temperature comparison data set WDdz includes the temperature difference ratio WC and the next test section temperature evaluation WP. The temperature comparison data set WDdz is calculated as follows:

[0058]

[0059] By calculating the temperature difference ratio WC and the next test section temperature evaluation WP, the temperature data is evaluated and predicted in real time, and abnormal temperature conditions are discovered in time. In the above formula, WDsj i is the i-th temperature data in the temperature data set, WDsj b is the standard temperature during material transportation, α is the weight of temperature data during transportation, WDsj s is the real-time temperature, WDsj s +WD s-1 WDsj is the sum of the real-time temperature and the temperature before the real-time temperature. s-2 +WD s-3 is the sum of the second and third temperatures before the real-time temperature, (WDsj s +WD s-1 )-(WDsj s-2 +WD s-3 ) is the difference between the sum of the real-time temperature and the temperature before the real-time temperature and the sum of the second and third temperatures before the real-time temperature;

[0060] Calculate the humidity control data group SDdz based on the humidity data set;

[0061] The humidity control data set SDdz includes the humidity difference ratio SC and the humidity evaluation SP of the next test section. The calculation formula of the humidity control data set SDdz is:

[0062]

[0063] By calculating the humidity difference ratio SC and the humidity evaluation SP of the next test section, the humidity data can be evaluated and predicted in real time, and abnormal humidity can be found in time. In the above formula, SDsj i is the i-th humidity data in the humidity data set, SDsj b is the standard humidity during material transportation, β is the weight of humidity data during transportation, SDsj s For real-time humidity data, SDsj s +SDs s-1 SDsj is the sum of the real-time humidity and the humidity before the real-time humidity. s-2 +SDs s-3 is the sum of the second and third humidity before the real-time humidity, (SDsj s +SDs s-1)-(SDsj s-2 +SDs s-3 ) is the difference between the sum of the real-time humidity and the humidity before the real-time humidity and the sum of the second and third humidity before the real-time humidity;

[0064] Calculate the gas concentration comparison data set QTdz based on the gas concentration data set;

[0065] The gas concentration control data set QTdz includes an oxygen concentration control value YQdz, a carbon dioxide concentration control value EYdz, and an ethylene concentration control value YXdz. The calculation formula of the gas concentration control data set QTdz is as follows:

[0066]

[0067] Oxygen concentration can slow down the oxidation process of food and extend the shelf life of perishable foods such as fruits and vegetables. Carbon dioxide concentration can inhibit the growth of certain microorganisms and reduce the metabolic rate, thereby further extending the shelf life of food. Ethylene is a natural plant hormone that can promote the ripening and aging of fruits. By controlling or removing ethylene, the aging process of fruits can be effectively delayed. In the above formula, QTyq s is the real-time oxygen concentration value in the gas concentration data set, QTyq b is the standard value of oxygen concentration, QTey s is the real-time carbon dioxide concentration value in the gas concentration dataset, QTey b is the standard value of carbon dioxide concentration, QTyx s is the real-time ethylene concentration value in the gas concentration data set, QTyx b is the standard value of ethylene concentration;

[0068] Calculate the vibration index ZDzs based on the vibration data set;

[0069] The calculation formula of vibration index ZDzs is:

[0070]

[0071] The vibration index can detect excessive vibration in time, reduce physical damage to food, and thus ensure food safety. In the above formula, ZDsj s For real-time vibration data in the vibration data set, ZDsj b is the standard vibration data, γ is the corresponding weight of the vibration value during transportation;

[0072] The transport data analysis module is internally provided with a temperature data comparison threshold, a humidity data comparison threshold, a gas concentration data comparison threshold, and a vibration data comparison threshold. The transport data analysis module compares the temperature data comparison threshold, the humidity data comparison threshold, and the gas concentration data comparison threshold with the temperature control data group WDdz, the humidity control data group SDdz, and the gas concentration control data group QTdz, respectively, to determine whether the current equipment has a fault;

[0073] The temperature data comparison threshold includes the temperature difference ratio threshold and the temperature conventional threshold. When the temperature difference ratio WC is greater than the temperature difference ratio threshold or the temperature evaluation WP of the next test section is greater than the temperature conventional threshold, it means that the temperature is abnormal, and a temperature abnormality signal is sent to the early warning alarm module for alarm;

[0074] The humidity data comparison threshold includes a humidity difference ratio threshold and a humidity regular threshold. When the humidity difference ratio SC is greater than the humidity difference ratio threshold or the humidity assessment SP of the next test period is greater than the humidity regular threshold, it means that the humidity is abnormal, and a humidity abnormality signal is sent to the early warning module for an alarm.

[0075] The gas concentration data comparison thresholds include oxygen concentration thresholds, carbon dioxide concentration thresholds and ethylene concentration thresholds. When the oxygen concentration comparison value YQdz, carbon dioxide concentration comparison value EYdz and ethylene concentration comparison value YXdz in the gas concentration comparison data group QTdz are greater than the corresponding oxygen concentration thresholds, carbon dioxide concentration thresholds and ethylene concentration thresholds, it means that the transported goods in the vehicle have deteriorated, and a gas abnormality signal is sent to the early warning module for alarm;

[0076] The transport data analysis module compares the vibration data comparison threshold with the vibration index ZDzs. When the vibration index ZDzs is greater than the vibration data comparison threshold, it means that the current road conditions or vehicle speed produce large vibrations, which may cause collisions with transported objects. The abnormal vibration data is sent to the early warning module for an alarm.

[0077] When the early warning module receives abnormal temperature signals, abnormal humidity signals, and abnormal gas signals, it sends a maintenance signal to the driver to remind the next server to perform equipment maintenance inspection. When the early warning module receives abnormal vibration data, it sends a deceleration signal to the driver to remind him to slow down and reduce vibration.

[0078] The specific method of the above-mentioned logistics management system based on big data technology is as follows:

[0079] The following steps are involved:

[0080] Step 1: Collect temperature data, humidity data, gas concentration data and vibration data in the car, and form corresponding data sets after numbering;

[0081] Step 2: Calculate the temperature control data group WDdz, humidity control data group SDdz, gas concentration control data group QTdz, and vibration index ZDzs according to the data set;

[0082] Step 3: judging whether there is an abnormality for the corresponding threshold value according to the calculation result, and sending a corresponding abnormal signal in the case of an abnormality;

[0083] Step 4: Issue corresponding warnings based on corresponding abnormal signals.

[0084] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A logistics management system based on big data technology, characterized by: Including transportation data collection module, transportation data analysis module and early warning module; The transport data acquisition module collects the temperature data WDsj in the transport compartment, and records them by number to form a temperature data set, collects the humidity data SDsj, and records them by number to form a humidity data set, collects the gas concentration data QTsj, and records them by number to form a gas concentration data set, monitors the vibration data ZDsj, and records them by number to form a vibration data set, and the transport data acquisition module sends the above data sets to the transport data analysis module; The transport data analysis module calculates the temperature control data group WDdz according to the temperature data set, calculates the humidity control data group SDdz according to the humidity data set, calculates the gas concentration control data group QTdz according to the gas concentration data set, and calculates the vibration index ZDzs according to the vibration data set; The transport data analysis module is internally provided with a temperature data comparison threshold, a humidity data comparison threshold, a gas concentration data comparison threshold, and a vibration data comparison threshold. The transport data analysis module compares the temperature data comparison threshold, the humidity data comparison threshold, and the gas concentration data comparison threshold with the temperature control data group WDdz, the humidity control data group SDdz, and the gas concentration control data group QTdz, respectively, to determine whether the current equipment has a fault. The transport data analysis module compares the vibration data comparison threshold with the vibration index ZDzs to determine whether the current road condition and vehicle speed will cause damage to the transported goods, and then determines whether there is a fault or a vulnerable item and sends an abnormal signal to the early warning module; The early warning module issues a corresponding alarm according to the corresponding signal.

2. According to claim 1, a logistics management system based on big data technology is characterized by: The expression of the temperature data set is: {WDsj1, WDsj2, WDsj3, . . . , WDsj n }, where WDsj1 represents the first temperature data collected in the temperature data set, WDsj n Represents the last collected temperature data in the temperature data set; The expression of the humidity data set is: {SDsj1, SDsj2, SDsj3, . . . , SDsj n }, where SDsj1 represents the humidity data collected for the first time in the humidity data set, SDsj n Represents the humidity data collected last time in the humidity dataset; The expression of the gas concentration data set is: {QTsj1, QTsj2, QTsj3, . . . , QTsj n }, where QTsj1 represents the gas concentration data collected for the first time in the gas concentration data set, QTsj n Represents the gas concentration data collected last time in the gas concentration data set. The gas concentration data includes oxygen concentration, carbon dioxide concentration, and ethylene concentration. The numbers are: QTyq, QTeh, and QTyx; The expression of the vibration data set is: {ZDsj1, ZDsj2, ZDsj3, . . . , ZDsj n }, where ZDsj1 represents the vibration data collected for the first time in the vibration data set, and ZDsj n Represents the last collected vibration data in the vibration data set; In the above expression, n represents that there are a total of n data collected in the data set.

3. The logistics management system based on big data technology according to claim 2 is characterized by: The temperature comparison data set WDdz includes the temperature difference ratio WC and the next test section temperature evaluation WP. The temperature comparison data set WDdz is calculated as follows: In the above formula, WDsj i is the i-th temperature data in the temperature data set, WDsj b is the standard temperature during material transportation, α is the weight of temperature data during transportation, WDsj s is the real-time temperature, WDsj s +WD s-1 WDsj is the sum of the real-time temperature and the temperature before the real-time temperature. s-2 +WD s-3 is the sum of the second and third temperatures before the real-time temperature, (WDsj s +WD s-1 )-(WDsj s-2 +WD s-3 ) is the difference between the sum of the real-time temperature and the temperature before the real-time temperature and the sum of the second and third temperatures before the real-time temperature.

4. The logistics management system based on big data technology according to claim 2 is characterized by: The humidity comparison data set SDdz includes the humidity difference ratio SC and the next test section humidity assessment SP. The calculation formula of the humidity comparison data set SDdz is: In the above formula, SDsj i is the i-th humidity data in the humidity data set, SDsj b is the standard humidity during material transportation, β is the weight of humidity data during transportation, SDsj s For real-time humidity data, SDsj s +SDs s-1 SDsj is the sum of the real-time humidity and the humidity before the real-time humidity. s-2 +SDs s-3 is the sum of the second and third humidity before the real-time humidity, (SDsj s +SDs s-1 )-(SDsj s-2 +SDs s-3 ) is the difference between the real-time humidity and the sum of the humidity before the real-time humidity and the sum of the second and third humidity before the real-time humidity.

5. The logistics management system based on big data technology according to claim 2 is characterized by: The gas concentration control data set QTdz includes an oxygen concentration control value YQdz, a carbon dioxide concentration control value EYdz, and an ethylene concentration control value YXdz. The calculation formula of the gas concentration control data set QTdz is as follows: In the above formula, QTyq s is the real-time oxygen concentration value in the gas concentration data set, QTyq b is the standard value of oxygen concentration, QTey s is the real-time carbon dioxide concentration value in the gas concentration dataset, QTey b is the standard value of carbon dioxide concentration, QTyx s is the real-time ethylene concentration value in the gas concentration data set, QTyx b It is the standard value of ethylene concentration.

6. The logistics management system based on big data technology according to claim 2 is characterized by: The calculation formula of the vibration index ZDzs is: In the above formula, ZDsj s For real-time vibration data in the vibration data set, ZDsj b is the standard vibration data, and γ is the corresponding weight of the vibration value during transportation.

7. A logistics management system based on big data technology according to claims 3, 4 and 5, characterized in that: The temperature data comparison threshold includes a temperature difference ratio threshold and a temperature conventional threshold. When the temperature difference ratio WC is greater than the temperature difference ratio threshold or the temperature evaluation WP of the next test section is greater than the temperature conventional threshold, it means that the temperature is abnormal, and a temperature abnormality signal is sent to the early warning alarm module for alarm; The humidity data comparison threshold includes a humidity difference ratio threshold and a humidity conventional threshold. When the humidity difference ratio SC is greater than the humidity difference ratio threshold or the humidity assessment SP of the next test period is greater than the humidity conventional threshold, it means that the humidity is abnormal, and a humidity abnormality signal is sent to the early warning module for alarm; The gas concentration data comparison thresholds include an oxygen concentration threshold, a carbon dioxide concentration threshold and an ethylene concentration threshold. When the oxygen concentration control value YQdz, the carbon dioxide concentration control value EYdz and the ethylene concentration control value YXdz in the gas concentration comparison data group QTdz are greater than the corresponding oxygen concentration threshold, the carbon dioxide concentration threshold and the ethylene concentration threshold, it indicates that the transported goods in the vehicle have deteriorated, and a gas abnormality signal is sent to the early warning module for alarm.

8. The logistics management system based on big data technology according to claim 6 is characterized by: When the vibration index ZDzs is greater than the vibration data comparison threshold, it means that the current road conditions or vehicle speed produce large vibrations, which may easily cause collisions with transported objects, and the vibration data anomaly is sent to the early warning module for an alarm.

9. A logistics management system based on big data technology according to claims 7 and 8, characterized in that: When the early warning module receives abnormal temperature signals, abnormal humidity signals, and abnormal gas signals, it sends a maintenance signal to the driver to remind the next server to perform equipment maintenance inspection. When the early warning module receives abnormal vibration data, it sends a deceleration signal to the driver to remind him to decelerate and reduce vibration.

10. A logistics management method based on big data technology, characterized in that: The following steps are involved: Step 1: Collect temperature data, humidity data, gas concentration data and vibration data in the car, and form corresponding data sets after numbering; Step 2: Calculate the temperature control data group WDdz, humidity control data group SDdz, gas concentration control data group QTdz, and vibration index ZDzs according to the data set; Step 3: judging whether there is an abnormality for the corresponding threshold value according to the calculation result, and sending a corresponding abnormal signal in the case of an abnormality; Step 4: Issue corresponding warnings based on corresponding abnormal signals.

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