A method, device and medium for automatically testing agricultural machinery navigation function of HIL

Abstract

The application discloses a kind of HIL automatic test agricultural machine navigation function method, equipment and medium, it is related to vehicle simulation test technical field.The method comprises the following steps: according to high-precision map and three-dimensional terrain data, construct farmland simulation environment, and render agricultural vehicle model, implement model in farmland simulation environment;According to the initial position of agricultural vehicle model, generate simulation positioning signal, and send to the agricultural machine navigation controller to be measured;Agricultural machine navigation controller executes path planning according to simulation positioning signal, generates steering control instruction;According to steering control instruction, simulate steering wheel corner response, and return through bus The data feedback of simulation is returned;According to simulation feedback data, correct deviation, and update the position, speed and attitude information of agricultural vehicle model in farmland simulation environment, and send to agricultural machine navigation controller again.The application realizes path planning to agricultural machine navigation controller in laboratory environment by the above method.

Description

Technical Field

[0001] This application relates to the field of vehicle simulation testing technology, and in particular to a method, equipment and medium for HIL automated testing of agricultural machinery navigation functions. Background Technology

[0002] With the rapid development of agricultural machinery towards intelligence and automation, the agricultural machinery navigation system, as the core control unit of intelligent agricultural machinery, faces increasing demands for functional complexity and safety. Agricultural machinery navigation systems need to maintain stable and reliable navigation and control performance under various extreme conditions, including slopes, dry land, paddy fields, nighttime, GNSS signal loss, and rainy / foggy weather. This places extremely high demands on the testing and verification of navigation systems. Traditional testing of agricultural machinery navigation functions mainly relies on real-world field testing, i.e., testing large agricultural machinery equipment in a real farmland environment. However, this method has drawbacks such as high safety risks, low testing efficiency, insufficient testing coverage, and high costs.

[0003] Currently, hardware-in-the-loop (HIL) testing technology is widely used in the automotive industry. However, existing HIL testing devices mainly consist of general-purpose components such as real-time machines, communication interfaces, I / O interfaces, and user interfaces, lacking dedicated simulation scenarios, dedicated positioning signal simulation, and steering closed-loop feedback capabilities for agricultural machinery navigation systems. Specifically, existing solutions cannot construct virtual farmland simulation environments that include elements such as field boundaries, terrain slope, and paddy field soil. They cannot achieve dynamic linkage simulation of positioning signals with farmland terrain and soil moisture, nor can they perform closed-loop verification of the entire chain of functions of agricultural machinery navigation controllers, including path planning, path decision-making, navigation control, steering execution, and human-machine interaction. Furthermore, existing HIL testing solutions only test single-vehicle controllers, lacking the ability to verify navigation functions in scenarios where multiple agricultural machines operate collaboratively in the same farmland. Based on the above analysis, the problems and deficiencies of existing technologies are as follows: Existing technologies for testing agricultural machinery navigation functions rely on real-vehicle field testing, which leads to high safety risks, low efficiency, and insufficient coverage. Furthermore, existing HIL testing devices lack dedicated simulation scenarios and closed-loop testing capabilities for agricultural machinery navigation systems. Summary of the Invention

[0004] This application provides a method, device, and medium for automated HIL testing of agricultural machinery navigation functions, which can solve the problems of high safety risks, low efficiency, and insufficient coverage caused by the reliance on actual vehicle field testing for agricultural machinery navigation function testing in the prior art, as well as the lack of dedicated simulation scenarios and closed-loop testing capabilities for agricultural machinery navigation systems in existing HIL testing devices.

[0005] In a first aspect, embodiments of this application provide a method for HIL automated testing of agricultural machinery navigation functions. The method includes: constructing a farmland simulation environment based on high-precision maps and three-dimensional terrain data, and rendering an agricultural machinery vehicle model in the farmland simulation environment; generating a simulated positioning signal based on the initial position of the agricultural machinery vehicle model and sending it to the agricultural machinery navigation controller under test; the agricultural machinery navigation controller performing path planning based on the simulated positioning signal and generating steering control commands; simulating steering wheel angle response based on the steering control commands and transmitting simulated feedback data back via a bus; correcting deviations based on the simulated feedback data, updating the position, speed, and attitude information of the agricultural machinery vehicle model in the farmland simulation environment, and sending it back to the agricultural machinery navigation controller.

[0006] In one implementation of this application, the steering wheel angle response is simulated according to the steering control command, and the simulation feedback data is transmitted back via a bus. Specifically, this includes: the agricultural machinery navigation controller sending the steering control command to the electric steering wheel simulation unit of the agricultural machinery vehicle model via the bus; the electric steering wheel simulation unit of the agricultural machinery vehicle model adjusting the calculation parameters of the longitudinal and lateral forces between the tires and the ground in the agricultural machinery vehicle model according to the ground adhesion coefficient under the current working conditions, calculating the steering resistance according to the load of the implement model on the agricultural machinery vehicle model, and adjusting the steering angle according to the steering control command sent by the navigation controller to complete the steering control command; during the execution of the steering control command, the electric steering wheel simulation unit of the agricultural machinery vehicle model feeds back the current state of the steering wheel to the agricultural machinery navigation controller via the bus according to the set signal transmission protocol.

[0007] In one implementation of this application, a simulated positioning signal is generated based on the initial position of the agricultural machinery vehicle model and sent to the agricultural machinery navigation controller under test. Specifically, this includes: acquiring terrain slope data and paddy field soil data of the current location of the agricultural machinery vehicle model; calculating the surface dielectric constant of the soil moisture content based on the paddy field soil data, controlling the reflection coefficient of the positioning signal, and adjusting the intensity of the multipath effect; calculating the influence intensity of occlusion and elevation angle changes on the multipath effect based on the terrain slope data, and superimposing it on the propagation path loss model of the positioning signal simulation unit to generate a simulated positioning signal that is dynamically linked to the farmland soil moisture content and terrain slope; and sending the simulated positioning signal to the agricultural machinery navigation controller under test via a bus.

[0008] In one implementation of this application, a farmland simulation environment is constructed based on high-precision maps and 3D terrain data. Specifically, this includes: constructing farmland soil regions, crop growth cycle simulation models, farm roads, farm machinery hangars, and static obstacles in the farmland simulation environment based on high-precision maps and 3D terrain data; predefining and calibrating vehicle entry and exit paths, vehicle transfer paths, and obstacle avoidance paths in the farmland simulation environment; adjusting the soil's wet and dry intensities according to the simulated weather environment in the farmland simulation environment to generate different paddy field soil data; dividing different soil moisture content regions in the farmland simulation environment based on the paddy field soil data, and configuring corresponding wheel slip ratio models in each soil moisture content region; binding the wheel slip ratio models to the drive wheels and steering wheels of the farm machinery vehicle model, so that the farm machinery vehicle model generates different slip responses when driving in different soil moisture content regions.

[0009] In one implementation of this application, the method further includes: after the agricultural machinery navigation controller under test generates a path plan based on the simulated positioning signal, obtaining the target operation path; calculating the path coverage, number of turns, and proportion of driving on slopes of the target operation path based on field boundary information, obstacle information, and terrain slope data; comparing the path coverage, number of turns, and proportion of driving on slopes with a preset operation efficiency threshold to generate a path quality evaluation result for the target operation path.

[0010] In one implementation of this application, the method further includes: obtaining the real-time position of the agricultural machinery vehicle model in the farmland simulation environment at the current moment, and determining the type of positioning signal obstruction corresponding to the real-time position according to preset test cases; determining the positioning signal attenuation parameter according to the type of positioning signal obstruction; superimposing the positioning signal attenuation parameter onto the simulated positioning signal to generate an obstructed simulated positioning signal, and sending the obstructed simulated positioning signal to the agricultural machinery navigation controller under test.

[0011] In one implementation of this application, the method further includes: deploying a first agricultural machinery vehicle model and a second agricultural machinery vehicle model in a farmland simulation environment; configuring independent simulated positioning signal channels for the first agricultural machinery vehicle model and the second agricultural machinery vehicle model, so that the first agricultural machinery vehicle model and the second agricultural machinery vehicle model respectively receive their respective first simulated positioning signals and second simulated positioning signals; issuing a global farmland planning path and an independent local target operation path for each agricultural machinery vehicle model to the first agricultural machinery vehicle model and the second agricultural machinery vehicle model, so that the first agricultural machinery vehicle model and the second agricultural machinery vehicle model travel along their respective assigned independent local target operation paths.

[0012] In one implementation of this application, after configuring independent analog positioning signal channels for the first agricultural machinery vehicle model and the second agricultural machinery vehicle model, the method further includes: acquiring the real-time position and real-time speed of the first agricultural machinery vehicle model when it travels along the corresponding target operation path; triggering the avoidance path replanning of the first agricultural machinery vehicle model and / or the second agricultural machinery vehicle model when the relative distance between the first agricultural machinery vehicle model and the second agricultural machinery vehicle model is less than a preset safe distance threshold; and recording the response time and path deviation of the avoidance path replanning as an evaluation index of the cooperative navigation function.

[0013] Secondly, embodiments of this application also provide an apparatus for HIL automated testing of agricultural machinery navigation functions, the apparatus including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: perform any of the steps of a method for HIL automated testing of agricultural machinery navigation functions.

[0014] Thirdly, embodiments of this application also provide a non-volatile computer storage medium for HIL automated testing of agricultural machinery navigation functions, storing computer-executable instructions, wherein the computer-executable instructions are configured to execute any one of the steps of a method for HIL automated testing of agricultural machinery navigation functions.

[0015] This application provides a method, device, and medium for automated testing of agricultural machinery navigation functions using HIL (Hybrid Information Module). By constructing a virtual simulation closed-loop test link that is linked in real time with farmland terrain, soil moisture, and steering dynamics, the agricultural machinery navigation controller can receive positioning signals and steering resistance feedback coupled with real farmland working conditions in a laboratory environment. It can then complete fully automated verification of path planning, steering execution, and multi-machine collaborative operation, solving the problems of poor safety, low efficiency, insufficient working condition coverage, and high cost in actual field testing. At the same time, it supports soil zoning slip rate binding, path quality quantitative evaluation, and multi-machine obstacle avoidance collaborative testing, significantly improving testing efficiency, coverage, and simulation realism. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a method for automated testing of agricultural machinery navigation functions using HIL (Hybrid Information Processing) is provided in this application embodiment. Figure 2 This is a schematic diagram of the internal structure of a device for automated testing of agricultural machinery navigation functions using HIL, provided as an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] This application provides a method, device, and medium for automated HIL testing of agricultural machinery navigation functions, which solves the problems of high safety risks, low efficiency, and insufficient coverage caused by the reliance on actual vehicle field testing for agricultural machinery navigation function testing in the prior art, as well as the lack of dedicated simulation scenarios and closed-loop testing capabilities for agricultural machinery navigation systems in existing HIL testing devices.

[0019] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0020] Figure 1 This application provides a flowchart of a method for automated testing of agricultural machinery navigation functions using HIL (Hybrid Information Processing). Figure 1 As shown in the figure, the method for automating the testing of agricultural machinery navigation functions using HIL provided in this application embodiment specifically includes the following steps: Step 10: Construct a farmland simulation environment based on high-precision maps and 3D terrain data, and render agricultural machinery and vehicle models in the farmland simulation environment.

[0021] In this step, the simulation scene rendering unit, based on digital twin technology, acquires farm data from high-precision maps and 3D terrain data. This farm data includes, but is not limited to, field boundary information, farm road information, hangar information, terrain slope information, soil moisture information, and the distribution of trees and crops. Based on this farm data, the simulation scene rendering unit constructs a virtual farmland simulation environment consistent with the real farmland operation environment. Within this virtual environment, it renders models of agricultural machinery and implements, as well as models of terrain features such as hangars, farm roads, crops, farmland plots, wells, utility poles, trees, and grass. The simulation scene rendering unit supports custom configuration of terrain slope, soil parameters, obstacles, weather conditions, and lighting parameters according to testing requirements to simulate farmland operation scenarios under different working conditions.

[0022] As an optional embodiment, a farmland simulation environment is constructed based on high-precision maps and 3D terrain data, which may specifically include: Step 101: Constructing farmland soil regions, crop growth cycle simulation models, farm roads, farm machinery hangars, and static obstacles in the farmland simulation environment based on high-precision maps and 3D terrain data; Step 102: Predefining and calibrating vehicle entry and exit paths, vehicle transfer paths, and obstacle avoidance paths in the farmland simulation environment; Step 103: Adjusting the soil wet and dry intensities according to the simulated weather environment in the farmland simulation environment to generate different paddy field soil data, dividing different soil moisture content regions according to the paddy field soil data, and configuring corresponding wheel slip rate models in each soil moisture content region.

[0023] In this step, agricultural machinery is deployed in a farmland simulation environment, with static obstacles such as graves, utility poles, water towers, and trees randomly placed, along with dynamic obstacles such as pedestrians and vehicles. Areas with soil moisture content above a first preset threshold are designated as high-moisture paddy fields, areas with soil moisture content below a second preset threshold are designated as low-moisture dry fields, and areas with soil moisture content between the first and second preset thresholds are designated as medium-moisture fields. For each soil moisture content area, the simulation scene rendering unit is configured with a corresponding wheel slip ratio model. This model characterizes the relationship between wheel driving force and ground adhesion when agricultural machinery travels in that soil moisture content area, as well as the response characteristics of wheel slip ratio as soil moisture content changes. The model parameters for the wheel slip ratio model differ for different soil moisture content areas to reflect the differences in agricultural machinery dynamic response under different soil conditions.

[0024] Step 102: Bind the wheel slip ratio model to the drive wheels and steering wheels of the agricultural machinery vehicle model so that the agricultural machinery vehicle model produces different slip responses when driving in different soil moisture content areas.

[0025] In this step, when the agricultural machinery vehicle model travels from the first soil moisture content area to the second soil moisture content area in the virtual farmland simulation environment, the drive wheels and steering wheels of the agricultural machinery vehicle model automatically switch to the wheel slip ratio model corresponding to the second soil moisture content area.

[0026] Through the above binding, when the agricultural vehicle model travels in areas with different soil moisture contents, the vehicle dynamics model calculates the longitudinal and lateral forces between the drive wheels and steering wheels and the ground based on the wheel slip ratio model of the current area, generating a slip response that matches the soil moisture content of that area. For example, when traveling in paddy fields with high moisture content, the drive wheel slip ratio increases, the adhesion decreases, and the steering wheel response is delayed; when traveling in dry fields with low moisture content, the drive wheel slip ratio decreases, the adhesion increases, and the steering response is sensitive.

[0027] Step 20: Generate a simulated positioning signal based on the initial position of the agricultural machinery vehicle model and send it to the agricultural machinery navigation controller under test.

[0028] In this step, the receiver positioning signal simulation unit acquires the initial position information of the agricultural machinery vehicle model in the simulation scene rendering unit, generates a simulated positioning signal containing the position, speed, heading and attitude information of the agricultural machinery vehicle in real time, and sends it to the agricultural machinery navigation controller under test. It supports the real-time output of GPS, Beidou, GLONASS and Galileo multi-constellation multi-frequency positioning signals, and synchronously sends the farmland boundary geographic information to the agricultural machinery navigation controller as the constraint boundary for path planning.

[0029] As an optional embodiment, a simulated positioning signal is generated based on the initial position of the agricultural machinery vehicle model and sent to the agricultural machinery navigation controller under test. Specifically, it may include: Step 201: Obtaining the terrain slope data and paddy field soil data of the current location of the agricultural machinery vehicle model.

[0030] In this step, the receiver positioning signal simulation unit obtains the current position of the agricultural machinery vehicle model in real time from the simulation scene rendering unit, and queries the terrain slope data and soil data corresponding to the position. The terrain slope data includes the slope angle and slope aspect, and the soil data includes soil moisture content, soil type and dielectric constant.

[0031] Step 202: Calculate the surface dielectric constant of the soil moisture content based on the paddy field soil data, and then control the reflection coefficient of the positioning signal to adjust the intensity of the multipath effect; calculate the influence intensity of shading and elevation angle changes on the multipath effect based on the terrain slope data.

[0032] In this step, the paddy field soil data and the multipath effect are generally positively correlated. The higher the soil moisture content, the greater the surface dielectric constant, and the higher the reflection coefficient of the GNSS satellite signal. The intensity of the multipath effect formed after the signal is reflected by the water surface and wet soil will be enhanced, increasing the influence of the multipath effect (when the farmland is covered with crops, the enhancing trend of the multipath effect will be offset by the vegetation blocking effect). The correlation between the slope angle and the multipath effect is that within a certain slope range, the intensity of the multipath effect increases synchronously with the increase of the slope angle. Beyond the slope range, terrain blocking weakens the reflected signal energy, and the multipath effect gradually decreases with the increase of the slope angle. The matching degree between the slope aspect and the satellite azimuth directly determines the multipath intensity. When the slope aspect is directly opposite to the satellite azimuth, the slope surface will form a strong specular reflection, and the multipath effect will reach its peak. When the slope aspect is perpendicular to or opposite to the satellite azimuth, the reflected signal is difficult to enter the antenna, and the multipath effect will be greatly attenuated.

[0033] Step 203: Overlay the terrain slope data and paddy field soil data into the propagation path loss model of the positioning signal simulation unit to generate a positioning signal that is dynamically linked to the farmland soil moisture content and terrain slope.

[0034] In this step, the basic expression for the propagation path loss model is: P L =P L0 +L attenuation +L scattering , where P L0 For free space path loss, L attenuation L represents the occlusion loss corresponding to the terrain attenuation coefficient. scattering This represents the scattering loss corresponding to the multipath scattering coefficient. When the agricultural vehicle model travels in different terrain areas, the receiver positioning signal simulation unit updates the above coefficients in real time, dynamically adjusting the strength of the simulated positioning signal to achieve linkage between the positioning signal and the simulation scene.

[0035] Step 30: The agricultural machinery navigation controller performs path planning based on the simulated positioning signal and generates steering control commands.

[0036] In this step, after receiving the simulated positioning signal and farmland boundary geographic information sent by the receiver positioning signal simulation unit, the agricultural machinery navigation controller configures the vehicle's driving speed. Under the control of the agricultural machinery navigation controller, the vehicle travels along the planned operation path according to the set driving speed, and the implements execute actions according to the control instructions. Based on the position, boundary, height and other information of static obstacles, a target operation path to avoid static obstacles is generated. The internal path planning module generates the optimal operation path based on the farmland boundary geographic information and the operation task. The path decision module dynamically adjusts the local tracking path strategy based on the current position of the agricultural machinery vehicle model. The control module converts the path decision instructions into control signals containing steering angle and speed settings. The steering module generates steering control instructions and outputs them to the motor steering wheel model simulation unit.

[0037] Step 40: Simulate the steering wheel angle response according to the steering control command, and transmit the simulation feedback data back via the bus.

[0038] In this step, the motor steering wheel model simulation unit receives steering control commands from the steering module of the agricultural machinery navigation controller, simulates the electric steering wheel angle response and steering resistance feedback based on the vehicle dynamics model, and transmits the simulated feedback data containing steering wheel angle status and steering torque data back to the control module of the agricultural machinery navigation controller via the CAN bus, so that the control module can correct the control signal deviation in real time based on the transmitted data.

[0039] As an optional embodiment, the steering wheel angle response is simulated according to the steering control command, and the simulation feedback data is transmitted back via the bus. Specifically, it may include: Step 401: The agricultural machinery navigation controller sends the steering control command to the electric steering wheel simulation unit of the agricultural machinery vehicle model via the CAN bus; Step 402: The electric steering wheel simulation unit of the agricultural machinery vehicle model adjusts the calculation parameters of the longitudinal force and lateral force between the tires and the ground in the agricultural machinery vehicle model according to the ground adhesion coefficient under the current working conditions; Step 403: The electric steering wheel simulation unit of the agricultural machinery vehicle model calculates the steering resistance according to the load of the implement model on the agricultural machinery vehicle model; Step 404: The electric steering wheel simulation unit of the agricultural machinery vehicle model adjusts the steering angle according to the steering control command sent by the navigation controller and executes the steering control command; Step 405: The electric steering wheel simulation unit of the agricultural machinery vehicle model feeds back the current state of the steering wheel to the agricultural machinery navigation controller according to the set CAN signal transmission protocol, thereby completing the closed-loop test link of steering.

[0040] In this step, the calculated ground adhesion coefficient is input into the vehicle dynamics model as a calculation parameter for the tire model, dynamically adjusting the calculation parameters of the tire's longitudinal and lateral forces. For example, when the ground adhesion coefficient decreases, the peak adhesion coefficient and slip ratio parameters of the tire model are adjusted to match the mechanical response characteristics under low-adhesion road surfaces. Based on the adjusted longitudinal and lateral force calculation parameters, and considering the dynamic characteristics of agricultural machinery vehicles with high center of gravity and large ground contact area, the vehicle dynamics model calculates the torque response during steering, generating a steering wheel angle response and steering resistance feedback value that matches the current farmland terrain and soil conditions, and reports this to the agricultural machinery navigation controller via the CAN bus.

[0041] Step 50: Correct the deviation based on the simulation feedback data, update the position, speed and attitude information of the agricultural machinery vehicle model in the farmland simulation environment, and send it again to the agricultural machinery navigation controller.

[0042] As an optional embodiment, the method may further include: after the agricultural machinery navigation controller under test generates a path plan based on the simulated positioning signal, obtaining the target operation path; calculating the path coverage, number of turns, and proportion of driving on slopes of the target operation path based on field boundary information, obstacle information, and terrain slope data; comparing the path coverage, number of turns, and proportion of driving on slopes with a preset operation efficiency threshold to generate a path quality evaluation result for the target operation path.

[0043] In this step, when the path coverage rate is greater than or equal to the preset coverage threshold, it is determined that the path coverage quality is qualified; when the number of turns is less than or equal to the preset turn number threshold, it is determined that the path economy is qualified; when the proportion of sloping land driving is less than or equal to the preset sloping land proportion threshold, it is determined that the path safety is qualified. Based on the above comparison results, a path quality evaluation result of the target operation path is generated and displayed through a graphical interface on the test management terminal.

[0044] As an optional embodiment, the method may further include: obtaining the real-time position of the agricultural machinery vehicle model in the farmland simulation environment at the current moment, and determining the type of positioning signal occluder corresponding to the real-time position according to the preset test case; determining the positioning signal attenuation parameter according to the type of positioning signal occluder; superimposing the positioning signal attenuation parameter onto the simulated positioning signal to generate an occluded simulated positioning signal, and sending the occluded simulated positioning signal to the tested agricultural machinery navigation controller.

[0045] In this step, different occluder types correspond to different signal attenuation characteristics. The receiver positioning signal simulation unit queries and determines the corresponding positioning signal attenuation parameter from the preset occluder-attenuation parameter mapping relationship according to the occluder type. The attenuation parameters corresponding to different types of occluders are different. For example, the attenuation parameter corresponding to tree occlusion is the first attenuation value, the attenuation parameter corresponding to building occlusion is the second attenuation value, and the attenuation parameter corresponding to terrain undulation occlusion is the third attenuation value. The receiver positioning signal simulation unit superimposes the positioning signal attenuation parameter onto the basic simulated positioning signal to generate an occluded simulated positioning signal carrying the occlusion effect, and sends the occluded simulated positioning signal to the tested agricultural machinery navigation controller, so that the agricultural machinery navigation controller performs path planning and navigation control decisions under the condition of simulated positioning signal occlusion to verify its navigation performance in the signal occlusion scenario.

[0046] As an optional embodiment, the method may further include: deploying the first agricultural machinery vehicle model and the second agricultural machinery vehicle model in the farmland simulation environment; configuring independent simulated positioning signal channels for the first agricultural machinery vehicle model and the second agricultural machinery vehicle model, so that the first agricultural machinery vehicle model and the second agricultural machinery vehicle model receive their respective corresponding first simulated positioning signal and second simulated positioning signal, and sending the global farmland planning path and the independent local target operation path of each agricultural machinery vehicle model to the first agricultural machinery vehicle model and the second agricultural machinery vehicle model, so that the first agricultural machinery vehicle model and the second agricultural machinery vehicle model travel along their respective allocated independent local target operation paths.

[0047] In this step, the simulation scene rendering unit simultaneously renders the first agricultural machinery vehicle model and the second agricultural machinery vehicle model in the same virtual farmland simulation environment, and associates the first agricultural machinery vehicle model with the first agricultural machinery navigation controller under test, and associates the second agricultural machinery vehicle model with the second agricultural machinery navigation controller under test, so as to simulate the scenario of multiple agricultural machines working together in the same farmland.

[0048] Specifically, the receiver positioning signal simulation unit configures independent simulated positioning signal channels for the first and second agricultural machinery vehicle models, including a first channel and a second channel. The first channel generates a first simulated positioning signal based on the real-time position of the first agricultural machinery vehicle model in the farmland simulation environment and sends it to the first agricultural machinery navigation controller under test; the second channel generates a second simulated positioning signal based on the real-time position of the second agricultural machinery vehicle model in the farmland simulation environment and sends it to the second agricultural machinery navigation controller under test. The first and second simulated positioning signals are independent of each other, enabling the first and second agricultural machinery navigation controllers under test to independently execute path planning and navigation control decisions, verifying the functional correctness of each agricultural machinery navigation controller and their mutual signal anti-interference capabilities under multi-machine collaborative operation conditions.

[0049] As an optional embodiment, after configuring independent analog positioning signal channels for the first agricultural machinery vehicle model and the second agricultural machinery vehicle model, the method may further include: acquiring the real-time position and real-time speed of the first agricultural machinery vehicle model when it travels along the corresponding target operation path; triggering the avoidance path replanning of the first agricultural machinery vehicle model and / or the second agricultural machinery vehicle model when the relative distance between the first agricultural machinery vehicle model and the second agricultural machinery vehicle model is less than a preset safe distance threshold; and recording the response time and path deviation of the avoidance path replanning as an evaluation index of the cooperative navigation function.

[0050] In this step, the receiver positioning signal simulation unit acquires the real-time position and speed of the first agricultural machinery vehicle model in the farmland simulation environment through the first channel, and synchronously sends the real-time position and speed to the test management terminal or the simulation scene rendering unit.

[0051] Specifically, the simulation scene rendering unit or test management terminal calculates the relative distance between the first and second agricultural machinery vehicle models in real time based on their real-time positions. When the relative distance is less than a preset safe distance threshold, the simulation scene rendering unit sends an avoidance trigger signal to the first and / or second agricultural machinery navigation controller under test, causing the corresponding agricultural machinery navigation controller to trigger avoidance path replanning.

[0052] Specifically, the test management terminal records the response time from the triggering of the avoidance path replanning to the time when the first and second agricultural machinery vehicle models reach a safe distance again, as well as the path deviation of the actual driving path of the agricultural machinery vehicle model after the avoidance replanning relative to the original target operation path. The response time and path deviation are used as evaluation indicators for the cooperative navigation function.

[0053] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a device for automated testing of agricultural machinery navigation functions using HIL (Hydraulic Instruction), the structure of which is as follows: Figure 2 As shown.

[0054] Figure 2 This is a schematic diagram of the internal structure of a device for automated testing of agricultural machinery navigation functions using HIL (Hydraulic Instruction) provided in an embodiment of this application. Figure 2 As shown, the device includes: At least one processor 201; And a memory 202 that is communicatively connected to at least one processor; The memory 202 stores instructions that can be executed by at least one processor, which are executed by at least one processor 201 to enable at least one processor 201 to: perform any of the steps of a method for automating the testing of agricultural machinery navigation functions using HIL.

[0055] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for HIL automated testing of agricultural machinery navigation functions stores computer-executable instructions, which are configured to execute any one of the steps of a method for HIL automated testing of agricultural machinery navigation functions.

[0056] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0057] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0058] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0059] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0060] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0061] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0062] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0063] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0064] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0065] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0066] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for automated testing of agricultural machinery navigation functions using HIL (Hybrid Information Processing) technology, characterized in that... The method includes: A farmland simulation environment is constructed based on high-precision maps and 3D terrain data, and agricultural machinery vehicle models and implement models are rendered in the farmland simulation environment. A simulated positioning signal is generated based on the initial position of the agricultural machinery vehicle model and sent to the agricultural machinery navigation controller under test. The agricultural machinery navigation controller performs path planning based on the simulated positioning signal and generates steering control commands; The steering wheel angle response is simulated according to the steering control command, and the simulated feedback data is transmitted back via the bus. The deviation is corrected based on the simulation feedback data, and the position, speed, and attitude information of the agricultural machinery vehicle model in the farmland simulation environment are updated and sent back to the agricultural machinery navigation controller.

2. The method for automated testing of agricultural machinery navigation function using HIL according to claim 1, characterized in that, The steering wheel angle response is simulated according to the steering control command, and the simulated feedback data is transmitted back via the bus, specifically including: The agricultural machinery navigation controller sends the steering control command to the electric steering wheel simulation unit of the agricultural machinery vehicle model via a bus; The electric steering wheel simulation unit of the agricultural machinery vehicle model adjusts the calculation parameters of the longitudinal and lateral forces between the tires and the ground in the agricultural machinery vehicle model according to the ground adhesion coefficient under the current working conditions, calculates the steering resistance according to the load of the implement model on the agricultural machinery vehicle model, and adjusts the steering angle according to the steering control command sent by the navigation controller to complete the steering control command. During the execution of steering control commands, the electric steering wheel simulation unit of the agricultural machinery vehicle model feeds back the current state of the steering wheel to the agricultural machinery navigation controller via a bus according to the set signal transmission protocol.

3. The method for automated testing of agricultural machinery navigation function using HIL according to claim 1, characterized in that, The step of generating a simulated positioning signal based on the initial position of the agricultural machinery vehicle model and sending it to the tested agricultural machinery navigation controller specifically includes: Obtain the terrain slope data and paddy field soil data of the current location of the agricultural machinery vehicle model; The surface dielectric constant of the soil moisture content is calculated based on the paddy field soil data, the reflection coefficient of the positioning signal is controlled, and the intensity of the multipath effect is adjusted. The influence intensity of occlusion and elevation angle changes on the multipath effect is calculated based on the terrain slope data, and then superimposed on the propagation path loss model of the positioning signal simulation unit to generate a simulated positioning signal that is dynamically linked to farmland soil moisture content and terrain slope. The simulated positioning signal is sent to the agricultural machinery navigation controller under test via a bus.

4. The method for automated testing of agricultural machinery navigation function using HIL according to claim 1, characterized in that, A farmland simulation environment is constructed based on high-precision maps and 3D terrain data, specifically including: Based on the high-precision map and 3D terrain data, a simulation model of farmland soil area, crop growth cycle, machine-cultivated road, agricultural machinery hangar, and static obstacles are constructed in the farmland simulation environment. In the farmland simulation environment, predefined calibrations of vehicle entry and exit paths, vehicle transfer paths, and obstacle avoidance paths are completed. Different paddy field soil data are generated by adjusting the soil's wet and dry intensities based on the simulated weather conditions in the farmland simulation environment. Based on the paddy field soil data, different soil moisture content regions are divided in the farmland simulation environment, and corresponding wheel slip rate models are configured in each soil moisture content region. The wheel slip ratio model is bound to the drive wheels and steering wheels of the agricultural machinery vehicle model, so that the agricultural machinery vehicle model produces different slip responses when driving in different soil moisture content areas.

5. The method for automated testing of agricultural machinery navigation function using HIL according to claim 1, characterized in that, The method further includes: After the agricultural machinery navigation controller under test generates the path plan based on the simulated positioning signal, the target operation path is obtained; Based on the field boundary information, terrain obstacle information, and terrain slope data, calculate the path coverage, number of turns, and proportion of driving on slopes for the target operation path; The path coverage, the number of turns, and the proportion of driving on slopes are compared with a preset work efficiency threshold to generate a path quality evaluation result for the target work path.

6. The method for automated testing of agricultural machinery navigation function using HIL according to claim 1, characterized in that, The method further includes: The real-time position of the agricultural machinery vehicle model in the farmland simulation environment is obtained at the current moment, and the type of positioning signal obstruction corresponding to the real-time position is determined according to the preset test cases. Determine the positioning signal attenuation parameters based on the type of obstruction to the positioning signal; The positioning signal attenuation parameter is superimposed on the simulated positioning signal to generate an occlusion simulated positioning signal, and the occlusion simulated positioning signal is sent to the agricultural machinery navigation controller under test.

7. The method for automated testing of agricultural machinery navigation function using HIL according to claim 1, characterized in that, The method further includes: Deploy the first and second agricultural machinery vehicle models in the farmland simulation environment; Independent analog positioning signal channels are configured for the first agricultural machinery vehicle model and the second agricultural machinery vehicle model, so that the first agricultural machinery vehicle model and the second agricultural machinery vehicle model respectively receive their respective first analog positioning signal and second analog positioning signal; The first agricultural machinery vehicle model and the second agricultural machinery vehicle model are issued a global farmland planning path and an independent local target operation path for each agricultural machinery vehicle model, so that the first agricultural machinery vehicle model and the second agricultural machinery vehicle model travel along their respective assigned independent local target operation paths.

8. The method for automated testing of agricultural machinery navigation function using HIL according to claim 7, characterized in that, After configuring independent analog positioning signal channels for the first agricultural machinery vehicle model and the second agricultural machinery vehicle model, the method further includes: Obtain the real-time position and speed of the first agricultural machinery vehicle model as it travels along the corresponding target operation path; When the relative distance between the first agricultural machinery vehicle model and the second agricultural machinery vehicle model is less than a preset safe distance threshold, the avoidance path replanning of the first agricultural machinery vehicle model and / or the second agricultural machinery vehicle model is triggered. The response time and path deviation of the avoidance path replanning are recorded and output as evaluation indicators of the cooperative navigation function.

9. A device for automated testing of agricultural machinery navigation functions using HIL (Hybrid Information Processing) technology, characterized in that: The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: The steps of performing the HIL automated testing method for agricultural machinery navigation functions as described in any one of claims 1-8.

10. A non-volatile computer storage medium for HIL automated testing of agricultural machinery navigation functions, storing computer-executable instructions, characterized in that, The computer-executable instructions are set as follows: The steps of performing the HIL automated testing method for agricultural machinery navigation functions as described in any one of claims 1-8.

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