Unmanned driving system of crawler agricultural machine

Abstract

The invention relates to the technical field of automatic control, in particular to a tracked agricultural machine unmanned driving system which comprises an initial parameter setting module, a behavior decision module, a strategy execution monitoring module, an environment adjustment module, a resource allocation module and a completion degree evaluation module. According to the invention, by automatically sensing topographic features and automatically adjusting operation parameters, the operation adaptability and efficiency of the agricultural machinery are significantly improved, the system can analyze topographic and obstacle information in real time and optimize a driving path and the operation parameters, the operation safety and accuracy of the agricultural machinery are significantly improved, and continuous monitoring and performance analysis of operation data are realized. The system can adjust driving parameters in real time according to environment changes, dynamic management and optimization of energy use are achieved, the operation cost is reduced, the resource utilization efficiency is improved, the overall operation efficiency and the task completion degree are evaluated and optimized, and the precision and the yield of agricultural production are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of automatic control, and in particular to an unmanned driving system for a crawler agricultural machine. Background Art

[0002] The field of automatic control technology involves the use of computers or automated tools to monitor and adjust the operation of equipment and systems. It is mainly used to improve efficiency, accuracy and safety while reducing or eliminating the need for human operation. In modern industrial and production processes, automatic control systems are a core component and are widely used in many fields such as manufacturing, transportation, defense and household appliances. Automatic control technology relies on sensors to collect data, controllers to process data, and actuators to execute control commands to achieve precise control of machinery and systems. The system includes feedback mechanisms that can adjust performance in real time to achieve predetermined goals.

[0003] Among them, the unmanned driving system of crawler agricultural machinery refers to the use of automatic control technology to manage and operate agricultural machinery, completing various agricultural tasks such as planting, tilling, and harvesting without manual driving. The main purpose of this system is to improve the efficiency and accuracy of agricultural production, reduce labor intensity, and improve the safety and reliability of operations. By integrating advanced sensors and navigation technology, unmanned agricultural machinery can operate autonomously in complex farmland environments, optimize the operation process and increase crop yields.

[0004] Existing technologies face the problems of insufficient terrain adaptability and inefficient resource management in automated control systems. In actual operation, traditional agricultural machinery cannot automatically sense terrain changes, making it difficult to ensure operating efficiency and safety. Especially under complex or changeable terrain conditions, the fixed parameter settings of machinery are difficult to meet the needs of different operating environments. Energy management in existing systems mostly relies on fixed algorithms and fails to optimize according to real-time operating status or environmental changes, resulting in energy waste and increased operating costs. The shortcomings are that in modern agricultural production where high efficiency and low cost are increasingly emphasized, the breadth and depth of the application of automation technology are limited, making it difficult to achieve ideal production efficiency and operating safety. Summary of the invention

[0005] In order to solve the problems of insufficient terrain adaptability and inefficient resource management faced by the existing technology. In actual operation, traditional agricultural machinery cannot automatically sense terrain changes, which makes it difficult to ensure operating efficiency and safety. Especially under complex or changeable terrain conditions, the fixed parameter settings of the machinery are difficult to meet the needs of different operating environments. Energy management in existing systems mostly relies on fixed algorithms, and fails to optimize according to real-time operating status or environmental changes, resulting in energy waste and increased operating costs. The shortcomings are that in modern agricultural production where high efficiency and low cost are increasingly emphasized, the breadth and depth of the application of automation technology are limited, resulting in technical problems that make it difficult to achieve ideal production efficiency and operating safety. An embodiment of the present invention provides an unmanned driving system for tracked agricultural machinery. The technical solution is as follows:

[0006] On the one hand, a crawler agricultural machinery unmanned driving system is provided, the system comprising: The initial parameter setting module uses the crawler agricultural machine to automatically sense the terrain, collect terrain feature data, record soil moisture and hardness, automatically adjust the crawler angle and ground pressure, match the terrain's operational requirements, and obtain the initial configuration of the operation; The behavior decision module adopts the initial configuration of the operation, analyzes the real-time terrain and obstacle data, dynamically adjusts the steering angle and speed, adjusts the driving path of the unmanned driving, and obtains the strategy adjustment result; The strategy execution monitoring module uses the strategy adjustment results to continuously monitor the unmanned driving path of the crawler agricultural machinery, collect operation data and analyze the performance of the crawler agricultural machinery, adjust the driving parameters, and obtain the operation analysis results; The environment adjustment module adjusts the operating parameters of the unmanned crawler agricultural machinery in real time according to the operation analysis results, adjusts the crawler settings according to the soil and terrain changes, and obtains adaptive adjustment data; The resource allocation module uses the adaptive adjustment data to perform energy management, analyze and optimize the fuel and power distribution of the crawler agricultural machinery, adjust the energy use strategy, and obtain a resource allocation optimization plan; The completion evaluation module implements the resource allocation optimization plan, evaluates the operation efficiency and task completion, adjusts the operation strategy in the future time period through data comparison and analysis, and obtains the unmanned driving task evaluation result.

[0007] As a further solution of the present invention, the initial operation configuration includes an adjusted track angle, ground pressure, and a matched terrain type; the strategy adjustment result includes a steering angle, a set driving speed, and an optimized driving path; the operation analysis result includes driving path data, performance indicators obtained by analysis, and adjusted driving parameters; the adaptive adjustment data includes track setting parameters adjusted according to soil moisture and terrain hardness, a track setting regulator, and adaptability inspection equipment; the resource allocation optimization plan includes an optimized fuel usage plan, a power distribution strategy, and predicted operating cost-effectiveness; the unmanned driving task evaluation result includes the evaluated operating efficiency, task completion, and operating strategy in the future time period.

[0008] As a further solution of the present invention, the initial parameter setting module includes: The terrain sensing submodule uses crawler agricultural machinery to automatically sense the terrain, continuously scan and record the inclination and concavity of the terrain, map the three-dimensional structure of the terrain, and obtain the terrain feature recognition results; The soil detection submodule uses underground detection radar and humidity sensor to measure the humidity value and hardness level of multiple layers of soil according to the terrain feature recognition result, analyzes the physical properties of the soil, and obtains the soil physical property analysis result; The track adjustment submodule adopts the soil physical property analysis results and dynamically adjusts the track angle and ground pressure through real-time monitored soil data to match the operation requirements of various terrains and obtain the initial operation configuration.

[0009] As a further solution of the present invention, the behavior decision module includes: The terrain obstacle analysis submodule adopts the initial configuration of the operation, collects data of the surrounding terrain and obstacles, identifies and classifies the size and location of multiple types of obstacles, and obtains terrain obstacle records; The path verification submodule identifies the driving path of the unmanned crawler agricultural machinery based on the terrain obstacle record, adjusts the steering angle and speed parameters in combination with the real-time terrain conditions, and verifies the safety of the path to obtain an optimized driving path; The operation safety submodule uses the optimized driving path to perform unmanned driving speed control and automatic steering adjustment, monitor the working environment and the state of the crawler agricultural machinery, optimize the operating efficiency and operation safety of the crawler agricultural machinery, and obtain strategy adjustment results.

[0010] As a further solution of the present invention, the policy execution monitoring module includes: Based on the strategy adjustment result, the path monitoring submodule tracks the real-time driving path of the unmanned crawler agricultural machinery in real time through the GPS and ground scanning radar installed on the crawler agricultural machinery, records the deviations and obstacles of the driving path, and generates driving monitoring results; The data analysis submodule uses the driving monitoring results to collect key performance data of the crawler agricultural machinery during the unmanned driving process, including speed, steering angle and track pressure, analyze the performance of the crawler agricultural machinery on differentiated terrains, evaluate performance efficiency, and obtain performance analysis results; The parameter adjustment submodule automatically adjusts the driving speed and steering sensitivity according to the performance analysis results and the environmental sensor data, optimizes driving stability and adaptability, matches the real-time changing working environment, and obtains the operation analysis results.

[0011] As a further solution of the present invention, the environment adjustment module includes: The change tracking submodule uses the operation analysis results to continuously track the changes in soil moisture and terrain hardness, calculates the speed and power output parameter values ​​of the crawler agricultural machinery, and obtains the operation parameter adjustment record; The track setting submodule adjusts the track tension and angle according to the operation parameter adjustment record, detects the ground pressure and the track grip, verifies the matching degree between the track configuration and the real-time terrain and soil conditions, and obtains the track configuration adjustment record; The adaptability test submodule adopts the track configuration adjustment record to conduct performance testing of unmanned tracked agricultural machinery, analyzes the adaptability of the track to differentiated soil and terrain, evaluates the overall adaptability of the tracked agricultural machinery through dynamic simulation and field testing, and obtains adaptive adjustment data.

[0012] As a further solution of the present invention, the formula for calculating the speed and power output parameter values ​​of the crawler agricultural machine is: ; in, are the speed and power output values ​​of the crawler agricultural machinery, A value representing soil moisture. A value representing the hardness of the terrain. Represents the speed setting of the previous cycle, , and is the weight parameter.

[0013] As a further solution of the present invention, the resource allocation module includes: The energy usage monitoring submodule monitors the energy consumption of the unmanned crawler agricultural machinery in real time based on the adaptive adjustment data, records the fuel usage rate and power consumption, adjusts the energy supply plan, checks the sustainability and efficiency of energy usage, and obtains energy management results; The usage pattern analysis submodule uses the energy management results to analyze the usage patterns of fuel and electricity, automatically adjust energy allocation in the differentiated operation stage, formulate and optimize energy usage strategies, and obtain energy allocation optimization data; The cost-benefit analysis submodule calculates the energy cost-benefit optimization value according to the energy allocation optimization data, evaluates the economic effect of the differentiated energy use strategy, and obtains the resource allocation optimization plan.

[0014] As a further solution of the present invention, the formula for calculating the energy cost-effectiveness optimization value is: ; in, Optimizing value for energy cost-effectiveness, Representative The weight coefficient of energy cost in the optimization calculation, Representative The real-time consumption value of energy items, Represents the average energy consumption value, Representative The weight coefficient of the benefit of each operation, Representative The total time of the operation process, is the number of energy projects, The number of jobs.

[0015] As a further solution of the present invention, the completion evaluation module includes: The operation speed evaluation submodule implements the resource allocation optimization scheme, collects the operation speed and resource consumption data of the crawler agricultural machinery, evaluates the energy utilization efficiency and operation speed, and obtains the resource utilization analysis results; The situation analysis submodule compares the completion status of the planned and real-time operations according to the resource usage analysis results, analyzes the task progress and the achievement rate of the quality standards, and obtains the task completion analysis results; The operation strategy adjustment submodule adjusts the operation strategy and parameters in the future time period based on the task completion analysis results and recent operation data, optimizes the efficiency and cost of the operation plan, verifies the operation adaptability and economy of unmanned driving of crawler agricultural machinery, and obtains the unmanned driving task evaluation results.

[0016] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: By automatically sensing terrain features and automatically adjusting operating parameters, the adaptability and efficiency of agricultural machinery operations can be significantly improved. The system can analyze terrain and obstacle information in real time, optimize driving paths and operating parameters, and significantly improve the safety and accuracy of agricultural machinery operations in complex farmland environments. Continuous monitoring and performance analysis of operating data enable the system to adjust driving parameters in real time according to environmental changes, further ensuring the continuity and accuracy of operations. Dynamic management and optimization of energy use can be achieved, operating costs can be reduced, and resource utilization efficiency can be improved. Overall operating efficiency and task completion can be evaluated and optimized, which significantly improves the accuracy and output of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the drawings without creative work.

[0018] Figure 1 is a schematic diagram of an unmanned driving system for a crawler agricultural machinery provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the system framework of the present invention; Figure 3 It is a flow chart of the initial parameter setting module in the present invention; Figure 4 It is a flow chart of the behavior decision module in the present invention; Figure 5 This is a flow chart of the strategy execution monitoring module in the present invention; Figure 6 It is a flow chart of the environment adjustment module in the present invention; Figure 7 It is a flow chart of the resource allocation module in the present invention; Figure 8 It is a flowchart of the completion evaluation module in the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0020] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0021] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0022] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0023] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0024] The embodiment of the present invention provides an unmanned driving system for a crawler agricultural machine, such as Figure 1-2 The schematic diagram of the unmanned driving system of crawler agricultural machinery shown in the figure includes: The initial parameter setting module uses the crawler agricultural machinery to automatically sense the terrain, collect terrain feature data, record soil moisture and hardness, automatically adjust the crawler angle and ground pressure, match the operation requirements of differentiated terrain, and obtain the initial operation configuration; The behavior decision module adopts the initial configuration of the operation, analyzes the real-time terrain and obstacle data, dynamically adjusts the steering angle and speed, adjusts the driving path of the unmanned driving, optimizes the operating efficiency and safety of the crawler agricultural machinery, and obtains the strategy adjustment results; The strategy execution monitoring module uses the strategy adjustment results to continuously monitor the unmanned driving path of the crawler agricultural machinery, collect operation data and analyze the performance of the crawler agricultural machinery, adjust the driving parameters according to environmental changes, and obtain the operation analysis results; The environmental adjustment module adjusts the operating parameters of the unmanned crawler agricultural machinery in real time according to the operation analysis results, adjusts the crawler settings according to soil and terrain changes, checks the adaptability of the crawler agricultural machinery, and obtains adaptability adjustment data; The resource allocation module adaptively adjusts data, performs energy management, analyzes and optimizes the fuel and power distribution of crawler agricultural machinery, adjusts energy use strategies, optimizes operation cost-effectiveness, and obtains resource allocation optimization solutions; The completion evaluation module implements the resource allocation optimization plan, evaluates the operation efficiency and task completion, adjusts the operation strategy in the future time period through data comparison and analysis, and obtains the unmanned driving task evaluation results; The initial operation configuration includes the adjusted track angle, ground pressure, and matching terrain type. The strategy adjustment results include the steering angle, set driving speed, and optimized driving path. The operation analysis results include driving path data, performance indicators obtained from the analysis, and adjusted driving parameters. The adaptive adjustment data includes track setting parameters adjusted according to soil moisture and terrain hardness, track setting regulators, and adaptability inspection equipment. The resource allocation optimization plan includes the optimized fuel usage plan, power distribution strategy, and predicted operating cost-effectiveness. The unmanned driving task evaluation results include the evaluated operating efficiency, task completion, and operating strategies in the future time period.

[0025] Specifically, if Figure 2 , 3 As shown, the initial parameter setting module includes: The terrain sensing submodule uses crawler agricultural machinery to automatically sense the terrain, continuously scan and record the inclination and concavity of the terrain, map the three-dimensional structure of the terrain, and obtain the execution process of the terrain feature recognition result as follows; Tracked agricultural machinery deploys a series of sensors and scanning equipment to capture the inclination and unevenness of the ground in real time. The equipment continuously obtains terrain data through scanning, and the sensors record the inclination data. The inclination and unevenness data are deeply analyzed and structured through data processing algorithms. The data is integrated to form a continuous three-dimensional terrain map. The three-dimensional structural mapping of the terrain can help operators better understand the terrain characteristics of the working area, provide the necessary information basis for further operations, and obtain terrain feature recognition results.

[0026] The soil detection submodule uses underground detection radar and humidity sensor to measure the humidity value and hardness level of multiple layers of soil according to the results of terrain feature recognition, analyzes the physical properties of the soil, and obtains the execution process of the soil physical property analysis results as follows; Using underground detection radar and humidity sensor, according to the formula: ; Calculate the multi-layer moisture value of the soil, where Represents the average moisture content of the soil, Representative The humidity value of the layer, Representative The depth of the layer, Represents the total detection depth, is the number of soil layers; The underground detection radar detects the moisture values ​​of the three layers of soil respectively. , , ; The corresponding layer depth cm, cm, cm; Total detection depth cm, the average humidity is calculated as follows: ; ; ; ; The results show that the overall moisture content of the soil remains at a relatively stable level within the detection depth, which is helpful for further analysis of soil physical properties.

[0027] The track adjustment submodule uses the soil physical property analysis results and real-time monitored soil data to dynamically adjust the track angle and ground pressure to match the operation requirements of various terrains. The execution process of obtaining the initial configuration of the operation is as follows; Through the implementation details of the soil physical property analysis results, the sensor continuously monitors the physical parameters of the soil such as moisture and hardness, and dynamically adjusts the angle and ground pressure of the track according to the monitoring data. This process involves real-time analysis of data and regulation technology of the track mechanical structure. By adjusting the track angle and ground pressure to match the operating requirements under different terrain conditions, it ensures that the agricultural machinery can operate efficiently and stably under various soil conditions, provides support for the efficient operation of agricultural machinery in different terrains, and obtains the initial configuration of the operation.

[0028] Specifically, if Figure 2 , 4 As shown, the behavior decision module includes: The terrain obstacle analysis submodule adopts the initial configuration of the operation, collects data of the surrounding terrain and obstacles, identifies and classifies the size and location of multiple types of obstacles, and obtains the execution process of terrain obstacle records as follows; Initializing the system settings of unmanned tracked agricultural machinery involves the configuration of GPS positioning accuracy, sensor sensitivity adjustment, and terrain data input interface. When collecting data, the system will automatically start terrain scanning in the area according to the set geographic fence, and use lidar and multi-band imaging technology to continuously collect terrain elevation data, slope information, and three-dimensional structure data of obstacles. During data collection, the system compares each data point with previously collected terrain data, identifies new or moving obstacles, and updates local terrain maps. Obstacle classification uses a shape- and material-based machine learning algorithm to determine the type and size of the obstacle by analyzing the reflectivity and geometric dimensions of the obstacle, and obtains a terrain obstacle record.

[0029] The path verification submodule identifies the driving path of the unmanned crawler agricultural machinery based on the terrain obstacle records, adjusts the steering angle and speed parameters based on the real-time terrain conditions, and verifies the safety of the path. The execution process of the optimized driving path is as follows; Based on the terrain obstacle record, identify the appropriate driving path according to the formula: ; Calculate the optimal speed of the driving path, where Represents the optimized speed, represents the turning radius, represents the acceleration due to gravity, represents the terrain inclination angle, Represents the friction coefficient between the ground and the track; According to the inclination angle of the terrain and the ground friction coefficient, the optimal driving speed of the unmanned crawler agricultural machinery under different terrain conditions can be calculated. For example, the inclination angle of the terrain = , friction coefficient =0.4, turning radius =50 meters, acceleration due to gravity =9.81m / s 2 , terrain inclination and friction coefficient data are obtained through terrain scanning and surface material analysis, and the values ​​are substituted into the formula: ; The results show that under this terrain condition, the agricultural machinery should adjust its speed to 12.21 m / s to optimize driving performance and maintain stability.

[0030] The operation safety submodule uses the optimized driving path to perform unmanned driving speed control and automatic steering adjustment, monitor the working environment and crawler agricultural machinery status, optimize the operating efficiency and operation safety of crawler agricultural machinery, and obtain the execution process of the strategy adjustment results as follows; Carry out speed control and automatic steering adjustment. The system monitors the environment and the status of agricultural machinery in real time, updates the terrain map and obstacle database, and on this basis, adjusts the driving speed and steering parameters of the agricultural machinery to ensure that the agricultural machinery completes the scheduled journey safely and efficiently. Monitor the working environment, including detecting terrain changes, obstacle dynamics and meteorological conditions in the working area, and optimize operations by analyzing real-time collected data and adjusting driving parameters to adapt to the new working environment. The optimization of operating efficiency is evaluated based on the operating efficiency and safety record of the agricultural machinery. The system continuously adjusts the driving strategy through real-time data analysis to improve operating efficiency and ensure safety, and obtains the results of strategy adjustment.

[0031] Specifically, if Figure 2 , 5 As shown, the policy execution monitoring module includes: Based on the strategy adjustment results, the path monitoring submodule tracks the real-time driving path of the unmanned crawler agricultural machinery through the GPS and ground scanning radar installed on the crawler agricultural machinery, records the deviations and obstacles of the driving path, and generates the execution process of the driving monitoring results as follows; Initialize the GPS system and ground scanning radar to ensure that the accuracy and response speed meet the requirements of unmanned crawler agricultural machinery. Real-time tracking begins. The real-time position and driving path of the crawler agricultural machinery are continuously monitored by GPS and ground scanning radar, and the real-time deviations and obstacles of the driving path are recorded. The system automatically marks the degree of deviation from the preset route and the type and location of obstacles encountered. In this process, real-time processing and analysis of data rely on high-performance data processing units to ensure the accuracy and timeliness of monitoring data and obtain driving monitoring results.

[0032] The data analysis submodule uses the driving monitoring results to collect key performance data of the crawler agricultural machinery during unmanned driving, including speed, steering angle and track pressure, analyze the performance of the crawler agricultural machinery on differentiated terrains, evaluate the performance efficiency, and obtain the performance analysis results. The execution process is as follows; The system collects and analyzes key performance data of unmanned tracked agricultural machinery, including speed, steering angle and track pressure. During the data analysis process, the system uses advanced data analysis algorithms, such as time series analysis and machine learning models, to classify and process the driving data of agricultural machinery under different terrain conditions, evaluate the performance and efficiency of tracked agricultural machinery on various terrains, and demonstrate the adaptability and efficiency of tracked agricultural machinery by comparing the driving data of various terrains, so as to maintain the optimal performance under changing terrain conditions and obtain performance analysis results.

[0033] The parameter adjustment submodule automatically adjusts the driving speed and steering sensitivity according to the performance analysis results and combines the environmental sensor data to optimize driving stability and adaptability and match the real-time changing operating environment. The execution process of obtaining the operation analysis results is as follows; Adjust driving speed and steering sensitivity according to the formula: ; Calculate the adjusted driving speed, where represents the adjusted speed, Represents the basic speed, represents the adjustment factor based on terrain differences, represents the deviation from the ideal path; Taking into account environmental sensor data and terrain changes, the crawler agricultural machinery needs to adjust its speed to adapt to the actual situation. For example, if the base speed is 10m / s, the deviation is 0.2, and the adjustment coefficient is 0.05, the adjusted speed is: ; The results show that by slightly adjusting the driving speed, the tracked agricultural machinery can better adapt to the current terrain and environmental conditions and maintain driving stability.

[0034] Specifically, if Figure 2 , 6 As shown, the environment adjustment module includes: The change tracking submodule uses the operation analysis results to continuously track the changes in soil moisture and terrain hardness, calculates the speed and power output parameter values ​​of the crawler agricultural machinery, and obtains the execution process of the operation parameter adjustment record as follows; The formula for calculating the speed and power output parameter values ​​of crawler agricultural machinery is: ; in, are the speed and power output values ​​of the crawler agricultural machinery, A value representing soil moisture. A value representing the hardness of the terrain. Represents the speed setting of the previous cycle, , and is the weight parameter; Parameter meaning and setting value: is the value of soil moisture, which is obtained through real-time monitoring by the soil moisture sensor and represents the moisture state of the soil. The current value is set to 30%, that is, ; is the value of terrain hardness, which is monitored by the terrain hardness measuring device and reflects the compressive strength of the ground. The current average hardness value is set to 5.0MPa, that is, ; The speed setting of the previous cycle is used to obtain historical data through the data recording device to consider the impact of historical operating conditions on the current settings. The setting speed of the previous cycle is set to 1.5m / s, that is, ; , and is the weight parameter, which is obtained through previous data analysis and experimental adjustment. , , ,The weight setting is set according to the influence of soil moisture and terrain hardness and the frequency of historical speed changes; Substitute the parameters into the formula for calculation: ; ; ; ; The calculation results show that the crawler agricultural machinery should be adjusted to a speed and power output close to 15.0 units to adapt to the current soil moisture and terrain hardness conditions. This result is used to guide the real-time adjustment of agricultural machinery under different terrain and soil conditions and optimize the overall operating efficiency.

[0035] The track setting submodule adjusts the track tension and angle according to the operation parameter adjustment record, detects the ground pressure and track grip, verifies the matching degree between the track configuration and the real-time terrain and soil conditions, and obtains the execution process of the track configuration adjustment record as follows; The tension and angle of the track are monitored and adjusted to adapt to different terrain and soil conditions. Sensor data is used to measure the ground pressure and grip of the track. The parameters are adjusted in real time based on the physical properties of the soil and real-time terrain data. In order to ensure that the track configuration matches the current environment, the elastic modulus of the track material and the friction coefficient of the ground must also be considered during the adjustment process. In this way, the working efficiency of the machine under different soil and terrain conditions can be optimized, and a record of track configuration adjustments can be obtained.

[0036] The adaptability test submodule uses the track configuration adjustment record to conduct the performance test of the unmanned crawler agricultural machinery, analyzes the adaptability of the crawler to differentiated soil and terrain, and evaluates the overall adaptability of the crawler agricultural machinery through dynamic simulation and field testing. The execution process of obtaining the adaptability adjustment data is as follows; The performance test is based on the recorded adjustment data to analyze the track's adaptability to differentiated soils and terrains. The test uses a combination of dynamic simulation and field testing to ensure that the track system can demonstrate optimal performance under different environmental conditions. The evaluation includes the track's load distribution, grip, and interaction with the soil. The field test is performed on simulated complex terrain to ensure the authenticity of the data and the comprehensiveness of the test, and to obtain adaptive adjustment data.

[0037] Specifically, if Figure 2 , 7 As shown, the resource allocation module includes: The energy usage monitoring submodule monitors the energy consumption of unmanned crawler agricultural machinery in real time based on adaptive adjustment data, records fuel usage and power consumption, adjusts energy supply plans, checks the sustainability and efficiency of energy usage, and obtains the execution process of energy management results as follows; By recording fuel usage and electricity consumption, optimizing energy supply plans, and monitoring the process, a variety of sensors and data acquisition systems are used to capture every aspect of energy consumption in detail and analyze the sustainability and efficiency of energy use. This data helps determine energy demand under different operating conditions and fluctuations in energy consumption under different environmental conditions. Through detailed analysis, the machine can automatically adjust energy distribution to adapt to different working environments and operating requirements, providing solid data support for the formulation and optimization of future energy strategies and obtaining energy management results.

[0038] The usage pattern analysis submodule uses the energy management results to analyze the usage patterns of fuel and electricity, automatically adjusts energy allocation in the differentiated operation stage, formulates and optimizes energy usage strategies, and obtains the execution process of energy allocation optimization data as follows; Based on the detailed energy usage data collected from each operation stage, energy allocation is automatically adjusted to adapt to different operation requirements. The formulated and optimized energy usage strategy takes into account the energy demand and consumption pattern under various operation conditions. During the optimization process, the energy consumption rate of each stage, as well as the efficiency and cost of each type of energy, are recorded and analyzed in detail, making energy allocation more accurate and efficient, and obtaining energy allocation optimization data.

[0039] The cost-benefit analysis submodule calculates the energy cost-benefit optimization value based on the energy allocation optimization data, evaluates the economic effect of the differentiated energy use strategy, and obtains the execution process of the resource allocation optimization plan as follows; The formula for calculating the energy cost-effectiveness optimization value is: ; in, Optimizing value for energy cost-effectiveness, Representative The weight coefficient of energy cost in the optimization calculation, Representative The real-time consumption value of energy items, Represents the average energy consumption value, Representative The weight coefficient of the benefit of each operation, Representative The total time of the operation process, is the number of energy projects, is the number of jobs; Parameter meaning and setting value: For the The weight coefficient of the energy cost is set to 0.2, reflecting the proportion of this energy in the total cost; For the The actual consumption value of the energy item is set to 500 units and collected through the energy monitoring system; is the average consumption value of all energy, set to 450 units, by calculating all The average value of For the The weight coefficient of the activity benefit is set to 0.3, reflecting the proportion of the activity in the total benefit; For the The total time of the operation process is set to 8 hours and obtained through the operation record; Substitute the parameters into the formula for calculation: 1. Calculation : ; 2. Calculation : ; 3. Calculate the square root of the denominator: ; 4. Calculation : ; The results show the degree of optimization of resource allocation under the current energy consumption and operational benefit weights, and this value can be used to further analyze and adjust the energy use strategy to improve economic benefits.

[0040] Specifically, if Figure 2 , 8 As shown, the completion assessment module includes: The operation speed evaluation submodule implements the resource allocation optimization plan, collects the operation speed and resource consumption data of crawler agricultural machinery, evaluates the energy use efficiency and operation speed, and obtains the resource use analysis results. The execution process is as follows; By evaluating energy utilization efficiency and operation speed, the relationship between operation speed and energy consumption is accurately recorded and analyzed during the optimization process to find the operation point with the best energy efficiency. The evaluation work accurately calculates the amount of work completed per unit of energy through real-time data monitoring and historical data comparison, reflecting the rationality of resource allocation and the optimization space of operation, providing data support for further improving operation efficiency and obtaining resource utilization analysis results.

[0041] The situation analysis submodule compares the completion status of the planned and real-time operations based on the resource usage analysis results, analyzes the task progress and the achievement rate of the quality standards, and obtains the task completion analysis results. The execution process is as follows; Compare the completion status of the planned and real-time operations according to the formula: ; Calculate the task completion, where Represents the percentage of task completion. Represents the actual amount of tasks completed. Represents the amount of tasks in the plan; set up tasks, Tasks, substitute the formula into: ; The result shows that the actual task completion rate is 75%, which reflects the comparison between the current work and the plan, and is used to analyze the task progress and the achievement rate of quality standards.

[0042] The operation strategy adjustment submodule adjusts the operation strategy and parameters in the future time period based on the task completion analysis results and recent operation data, optimizes the efficiency and cost of the operation plan, verifies the operation adaptability and economy of the crawler agricultural machinery unmanned driving, and obtains the unmanned driving task evaluation results. The execution process is as follows;

[0043] Adjust the operation strategies and parameters in the future time period, optimize the efficiency and cost of the operation plan through real-time monitoring and data analysis. During the adjustment process, consider the impact of various operating environments on the strategy in detail to ensure the adaptability and economy of unmanned operation of crawler agricultural machinery, demonstrate the effectiveness of strategy adjustment and optimization of operation costs, provide empirical data for continuous improvement and refined management, and obtain unmanned driving task evaluation results.

[0044] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An unmanned driving system for crawler agricultural machinery, characterized in that: The system comprises: The initial parameter setting module uses the crawler agricultural machine to automatically sense the terrain, collect terrain feature data, record soil moisture and hardness, automatically adjust the crawler angle and ground pressure, match the terrain's operational requirements, and obtain the initial configuration of the operation; The behavior decision module adopts the initial configuration of the operation, analyzes the real-time terrain and obstacle data, dynamically adjusts the steering angle and speed, adjusts the driving path of the unmanned driving, and obtains the strategy adjustment result; The strategy execution monitoring module uses the strategy adjustment results to continuously monitor the unmanned driving path of the crawler agricultural machinery, collect operation data and analyze the performance of the crawler agricultural machinery, adjust the driving parameters, and obtain the operation analysis results; The environment adjustment module adjusts the operating parameters of the unmanned crawler agricultural machinery in real time according to the operation analysis results, adjusts the crawler settings according to the soil and terrain changes, and obtains adaptive adjustment data; The resource allocation module uses the adaptive adjustment data to perform energy management, analyze and optimize the fuel and power distribution of the crawler agricultural machinery, adjust the energy use strategy, and obtain a resource allocation optimization plan; The completion evaluation module implements the resource allocation optimization plan, evaluates the operation efficiency and task completion, adjusts the operation strategy in the future time period through data comparison and analysis, and obtains the unmanned driving task evaluation result.

2. The unmanned driving system for crawler agricultural machinery according to claim 1, characterized in that: The initial operation configuration includes the adjusted track angle, ground pressure, and matched terrain type; the strategy adjustment result includes the steering angle, the set driving speed, and the optimized driving path; the operation analysis result includes the driving path data, the performance indicators obtained by analysis, and the adjusted driving parameters; the adaptive adjustment data includes the track setting parameters adjusted according to soil moisture and terrain hardness, the track setting regulator, and the adaptability inspection equipment; the resource allocation optimization plan includes the optimized fuel usage plan, power distribution strategy, and predicted operation cost-effectiveness; the unmanned driving task evaluation result includes the evaluated operation efficiency, task completion, and operation strategy in the future time period.

3. The unmanned driving system for crawler agricultural machinery according to claim 1, characterized in that: The initial parameter setting module includes: The terrain sensing submodule uses crawler agricultural machinery to automatically sense the terrain, continuously scan and record the inclination and concavity of the terrain, map the three-dimensional structure of the terrain, and obtain the terrain feature recognition results; The soil detection submodule uses underground detection radar and humidity sensor to measure the humidity value and hardness level of multiple layers of soil according to the terrain feature recognition result, analyzes the physical properties of the soil, and obtains the soil physical property analysis result; The track adjustment submodule adopts the soil physical property analysis results and dynamically adjusts the track angle and ground pressure through real-time monitored soil data to match the operation requirements of various terrains and obtain the initial operation configuration.

4. The unmanned driving system for crawler agricultural machinery according to claim 1, characterized in that: The behavior decision module includes: The terrain obstacle analysis submodule adopts the initial configuration of the operation, collects data of the surrounding terrain and obstacles, identifies and classifies the size and location of multiple types of obstacles, and obtains terrain obstacle records; The path verification submodule identifies the driving path of the unmanned crawler agricultural machinery based on the terrain obstacle record, adjusts the steering angle and speed parameters in combination with the real-time terrain conditions, and verifies the safety of the path to obtain an optimized driving path; The operation safety submodule uses the optimized driving path to perform unmanned driving speed control and automatic steering adjustment, monitor the working environment and the state of the crawler agricultural machinery, optimize the operating efficiency and operation safety of the crawler agricultural machinery, and obtain strategy adjustment results.

5. The unmanned driving system for crawler agricultural machinery according to claim 1, characterized in that: The policy execution monitoring module includes: Based on the strategy adjustment result, the path monitoring submodule tracks the real-time driving path of the unmanned crawler agricultural machinery in real time through the GPS and ground scanning radar installed on the crawler agricultural machinery, records the deviations and obstacles of the driving path, and generates driving monitoring results; The data analysis submodule uses the driving monitoring results to collect key performance data of the crawler agricultural machinery during the unmanned driving process, including speed, steering angle and track pressure, analyze the performance of the crawler agricultural machinery on differentiated terrains, evaluate performance efficiency, and obtain performance analysis results; The parameter adjustment submodule automatically adjusts the driving speed and steering sensitivity according to the performance analysis results and the environmental sensor data, optimizes driving stability and adaptability, matches the real-time changing working environment, and obtains the operation analysis results.

6. The unmanned driving system for crawler agricultural machinery according to claim 1, characterized in that: The environment adjustment module comprises: The change tracking submodule uses the operation analysis results to continuously track the changes in soil moisture and terrain hardness, calculates the speed and power output parameter values ​​of the crawler agricultural machinery, and obtains the operation parameter adjustment record; The track setting submodule adjusts the track tension and angle according to the operation parameter adjustment record, detects the ground pressure and the track grip, verifies the matching degree between the track configuration and the real-time terrain and soil conditions, and obtains the track configuration adjustment record; The adaptability test submodule adopts the track configuration adjustment record to conduct performance testing of unmanned tracked agricultural machinery, analyzes the adaptability of the track to differentiated soil and terrain, evaluates the overall adaptability of the tracked agricultural machinery through dynamic simulation and field testing, and obtains adaptive adjustment data.

7. The unmanned driving system for crawler agricultural machinery according to claim 6, characterized in that: The formula for calculating the speed and power output parameter values ​​of the crawler agricultural machine is: ； in, are the speed and power output values ​​of the crawler agricultural machinery, A value representing soil moisture. A value representing the hardness of the terrain. Represents the speed setting of the previous cycle, , and is the weight parameter.

8. The unmanned driving system for crawler agricultural machinery according to claim 1, characterized in that: The resource allocation module comprises: The energy usage monitoring submodule monitors the energy consumption of the unmanned crawler agricultural machinery in real time based on the adaptive adjustment data, records the fuel usage rate and power consumption, adjusts the energy supply plan, checks the sustainability and efficiency of energy usage, and obtains energy management results; The usage pattern analysis submodule uses the energy management results to analyze the usage patterns of fuel and electricity, automatically adjust energy allocation in the differentiated operation stage, formulate and optimize energy usage strategies, and obtain energy allocation optimization data; The cost-benefit analysis submodule calculates the energy cost-benefit optimization value according to the energy allocation optimization data, evaluates the economic effect of the differentiated energy use strategy, and obtains the resource allocation optimization plan.

9. The unmanned driving system for crawler agricultural machinery according to claim 8, characterized in that: The formula for calculating the energy cost-effectiveness optimization value is: ； in, Optimizing value for energy cost-effectiveness, Representative The weight coefficient of energy cost in the optimization calculation, Representative The real-time consumption value of energy items, Represents the average energy consumption value, Representative The weight coefficient of the benefit of each operation, Representative The total time of the operation process, is the number of energy projects, The number of jobs.

10. The unmanned driving system for crawler agricultural machinery according to claim 1, characterized in that: The completion assessment module includes: The operation speed evaluation submodule implements the resource allocation optimization scheme, collects the operation speed and resource consumption data of the crawler agricultural machinery, evaluates the energy utilization efficiency and operation speed, and obtains the resource utilization analysis results; The situation analysis submodule compares the completion status of the planned and real-time operations according to the resource usage analysis results, analyzes the task progress and the achievement rate of the quality standards, and obtains the task completion analysis results; The operation strategy adjustment submodule adjusts the operation strategy and parameters in the future time period based on the task completion analysis results and recent operation data, optimizes the efficiency and cost of the operation plan, verifies the operation adaptability and economy of unmanned driving of crawler agricultural machinery, and obtains the unmanned driving task evaluation results.

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