Agricultural machine operation area calculation method based on Internet of Vehicles big data

Abstract

The invention discloses an agricultural machinery operation area calculation method based on Internet of Vehicles big data, belongs to the technical field of Internet of Vehicles big data, and solves the technical problems that a traditional operation area measurement mode depends on manual investigation or simple estimation, and is generally low in efficiency, high in labor cost and large in error. The method comprises the steps that a vehicle-mounted terminal with a networking function is installed on an agricultural machine vehicle, the vehicle-mounted terminal is in butt joint with a whole vehicle CAN bus, efficient communication with a vehicle ECU is achieved, and the vehicle-mounted terminal collects key operation data of the engine rotating speed, the vehicle speed, the vehicle position, time and the operation breadth in real time; the vehicle-mounted terminal is stably connected with the cloud platform through a wireless network to realize real-time uploading and interaction of data; the cloud platform performs intelligent identification and calculation on the operation track, the operation state and the effective operation area of the agricultural machine based on mass operation data in combination with a big data analysis technology so as to efficiently and accurately generate an operation area statistical result.

Description

Technical Field

[0001] This invention relates to the field of vehicle-to-everything (V2X) big data technology, and more specifically, to a method for calculating the area of ​​agricultural machinery operations based on V2X big data. Background Technology

[0002] In recent years, the mechanization of agriculture has accelerated, the number of agricultural machinery and equipment has continued to grow, and intelligent technologies have been rapidly integrated into all aspects of agricultural production. Especially in field operations, intelligent driving agricultural machinery has achieved large-scale application, driving agricultural production towards higher efficiency and precision. Against this backdrop, the area of ​​agricultural machinery operations, as core data, is not only related to the billing and settlement of operational services, but also an important basis for evaluating operational efficiency and management effectiveness.

[0003] Traditional methods of measuring farmland area rely on manual surveying or simple estimation, which are generally inefficient, costly in terms of labor, and prone to errors. This is especially true when dealing with large areas of irregular or complex farmland, where rapid and accurate statistics are difficult to achieve. This extensive measurement model is no longer sufficient to meet the needs of modern agricultural precision management. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the above-mentioned shortcomings of the prior art. The purpose of the present invention is to provide a method for calculating the agricultural machinery operation area based on vehicle network big data, which can improve the automation and intelligence level of operation area measurement and provide an efficient and reliable digital solution for agricultural machinery management and services.

[0005] The technical solution of this invention is: a method for calculating the agricultural machinery operation area based on vehicle-to-everything (V2X) big data. This involves installing a vehicle-mounted terminal with network connectivity on the agricultural machinery. The terminal interfaces with the vehicle's CAN bus to achieve efficient communication with the vehicle's ECU. The terminal collects key operational data in real time, including engine speed, vehicle speed, vehicle position, time, and operating width. It also establishes a stable connection with a cloud platform via a wireless network, enabling real-time data upload and interaction. The cloud platform, based on massive amounts of operational data and combined with big data analytics, intelligently identifies and calculates the agricultural machinery's operation trajectory, operating status, and effective operating area, thereby accurately generating statistical results for the operating area.

[0006] As a further improvement, the data is processed and calculated using big data analytics techniques as follows: Engine speed, vehicle speed, location, and time are extracted from vehicle network data, and the data is cleaned according to the reasonable value range corresponding to the data. The operation status of agricultural machinery is determined based on engine speed, vehicle speed, and time. The dataset is then segmented, and the segmented data of the operation is selected. For each data segment, the location information of the trajectory points with continuous time sequence is judged based on vehicle speed and time. For unreasonable trajectory points, the actual mileage is calculated using vehicle speed, and finally one or more operation segments are formed.

[0007] Furthermore, the following specific steps are included: Step 1. Extract relevant data of the agricultural machinery within the time range from the vehicle network data of the agricultural machinery. Determine the working status of the agricultural machinery based on engine speed, vehicle speed, and time, and use the data that is in the working status as the working data. Step 2. Sort the job data in chronological order and calculate the time difference ΔT between two adjacent data sets; Step 3. Determine whether the position coordinates of two adjacent data points are reasonable based on the time difference ΔT, correct unreasonable position coordinates, and finally obtain a dataset that identifies the operation status and the corrected coordinates; Step 4. Calculate the work area A1, ..., An for each data point in the work status; Step 5. Calculate the area S1 of repeated operations for each data point; Step 6. Calculate the actual work area S = (A1 + ... + An) - repeated work area S1.

[0008] Furthermore, in step 2, if the time difference ΔT is greater than the data acquisition frequency C, the time difference ΔT is set to the data acquisition frequency C.

[0009] Furthermore, step 3 specifically includes: using the Haversine formula to calculate the coordinate distance Li between two adjacent data points, calculating the vehicle speed Vi = coordinate distance Li / time difference ΔT, and judging whether the position coordinates are reasonable by the difference between the calculated vehicle speed Vi and the actual collected vehicle speed V; if the position coordinates are unreasonable, then taking the coordinates of the data before and after the unreasonable coordinates for linear fitting to correct the coordinates.

[0010] Further, in step 4, the WGS84 coordinates of the location information in the dataset are converted to the WebMercator metric coordinate system, sorted in chronological order, and the working area A1, ..., An of each data point in the working state is calculated by combining the working status and working width.

[0011] Furthermore, the formula for converting WGS84 coordinates to Web Mercator is as follows: X = Longitude × 20037508.34 / 180; Y = log(tan((90° + latitude) × π / 360)) / (π / 360) × 20037508.34 / 180.

[0012] Further, in step 5, the vertex coordinates (A1-1, A1-2, A1-3, A1-4), ..., (An-1, An-2, An-3, An-4) of the work area of ​​each data rectangle are determined, and the intersection of the vertex coordinates of all work areas is obtained by combining Boolean geometric operations to obtain the repeated work area S1.

[0013] Beneficial effects Compared with the prior art, the advantages of this invention are as follows: 1. This invention eliminates the need for operation path planning and complex calculations, reducing the cost of calculating the operating area. The entire process requires no additional sensors or manual intervention, significantly reducing system deployment costs and maintenance complexity, and improving the automation and intelligence level of operating area measurement. It provides an efficient and reliable digital solution for agricultural machinery management and services.

[0014] 2. The working area calculation of this invention can be adapted to the calculation of agricultural machinery working area in complex scenarios, without the need to construct the outer boundary of the working area.

[0015] 3. The present invention is closer to the actual operation situation in identifying missed tillage and repeated tillage. Attached Figure Description

[0016] Figure 1 This is a flowchart of the present invention; Figure 2 A flowchart for data processing and computation; Figure 3 This is a schematic diagram of trajectory breakdown. Detailed Implementation

[0017] The present invention will be further described below with reference to specific embodiments shown in the accompanying drawings.

[0018] See Figures 1-3 A method for calculating the agricultural machinery operation area based on vehicle-to-everything (V2X) big data is proposed. This method involves installing a network-enabled vehicle-mounted terminal on the agricultural machinery. The terminal interfaces with the vehicle's CAN bus to achieve efficient communication with the vehicle's ECU. The terminal collects key operational data in real time, including engine speed, vehicle speed, vehicle position, time, and operating width. It establishes a stable connection with a cloud platform via a wireless network, enabling real-time data upload and interaction. The cloud platform, based on massive operational data and combined with big data analytics, intelligently identifies and calculates the agricultural machinery's operation trajectory, operating status, and effective operating area, thereby accurately generating statistical results for the operating area.

[0019] The data is processed and calculated using big data analytics techniques as follows: Engine speed, vehicle speed, location, and time are extracted from vehicle network data, and the data is cleaned according to the reasonable value range corresponding to the data. The operation status of agricultural machinery is determined based on engine speed, vehicle speed, and time. The dataset is then segmented, and the segmented data of the operation is selected. For each data segment, the location information of the trajectory points with continuous time sequence is judged based on vehicle speed and time. For unreasonable trajectory points, the actual mileage is calculated using vehicle speed, and finally one or more operation segments are formed.

[0020] The calculation process includes the following specific steps 1 to 6: Step 1. Extract relevant data of the agricultural machinery within the time range from the vehicle network data of the agricultural machinery. Determine the operating status of the agricultural machinery based on engine speed, vehicle speed, and time, and use the data that indicates the operating status as the operating data.

[0021] Step 2. Sort the job data in chronological order and calculate the time difference ΔT between two adjacent data sets.

[0022] Step 3. Determine whether the position coordinates of two adjacent data points are reasonable based on the time difference ΔT, correct unreasonable position coordinates, and finally obtain a dataset that identifies the operation status and the corrected coordinates.

[0023] Specifically, the Haversine formula is used to calculate the coordinate distance Li between two adjacent data points, and the vehicle speed Vi is calculated as coordinate distance Li / time difference ΔT. The difference between the calculated vehicle speed Vi and the actual collected vehicle speed V is used to determine whether the position coordinates are reasonable. If the position coordinates are unreasonable, the coordinates of the data before and after the unreasonable coordinates are used for linear fitting to correct the coordinates.

[0024] Step 4. Calculate the work area A1, ..., An for each data point in the work status.

[0025] Specifically, the WGS84 coordinates of the location information in the dataset are converted to the Web Mercator metric coordinate system, sorted in chronological order, and the working area A1, ..., An of each data point in the working state is calculated by combining the working status and working width.

[0026] The formula for converting WGS84 coordinates to Web Mercator is as follows: X = Longitude × 20037508.34 / 180; Y = log(tan((90° + latitude) × π / 360)) / (π / 360) × 20037508.34 / 180.

[0027] Step 5. Calculate the area S1 of repeated operations for each data point.

[0028] Specifically: Determine the vertex coordinates (A1-1, A1-2, A1-3, A1-4), ..., (An-1, An-2, An-3, An-4) of the area of ​​each data rectangle. Then, use Boolean geometric operations to find the intersection of the vertex coordinates of all the areas to obtain the repeated area S1. Figure 3 As shown.

[0029] Step 6. Calculate the actual work area S = (A1 + ... + An) - repeated work area S1.

[0030] In step 2, if the time difference ΔT is greater than the data acquisition frequency C, setting the time difference ΔT to the data acquisition frequency C can ensure that valid data is acquired.

[0031] This invention combines big data analytics to intelligently identify and calculate agricultural machinery operation trajectories, operational status, and effective operating areas, thereby accurately generating statistical results for the operating area. The entire process requires no additional sensors or manual intervention, significantly reducing system deployment costs and maintenance complexity, and improving the automation and intelligence of operating area calculation. It provides an efficient and reliable digital solution for agricultural machinery management and services.

[0032] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention, and these will not affect the effectiveness of the implementation of the present invention or the practicality of the patent.

Claims

1. A method for calculating the area of ​​agricultural machinery operations based on vehicle-to-everything (V2X) big data, characterized in that, By installing a network-enabled vehicle terminal on agricultural machinery, the vehicle terminal interfaces with the vehicle's CAN bus to achieve efficient communication with the vehicle's ECU. The vehicle terminal collects key operational data in real time, including engine speed, vehicle speed, vehicle position, time, and working width. The vehicle terminal establishes a stable connection with a cloud platform via a wireless network, enabling real-time data upload and interaction. Based on massive operational data and combined with big data analytics, the cloud platform intelligently identifies and calculates the agricultural machinery's operating trajectory, operating status, and effective operating area, thereby accurately generating statistical results of the operating area.

2. The method for calculating agricultural machinery operation area based on vehicle network big data according to claim 1, characterized in that, The data is processed and calculated using big data analytics techniques as follows: Engine speed, vehicle speed, location, and time are extracted from vehicle network data, and the data is cleaned according to the reasonable value range corresponding to the data. The operation status of agricultural machinery is determined based on engine speed, vehicle speed, and time. The dataset is then segmented, and the segmented data of the operation is selected. For each data segment, the location information of the trajectory points with continuous time sequence is judged based on vehicle speed and time. For unreasonable trajectory points, the actual mileage is calculated using vehicle speed, and finally one or more operation segments are formed.

3. The method for calculating agricultural machinery operation area based on vehicle network big data according to claim 2, characterized in that, The specific steps include the following: Step 1. Extract relevant data of the agricultural machinery within the time range from the vehicle network data of the agricultural machinery. Determine the working status of the agricultural machinery based on engine speed, vehicle speed, and time, and use the data that is in the working status as the working data. Step 2. Sort the job data in chronological order and calculate the time difference ΔT between two adjacent data sets; Step 3. Determine whether the position coordinates of two adjacent data points are reasonable based on the time difference ΔT, correct unreasonable position coordinates, and finally obtain a dataset that identifies the operation status and the corrected coordinates; Step 4. Calculate the work area A1, ..., An for each data point in the work status; Step 5. Calculate the area S1 of repeated operations for each data point; Step 6. Calculate the actual work area S = (A1 + ... + An) - repeated work area S1.

4. The method for calculating agricultural machinery operation area based on vehicle network big data according to claim 3, characterized in that, In step 2, if the time difference ΔT is greater than the data acquisition frequency C, the time difference ΔT is set to the data acquisition frequency C.

5. The method for calculating agricultural machinery operation area based on vehicle network big data according to claim 3, characterized in that, Step 3 specifically includes: using the Haversine formula to calculate the coordinate distance Li between two adjacent data points, calculating the vehicle speed Vi = coordinate distance Li / time difference ΔT, and judging whether the position coordinates are reasonable by the difference between the calculated vehicle speed Vi and the actual collected vehicle speed V; if the position coordinates are unreasonable, then taking the coordinates of the data before and after the unreasonable coordinates and performing linear fitting to correct the coordinates.

6. The method for calculating agricultural machinery operation area based on vehicle network big data according to claim 3, characterized in that, In step 4, the WGS84 coordinates of the location information in the dataset are converted to the Web Mercator metric coordinate system, sorted in chronological order, and the working area A1, ..., An of each data point in the working state is calculated by combining the working status and working width.

7. The method for calculating agricultural machinery operation area based on vehicle network big data according to claim 6, characterized in that, The formula for converting WGS84 coordinates to Web Mercator is as follows: X = Longitude × 20037508.34 / 180; Y = log(tan((90° + latitude) × π / 360)) / (π / 360) × 20037508.34 / 180.

8. The method for calculating agricultural machinery operation area based on vehicle network big data according to claim 3, characterized in that, In step 5, determine the vertex coordinates (A1-1, A1-2, A1-3, A1-4), ..., (An-1, An-2, An-3, An-4) of the work area of ​​each data rectangle, and use Boolean geometric operations to find the intersection of the vertex coordinates of all work areas to obtain the repeated work area S1.

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