Method for detecting dust in an agricultural production environment

Abstract

The application discloses a kind of agricultural production environment dust detection methods, and the dust detection of agricultural production environment is carried out by unmanned aerial vehicle and dust detection device.First, the corner point longitude and latitude of the region to be detected is determined;First coordinate system is established;With the projection position of the tail of the central axis of the agricultural machinery body on the plane as the origin of the coordinate system, a second coordinate system is established;After the tail of the agricultural machinery body reaches the dust detection starting point, the unmanned aerial vehicle flies at a set height according to the M-type path from the origin of the first plane coordinate system;Fixed frequency recording detection data;The region to be detected is rasterized in the second coordinate system;Using inverse distance weighting difference algorithm, calculate the dust concentration of each grid;Obtain the dust concentration spatial distribution of the set height;Repeat to obtain the dust concentration spatial distribution by flying at a set height multiple times at different set heights.

Description

Technical Field

[0001] This invention relates to the field of agricultural production, and in particular to a method for detecting dust in agricultural production environments. Background Technology

[0002] In agricultural production activities, especially during tilling and harvesting, agricultural machinery interacts with the soil and crops, disturbing dust adhering to the ground or crop surfaces and generating large amounts of dust. This dust, suspended around the machinery, seriously affects the health of operators, their visibility, and production safety. Currently, most research on dust in agricultural production environments remains at the stage of qualitative analysis, mainly due to the lack of flexible and efficient detection methods to obtain spatial distribution data of dust in agricultural production environments. Dust generated during agricultural machinery operation is characterized by unpredictable emission sources, rapid dust changes, and large particle sizes. Therefore, highly mobile, rapid, and multi-particle-size dust detection methods are of great significance for studying the generation and diffusion patterns of dust in agricultural production environments.

[0003] Currently, the main methods for detecting dust in agricultural production environments include: setting up particulate matter samplers near the operating path of harvesting machinery for continuous sampling, collecting airborne dust through negative pressure fans, cutters, and filter membranes, and calculating the average dust concentration at a fixed location over a period of time based on changes in filter membrane quality and fan airflow after a relatively long sampling period; or using a micro-oscillation balance or beta-ray particulate matter detector installed in a stable mounting position for long-term sampling to measure the average dust concentration at a fixed location over a period of time; or using a handheld dust concentration meter to measure the dust concentration near harvesting machinery in real time; or using a small unmanned vehicle equipped with particulate matter sensors to detect the concentrations of PM2.5 and PM10 in livestock sheds and poultry houses.

[0004] Traditional dust detection methods, such as filtration and weighing, micro-oscillation balance, and beta-ray methods, typically require continuous operation for 60 minutes or even longer to obtain a single data point. The data represents an average value over the specified time period, failing to provide real-time monitoring of dust concentrations at different locations around agricultural machinery. Furthermore, measuring dust concentrations at different locations within a defined area requires deploying multiple detection devices. While handheld detectors can measure dust concentrations around agricultural machinery, manual operation and readings are inflexible, pose health risks to personnel, and present safety hazards. Small, unmanned particulate matter detection methods can detect particulate matter concentrations at different locations within a specific area, but their intelligent detection altitude is too low, and the detection path is limited by surface conditions. Therefore, existing detection technologies lack flexible and efficient detection methods and scientific data processing techniques, failing to provide spatial distribution data of dust concentrations in the agricultural production environment within the tested area. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting dust in agricultural production environments, which can flexibly and efficiently obtain spatial distribution data of dust concentration in agricultural production environments within the test area.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: This invention discloses a method for detecting dust in agricultural production environments, which uses a drone and a dust detection device to detect dust in the agricultural production environment. The dust detection device includes a primary positioning and elevation column, a positioning module, a secondary positioning and elevation column, a pre-sampling filter cover, a light-scattering particulate matter sensor, a microcontroller, a wireless data transmission module, an input / output module, and a PCB board. The light-scattering particulate matter sensor is sequentially connected and fixed to the drone via the primary positioning and elevation column, the PCB board, and the secondary positioning and elevation column. The pre-sampling filter cover is installed at the sampling port of the light-scattering particulate matter sensor. The positioning module, the microcontroller, the wireless data transmission module, and the input / output module are also mounted on the PCB board. The dust detection method is specifically as follows: S1, determine the latitude and longitude of the corner points of the area to be detected; S2, establish the first coordinate system with a certain corner point as the origin of the coordinate system; S3. Establish a second coordinate system with the projection position of the tail of the central axis of the agricultural machinery body on the plane as the origin of the coordinate system; S4. After the tail of the agricultural machinery body reaches the dust detection start point, the UAV flies from the origin of the first plane coordinate system in an M-shaped path at a set altitude. S5, the dust detection device carried by the UAV records detection data at a fixed frequency and sends it to the data processing terminal through the wireless data transmission module; S6. Using the central axis of the agricultural machinery body as the axis of symmetry, the area to be detected is rasterized in the second coordinate system. S7. Calculate the dust concentration of each grid cell using the inverse distance weighted difference algorithm; obtain the spatial distribution of dust concentration at the set height; S8 involves repeatedly flying at different set altitudes and executing steps S5-S7 to obtain the spatial distribution of dust concentration.

[0007] Preferably, the positioning module adopts dual-mode positioning of BDS and GPS.

[0008] Preferably, the light-scattering particulate sensor is located directly above the center of the plane where the UAV rotor is located, at a distance of 10 cm or more from the UAV rotor, to avoid the airflow disturbance caused by the UAV rotor affecting the detection results.

[0009] Preferably, the dust detection start point is the first intersection of the flight path of the UAV and the extended line of the central axis of the agricultural machinery body, which is 1.5 meters from the tail of the agricultural machinery body.

[0010] Preferably, the detection data includes data acquisition time, coordinates of the acquisition point in the first coordinate system, and dust concentration at the acquisition point.

[0011] Preferably, step S7 specifically includes: S7.1, Convert the coordinates of the first coordinate system of the collection point to the coordinates of the second coordinate system; S7.2, fill the dust concentration at the collection point into the corresponding grid, and fill the corresponding symmetrical grid with the axis of symmetry as the center; S7.3 uses the inverse distance weighted interpolation method to calculate the dust concentration in other grid cells.

[0012] Preferably, the pre-filter cover of the sampling port is used to prevent straw debris in the agricultural production environment from damaging the light scattering particulate matter sensor.

[0013] Preferably, it can display the dust concentration at various locations in the agricultural production environment in real time on a mobile device and store the data.

[0014] The advantages of this invention are as follows: For agricultural production environments, a pre-filter cover is installed at the sampling port of the particulate matter sensor to prevent straw and other debris generated during agricultural machinery operations from entering the dark chamber of the light-scattering particulate matter sensor and damaging it; the BDS / GPS dual-mode positioning module and the wireless data transmission module and their antennas are arranged separately to avoid interference between signals; through multi-level positioning elevation columns and PCB boards, the light-scattering particulate matter sensor is ensured to be installed at least 10cm above the plane of the motor and rotor, avoiding interference from airflow generated by rotor disturbance on the detection and sampling; and by utilizing UAV technology and employing reasonable detection and data processing methods, the spatial distribution of dust data around mobile pollution sources in agricultural production environments is constructed. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the drone and dust detection device used in the method described in this invention.

[0016] Figure 2 This is a schematic diagram illustrating the implementation of the method described in Embodiment 2 of the present invention.

[0017] Figure 3 This is a schematic diagram illustrating the effect of rasterizing the rectangular detection area in Embodiment 2 of the present invention. Detailed Implementation

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: like Figure 1 As shown, this application proposes a method for detecting dust in agricultural production environments, which uses a drone and a dust detection device to detect dust in the agricultural production environment. Figure 1 As shown, the present invention uses a quadcopter drone, and a dust detection device is installed directly above the quadcopter drone.

[0020] The quadcopter drone includes a battery 1, landing gear 2, motors and rotors 3, and a flight control system 4.

[0021] The dust detection device includes a primary positioning and elevation column 5, a positioning module 6, a secondary positioning and elevation column 7, a pre-filter cover for the sampling port 8, a light scattering particulate matter sensor 9, a microcontroller 10, a wireless data transmission module 11, an input / output module 12, and a PCB board 13.

[0022] The light-scattering particulate sensor is sequentially connected and fixed to the UAV via the primary positioning and elevation column 5, the PCB board 13, and the secondary positioning and elevation column 7. The light-scattering particulate sensor is installed above the plane of the UAV rotor, at a distance greater than 10 cm from the plane of the UAV rotor, to avoid interference from airflow generated by the UAV rotor's movement on the detection and sampling.

[0023] The sampling port pre-filter is installed at the sampling port of the light scattering particulate matter sensor to prevent straw and other debris generated during agricultural machinery operations from entering the detection dark chamber of the light scattering particulate matter sensor 9 and damaging the sensor. The positioning module, the microcontroller, the wireless data transmission module, and the input / output module are also mounted on the PCB board, and their arrangement can be adjusted according to available space.

[0024] The positioning module uses dual-mode positioning with BDS and GPS.

[0025] During operation, the dust concentration at spatial locations is detected by a light-scattering particulate sensor 9; latitude and longitude information is acquired by a BDS / GPS dual-mode positioning module 6 and converted into surface coordinates of the spatial locations; the dust concentration and coordinate information of the spatial locations are processed by a microcontroller 10 and then sent to a mobile terminal by a wireless data transmission module 11; the mobile terminal receives and stores the data via an app. The mobile terminal can also display the dust concentration at various locations in the agricultural production environment in real time.

[0026] The dust detection method is specifically as follows: S1, determine the latitude and longitude of the corner points of the area to be detected; S2, establish the first coordinate system with a certain corner point as the origin of the coordinate system; S3. Establish a second coordinate system with the projection position of the tail of the central axis of the agricultural machinery body on the plane as the origin of the coordinate system; S4. After the tail of the agricultural machinery body reaches the dust detection start point, the UAV flies at a fixed altitude from the origin of the first plane coordinate system in an M-shaped path. The dust detection start point is the first intersection of the flight path of the UAV and the extended line of the central axis of the agricultural machinery body, which is 1.5 meters from the tail of the agricultural machinery body.

[0027] S5, the dust detection device carried by the UAV records detection data at a fixed frequency and sends it to the data processing terminal through the wireless data transmission module; the detection data includes data acquisition time, coordinates of the acquisition point in the first coordinate system, and dust concentration at the acquisition point.

[0028] S6. Using the central axis of the agricultural machinery body as the axis of symmetry, the area to be detected is rasterized in the second coordinate system. S7, using the inverse distance weighted difference algorithm, calculate the dust concentration of each grid cell; obtain the spatial distribution of dust concentration at the set height; specifically including: S7.1, Convert the coordinates of the first coordinate system of the collection point to the coordinates of the second coordinate system; S7.2, fill the dust concentration at the collection point into the corresponding grid, and fill the corresponding symmetrical grid with the axis of symmetry as the center; S7.3 uses the inverse distance weighted interpolation method to calculate the dust concentration in other grid cells.

[0029] S8 involves repeatedly flying at different set altitudes and executing steps S5-S7 to obtain the spatial distribution of dust concentration.

[0030] Example 2: like Figure 2 As shown, taking a rectangular area to be detected as an example, the specific implementation steps of the dust detection method of the present invention are explained.

[0031] Figure 2 The rectangular region to be detected has a side length of a×b. First: S1, determine the latitude and longitude of the corner points of the area to be detected; such as Figure 2 As shown, the four corner points of the rectangular area to be detected are O, A, B, and C. Determine the latitude and longitude of three of the corner points O, A, and B.

[0032] S2. Establish a first coordinate system with point O as the origin; this first coordinate system is also called the surface coordinate system, and it uses X... e OY e If we express that the coordinates of any point I within the region to be detected in the first coordinate system are (x, y, y), then the coordinates of that point I in the first coordinate system are expressed as (x, y ... eI y eI The value can be calculated using Heron's formula based on the latitude and longitude of the point.

[0033] S3. A second coordinate system is established with the projection of the tail of the central axis 16 of the agricultural machinery body 19 onto the plane as the origin; this second coordinate system is also called the volume coordinate system, denoted by X. b OY b express.

[0034] S4. After the tail of the agricultural machinery body 19 reaches the dust detection start point 17, the UAV flies from the origin of the first plane coordinate system in an M-shaped path at a set altitude. like Figure 2 As shown, agricultural machinery 19 enters the detection area from the far end along the central axis 16 at a speed v P During the operation, when the tail of the harvesting machinery reaches the measurement start position 17, the flight measurement start command is issued, and this time is recorded as T0. The UAV carrying a dust detection device travels at a speed v d The measurement is performed by flying at a constant altitude along an M-shaped path from a height h above the origin O of the first plane coordinate system.

[0035] S5, During flight measurement, detection data is recorded at a fixed frequency, such as transmitting spatial location data to the mobile receiver once per second. The detection data includes the data acquisition time T. i Dust concentration C at the collection point i The first coordinate system coordinates of the sampling point (x eI y eI When the detection end point 20 is reached, the flight measurement is completed. The detection data is sent to the data processing terminal through the wireless data transmission module.

[0036] According to Heron's formula, the coordinates (x, y) of any point I within the rectangular detection region are ultimately determined. eI y eI The calculation formula for ) is as follows: in D IO This represents the distance between the origin O and point I, and so on. Except... D OA = a , D OB = bIn addition, the distance between any other two points can be calculated using the Haversine formula based on the longitude (lon) and latitude (lat) of these two points. The specific calculation formula is as follows: in: D It is the spherical distance between two points, in meters; r It is the average radius of the Earth, typically expressed as approximately 6.371 × 10⁻⁶. 6 rice; lat1 and lat2 are the latitudes of the two points, expressed in radians; Δlat = lat2 − lat1 is the difference in latitude between two points, expressed in radians; Δlon = lon2 − lon1 is the difference in longitude between the two points, expressed in radians.

[0037] The dust detection start point should be ensured that when the UAV device 15, used for detecting dust in the agricultural production environment, first crosses the central axis after measurement, its coordinates are exactly 1.5m directly behind the tail of the agricultural machinery 19, in order to obtain measurement data for key spatial points. That is, the dust detection start point is the point where the UAV's flight path first intersects the extended line of the central axis of the agricultural machinery body, 1.5 meters behind the agricultural machinery body. This can be calculated using the following formula: the dust detection start point, represented by P, is located at X... e OY e Coordinates in the first coordinate system (x) eP y eP )for: S6, using the central axis of the agricultural machinery body as the axis of symmetry, the area to be detected is rasterized in the second coordinate system; as shown... Figure 3 The image shows a schematic diagram illustrating the effect of rasterizing a rectangular area to be detected.

[0038] S7, using the inverse distance weighted difference algorithm, calculate the dust concentration of each grid cell; obtain the spatial distribution of dust concentration at the set height; specifically including: S7.1, convert the coordinates of the first coordinate system of the acquisition point to the coordinates of the second coordinate system; that is, convert the coordinates of the first coordinate system of the acquisition point I obtained by flight measurement (x eI y eI ) Convert the coordinates to the second coordinate system (x) bI y bI The calculation formula is as follows: S7.2, fill the dust concentration at the collection point into the corresponding grid, and with the axis of symmetry as the center, fill the dust concentration at the collection point into the corresponding symmetrical grid; S7.3 uses inverse distance weighted interpolation to calculate the dust concentration C in other grid cells. The calculation formula is as follows: in: C i The closest grid to the calculated interpolation n The first point i Dust concentration values ​​at each point; d i It is the distance between it and the raster from which the interpolation is calculated.

[0039] S8 involves repeatedly flying at different set altitudes and executing steps S5-S7 to obtain the spatial distribution of dust concentration.

[0040] The dust detection method for agricultural production environments described in this invention employs a high-temporal-resolution, low-cost light-scattering particulate matter sensor to detect dust data, achieving a dust data detection frequency of up to 1 time per second. By installing a pre-filter at the sampling port of the particulate matter sensor, it prevents straw and other debris generated during agricultural machinery operations from entering the dark chamber of the light-scattering particulate matter sensor and damaging it. The layout of each module in the dust detection device is designed to avoid interference between signals and interference from airflow generated by rotor disturbances on the particulate matter sampler's detection. By using a drone as a power platform, it avoids direct exposure of detection personnel to high-concentration dust environments while rapidly acquiring dust data from different spatial locations at different altitudes. Spatiotemporal transformation is achieved through coordinate transformation, thereby enabling the detection of dynamic dust emission sources. A method for constructing complete spatial dust data around the operating path of agricultural machinery is developed through rasterization of the dust detection area in the volume coordinate system, filling of original measurement data, and generation of symmetrical and interpolated data.

Claims

1. A method for detecting dust in agricultural production environments, comprising using a drone and a dust detection device to detect dust in the agricultural production environment; characterized in that, The dust detection device includes a primary positioning and elevation column, a positioning module, a secondary positioning and elevation column, a pre-sampling filter cover, a light-scattering particulate matter sensor, a microcontroller, a wireless data transmission module, an input / output module, and a PCB board. The light-scattering particulate matter sensor is sequentially connected and fixed to the UAV via the primary positioning and elevation column, the PCB board, and the secondary positioning and elevation column. The pre-sampling filter cover is installed at the sampling port of the light-scattering particulate matter sensor. The positioning module, the microcontroller, the wireless data transmission module, and the input / output module are also mounted on the PCB board. The dust detection method is specifically as follows: S1, determine the latitude and longitude of the corner points of the area to be detected; S2, establish the first coordinate system with a certain corner point as the origin of the coordinate system; S3. Establish a second coordinate system with the projection position of the tail of the central axis of the agricultural machinery body on the plane as the origin of the coordinate system; S4. After the tail of the agricultural machinery body reaches the dust detection start point, the UAV flies from the origin of the first coordinate system in an M-shaped path at a set altitude. S5, the dust detection device carried by the UAV records detection data at a fixed frequency and sends it to the data processing terminal through the wireless data transmission module; the detection data includes data acquisition time, coordinates of the acquisition point in the first coordinate system, and dust concentration at the acquisition point; S6. Using the central axis of the agricultural machinery body as the axis of symmetry, the area to be detected is rasterized in the second coordinate system. S7 uses the inverse distance weighted difference algorithm to calculate the dust concentration of each grid cell; Obtaining the spatial distribution of dust concentration at the set height; specifically including: S7.1, Convert the coordinates of the first coordinate system of the collection point to the coordinates of the second coordinate system; S7.2, fill the dust concentration at the collection point into the corresponding grid, and fill the corresponding symmetrical grid with the axis of symmetry as the center; S7.3 Calculate the dust concentration in other grid cells using the inverse distance weighted interpolation method; S8 involves repeatedly flying at different set altitudes and executing steps S5-S7 to obtain the spatial distribution of dust concentration.

2. The method for detecting dust in agricultural production environments according to claim 1, characterized in that: The positioning module uses BDS and GPS dual-mode positioning.

3. The method for detecting dust in agricultural production environments according to claim 1, characterized in that: The light-scattering particulate sensor is located directly above the center of the plane where the drone rotor is located, at a distance of 10 centimeters or more from the drone rotor, to avoid the drone rotor's turbulent airflow affecting the detection results.

4. The method for detecting dust in agricultural production environments according to claim 1, characterized in that: The dust detection start point is the first intersection of the UAV's flight path and the extended line of the central axis of the agricultural machinery body, located 1.5 meters from the tail of the agricultural machinery body.

5. The method for detecting dust in agricultural production environments according to claim 1, characterized in that: The pre-filter cover at the sampling port is used to prevent straw debris in the agricultural production environment from damaging the light scattering particulate matter sensor.

6. The method for detecting dust in agricultural production environments according to claim 1, characterized in that: It can display the dust concentration at various locations in the agricultural production environment on mobile devices and store the data.

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