A method for calculating the operation of a slag car

Abstract

The present application relates to a kind of calculation method of slag car operation trip, through the vehicle information reported on networked vehicle, positioning information, vehicle total fuel consumption, total mileage, time of occurrence and other data are stored in the database of big data platform.In the user query a period of time history journey, big data platform carries out real-time calculation, saves the calculation process in database, after completion of calculation, the result is shown to user, solve the false positive rate of electronic fence mode is too high and the problems such as traditional barrier plus license plate recognition mode equipment investment cost is too high, management difficulty.

Description

Technical Field

[0001] This invention belongs to the field of construction waste truck technology, specifically relating to a method for calculating the number of trips a construction waste truck can operate. Background Technology

[0002] During operation, the contractor needs to settle freight charges based on the number of trips made by the dump trucks. The number of trips is therefore of particular concern to both the insurer and the contractor. Currently, common vehicle networking methods involve using electronic fences to delineate the map area of ​​the construction site and disposal plant. When a vehicle passes through the electronic fence, the route is recorded, and the number of trips is calculated based on the number of recorded trips. Alternatively, traditional methods using barriers and license plate recognition can be used to calculate the number of trips.

[0003] Using electronic fences to calculate vehicle transport trips can lead to issues such as missed records and vehicles passing through fences without actually transporting construction waste being recorded abnormally. This results in a high false alarm rate and low accuracy for electronic fences. Furthermore, while using gate barriers and license plate recognition for calculating vehicle transport trips is more accurate than electronic fences, all construction sites and disposal plants need to install both devices, leading to high equipment costs and relatively difficult management. Summary of the Invention

[0004] This invention provides a method for calculating the number of trips of a dump truck. The technical problems to be solved are: the false alarm rate of electronic fences and the high equipment investment cost and management difficulties of traditional gate and license plate recognition methods.

[0005] To address the above technical problems, this invention provides a method for calculating the number of trips undertaken by dump trucks, characterized by the following specific steps:

[0006] Step 1: Enter the start and end times of the vehicle's operation.

[0007] Step 2: Based on the time parameters input by the user, the big data platform retrieves the vehicle location data, occurrence time, total fuel consumption, total vehicle mileage, and data ID from the big data platform.

[0008] Step 3, Data Segmentation: Sort the data from Step 2 in ascending order by time. For data where the time interval between two adjacent vehicle data points is greater than the critical value T1, classify them as two transport trips and divide the data into multiple segments as the result of multiple trips.

[0009] Step 4: Fragmented Data: The multiple trip results calculated in Step 3 are re-verified based on the fuel consumption, distance, and time taken for each trip. Data segments that meet any one of the following three conditions are marked as fragmented data and treated as invalid data segments: fuel consumption per vehicle is less than the critical value O, distance is less than the critical value D1, and transport time is less than the critical value T2. If none of the above three conditions are met, the data segment is considered a valid data segment.

[0010] Step 5: Merging Fragmented Data: Obtain the data segment closest to the start time of the fragmented data as the comparison data segment. Calculate the distance between the start position of the fragmented data and the start position of the comparison data segment. Fragmented data with a distance less than the critical value D2 will be merged into the comparison data segment. For fragmented data segments that do not meet the judgment criteria, obtain the comparison data segment closest to the end time of the fragmented data and verify it. Fragmented data that still does not meet the above conditions will be discarded.

[0011] Step 6: Finally, the processed results are returned to the user for display through the big data platform.

[0012] Beneficial effects: This invention utilizes connected vehicles to report vehicle information, storing data such as location information, total fuel consumption, total mileage, and time of occurrence in the database of a big data platform. When a user queries historical travel records for a period of time, the big data platform performs real-time calculations, saves the calculation process in the database, and displays the results to the user after the calculation is complete. This solves the problems of high false alarm rates in electronic fence methods and high equipment investment costs and management difficulties in traditional barrier gate and license plate recognition methods. Attached Figure Description

[0013] Figure 1 This is a flowchart of the method of the present invention;

[0014] Figure 2 The detailed design steps for trip calculation in this invention are as follows. Detailed Implementation

[0015] To make the objectives, contents, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in further detail below.

[0016] The present invention proposes a method for calculating the number of trips of a dump truck, the specific steps of which are as follows:

[0017] Step 1: The user enters the start and end times of the vehicle's operation.

[0018] Step 2: Based on the time parameters input by the user, the big data platform retrieves the vehicle location data, occurrence time, total fuel consumption, total vehicle mileage, and data ID from the big data platform.

[0019] Step 3: Data Segmentation: Sort the data from Step 2 in ascending order by time. For data where the time interval between two adjacent vehicle data points is greater than the threshold T1 (T1 can be adjusted based on the actual unloading time in business operations), classify them as two separate transport trips. Then, divide the data into multiple segments to create a multi-segment trip result. The specific method is as follows:

[0020] After sorting the data by time, iterate through it. Within the iteration loop, check if the data exists in the Redis database. If the data is not found in Redis, then iterate through the two most recent data entries and check their occurrence times. i+1 time i Compare them, if time i+1 -time i ≥T1, mark Time i+1 Point is the starting point, time i Discard invalid points. If time i+1 -time i <T1, marked with time i Starting point, time i+1 No processing is performed, and the time is set. i The vehicle location data at the starting point, the time of occurrence, the total fuel consumption, the total mileage of the vehicle, the data ID, and the tag are stored in the Redis database.

[0021] If there is data in Redis, retrieve the time of the latest data from Redis. r With time i Compare, if time i -time r If <T1, then this data is recorded as data within this segment, and the time is set. i The data is stored in Redis. If time... i -time r If ≥T1, then mark time. r Mark the end of this data segment with the time marker. i This marks the starting point of the second data segment, and two data entries along with their tags are stored in Redis. The process continues iterating until all data has been traversed. This completes the data segmentation operation.

[0022] Step 4: Fragmented Data: The multiple trip results calculated in Step 3 are re-verified based on the fuel consumption, distance, and time taken for each trip. Data segments that meet any one of the following three conditions are marked as fragmented data and considered invalid: fuel consumption per trip is less than the critical value O (which can be adjusted based on actual vehicle idling time and average fuel consumption per 100 kilometers); distance is less than the critical value D1 (which can be adjusted based on the size of the disposal plant and construction site and the distance between them); and transport time is less than the critical value T2 (which can be adjusted based on the actual transport time from the construction site to the disposal plant). If none of the above three conditions are met, the data segment is considered valid.

[0023] Step 5: Fragmented Data Merging: Obtain the data segment with the closest start time to the fragmented data as the comparison data segment. Calculate the distance between the start position of the fragmented data and the start position of the comparison data segment. Fragmented data with a distance less than the critical value D2 (D2 can be adjusted based on the size of the disposal plant / construction site and the distance between them) are merged into the comparison data segment. For fragmented data segments that do not meet the judgment criteria, obtain the comparison data segment with the closest end time to the fragmented data and verify it. Fragmented data that still does not meet the above conditions are discarded.

[0024] Step 6: Finally, the processed results are returned to the user for display through the big data platform.

[0025] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for calculating the number of trips of a dump truck, characterized in that, The specific steps are as follows: Step 1: Enter the start and end times of the vehicle's operation. Step 2: Based on the time parameters input by the user, the big data platform retrieves the vehicle location data, occurrence time, total fuel consumption, total vehicle mileage, and data ID from the big data platform. Step 3, Data Segmentation: Sort the data from Step 2 in ascending order by time. For data where the time interval between two adjacent vehicle data points is greater than the critical value T1, classify them as two transport trips and divide the data into multiple segments as the result of multiple trips. Step 4: Fragmented Data Identification: The multiple trip results calculated in Step 3 are subjected to secondary verification based on the fuel consumption, distance, and time spent for each trip. Data segments that meet any one of the following three conditions are marked as fragmented data and treated as invalid data segments: fuel consumption per vehicle is less than the critical value O, distance is less than the critical value D1, and transport time is less than the critical value T2. If none of the above three conditions are met, the data segment is considered a valid data segment. Step 5: Merging Fragmented Data: Obtain the data segment closest to the start time of the fragmented data as the comparison data segment. Calculate the distance between the start position of the fragmented data and the start position of the comparison data segment. Fragmented data with a distance less than the critical value D2 will be merged into the comparison data segment. For fragmented data segments that do not meet the judgment criteria, obtain the comparison data segment closest to the end time of the fragmented data and verify it. Fragmented data that still does not meet the above conditions will be discarded. Step 6: Finally, the processed results are returned to the user for display through the big data platform.

2. The method for calculating the number of trips of a dump truck according to claim 1, characterized in that, The T1 value is adjusted based on the actual unloading time in business operations.

3. The method for calculating the number of trips of a dump truck according to claim 1, characterized in that, The O value is adjusted based on the vehicle's actual idling time and average fuel consumption per 100 kilometers.

4. The method for calculating the number of trips of a dump truck according to claim 1, characterized in that, The D1 value is adjusted based on the size of the disposal plant and the construction site, as well as the distance between them.

5. The method for calculating the number of trips of a dump truck according to claim 1, characterized in that, The T2 value is adjusted based on the actual transportation time from the construction site to the disposal plant in the business.

6. The method for calculating the number of trips of a dump truck according to claim 1, characterized in that, The D2 value is adjusted based on the size of the disposal plant and the construction site, as well as the distance between them.

7. The method for calculating the number of trips of a dump truck according to claim 1, characterized in that, The specific method for S3 is as follows: After sorting the data by time, iterate through it. Within the iteration loop, check if the data exists in the Redis database. If the data is not found in Redis, then check the timestamps of the two most recent data entries in the loop. i+1 time i Compare them, if time i+1 -time i ≥T1, mark Time i+1 Point is the starting point, time i Discard invalid points; if time i+1 -time i <T1, marked with time i Starting point, time i+1 No processing is performed, and the time is set. i The vehicle location data at the starting point, the time of occurrence, the total fuel consumption, the total mileage of the vehicle, the data ID, and the tag are stored in the Redis database; If there is data in Redis, retrieve the time of the latest data from Redis. r With time i Compare, if time i -time r If <T1, then this data is recorded as data within this segment, and the time is set. i The data is stored in Redis; if time... i -time r If ≥T1, then mark time. r Mark the end of this data segment with the time marker. i Set the starting point for the second data segment and save the two data segments and their markers in Redis; continue looping until the data traversal is complete, thus completing the data segmentation operation.

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