A logistics monitoring system and method based on big data

Abstract

The present invention relates to the field of logistics monitoring technology, and specifically to a logistics monitoring system and method based on big data, comprising the following steps: obtaining operation monitoring data of a logistics vehicle; wherein the operation monitoring data includes a freezing temperature average; 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; based on the high logistics transportation risk signal, obtaining operation monitoring data again to generate a logistics processing signal; based on the transit signal, controlling the speed of logistics transportation; the present invention performs reasonable transit processing on the current logistics according to the shortest processing time of the logistics obtained by analysis, thereby ensuring the fluidity of the logistics while ensuring the quality of the logistics products.

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, originally meaning "physical distribution" or "goods delivery";

[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 logistics orders. The data processing unit transmits the logistics orders, logistics delivery efficiency, and logistics transportation efficiency to the monitoring unit and the scoring unit respectively.

[0004] In the existing technology, the location of the customer's mobile phone number is obtained through the setting of the 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 logistics field, there is also a special method 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 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 during 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 deal with 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 logistics transportation risk signal;

[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 a logistics processing signal;

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

[0014] Step 5: Based on the transit 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 average freezing temperature 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, 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 a continuous unqualified fluctuation value; and the number of continuous unqualified values ​​during the transportation process is marked as the continuous unqualified number;

[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 logistics freezing temperature unqualified signal is:

[0020] 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;

[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 continuous unqualified values ​​is:

[0023] During the transportation process of the logistics vehicle, the first start time of generating the logistics freezing temperature unqualified signal is obtained, and the first start time is marked as the 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 failure cutoff value and the continuous failure initial value is calculated to obtain the continuous failure 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] Calculate the sum of the average values ​​of all consecutive unqualified freezing temperatures during the logistics transportation process to obtain the total value of consecutive unqualified freezing temperatures;

[0030] Calculate the difference between the total value of continuous unqualified freezing temperature and the continuous unqualified freezing temperature warning value, and take the absolute value to obtain the continuous unqualified freezing temperature warning difference;

[0031] and screening based on the mean values ​​of all consecutive unqualified freezing temperatures 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, obtain the distance between the logistics vehicle and the nearest logistics transfer station, obtain the time when the logistics vehicle arrives at the nearest logistics transfer station, and mark it as the nearest transfer time;

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

[0035] If the shortest processing time of logistics 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 consecutive failure risk single value is:

[0037] The continuous unqualified value is multiplied 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, and mark the continuous unqualified value corresponding to the first one in the arrangement as the target continuous unqualified value;

[0039] Obtain the mean of all freezing temperatures of the target continuous unqualified values, perform sum calculation, 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] Acquisition 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, it monitors and analyzes logistics products to obtain transportation risk values;

[0044] Assessment 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, it obtains the operation monitoring data and generates the logistics processing signal;

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

[0048] Control module: controls the speed of logistics transportation based on transit signals.

[0049] Beneficial effects of the present invention:

[0050] (1) The present invention obtains operation monitoring data of logistics vehicles; based on the operation monitoring data of logistics vehicles, the logistics products are monitored and analyzed to obtain a 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 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 time and quantity dimensions, and monitors the logistics in real time during transportation, thereby accurately assessing the risks of logistics;

[0051] (2) The present invention obtains operation monitoring data based on the high risk signal of logistics transportation and generates 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, based on the shortest processing time of logistics obtained by analysis, the current logistics is reasonably transited, and the flowability of logistics is also guaranteed under the premise of ensuring the quality of logistics products. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall 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] Multiple 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 value is obtained, the real-time average freezing temperature value is compared with the real-time average freezing temperature threshold value;

[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 during the transportation process, which generates the logistics refrigeration temperature unqualified signal, 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, which is marked as the continuous unqualified number;

[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 and its value 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 the logistics freezing temperature unqualified signal is obtained, and the first start time is marked as the 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] Calculate the difference between the continuous unqualified cut-off value and the continuous unqualified initial value 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 logistics transportation risk signal;

[0075] Among them, the logistics and transportation risk high and low signals include logistics and transportation risk high signals or logistics and transportation risk low 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 a high logistics and transportation risk signal indicates that the frequency of temperature fluctuations in cold chain transportation is high, which has a significant impact on the quality of products inside the logistics vehicle. A low logistics and transportation risk signal indicates that the frequency of temperature fluctuations in cold chain transportation is low, which has a minimal impact on the quality of products inside the logistics vehicle.

[0080] The technical solution of the embodiment of the present invention is as follows: obtaining operation monitoring data of the logistics vehicle; based on the operation monitoring data of the logistics vehicle, monitoring and analyzing the logistics products to obtain a transportation risk value; based on the transportation risk value, comparing the transportation risk value with the 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 the 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 a logistics processing signal;

[0084] Among them, the logistics processing signal includes a transfer signal or a maintenance signal;

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

[0086] Calculate the sum of the average values ​​of all consecutive unqualified freezing temperatures during the logistics transportation process to obtain the total value of consecutive unqualified freezing temperatures;

[0087] Calculate the difference between the total value of continuous unqualified freezing temperature and the continuous unqualified freezing temperature warning value, and take the absolute value to obtain the continuous unqualified freezing temperature warning difference;

[0088] and screening based on the mean values ​​of all consecutive unqualified freezing temperatures 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, obtain the distance between the logistics vehicle and the nearest logistics transfer station, obtain the time when the logistics vehicle arrives at the nearest logistics transfer station, and mark it as the nearest transfer time;

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

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

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

[0094] It should be explained that the transfer signal indicates that the current logistics vehicle has an 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 vehicle's cooling temperature is abnormal, but the abnormality is too far away to reach the temperature warning standard, making it impossible to transport the products in the vehicle to the transfer station for processing. The vehicle can utilize the spare refrigeration equipment and arrange for technicians to perform maintenance.

[0096] Specifically, the process of determining the maximum consecutive failure risk value is as follows:

[0097] The continuous unqualified value is multiplied 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, and mark the continuous unqualified value corresponding to the first one in the arrangement as the target continuous unqualified value;

[0099] Obtain the average 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: Control the speed of logistics transportation based on the transfer signal;

[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 also guaranteed under the premise of 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, which includes:

[0105] Acquisition 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, it monitors and analyzes logistics products to obtain transportation risk values;

[0107] Assessment 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, it obtains the operation monitoring data and generates the logistics processing signal;

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

[0111] Control module: controls the speed of logistics transportation based on transit signals.

[0112] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas 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. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope 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: S1: Obtaining operation monitoring data of the logistics vehicle; wherein the operation monitoring data includes the average freezing temperature; S2: Based on the operation monitoring data of logistics vehicles, the logistics products are monitored and analyzed to obtain the transportation risk value; In S2, 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; 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; Obtain the continuous unqualified value of the logistics vehicle generating the logistics refrigeration temperature unqualified signal during the transportation process. Specifically, the process of obtaining the continuous unqualified value is as follows: During the transportation process of the logistics vehicle, the first start time of generating the logistics refrigeration temperature unqualified signal is obtained, and the first start time is marked as the continuous unqualified initial value; with the start time as the starting point, the logistics refrigeration temperature unqualified signal is continuously generated within the continuous time until the second start time of generating the logistics refrigeration temperature qualified signal is marked as the continuous unqualified cutoff value; the difference between the continuous unqualified cutoff value and the continuous unqualified initial value is calculated to obtain the continuous unqualified value; Calculate the variance of all consecutive unqualified values ​​to obtain the continuous unqualified fluctuation value; and the number of consecutive unqualified values ​​during the transportation process is marked as the number of consecutive unqualified values; According to the continuous unqualified fluctuation value ZBb and the continuous unqualified number ZBg, the transportation risk value ZYF is calculated; S3: 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; S4: Based on the high risk signal of logistics transportation, the operation monitoring data is obtained to generate a logistics processing signal; when the high risk signal of logistics transportation is obtained, the average freezing temperature corresponding to the consecutive unqualified values ​​during the transportation process is obtained and marked as the continuous unqualified freezing temperature average; Calculate the sum of the average values ​​of all consecutive unqualified freezing temperatures during the logistics transportation process to obtain the total value of consecutive unqualified freezing temperatures; Calculate the difference between the total value of continuous unqualified freezing temperature and the continuous unqualified freezing temperature warning value, and take the absolute value to obtain the continuous unqualified freezing temperature warning difference; and screening based on the mean values ​​of all consecutive unqualified freezing temperatures 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, obtain the distance between the logistics vehicle and the nearest logistics transfer station, obtain the time when the logistics vehicle arrives at the nearest logistics transfer station, and mark it as the nearest transfer time; Compare the shortest possible logistics processing time with the nearest transit time; If the shortest processing time for logistics is greater than or equal to the latest transit time, a transit signal is generated; Step 5: Control the speed of logistics transportation based on the transfer signal; When the transfer signal is received, the distance from the logistics vehicle to the nearest logistics transfer station is obtained and marked as the transfer distance LZ; through the formula , calculate the transfer speed VZ; where TC is the shortest time that logistics can be processed.

2. A logistics monitoring method based on big data according to claim 1, characterized in that: In step 1, the process of obtaining the real-time average freezing temperature is as follows: 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. 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.

4. A logistics monitoring method based on big data according to claim 3, characterized in that: The process of determining the maximum consecutive failure risk value is as follows: The continuous unqualified value is multiplied 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, and mark the continuous unqualified value corresponding to the first one in the arrangement as the target continuous unqualified value; Obtain the mean of all freezing temperatures for the target continuous unqualified values, perform sum calculations, and obtain the maximum continuous unqualified risk single value.

5. A logistics monitoring system based on big data, characterized by: The system is used to perform the method according to any one of claims 1 to 4 above, and the system comprises: Acquisition 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, it monitors and analyzes logistics products to obtain transportation risk values; Assessment 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, it obtains the operation monitoring data and generates the logistics processing signal; Among them, the logistics processing signal includes the transfer signal; Control module: controls the speed of logistics transportation based on transit signals.

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