A method for calculating travel statistics in the cloud

Abstract

The present invention discloses a method for cloud-based calculation of travel statistics, which uses vehicle travel data collected on a cloud server and analyzes it according to a certain algorithm based on the data uploaded by the terminal. The start time of the trip uploaded by the vehicle is compared with the server time. If the difference is large, it is determined that the terminal time calibration is inaccurate. The start time of this trip uses the server time, and the vehicle's most recent data on the platform is queried. If it is close to the start time of this trip, then this trip is actually a continuation of the previous trip. After the vehicle stops, the platform analyzes the deadline for uploading terminal data as the end time of this trip, dynamically modifies the trip data, and when the vehicle starts to upload the end time of this trip next time, it is compared with the platform data to achieve the purpose of making the industrial vehicle travel data more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial vehicles, and in particular to a method for calculating travel statistics in the cloud. Background Art

[0002] In recent years, with the development of the economy, the sales growth rate of industrial vehicles has also increased. Fleet managers and drivers are also increasingly concerned about vehicle driving data; Figure 1 As mentioned above, during the use of an industrial vehicle, after the terminal starts logging in, it sends a frame of trip start data and then begins sending normal data to start working. After the work is completed, the terminal shuts down and uploads the trip end time the next time it is turned on. After receiving the trip start time, the terminal records the trip and confirms the complete trip by receiving the trip end time. However, this method can cause technical problems: trip time deviations due to inaccurate terminal time calibration, loss of the last trip data due to failure to upload the last trip after the vehicle is shut down, and discrepancies between the platform data and the actual trip data due to the calculation of the trip after the vehicle is restarted.

[0003] Therefore, it is a problem worth studying to provide a method for cloud-based calculation of travel statistics by collecting vehicle travel data on a cloud server and analyzing the data uploaded by the terminal according to a certain algorithm. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for calculating travel statistics in the cloud by collecting vehicle travel data on a cloud server and analyzing the data uploaded by the terminal according to a certain algorithm.

[0005] The object of the present invention is achieved like this:

[0006] A method for calculating travel statistics in the cloud includes the following steps:

[0007] Step 1: Install an on-board terminal on the vehicle to collect terminal data through CAN communication, and transmit the collected data through 4G or 5G network to upload the vehicle data collected by the terminal to the cloud server;

[0008] Step 2: Start the vehicle and upload the trip start time, then start uploading the driving data. After the trip is completed, shut down the vehicle and wait for the next startup to upload the last trip end time.

[0009] Step 3: After receiving the trip start time, the terminal records the trip. After receiving the trip end time, it can confirm a complete trip and complete the trip data;

[0010] Step 4: The platform compares the vehicle's uploaded trip start time with the time the vehicle sent data to determine the start time. After the vehicle completes the trip, it stops sending data. The platform dynamically scans the vehicle's trip data and dynamically modifies the trip end time based on the offline time if the vehicle is offline.

[0011] Step 5: The next time the vehicle is started, it will upload the end time of the last trip. The platform will compare the vehicle's last offline time with the trip end time. If the times are close, it will be considered a restart and the trip will not be recorded, making the platform data more consistent with the actual situation.

[0012] In step 4, the platform dynamically scans the trip data through a scheduled task, searches for the end time of the vehicle upload data in the database based on the trip start time, and compares the trip start time in the database with the start time of the vehicle upload data to determine the start time.

[0013] In step 5, the platform compares the vehicle's last offline time and the trip end time through the following operations: (1) dynamically scans the trip through a scheduled task and searches the database for the trip's offline time; (2) determines whether the vehicle is online, searches the database for the vehicle's last offline time if the vehicle is online, and searches the database for the vehicle's last data upload time if the vehicle is offline; (3) compares the vehicle's offline time and the trip end time.

[0014] Positive and beneficial effects: The present invention aims to determine the actual start and end time of the vehicle's work through real-time analysis in the cloud when the vehicle travel data uploaded by the terminal is inaccurate, thereby determining a trip, dynamically modifying the travel data, and displaying more accurate results on the platform, thereby achieving the purpose of making industrial vehicle travel data more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of uploading travel data to the existing technology terminal;

[0016] Figure 2 Flowchart of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be further described below with reference to the accompanying drawings and examples.

[0018] like Figure 2 As shown, a method for calculating travel statistics in the cloud includes the following steps:

[0019] Step 1: Install an on-board terminal on the vehicle to collect terminal data through CAN communication, and transmit the collected data through the 4G or 5G network to upload the entire vehicle data collected by the terminal to the cloud server.

[0020] Step 2: Start the vehicle and upload the start time of the trip, then start uploading the driving data. After the trip is completed, shut down the vehicle and wait for the next startup to upload the end time of the previous trip.

[0021] Step 3: After receiving the trip start time, the terminal records the trip. After receiving the trip end time, it can determine a complete trip and complete the trip data.

[0022] Step 4: The platform determines the start time by comparing the start time of the vehicle's uploaded trip with the time the vehicle sends data. The vehicle stops sending data after the trip ends. The platform dynamically scans the vehicle's trip data at a fixed time. After determining that the vehicle is offline, it dynamically modifies the trip end time based on the offline time. The platform dynamically scans the trip data through a scheduled task. Based on the trip start time, it searches the database for the end time of the vehicle's uploaded data, and determines the start time by comparing the trip start time in the database with the start time of the vehicle's uploaded data.

[0023] Step 5: The next time the vehicle is started, it will upload the last trip end time. The platform will compare the vehicle's last offline time with the trip end time. If the times are close, it will be judged as a restart and the trip will not be recorded, making the platform data more consistent with the actual situation. The platform compares the vehicle's last offline time and the trip end time through the following operations: (1) Dynamically scan the trip from the database through a scheduled task; (2) Determine whether the vehicle is online. If it is online, find the last vehicle offline time from the database. If it is offline, find the time when the vehicle last uploaded data from the database as the vehicle offline time; (3) Compare the vehicle offline time and the trip end time.

[0024] The following are examples of the use of the present invention:

[0025] 1. The test vehicle equipped with the vehicle terminal is turned on. The vehicle terminal will upload the data frame of the vehicle trip start.

[0026] 2. The trip start data frame is uploaded to the server, which saves the trip record. The test vehicle continues to work, and the on-board terminal continues to upload the vehicle's normal working data;

[0027] 3. The vehicle is powered off after the work is completed. At this point, a trip ends. Since the terminal timing may be inaccurate, the platform compares the trip start time uploaded by the test vehicle with the time when the vehicle's normal data is uploaded to determine the correct trip start time.

[0028] 4. The platform dynamically scans the trip data through scheduled tasks and determines that the trip data is incomplete and lacks the trip end time. The end time of the trip is determined based on the last frame of normal vehicle data uploaded by the test vehicle.

[0029] The terminal synchronization time is inaccurate, resulting in the start time of the uploaded trip not being consistent with the actual time. By modifying the trip start time with the present invention, the data accuracy is improved.

[0030] When the terminal is offline, the trip end time can be obtained only when the terminal is online next time. The last trip data is accurate. The trip end time is dynamically modified by the present invention to improve data integrity.

[0031] Frequent terminal restarts will submit a lot of invalid travel data. The present invention dynamically modifies the end time to improve data authenticity.

[0032] The present invention aims to determine the actual start and end time of a vehicle's work through real-time analysis in the cloud when the vehicle trip data uploaded by the terminal is inaccurate, thereby determining a trip, dynamically modifying the trip data, and displaying more accurate results on the platform, thereby achieving the purpose of making industrial vehicle trip data more accurate.

Claims

1. A method for calculating travel statistics in the cloud, characterized by: The following steps are involved: Step 1: Install an on-board terminal on the vehicle to collect terminal data through CAN communication, and transmit the collected data through 4G or 5G network to upload the vehicle data collected by the terminal to the cloud server; Step 2: Start the vehicle and upload the trip start time, then start uploading the driving data. After the trip is completed, shut down the vehicle and wait for the next startup to upload the last trip end time. Step 3: After receiving the trip start time, the terminal records the trip. After receiving the trip end time, it can confirm a complete trip and complete the trip data; Step 4: The platform determines the start time by comparing the vehicle's uploaded trip start time with the vehicle's data transmission time. The vehicle stops transmitting data after the trip ends. The platform dynamically scans the vehicle's trip data at regular intervals. If the vehicle is offline, the platform dynamically modifies the trip end time based on the offline time. The platform dynamically scans the trip data through a scheduled task. Based on the trip start time, it searches the database for the end time of the vehicle's uploaded data. The platform then compares the trip start time in the database with the start time of the vehicle's uploaded data to determine the start time. Step 5: The vehicle uploads the last trip end time when it is started next time. The platform compares the vehicle's last offline time and the trip end time. If the time is close, it is judged as a restart and the trip is not recorded, so that the platform data is more in line with the actual situation. In step 5, the platform compares the vehicle's last offline time and the trip end time through the following operations: (1) Dynamically scan the trip from the database through a scheduled task; (2) Determine whether the vehicle is online. If it is online, find the last vehicle offline time from the database. If it is offline, find the time when the vehicle last uploaded data from the database as the vehicle offline time; (3) Compare the vehicle offline time and the trip end time.

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