Agricultural machinery intelligent control method and system based on heterogeneous double chips

Abstract

The invention discloses an agricultural machinery intelligent control method and system based on heterogeneous double chips, and relates to the technical field of electric digital data processing. The agricultural machinery intelligent control method based on the heterogeneous double chips comprises the following steps: acquiring a collaborative energy efficiency score; acquiring an obstacle avoidance coefficient; acquiring a path planning coincidence index; and judging the path planning progress. Whether the image data is acquired or not is judged through the acquired collaborative energy efficiency score, then whether the path planning instruction of the first time period is sent or not is judged based on the acquired obstacle avoidance coefficient, and meanwhile whether the path planning instruction of the second time period is sent or not is judged based on the acquired path planning coincidence index. Finally, whether path planning is completed is judged, the effect of improving the planning accuracy of the driving path of the specified agricultural machine in the heterogeneous dual-chip cooperative control process is achieved, and the problem that in the prior art, the path planning accuracy of the heterogeneous dual-chip cooperative control farmland irrigation machine in the driving path process is low is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and in particular to an intelligent control method and system for agricultural machinery based on heterogeneous dual chips. Background Art

[0002] With the continuous development of chip technology and software technology, chip computing power continues to increase, and software algorithms are becoming increasingly intelligent, driving the rapid development of artificial intelligence. Driven by the wave of technology in the field of artificial intelligence, agricultural machinery is also developing in the direction of intelligence, especially agricultural machinery for driving operations. With the support of intelligent technologies such as GNSS, I MU, RTK, visual perception, radar perception, and networking, unmanned operations can be realized in specific scenarios, such as unmanned land leveling, unmanned vegetable and fruit picking, unmanned sowing and fertilization, etc. The centralized controller combines multiple chips through chips with multiple application interface types and high computing power. It can be compatible with the intelligent control of various agricultural machinery in a platform-based manner. Through a variety of electrical interfaces and supporting software platforms, it can achieve compatibility with various scenarios and various operation requirements, and perform pre-installed applications on agricultural machinery to ensure the functional diversity and integrity of agricultural machinery.

[0003] In the prior art, by adopting the after-market modification method of traditional agricultural machinery, a combined navigation and lateral and longitudinal control mechanism is additionally installed on the original traditional agricultural machinery. A feedforward controller containing a PID algorithm is usually used to obtain the feedforward expected turning angle of the agricultural machinery, and then the path tracking turning angle of the agricultural machinery is obtained. The electric steering wheel is controlled in combination with the current turning angle of the agricultural machinery to realize the path tracking of the agricultural machinery.

[0004] For example, the intelligent control method, device and engineering machinery equipment of the invention patent announcement with publication number: CN114610200A include: responding to the selection and connection operations of multiple algorithm framework nodes to determine the target algorithm framework corresponding to the target function and the one-way graph data structure corresponding to the target algorithm framework; initializing the target algorithm framework based on the one-way graph data structure; acquiring the measurement data of the measuring device of the engineering machinery equipment; triggering the operation of the initialized target algorithm framework according to the measurement data, and controlling the engineering machinery equipment corresponding to the target function.

[0005] For example, a patent application with publication number: CN114816693A discloses a method for implementing a process automation robot that can be independently scheduled and run, including: a development user logs in to the RPA process designer to create a process project; publishes the created process project data to a local path; a user logs in to the RPA robot and selects the process project to be imported on the RPA robot operation interface; creates a default task for the user based on the process project information; after confirming the import of the process project, the newly imported process project is displayed in the process management interface, and the process to be run is selected and task management is performed.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, due to the diversity and complexity of the farmland environment, the existing feedforward controller based on the PID algorithm may not be able to adapt well to various farmland scenarios, such as undulating terrain and uneven crop distribution, so that the existing control system may not be able to accurately identify the farmland boundaries, resulting in inaccurate path tracking or failure to complete the expected operations. There is a problem of low path planning accuracy during the driving process of heterogeneous dual-chip collaborative control of farmland irrigation machinery. Summary of the invention

[0008] The embodiments of the present application solve the problem of low path planning accuracy in the driving path of farmland irrigation machinery controlled by heterogeneous dual chips in the prior art by providing an intelligent control method and system for agricultural machinery based on heterogeneous dual chips, thereby improving the accuracy of driving path planning of designated agricultural machinery in the process of heterogeneous dual chip collaborative control.

[0009] The embodiment of the present application provides an intelligent control method for agricultural machinery based on heterogeneous dual chips, comprising the following steps: step one, real-time monitoring of the collaborative working state between a first chip and a second chip to obtain a collaborative energy efficiency score, and judging whether to obtain image data based on the obtained collaborative energy efficiency score, wherein the collaborative energy efficiency score is used to quantify the energy efficiency of the first chip and the second chip during the collaborative working process; step two, if image data is obtained, obstacle perception and recognition are performed on the operation process of a specified agricultural machinery according to the obtained image data to obtain an obstacle avoidance coefficient, and judging whether to send a path planning instruction for a first time period to a microcontroller MCU based on the obtained obstacle avoidance coefficient, wherein the obstacle avoidance coefficient is used to quantify the obstacle avoidance coefficient of the specified agricultural machinery during the operation process. The urgency of obstacles; Step three, if the path planning instruction for the first time period is sent, the actual driving status of the designated agricultural machinery at the end of the first time period is monitored in real time to obtain the path planning compliance index, and at the same time, based on the obtained path planning compliance index, it is judged whether to send the path planning instruction for the second time period to the microcontroller MCU, and the path planning compliance index is used to quantify the degree of compliance between the actual driving path of the designated agricultural machinery in the first time period and the reference driving path; Step four, if the path planning instruction for the second time period is sent, the first driving path and the second driving path are obtained, and at the same time, the operating status of the designated agricultural machinery on the third driving path is monitored in real time, and whether the path planning is completed is judged according to the corresponding path planning compliance index at the end of the third time period.

[0010] Furthermore, the collaborative energy efficiency score is obtained by the following method: E1, when the end time point of the first chip is equal to the end time point of the second chip, execute E2, the end time point of the first chip is the time point when the collaborative work of the first chip ends, and the end time point of the second chip is the time point when the collaborative work of the second chip ends; E2, respectively obtain the average task allocation duration and the average task execution duration of the first chip and the second chip, and at the same time obtain the task allocation efficiency score and the task execution efficiency score in combination with the maximum allowed task allocation duration and the maximum allowed task execution duration in the database; E3, real-time monitoring of the power consumption data of the first chip and the second chip during the collaborative work process and evaluation to obtain the power consumption management efficiency score, and at the same time obtain the collaborative task processing efficiency score of the first chip and the second chip at the end of the collaborative work; E4, process the obtained task allocation efficiency score, task execution efficiency score, power consumption management efficiency score and collaborative task processing efficiency score to obtain a collaborative energy efficiency score; the collaborative task processing efficiency score is the ratio of the collaborative task processing volume of the first chip and the second chip at the end of the collaborative work to the total amount of collaborative tasks to be processed.

[0011] Furthermore, the specific steps for obtaining the obstacle avoidance coefficient include: when the obtained synergistic energy efficiency score is greater than the synergistic energy efficiency score preset in the database, obtaining the actual driving position of the specified agricultural machinery at the current perception moment according to the obtained kinematic model, and obtaining the shortest obstacle distance and the target position distance and performing de-unitization processing; judging whether the obtained shortest obstacle distance is less than the obtained target position distance, if so, obtaining the actual perception parameters of the specified agricultural machinery at the current perception moment according to the obtained kinematic model and performing de-unitization processing, and at the same time combining the maximum allowable steering speed in the database and the obtained synergistic energy efficiency score to obtain the obstacle avoidance coefficient, otherwise the corresponding obstacle avoidance coefficient is recorded as 0; the actual perception parameters include the actual steering radius, the actual steering angle and the actual steering speed.

[0012] Furthermore, the specific limiting expression of the obstacle avoidance coefficient is:

[0013]

[0014] Where t is the number of the current perception moment of the specified agricultural machinery, t = 1, 2, ..., T, T is the total number of the current perception moments of the specified agricultural machinery, e is a natural constant, GUI represents the obstacle avoidance coefficient of the specified agricultural machinery at the current perception moment, SU t represents the actual steering speed of the specified agricultural machinery at the current sensing time t, SU max represents the maximum allowable steering speed, XIE represents the synergistic energy efficiency score of the chip in the controller of the specified agricultural machinery, D1 t represents the shortest obstacle distance of the specified agricultural machinery at the current sensing time t, D2 t represents the target position distance of the specified agricultural machinery at the current sensing time t, R t represents the actual turning radius of the specified agricultural machinery at the current sensing time t, δ t Represents the actual steering angle of the specified agricultural machinery at the current sensing time t.

[0015] Furthermore, the path planning compliance index is obtained by the following method: when the obtained obstacle avoidance coefficient is less than the obstacle avoidance coefficient preset in the database, the corresponding number of path planning instruction updates during the driving process of the specified agricultural machinery in the first time period is obtained, and the actual distance deviation and the actual steering angle deviation at the end of the first time period are obtained; a preset terrain undulation correction factor is obtained from the database, and the path planning compliance index is obtained by combining the total length of the reference driving path in the database, the maximum allowable steering angle, the maximum allowable number of path planning instruction updates in the first time period, and the obtained obstacle avoidance coefficient; the actual distance deviation represents the straight-line distance between the stop position and the target stop position of the specified agricultural machinery at the end of the first time period; the actual steering angle deviation represents the angle difference between the stop steering angle and the target stop steering angle of the specified agricultural machinery at the end of the first time period.

[0016] An embodiment of the present application provides an intelligent control system for agricultural machinery based on heterogeneous dual chips, including: a collaborative energy efficiency score acquisition module, an obstacle avoidance coefficient acquisition module, a path planning compliance index acquisition module and a path planning progress judgment module; wherein the collaborative energy efficiency score acquisition module is used to monitor the collaborative working state between the first chip and the second chip in real time to obtain a collaborative energy efficiency score, and at the same time determine whether to obtain image data based on the obtained collaborative energy efficiency score, and the collaborative energy efficiency score is used to quantify the energy efficiency of the first chip and the second chip during the collaborative working process; the obstacle avoidance coefficient acquisition module is used to, if image data is obtained, perform obstacle perception and recognition on the operation process of the specified agricultural machinery according to the obtained image data to obtain an obstacle avoidance coefficient, and at the same time determine whether to send a path planning instruction for a first time period to the microcontroller MCU based on the obtained obstacle avoidance coefficient, and the obstacle avoidance coefficient is used to determine whether to send a path planning instruction for a first time period to the microcontroller MCU based on the obtained obstacle avoidance coefficient. The avoidance coefficient is used to quantify the urgency of the designated agricultural machinery to avoid obstacles during operation; the path planning compliance index acquisition module is used to monitor the actual driving status of the designated agricultural machinery at the end of the first time period in real time to obtain the path planning compliance index if the path planning instruction of the first time period is sent, and at the same time, judge whether to send the path planning instruction of the second time period to the microcontroller MCU based on the acquired path planning compliance index. The path planning compliance index is used to quantify the degree of compliance between the actual driving path of the designated agricultural machinery in the first time period and the reference driving path; the path planning progress judgment module is used to obtain the first driving path and the second driving path if the path planning instruction of the second time period is sent, and at the same time, monitor the operating status of the designated agricultural machinery on the third driving path in real time, and judge whether the path planning is completed according to the corresponding path planning compliance index at the end of the third time period.

[0017] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0018] 1. Determine whether to acquire image data based on the acquired collaborative energy efficiency score, and then determine whether to send the path planning instruction for the first time period based on the acquired obstacle avoidance coefficient. At the same time, determine whether to send the path planning instruction for the second time period based on the acquired path planning compliance index. Finally, determine whether the path planning is completed based on the corresponding path planning compliance index at the end of the third time period, thereby achieving more accurate acquisition of the path planning compliance index, and further achieving improved accuracy in the driving path planning of designated agricultural machinery during the heterogeneous dual-chip collaborative control process, effectively solving the problem of low path planning accuracy in the driving path of heterogeneous dual-chip collaborative control of farmland irrigation machinery in the prior art.

[0019] 2. The power consumption data of the first chip and the second chip during the collaborative work are monitored in real time and evaluated to obtain the power consumption management efficiency score, and then the collaborative task processing efficiency score of the first chip and the second chip at the end of the collaborative work is obtained. Finally, the obtained task allocation efficiency score, task execution efficiency score, power consumption management efficiency score and collaborative task processing efficiency score are processed to obtain the collaborative energy efficiency score, thereby improving the accuracy of obtaining the collaborative energy efficiency score, and further achieving a more accurate evaluation of the collaborative energy efficiency of the first chip and the second chip.

[0020] 3. The actual driving position of the designated agricultural machinery at the current perception moment is obtained through the obtained kinematic model, and the shortest obstacle distance and the target position distance are obtained and de-unitized. When the obtained shortest obstacle distance is less than the obtained target position distance, the actual perception parameters of the designated agricultural machinery at the current perception moment are obtained according to the obtained kinematic model and de-unitized, and the obstacle avoidance coefficient is obtained based on the obtained kinematic model, thereby improving the accuracy of obtaining the obstacle avoidance coefficient, and further achieving a more accurate evaluation of the accuracy of the path planning of the designated agricultural machinery. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of an intelligent control method for agricultural machinery based on heterogeneous dual chips provided in an embodiment of the present application;

[0022] Figure 2 A schematic diagram of the structure of an intelligent control system for agricultural machinery based on heterogeneous dual chips provided in an embodiment of the present application;

[0023] Figure 3 This is a specific architecture diagram of the agricultural machinery intelligent control system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The embodiment of the present application solves the problem of low path planning accuracy of heterogeneous dual-chip collaborative control of farm irrigation machinery in the driving path process in the prior art by providing an intelligent control method and system for agricultural machinery based on heterogeneous dual chips. The collaborative working state between the first chip and the second chip is monitored in real time to obtain a collaborative energy efficiency score. At the same time, it is determined whether to obtain image data based on the acquired collaborative energy efficiency score. If the image data is obtained, obstacle perception and recognition are performed on the operation process of the designated agricultural machinery according to the acquired image data to obtain an obstacle avoidance coefficient. Then, based on the acquired obstacle avoidance coefficient, it is determined whether to send a path planning instruction for a first time period to the microcontroller MCU. If the path planning instruction for the first time period is sent, the actual driving state of the designated agricultural machinery at the end of the first time period is monitored in real time to obtain a path planning compliance index. At the same time, based on the acquired path planning compliance index, it is determined whether to send a path planning instruction for a second time period to the microcontroller MCU. If the path planning instruction for the second time period is sent, the first driving path and the second driving path are obtained. Finally, the operating state of the designated agricultural machinery on the third driving path is monitored in real time, and the path planning compliance index corresponding to the end of the third time period is determined whether the path planning is completed, thereby improving the accuracy of the driving path planning of the designated agricultural machinery in the process of heterogeneous dual-chip collaborative control.

[0025] The technical solution in the embodiment of the present application is to solve the problem of low path planning accuracy in the driving path of the above-mentioned heterogeneous dual-chip collaborative control farm irrigation machinery. The overall idea is as follows:

[0026] The acquired collaborative energy efficiency score is used to determine whether to acquire image data, and then based on the acquired obstacle avoidance coefficient, it is determined whether to send the path planning instructions for the first time period. At the same time, based on the acquired path planning compliance index, it is determined whether to send the path planning instructions for the second time period. Finally, based on the corresponding path planning compliance index at the end of the third time period, it is determined whether the path planning is completed, thereby achieving the effect of improving the accuracy of the driving path planning of specified agricultural machinery during the heterogeneous dual-chip collaborative control process.

[0027] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0028] like Figure 1As shown, it is a flow chart of the intelligent control method of agricultural machinery based on heterogeneous dual chips provided in an embodiment of the present application. The intelligent control method of agricultural machinery based on heterogeneous dual chips provided in an embodiment of the present application includes the following steps: Step 1, real-time monitoring of the collaborative working state between the first chip and the second chip to obtain a collaborative energy efficiency score, and judging whether to obtain image data based on the obtained collaborative energy efficiency score, the collaborative energy efficiency score is used to quantify the energy efficiency of the first chip and the second chip in the collaborative working process; Step 2, if image data is obtained, obstacle perception and identification are performed on the operation process of the specified agricultural machinery according to the obtained image data to obtain an obstacle avoidance coefficient, and judging whether to send a path planning instruction for the first time period to the microcontroller MCU based on the obtained obstacle avoidance coefficient, the obstacle avoidance coefficient is used to Quantify the urgency of designated agricultural machinery in avoiding obstacles during operation; Step three, if a path planning instruction for a first time period is sent, the actual driving status of the designated agricultural machinery at the end of the first time period is monitored in real time to obtain a path planning compliance index, and at the same time, based on the obtained path planning compliance index, determine whether to send a path planning instruction for a second time period to the microcontroller MCU. The path planning compliance index is used to quantify the degree of compliance between the actual driving path of the designated agricultural machinery in the first time period and the reference driving path; Step four, if a path planning instruction for the second time period is sent, the first driving path and the second driving path are obtained, and at the same time, the operating status of the designated agricultural machinery on the third driving path is monitored in real time, and whether the path planning is completed is determined according to the corresponding path planning compliance index at the end of the third time period.

[0029] Among them, the first chip represents the main processor MPU (Memory Protection Unit) based on ARM (Advanced RICS Machines) architecture; the second chip represents the microcontroller MCU (MicroController Unit) based on AURIX architecture; the path planning instruction is used to adjust the driving state of the specified agricultural machinery; the reference driving path represents the total length of the driving path of the specified agricultural machinery after the microcontroller MCU adjusts according to the path planning instruction; the first driving path is the actual driving path of the specified agricultural machinery in the first time period; the second driving path is the actual driving path of the specified agricultural machinery in the second time period.

[0030] In this embodiment, the designated agricultural machinery is usually an unmanned farm irrigation machinery; collaborative work indicates the process of jointly processing tasks on the first chip and the second chip, and the collaborative work state indicates the state of the first chip and the second chip in jointly processing tasks, usually expressed in binary, 0 indicates that the joint processing is not completed, and 1 indicates that the joint processing is completed; the first chip is used to perform image processing of the designated agricultural machinery and algorithm calculations of the designated agricultural machinery in the intelligent control process; the second chip is responsible for real-time path control and task scheduling of the designated agricultural machinery; the sum of the paths corresponding to the first driving path, the second driving path and the third driving path is the total length of the reference driving path; the total duration corresponding to the first time period, the second time period and the third time period is the actual total driving duration of the designated agricultural machinery.

[0031] Compared with the existing technology, this example can give full play to the advantages of each chip through the collaborative control of heterogeneous dual chips, achieve functional complementarity and performance improvement. This collaborative control method helps to improve the intelligent control level of agricultural machinery, reduce energy consumption and costs, and improve work efficiency and safety. Secondly, through real-time monitoring of the collaborative working status of the dual chips, accurate obstacle perception and recognition and path planning, and improved driving path planning accuracy, the accuracy of driving path planning of specified agricultural machinery is improved during the collaborative control of heterogeneous dual chips.

[0032] Furthermore, the collaborative energy efficiency score is obtained by the following method: E1, when the end time point of the first chip is equal to the end time point of the second chip, execute E2, the end time point of the first chip is the time point when the collaborative work of the first chip ends, and the end time point of the second chip is the time point when the collaborative work of the second chip ends; E2, respectively obtain the average task allocation duration and the average task execution duration of the first chip and the second chip, and at the same time combine the maximum allowed task allocation duration and the maximum allowed task execution duration in the database to obtain the task allocation efficiency score and the task execution efficiency score, that is, and E3, real-time monitoring of the power consumption data of the first chip and the second chip during the collaborative work process and evaluation to obtain the power consumption management efficiency score, i.e. At the same time, the collaborative task processing efficiency scores of the first chip and the second chip at the end of the collaborative work are obtained, that is, E4, processes the acquired task allocation efficiency score, task execution efficiency score, power consumption management efficiency score and collaborative task processing efficiency score to obtain a collaborative energy efficiency score; the power consumption data includes the actual power consumption and the historical minimum power consumption in the database; the collaborative task processing efficiency score is the ratio of the collaborative task processing volume of the first chip and the second chip at the end of the collaborative work to the total amount of collaborative tasks to be processed.

[0033] Among them, when the end time point of the first chip is equal to the end time point of the second chip, the specific restriction expression of the synergistic energy efficiency score is:

[0034]

[0035] Where n is the chip number in the controller of the specified agricultural machinery, n=1,2, e is a natural constant, XIE represents the synergistic energy efficiency score of the chip in the controller of the specified agricultural machinery, AU n represents the average task allocation time of the tasks on the nth chip at the end of the collaborative work, AU max Indicates the maximum allowed task allocation duration, BU n represents the average task execution time of the tasks on the nth chip at the end of the collaborative work, BU max Indicates the maximum allowed task execution time, LU min Indicates the historical minimum power consumption, LU n represents the actual power consumption of the nth chip at the end of the collaborative work, G2 represents the collaborative task processing volume of the first chip and the second chip at the end of the collaborative work, and G1 represents the total amount of collaborative tasks to be processed by the first chip and the second chip before the start of the collaborative work.

[0036] The maximum allowable task allocation duration and the maximum allowable task execution duration respectively represent the maximum values ​​of the historical task allocation duration and the historical task execution duration of the agricultural machinery chip in the database at the end of each historical collaborative work; the historical minimum power consumption represents the historical minimum power consumption of the agricultural machinery chip in the database at the end of each historical collaborative work.

[0037] The aforementioned database is a database for storing various types of setting data established before the design of the intelligent control method and system of agricultural machinery based on heterogeneous dual chips. The database includes but is not limited to a preset synergistic energy efficiency score, a preset obstacle avoidance coefficient, a terrain undulation correction factor, a preset path planning compliance index, and a first time period, a second time period, and a third time period. The various values ​​therein are directly set by technical personnel. Among them, the setting basis of the preset path planning compliance index can be determined according to the actual working scenario of the specified agricultural machinery. For example, the preset path planning compliance index is represented by the sum and average of the historical path planning compliance indices of the agricultural machinery in the database in each historical time period. In addition, the various values ​​in the database can be set and fine-tuned by technical personnel according to actual debugging.

[0038] In this embodiment, the end time point of the first chip and the end time point of the second chip are recorded by a timer, the collaborative task processing volume and the total amount of collaborative tasks to be processed are recorded by a counter, the actual power consumption is monitored by a power sensor, and the average task allocation duration and the average task execution duration respectively represent the results of summing and averaging the allocation duration and execution duration of the tasks on the corresponding chips during the collaborative work process, wherein the allocation duration and execution duration are recorded by a timer.

[0039] This example ensures that the first chip and the second chip complete their respective tasks at the same time point by considering the end time point of the first chip to be equal to the end time point of the second chip, significantly improving computing efficiency and throughput. This synchronization state helps to reduce the delay caused by waiting for other components (components other than the first chip and the second chip, for example, the actuator on the motor in the agricultural machinery, which drives the action of the agricultural machinery according to the received control instructions) to complete the corresponding task two, making the administrator's tracking and debugging work more timely and reliable.

[0040] It should be understood that the collaborative energy efficiency score decreases with the increase of actual power consumption, average task allocation time and average task execution time, and increases with the increase of collaborative task processing volume. When the actual power consumption is equal to the historical minimum power consumption and the collaborative task processing volume is equal to the collaborative task processing volume to be processed, it indicates that the corresponding collaborative energy consumption is the minimum and the designated agricultural machinery completes the collaborative processing task at the end of the collaborative work.

[0041] It should be noted that the actual power consumption also indirectly affects the value of the average task allocation duration. When the actual power consumption increases, it may be necessary to adjust the resource allocation strategy to reduce energy consumption. For example, the computing resources (such as CPU) allocated to certain tasks may be reduced to reduce power consumption. This adjustment may cause uneven task allocation on the first chip and the second chip, thereby delaying the average allocation duration of certain tasks.

[0042] The average task execution time also indirectly affects the value of the collaborative task processing volume. When the task execution time increases, it means that the throughput of the first chip and the second chip when collaboratively processing certain tasks is reduced, because the first chip or the second chip requires longer time to complete tasks with the same data, which limits the total amount of collaborative tasks that can be processed within a given time.

[0043] By considering the above-mentioned indirect impact mechanism, the energy efficiency of the heterogeneous dual chips in the collaborative work process can be evaluated more accurately, and the resource allocation and task scheduling strategies can be adjusted accordingly. These adjustments help to optimize the overall performance of the system, improve task execution efficiency and throughput, thereby ensuring that agricultural machinery can travel along the planned path more accurately, thereby achieving an improvement in the accuracy of the planning of the driving path of the specified agricultural machinery during the collaborative control of the heterogeneous dual chips, and effectively solving the problem of low path planning accuracy of the heterogeneous dual chip collaborative control of farmland irrigation machinery during the driving path in the prior art.

[0044] Furthermore, the specific process of determining whether to acquire image data based on the acquired synergy energy efficiency score is as follows: determining whether the acquired synergy energy efficiency score is greater than the synergy energy efficiency score preset in the database: if the acquired synergy energy efficiency score is greater than the synergy energy efficiency score preset in the database, it indicates that the synergy performance between the first chip and the second chip meets the expected requirements and image data is acquired; if the acquired synergy energy efficiency score is not greater than the synergy energy efficiency score preset in the database, it indicates that the synergy performance between the first chip and the second chip does not meet the expected requirements, and image data is not acquired at this time, and a synergy maintenance instruction is sent to the main processor MPU.

[0045] Among them, the image data includes the environment image of the specified agricultural machinery, the crop growth status image and the obstacle detection image; the obstacle detection image represents the two-dimensional coordinate map of the specified agricultural machinery containing obstacle marks after obstacle perception and identification; the image data is obtained by the camera interface circuit by converting the collected image signal into a digital signal, and can be directly recognized by the main processor MPU.

[0046] In this embodiment, the preset synergy energy efficiency score is represented by the sum and average of the historical synergy energy efficiency scores of the chips in the controllers of the agricultural machinery in the historical collaborative work process in the database; this example ensures that image data is only acquired when the chip collaborative performance meets the expected requirements by judging the numerical change relationship between the synergy energy efficiency score and the preset synergy energy efficiency score, thereby avoiding image data errors or losses caused by chip collaboration problems, improving the accuracy and reliability of image data acquisition, and achieving more accurate acquisition of the actual driving path of the specified agricultural machinery.

[0047] Furthermore, obstacle perception and identification are performed on the operation process of the specified agricultural machinery based on the acquired image data, and then the kinematic model of the specified agricultural machinery corresponding to the actual driving state is obtained. The specific steps of obtaining the kinematic model are: obtaining a two-dimensional coordinate map of the specified agricultural machinery and marking the actual coordinate parameters of the specified agricultural machinery in the two-dimensional coordinate map, and at the same time monitoring the actual driving parameters of the specified agricultural machinery during the operation process in real time and processing them to obtain the kinematic model of the specified agricultural machinery during the operation process; the actual coordinate parameters include coordinate position, body inclination angle and body length; the actual driving parameters include driving speed, driving angle, driving steering angle and turning radius.

[0048] The kinematic model is used to visualize the position change and steering angle change of the specified agricultural machinery during operation. The specific expression of the kinematic model is:

[0049]

[0050] In the formula, represents the first-order derivative of the lateral travel distance of the specified agricultural machinery during operation, v represents the travel speed of the specified agricultural machinery, θ represents the travel angle of the specified agricultural machinery, Represents the first-order derivative of the longitudinal travel distance of a specified agricultural machinery during operation. represents the first-order derivative of the driving angle of the specified agricultural machinery during operation, ρ represents the driving steering angle of the specified agricultural machinery, σ represents the body tilt angle of the specified agricultural machinery, and w σ It indicates the turning radius of the agricultural machinery at the specified tilt angle of the fuselage, r σ It represents the turning radius of the specified agricultural machinery under the driving steering angle, and a represents the fuselage length of the specified agricultural machinery.

[0051] In this embodiment, the coordinate position is obtained in real time through a GPS (Global Positioning System) positioning instrument, the driving speed is measured by a LP-DS201 speed sensor, the driving angle and the driving steering angle are obtained by a vehicle handover measuring device, the fuselage tilt angle is measured by a three-axis inclinometer, the turning radius is measured by a rangefinder, and the fuselage length is a fixed parameter of the specified agricultural machinery, which has been determined in the design stage.

[0052] This example uses the existing kinematic model of agricultural machinery during operation to visualize the actual position and actual steering angle changes of the specified agricultural machinery during operation over time, and provides the specified agricultural machinery with an optimal path selection plan to avoid obstacles, which helps to further optimize the control strategy of the specified agricultural machinery, improve operation efficiency and safety, and thus realize intelligent monitoring of agricultural machinery in complex operating environments.

[0053] Furthermore, the specific steps for obtaining the obstacle avoidance coefficient include: when the obtained synergy energy efficiency score is greater than the synergy energy efficiency score preset in the database, the actual driving position of the specified agricultural machinery at the current sensing moment is obtained according to the obtained kinematic model, and the shortest obstacle distance and the target position distance are obtained and de-unitized. The shortest obstacle distance represents the distance between obstacles with the shortest straight-line distance in the same driving direction as the specified agricultural machinery, that is, D1 t , the target position distance represents the distance between the actual driving position and the target driving position, that is, D2 t ; Determine whether the obtained shortest obstacle distance is less than the obtained target position distance. If so, obtain the actual perception parameters of the specified agricultural machinery at the current perception moment according to the obtained kinematic model and perform de-unitization processing. At the same time, combine the maximum allowable steering speed in the database and the obtained synergistic energy efficiency score to obtain the obstacle avoidance coefficient. Otherwise, it indicates that there is no obstacle in the current working area of ​​the specified agricultural machinery, that is, no obstacle avoidance is required and the corresponding obstacle avoidance coefficient is recorded as 0; the actual perception parameters include the actual steering radius, the actual steering angle and the actual steering speed, that is, R t , δ t and SU t .

[0054] When the obtained synergistic energy efficiency score is greater than the synergistic energy efficiency score preset in the database, the specific restriction expression of the obstacle avoidance coefficient is:

[0055]

[0056] Where t is the number of the current perception moment of the specified agricultural machinery, t = 1, 2, ..., T, T is the total number of the current perception moments of the specified agricultural machinery, e is a natural constant, GUI represents the obstacle avoidance coefficient of the specified agricultural machinery at the current perception moment, SU t represents the actual steering speed of the specified agricultural machinery at the current sensing time t, SU max represents the maximum allowable steering speed, XIE represents the synergistic energy efficiency score of the chip in the controller of the specified agricultural machinery, D1 t represents the shortest obstacle distance of the specified agricultural machinery at the current sensing time t, D2 t represents the target position distance of the specified agricultural machinery at the current sensing time t, R t represents the actual turning radius of the specified agricultural machinery at the current sensing time t, δ t Represents the actual steering angle of the specified agricultural machinery at the current sensing time t.

[0057] In this embodiment, the maximum allowable steering speed represents the maximum value of the historical steering speeds of the agricultural machinery in the database during historical operations; the shortest distance to the obstacle and the distance to the target position are measured by a rangefinder, the actual steering speed is obtained by a vehicle speed sensor installed on the transmission of the specified agricultural machinery, the actual steering angle is obtained by an angle sensor installed on the steering gear of the specified agricultural machinery, and the actual steering radius is displayed in real time on the controller display screen on the specified agricultural machinery.

[0058] Specifically, to simplify the analysis, define RT t =R t *sinδ t , where RT t It represents the actual steering coefficient of the specified agricultural machinery at the current sensing time t, where the actual steering angle ranges from 0° to 90° and the maximum allowable steering speed is 0.15 m / s. t <D2 t When , the obstacle avoidance coefficient change statistics are shown in Table 1:

[0059] Table 1 Statistics of changes in obstacle avoidance coefficient

[0060]

[0061] It is important to understand that when D1 t <D2 t It can be seen from the first, second and third groups of data in Table 1 that the obstacle avoidance coefficient increases with the increase of the synergistic energy efficiency score and the actual turning speed. It can be seen from the fourth, fifth and sixth rows of data in Table 1 that the obstacle avoidance coefficient decreases with the increase of the shortest obstacle distance, the target position distance and the actual turning coefficient.

[0062] It should be noted that the synergistic energy efficiency score also indirectly affects the values ​​of the actual steering speed and the actual steering angle. When the synergistic energy efficiency score increases, it means that the first chip and the second chip can process the corresponding tasks more quickly and make more accurate driving decisions, so that the agricultural machinery can respond to various situations more flexibly during driving, thereby improving the response speed and steering performance of the agricultural machinery.

[0063] The actual steering coefficient also indirectly affects the value of the shortest distance to the obstacle. When the actual steering coefficient increases, the agricultural machinery will respond more quickly and the steering angle will be larger after receiving the steering command. This rapid and large-scale steering behavior may cause the agricultural machinery to get closer to the obstacle on the driving path, thereby reducing the shortest distance to the obstacle.

[0064] By considering the above-mentioned indirect influence mechanism, the dynamic behavior of agricultural machinery during driving can be predicted more accurately. This prediction capability enables the heterogeneous dual-chip collaborative control system to more accurately plan the driving path of agricultural machinery to avoid potential collision risks. At the same time, this flexibility further enhances the accuracy and reliability of path planning, thereby achieving an improvement in the accuracy of driving path planning for specified agricultural machinery during heterogeneous dual-chip collaborative control, and effectively solving the problem of low path planning accuracy in the driving path of heterogeneous dual-chip collaborative control of farmland irrigation machinery in the prior art.

[0065] Furthermore, the specific process of judging whether to send the path planning instruction for the first time period to the microcontroller MCU based on the obtained obstacle avoidance coefficient is as follows: judging whether the obtained obstacle avoidance coefficient is less than the obstacle avoidance coefficient preset in the database: if the obtained obstacle avoidance coefficient is less than the obstacle avoidance coefficient preset in the database, it indicates that the specified agricultural machinery does not avoid obstacles at the current working moment, and at this time, the path planning instruction for the first time period is sent to the microcontroller MCU; if the obtained obstacle avoidance coefficient is not less than the obstacle avoidance coefficient preset in the database, it indicates that the specified agricultural machinery avoids obstacles at the current working moment, and at this time, the obstacle avoidance instruction is sent to the microcontroller MCU.

[0066] Among them, the obstacle avoidance instruction includes a first obstacle avoidance instruction and a second obstacle avoidance instruction; the first obstacle avoidance instruction represents the obstacle avoidance instruction corresponding to when the obtained obstacle avoidance coefficient is greater than the obstacle avoidance coefficient preset in the database; the second obstacle avoidance instruction represents the obstacle avoidance instruction corresponding to when the obtained obstacle avoidance coefficient is equal to the obstacle avoidance coefficient preset in the database.

[0067] In this embodiment, the preset obstacle avoidance coefficient is represented by the sum and average of the historical obstacle avoidance coefficients of the agricultural machinery in the database at each historical perception moment; this example can more accurately judge whether the agricultural machinery needs to avoid obstacles by using the numerical relationship between the obstacle avoidance coefficient and the preset obstacle avoidance coefficient, thereby avoiding driving safety problems caused by misjudgment, and sending different levels of obstacle avoidance instructions according to the value of the obstacle avoidance coefficient, which can more flexibly respond to different obstacle avoidance needs, improve the driving efficiency and safety of the specified agricultural machinery, and has strong versatility and adaptability to the working environments of different types of agricultural machinery.

[0068] Furthermore, the path planning compliance index is obtained by the following method: when the obtained obstacle avoidance coefficient is less than the obstacle avoidance coefficient preset in the database, the corresponding number of path planning instruction updates during the driving process of the specified agricultural machinery within the first time period is obtained, and the actual distance deviation and the actual steering angle deviation at the end of the first time period are obtained; a preset terrain undulation correction factor is obtained from the database, and the path planning compliance index is obtained by combining the total length of the reference driving path in the database, the maximum allowable steering angle, the maximum allowable number of path planning instruction updates within the first time period, and the obtained obstacle avoidance coefficient.

[0069] When the obtained obstacle avoidance coefficient is less than the obstacle avoidance coefficient preset in the database, the specific restriction expression of the path planning compliance index is:

[0070]

[0071] Where, e is a natural constant, FUH represents the path planning compliance index of the specified agricultural machinery in the first time period, GUI represents the obstacle avoidance coefficient of the specified agricultural machinery in the process of obstacle perception and identification, μ represents the terrain undulation correction factor, ΔQ1 represents the actual distance deviation of the specified agricultural machinery at the end of the first time period, ΔQ10 represents the total length of the reference driving path, ΔQ2 represents the actual steering angle deviation of the specified agricultural machinery at the end of the first time period, and ΔQ2 represents the actual steering angle deviation of the specified agricultural machinery at the end of the first time period. max represents the maximum allowable steering angle, Q3 represents the number of path planning instruction updates for the specified agricultural machinery at the end of the first time period, Q3 max Indicates the maximum number of allowed path planning instruction updates.

[0072] Among them, the actual distance deviation represents the straight-line distance between the stopping position of the designated agricultural machinery at the end of the first time period and the target stopping position; the actual steering angle deviation represents the angle difference between the stopping steering angle of the designated agricultural machinery at the end of the first time period and the target stopping steering angle; the total length of the reference driving path is set by the administrator, such as 6m, and the number of path planning instruction updates is recorded by a counter. The maximum allowable number of path planning instruction updates in the first time period is usually set to 6 times, and the maximum allowable steering angle is usually set to 90°.

[0073] The terrain undulation correction factor in this example is a correction value of the influence of the terrain undulation of the area where the agricultural machinery is located during the operation process on the path planning compliance index acquisition process, which is preset in the database. Specifically, the database stores the terrain undulation correction factor corresponding to the path planning compliance index. There is a pre-set mapping relationship between this correction factor and the path planning compliance index. This mapping relationship can be one-to-one or many-to-one. In practical applications, the real-time terrain undulation and path planning compliance index can be input into this mapping relationship to quickly obtain the corresponding terrain undulation correction factor. The value range of the terrain undulation correction factor in this example is usually 0 to 1.

[0074] It should be understood that the actual steering angle deviation is less than the maximum allowable steering angle, and the path planning compliance index decreases with the increase of the actual distance deviation, the number of path planning instruction updates and the actual steering angle deviation, and increases with the increase of the obstacle avoidance coefficient. Among them, the obstacle avoidance coefficient also indirectly affects the value of the actual distance deviation. When the obstacle avoidance coefficient increases, it means that the specified agricultural machinery needs to urgently avoid obstacles at the current moment, which usually leads to more frequent path adjustments of agricultural machinery during driving, causing the agricultural machinery to deviate from the originally planned driving path, thereby resulting in actual distance deviation.

[0075] The actual steering angle deviation also indirectly affects the value of the number of path planning instruction updates. When the actual steering angle deviation increases, it means that the steering performance of the agricultural machinery is significantly different from expectations. In order to correct this difference, the path planning instructions need to be updated more frequently to ensure that the agricultural machinery can travel along the expected path and guide the agricultural machinery back to the reference driving path. Therefore, the increase in the actual steering angle deviation indirectly increases the demand for the number of path planning instruction updates.

[0076] By considering the above-mentioned indirect influence mechanism, we can have a more comprehensive understanding of the interaction between various factors in agricultural machinery driving path planning. This understanding helps to design more accurate and efficient path planning algorithms, thereby achieving improved accuracy in designated agricultural machinery driving path planning during heterogeneous dual-chip collaborative control, and effectively solving the problem of low path planning accuracy during heterogeneous dual-chip collaborative control of designated agricultural machinery driving paths in the prior art.

[0077] like Figure 2As shown, it is a structural schematic diagram of an agricultural machinery intelligent control system based on heterogeneous dual chips provided in an embodiment of the present application. The agricultural machinery intelligent control system based on heterogeneous dual chips provided in an embodiment of the present application includes: a collaborative energy efficiency score acquisition module, an obstacle avoidance coefficient acquisition module, a path planning compliance index acquisition module and a path planning progress judgment module; wherein the collaborative energy efficiency score acquisition module is used to monitor the collaborative working state between the first chip and the second chip in real time to obtain a collaborative energy efficiency score, and at the same time, judge whether to obtain image data based on the obtained collaborative energy efficiency score, and the collaborative energy efficiency score is used to quantify the energy efficiency of the first chip and the second chip in the collaborative working process; the obstacle avoidance coefficient acquisition module is used to, if image data is obtained, perform obstacle perception and recognition on the operation process of the specified agricultural machinery according to the obtained image data to obtain an obstacle avoidance coefficient, and at the same time, judge whether to send an obstacle avoidance coefficient to the microcontroller MCU based on the obtained obstacle avoidance coefficient. The path planning instruction of the first time period is sent, and the obstacle avoidance coefficient is used to quantify the urgency of the designated agricultural machinery to avoid obstacles during the operation process; the path planning compliance index acquisition module is used to monitor the actual driving status of the designated agricultural machinery at the end of the first time period in real time to obtain the path planning compliance index if the path planning instruction of the first time period is sent, and at the same time, based on the obtained path planning compliance index, determine whether to send the path planning instruction of the second time period to the microcontroller MCU, and the path planning compliance index is used to quantify the degree of compliance between the actual driving path of the designated agricultural machinery in the first time period and the reference driving path; the path planning progress judgment module is used to obtain the first driving path and the second driving path if the path planning instruction of the second time period is sent, and at the same time, monitor the operating status of the designated agricultural machinery on the third driving path in real time, and determine whether the path planning is completed according to the corresponding path planning compliance index at the end of the third time period.

[0078] In this embodiment, if Figure 3 As shown in FIG. 1 , a specific architecture diagram of the intelligent control system for agricultural machinery provided in an embodiment of the present application is provided, which realizes real-time driving state monitoring and intelligent control of a specified agricultural machinery during operation. Figure 3 It can be seen that the first chip MPU and the second chip MCU are connected through various components to work together. S1 represents the power input module; S8 represents the power voltage conversion module, which steps down the power voltage introduced by S1; S9, S10, and S11 represent the camera interface circuit module, which is composed of six cameras, S2, S3, S4, S5, S6, and S7; the transmitted image signal is deserialized; S12 represents a radar device that uses laser echo for obstacle detection, and supports 16, 32, 64, and 128 lines; S13 represents the power output module, which provides power for other electronic components of agricultural machinery except MPU / MCU (including: sensor power supply, relay drive), and supports 12V, 5V, and 3.3V.

[0079] s14 represents the power management module, which provides power for the MPU and MCU; s15 represents the memory unit module, RAM medium, which supports a maximum of 4GB and provides a program running space carrier for the MPU; s16 represents the data storage unit, Flash medium, which supports a maximum of 64GB. After being awakened by the aforementioned s14, the MPU loads the relevant program from this module to the aforementioned s15 for operation to realize the operation of agricultural machinery; s17 represents a micro data storage unit, which supports a maximum of 4MB and is used for system booting and initialization of the MPU; s18 represents a video output interface, which can output data to the display screen in the form of a video stream, and realize the function of real-time display of the data obtained by the MPU through the aforementioned s2, s3, s4, s5, s6, s7, and s12; s19 represents a serial communication input and output interface, which can realize full-duplex communication.

[0080] s20 represents the standard USB3.0 interface, which can be used to realize data interaction between various agricultural machinery and external devices, so as to achieve compatibility with various operation scenarios; s21 represents the standard CAN FD interface, supporting millimeter wave radar or integrated navigation data communication; s22 represents Ethernet data communication PHY module, used to establish communication between the aforementioned s12 and MPU; s23 represents ARM architecture SOC, used to carry out data input and output processing of the aforementioned s2, s2, s3, s4, s5, s6, s7, s12, s18, s19, s20, and s21; s24 represents a universal RJ45 Ethernet interface, supporting 1000Mbps, providing a software debugging entry for the MPU; s25 represents an Ethernet SWITCH module, serving as a gateway for connecting the MPU and MCU to the external network; s26 represents a universal GPIO interface, realizing time synchronization between the MPU and MCU; s27 represents a universal SPI interface; s28 represents a pluggable FLASH storage medium interface; s29 represents a universal Bluetooth interface; s30 represents a universal UART interface; s31 represents a universal CAN interface, which can be used for signal interaction with other control modules of agricultural machinery.

[0081] To summarize, the embodiment of the present application determines whether to acquire image data through the acquired collaborative energy efficiency score, and then determines whether to send the path planning instruction for the first time period based on the acquired obstacle avoidance coefficient, and at the same time determines whether to send the path planning instruction for the second time period based on the acquired path planning compliance index, and finally determines whether the path planning is completed according to the corresponding path planning compliance index at the end of the third time period, thereby achieving more accurate acquisition of the path planning compliance index, and further achieving improved accuracy in the driving path planning of specified agricultural machinery during the heterogeneous dual-chip collaborative control process, effectively solving the problem of low path planning accuracy in the driving path process of heterogeneous dual-chip collaborative control of farmland irrigation machinery in the prior art.

[0082] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0084] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0085] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0086] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0087] 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 equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent control method for agricultural machinery based on heterogeneous dual chips, characterized in that: The following steps are involved: Step 1: monitor the collaborative working state between the first chip and the second chip in real time to obtain a collaborative energy efficiency score, and determine whether to obtain image data based on the obtained collaborative energy efficiency score, wherein the collaborative energy efficiency score is used to quantify the energy efficiency of the first chip and the second chip during the collaborative working process; Step 2: If the image data is obtained, obstacle perception and identification are performed on the operation process of the specified agricultural machinery according to the obtained image data to obtain an obstacle avoidance coefficient, and at the same time, based on the obtained obstacle avoidance coefficient, it is determined whether to send a path planning instruction for the first time period to the microcontroller MCU, wherein the obstacle avoidance coefficient is used to quantify the urgency of the specified agricultural machinery to avoid obstacles during the operation process; Step three: if the path planning instruction for the first time period is sent, the actual driving state of the designated agricultural machinery at the end of the first time period is monitored in real time to obtain a path planning compliance index, and based on the obtained path planning compliance index, it is determined whether to send the path planning instruction for the second time period to the microcontroller MCU, wherein the path planning compliance index is used to quantify the degree of compliance between the actual driving path of the designated agricultural machinery in the first time period and the reference driving path; Step 4: If the path planning instruction for the second time period is sent, the first driving path and the second driving path are obtained, and the operating status of the designated agricultural machinery on the third driving path is monitored in real time, and whether the path planning is completed is determined based on the corresponding path planning compliance index at the end of the third time period.

2. The agricultural machinery intelligent control method based on heterogeneous dual chips as claimed in claim 1, characterized in that: The first chip represents a main processor MPU based on ARM architecture; The second chip represents a microcontroller MCU based on the AURIX architecture; The path planning instruction is used to adjust the driving state of the designated agricultural machinery; The reference driving path represents the total length of the driving path of the designated agricultural machinery after the microcontroller MCU adjusts the path planning instruction; The first driving path is the actual driving path of the designated agricultural machinery within the first time period; The second driving path is the actual driving path of the designated agricultural machinery within the second time period.

3. The agricultural machinery intelligent control method based on heterogeneous dual chips as claimed in claim 1, characterized in that: The synergy energy efficiency score is obtained by the following method: E1, when the end time point of the first chip is equal to the end time point of the second chip, execute E2, the end time point of the first chip is the time point when the collaborative work of the first chip ends, and the end time point of the second chip is the time point when the collaborative work of the second chip ends; E2, respectively obtain the average task allocation duration and the average task execution duration of the first chip and the second chip, and simultaneously obtain the task allocation efficiency score and the task execution efficiency score in combination with the maximum allowed task allocation duration and the maximum allowed task execution duration in the database; E3, real-time monitoring of power consumption data of the first chip and the second chip during the collaborative work process and evaluation to obtain a power consumption management efficiency score, and at the same time obtaining a collaborative task processing efficiency score of the first chip and the second chip at the end of the collaborative work; E4, processing the obtained task allocation efficiency score, task execution efficiency score, power consumption management efficiency score and collaborative task processing efficiency score to obtain a collaborative energy efficiency score; The collaborative task processing efficiency score is the ratio of the collaborative task processing volume of the first chip and the second chip at the end of the collaborative work to the total amount of collaborative tasks to be processed.

4. The agricultural machinery intelligent control method based on heterogeneous dual chips as claimed in claim 1, characterized in that: The specific process of determining whether to acquire image data based on the acquired synergistic energy efficiency score is as follows: Determine whether the obtained synergy energy efficiency score is greater than the synergy energy efficiency score preset in the database: If the obtained synergy energy efficiency score is greater than the synergy energy efficiency score preset in the database, then acquiring image data; If the obtained collaborative energy efficiency score is not greater than the collaborative energy efficiency score preset in the database, the image data is not obtained and a collaborative maintenance instruction is sent to the main processor MPU; The image data includes an environment image of a designated agricultural machine, a crop growth condition image, and an obstacle detection image.

5. The agricultural machinery intelligent control method based on heterogeneous dual chips as claimed in claim 1, characterized in that: The obstacle perception and recognition is performed on the operation process of the designated agricultural machinery according to the acquired image data, and then the kinematic model of the designated agricultural machinery corresponding to the actual driving state is acquired. The specific steps of acquiring the kinematic model are: Obtain a two-dimensional coordinate diagram of the specified agricultural machinery and mark the actual coordinate parameters of the specified agricultural machinery in the two-dimensional coordinate diagram, and simultaneously monitor the actual driving parameters of the specified agricultural machinery in real time during the operation process and process them to obtain a kinematic model of the specified agricultural machinery during the operation process; The actual coordinate parameters include coordinate position, fuselage inclination angle and fuselage length; The kinematic model is used to visualize the position change and steering angle change of the specified agricultural machinery during operation; The actual driving parameters include driving speed, driving angle, driving steering angle and turning radius.

6. The agricultural machinery intelligent control method based on heterogeneous dual chips as claimed in claim 1, characterized in that: The specific steps of obtaining the obstacle avoidance coefficient include: When the obtained synergy energy efficiency score is greater than the preset synergy energy efficiency score in the database, the actual driving position of the specified agricultural machinery at the current sensing moment is obtained according to the obtained kinematic model, and the shortest distance to the obstacle and the distance to the target position are obtained and de-unitized; Determine whether the obtained shortest obstacle distance is less than the obtained target position distance. If so, obtain the actual perception parameters of the specified agricultural machinery at the current perception moment according to the obtained kinematic model and perform de-unitization processing. At the same time, combine the maximum allowable steering speed in the database and the obtained synergistic energy efficiency score to obtain the obstacle avoidance coefficient. Otherwise, the corresponding obstacle avoidance coefficient is recorded as 0. The actual perception parameters include an actual steering radius, an actual steering angle, and an actual steering speed.

7. The agricultural machinery intelligent control method based on heterogeneous dual chips as claimed in claim 6, characterized in that: The specific limiting expression of the obstacle avoidance coefficient is: Where t is the number of the current perception moment of the specified agricultural machinery, t = 1, 2, ..., T, T is the total number of the current perception moments of the specified agricultural machinery, e is a natural constant, GUI represents the obstacle avoidance coefficient of the specified agricultural machinery at the current perception moment, SU t represents the actual steering speed of the specified agricultural machinery at the current sensing time t, SU max represents the maximum allowable steering speed, XIE represents the synergistic energy efficiency score of the chip in the controller of the specified agricultural machinery, D1 t represents the shortest obstacle distance of the specified agricultural machinery at the current sensing time t, D2 t represents the target position distance of the specified agricultural machinery at the current sensing time t, R t represents the actual turning radius of the specified agricultural machinery at the current sensing time t, δ t Represents the actual steering angle of the specified agricultural machinery at the current sensing time t.

8. The agricultural machinery intelligent control method based on heterogeneous dual chips as claimed in claim 1, characterized in that: The specific process of judging whether to send the path planning instruction for the first time period to the microcontroller MCU based on the obtained obstacle avoidance coefficient is as follows: Determine whether the obtained obstacle avoidance coefficient is less than the obstacle avoidance coefficient preset in the database: If the obtained obstacle avoidance coefficient is less than the obstacle avoidance coefficient preset in the database, a path planning instruction for the first time period is sent to the microcontroller MCU; If the obtained obstacle avoidance coefficient is not less than the obstacle avoidance coefficient preset in the database, an obstacle avoidance instruction is sent to the microcontroller MCU; The obstacle avoidance instruction includes a first obstacle avoidance instruction and a second obstacle avoidance instruction; The first obstacle avoidance instruction represents an obstacle avoidance instruction corresponding to when the acquired obstacle avoidance coefficient is greater than the obstacle avoidance coefficient preset in the database; The second obstacle avoidance instruction represents an obstacle avoidance instruction corresponding to when the acquired obstacle avoidance coefficient is equal to the obstacle avoidance coefficient preset in the database.

9. The agricultural machinery intelligent control method based on heterogeneous dual chips as claimed in claim 1, characterized in that: The path planning compliance index is obtained by the following method: When the obtained obstacle avoidance coefficient is less than the obstacle avoidance coefficient preset in the database, the corresponding number of path planning instruction updates during the driving process of the specified agricultural machinery in the first time period is obtained, and the actual distance deviation and the actual steering angle deviation at the end of the first time period are obtained; A preset terrain undulation correction factor is obtained from a database, and a path planning compliance index is obtained by combining the total length of the reference driving path in the database, the maximum allowable steering angle, the maximum allowable path planning instruction update times in the first time period, and the obtained obstacle avoidance coefficient; The actual distance deviation represents the straight-line distance between the stop position of the designated agricultural machinery at the end of the first time period and the target stop position; The actual steering angle deviation represents an angle difference between a stop steering angle of the designated agricultural machine at the end of the first driving time period and a target stop steering angle.

10. The intelligent control system for agricultural machinery based on heterogeneous dual chips is characterized by: include: Collaborative energy efficiency score acquisition module, obstacle avoidance coefficient acquisition module, path planning compliance index acquisition module and path planning progress judgment module; The collaborative energy efficiency score acquisition module is used to monitor the collaborative working state between the first chip and the second chip in real time to acquire the collaborative energy efficiency score, and determine whether to acquire image data based on the acquired collaborative energy efficiency score, and the collaborative energy efficiency score is used to quantify the energy efficiency of the first chip and the second chip during the collaborative working process; The obstacle avoidance coefficient acquisition module is used to obtain image data, then perform obstacle perception and recognition on the operation process of the specified agricultural machinery according to the obtained image data to obtain the obstacle avoidance coefficient, and at the same time determine whether to send a path planning instruction for the first time period to the microcontroller MCU based on the obtained obstacle avoidance coefficient, and the obstacle avoidance coefficient is used to quantify the urgency of the specified agricultural machinery to avoid obstacles during the operation process; The path planning compliance index acquisition module is used to monitor the actual driving state of the designated agricultural machinery at the end of the first time period in real time to obtain the path planning compliance index if the path planning instruction of the first time period is sent, and to judge whether to send the path planning instruction of the second time period to the microcontroller MCU based on the acquired path planning compliance index, and the path planning compliance index is used to quantify the degree of compliance between the actual driving path of the designated agricultural machinery in the first time period and the reference driving path; The path planning progress judgment module is used to obtain the first driving path and the second driving path if the path planning instruction for the second time period is sent, and at the same time monitor the operating status of the specified agricultural machinery on the third driving path in real time, and judge whether the path planning is completed according to the corresponding path planning compliance index at the end of the third time period.

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