Logistics monitoring system and method based on big data

Abstract

The invention relates to the technical field of logistics monitoring, in particular to a logistics monitoring system and method based on big data, and the method comprises the following steps: obtaining the operation monitoring data of a logistics vehicle; wherein the operation monitoring data comprises a freezing temperature mean value; monitoring and analyzing the logistics product based on the operation monitoring data of the logistics vehicle to obtain a transportation risk value; based on the transportation risk value, the transportation risk value is compared with a transportation risk threshold value, and a logistics transportation risk high-low signal is generated; based on the logistics transportation risk high signal, operation monitoring data is acquired, and a logistics processing signal is generated; controlling the speed of logistics transportation based on the transfer signal; according to the logistics processing shortest time obtained through analysis, the current logistics is subjected to reasonable transfer processing, and on the premise that the quality of logistics products is guaranteed, the circulation of the logistics is also guaranteed.

Description

Technical Field

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

[0002] The concept of logistics was first formed in the United States in the 1930s, and originally meant "physical distribution" or "goods distribution";

[0003] Chinese patent CN111461626B discloses a logistics monitoring system based on big data, including a collection unit, a positioning unit, a data processing unit, a monitoring unit, a scoring unit and an intelligent device; the collection unit collects logistics information related to logistics transportation and transmits it to the data processing unit, the data processing unit receives the relevant logistics information and processes it to obtain a logistics order, and transmits it together with the logistics sending efficiency and the logistics transportation efficiency to the monitoring unit and the scoring unit respectively;

[0004] In the prior art, the location of the customer's mobile phone number is obtained through the setting of a positioning unit, and the order address is marked in the virtual coordinates, so as to calculate the difference between the location of the mobile phone number and the order address, so as to select a reasonable delivery time and save the time and energy consumed by manual phone calls; that is, the current logistics monitoring focuses on: the reasonable setting of delivery time; but in the field of logistics, there is also a special mode of cold chain logistics, and it is particularly important to monitor its temperature during the logistics transportation process, because in the logistics transportation, if there is an abnormal temperature fluctuation, it will cause the quality of the transported logistics products to change. Therefore, it is currently impossible to effectively monitor the temperature in the cold chain logistics accurately, and based on the results obtained by the monitoring, make a quick response to the logistics to deal with logistics products at abnormal temperatures. Summary of the invention

[0005] The purpose of the present invention is to provide a logistics monitoring system and method based on big data. The technical problem solved by the present invention is: in cold chain logistics, temperature monitoring is particularly important, because in logistics transportation, if abnormal temperature fluctuations occur, it will cause changes in the quality of the transported logistics products. Therefore, it is currently impossible to effectively monitor the temperature in cold chain logistics accurately, and based on the results obtained from the monitoring, make a quick response to the logistics to handle logistics products at abnormal temperatures.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A logistics monitoring method based on big data includes the following steps:

[0008] Step 1: Obtain the operation monitoring data of the logistics vehicle; wherein the operation monitoring data includes the average freezing temperature;

[0009] Step 2: Based on the operation monitoring data of the logistics vehicle, monitor and analyze the logistics products to obtain the transportation risk value;

[0010] Step 3: Based on the transportation risk value, compare the transportation risk value with the transportation risk threshold to generate a high or low signal of logistics transportation risk;

[0011] Among them, the high and low signals of logistics and transportation risks include high signals of logistics and transportation risks;

[0012] Step 4: Based on the high risk signal of logistics transportation, obtain the operation monitoring data and generate the logistics processing signal;

[0013] Among them, the logistics processing signal includes a transfer signal;

[0014] Step 5: Based on the transfer signal, control the speed of logistics transportation.

[0015] As a further solution of the present invention: in step 1, the process of obtaining the real-time freezing temperature mean is:

[0016] The temperature values ​​at various locations in the logistics vehicle are collected, and the temperature values ​​at the same time are averaged to obtain the real-time average freezing temperature.

[0017] As a further solution of the present invention: in step 2, the continuous unqualified values ​​of the logistics vehicle generating the logistics refrigeration temperature unqualified signal during the transportation process are obtained, and the variance of all the continuous unqualified values ​​is calculated to obtain the continuous unqualified fluctuation value; and the number of continuous unqualified values ​​during the transportation process is marked as the number of continuous unqualified values;

[0018] The continuous unqualified fluctuation value and the continuous unqualified number are marked as ZBb and ZBg respectively, and the transportation risk value ZYF is calculated.

[0019] As a further solution of the present invention: the process of generating the signal of unqualified logistics freezing temperature is as follows:

[0020] When the real-time freezing temperature average is obtained, the real-time freezing temperature average is compared with the real-time freezing temperature average threshold;

[0021] If the real-time freezing temperature average is greater than or equal to the real-time freezing temperature average threshold, it means that the temperature of the logistics products in the logistics vehicle does not meet the transportation conditions, and a logistics freezing temperature unqualified signal is generated.

[0022] As a further solution of the present invention: the process of obtaining the continuous unqualified values ​​is:

[0023] During the transportation process of the logistics vehicle, the first start time of generating a signal of unqualified logistics freezing temperature is obtained, and the first start time is marked as a continuous unqualified initial value;

[0024] Taking the start time as the starting point, continuously generating the logistics freezing temperature unqualified signal within the continuous time until the second start time when the logistics freezing temperature qualified signal is generated, and marking the second start time as the continuous unqualified cutoff value;

[0025] The difference between the continuous unqualified cut-off value and the continuous unqualified initial value is calculated to obtain the continuous unqualified value.

[0026] As a further solution of the present invention: in step 3, if the transportation risk value is greater than or equal to the transportation risk threshold, a high logistics transportation risk signal is generated.

[0027] As a further solution of the present invention: in step 4, the process of generating the transfer signal is:

[0028] When a high risk signal for logistics transportation is obtained, the average freezing temperature corresponding to the consecutive unqualified values ​​during the transportation process is obtained and marked as the average continuous unqualified freezing temperature;

[0029] The mean values ​​of all the consecutive unqualified freezing temperatures during the logistics transportation process are summed to obtain the total value of the consecutive unqualified freezing temperatures;

[0030] The difference between the total value of the continuous unqualified freezing temperature and the warning value of the continuous unqualified freezing temperature is calculated, and the absolute value is taken to obtain the warning difference of the continuous unqualified freezing temperature;

[0031] And based on the mean values ​​of all consecutive unqualified freezing temperatures, screening is performed to determine the maximum consecutive unqualified risk single value;

[0032] Calculate the ratio of the continuous unqualified freezing temperature warning difference to the maximum continuous unqualified risk single value to obtain the continuous unqualified freezing temperature warning ratio. Multiply the continuous unqualified freezing temperature warning ratio by the target continuous unqualified value to obtain the shortest logistics processing time.

[0033] At the same time, the distance between the logistics vehicle and the nearest logistics transfer station is obtained, and the time when the logistics vehicle arrives at the nearest logistics transfer station is obtained, which is marked as the nearest transfer time;

[0034] Compare the shortest logistics processing time with the nearest transit time;

[0035] If the shortest logistics processing time is greater than or equal to the latest transit time, a transit signal is generated.

[0036] As a further solution of the present invention: the process of determining the maximum continuous unqualified risk single value is:

[0037] The continuous unqualified value is calculated by multiplying the continuous unqualified value by the maximum value of the continuous unqualified freezing temperature to obtain the continuous unqualified risk single value;

[0038] Arrange each continuous unqualified risk single value from large to small according to the value, and mark the continuous unqualified value corresponding to the first one in the arrangement as the target continuous unqualified value;

[0039] Obtain the mean values ​​of all freezing temperatures of the target continuous unqualified values, perform sum calculations, and obtain the maximum continuous unqualified risk single value.

[0040] As a further solution of the present invention: in step 5, when the transfer signal is obtained, the distance from the logistics vehicle to the nearest logistics transfer station is obtained, marked as the transfer distance LZ; and the transfer speed VZ is calculated.

[0041] A logistics monitoring system based on big data, the system comprising:

[0042] Collection module: obtains the operation monitoring data of the logistics vehicle; the operation monitoring data includes the average freezing temperature;

[0043] Analysis module: Based on the operation monitoring data of logistics vehicles, the logistics products are monitored and analyzed to obtain the transportation risk value;

[0044] Evaluation module: Based on the transportation risk value, the transportation risk value is compared with the transportation risk threshold to generate a high or low signal of logistics transportation risk;

[0045] Among them, the high and low signals of logistics and transportation risks include high signals of logistics and transportation risks;

[0046] Processing module: Based on the high risk signal of logistics transportation, the operation monitoring data is obtained to generate logistics processing signals;

[0047] Among them, the logistics processing signal includes a transfer signal;

[0048] Control module: Control the speed of logistics transportation based on the transfer signal.

[0049] Beneficial effects of the present invention:

[0050] (1) The present invention obtains the operation monitoring data of the logistics vehicle; based on the operation monitoring data of the logistics vehicle, the logistics products are monitored and analyzed to obtain the transportation risk value; based on the transportation risk value, the transportation risk value is compared with the transportation risk threshold to generate a high or low signal of the logistics transportation risk; the present invention monitors and analyzes the cold chain logistics, obtains the freezing temperature during the transportation process, processes the data according to the freezing temperature in the time and quantity dimensions, and monitors the logistics in real time during the transportation process, so as to accurately evaluate the risk of the logistics;

[0051] (2) The present invention obtains operation monitoring data based on high risk signals of logistics transportation and generates logistics processing signals; based on the transit signal, the speed of logistics transportation is controlled; the present invention analyzes the risks of logistics transportation based on the operation monitoring data and reasonably processes the current logistics to ensure the quality of logistics products. Furthermore, based on the shortest processing time of logistics obtained from the analysis, the current logistics is reasonably transited, thereby ensuring the flow of logistics while ensuring the quality of logistics products. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described below in conjunction with the accompanying drawings.

[0053] Figure 1 is a flowchart of Embodiment 1 of the present invention;

[0054] Figure 2 is a flowchart of Embodiment 2 of the present invention;

[0055] Figure 3 It is a system block diagram of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0056] 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.

[0057] Example 1

[0058] See also Figure 1 As shown, the present invention is a logistics monitoring method based on big data, comprising the following steps:

[0059] Step 1: Obtain the operation monitoring data of the logistics vehicle;

[0060] Among them, the operation monitoring data includes the real-time average freezing temperature;

[0061] In some embodiments, during the cold chain logistics process, the real-time average freezing temperature of the logistics vehicle during transportation is obtained;

[0062] Specifically, the process of obtaining the real-time average freezing temperature of the logistics vehicle during transportation is as follows:

[0063] A plurality of temperature sensors are installed in the refrigerated compartment of the logistics vehicle. The temperature values ​​at various locations in the logistics vehicle are collected by the temperature sensors, and the temperature values ​​at the same time are averaged to obtain the real-time average freezing temperature.

[0064] Step 2: Based on the operation monitoring data of the logistics vehicle, monitor and analyze the logistics products to obtain the transportation risk value;

[0065] In some embodiments, when the real-time average freezing temperature is obtained, the real-time average freezing temperature is compared with the real-time average freezing temperature threshold;

[0066] If the real-time freezing temperature average is less than the real-time freezing temperature average threshold, it means that the temperature of the logistics products in the logistics vehicle meets the transportation conditions, and a logistics freezing temperature qualified signal is generated;

[0067] If the real-time freezing temperature average is greater than or equal to the real-time freezing temperature average threshold, it means that the temperature of the logistics products in the logistics vehicle does not meet the transportation conditions, and a logistics freezing temperature unqualified signal is generated;

[0068] Obtain the continuous unqualified values ​​of the logistics vehicle generating the logistics refrigeration temperature unqualified signal during the transportation process, calculate the variance of all the continuous unqualified values ​​to obtain the continuous unqualified fluctuation value; and the number of continuous unqualified values ​​during the transportation process, marked as the number of continuous unqualified values;

[0069] The continuous unqualified fluctuation value and the continuous unqualified number are marked as ZBb and ZBg respectively, and the formula The transportation risk value ZYF is calculated, where a is the error factor, which is 0.61;

[0070] Specifically, the process of obtaining continuous unqualified values ​​is as follows:

[0071] During the transportation process of the logistics vehicle, the first start time of generating a signal of unqualified logistics freezing temperature is obtained, and the first start time is marked as a continuous unqualified initial value;

[0072] Taking the start time as the starting point, continuously generating the logistics freezing temperature unqualified signal within the continuous time until the second start time when the logistics freezing temperature qualified signal is generated, and marking the second start time as the continuous unqualified cutoff value;

[0073] The difference between the continuous unqualified cut-off value and the continuous unqualified initial value is calculated to obtain the continuous unqualified value;

[0074] Step 3: Based on the transportation risk value, compare the transportation risk value with the transportation risk threshold to generate a high or low signal of logistics transportation risk;

[0075] Among them, the high and low signals of logistics and transportation risks include high logistics and transportation risk signals or low logistics and transportation risk signals;

[0076] In some embodiments, obtaining a transportation risk value, and comparing the transportation risk value to a transportation risk threshold;

[0077] If the transportation risk value is greater than or equal to the transportation risk threshold, a high logistics transportation risk signal is generated;

[0078] If the transportation risk value is greater than or equal to the transportation risk threshold, a low logistics transportation risk signal is generated;

[0079] It should be explained that: the high signal of logistics and transportation risk means that the frequency of temperature fluctuations in logistics vehicles during cold chain transportation is high, which has a greater impact on the quality of products in the logistics vehicles; the low signal of logistics and transportation risk means that the frequency of temperature fluctuations in logistics vehicles during cold chain transportation is low, which has a smaller impact on the quality of products in the logistics vehicles;

[0080] The technical solution of the embodiment of the present invention is as follows: obtaining operation monitoring data of a logistics vehicle; based on the operation monitoring data of the logistics vehicle, monitoring and analyzing logistics products to obtain a transportation risk value; based on the transportation risk value, comparing the transportation risk value with a transportation risk threshold to generate a high and low logistics transportation risk signal; the present invention monitors and analyzes cold chain logistics, obtains the freezing temperature during transportation, processes data based on the freezing temperature in the dimensions of time and quantity, and monitors logistics in real time during transportation, so as to accurately assess the risks of logistics.

[0081] Example 2

[0082] See also Figure 2 As shown, the present invention is a logistics monitoring method based on big data, which also includes the following steps:

[0083] Step 4: Based on the high risk signal of logistics transportation, obtain the operation monitoring data and generate the logistics processing signal;

[0084] The logistics processing signal includes a transfer signal or a maintenance signal;

[0085] In some embodiments, when a high risk signal for logistics transportation is obtained, the average freezing temperature corresponding to consecutive unqualified values ​​during transportation is obtained and marked as the average consecutive unqualified freezing temperature;

[0086] The mean values ​​of all the consecutive unqualified freezing temperatures during the logistics transportation process are summed to obtain the total value of the consecutive unqualified freezing temperatures;

[0087] The difference between the total value of the continuous unqualified freezing temperature and the warning value of the continuous unqualified freezing temperature is calculated, and the absolute value is taken to obtain the warning difference of the continuous unqualified freezing temperature;

[0088] And based on the mean values ​​of all consecutive unqualified freezing temperatures, screening is performed to determine the maximum consecutive unqualified risk single value;

[0089] Calculate the ratio of the continuous unqualified freezing temperature warning difference to the maximum continuous unqualified risk single value to obtain the continuous unqualified freezing temperature warning ratio. Multiply the continuous unqualified freezing temperature warning ratio by the target continuous unqualified value to obtain the shortest logistics processing time.

[0090] At the same time, the distance between the logistics vehicle and the nearest logistics transfer station is obtained, and the time when the logistics vehicle arrives at the nearest logistics transfer station is obtained, which is marked as the nearest transfer time;

[0091] Compare the shortest logistics processing time with the nearest transit time;

[0092] If the shortest processing time of logistics is greater than or equal to the latest transit time, a transit signal is generated;

[0093] If the shortest logistics processing time is less than the latest transit time, a maintenance signal is generated;

[0094] It needs to be explained that the transfer signal indicates that the current logistics vehicle has abnormal cooling temperature, but there is a long time before the abnormality reaches the temperature warning standard. The long time gap can be used to transport the products in the logistics vehicle to the transfer station for processing;

[0095] The maintenance signal indicates that the cooling temperature of the current logistics vehicle is abnormal, but the abnormality is far from reaching the temperature warning standard, which makes it impossible to transport the products in the logistics vehicle to the transfer station for processing. The spare refrigeration equipment in the logistics vehicle can be used, and technicians can be arranged to repair it.

[0096] Specifically, the process of determining the maximum continuous non-conformity risk single value is as follows:

[0097] The continuous unqualified value is calculated by multiplying the continuous unqualified value by the maximum value of the continuous unqualified freezing temperature to obtain the continuous unqualified risk single value;

[0098] Arrange each continuous unqualified risk single value from large to small according to the value, and mark the continuous unqualified value corresponding to the first one in the arrangement as the target continuous unqualified value;

[0099] Obtain the average values ​​of all freezing temperatures of the target continuous unqualified values, perform sum calculations, and obtain the maximum continuous unqualified risk single value;

[0100] Step 5: Based on the transfer signal, control the speed of logistics transportation;

[0101] In some embodiments, when a transfer signal is received, the distance from the logistics vehicle to the nearest logistics transfer station is obtained, which is marked as the transfer distance LZ; The transfer speed VZ is calculated; where TC is the shortest time that logistics can be processed;

[0102] The technical solution of the embodiment of the present invention is as follows: based on the high risk signal of logistics transportation, the operation monitoring data is obtained to generate a logistics processing signal; based on the transit signal, the speed of logistics transportation is controlled; the present invention analyzes the risk of logistics transportation based on the operation monitoring data and reasonably processes the current logistics to ensure the quality of logistics products. Furthermore, according to the shortest processing time of logistics obtained by analysis, the current logistics is reasonably transited, and the flowability of logistics is guaranteed while ensuring the quality of logistics products.

[0103] Example 3

[0104] See also Figure 3 As shown, the present invention is a logistics monitoring system based on big data, the system comprising:

[0105] Collection module: obtains the operation monitoring data of the logistics vehicle; the operation monitoring data includes the average freezing temperature;

[0106] Analysis module: Based on the operation monitoring data of logistics vehicles, the logistics products are monitored and analyzed to obtain the transportation risk value;

[0107] Evaluation module: Based on the transportation risk value, the transportation risk value is compared with the transportation risk threshold to generate a high or low signal of logistics transportation risk;

[0108] Among them, the high and low signals of logistics and transportation risks include high signals of logistics and transportation risks;

[0109] Processing module: Based on the high risk signal of logistics transportation, the operation monitoring data is obtained to generate logistics processing signals;

[0110] Among them, the logistics processing signal includes a transfer signal;

[0111] Control module: Control the speed of logistics transportation based on the transfer signal.

[0112] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0113] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A logistics monitoring method based on big data, characterized in that: The following steps are involved: Step 1: Obtain the operation monitoring data of the logistics vehicle; wherein the operation monitoring data includes the average freezing temperature; Step 2: Based on the operation monitoring data of the logistics vehicle, monitor and analyze the logistics products to obtain the transportation risk value; Step 3: Based on the transportation risk value, compare the transportation risk value with the transportation risk threshold to generate a high or low signal of logistics transportation risk; Among them, the high and low signals of logistics and transportation risks include high signals of logistics and transportation risks; Step 4: Based on the high risk signal of logistics transportation, obtain the operation monitoring data and generate the logistics processing signal; Among them, the logistics processing signal includes a transfer signal; Step 5: Based on the transfer signal, control the speed of logistics transportation.

2. According to the logistics monitoring method based on big data according to claim 1, it is characterized in that: In step 1, the process of obtaining the real-time average freezing temperature is: The temperature values ​​at various locations in the logistics vehicle are collected, and the temperature values ​​at the same time are averaged to obtain the real-time average freezing temperature.

3. According to the logistics monitoring method based on big data in claim 1, it is characterized in that: In step 2, the continuous unqualified values ​​of the logistics vehicle generating the logistics refrigeration temperature unqualified signal during the transportation process are obtained, and the variance of all the continuous unqualified values ​​is calculated to obtain the continuous unqualified fluctuation value; And the number of consecutive unqualified values ​​during transportation, marked as the number of consecutive unqualified values; The continuous unqualified fluctuation value and the continuous unqualified number are marked as ZBb and ZBg respectively, and the transportation risk value ZYF is calculated.

4. According to the logistics monitoring method based on big data as described in claim 3, it is characterized in that: The generation process of the logistics freezing temperature unqualified signal is as follows: When the real-time freezing temperature average is obtained, the real-time freezing temperature average is compared with the real-time freezing temperature average threshold; If the real-time freezing temperature average is greater than or equal to the real-time freezing temperature average threshold, it means that the temperature of the logistics products in the logistics vehicle does not meet the transportation conditions, and a logistics freezing temperature unqualified signal is generated.

5. The logistics monitoring method based on big data according to claim 4 is characterized in that: The process of obtaining continuous unqualified values ​​is as follows: During the transportation process of the logistics vehicle, the first start time of generating a signal of unqualified logistics freezing temperature is obtained, and the first start time is marked as a continuous unqualified initial value; Taking the start time as the starting point, continuously generating the logistics freezing temperature unqualified signal within the continuous time until the second start time when the logistics freezing temperature qualified signal is generated, and marking the second start time as the continuous unqualified cutoff value; The difference between the continuous unqualified cut-off value and the continuous unqualified initial value is calculated to obtain the continuous unqualified value.

6. The logistics monitoring method based on big data according to claim 1 is characterized in that: In step 3, if the transportation risk value is greater than or equal to the transportation risk threshold, a high logistics transportation risk signal is generated.

7. The logistics monitoring method based on big data according to claim 1 is characterized in that: In step 4, the generation process of the transfer signal is: When a high risk signal for logistics transportation is obtained, the average freezing temperature corresponding to the consecutive unqualified values ​​during the transportation process is obtained and marked as the average continuous unqualified freezing temperature; The mean values ​​of all the consecutive unqualified freezing temperatures during the logistics transportation process are summed to obtain the total value of the consecutive unqualified freezing temperatures; The difference between the total value of the continuous unqualified freezing temperature and the continuous unqualified freezing temperature warning value is calculated, and the absolute value is taken to obtain the continuous unqualified freezing temperature warning difference; And based on the mean values ​​of all consecutive unqualified freezing temperatures, screening is performed to determine the maximum consecutive unqualified risk single value; Calculate the ratio of the continuous unqualified freezing temperature warning difference to the maximum continuous unqualified risk single value to obtain the continuous unqualified freezing temperature warning ratio. Multiply the continuous unqualified freezing temperature warning ratio by the target continuous unqualified value to obtain the shortest logistics processing time. At the same time, the distance between the logistics vehicle and the nearest logistics transfer station is obtained, and the time when the logistics vehicle arrives at the nearest logistics transfer station is obtained, which is marked as the nearest transfer time; Compare the shortest logistics processing time with the nearest transit time; If the shortest logistics processing time is greater than or equal to the latest transit time, a transit signal is generated.

8. The logistics monitoring method based on big data according to claim 7 is characterized in that: The process of determining the maximum continuous non-conformity risk single value is as follows: The continuous unqualified value is calculated by multiplying the continuous unqualified value by the maximum value of the continuous unqualified freezing temperature to obtain the continuous unqualified risk single value; Arrange each continuous unqualified risk single value from large to small according to the value, and mark the continuous unqualified value corresponding to the first one in the arrangement as the target continuous unqualified value; Obtain the mean values ​​of all freezing temperatures of the target continuous unqualified values, perform sum calculations, and obtain the maximum continuous unqualified risk single value.

9. The logistics monitoring method based on big data according to claim 1 is characterized in that: In step 5, when the transfer signal is obtained, the distance from the logistics vehicle to the nearest logistics transfer station is obtained and marked as the transfer distance LZ; The transfer speed VZ is calculated.

10. A logistics monitoring system based on big data, characterized in that: The system is used to execute the method described in any one of claims 1 to 9, and the system comprises: Collection module: obtains the operation monitoring data of the logistics vehicle; the operation monitoring data includes the average freezing temperature; Analysis module: Based on the operation monitoring data of logistics vehicles, the logistics products are monitored and analyzed to obtain the transportation risk value; Evaluation module: Based on the transportation risk value, the transportation risk value is compared with the transportation risk threshold to generate a high or low signal of logistics transportation risk; Among them, the high and low signals of logistics and transportation risks include high signals of logistics and transportation risks; Processing module: Based on the high risk signal of logistics transportation, the operation monitoring data is obtained to generate logistics processing signals; Among them, the logistics processing signal includes a transfer signal; Control module: Control the speed of logistics transportation based on the transfer signal.

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