Multi-source fusion agricultural machine positioning platform based on Beidou satellite positioning

Abstract

The invention belongs to the technical field of agricultural machinery, and provides a Beidou satellite positioning-based multi-source fusion agricultural machinery positioning platform, which comprises an initial position information acquisition module for acquiring initial position information of agricultural machinery based on a Beidou satellite positioning technology; the relative position information acquisition module is used for acquiring relative position information of the agricultural machinery based on the measuring equipment; the visual position information acquisition module is used for acquiring visual position information of the agricultural machine based on the camera device and the image processing device; the multi-source information fusion module is used for performing data fusion by adopting an extended Kalman filtering algorithm based on the initial position information, the relative position information and the visual position information to obtain target position information of the agricultural machine; and the agricultural machine operation control module controls the agricultural machine to operate according to a set path according to the target position information. According to the invention, multi-source information of Beidou satellite positioning, field measurement and visual positioning is fused, so that the positioning precision and reliability of the agricultural machine can be improved, and the requirements of precise agricultural operation can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural machinery, and particularly to a multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning. Background Art

[0002] With the development of agricultural modernization, the popularization level of agricultural machinery mechanized operation has been continuously improved, which puts forward higher and higher requirements for the precision of agricultural machinery.

[0003] Traditional agricultural machinery positioning mainly relies on a single global navigation satellite system, such as GPS. Due to the influence of the operation area and environment of agricultural machinery, this positioning method is vulnerable to signal occlusion, multipath effect, weather interference, etc. in a complex farmland environment, resulting in a decrease in positioning accuracy and difficulty in meeting the high-precision operation requirements in a complex farmland environment.

[0004] Existing improvement schemes usually adopt a loose coupling combination of satellite positioning and inertial navigation. However, inertial sensors have cumulative errors, and the positioning drift is significant after long-term operation. In addition, it is difficult for single data source fusion to cope with dynamic complex environments such as terrain undulation and crop occlusion in farmland.

[0005] Therefore, it is necessary to provide a multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning. Summary of the Invention

[0006] The present invention provides a multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning. By fusing multi-source information of Beidou satellite positioning, field measurement, and visual positioning, the positioning accuracy and reliability of agricultural machinery can be improved, and the requirements of precision agriculture operations can be met.

[0007] The present invention provides a multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning, including:

[0008] An initial position information acquisition module, configured to acquire the initial position information of the agricultural machinery by receiving Beidou satellite signals based on Beidou satellite positioning technology;

[0009] A relative position information acquisition module, configured to acquire the relative position information of the agricultural machinery by measuring the angular velocity and acceleration during the operation of the agricultural machinery based on a measuring device;

[0010] A visual position information acquisition module, configured to collect and process the farmland environment images during the operation of the agricultural machinery based on a camera device and an image processing device, and acquire the visual position information of the agricultural machinery;

[0011] A multi-source information fusion module, configured to perform data fusion on the initial position information, relative position information, and visual position information by using an extended Kalman filter algorithm to obtain the target position information of the agricultural machinery;

[0012] The agricultural machinery operation control module is used to control the agricultural machinery to operate according to the set path based on the target position information.

[0013] Furthermore, the initial position information acquisition module includes a positioning device configuration unit and a signal processing unit;

[0014] The positioning device configuration unit is used to configure the positioning device to receive the Beidou positioning signal of the agricultural machinery; the positioning device includes but is not limited to a Beidou receiving antenna, a noise amplifier, a band-pass filter, a down-converter, a baseband processing device, and a navigation solution device;

[0015] The signal processing unit is used to process the Beidou positioning signal by using the baseband processing device and the navigation solution device to obtain the initial position information of the agricultural machinery.

[0016] Furthermore, processing the Beidou positioning signal by using the baseband processing device and the navigation solution device to obtain the initial position information of the agricultural machinery includes:

[0017] Using the analog-to-digital converter, digital down-converter, and correlator in the baseband processing device to perform analog-to-digital conversion, digital down-conversion, and correlation processing on the Beidou positioning signal after down-conversion processing by the down-converter, and extracting the pseudorange and carrier phase information of the Beidou positioning signal;

[0018] Based on the navigation solution device, using the least squares method or the Kalman filtering algorithm, and using the pseudorange and carrier phase information to solve the three-dimensional position, speed, and time of the agricultural machinery to obtain the initial position information of the agricultural machinery.

[0019] Furthermore, the relative position information acquisition module includes a measurement device configuration unit and a relative position information acquisition unit;

[0020] The measurement device configuration unit is used to configure the measurement device, and the measurement device includes a three-axis gyroscope and a three-axis accelerometer; the three-axis gyroscope is used to measure the angular velocity of the agricultural machinery; the three-axis accelerometer is used to measure the acceleration of the agricultural machinery;

[0021] The relative position information acquisition unit is used to solve according to the angular velocity of the agricultural machinery measured by the three-axis gyroscope by using the quaternion method or the direction cosine matrix method to obtain the attitude angle of the agricultural machinery; integrate the acceleration of the agricultural machinery measured by the three-axis accelerometer to obtain the speed of the agricultural machinery, and integrate the speed to obtain the relative position of the agricultural machinery; and form the relative position information of the agricultural machinery according to the attitude angle and relative position of the agricultural machinery.

[0022] Furthermore, the visual position information acquisition module includes a camera and processing device configuration unit and a visual position information acquisition unit;

[0023] The camera and processing device configuration unit is used to configure a camera, a lidar, and an image processing device;

[0024] A visual position information acquisition unit, configured to collect farmland environment images based on a camera and a lidar, and process the farmland environment images by using an image processing device to obtain visual position information.

[0025] Further, collecting farmland environment images based on a camera and a lidar, and processing the farmland environment images by using an image processing device to obtain visual position information, including:

[0026] Collecting farmland environment images by using a camera and a lidar;

[0027] Using an image processing device to preprocess the farmland environment images to obtain preprocessed images; the preprocessing includes one or more of graying, denoising, and edge detection;

[0028] Extracting feature points from the preprocessed images by using a feature point extraction algorithm; the feature point extraction algorithm includes, but is not limited to, scale-invariant feature transform, speeded-up robust features, oriented FAST and rotated BRIEF;

[0029] Adopting a KNN feature matching algorithm to match the feature points with the feature points of the previous frame of image to obtain a matching result;

[0030] Estimating the movement direction and rotation angle of the agricultural machine according to the matching result;

[0031] Calculating to obtain the visual position information of the agricultural machine according to the movement direction and rotation angle of the agricultural machine.

[0032] Further, adopting an extended Kalman filter algorithm for data fusion, including:

[0033] Setting a state vector X and setting a state equation; the state vector X includes the position, speed, and attitude angle of the agricultural machine; the state equation is:

[0034] X k = F k-1 * X k-1 + W k-1

[0035] wherein, F k-1 is a state transition matrix, W k-1 is a process noise, X k represents the state at time k, and k represents time;

[0036] Setting an observation vector and setting an observation equation; the observation vector includes the observed value of the initial position information, the observed value of the relative position information, and the observed value of the visual position information; the observation equation is:

[0037] Z k = H k * Xk +V k

[0038] Among them, H k is the observation matrix, V k is the observation noise, Z k represents the observation state at time k, where k represents the time;

[0039] Predict the state vector and covariance matrix according to the state equation;

[0040] Update the state vector and covariance matrix according to the observation equation.

[0041] Furthermore, the multi-source information fusion module further includes an information weight allocation unit; the information weight allocation unit is used to allocate weights to the initial position information, relative position information, and visual position information, specifically as follows:

[0042] If the intensity of the Beidou positioning signal is greater than the set first intensity threshold, weights are allocated to the initial position information, relative position information, and visual position information according to the set first weight allocation strategy; the first weight allocation strategy focuses on the allocation of the weight of the initial position information;

[0043] If the intensity of the Beidou positioning signal is less than the set first intensity threshold and greater than the set second intensity threshold, weights are allocated to the initial position information, relative position information, and visual position information according to the set second weight allocation strategy; the second weight allocation strategy focuses on the allocation of the weight of the visual position information;

[0044] If the intensity of the Beidou positioning signal is less than the set second intensity threshold, weights are allocated according to the confidence levels of the initial position information, relative position information, and visual position information. The weight calculation formula is:

[0045]

[0046] C i is the confidence level of the initial position information, relative position information, or visual position information, w i is the weight value of the initial position information, relative position information, or visual position information; i = 1, 2, 3; n = 3; j = 1, 2, 3; Weights are allocated according to the calculated weight values.

[0047] Furthermore, the multi-source information fusion module further includes an error correction unit. The error correction unit includes an initial position information correction subunit and a relative position information correction subunit;

[0048] An initial position information correction subunit, which is used to eliminate multipath noise in the Beidou positioning signal by using wavelet transform, generate a topographic elevation map of the farmland environment by using lidar point cloud data, compare the data of the topographic elevation map with the Beidou elevation data, correct the satellite positioning error, so as to correct the initial position information;

[0049] A relative position information correction subunit, which is used to predict the zero bias error of the relative position information by using the LSTM network model. By inputting the angular velocity and acceleration of the agricultural machinery into the LSTM network model, the predicted value of the zero bias error is obtained and fed back to the calculation process of the extended Kalman filter algorithm to correct the error of the attitude angle of the agricultural machinery in real time.

[0050] Furthermore, according to the target position information, controlling the agricultural machinery to operate according to the set path includes:

[0051] Planning and setting the operation path of the agricultural machinery according to the farmland area map and the operation tasks of the agricultural machinery;

[0052] According to the target position information of the agricultural machinery and the target position to be operated, controlling the steering and speed of the agricultural machinery so that the agricultural machinery travels along the operation path.

[0053] Compared with the prior art, the present invention has the following advantages and beneficial effects: By fusing multi-source information of Beidou satellite positioning, field measurement, and visual positioning, the positioning accuracy and reliability of agricultural machinery can be improved, and the requirements of precision agriculture operations can be met.

[0054] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification and the drawings.

[0055] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0056] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0057] Figure 1 It is a schematic structural diagram of a multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning;

[0058] Figure 2 It is a schematic structural diagram of an initial position information acquisition module;

[0059] Figure 3 It is a schematic structural diagram of a relative position information acquisition module. Detailed Implementation Manner

[0060] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0061] The present invention provides a multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning, as Figure 1 shown, including:

[0062] An initial position information acquisition module, configured to acquire the initial position information of the agricultural machinery by receiving Beidou satellite signals based on Beidou satellite positioning technology;

[0063] A relative position information acquisition module, configured to acquire the relative position information of the agricultural machinery by measuring the angular velocity and acceleration during the operation of the agricultural machinery based on a measuring device;

[0064] A visual position information acquisition module, configured to collect and process the farmland environment images during the operation of the agricultural machinery based on a camera device and an image processing device, and acquire the visual position information of the agricultural machinery;

[0065] A multi-source information fusion module, configured to perform data fusion using an extended Kalman filter algorithm based on the initial position information, relative position information, and visual position information to obtain the target position information of the agricultural machinery;

[0066] An agricultural machinery operation control module, configured to control the agricultural machinery to operate according to a set path based on the target position information.

[0067] The working principle of the above technical solution is as follows: In order to implement a multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning, the present invention proposes an initial position information acquisition module, which is used to acquire the initial position information of the agricultural machinery by receiving Beidou satellite signals based on Beidou satellite positioning technology; a relative position information acquisition module is proposed, which is used to acquire the relative position information of the agricultural machinery by measuring the angular velocity and acceleration during the operation of the agricultural machinery based on a measuring device; a visual position information acquisition module is proposed, which is used to collect and process the farmland environment images during the operation of the agricultural machinery based on a camera device and an image processing device, and acquire the visual position information of the agricultural machinery; a multi-source information fusion module is proposed, which is used to perform data fusion using an extended Kalman filter algorithm based on the initial position information, relative position information, and visual position information to obtain the target position information of the agricultural machinery; an agricultural machinery operation control module is proposed, which is used to control the agricultural machinery to operate according to a set path based on the target position information.

[0068] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment and fusing multi-source information such as Beidou satellite positioning, field measurement, and visual positioning, the positioning accuracy and reliability of agricultural machinery can be improved, and the requirements of precision agriculture operations can be met.

[0069] In one embodiment, as Figure 2 shown, the initial position information acquisition module includes a positioning device configuration unit and a signal processing unit;

[0070] The positioning device configuration unit is used to configure a positioning device to receive the Beidou positioning signal of the agricultural machine; the positioning device includes but is not limited to a Beidou receiving antenna, a noise amplifier, a band-pass filter, a down-converter, a baseband processing device, and a navigation solution device;

[0071] The signal processing unit is used to process the Beidou positioning signal by using the baseband processing device and the navigation solution device to obtain the initial position information of the agricultural machine.

[0072] The working principle of the above technical solution is as follows: In order to realize the function of the initial position information acquisition module, the present invention proposes a positioning device configuration unit and a signal processing unit; among them, the positioning device configuration unit is used to configure a positioning device to receive the Beidou positioning signal of the agricultural machine; the positioning device includes but is not limited to a Beidou receiving antenna, a noise amplifier, a band-pass filter, a down-converter, a baseband processing device, and a navigation solution device; the signal processing unit is used to process the Beidou positioning signal by using the baseband processing device and the navigation solution device to obtain the initial position information of the agricultural machine.

[0073] The beneficial effect of the above technical solution is: By adopting the solution provided in this embodiment, through configuring the Beidou positioning device and processing the Beidou positioning signal, it is possible to ensure obtaining the accurate initial position information of the agricultural machine.

[0074] In one embodiment, using the baseband processing device and the navigation solution device to process the Beidou positioning signal to obtain the initial position information of the agricultural machine includes:

[0075] Using the analog-to-digital converter, digital down-converter, and correlator in the baseband processing device to perform analog-to-digital conversion, digital down-conversion, and correlation processing on the Beidou positioning signal after being down-converted by the down-converter, and extracting the pseudorange and carrier phase information of the Beidou positioning signal;

[0076] Based on the navigation solution device, using the least squares method or the Kalman filtering algorithm, and using the pseudorange and carrier phase information to solve the three-dimensional position, speed, and time of the agricultural machine to obtain the initial position information of the agricultural machine.

[0077] The working principle of the above technical solution is as follows: In order to process the Beidou positioning signal using the baseband processing device and the navigation solution device to obtain the initial position information of the agricultural machinery, the present invention first uses the analog-to-digital converter, digital down-converter, and correlator in the baseband processing device to perform analog-to-digital conversion, digital down-conversion, and correlation processing on the Beidou positioning signal after down-conversion processing by the down-converter, and extracts the pseudorange and carrier phase information of the Beidou positioning signal; then based on the navigation solution device, using the least squares method or the Kalman filter algorithm, the three-dimensional position, speed, and time of the agricultural machinery are calculated using the pseudorange and carrier phase information to obtain the initial position information of the agricultural machinery.

[0078] The beneficial effect of the above technical solution is as follows: By adopting the solution provided in this embodiment, through the processing and conversion of the Beidou positioning signal, comprehensive initial position information of the agricultural machinery can be guaranteed to be obtained.

[0079] In one embodiment, as Figure 3 shown, the relative position information acquisition module includes a measurement device configuration unit and a relative position information acquisition unit;

[0080] The measurement device configuration unit is used to configure the measurement device, and the measurement device includes a three-axis gyroscope and a three-axis accelerometer; the three-axis gyroscope is used to measure the angular velocity of the agricultural machinery; the three-axis accelerometer is used to measure the acceleration of the agricultural machinery;

[0081] The relative position information acquisition unit is used to calculate the attitude angle of the agricultural machinery by the quaternion method or the direction cosine matrix method according to the angular velocity of the agricultural machinery measured by the three-axis gyroscope; integrate the acceleration of the agricultural machinery measured by the three-axis accelerometer to obtain the speed of the agricultural machinery, and integrate the speed to obtain the relative position of the agricultural machinery; and form the relative position information of the agricultural machinery according to the attitude angle and relative position of the agricultural machinery.

[0082] The working principle of the above technical solution is as follows: In order to implement the functions of the relative position information acquisition module, the present invention proposes a measurement device configuration unit for configuring the measurement device, and the measurement device includes a three-axis gyroscope and a three-axis accelerometer; the three-axis gyroscope is used to measure the angular velocity of the agricultural machinery; the three-axis accelerometer is used to measure the acceleration of the agricultural machinery; and a relative position information acquisition unit is proposed for calculating the attitude angle of the agricultural machinery by the quaternion method or the direction cosine matrix method according to the angular velocity of the agricultural machinery measured by the three-axis gyroscope; integrating the acceleration of the agricultural machinery measured by the three-axis accelerometer to obtain the speed of the agricultural machinery, and integrating the speed to obtain the relative position of the agricultural machinery; and forming the relative position information of the agricultural machinery according to the attitude angle and relative position of the agricultural machinery.

[0083] The beneficial effect of the above technical solution is as follows: By adopting the solution provided in this embodiment, through the three-axis gyroscope and the three-axis accelerometer, the angular velocity and acceleration of the agricultural machinery are measured, and the relative position of the agricultural machinery can be guaranteed to be obtained.

[0084] In one embodiment, the visual position information acquisition module includes a camera and processing device configuration unit and a visual position information acquisition unit;

[0085] The camera and processing device configuration unit is used to configure a camera, a lidar, and an image processing device;

[0086] The visual position information acquisition unit is used to collect farmland environment images based on the camera and the lidar, and use the image processing device to process the farmland environment images to obtain visual position information.

[0087] The working principle of the above technical solution is as follows: To implement the functions of the visual position information acquisition module, the present invention proposes a camera and processing device configuration unit and a visual position information acquisition unit; through the camera and processing device configuration unit, a camera, a lidar, and an image processing device can be configured; through the visual position information acquisition unit, farmland environment images can be collected based on the camera and the lidar, and the image processing device can be used to process the farmland environment images to obtain visual position information.

[0088] The beneficial effect of the above technical solution is as follows: By adopting the solution provided in this embodiment, by configuring a camera device and using the camera device to acquire visual position information, accurate visual position information can be guaranteed to be obtained.

[0089] In one embodiment, collecting farmland environment images based on a camera and a lidar, and using an image processing device to process the farmland environment images to obtain visual position information includes:

[0090] Collecting farmland environment images using the camera and the lidar;

[0091] Using the image processing device to preprocess the farmland environment images to obtain preprocessed images; the preprocessing includes one or more of grayscale conversion, denoising, and edge detection;

[0092] Using a feature point extraction algorithm to extract feature points from the preprocessed images; the feature point extraction algorithm includes but is not limited to scale-invariant feature transform, speeded-up robust features, oriented FAST and rotated BRIEF;

[0093] Adopting a KNN feature matching algorithm to match the feature points with the feature points of the previous frame of image to obtain a matching result;

[0094] Estimating the movement direction and rotation angle of the agricultural machine according to the matching result;

[0095] Calculating and obtaining the visual position information of the agricultural machine according to the movement direction and rotation angle of the agricultural machine.

[0096] The working principle of the above technical solution is as follows: In order to collect farmland environment images based on a camera and a lidar, and use an image processing device to process the farmland environment images to obtain visual position information, the present invention first uses the camera and the lidar to collect farmland environment images; then uses the image processing device to preprocess the farmland environment images to obtain preprocessed images; the preprocessing includes one or more of grayscale conversion, denoising, and edge detection; then uses a feature point extraction algorithm to extract feature points from the preprocessed images; the feature point extraction algorithm includes, but is not limited to, Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Oriented FAST and Rotated BRIEF (ORB); then uses the KNN feature matching algorithm to match the feature points with the feature points of the previous frame of image to obtain a matching result; finally, according to the matching result, estimate the movement direction and rotation angle of the agricultural machine; according to the movement direction and rotation angle of the agricultural machine, calculate and obtain the visual position information of the agricultural machine.

[0097] The beneficial effect of the above technical solution is as follows: By adopting the solution provided in this embodiment, accurate visual position information can be obtained by processing the farmland environment images.

[0098] In one embodiment, an Extended Kalman Filter (EKF) algorithm is used for data fusion, including:

[0099] Set the state vector X and set the state equation; the state vector X includes the position, speed, and attitude angle of the agricultural machine; the state equation is:

[0100] X k =F k-1 *X k-1 +W k-1

[0101] where F k-1 is the state transition matrix, W k-1 is the process noise, X k represents the state at time k, and k represents time;

[0102] Set the observation vector and set the observation equation; the observation vector includes the observed values of the initial position information, the relative position information, and the visual position information; the observation equation is:

[0103] Z k =H k *X k +V k

[0104] where H k is the observation matrix, V k is the observation noise, Z k represents the observed state at time k, and k represents time;

[0105] Predict the state vector and covariance matrix according to the state equation;

[0106] Update the state vector and covariance matrix according to the observation equation.

[0107] The working principle of the above technical solution is as follows: The process of data fusion using the extended Kalman filter algorithm in the present invention is as follows: First, set the state vector X and the state equation; the state vector X includes the position, speed, and attitude angle of the agricultural machinery; the state equation is:

[0108] X k = F k-1 * X k-1 + W k-1

[0109] where F k-1 is the state transition matrix, W k-1 is the process noise, X k represents the state at time k, and k represents time; then set the observation vector and the observation equation; the observation vector includes the observed value of the initial position information, the observed value of the relative position information, and the observed value of the visual position information; the observation equation is:

[0110] Z k = H k * X k + V k

[0111] where H k is the observation matrix, V k is the observation noise, Z k represents the observed state at time k, and k represents time; finally, predict the state vector and covariance matrix according to the state equation; update the state vector and covariance matrix according to the observation equation.

[0112] The beneficial effect of the above technical solution is: By adopting the solution provided in this embodiment and performing data fusion using the extended Kalman filter algorithm, accurate fusion data can be ensured.

[0113] In one embodiment, the multi-source information fusion module further includes an information weight allocation unit; the information weight allocation unit is used to allocate weights to the initial position information, relative position information, and visual position information, specifically:

[0114] When the intensity of the Beidou positioning signal is greater than the set first intensity threshold, the weights of the initial position information, relative position information, and visual position information are allocated according to the set first weight allocation strategy; the first weight allocation strategy focuses on the allocation of the weight of the initial position information;

[0115] When the intensity of the Beidou positioning signal is less than the set first intensity threshold and greater than the set second intensity threshold, weight distribution is performed on the initial position information, relative position information, and visual position information according to the set second weight distribution strategy; the second weight distribution strategy focuses on the distribution of the weight of the visual position information.

[0116] When the intensity of the Beidou positioning signal is less than the set second intensity threshold, weight distribution is performed according to the confidence levels of the initial position information, relative position information, and visual position information. The weight calculation formula is:

[0117]

[0118] C i is the confidence level of the initial position information, relative position information, or visual position information, and w i is the weight value of the initial position information, relative position information, or visual position information; i = 1, 2, 3; n = 3; j = 1, 2, 3; Weight distribution is performed according to the calculated weight values.

[0119] The working principle of the above technical solution is as follows: In order to achieve precise fusion of multi-source information, the present invention proposes a weight distribution unit to distribute the weights of the initial position information, relative position information, and visual position information, which is specifically divided into three categories of situations. The first category of situation is: When the intensity of the Beidou positioning signal is greater than the set first intensity threshold, weight distribution is performed on the initial position information, relative position information, and visual position information according to the set first weight distribution strategy; the first weight distribution strategy focuses on the distribution of the weight of the initial position information. The second category of situation is: When the intensity of the Beidou positioning signal is less than the set first intensity threshold and greater than the set second intensity threshold, weight distribution is performed on the initial position information, relative position information, and visual position information according to the set second weight distribution strategy; the second weight distribution strategy focuses on the distribution of the weight of the visual position information. The third category of situation is: When the intensity of the Beidou positioning signal is less than the set second intensity threshold, weight distribution is performed according to the confidence levels of the initial position information, relative position information, and visual position information. The weight calculation formula is:

[0120]

[0121] C i is the confidence level of the initial position information, relative position information, or visual position information, and w i is the weight value of the initial position information, relative position information, or visual position information; i = 1, 2, 3; n = 3; j = 1, 2, 3; Weight distribution is performed according to the calculated weight values.

[0122] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment and allocating weights by distinguishing three types of situations, weight allocation for initial position information, relative position information, or visual position information can be achieved.

[0123] In one embodiment, the multi-source information fusion module further includes an error correction unit, and the error correction unit includes an initial position information correction subunit and a relative position information correction subunit;

[0124] The initial position information correction subunit is used to eliminate multipath noise in the Beidou positioning signal by using wavelet transform, generate a topographic elevation map of the farmland environment by using lidar point cloud data, compare the data of the topographic elevation map with the Beidou elevation data, and correct the satellite positioning error to correct the initial position information;

[0125] The relative position information correction subunit is used to predict the zero bias error of the relative position information by using an LSTM network model. By inputting the angular velocity and acceleration of the agricultural machine into the LSTM network model, the zero bias error prediction value is output, and the zero bias error prediction value is fed back to the calculation process of the extended Kalman filter algorithm to correct the error of the attitude angle of the agricultural machine in real time.

[0126] The working principle of the above technical solution is as follows: In order to correct the errors of multi-source information, the present invention also proposes an error correction unit. First, the initial position information is corrected. Specifically, wavelet transform is used to eliminate multipath noise in the Beidou positioning signal, a topographic elevation map of the farmland environment is generated by using lidar point cloud data, and the data of the topographic elevation map is compared with the Beidou elevation data to correct the satellite positioning error to correct the initial position information; then, the relative position information is corrected. Specifically, an LSTM network model is used to predict the zero bias error of the relative position information. By inputting the angular velocity and acceleration of the agricultural machine into the LSTM network model, the zero bias error prediction value is output, and the zero bias error prediction value is fed back to the calculation process of the extended Kalman filter algorithm to correct the error of the attitude angle of the agricultural machine in real time.

[0127] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment and correcting the errors of multi-source information, the accuracy of multi-source information can be enhanced.

[0128] In one embodiment, according to the target position information, controlling the agricultural machine to operate according to a set path includes:

[0129] Planning and setting the operation path of the agricultural machine according to the farmland area map and the agricultural machine operation task;

[0130] Controlling the steering and speed of the agricultural machine according to the target position information of the agricultural machine and the target position to be operated, so that the agricultural machine travels along the operation path.

[0131] The working principle of the above technical solution is as follows: In order to control the agricultural machinery to operate according to the set path based on the target position information, the present invention first plans and sets the operation path of the agricultural machinery according to the farmland area map and the agricultural machinery operation task; then, according to the target position information of the agricultural machinery and the target position to be operated, it controls the steering and speed of the agricultural machinery so that the agricultural machinery travels along the operation path.

[0132] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, by controlling the steering and speed of the agricultural machinery according to the target position information of the agricultural machinery and the target position to be operated, so that the agricultural machinery travels along the operation path, the operation control of the agricultural machinery can be realized.

[0133] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning, characterized in that Including: An initial position information acquisition module, which is used to acquire the initial position information of agricultural machinery based on Beidou satellite positioning technology by receiving Beidou satellite signals; A relative position information acquisition module, which is used to acquire the relative position information of agricultural machinery based on a measuring device by measuring the angular velocity and acceleration during the operation of agricultural machinery; A visual position information acquisition module, which is used to acquire the visual position information of agricultural machinery by collecting and processing the farmland environment images during the operation of agricultural machinery based on a camera device and an image processing device; A multi-source information fusion module, which is used to perform data fusion on the initial position information, relative position information, and visual position information by using an extended Kalman filter algorithm to obtain the target position information of agricultural machinery; An agricultural machinery operation control module, which is used to control the agricultural machinery to operate according to a set path based on the target position information.

2. The multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning according to claim 1, characterized in that, The initial position information acquisition module includes a positioning device configuration unit and a signal processing unit; The positioning device configuration unit is used to configure a positioning device to receive the Beidou positioning signal of agricultural machinery; the positioning device includes but is not limited to a Beidou receiving antenna, a noise amplifier, a band-pass filter, a down-converter, a baseband processing device, and a navigation solution device; The signal processing unit is used to process the Beidou positioning signal by using the baseband processing device and the navigation solution device to obtain the initial position information of agricultural machinery.

3. The multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning according to claim 2, wherein, Processing the Beidou positioning signal by using the baseband processing device and the navigation solution device to obtain the initial position information of agricultural machinery includes: Using the analog-to-digital converter, digital down-converter, and correlator in the baseband processing device to perform analog-to-digital conversion, digital down-conversion, and correlation processing on the Beidou positioning signal after being down-converted by the down-converter to extract the pseudorange and carrier phase information of the Beidou positioning signal; Based on the navigation solution device, using the least squares method or the Kalman filter algorithm, and using the pseudorange and carrier phase information to solve the three-dimensional position, velocity, and time of agricultural machinery to obtain the initial position information of agricultural machinery.

4. The multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning according to claim 1, characterized in that, The relative position information acquisition module includes a measuring device configuration unit and a relative position information acquisition unit; The measuring device configuration unit is used to configure a measuring device, and the measuring device includes a three-axis gyroscope and a three-axis accelerometer; the three-axis gyroscope is used to measure the angular velocity of agricultural machinery; The three-axis accelerometer is used to measure the acceleration of agricultural machinery; The relative position information acquisition unit is used to solve the attitude angle of agricultural machinery by using the quaternion method or the direction cosine matrix method according to the angular velocity of agricultural machinery measured by the three-axis gyroscope; integrate the acceleration of agricultural machinery measured by the three-axis accelerometer to obtain the velocity of agricultural machinery, and integrate the velocity to obtain the relative position of agricultural machinery; compose the relative position information of agricultural machinery according to the attitude angle and relative position of agricultural machinery.

5. The multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning according to claim 1, characterized in that The visual position information acquisition module includes a camera and processing device configuration unit and a visual position information acquisition unit; The camera and processing device configuration unit is used to configure a camera, a lidar, and an image processing device; The visual position information acquisition unit is used to collect farmland environment images based on the camera and the lidar, and use the image processing device to process the farmland environment images to obtain visual position information.

6. The multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning according to claim 5, characterized in that, Collect farmland environment images based on cameras and lidar, and use image processing equipment to process the farmland environment images to obtain visual position information, including: Collect farmland environment images using cameras and lidar; Use image processing equipment to preprocess the farmland environment images to obtain preprocessed images; The preprocessing includes one or more of grayscale conversion, denoising, and edge detection; Use feature point extraction algorithms to extract feature points from the preprocessed images; Feature point extraction algorithms include, but are not limited to, Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Oriented FAST and Rotated BRIEF (ORB); Adopt the KNN feature matching algorithm to match the feature points with the feature points of the previous frame image to obtain a matching result; Estimate the movement direction and rotation angle of the agricultural machinery according to the matching result; Calculate the visual position information of the agricultural machinery according to the movement direction and rotation angle of the agricultural machinery.

7. The multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning according to claim 1, characterized in that Adopt the Extended Kalman Filter algorithm for data fusion, including: Set the state vector X and set the state equation; The state vector X includes the position, speed, and attitude angle of the agricultural machinery; The state equation is: X k = F k-1 * X k-1 + W k-1 Among them, F k-1 is the state transition matrix, W k-1 is the process noise, X k represents the state at time k, where k represents time; Set the observation vector and set the observation equation; The observation vector includes the observed values of the initial position information, relative position information, and visual position information; The observation equation is: Z k = H k * X k + V k Among them, H k is the observation matrix, V k is the observation noise, Z k represents the observation state at time k, X k represents the state at time k, and k represents time; Predict the state vector and covariance matrix according to the state equation; Update the state vector and covariance matrix according to the observation equation.

8. The multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning according to claim 7, characterized in that, The multi-source information fusion module also includes an information weight allocation unit; The information weight allocation unit is used to allocate weights to the initial position information, relative position information, and visual position information, specifically: When the intensity of the Beidou positioning signal is greater than the set first intensity threshold, allocate weights to the initial position information, relative position information, and visual position information according to the set first weight allocation strategy; The first weight allocation strategy focuses on the allocation of the weight of the initial position information; When the intensity of the Beidou positioning signal is less than the set first intensity threshold and greater than the set second intensity threshold, allocate weights to the initial position information, relative position information, and visual position information according to the set second weight allocation strategy; The second weight allocation strategy focuses on the allocation of the weight of the visual position information; When the intensity of the Beidou positioning signal is less than the set second intensity threshold, allocate weights according to the confidence levels of the initial position information, relative position information, and visual position information. The weight calculation formula is: C i is the confidence of the initial position information, relative position information or visual position information, w i is the weight value of the initial position information, relative position information or visual position information; i = 1, 2, 3; n = 3; j = 1, 2, 3; Allocate weights according to the calculated weight values.

9. The multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning according to claim 7, characterized in that The multi-source information fusion module also includes an error correction unit. The error correction unit includes an initial position information correction subunit and a relative position information correction subunit; The initial position information correction subunit is used to use wavelet transform to remove the multipath noise in the Beidou positioning signal, generate a terrain elevation map of the farmland environment using lidar point cloud data, and compare the data of the terrain elevation map with the Beidou elevation data to correct the satellite positioning error, so as to correct the initial position information; A relative position information correction subunit is used to predict the zero bias error of the relative position information by adopting an LSTM network model. By inputting the angular velocity and acceleration of the agricultural machine into the LSTM network model, a zero bias error prediction value is output and fed back to the calculation process of the extended Kalman filter algorithm to correct the error of the attitude angle of the agricultural machine in real time.

10. The multi-source fusion agricultural machinery positioning platform based on Beidou satellite positioning according to claim 1, characterized in that According to the target position information, control the agricultural machine to operate according to the set path, including: Plan and set the operation path of the agricultural machine according to the farmland area map and the agricultural machine operation task; According to the target position information of the agricultural machine and the target position to be operated, control the steering and speed of the agricultural machine so that the agricultural machine travels along the operation path.

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