Field driving auxiliary driving system and method for rice direct seeding machine based on Beidou

Abstract

This invention discloses a BeiDou-based field driving assistance system and method for rice direct seeding machines, comprising: a direct seeding machine, a central control computer, a soil mechanical property testing module, a paddy field structure variation prediction model, an assistance driving module, a field path planning module, and a BeiDou navigation module. The assistance driving system of this invention can predict the degree of paddy field structure variation based on a mathematical model of axle load and driving resistance, based on the tested subsidence depth. Utilizing the BeiDou navigation system, it plans the path for multiple trips of the agricultural machinery according to the principles of low driving resistance and minimal interference with seeds and seedlings, thereby reducing driving resistance and minimizing damage to seeds and seedlings. Simultaneously, it can increase the straightness of the agricultural machinery's movement, improve the precision of rice direct seeding, and further realize precision agriculture.

Description

Technical Field

[0001] This invention relates to the technical field of agricultural machinery and equipment, and more specifically, to a Beidou-based field driving assistance system and method for rice direct seeding machines. Background Technology

[0002] Paddy field soil generally refers to the soil used for rice cultivation. Over long periods of rice cultivation, rice root and stalk residues decompose deep into the soil, forming organic matter. Through physicochemical processes, sedimentation, and seasonal wet-dry cycles, the soil naturally forms layers. These layers typically include a water layer, a soil slurry layer, a topsoil layer, and a subsoil. The physical morphology of the subsoil significantly influences its bearing capacity and shear properties. These influences can lead to problems such as significantly increased rolling resistance of the dynamic chassis, severe tilting, and deep sinking.

[0003] Direct seeding of rice refers to a planting method in which rice seeds are sown directly into the paddy field, eliminating the need for seedling raising and cultivation. With the widespread application of chemical herbicides and the increasing mechanization of agriculture, direct seeding technology has significantly promoted the development of direct seeding rice cultivation in my country. Besides the direct seeding process, direct seeding also includes field management processes such as fertilizer management, water management, and traffic management, requiring agricultural machinery to pass through the paddy field multiple times for related operations. The repeated compaction of the paddy field by the impeller leads to changes in the soil structure, especially the hard subsoil. Since the supporting force required for agricultural machinery during operation is mainly provided by the hard subsoil, the increased resistance caused by these changes in soil structure can lead to serious malfunctions such as getting stuck. Furthermore, the wheels can interfere with and damage the planted seeds and seedlings. Therefore, to ensure the safe, stable, and efficient operation of agricultural machinery in paddy fields, new technologies must be adopted to address the problems of soil structure changes and interference with and damage to seeds and seedlings caused by the repeated passage of agricultural machinery through the paddy field during direct seeding of rice.

[0004] The current technical shortcomings of rice direct-seeding machinery in the field are manifested in the following aspects:

[0005] (1) Traditional rice direct seeding machinery relies mainly on experience to estimate the driving resistance during the field process. The driving resistance cannot be predicted before the agricultural machinery passes through multiple times, which may lead to increased resistance or even getting stuck.

[0006] (2) Traditional rice direct seeding machinery does not plan its movement in the field, so it will crush the seeds and seedlings when it passes through the field multiple times, causing a certain degree of damage to the crops and reducing the yield.

[0007] (3) Traditional rice direct seeding machinery is driven manually in the field, and the straightness of the driving trajectory is low, which reduces the utilization rate of the field and affects the precision of sowing. Summary of the Invention

[0008] The present invention aims to overcome at least one of the defects (deficiencies) of the prior art and provide a Beidou-based field driving assistance system and method for rice direct seeding machines, which solves the problems of low automation and insufficient operational sensitivity of traditional rice direct seeding machinery.

[0009] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0010] A BeiDou-based field driving assistance system for rice direct seeders includes:

[0011] A direct seeding machine is used to travel in the field. The initial sinking position of the direct seeding machine in the field is taken as the origin of the coordinate system. The initial sinking depth of the direct seeding machine when it first travels through the farmland is obtained, as well as the relative sinking depth of the direct seeding machine when it travels along the same path multiple times in the future, compared with the initial sinking depth.

[0012] The central control computer is used to control the operation of the entire field driving path planning assistance system;

[0013] The soil mechanical properties testing module is used to test and obtain soil texture, soil compaction at different depths, and moisture content.

[0014] The soil structure variation prediction model has an internal mathematical model; the mathematical model stores the driving resistance values ​​of the direct seeding machine at different relative subsidence depths; the driving resistance values ​​include the maximum driving resistance value of the direct seeding machine before the failure of the hard soil sublayer;

[0015] The soil structure variation prediction model is connected to the soil mechanical property testing module. Based on the differences in soil texture data obtained by the soil mechanical property testing module, the initial subsidence depth is corrected through a self-learning method to obtain the baseline subsidence depth. The value of the relative subsidence depth changes as the value of the baseline subsidence depth changes, thereby obtaining the target relative subsidence depth.

[0016] The soil structure variation prediction model imports the relative subsidence depth of the target into a mathematical model to obtain the corresponding predicted driving resistance value. By comparing the predicted driving resistance value with the maximum driving resistance value, it determines whether the live broadcasting machine is suitable to continue driving on the current path.

[0017] If the predicted driving resistance value is less than the maximum driving resistance value, it indicates that the live broadcasting machine is suitable to continue traveling on the current path; if the predicted driving resistance value is greater than or equal to the maximum driving resistance value, it indicates that the live broadcasting machine is not suitable to continue traveling on the current path.

[0018] According to the present invention, a field driving assistance system for a rice direct seeding machine based on Beidou also includes an assistance driving module;

[0019] The assisted driving module is connected to the live broadcasting machine and the soil structure variation prediction model respectively, and is used to adjust the driving direction of the live broadcasting machine in real time to ensure that the live broadcasting machine travels along the predetermined path.

[0020] According to a Beidou-based field driving assistance system for rice direct seeders, the central control computer further includes a field path planning module.

[0021] The field path planning module is signal-connected to the soil structure variation prediction model and the driving assistance module, respectively, and is used to plan a new driving path for the direct seeding machine and transmit the new driving path signal to the driving assistance module.

[0022] According to the present invention, a field driving assistance system for rice direct seeding machines based on BeiDou navigation also includes a BeiDou navigation module.

[0023] The Beidou navigation module is connected to the assisted driving module and is used to collect the latitude and longitude information of the live broadcasting machine at different driving positions. The latitude and longitude information is then transmitted to the assisted driving module, which adjusts the driving direction of the live broadcasting machine in real time according to the latitude and longitude information to ensure that the live broadcasting machine travels along the predetermined path.

[0024] According to a Beidou-based field driving assistance system for rice direct seeders, the assistance module includes an electric power steering device connected to a steering wheel on the direct seeder, which is used to turn the steering wheel on the direct seeder to adjust the driving direction of the direct seeder.

[0025] According to the present invention, a field driving assistance system for a rice direct seeding machine based on Beidou is provided. The direct seeding machine includes a power chassis and a direct seeding mechanism. The power chassis is used to drive the direct seeding mechanism to travel in the field, and the direct seeding mechanism is used to perform crop operations.

[0026] A driving method for a BeiDou-based rice direct seeding machine field driving assistance system, applied to the aforementioned BeiDou-based rice direct seeding machine field driving assistance system, includes the following steps:

[0027] S1: Mathematical model for constructing a soil structure variation prediction model;

[0028] S2: Determine the current travel path of the live broadcasting machine and obtain the texture data of the current travel path through the soil mechanical properties testing module;

[0029] S3: Control the live streaming device to move to the initial position, and set the sinking position of the live streaming device at the initial position as the origin of the live streaming device's coordinates;

[0030] S4: Control the live broadcasting machine to drive on the farmland, and take the coordinate origin of step S3 as the starting point to measure the first sinking depth when the live broadcasting machine first drives through the farmland;

[0031] S5: The soil structure variation prediction model uses the soil texture data obtained from the soil mechanical properties testing module to correct the initial subsidence depth through a self-learning method to obtain the baseline subsidence depth;

[0032] S6: Control the live streaming machine to continue traveling on the same path and obtain the target relative depression depth compared with the baseline depression depth when the live streaming machine is traveling;

[0033] S7: The soil structure variation prediction model imports the target relative subsidence depth obtained in step S6 into the mathematical model, and compares the value of the relative subsidence depth stored in the mathematical model with the value of the target relative subsidence depth to obtain the predicted driving resistance value of the live broadcast machine as it travels along the current driving path.

[0034] S8: The soil structure variation prediction model compares the predicted driving resistance value with the maximum driving resistance value stored in the mathematical model to predict whether the live broadcasting machine is suitable to continue driving on the current path.

[0035] S9: If the predicted driving resistance value is less than the maximum driving resistance value, repeat step S6 until the operation is completed;

[0036] S10: When the predicted driving resistance value is greater than or equal to the maximum driving resistance value, it indicates that the live broadcasting machine is not suitable to continue driving on the current path.

[0037] According to a Beidou-based field driving assistance method for rice direct seeding machines, the field driving path planning assistance system further includes an assistance driving module, a field path planning module, and a Beidou navigation module.

[0038] The driver assistance module is signal-connected to the direct seeding machine and the soil structure variation prediction model, respectively; the field path planning module and the Beidou navigation module are signal-connected to the driver assistance module, respectively.

[0039] In steps S4 and S6, the specific steps for controlling the live broadcast machine to travel along the current driving path are as follows:

[0040] The Beidou navigation module collects the latitude and longitude information of the live broadcasting machine at different driving positions and transmits the latitude and longitude information to the driving assistance module; the driving assistance module adjusts the driving direction of the live broadcasting machine in real time according to the latitude and longitude information to ensure that the live broadcasting machine travels along the set path;

[0041] Step S10 also includes the following steps:

[0042] S101: The soil structure variation prediction model will transmit a signal indicating that the direct seeding machine is not suitable to continue driving on the current path to the auxiliary driving module, and the auxiliary driving module will transmit the signal to the field path planning module.

[0043] S102: The field path planning module plans a new path based on the principles of low driving resistance, minimal driving trajectory, and minimal interference with crops, and transmits the path to the assisted driving module.

[0044] S103: The assisted driving module resets the new path to the current driving path of the live broadcast machine and repeats step S4.

[0045] According to the present invention, in a BeiDou-based field driving assistance method for rice direct seeding machines, the method for constructing the mathematical model in step S1 is as follows:

[0046] S11: Control the live broadcasting machine to the initial position of the path to be traveled, and set the origin position of the live broadcasting machine's coordinates based on the depression position of the live broadcasting machine at the initial position.

[0047] S12: Control the live broadcasting machine to travel on the path to be traveled, and take the coordinate origin position of step S11 as the starting point to measure the vertical load, initial sinking depth and travel resistance value when the live broadcasting machine travels through the path to be traveled for the first time.

[0048] S13: Using the initial sinking depth as the reference sinking depth, the live broadcasting machine is operated to travel on the path to be traveled for the nth time, and the relative sinking depth of the live broadcasting machine compared with the reference sinking depth when it travels through the path to be traveled for the nth time, and the driving resistance value corresponding to the relative sinking depth is measured; where n>1.

[0049] S14: Input the data from steps S12 and S13 to obtain the corresponding data table; and perform curve fitting based on the data table to obtain the fitting function, which is the mathematical model.

[0050] According to a Beidou-based field driving assistance method for rice direct seeding machines, the vertical load of the direct seeding machine is measured by a six-axis force sensor; the driving resistance value of the direct seeding machine is calculated by the vertical load and the sinking depth value.

[0051] The vertical load varies among different live streaming machines; the greater the vertical load, the greater the sinking depth of the live streaming machine and the greater the driving resistance; the six-axis force sensor is preferably a six-axis force sensor.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] This invention discloses a BeiDou-based field driving assistance system and method for rice direct seeders, specifically a BeiDou navigation-based field driving path planning assistance system and method. During the automated sowing, fertilization, and weeding processes of crops, direct seeders need to travel through the field multiple times. However, the movement of the direct seeder can damage the soil structure, which in turn increases the driving resistance of the direct seeder. When the hard subsoil structure is damaged, the direct seeder may get stuck, preventing it from continuing to travel in the field. On the other hand, frequently changing the direct seeder's path can easily interfere with crops, leading to low crop yields. Therefore, when operating a direct seeder in the field, it is necessary to constrain the direct seeder to travel on its original trajectory as much as possible, while also preventing the hard subsoil from becoming damaged.

[0054] In this invention, the soil structure variation prediction model compares the relative subsidence depth of the direct seeding machine along the current path with the relative subsidence depth in the mathematical model each time to obtain the driving resistance value of the direct seeding machine along the current path. By comparing this driving resistance value with the maximum driving resistance value before the hard subsurface fails, it predicts whether the direct seeding machine will cause the hard subsurface to fail if it continues to travel along the current path. If the hard subsurface fails, a new path is planned for the direct seeding machine through the field path planning module. If the hard subsurface does not fail, the direct seeding machine is instructed to continue traveling along the current path until the operation is completed.

[0055] Each time the direct seeding machine travels in the field, the driving assistance module adjusts its direction in real time to ensure it follows the predetermined path. Specifically, the driving assistance module uses real-time latitude and longitude information provided by the BeiDou navigation module and an electric power steering system to adjust the direction in real time to ensure the machine stays on the planned route. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the structure of the present invention.

[0057] Figure 2 This is a flowchart of the process of the present invention. Detailed Implementation

[0058] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the invention. To better illustrate the following embodiments, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions; it is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0059] Example 1

[0060] like Figure 1As shown, this embodiment discloses a BeiDou-based field driving assistance system for rice direct seeders, including a direct seeder 100, a central control computer 200, an assistance driving module 300, and a BeiDou navigation module 400. The direct seeder 100 is a precision-drilling direct seeder for rice, used for driving in the field. The system uses the initial depression position of the direct seeder in the field as the coordinate origin, obtaining the initial depression depth of the direct seeder during its first passage through the farmland, and the relative depression depths of the direct seeder during subsequent passages along the same path compared to the initial depression depth. The direct seeder 100 includes a power chassis and a direct seeding mechanism. The power chassis drives the direct seeding mechanism in the field, and the direct seeding mechanism performs agricultural operations such as sowing, fertilizing, weeding, and harvesting.

[0061] The central control computer 200 is used to control the operation of the entire field driving path planning assisted driving system; specifically, it also includes a soil mechanical property testing module, a soil structure variation prediction model, and a field path planning module.

[0062] The soil mechanical properties testing module is used to obtain soil texture data, soil compaction at different depths, and moisture content. Soil texture data can be obtained by testing with a soil texture analyzer, soil compaction data can be obtained by testing with a soil strength tester, and soil moisture content can be measured by the drying method.

[0063] The soil structure variation prediction model contains a mathematical model that stores the driving resistance values ​​of the direct seeding machine 100 at different relative subsidence depths. Specifically, this includes the maximum driving resistance value of the direct seeding machine 100 before the failure of the hard subsoil. The driving resistance value is related to the subsidence depth of the direct seeding machine 100 and its vertical load. Different direct seeding machines 100 have different vertical loads; the greater the vertical load, the greater the subsidence depth of the direct seeding machine 100, and the greater the driving resistance. The vertical load of the direct seeding machine 100 can be measured using a six-axis force sensor.

[0064] Specifically, the soil structure variation prediction model signal is connected to the soil mechanical property testing module, and the initial subsidence depth is corrected through a self-learning method based on the differences in soil texture data obtained by the soil mechanical property testing module to obtain the baseline subsidence depth; wherein, the value of the relative subsidence depth changes with the value of the baseline subsidence depth, thereby obtaining the target relative subsidence depth.

[0065] The soil structure variation prediction model imports the relative subsidence depth of the target into the mathematical model to obtain the corresponding predicted driving resistance value. By comparing the predicted driving resistance value with the maximum driving resistance value, it determines whether the live broadcasting machine 100 is suitable to continue driving on the current path.

[0066] If the predicted driving resistance value is less than the maximum driving resistance value, the live broadcasting machine 100 is indicated to be suitable to continue driving on the current path; if the predicted driving resistance value is greater than or equal to the maximum driving resistance value, the live broadcasting machine 100 is indicated to be unsuitable to continue driving on the current path.

[0067] Furthermore, the assisted driving module 300 is connected to the live broadcast machine 100 and the soil structure variation prediction model respectively, and is used to adjust the driving direction of the live broadcast machine 100 in real time to ensure that the live broadcast machine 100 travels along the predetermined path.

[0068] Furthermore, the field path planning module is connected to the soil structure variation prediction model and the auxiliary driving module 300 to plan a new driving path for the direct seeding machine 100 and transmit the new driving path signal to the auxiliary driving module 300. Specifically, when the direct seeding machine 100 is not suitable to continue driving on the current path, the soil structure variation prediction model transmits a signal to the field path planning module. The field path planning module plans a new driving path based on the principles of low driving resistance, minimal driving trajectory, and low interference with crops, and transmits the new driving path signal to the auxiliary driving module 300. The auxiliary driving module 300 then adjusts the driving direction of the direct seeding machine 100 so that it drives along the new driving path.

[0069] Furthermore, the Beidou navigation module 400 is signal-connected to the driver assistance module 300 to collect the latitude and longitude information of the live broadcasting device 100 at different driving positions and transmit this information to the driver assistance module 300. The driver assistance module 300 adjusts the driving direction of the live broadcasting device 100 in real time based on the latitude and longitude information to ensure that the live broadcasting device 100 travels along a predetermined path. The Beidou navigation module is a Beidou vehicle-mounted antenna, which, through connection with Beidou navigation satellite signals, enables the collection of latitude and longitude information of the live broadcasting device 100 at different driving positions.

[0070] Furthermore, the driver assistance module 300 includes an electric power steering device connected to the steering wheel on the live broadcast machine 100, which is used to turn the steering wheel on the live broadcast machine 100 to adjust the driving direction of the live broadcast machine 100.

[0071] This embodiment also discloses a driving method for the above-mentioned Beidou-based rice direct seeding machine field driving assistance driving system 300, including the following steps:

[0072] S1: Mathematical model for constructing a soil structure variation prediction model;

[0073] S2: Determine the current driving path of the live broadcast machine 100 and obtain the texture data of the current driving path through the soil mechanical properties testing module;

[0074] S3: Control the live broadcast machine 100 to move to the initial position, and set the depression position of the live broadcast machine 100 at the initial position as the origin position of the coordinates of the live broadcast machine 100.

[0075] S4: Control the live broadcast machine 100 to travel along the current travel path, and take the coordinate origin position of step S3 as the starting point to measure the first sinking depth of the live broadcast machine 100 when it first travels through the farmland.

[0076] S5: The soil structure variation prediction model uses the soil texture data obtained from the soil mechanical properties testing module to correct the initial subsidence depth through a self-learning method to obtain the baseline subsidence depth;

[0077] S6: Control the live broadcast machine 100 to continue traveling on the same path, and obtain the target relative depression depth compared with the baseline depression depth when the live broadcast machine 100 is traveling;

[0078] S7: The soil structure variation prediction model imports the target relative subsidence depth obtained in step S6 into the mathematical model, and compares the value of the relative subsidence depth stored in the mathematical model with the value of the target relative subsidence depth to obtain the predicted driving resistance value of the live broadcast machine 100 as it travels along the current driving path.

[0079] S8: The soil structure variation prediction model compares the predicted driving resistance value with the maximum driving resistance value stored in the mathematical model to predict whether the live broadcast machine 100 is suitable to continue driving on the current path.

[0080] S9: If the predicted driving resistance value is less than the maximum driving resistance value, repeat step S6 until the operation is completed;

[0081] S10: When the predicted driving resistance value is greater than or equal to the maximum driving resistance value, the live broadcast machine 100 is not suitable to continue driving on the current path.

[0082] Specifically, in steps S4 and S6, the specific steps for controlling the live broadcast machine 100 to travel along the current travel path are as follows:

[0083] The Beidou navigation module 400 collects the latitude and longitude information of the live broadcast machine 100 at different driving positions and transmits the latitude and longitude information to the driving assistance module 300. The driving assistance module 300 adjusts the driving direction of the live broadcast machine 100 in real time according to the latitude and longitude information to ensure that the live broadcast machine 100 travels along the set path.

[0084] Furthermore, step S10 also includes the following steps:

[0085] S101: The soil structure variation prediction model will transmit a signal indicating that the direct seeding machine 100 is not suitable to continue driving on the current path to the auxiliary driving module 300, and the auxiliary driving module 300 will transmit the signal to the field path planning module.

[0086] S102: The field path planning module plans a new path based on the principles of low driving resistance, minimal driving trajectory, and minimal disturbance to crops, and transmits the path to the assisted driving module 300.

[0087] S103: The driver assistance module 300 resets the new path to the current driving path of the live broadcast machine 100 and repeats step S4.

[0088] Furthermore, in step S1, the method for constructing the mathematical model is as follows:

[0089] S11: Control the live broadcast machine 100 to travel to the initial position of the path to be traveled, and set the depression position of the live broadcast machine 100 at the initial position as the origin position of the coordinates of the live broadcast machine 100.

[0090] S12: Control the live broadcast machine 100 to travel on the path to be traveled, and take the coordinate origin position of step S11 as the starting point to measure the vertical load, the first sinking depth and the travel resistance value when the live broadcast machine 100 travels through the path to be traveled for the first time.

[0091] S13: Using the initial sinking depth as the reference sinking depth, the live streaming machine 100 is operated to travel on the path to be traveled for the nth time, and the relative sinking depth of the live streaming machine 100 compared with the reference sinking depth when it travels through the path to be traveled for the nth time, and the driving resistance value corresponding to the relative sinking depth is measured; where n>1.

[0092] S14: Input the data from steps S12 and S13 to obtain the corresponding data table; and perform curve fitting based on the data table to obtain the fitting function, thus obtaining the mathematical model.

[0093] The vertical load of the live streaming machine 100 is measured by a six-axis force sensor; the driving resistance value of the live streaming machine 100 is calculated by the vertical load and the sinking depth value.

[0094] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A BeiDou-based field driving assistance system for rice direct seeding machines, characterized in that, include: Direct seeding machine: used for field operations; wherein, the initial sinking depth of the direct seeding machine at its initial position in the field is taken as the origin of the coordinate system, and the initial sinking depth of the direct seeding machine when it first travels through the farmland is obtained, as well as the relative sinking depth of the direct seeding machine when it travels along the same path multiple times thereafter compared to the initial sinking depth; The central control computer is used to control the operation of the entire field driving assistance system; The soil mechanical properties testing module is used to test and obtain soil texture data, soil compaction at different depths, and moisture content. The soil structure variation prediction model has an internal mathematical model; the mathematical model stores the driving resistance values ​​of the direct seeding machine at different relative subsidence depths; the driving resistance values ​​include the maximum driving resistance value of the direct seeding machine before the failure of the hard soil sublayer; The soil structure variation prediction model is connected to the soil mechanical property testing module. Based on the differences in soil texture data obtained by the soil mechanical property testing module, the initial subsidence depth is corrected through a self-learning method to obtain the baseline subsidence depth. The value of the relative subsidence depth changes as the value of the baseline subsidence depth changes, thereby obtaining the target relative subsidence depth. The soil structure variation prediction model imports the relative subsidence depth of the target into a mathematical model to obtain the corresponding predicted driving resistance value. By comparing the predicted driving resistance value with the maximum driving resistance value, it determines whether the live broadcasting machine is suitable to continue driving on the current path. If the predicted driving resistance value is less than the maximum driving resistance value, it indicates that the live broadcasting machine is suitable to continue traveling on the current path; if the predicted driving resistance value is greater than or equal to the maximum driving resistance value, it indicates that the live broadcasting machine is not suitable to continue traveling on the current path.

2. The field driving assistance system for a rice direct seeding machine based on Beidou as described in claim 1, characterized in that, It also includes a driver assistance module; The assisted driving module is connected to the live broadcasting machine and the soil structure variation prediction model respectively, and is used to adjust the driving direction of the live broadcasting machine in real time to ensure that the live broadcasting machine travels along the predetermined path.

3. The Beidou-based field driving assistance system for rice direct seeding machines according to claim 2, characterized in that, It also includes a field path planning module; The field path planning module is signal-connected to the soil structure variation prediction model and the driving assistance module, respectively, and is used to plan a new driving path for the direct seeding machine and transmit the new driving path signal to the driving assistance module.

4. The field driving assistance system for a rice direct seeding machine based on Beidou as described in claim 2, characterized in that, It also includes the BeiDou navigation module. The Beidou navigation module is connected to the assisted driving module and is used to collect the latitude and longitude information of the live broadcasting machine at different driving positions. The latitude and longitude information is then transmitted to the assisted driving module, which adjusts the driving direction of the live broadcasting machine in real time according to the latitude and longitude information to ensure that the live broadcasting machine travels along the predetermined path.

5. The field driving assistance system for a rice direct seeding machine based on Beidou as described in claim 2, characterized in that, The driver assistance module includes an electric power steering device, which is connected to the steering wheel on the live broadcast machine and is used to turn the steering wheel on the live broadcast machine to adjust the driving direction of the live broadcast machine.

6. The field driving assistance system for a rice direct seeding machine based on Beidou as described in claim 1, characterized in that, The direct seeding machine includes a power chassis and direct seeding machinery. The power chassis is used to drive the direct seeding machinery in the field, and the direct seeding machinery is used for crop operations.

7. A driving method for a Beidou-based field driving assistance system for rice direct seeding machines as described in claim 1 or 6. The method, characterized in that, Includes the following steps: S1: Mathematical model for constructing a soil structure variation prediction model; S2: Determine the current travel path of the live broadcasting machine and obtain the texture data of the current travel path through the soil mechanical properties testing module; S3: Control the live streaming device to move to the initial position, and set the sinking position of the live streaming device at the initial position as the origin of the live streaming device's coordinates; S4: Control the live broadcasting machine to drive on the farmland, and take the coordinate origin of step S3 as the starting point to measure the first sinking depth when the live broadcasting machine first drives through the farmland; S5: The soil structure variation prediction model uses the soil texture data obtained from the soil mechanical properties testing module to correct the initial subsidence depth through a self-learning method, and obtains the baseline subsidence depth. S6: Control the live streaming machine to continue traveling on the same path and obtain the target relative depression depth compared with the baseline depression depth when the live streaming machine is traveling; S7: The soil structure variation prediction model imports the target relative subsidence depth obtained in step S6 into the mathematical model, and compares the value of the relative subsidence depth stored in the mathematical model with the value of the target relative subsidence depth to obtain the predicted driving resistance value of the live broadcast machine as it travels along the current driving path. S8: The soil structure variation prediction model compares the predicted driving resistance value with the maximum driving resistance value stored in the mathematical model to predict whether the live broadcasting machine is suitable to continue driving on the current path. S9: If the predicted driving resistance value is less than the maximum driving resistance value, repeat step S6 until the operation is completed; S10: When the predicted driving resistance value is greater than or equal to the maximum driving resistance value, it indicates that the live broadcasting machine is not suitable to continue driving on the current path.

8. The driving method of the Beidou-based rice direct seeding machine field driving assistance driving system according to claim 7, characterized in that, The field driving assistance system also includes a driving assistance module, a field path planning module, and a Beidou navigation module; The driver assistance module is signal-connected to the direct seeding machine and the soil structure variation prediction model, respectively; the field path planning module and the Beidou navigation module are signal-connected to the driver assistance module, respectively. In steps S4 and S6, the specific steps for controlling the live broadcast machine to travel along the current driving path are as follows: The Beidou navigation module collects the latitude and longitude information of the live broadcasting machine at different driving positions and transmits the latitude and longitude information to the driving assistance module; the driving assistance module adjusts the driving direction of the live broadcasting machine in real time according to the latitude and longitude information to ensure that the live broadcasting machine travels along the set path; Step S10 also includes the following steps: S101: The soil structure variation prediction model will transmit a signal indicating that the direct seeding machine is not suitable to continue driving on the current path to the auxiliary driving module, and the auxiliary driving module will transmit the signal to the field path planning module. S102: The field path planning module plans a new path based on the principles of low driving resistance, minimal driving trajectory, and minimal interference with crops, and transmits the path to the assisted driving module. S103: The assisted driving module resets the new path to the current driving path of the live broadcast machine and repeats step S4.

9. The driving method of the Beidou-based rice direct seeding machine field driving assistance driving system according to claim 7, characterized in that, In step S1, the method for constructing the mathematical model is as follows: S11: Control the live broadcast camera to the initial position of the path to be traveled, and determine the position of the live broadcast camera at that initial position. Set the coordinate origin position of the live streaming device; S12: Control the live broadcasting machine to travel on the path to be traveled, and take the coordinate origin position of step S11 as the starting point to measure the vertical load, initial sinking depth and travel resistance value when the live broadcasting machine travels through the path to be traveled for the first time. S13: Using the initial sinking depth as the reference sinking depth, the live broadcasting machine is operated to travel on the path to be traveled for the nth time, and the relative sinking depth of the live broadcasting machine compared with the reference sinking depth when it travels through the path to be traveled for the nth time, and the driving resistance value corresponding to the relative sinking depth is measured; where n>1. S14: Input the data from steps S12 and S13 to obtain the corresponding data table; and perform curve fitting based on the data table to obtain the fitting function, which is the mathematical model.

10. The driving method of the Beidou-based rice direct seeding machine field driving assistance driving system according to claim 9, characterized in that, The vertical load of the live streaming machine is measured by a six-axis force sensor; the driving resistance value of the live streaming machine is calculated by the vertical load and the sinking depth value.

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