A tractor spatial domain load spectrum construction method, device and system

Abstract

The application provides a tractor space domain load spectrum construction method, device and system, and belongs to the technical field of agricultural machinery engineering. The method comprises the following steps: acquiring three-dimensional point cloud data with geographical coordinates by using a UAV carrying a laser radar and a positioning module; removing interference information by a DBSCAN algorithm, extracting geometric and spatial features of a typical area, and constructing a surface feature quantization model; collecting tractor operation load data by using multiple types of sensors carried by the tractor; aligning the load data and the geographical coordinates based on a time sequence, anchoring the load features to the corresponding area of the surface feature quantization model based on a geographical space mapping relationship, establishing a dynamic coupling relationship between the surface conditions and the load changes, and generating a space domain load spectrum. The application combines the farmland surface features and the load spectrum, solves the limitation that the traditional load spectrum lacks environmental information, improves the precision of tractor fatigue analysis, life prediction and structure optimization, and is suitable for various agricultural machines and terrain environments.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agricultural machinery engineering, in particular to a tractor spatial domain load spectrum construction method, device and system. BACKGROUND

[0002] With the development of agricultural mechanization, improving the working efficiency and service life of tractors has become a research focus. During the plowing and land preparation process, the tractor will produce variable loads due to factors such as ground conditions, driving speed, and work load, which will affect its stability, performance, and service life. Therefore, accurately constructing a tractor load spectrum to describe the load variation characteristics of the tractor under different working conditions is crucial for positive durability design of the tractor, and has important practical significance in aspects such as fatigue analysis, life prediction, and structure optimization.

[0003] However, the traditional tractor load spectrum still has certain limitations. At present, the construction method of the load spectrum mainly relies on installing various detection sensors such as strain gauge sensors, torque sensors, and angle sensors at key parts of the tractor (frame, power output shaft, suspension rod, etc.) to monitor the load changes of the tractor in real time during the plowing and land preparation process. Although this type of load spectrum can reflect the working state of the tractor body, it does not fully consider the farmland surface information during work. For example, different ground conditions (such as surface unevenness) will significantly affect the distribution of loads, and such information is not considered by the traditional load spectrum method, resulting in the constructed load spectrum being unable to limit the application range and applicable scenarios, causing insufficient usability of the traditional load spectrum, and making it difficult to be used in the verification stage of positive durability design.

[0004] In addition, the current prototype verification still heavily relies on bench conventional loading tests, and the traditional load spectrum used has a large difference with the actual working condition characteristics, resulting in insufficient precision of the test results, and being time-consuming and laborious, and being unable to effectively reflect the dynamic performance of the tractor in the real working environment. Therefore, the current load spectrum construction method needs to be improved, especially in more accurately integrating farmland surface information into the load spectrum to improve its application value in actual engineering.

[0005] At present, the traditional tractor load spectrum only reflects the working state of the machine body, lacks farmland surface information during work, and cannot limit the application range and applicable scenarios, resulting in insufficient usability of the load spectrum, and making it difficult to be used in the verification stage of positive durability design. The current prototype verification still heavily relies on bench conventional loading tests, and the traditional load spectrum used has a large difference with the actual working condition characteristics, resulting in insufficient precision of the test results and being time-consuming and laborious. SUMMARY

[0006] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a method, device, and system for constructing a tractor spatial domain load spectrum, which combines farmland surface features with load spectrum, solves the limitation of traditional load spectrum lacking environmental information, improves the accuracy of tractor fatigue analysis, life prediction, and structural optimization, and is applicable to various agricultural machinery and terrain environments.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A method for constructing the spatial domain load spectrum of a tractor includes the following steps:

[0009] S1. Using drones equipped with lidar and positioning modules, a comprehensive scan of farmland is conducted to obtain three-dimensional point cloud data with geographic coordinates.

[0010] S2. By preprocessing the data, interference information in the three-dimensional point cloud data is removed, and the geometric and spatial features of typical areas are extracted to construct a quantitative model of surface features.

[0011] S3. Synchronously collect tractor load data through multiple types of sensors mounted on the tractor;

[0012] S4. Using the time series as a reference, align the load data with geographic coordinates, and based on the geospatial mapping relationship, anchor the load features to the corresponding area of ​​the surface feature quantification model, establish a dynamic coupling relationship between surface conditions and load changes, and generate a load spectrum that integrates spatial location information.

[0013] Preferably, in step S1, the positioning module is an RTK receiver, the lidar is a single-line two-dimensional lidar, and the scanning parameters include flight altitude. h ,speed v and scanning angle θ The scan width is h ·tan θ .

[0014] Preferably, in step S2, the data preprocessing uses the density-based clustering algorithm DBSCAN to remove residual crop point clouds from the three-dimensional point cloud data. The typical areas include straight operation areas, turning areas, field ridges, and uneven areas of the ground surface.

[0015] Preferably, in step S2, the geometric and spatial features include height difference, local fluctuation points, angle abrupt changes, and point cloud density, and the surface feature quantification model includes concave-convex area, height difference, undulation angle, and roughness index.

[0016] Preferably, in step S3, the multi-type sensor includes an RTK receiver, an inertial measurement unit, a torque sensor, a force sensor and a vibration sensor, and the multi-source data timestamp synchronization is realized through a CAN bus and a.NET communication protocol.

[0017] Preferably, in step S4, the geographic space mapping relationship includes mapping the tractor working load data to a three-dimensional farmland point cloud model according to geographic coordinates to form a position-time-load three-dimensional correlation data structure.

[0018] Preferably, before step S1, further comprising: performing hardware self-checking and communication protocol verification on the unmanned aerial vehicle sensor and the tractor data acquisition system.

[0019] Preferably, the spatial domain load spectrum is used for tractor fatigue life prediction, structure strength optimization or working condition adaptability evaluation.

[0020] The application also provides a tractor spatial domain load spectrum construction device, comprising:

[0021] A farmland terrain acquisition device, the terrain acquisition device comprising a large-load unmanned aerial vehicle, a single-line two-dimensional laser radar and an RTK receiver, the large-load unmanned aerial vehicle carrying the single-line two-dimensional laser radar and the RTK receiver, supporting farmland scanning at a preset flight height h , speed v and scanning angle θ ;

[0022] A tractor load acquisition device, the tractor load acquisition device integrating an RTK receiver, an inertial measurement unit, a torque sensor and a force sensor, and realizing multi-source data synchronous acquisition through a CAN bus;

[0023] A data processing device, the data processing device comprising an edge computing unit and a cloud server, the edge computing unit being in communication connection with the cloud server, the edge computing unit pre-processing point cloud and load data, and the cloud server executing surface feature quantization and load-geographic coordinate mapping algorithm based on the pre-processing result.

[0024] The application also provides a tractor spatial domain load spectrum construction system, comprising:

[0025] A surface information acquisition module composed of an unmanned aerial vehicle platform, a laser radar and a positioning module, used for acquiring farmland three-dimensional point cloud and geographic coordinates;

[0026] A load data acquisition module composed of multi-type sensors and a data synchronization unit, used for acquiring tractor working load data;

[0027] The spatio-temporal fusion module is configured to map the load data to the surface feature quantization model based on the geographic coordinates, establish a dynamic coupling relationship between the surface condition and the load change, and generate a spatial domain load spectrum.

[0028] According to the embodiments of the present application, the following technical effects are achieved:

[0029] (1) The present application realizes the breakthrough of traditional load spectrum from single machine body state to space-time-load three-dimensional coupling by fusing the operation farmland surface three-dimensional point cloud data and tractor load data, and improves the scene adaptability of the load spectrum to the actual working condition. By constructing the spatial domain load spectrum, the dependence on the bench conventional loading test for the prototype verification is reduced, the test precision is improved and the research and development cost is reduced, and the tractor forward durability design cycle is shortened.

[0030] (2) The present application realizes high-precision quantitative modeling of the surface feature by collecting point cloud data with geographic coordinates by the unmanned aerial vehicle carrying the laser radar and the RTK receiver, combining the DBSCAN algorithm to remove interference and extract typical regional features, and solves the problem that the traditional load spectrum cannot limit the application range.

[0031] (3) The present application realizes the dynamic correlation analysis of the surface condition and the load change by synchronously collecting the load data by multiple types of sensors and aligning the time stamp based on the CAN bus, and mapping the load to the surface model based on the geographic coordinates, and improves the precision of fatigue analysis and life prediction. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings described in the following only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0033] Figure 1 The flow chart of the tractor spatial domain load spectrum construction method provided by the present application is shown in the figure.

[0034] Figure 2 The operation flow chart of the tractor load acquisition subsystem provided by the present application is shown in the figure.

[0035] Figure 3 The acquisition schematic diagram of the farmland surface information provided by the present application is shown in the figure. Figure 3 (a) in the figure is a structural schematic diagram of the tractor spatial domain load spectrum construction device, Figure 3 (b) in the figure is a schematic diagram of the surface information air acquisition parameter. DETAILED DESCRIPTION

[0036] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0037] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0038] As shown in the drawings, Figure 1 The present application provides a tractor spatial domain load spectrum construction method, comprising the following steps:

[0039] S1, using a UAV carrying a laser radar and a positioning module, covering the farmland for scanning, obtaining three-dimensional point cloud data with geographic coordinates;

[0040] S2, eliminating interference information in the three-dimensional point cloud data through data preprocessing, extracting geometric and spatial features of a typical area, and constructing a surface feature quantization model;

[0041] S3, synchronously collecting tractor operation load data through a plurality of types of sensors carried by the tractor;

[0042] S4, aligning the load data with the geographic coordinates based on a time sequence, and anchoring the load features to the corresponding area of the surface feature quantization model based on a geographic space mapping relationship, establishing a dynamic coupling relationship between the surface conditions and the load changes, and generating a load spectrum fused with spatial position information.

[0043] Specifically, in step S1, the positioning module is an RTK receiver, the laser radar is a single-line two-dimensional laser radar, the scanning parameters include flight height h , speed v and scanning angle θ , and the scanning width is h ·tan θ .

[0044] Specifically, in step S2, the data preprocessing adopts a density-based clustering algorithm DBSCAN to eliminate residual crop point clouds in the three-dimensional point cloud data, and the typical area includes a straight-line working area, a turning area, a field ridge and a surface concave-convex area. The geometric and spatial features include height difference, local fluctuation point, angle mutation and point cloud density, and the surface feature quantization model includes concave-convex area, height difference, fluctuation angle and roughness index.

[0045] Specifically, in step S3, the multi-type sensor includes an RTK receiver, an inertial measurement unit, a torque sensor, a force sensor and a vibration sensor, and time stamp synchronization of multi-source data is achieved through a CAN bus and a.NET communication protocol.

[0046] Specifically, in step S4, the geographic space mapping relationship includes mapping tractor working load data to a three-dimensional farmland point cloud model according to geographic coordinates, to form a position-time-load three-dimensional correlation data structure.

[0047] In addition, before step S1, a hardware self-check and a communication protocol verification are further included for the unmanned aerial vehicle sensor and the tractor data acquisition system. The space domain load spectrum is used for tractor fatigue life prediction, structure strength optimization or working condition adaptability evaluation.

[0048] Referring to Figure 3 (a), the present application further provides a tractor space domain load spectrum construction device, comprising:

[0049] a farmland terrain acquisition device, the terrain acquisition device comprising a heavy-load unmanned aerial vehicle, a single-line two-dimensional laser radar and an RTK receiver, the heavy-load unmanned aerial vehicle carrying the single-line two-dimensional laser radar and the RTK receiver, supporting farmland scanning at a preset flight height h , speed v and scanning angle θ ;

[0050] a tractor load acquisition device, the tractor load acquisition device integrating an RTK receiver, an inertial measurement unit, a torque sensor and a force sensor, and realizing multi-source data synchronous acquisition through a CAN bus;

[0051] a data processing device, the data processing device comprising an edge computing unit and a cloud server, the edge computing unit being in communication connection with the cloud server, the edge computing unit pre-processing point cloud and load data, and the cloud server executing surface feature quantization and load-geographic coordinate mapping algorithm based on a pre-processing result.

[0052] Meanwhile referring to Figure 1 , the present application further provides a tractor space domain load spectrum construction system, which is composed of a farmland terrain information acquisition subsystem and a tractor load acquisition subsystem, and specifically comprises:

[0053] a surface information acquisition module composed of an unmanned aerial vehicle platform, a laser radar and a positioning module, used for acquiring farmland three-dimensional point cloud and geographic coordinates;

[0054] a load data acquisition module composed of multi-type sensors and a data synchronization unit, used for acquiring tractor working load data;

[0055] The space-time fusion module is configured to map the load data to a surface feature quantization model based on geographic coordinates, establish a dynamic coupling relationship between the surface condition and the load change, and generate a spatial domain load spectrum.

[0056] In addition, the farmland terrain information acquisition subsystem is configured to acquire high-precision three-dimensional spatial information of the farmland region to be cultivated and leveled, and the acquisition unit is composed of a UAV platform with a built-in RTK receiver and a single-line two-dimensional laser radar. By setting a suitable flight height h and a laser radar scanning angle θ , and combining with a fixed flight speed v , a pre-planned flight route is flown to cover the target farmland region for scanning. In the flight process, the laser radar collects real-time surface point cloud data, and the RTK module accurately records the longitude, latitude and altitude of each scanning point. Since the laser radar scanning angle and the point cloud data have a one-to-one correspondence, efficient fusion and matching of the point cloud and the geographic spatial information can be achieved, and the spatial positioning information of the three-dimensional point cloud data of the surface is obtained. The result can be referred to (b) shown in Figure 3 .

[0057] The tractor load acquisition subsystem covers typical structural components of the tractor, including the engine, front and rear axles, wheels, power output shaft and suspension system, etc. Through RTK receivers, inertial measurement units, speed sensors, torque sensors, force sensors and other acquisition devices, real-time geographic position, attitude information, working speed, torque and speed of the power transmission system, traction and structural vibration and other multi-dimensional parameters are synchronously acquired. The working process of the tractor load acquisition subsystem is shown in Figure 2 . On this basis, the time sequence in the working process is taken as the reference, the transient load data collected during the field travel of the whole machine are matched with the real-time geographic position, and the load spectrum is further mapped to the three-dimensional farmland point cloud surface feature quantization model, so as to realize the geographic reference anchoring of the load data in the spatial dimension.

[0058] Compared with the prior art, the tractor spatial domain load spectrum construction method, device and system described above combine the farmland surface feature information to be cultivated and the load spectrum of the key components of the tractor, effectively solve the limitation that the traditional load spectrum can only reflect the working state of the tractor body, improve the usability of the load spectrum, and better support the positive durability design of the tractor, including fatigue analysis, life prediction and structure optimization, etc. The spatial domain load spectrum can comprehensively show the load change of the typical components of the tractor under different surface feature conditions, accurately capture the regional difference of the load spectrum of the key components under actual working conditions, and provide a solid theoretical basis and data support for the current prototype verification.

[0059] The principles and implementation manners of the present application are described by using specific examples in the present application, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, the specific implementation manners and application ranges will be changed according to the idea of the present application. In conclusion, the content of the present specification should not be understood as the limitation of the present application.

Claims

1. A method for constructing a tractor spatial domain load spectrum, characterized in that, The method comprises the following steps: S1, using a UAV to carry a laser radar and a positioning module to cover the farmland for scanning to obtain three-dimensional point cloud data with geographic coordinates; S2, removing interference information in the three-dimensional point cloud data through data preprocessing, extracting geometric and spatial features of a typical area, and constructing a surface feature quantization model; The geometric and spatial features include height difference, local fluctuation point, angle mutation and point cloud density, and the surface feature quantization model includes concave-convex area, height difference, fluctuation angle and roughness index; S3, synchronously collecting tractor working load data by a multi-type sensor carried by the tractor; S4, aligning the load data with the geographic coordinates based on a time sequence, anchoring load features to corresponding areas of the surface feature quantization model based on a geographic space mapping relationship, establishing a dynamic coupling relationship between surface conditions and load changes, and generating a load spectrum fused with spatial position information. The geographic space mapping relationship includes mapping the tractor working load data to a three-dimensional farmland point cloud model according to geographic coordinates to form a position-time-load three-dimensional correlation data structure.

2. The method according to claim 1, wherein, In step S1, the positioning module is an RTK receiver, the laser radar is a single-line two-dimensional laser radar, the scanning parameters include a flying height h , a speed v , and a scanning angle θ , and the scanning width is h ·tan In step S2, the data preprocessing uses a density-based clustering algorithm DBSCAN to remove residual crop point clouds in the three-dimensional point cloud data, and the typical area includes a straight working area, a turning area, a field ridge and a surface concave-convex area. .

3. The method of claim 1, wherein, In step S3, the multi-type sensor includes an RTK receiver, an inertial measurement unit, a torque sensor, a force sensor and a vibration sensor, and time stamp synchronization of multi-source data is realized through a CAN bus and a.NET communication protocol.

4. The method of claim 1, wherein, Before step S1, there is also a hardware self-checking and communication protocol verification of the UAV sensor and the tractor data acquisition system.

5. The method of claim 1, wherein, The spatial domain load spectrum is used for tractor fatigue life prediction, structure strength optimization or working condition adaptability evaluation.

6. The method of claim 1, wherein, Including:

7. An apparatus for performing the method of constructing a tractor spatial domain load spectrum according to any one of claims 1 to 6, characterized in that, A tractor load acquisition device, which integrates an RTK receiver, an inertial measurement unit, a torque sensor and a force sensor, and realizes synchronous acquisition of multi-source data through a CAN bus; The farmland topography acquisition device comprises a heavy-load unmanned aerial vehicle, a single-line two-dimensional laser radar and an RTK receiver, the heavy-load unmanned aerial vehicle is provided with the single-line two-dimensional laser radar and the RTK receiver, and supports farmland scanning according to a preset flight height h , speed v and scanning angle A data processing device, which comprises an edge computing unit and a cloud server, the edge computing unit is in communication connection with the cloud server, the edge computing unit preprocesses point cloud and load data, and the cloud server executes surface feature quantization and load-geographic coordinate mapping algorithm based on the preprocessing result. . Including: A surface information acquisition module composed of a UAV platform, a laser radar and a positioning module, used for obtaining farmland three-dimensional point cloud and geographic coordinates; 8. A system for performing the method of tractor spatial domain load spectrum construction according to any one of claims 1 to 6, characterized in that, A load data acquisition module composed of multi-type sensors and a data synchronization unit, used for collecting tractor working load data; A space-time fusion module, used for mapping load data to a surface feature quantization model based on geographic coordinates, establishing a dynamic coupling relationship between surface conditions and load changes, and generating a spatial domain load spectrum. ​ ​

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