Leveler for garden planting and leveling method thereof

Abstract

The present application relates to the technical field of agricultural machinery, and discloses a leveler for garden planting and a leveling method thereof, which comprises the following steps: collecting soil penetration resistance and volume water content, and calculating soil cutting resistance coefficient representing cutting difficulty; measuring traction resistance and scraper vertical load, and calculating scraper force balance coefficient representing force balance; monitoring body pitch angle and front ground longitudinal slope angle, and combining the aforementioned coefficients to calculate geometric-mechanical confidence degree reflecting the matching degree of working condition posture; obtaining adhesion contact area to calculate soil removal health coefficient; receiving backward roughness, and comprehensively calculating target scraper soil entry angle according to the aforementioned coefficients and confidence degrees, and adjusting the scraper according to the target scraper soil entry angle. The present application fuses multi-source information such as soil characteristics, mechanical balance, geometric posture and operation quality, constructs closed-loop adaptive control logic, dynamically optimizes the scraper soil entry angle, and significantly improves the intelligent level and quality consistency of leveling operation under complex soil conditions.

Description

Technical Field

[0001] This invention belongs to the field of agricultural machinery technology, and in particular relates to a leveling device for garden planting and its leveling method. Background Technology

[0002] In landscaping, farmland preparation, and other similar applications, scraper-type landscaping screeds achieve surface leveling and soil movement by adjusting the angle at which the scraper enters the soil. However, existing landscaping screeds generally suffer from the following shortcomings during operation: On the one hand, soil conditions are complex and variable, and the combination of penetration resistance and moisture content changes directly affect the cutting difficulty. Traditional leveling tools often rely on the operator's experience or use a fixed angle of attack, making it difficult to adaptively adjust according to real-time soil conditions. When encountering dry, hard, or sticky soil, problems such as difficulty in penetration, scraper floating, or severe adhesion can easily occur, leading to a decrease in leveling quality and reduced work efficiency.

[0003] On the other hand, existing leveling devices rarely take into account the coupling relationship between fuselage attitude, terrain slope, and scraper stress state. In actual operation, changes in fuselage pitch angle and longitudinal slope of the ground will change the relative geometric relationship between the scraper and the ground. If these changes are not analyzed in conjunction with mechanical parameters such as traction resistance and vertical load, it is impossible to accurately assess the suitability of the current working conditions and achieve precise angle of attack control.

[0004] In addition, the adhesion of the scraper back and the roughness of the finished leveled surface directly reflect the quality of the operation. However, existing levelers generally lack a closed-loop mechanism to feed these backward quality indicators back to the angle of attack decision, and cannot proactively adjust the operation parameters when a trend of quality deterioration is detected.

[0005] Therefore, there is an urgent need for a leveling method and device that can integrate multi-source information such as soil properties, mechanical balance, geometric posture and operation quality, and calculate and adjust the angle of the scraper entering the soil in real time. Summary of the Invention

[0006] The purpose of this invention is to provide a leveling device and a leveling method for garden planting, in order to solve the above-mentioned problems.

[0007] This invention is implemented as follows: a leveling method for garden planting, comprising the following steps: collecting soil penetration resistance and soil volumetric moisture content, and calculating the soil cutting resistance coefficient characterizing the ease of soil cutting; measuring traction resistance and scraper vertical load, and calculating the scraper force balance coefficient characterizing the force balance state of the scraper; monitoring the machine pitch angle and the longitudinal slope angle of the ground surface in front, and calculating the geometric-mechanical confidence level characterizing the degree of matching between the current working condition and the actual posture based on the scraper force balance coefficient and the soil cutting resistance coefficient; obtaining the adhesion contact area measured by the pressure sensor on the back of the scraper, and calculating the soil removal health coefficient characterizing the soil removal ability of the scraper; receiving the backward roughness of the leveled surface, and combining the soil cutting resistance coefficient, soil removal health coefficient, and geometric-mechanical confidence level to calculate and obtain the target scraper entry angle, and adjusting the current entry angle of the scraper.

[0008] A further technical solution involves calculating the soil cutting resistance coefficient as follows: obtaining the soil penetration resistance and soil volumetric moisture content; normalizing the current soil penetration resistance and soil volumetric moisture content to obtain the soil penetration resistance index and the moisture content suitability index; the soil penetration resistance index monotonically increases with increasing soil penetration resistance, and the moisture content suitability index takes a minimum value within the suitable moisture content range, monotonically increases when deviating from the suitable moisture content range, with the dry deviation and wet deviation increasing in the same direction; and taking the larger value between the soil penetration resistance index and the moisture content suitability index as the soil cutting resistance coefficient.

[0009] A further technical solution involves normalizing the soil penetration resistance and soil volumetric moisture content as follows: The current soil penetration resistance is compared to the maximum penetration resistance that the leveler design can handle, yielding a soil penetration resistance index. The current soil volumetric moisture content is compared with the lower limit of arable moisture content, the upper limit of arable moisture content, and the soil saturation moisture content. Normalization is performed based on the deviation of the soil volumetric moisture content from the nearest interval boundary, yielding a moisture content arableability index. The moisture content arableability index takes its global minimum value within the interval between the lower and upper limits of arable moisture content, and monotonically increases when deviating from this interval, with the growth direction of dry deviation and wet deviation being consistent.

[0010] A further technical solution involves calculating the scraper force balance coefficient as follows: obtaining the traction resistance and scraper vertical load; normalizing the deviation of the current traction resistance and scraper vertical load to obtain the traction resistance imbalance index and the vertical load imbalance index; using the root mean square of the sum of the squares of the traction resistance imbalance index and the vertical load imbalance index as the comprehensive imbalance metric, and performing complement processing on the comprehensive imbalance metric to obtain the scraper force balance coefficient.

[0011] A further technical solution is that the traction resistance imbalance index takes its minimum value when the traction resistance falls within the ideal traction resistance range, and increases monotonically when it deviates from this range; the vertical load imbalance index takes its minimum value when the scraper vertical load falls within the ideal vertical load range, and increases monotonically when it deviates from this range.

[0012] A further technical solution is that the soil removal health coefficient is calculated as follows: the adhesion contact area measured by the pressure sensor on the back of the scraper is obtained; the soil removal health coefficient is calculated based on the proportion of the adhesion contact area to the total monitored area, and the soil removal health coefficient decreases monotonically as the proportion of the adhesion contact area increases.

[0013] A further technical solution involves calculating the geometric-mechanical confidence level as follows: obtaining the fuselage pitch angle, the longitudinal slope angle of the ground ahead, the scraper force balance coefficient, and the soil cutting resistance coefficient; and substituting the scraper force balance coefficient, the soil cutting resistance coefficient, the fuselage pitch angle, and the longitudinal slope angle of the ground ahead into the formula. Obtaining geometric-mechanical confidence scores ,in, For the fuselage pitch angle, This represents the longitudinal slope of the ground ahead. The soil cutting resistance coefficient, This is the scraper force balance coefficient.

[0014] A further technical solution involves calculating the target scraper entry angle as follows: The back roughness of the leveled surface, soil cutting resistance coefficient, soil removal health coefficient, and geometric-mechanical confidence level are obtained. The ratio of the current back roughness of the leveled surface to the rated maximum allowable roughness is processed, and the upper limit of the ratio is truncated to 1. The complement of the ratio is taken as the surface quality factor. The geometric-mechanical confidence level is used as the adjustment intensity coefficient. The effective adjustment demand degree, which monotonically increases with the accumulation of deterioration, is generated by combining the degree of soil removal degradation reflected by the soil removal health coefficient and the degree of leveling degradation reflected by the surface quality factor. The soil cutting resistance coefficient is used as a decision-making balancing parameter to determine the target scraper entry angle between the scraper's design limit angle of attack and the angle of attack modulated by the effective adjustment demand degree. Specifically, the larger the soil cutting resistance coefficient, the closer the target scraper entry angle is to the scraper's design limit angle of attack; the smaller the soil cutting resistance coefficient, the more the target scraper entry angle is determined by the effective adjustment demand degree.

[0015] A leveling device for landscaping includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the leveling method for landscaping described above.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Strong adaptability and full coverage of working conditions: This invention constructs multi-dimensional evaluation indicators such as soil cutting resistance coefficient, scraper force balance coefficient and geometric-mechanical confidence degree, and uses soil cutting resistance as a dynamic weight adjustment decision strategy to achieve real-time optimal matching of scraper entry angle under various soil conditions such as dry and hard, sticky and wet or suitable for cultivation and complex terrain, effectively overcoming the limitations of traditional fixed angle of attack or single parameter judgment.

[0017] 2. Closed-Loop Quality Control and Refined Operation: This invention innovatively incorporates backward roughness and soil removal health coefficient as quality feedback into the angle-of-attack decision chain. Through geometric-mechanical confidence modulation, it generates an "effective adjustment demand," forming a complete closed loop from perception and evaluation to execution. This mechanism can proactively intervene when operational quality shows a deteriorating trend, balancing cutting assurance under extreme conditions with smoothing quality optimization in routine operations, significantly improving operational accuracy and efficiency. Attached Figure Description

[0018] Figure 1 This invention provides a flowchart of a leveling method for landscaping. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0021] like Figure 1 As shown, an embodiment of the present invention provides a leveling method for garden planting, comprising the following steps: Soil penetration resistance and soil volumetric water content are collected to calculate the soil cutting resistance coefficient, which characterizes the ease with which soil can be cut. Soil penetration resistance refers to the resistance encountered by a probe or sensor when entering the soil, reflecting the soil's compaction and hardness. Soil volumetric water content refers to the percentage of water in the soil by volume, affecting its plasticity, cohesion, and arability. The soil cutting resistance coefficient is an index calculated by combining soil penetration resistance and soil volumetric water content, used to quantify the ease with which soil can be cut.

[0022] The traction resistance and vertical load on the scraper are measured, and the scraper force balance coefficient, which characterizes the force balance state of the scraper, is calculated. The traction resistance refers to the forward pulling force required by the leveler to overcome soil resistance during operation. The vertical load on the scraper refers to the vertical pressure exerted by the scraper on the ground. The scraper force balance coefficient is calculated based on the traction resistance and vertical load on the scraper and is used to evaluate whether the scraper is in an ideal state of force balance during the cutting process.

[0023] The system monitors the fuselage pitch angle and the longitudinal slope angle of the ground in front, and calculates the geometric-mechanical confidence level, which characterizes the degree of matching between the current working condition and the actual attitude, based on the scraper force balance coefficient and the soil cutting resistance coefficient. The fuselage pitch angle refers to the tilt angle of the leveler body relative to the horizontal plane. The longitudinal slope angle of the ground in front refers to the slope of the ground in front of the leveler. The geometric-mechanical confidence level is calculated based on the fuselage pitch angle, the longitudinal slope angle of the ground in front, the scraper force balance coefficient, and the soil cutting resistance coefficient, and is used to characterize the degree of matching between the current working condition and the actual attitude.

[0024] The adhesion contact area measured by the pressure sensor on the back of the scraper is obtained, and the soil removal health coefficient, which characterizes the scraper's soil removal ability, is calculated. The pressure sensor on the back of the scraper is used to detect the adhesion between the back of the scraper and the soil, thereby obtaining the adhesion contact area. The soil removal health coefficient is calculated based on the adhesion contact area and is used to characterize the scraper's soil removal ability.

[0025] The system receives the backward roughness of the leveled surface and, in conjunction with the soil cutting resistance coefficient, soil removal health coefficient, and geometric-mechanical confidence level, calculates the target scraper entry angle (angle of attack) and adjusts the current entry angle of the scraper. The backward roughness of the leveled surface refers to the smoothness of the ground surface behind the scraper after the scraper operation. The target scraper entry angle (angle of attack) is calculated based on the backward roughness of the leveled surface, the soil cutting resistance coefficient, the soil removal health coefficient, and the geometric-mechanical confidence level, and is used to guide the adjustment of the scraper. Adjusting the current entry angle of the scraper involves changing the angle between the scraper and the ground through the actuator on the leveler to adapt to different operational needs.

[0026] The leveling method described in this application integrates multi-source information such as soil properties, mechanical balance, geometric orientation, and work quality, and adaptively calculates and adjusts the target scraper's entry angle based on this information, forming a closed-loop control system. This systematic solution enables the leveler to achieve higher work efficiency, more stable leveling quality, and lower energy consumption in complex work scenarios such as landscaping, significantly improving the intelligence and automation level of leveling operations and representing a significant technological advancement.

[0027] This application further proposes a method for calculating the soil cutting resistance coefficient as follows: Soil penetration resistance and soil volumetric water content are obtained. Soil penetration resistance refers to the force exerted by the soil against the penetration of a probe or tool. It is an important indicator of soil compaction and hardness, directly reflecting the soil's physical and mechanical properties. It can be obtained in real-time using handheld or vehicle-mounted soil penetrators. For example, a conical probe can be inserted into the soil at a constant speed, and the relationship between the penetration depth and the required force can be recorded. Alternatively, multiple penetration resistance sensors can be deployed in the work area using a pre-set sensor network to periodically collect data and transmit it to the control system. Soil volumetric water content refers to the percentage of water in the soil by volume. It is a key parameter affecting soil plasticity, cohesion, and shear strength, reflecting the soil's moisture level. It can be obtained through non-contact or contact measurements using dielectric constant sensors such as time domain reflectometers (TDR) or frequency domain reflectometers (FDR). These sensors can sense changes in the soil's dielectric constant in real time, thereby calculating the water content. Alternatively, distributed measurements can be performed using a soil moisture sensor array, and the collected data can be averaged or weighted to obtain the regional water content.

[0028] The soil penetration resistance and soil volumetric moisture content are normalized to obtain the soil penetration resistance index and the soil moisture content suitability index. The soil penetration resistance index increases monotonically with increasing soil penetration resistance, while the soil moisture content suitability index has a minimum value within the suitable soil moisture content range and increases monotonically when deviating from the suitable soil moisture content range. Furthermore, the growth direction of dry deviation and wet deviation is the same. The normalization process for soil penetration resistance and soil volumetric moisture content is as follows: The soil penetration resistance index is obtained by comparing the current soil penetration resistance with the maximum penetration resistance that the grader is designed to handle. This step aims to standardize the real-time measured soil penetration resistance into a dimensionless index, allowing it to be compared and comprehensively evaluated with other normalized parameters. This ratio directly reflects the proportional relationship between the current soil compaction and the equipment's design limits, thus quantifying its impact on cutting ease. The grader can be equipped with a penetration resistance sensor to measure the soil penetration resistance in real time. The processor receives this measurement and divides it by a preset maximum penetration resistance value to obtain the soil penetration resistance index. For example, if the maximum penetration resistance is 3 MPa and the current measurement is 1.5 MPa, the index is 0.5. Soil penetration resistance data can also be obtained through a soil hardness meter or soil compaction meter integrated into the grader. These devices typically output the penetration resistance value directly or through simple calculations. Subsequently, the control system performs mathematical operations on this value with the stored maximum penetration resistance to generate the corresponding soil penetration resistance index.

[0029] The current soil volumetric moisture content is compared with the lower limit, upper limit, and saturated moisture content of the suitable arable soil. A normalization calculation is performed based on the deviation of the soil volumetric moisture content from the nearest interval boundary to obtain the moisture content suitability index. The moisture content suitability index takes its global minimum value within the interval between the lower and upper limits of the suitable arable soil moisture content, and increases monotonically when deviating from this interval, with the growth direction of dryness deviation and wetness deviation being consistent. The specific formula for the normalization calculation is as follows: Substitute the current soil volumetric moisture content into the formula. Obtain the moisture content suitability index. ,in, This represents the current soil volumetric moisture content. The lower limit of suitable soil moisture content, The upper limit of suitable soil moisture content, The soil saturation moisture content is used as the saturation point. This step transforms the real-time measured soil volumetric moisture content into an index reflecting its deviation from the suitable cultivation range using a piecewise function. This index quantifies the impact of soil moisture content on cutting resistance; both excessively low (dry and hard) and excessively high (sticky and wet) moisture content increase cutting difficulty. The leveler can integrate a soil volumetric moisture sensor, such as a dielectric constant sensor or a time-domain reflectometer (TDR), to acquire the soil volumetric moisture content in real time. The processor compares this value with the preset lower limit of suitable soil moisture content. Upper limit of suitable soil moisture content and soil saturated water content Substituting the values ​​into the above segmented formula, we can obtain the moisture content suitability index. Alternatively, remote sensing technology or image processing-based methods, combined with a soil type database, can be used to estimate the soil volumetric water content of the work area. The estimated water content can then be... The value is input into the leveler's control system, which executes the above formula to calculate the moisture content suitability index. The formula is designed so that the index is 0 within the suitable arable range and increases monotonically when it deviates from the suitable arable range, thus accurately reflecting the nonlinear effect of moisture content on cutting resistance.

[0030] The grader design should be able to handle the maximum penetration resistance, lower limit of suitable tillage moisture content, upper limit of suitable tillage moisture content, and soil saturation moisture content. These parameters are the benchmark values ​​for normalizing soil penetration resistance and soil volumetric moisture content. They define the boundaries and ideal ranges of soil conditions and are the basis for quantifying the ease of soil cutting. These parameters can be pre-set and stored in the equipment's memory through technical specification manuals, design documents, or experimental data provided by the grader manufacturer. For example, the maximum penetration resistance can be determined based on design limitations such as grader cutter material and drive power; the lower limit of suitable tillage moisture content, upper limit of suitable tillage moisture content, and soil saturation moisture content can be pre-calibrated through statistical analysis of historical operation data within the work area. Specifically, multiple sets of historical data are obtained from multiple operations of the grader under the same or similar garden soil types. Each set of historical data includes the soil volumetric moisture content collected before or during operation, and at least one operational performance indicator such as the surface smoothness qualification rate and operation energy consumption or scraper adhesion degree obtained through quality assessment under the corresponding moisture content conditions. Using the constraint that the operational performance indicators meet a preset excellent threshold, a curve relating soil volumetric moisture content to the operational performance indicators is plotted. The lower and upper boundaries of the moisture content intervals where the operational performance indicators fall within the excellent range are respectively taken as the lower and upper limits of the suitable arable moisture content. Simultaneously, from historical soil moisture content measurement records of the same type of soil, the upper envelope value of soil volumetric moisture content under stable moisture content conditions after sufficient drainage following rain is extracted, or statistical regression is performed on the maximum moisture content values ​​measured multiple times, and the upper tolerance at a certain confidence level is taken as the soil saturated moisture content. The aforementioned calibrated lower, upper, and saturated moisture content of the suitable arable moisture content can be stored in the leveler's memory and retrieved by the processor during subsequent real-time normalization processing. Alternatively, these parameters can be dynamically calibrated and input by professionals conducting on-site soil testing and equipment performance evaluation during the initial deployment or periodic maintenance of the leveler.

[0031] The larger value between the soil penetration resistance index and the soil moisture content suitability index is taken as the soil cutting resistance coefficient. , , A value close to 0 indicates extremely low penetration resistance and a moisture content just within the optimal cultivation window, meaning the soil is very easily mowed. A value approaching 1 indicates either extremely firm soil or a moisture content approaching the limit of dry-hard or sticky-wet, where cutting resistance reaches its maximum. The larger of the soil penetration resistance index and the moisture content suitability index is taken as the soil cutting resistance coefficient. This is a decision-making logic aimed at identifying the factor with the most significant or adverse impact on the target outcome from multiple influencing factors. This "maximum" strategy ensures that the soil cutting resistance coefficient preferentially reflects the most severe cutting obstacle in the soil conditions; that is, whether the soil is too hard (high penetration resistance) or has unsuitable moisture content (too dry or too wet), as long as one of these factors reaches a high level, it will be factored into the coefficient. The captured and reflected information ensures sensitivity to the most unfavorable operating conditions.

[0032] This application addresses the problem of dynamically adapting to soil changes and accurately characterizing the ease of cutting by specifying the calculation method for the soil cutting resistance coefficient. The method first obtains soil penetration resistance and soil volumetric moisture content as basic inputs, as these data directly reflect the physical properties of the soil. Subsequently, these two physical quantities are normalized to generate a soil penetration resistance index and a moisture content suitability index, respectively. The soil penetration resistance index is designed to increase monotonically with increasing soil penetration resistance, thus linearly mapping the direct impact of penetration resistance on cutting. The moisture content suitability index takes a minimum value within the suitable moisture content range and increases monotonically when deviating from this range, with dry and wet deviations increasing in the same direction. This allows the index to uniformly handle cutting obstacles caused by excessively dry or wet moisture content. Finally, the larger value between the soil penetration resistance index and the moisture content suitability index is taken as the soil cutting resistance coefficient. This "maximum-based" strategy ensures that the coefficient primarily reflects the most severe cutting obstacles in soil conditions, whether it's excessively hard soil or unsuitable moisture content; as long as one of these factors reaches a high level, it will be factored into the coefficient. This data is captured and reflected, thus comprehensively covering the most unfavorable scenarios even with complex and variable soil conditions. The coefficient's value continuously varies between 0 and 1, reliably indicating the transition from extremely easy to extremely difficult cutting. In this way, this application provides a more accurate and robust assessment of soil cutting difficulty for calculating the target scraper's entry angle in the aforementioned leveling method. This allows the leveler to make more intelligent and precise angle-of-attack adjustments based on real-time soil conditions, effectively avoiding reduced work efficiency and lower leveling quality due to changes in soil conditions.

[0033] As a specific implementation method, in actual landscaping leveling operations, the leveler can first acquire real-time soil penetration resistance data through a soil penetration sensor integrated in front of the scraper. For example, the sensor can be a conical probe with a pressure sensor, inserted into the soil at a fixed speed and recording the resistance value. Simultaneously, the soil volumetric moisture content is acquired in real-time through a soil moisture sensor (e.g., a TDR sensor based on the dielectric constant principle) installed near the scraper or within the work area. Assuming that at a certain moment, the sensor measures a soil penetration resistance of 1.5 MPa and a soil volumetric moisture content of 20%, the system will then normalize this data. For example, for soil penetration resistance, a maximum design resistance can be set (e.g., 3 MPa), and the soil penetration resistance index can be simply calculated as the ratio of the current resistance to the maximum resistance, i.e., 1.5 MPa / 3 MPa = 0.5. For soil volumetric moisture content, a lower limit of 15% and an upper limit of 25% for suitable cultivation moisture content can be set, with a soil saturation moisture content of 40%. If the current moisture content of 20% falls within the suitable tillage range [15%, 25%], then the soil cutting resistance index can be set to 0. The system then compares these two normalized indices. In this example, the soil penetration resistance index is 0.5, and the soil cutting resistance index is 0. The larger of the two, 0.5, is taken as the current soil cutting resistance coefficient. This coefficient of 0.5 indicates that although the soil moisture content is ideal, the soil compaction (penetration resistance) has reached a moderate level, posing some obstacle to the cutting operation. The system will adjust accordingly. The value, combined with other parameters, is used to further calculate the target scraper entry angle, thereby guiding the scraper to make corresponding adjustments to adapt to the current soil cutting difficulty.

[0034] Through the above technical solution, this application can dynamically and accurately assess the ease of soil cutting. By normalizing soil penetration resistance and soil volumetric moisture content, and comprehensively considering the factor with a greater impact on cutting difficulty, a soil cutting resistance coefficient in the range of 0 to 1 is generated. This allows the leveler to overcome the limitations of single-parameter evaluation or experience-based judgment in traditional methods, effectively coping with complex and variable soil conditions. When the soil is too hard or the moisture content is unsuitable, this coefficient can promptly and accurately reflect the increase in cutting resistance, thus providing a reliable basis for subsequent calculation of the target scraper entry angle. This avoids problems such as difficulty in scraper entry, floating, or low work efficiency caused by distorted assessment of soil cutting difficulty, significantly improving the adaptability and accuracy of leveling operations.

[0035] This application further proposes a method for calculating the scraper force balance coefficient as follows: Obtaining traction resistance and scraper vertical load: This step aims to provide real-time, fundamental mechanical input data for assessing the force balance state of the scraper. This can be achieved by installing force sensors (e.g., tension / compression sensors or strain gauge sensors) on the leveler's traction mechanism to measure traction resistance in real time, and by installing pressure sensors or load cells at the scraper-body connection to obtain the scraper vertical load. Alternatively, traction resistance and scraper vertical load can be indirectly estimated by monitoring operating parameters such as current and pressure of the traction motor or hydraulic cylinder, combined with a pre-defined mechanical model or calibration curve.

[0036] The current traction resistance and scraper vertical load are normalized to obtain the traction resistance imbalance index and the vertical load imbalance index. The traction resistance imbalance index reaches its minimum value when the traction resistance falls within the ideal range, and increases monotonically when it deviates from this range. The vertical load imbalance index reaches its minimum value when the scraper vertical load falls within the ideal range, and increases monotonically when it deviates from this range. The specific method for normalizing the deviation of the traction resistance and scraper vertical load is as follows: Substitute the current traction resistance into the formula Obtain the traction resistance imbalance index ,in, For traction resistance, This represents the ideal lower limit for traction resistance. To achieve the ideal upper limit of traction resistance, This is the traction resistance limit value; this step aims to convert the traction resistance measured in real time... This is transformed into a dimensionless index that can characterize the degree to which it deviates from the ideal working range. By using piecewise function processing, the system can accurately distinguish between three situations: excessively low traction resistance, resistance within the ideal range, and excessively high traction resistance, and calculate the degree of imbalance for each. This calculation can be implemented in the leveler's control unit using embedded software. The control unit receives real-time traction resistance signals from a traction sensor (e.g., a strain gauge sensor mounted on the traction rod or drive shaft). Then, based on the preset ideal lower limit of traction resistance Ideal upper limit of traction resistance and traction resistance limit value Perform the above piecewise function calculation to output the traction resistance imbalance index. Alternatively, calculations can be performed using external processing units (such as in-vehicle computers or cloud servers).

[0037] Substitute the current vertical load on the scraper into the formula to obtain Obtain the vertical load imbalance index ,in, This represents the current vertical load on the scraper. This represents the ideal lower limit for vertical load. This represents the ideal upper limit for vertical load. This is the vertical load limit value; the procedure is similar to the calculation of the traction resistance imbalance index, and this step is used to apply the real-time measured vertical load of the scraper. This is transformed into a dimensionless index that characterizes the degree to which it deviates from the ideal working range. This helps assess whether the scraper is in the ideal cutting posture, avoiding soil clogging due to excessive load or floating due to insufficient load. Scraper vertical load This can be obtained through pressure sensors or load cells mounted on the scraper support structure. These sensors transmit the vertical load signal to the leveler's control unit, which then determines the load based on a preset ideal lower limit for the vertical load. Ideal upper limit of vertical load and vertical load limit value Perform the piecewise function calculation described above and output the vertical load imbalance index. Vertical load can also be indirectly obtained through hydraulic cylinder pressure sensors. When the scraper is raised, lowered, and pressurized by the hydraulic system, there is a corresponding relationship between the pressure in the hydraulic cylinder and the vertical load.

[0038] The ideal lower limit, ideal upper limit, and ultimate value of traction resistance, as well as the ideal lower limit, ideal upper limit, and ultimate value of vertical load, can also be calibrated by analyzing historical operating data of the leveler under the same or similar working conditions. First, collect historical data from multiple effective work segments. Each set of data should include real-time recorded traction resistance, scraper vertical load, and corresponding work quality evaluation indicators, such as fuel or electricity consumption per unit distance recorded by onboard sensors, flatness data obtained through 3D surface scanning, or the degree of soil accumulation in front of the scraper identified by a vision system. Using the work quality evaluation indicators meeting a preset excellent standard as a constraint, statistical analysis is performed on the historical data, and traction resistance distribution curves and scraper vertical load distribution curves are plotted respectively. The left and right boundary points of the concentrated range of traction resistance under excellent working conditions are determined as the ideal lower limit and ideal upper limit of traction resistance, respectively; similarly, the left and right boundary points of the concentrated distribution range of scraper vertical load under excellent working conditions are determined as the ideal lower limit and ideal upper limit of vertical load, respectively. The traction resistance limit can be determined based on historical data showing that the traction resistance continues to rise beyond the aforementioned ideal upper limit until the work efficiency significantly decreases or a safety alarm is triggered. Alternatively, it can be calibrated by referring to the theoretical maximum adhesion force when the grader's drive wheels are fully slipping. The vertical load limit can be determined based on the load corresponding to the highest pressure set by the hydraulic system's relief valve, or the maximum allowable load designed for the scraper support structure. The aforementioned calibrated threshold parameters can be pre-stored in the grader's memory for the processor to access during normalization deviation processing.

[0039] Using the root mean square of the sum of the squares of the traction resistance imbalance index and the vertical load imbalance index as a comprehensive imbalance metric, the scraper force balance coefficient is obtained by performing complement processing on the comprehensive imbalance metric. , , A value close to 1 indicates that both the traction force and vertical force fall exactly within their respective ideal ranges, and the scraper is in an ideal cutting posture, neither accumulating soil nor floating up. A value approaching 0 indicates that the force in at least one direction has significantly deviated from the ideal window, and the overall force balance has deteriorated to its limit; the specific calculation formula is as follows: in, This is the scraper force balance coefficient. The traction resistance imbalance index, This is the vertical load imbalance index. Through a comprehensive mathematical model, two independent imbalance indices are merged into a single, physically meaningful scraper force balance coefficient, which can comprehensively quantify the overall force balance state of the scraper. The formula uses a calculation method similar to Euclidean distance, averaging the sum of the squares of the two imbalance indices, taking the square root, and then subtracting the value from 1, thus mapping the result to the [0,1] interval. This method effectively reflects the combined influence of the two imbalance indices on the overall balance state. Alternatively, a weighted average method can also be used.

[0040] This scheme systematically evaluates the force balance state of the scraper through a series of steps. First, traction resistance and vertical load on the scraper are acquired in real time; these are key mechanical parameters reflecting the interaction between the scraper and the soil. This raw mechanical data forms the basis for all subsequent balance assessments. Next, these real-time acquired traction resistance and vertical load data undergo normalization deviation processing. This process compares the actual measured values ​​with a preset ideal working range, generating a traction resistance imbalance index and a vertical load imbalance index. These two indices quantify the degree to which the traction force and vertical force deviate from the ideal state. When the force value falls within the ideal range, the corresponding imbalance index reaches its minimum value (usually 0), indicating force balance in that direction; when the force value deviates from the ideal range, the imbalance index increases monotonically, reflecting the severity of the imbalance. This normalization process allows mechanical parameters of different dimensions to be uniformly measured and highlights their deviation from the ideal state. Subsequently, these two imbalance indices are substituted into a specific mathematical formula to calculate the scraper force balance coefficient. This formula uses a comprehensive approach to measure the traction-resistance imbalance index. and vertical load imbalance index Combining these factors, a coefficient between 0 and 1 is generated. This is the coefficient related to the scraper force balance. When the coefficient of friction approaches 1, it indicates that both the traction force and the vertical force are exactly within their ideal ranges. At this point, the scraper is in an ideal cutting posture, neither clogging the soil due to insufficient or excessive traction force, nor floating or over-compacting due to improper vertical load. Conversely, when the scraper force balance coefficient is close to 1, it indicates that both the traction force and the vertical force are within their ideal ranges. When the value approaches 0, it indicates that the force in at least one direction deviates significantly from the ideal window, the overall force balance has deteriorated to its limit, and the scraper may experience severe soil accumulation, lifting, or compaction problems. The ingenuity of this solution lies in simplifying the complex mechanical balance problem into an intuitive quantitative indicator: the scraper force balance coefficient. This coefficient not only reflects the deviation of a single mechanical parameter, but more importantly, it comprehensively considers the interaction between traction force and vertical force, providing a comprehensive assessment of the scraper's force balance. In the aforementioned leveling method used for landscaping, the scraper force balance coefficient serves as a key input for assessing the geometric-mechanical confidence level of the match between the current working condition and the actual posture. Its precise quantification capability allows the geometric-mechanical confidence level to more accurately reflect the actual working state of the scraper. In this way, this solution solves the problem of the lack of precise quantification of the scraper's force balance state in traditional methods, providing a reliable basis for subsequent angle-of-attack adjustments, thereby ensuring that the leveler can adaptively adjust according to real-time working conditions, improving the quality and efficiency of leveling operations.

[0041] As a specific implementation, the leveler can be equipped with multiple sensors to achieve the above method. For example, a high-precision tension / compression sensor can be installed on the traction rod to measure the traction resistance in real time. This sensor can be a resistance strain gauge sensor, and its output signal is amplified and converted from analog to digital before being acquired by the processor. Simultaneously, multiple load cells or pressure sensors can be installed on the support structure connecting the scraper to the leveler body to measure the vertical load borne by the scraper. These sensors can be arranged on the left and right sides and the middle of the scraper to obtain more comprehensive vertical load distribution information, and the total scraper vertical load is obtained by summing or weighted averaging. The processor pre-stores the ideal lower limit, ideal upper limit, and ultimate value of the traction resistance, as well as the ideal lower limit, ideal upper limit, and ultimate value of the vertical load. When the processor receives the real-time traction resistance value and the scraper vertical load value, it performs calculations according to a preset normalized deviation processing algorithm. For example, the ideal lower limit of the traction resistance is determined according to the equipment design specifications. The ideal upper limit for traction resistance is 5000N. The traction resistance limit is 8000N. The value is 12000N. Simultaneously, the ideal lower limit for vertical load is... The ideal upper limit for vertical load is 3000N. The vertical load limit is 6000N. The resistance is 9000N. These parameters are stored in the leveler's control unit. During actual operation, sensors on the leveler monitor the traction resistance in real time. Vertical load of scraper For example, if the currently measured traction resistance... The value is 4000N, which is below the ideal lower limit for traction resistance. (5000N). At this time, the control unit will Substitute the traction resistance imbalance index The calculation formula is as follows: The traction resistance imbalance index is obtained. A value of 0.2 indicates insufficient traction. If the currently measured traction resistance... If the resistance is 7000N, it falls within the ideal traction resistance range [5000N, 8000N]. At this point, the control unit will... Setting it to 0 indicates that the traction force is in an ideal equilibrium state. If the currently measured traction resistance... The value is 10,000 N, which is higher than the ideal upper limit for traction resistance. At this time, the control unit will Substitute the traction resistance imbalance index The calculation formula is as follows: The traction resistance imbalance index is obtained. A value of 0.5 indicates excessive traction. Similarly, for the vertical load on the scraper... If the currently measured vertical load on the scraper The value is 2500N, which is below the ideal lower limit for vertical load. (3000N). At this time, the control unit will... Substitute the vertical load imbalance index The calculation formula is as follows: The vertical load imbalance index is obtained. A value of approximately 0.167 indicates insufficient vertical load, and the scraper may float. If the currently measured vertical load on the scraper... The load is 5000N, which is within the ideal vertical load range [3000N, 6000N]. At this time, the control unit will... Setting it to 0 indicates that the vertical load is in an ideal equilibrium state. If the currently measured vertical load on the scraper... The load capacity is 7500N, which is higher than the ideal upper limit for vertical load. (6000N). At this time, the control unit will Substitute the vertical load imbalance index The calculation formula is as follows: The vertical load imbalance index is obtained. A value of 0.5 indicates excessive vertical load, potentially causing soil accumulation on the scraper. Through the above calculations, the system can accurately and in real-time obtain the traction resistance imbalance index. and vertical load imbalance index The processor then calculates the traction resistance imbalance index. and vertical load imbalance index Substitute into the formula In this way, the current scraper force balance coefficient can be calculated. For example, if the traction resistance is exactly within the ideal range, and the vertical load is also exactly within the ideal range, then... and All are 0, and the calculated scraper force balance coefficient is 0. A value of 1 indicates that the scraper is in a state of perfect force balance. If the traction resistance deviates significantly from the ideal range, resulting in... If the value is close to 1, and the vertical load is still within the ideal range, then... The coefficient of force balance of the scraper is 0 at this time. A value close to 0.29 indicates a severe imbalance in the traction force direction. The processor can use this calculated scraper force balance coefficient to further assess the suitability of the current working conditions and provide crucial information for subsequent adjustments to the scraper's soil entry angle.

[0042] Through the above technical solution, this application provides a method for accurately quantifying the force balance state of a scraper. By acquiring the traction resistance and vertical load of the scraper in real time and performing normalized deviation processing, the traction resistance imbalance index and vertical load imbalance index can be accurately obtained, thus clearly reflecting the degree of force deviation of the scraper in the horizontal and vertical directions. Furthermore, these two imbalance indices are comprehensively calculated into a scraper force balance coefficient, which can intuitively and comprehensively characterize the overall force balance state of the scraper, effectively avoiding the inaccurate assessment problem caused by the lack of quantitative indicators in traditional methods. This accurate force balance assessment allows the leveler to more accurately determine whether the scraper is in an ideal cutting posture, such as whether it is soil-laden or floating. In the above-mentioned leveling method for landscaping, the scraper force balance coefficient, as a key input for geometric-mechanical confidence calculation, significantly improves the accuracy of the assessment of the matching degree between the current working condition and the actual posture, thus providing a more reliable and refined basis for the subsequent calculation and adjustment of the target scraper entry angle, ultimately helping to achieve more precise leveling operations and improve work quality and efficiency.

[0043] This application further proposes a method for calculating the soil removal health coefficient as follows: The adhesion contact area is obtained from the pressure sensor on the back of the scraper. The adhesion contact area refers to the actual contact area between the scraper back and the soil. Its function is to directly quantify the degree of soil adhesion on the back of the scraper, providing objective data for subsequent soil removal capacity assessment. This area can be obtained in several ways. For example, an array of pressure sensors can be integrated on the back of the scraper. By detecting the pressure values ​​of each sensor unit in real time and determining which units have soil adhesion based on a preset pressure threshold, the area of ​​these adhered sensor units can be accumulated to obtain the area. Alternatively, image recognition technology can be used. A camera installed on the back of the scraper can acquire real-time images of the scraper back, and image processing algorithms can be used to identify and calculate the pixel area of ​​the adhered soil in the image, then convert it into the actual physical area.

[0044] The soil removal health coefficient is calculated based on the proportion of the adhesion contact area to the total monitored area. , , This indicates that there is no adhesion on the back and the soil flow is completely unobstructed. This indicates that the entire monitoring surface is completely covered with adhering soil, rendering the soil removal function ineffective; the soil removal health coefficient decreases monotonically as the proportion of the adhering contact area increases; the specific calculation formula is as follows: in, For the health coefficient of soil removal, The adhesive contact area is measured by the pressure sensor on the back of the scraper. The total monitoring area refers to the total area of ​​the effective region on the back of the scraper used to monitor soil adhesion. Its function is to serve as a benchmark for calculating the soil removal health coefficient, providing a standardized reference. This total area is usually a fixed value predetermined based on the scraper's design dimensions and the arrangement range of the sensor array, or it can be precisely defined based on the actual coverage and effective working area of ​​the sensor array on the back of the scraper. The formula converts the real-time acquired adhesion contact area and the total monitoring area into a standardized soil removal health coefficient. This calculation can be performed by the processor using floating-point operations, substituting the real-time acquired adhesion contact area and the preset total monitoring area into the formula. Alternatively, a pre-built lookup table (LUT) can be used to directly query and obtain the corresponding soil removal health coefficient value based on the ratio of the adhesion contact area to the total monitoring area. This soil removal health coefficient is a dimensionless parameter between 0 and 1, used to quantify the scraper's soil removal capability. Its function is to intuitively reflect the severity of soil adhesion on the back of the scraper, providing an important decision-making basis for subsequent angle of attack adjustments. This coefficient is calculated by the processor and output as a key parameter to the leveler's control system.

[0045] This application's solution introduces a soil removal health coefficient to accurately assess the soil removal capacity of a scraper. Specifically, the solution first obtains the adhesion contact area and the total monitored area measured by the pressure sensor on the back of the scraper. The adhesion contact area reflects the actual size of the area on the back of the scraper where soil adheres, while the total monitored area provides a benchmark for assessing the degree of adhesion. Subsequently, the adhesion contact area is substituted into the formula... Calculations are performed to obtain the soil removal health coefficient. This quantitative method allows for an objective and accurate characterization of the scraper's soil removal status. The introduction of this soil removal health coefficient forms a close synergy with the aforementioned leveling methods used in landscaping. In these methods, the soil removal health coefficient serves as a key input parameter for calculating the effective adjustment requirement, thus influencing the determination of the target scraper's entry angle. When there is severe adhesion on the scraper's back and the soil removal health coefficient is low, it indicates a decline in the scraper's soil removal ability, potentially leading to soil accumulation or poor leveling quality. In this case, the system calculates a more suitable effective adjustment requirement based on the lower soil removal health coefficient, combined with other operating parameters, and adjusts the target scraper's entry angle accordingly. This mechanism enables the leveler to dynamically and adaptively adjust the angle of attack based on the actual adhesion on the scraper's back, effectively avoiding decreased work efficiency and leveling quality problems caused by soil adhesion, and improving the intelligence and precision of leveling operations.

[0046] In one specific implementation, an array of multiple independent pressure-sensitive resistors or piezoelectric film sensors can be integrated on the back of the scraper. The size and spacing of each sensor unit are predetermined. When the leveler is operating, the processor periodically reads the output signal of each sensor unit. By setting a pressure threshold, for example, when the output pressure of a sensor unit exceeds the threshold, soil adhesion is considered to have occurred in that unit's area. The processor counts the number of all sensor units with adhesion and multiplies it by the area of ​​a single sensor unit to calculate the current adhesion contact area. Total monitored area This can be defined as the total area of ​​all sensor units. For example, if the sensor array consists of 100 sensor units, each with an area of ​​1 square centimeter, then the total monitored area is 100 square centimeters. The processor then calculates the resulting adhesive contact area. and the preset total monitoring area Substitute into the formula This allows for the real-time acquisition of the soil health coefficient. For example, if the measured adhesion contact area is 20 square centimeters, the soil removal health coefficient is 0.8. This coefficient is then transmitted to the leveler's control module for subsequent angle of attack decisions.

[0047] Through the above technical solution, this application provides a specific and quantitative method for calculating the soil removal health coefficient, effectively solving the problem of inaccurate soil removal capacity assessment in traditional leveling operations. This method directly obtains the adhesion contact area measured by the pressure sensor on the back of the scraper and performs standardized calculations based on the total monitored area, achieving an objective and real-time assessment of the scraper's soil removal capacity. This precise quantification allows the leveler to accurately grasp the severity of soil adhesion on the back of the scraper, thus providing a reliable basis for calculating the subsequent target scraper entry angle. When the soil removal health coefficient indicates worsening adhesion, the system can adjust the angle of attack in a timely manner, avoiding reduced work efficiency and lower leveling quality due to soil accumulation or adhesion. This significantly improves the adaptability and accuracy of leveling operations, ensuring the leveling quality and work efficiency of garden planting surfaces.

[0048] This application further proposes a method for calculating the geometric-mechanical confidence level as follows: The following parameters are obtained: fuselage pitch angle, longitudinal slope angle of the ground ahead, scraper force balance coefficient, and soil cutting resistance coefficient. The fuselage pitch angle refers to the tilt angle of the screed body relative to the horizontal plane, reflecting the overall attitude of the screed. This angle can be measured in real time using an inertial measurement unit (IMU) or a high-precision tilt sensor mounted on the screed body. For example, it can be accurately obtained using a fusion algorithm of accelerometers and gyroscopes based on microelectromechanical systems (MEMS) technology. The longitudinal slope angle of the ground ahead refers to the slope of the ground ahead of the screed along the direction of travel, reflecting the terrain features of the area to be screed. This angle can be obtained in various ways. For example, it can be obtained by scanning the ground ahead using a LiDAR sensor or ultrasonic sensor mounted on the front of the screed and calculating the longitudinal slope using point cloud data or distance data; alternatively, it can be obtained by using a visual sensor combined with image processing algorithms to identify surface features and estimate the slope. The scraper force balance coefficient and soil cutting resistance coefficient are obtained through the above calculations.

[0049] Substitute the scraper force balance coefficient, soil cutting resistance coefficient, fuselage pitch angle, and longitudinal slope angle of the ground in front into the formula. Obtaining geometric-mechanical confidence scores , , A value close to 1 indicates excellent geometric fit and force balance, with each factor matching the current soil conditions, resulting in a high degree of adaptability to the working conditions. A value approaching 0 indicates that at least one of the geometric and mechanical aspects has significantly deteriorated, and that this deterioration term is given a high weight under the current soil impedance, indicating a severe mismatch in working conditions. For the fuselage pitch angle, This represents the longitudinal slope of the ground ahead. The soil cutting resistance coefficient, This is the scraper force balance coefficient.

[0050] This application's solution obtains the fuselage pitch angle, the longitudinal slope angle of the ground ahead, the scraper force balance coefficient, and the soil cutting resistance coefficient, and substitutes these parameters into a specific mathematical model to calculate the geometric-mechanical confidence level. The core of this model lies in the weighted combination of geometric fit (represented by the cosine difference between the fuselage pitch angle and the longitudinal slope angle of the ground ahead) and the scraper force balance coefficient, with the soil cutting resistance coefficient serving as a dynamic weighting factor. Specifically, a high soil cutting resistance coefficient indicates harsh soil conditions and high cutting difficulty. In this case, the model assigns a higher weight to the scraper force balance coefficient to ensure that the leveler prioritizes the force balance of the scraper under harsh soil conditions, avoiding operation failure due to uneven force distribution. Conversely, a low soil cutting resistance coefficient indicates that the soil is easy to cut. In this case, the model assigns a higher weight to the geometric fit to optimize the relative geometric relationship between the scraper and the ground, thereby improving leveling accuracy and efficiency. This dynamic weighting mechanism enables the geometric-mechanical confidence score to sensitively reflect the degree of matching between the current working condition and the actual posture, and adaptively adjusts the evaluation focus according to soil conditions. This provides a more accurate and reliable working condition assessment in complex and ever-changing landscaping environments, laying a solid foundation for subsequent adjustments to the scraper's entry angle. In this way, the proposed solution effectively addresses the shortcomings of traditional leveling tools, such as the inability to coordinate geometric fit and force balance analysis and the fixed weights in evaluating working conditions, significantly improving the adaptability and intelligence of leveling operations.

[0051] As one specific implementation method, the leveler can be equipped with a high-precision inertial measurement unit (IMU) to acquire the fuselage pitch angle in real time. For example, a MEMS sensor integrating a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer can be used to fuse data through algorithms such as Kalman filtering, outputting a precise pitch angle. Simultaneously, a laser scanner, such as a single-line or multi-line lidar, can be installed at the front of the leveler to scan the ground surface at a frequency of tens of times per second, acquiring surface point cloud data. The control system can process this point cloud data to fit the longitudinal slope angle of the ground surface in front. Scraper force balance coefficient and soil cutting resistance coefficient The leveler control system then calculates the value based on real-time collected data such as traction resistance, scraper vertical load, soil penetration resistance, and soil volumetric moisture content, according to the method defined in the above scheme. For example, at a certain moment, assuming the measured fuselage pitch angle... The longitudinal slope angle of the ground ahead is 5 degrees. If the degree is 2, then the geometric fit term =0.9986. If the soil cutting resistance coefficient calculated at this point... The coefficient of force balance of the scraper is 0.7 (indicating relatively hard soil). If the confidence level is 0.85, then the geometry-mechanics confidence level is... =0.9526. This calculation result It will be sent to the subsequent decision-making module to guide the calculation of the target scraper's entry angle into the soil.

[0052] Through the above technical solution, this application can dynamically evaluate the degree of matching between the current leveling operation conditions and the actual posture. This method comprehensively considers the geometric posture of the leveler, the force balance of the scraper, and the cutting characteristics of the soil, and adaptively adjusts the weights of various factors according to soil conditions, thereby overcoming the limitations of traditional methods where geometric and mechanical parameters cannot be analyzed collaboratively and their weights are fixed. This precise and adaptive evaluation mechanism enables the leveler to more accurately judge the complexity and challenge of the current working environment, effectively avoiding working condition mismatch caused by deterioration of geometric or mechanical parameters. It provides a reliable basis for achieving more refined and intelligent scraper entry angle adjustment, thus significantly improving the quality and efficiency of landscaping planting leveling operations.

[0053] This application further proposes a method for calculating the target scraper's entry angle (angle of attack): Obtain the back roughness of the flat surface, soil cutting resistance coefficient, soil removal health coefficient, and geometric-mechanical confidence level; The current backward roughness of the smoothed surface is compared to the rated maximum permissible roughness. A minus function is used to truncate the ratio to a maximum of 1, and the complement of this ratio (1 minus the truncated ratio) is taken as the surface quality factor. This step aims to quantify the quality of the completed smoothed surface, transforming it into a standardized indicator for subsequent calculations. The current backward roughness of the smoothed surface can be acquired in real-time or near real-time using various methods such as laser scanners, ultrasonic sensors, or vision systems. Ratioting it to the rated maximum permissible roughness directly reflects the relative relationship between the current roughness and the desired roughness. The minus function truncation of the ratio to a maximum of 1 ensures that the ratio will not exceed 1 when the actual roughness is lower than the rated maximum permissible roughness, thus avoiding an overly optimistic assessment of the surface quality factor. The complement of the ratio (1 minus the ratio) is taken as the surface quality factor. , making A higher value indicates a smoother surface, and vice versa, providing a clear quantitative basis for subsequent adjustment needs. The rated maximum permissible roughness refers to the upper limit of roughness acceptable for the final leveled surface in specific landscaping or farmland leveling operations. This parameter is typically determined by operational requirements, crop type, or subsequent mechanical operation needs, serving as a benchmark for measuring leveling quality. It can be obtained through industry standards, expert experience, or statistical analysis of historical operational data.

[0054] Using the geometric-mechanical confidence level as the adjustment intensity coefficient, and combining the degree of soil deterioration reflected by the soil deterioration health coefficient and the degree of smoothness deterioration reflected by the surface quality factor, an effective adjustment demand degree that monotonically increases with the accumulation of deterioration degree is generated; the formula for calculating the effective adjustment demand degree is: in, To effectively regulate demand, , The larger the size, the greater the need for regulation. For geometric-mechanical confidence, For the health coefficient of soil removal, For surface quality factors; effectively adjust the degree of demand. It is a comprehensive indicator used to assess the urgency of adjusting the scraper's entry angle under current working conditions. The formula cleverly combines three key dimensions: geometric-mechanical confidence level. This reflects the degree of matching between the fuselage attitude, terrain slope, and the force balance of the scraper; the soil removal health coefficient. This reflects the scraper's soil removal ability, i.e., the adhesion to the back of the scraper; surface quality factor. This reflects the quality of the completed leveled surface. Among them, This indicates the degree of inadequacy in soil removal capacity. This indicates the degree of surface roughness. By using the geometric-mechanical confidence level M as a multiplicative factor and summing the effects of insufficient soil removal capacity and surface roughness, the demand level can be effectively adjusted when either factor deteriorates. These will all increase accordingly, thus accurately reflecting the necessity of regulation.

[0055] Using the soil cutting resistance coefficient as a decision-making parameter, the target scraper entry angle is determined between the scraper's design limit angle of attack and the angle of attack modulated by the effective adjustment demand. Specifically, the larger the soil cutting resistance coefficient, the closer the target scraper entry angle is to the scraper's design limit angle of attack; conversely, the smaller the soil cutting resistance coefficient, the more the target scraper entry angle is determined by the effective adjustment demand. The specific calculation formula is as follows: in, The angle of attack (angle of attack) of the target scraper into the soil. The soil cutting resistance coefficient, The scraper is designed with an extreme angle of attack. To effectively regulate soil demand, this formula aims to dynamically calculate the optimal scraper entry angle based on current soil conditions and regulation requirements. Soil cutting resistance coefficient. This reflects the ease or difficulty of soil cutting; a higher value indicates that the soil is more difficult to cut. When When it approaches 1, the first term of the formula Take the lead, target angle of attack Approaching the limit of the scraper design's angle of attack. To cope with extreme soil conditions, such as hard or sticky soil. When When it approaches 0, the second term of the formula Dominant, target scraper entry angle This will be mainly due to effective regulation of demand. This allows for precise adjustments based on actual needs when the soil is easily cuttable. This weighted average calculation method enables the target angle of attack to adaptively balance the difficulty of soil cutting with the requirements of operational quality. The scraper is designed with an extreme angle of attack. This refers to the maximum allowable soil penetration angle of the leveling scraper blade in its design. This angle is a physical limit resulting from a comprehensive consideration of factors such as equipment structure, power performance, and operational stability. It can be obtained through technical parameters and design manuals provided by the equipment manufacturer, or through actual testing and simulation.

[0056] The solution presented in this application achieves precise adaptive adjustment of the scraper's entry angle into the soil through a series of logically rigorous calculation steps. First, the system obtains the rated maximum permissible roughness and the scraper's design limit angle of attack. These serve as quality standards for leveling operations and physical boundaries for equipment operation, providing a fundamental reference for subsequent angle-of-attack calculations. Next, by real-time monitoring of the current backward roughness of the leveled surface and standardizing it against the rated maximum allowable roughness, the surface quality factor is obtained. This factor quantifies the flatness of the completed surface; a lower value indicates poorer flatness, thus directly reflecting the necessity of adjusting the angle of attack. Based on this, the system uses the surface quality factor... Geometry-mechanical confidence from other modules and soil removal health coefficient Integrate the data. Geometric-mechanical confidence level. This comprehensively reflects the degree of matching between the aircraft's attitude, terrain slope, and the force balance of the scraper, while the soil removal health coefficient... The soil removal efficiency of the scraper was then evaluated. These three key parameters were substituted into the formula. Calculate the effective adjustment of demand. The design of this formula makes It can comprehensively and dynamically reflect the urgency of angle-of-attack adjustment under current working conditions due to factors such as geometric attitude mismatch, reduced soil removal capacity, or poor leveling quality. Geometric-mechanical confidence level. As a multiplicative factor, this ensures that the feedback from soil removal capacity and surface quality can effectively drive the adjustment demand only when the working condition fit is high, avoiding ineffective or harmful adjustments under extremely unsuitable working conditions. Ultimately, the system will use the soil cutting resistance coefficient... (This reflects the ease or difficulty of soil cutting) and the calculated effective regulation requirement. Substitute into the target angle of attack calculation formula This formula cleverly balances soil properties with regulation requirements. When the soil cutting resistance coefficient... At a higher angle, the target angle of attack It will be more inclined to use a scraper design for extreme angle of attack. This is to provide sufficient cutting force. Conversely, when the soil is easily cut, the target angle of attack... The calculation will be more influenced by the effective regulation of demand. The dynamic weighted mechanism adjusts the scraper's entry angle based on the actual leveling quality and the scraper's working condition, making it a multi-dimensional and adaptive optimization process rather than a simple response to a single factor. This ensures that the leveler operates at the optimal angle of attack in complex and ever-changing landscaping environments, effectively solving the problem of decreased work quality caused by inaccurate adjustments.

[0057] The following is a concrete example to illustrate this. Suppose that in a certain landscaping leveling operation, the leveling tool is set to a maximum permissible roughness of 5 mm, and the scraper is designed with a maximum angle of attack. The angle is 30 degrees. At a certain moment, the system detects that the backward roughness of the current flat surface is 8 mm. At this point, the surface quality factor is first calculated. The ratio of the current back roughness of the flat surface (8 mm) to the rated maximum allowable roughness (5 mm) is calculated to obtain 1.6. Since this ratio is greater than 1, it remains 1 after truncation using the min function. Then, its complement is taken: 1 - min(8 / 5, 1) = 1 - 1 = 0. Therefore, the surface quality factor... A value of 0 indicates that the surface roughness has exceeded the allowable range, resulting in poor flatness quality. Simultaneously, the system obtains the current geometric-mechanical confidence level. A value of 0.8 (indicating good adaptability to working conditions), indicating a soil removal health coefficient. A value of 0.6 (indicating moderate soil removal ability with some adhesion). Substituting these values ​​into the effective adjustment demand level... The calculation formula is as follows: At this point, effectively adjusting the degree of demand is crucial. The value of 1.12 indicates a pressing need for adjustment. Furthermore, the system obtains the current soil cutting resistance coefficient. The value is 0.7 (indicating that the soil is relatively firm and difficult to cut). The soil cutting resistance coefficient is... Scraper design for extreme angle of attack and effectively regulate demand Substitute the target scraper entry angle into the soil The calculation formula is as follows: Therefore, the calculated target scraper entry angle is... The angle of attack is 31.08 degrees. The leveler will adjust the current angle of entry of the scraper into the soil based on this target angle of attack to cope with the current relatively firm soil conditions, general soil removal ability and poor leveling quality, thereby optimizing the operation results.

[0058] Through the above technical solution, this application can effectively solve the problem of decreased work quality caused by inaccurate adjustment in traditional leveling methods. This solution introduces a surface quality factor. By incorporating the surface roughness of the completed surface as feedback information into the angle of attack decision, real-time assessment and response to work quality are achieved. Simultaneously, geometric-mechanical confidence levels are... Health coefficient of soil removal and surface quality factor Taking all factors into consideration, the effective adjustment demand level is calculated. This avoids misjudgments caused by a single parameter, making regulation decisions more comprehensive and accurate. Based on this, the soil cutting resistance coefficient is also considered. Dynamically adjust the angle of the target scraper into the soil. This allows the angle of attack to adaptively balance the difficulty of soil cutting with the requirements of work quality. This multi-dimensional, adaptive angle of attack calculation and adjustment mechanism ensures that the leveler can always operate at the optimal angle of attack in complex and ever-changing garden planting environments, significantly improving the accuracy and efficiency of leveling operations and effectively improving the final leveling quality.

[0059] In some of the solutions described above in this application, steps for implementing the above-mentioned leveling method for garden planting are proposed to provide an automated leveling method. However, in its implementation process, there is a lack of a dedicated hardware device to execute the method in real time, which leads to reliance on external equipment or manual operation, making it impossible to achieve efficient and adaptive leveling operations. The quality of the operation is easily affected by human factors and the adjustment response is lagging.

[0060] In response, this application proposes a leveling device for garden planting, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned leveling method for garden planting.

[0061] Memory is a hardware device used to store data and instructions. Its role is to ensure that data can be persistently accessed and to support continuous data processing needs. Memory can be specifically random access memory (RAM) for temporary storage of runtime data, or read-only memory (ROM), flash memory, or hard disk drive (HDD / SSD) for storing the operating system, applications, and persistent data. For example, in embedded systems, EEPROM or NAND flash is often used to store configuration parameters and program code. The processor is the central processing unit that executes instructions and performs arithmetic and logical operations. Its role is to provide efficient computing power to quickly respond to changes in operating conditions and execute computational tasks in computer programs. The processor can be specifically a microcontroller (MCU), such as the ARM Cortex-M series, which integrates a CPU, memory, and peripheral interfaces, suitable for real-time control; or a microprocessor (MPU), such as the ARM Cortex-A series or x86 architecture processors, providing more powerful computing capabilities and more complex operating system support. A computer program, stored in memory and run on a processor, is a collection of instructions designed to guide the computer in performing specific tasks. Its role is to encode the complete logic of a leveling method, enabling the processor to automatically execute all steps from data acquisition to angle adjustment. Specifically, the computer program can be embedded firmware written in C / C++, running directly on a microcontroller to acquire sensor data, perform algorithm calculations, and control actuators; or it can be an application program written in high-level languages ​​such as Python and Java, running on a processor with an operating system, and achieving hardware interaction and algorithm execution by calling underlying drivers. The steps of the processor executing the computer program to implement a leveling method for landscaping refer to the processor processing, calculating, and making decisions based on the pre-stored instruction sequence, ultimately issuing control commands to adjust the operating parameters of the leveler. The purpose of this process is to transform the complex leveling operation from manual experience-based judgment into automated and intelligent control. Possible implementation methods include: the processor scheduling tasks through a real-time operating system (RTOS), periodically reading sensor data, executing the leveling algorithm, and updating the actuator's control quantity based on the calculation results; or, the processor running in a bare-metal environment, responding to sensor events through interrupt service routines, and sequentially executing the various calculation and control steps of the leveling method.

[0062] In one specific implementation, the leveling device described above can be integrated into an embedded control unit. Specifically, the memory can be a non-volatile flash memory (NAND Flash) for storing the operating system, leveling algorithm program, and historical operation data. Simultaneously, a dynamic random access memory (DRAM) is provided as the processor's working memory for temporarily storing real-time sensor data and intermediate calculation results. The processor can be a high-performance industrial-grade microcontroller, such as an STM32 series microcontroller based on the ARM Cortex-M4 or Cortex-A7 architecture, which possesses sufficient processing power and rich interface resources to process multi-channel sensor data in real time and perform complex floating-point operations. The computer program stored in the memory and executable on the processor can be written in C or C++ and compiled into firmware that can run directly on the microcontroller. This firmware contains the complete logic of the leveling method described above, including a sensor data acquisition driver, a data preprocessing module, a soil cutting resistance coefficient calculation module, a scraper force balance coefficient calculation module, a geometric-mechanical confidence calculation module, a soil removal health coefficient calculation module, a target scraper entry angle calculation module, and a scraper angle adjustment control module. When the processor executes the computer program, it acquires analog sensor signals through its integrated ADC (analog-to-digital converter) interface, obtains digital sensor data through digital interfaces such as SPI / I2C / CAN, performs calculations according to the program logic, and outputs control signals through PWM (pulse width modulation) or DAC (digital-to-analog converter) interfaces to drive the connected hydraulic valves or servo motors, thereby precisely adjusting the soil entry angle of the scraper.

[0063] Through the above technical solution, this application provides a dedicated leveling device that can execute the leveling method for landscaping planting in real time and automatically. This leveling device, by integrating a memory, processor, and computer program, transforms the complex leveling decision-making process from manual experience-based judgment to intelligent algorithm control based on multi-source sensor data. This effectively solves the problems of low efficiency, unstable work quality, and delayed adjustment response caused by reliance on external equipment or manual operation in traditional leveling operations. Specifically, this leveling device can accurately calculate and adaptively adjust the scraper's entry angle into the soil based on real-time changes in soil conditions, scraper stress state, machine posture, and quality feedback of the leveled surface. This avoids problems such as difficulty in entry into the soil, scraper floating, or severe adhesion under dry or sticky soil conditions, significantly improving the accuracy and efficiency of leveling operations and ensuring the leveling quality of the landscaping planting surface.

[0064] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A leveling method for garden planting, characterized in that, Includes the following steps: Soil penetration resistance and soil volumetric water content were collected, and the soil cutting resistance coefficient, which characterizes the ease of soil cutting, was calculated. Measure the traction resistance and vertical load on the scraper, and calculate the scraper force balance coefficient, which characterizes the force balance state of the scraper. Monitor the fuselage pitch angle and the longitudinal slope angle of the ground in front, and calculate the geometric-mechanical confidence level, which characterizes the degree of matching between the current working condition and the actual attitude, based on the scraper force balance coefficient and the soil cutting resistance coefficient. Obtain the adhesion contact area measured by the pressure sensor on the back of the scraper, and calculate the soil removal health coefficient, which characterizes the soil removal ability of the scraper. The back roughness of the flat surface is received, and combined with the soil cutting resistance coefficient, soil removal health coefficient and geometric-mechanical confidence level, the target scraper entry angle is calculated and the current entry angle of the scraper is adjusted.

2. The leveling method for garden planting according to claim 1, characterized in that, The soil cutting resistance coefficient is calculated as follows: Obtain soil penetration resistance and soil volumetric water content; The soil penetration resistance and soil volumetric moisture content are normalized to obtain the soil penetration resistance index and the moisture content tillage index. The soil penetration resistance index increases monotonically as the soil penetration resistance increases. The moisture content suitability index takes the minimum value within the suitable moisture content range and increases monotonically when deviating from the suitable moisture content range. The dry deviation and wet deviation grow in the same direction. The larger value between the soil penetration resistance index and the moisture content suitability index is taken as the soil cutting resistance coefficient.

3. The leveling method for garden planting according to claim 2, characterized in that, The soil penetration resistance and soil volumetric moisture content were normalized as follows: The soil penetration resistance index is obtained by comparing the current soil penetration resistance with the maximum penetration resistance that the grader design can handle. The current soil volumetric moisture content is compared with the lower limit of arable moisture content, the upper limit of arable moisture content, and the soil saturation moisture content. The soil volumetric moisture content is normalized according to the deviation of the soil volumetric moisture content from the nearest interval boundary to obtain the moisture content arable index. The soil moisture content suitability index takes the global minimum value within the range between the lower limit and the upper limit of soil moisture content. When it deviates from this range, it increases monotonically, and the growth direction of dry deviation and wet deviation is consistent.

4. The leveling method for garden planting according to claim 1, characterized in that, The scraper force balance coefficient is calculated as follows: Obtain the traction resistance and scraper vertical load; The current traction resistance and scraper vertical load are normalized to obtain the traction resistance imbalance index and the vertical load imbalance index. The root mean square of the sum of the squares of the traction resistance imbalance index and the vertical load imbalance index is used as the comprehensive imbalance metric. The scraper force balance coefficient is obtained by processing the complement of the comprehensive imbalance metric.

5. The leveling method for garden planting according to claim 4, characterized in that, The traction resistance imbalance index takes its minimum value when the traction resistance falls within the ideal range of traction resistance, and increases monotonically when it deviates from this range. The vertical load imbalance index takes its minimum value when the vertical load of the scraper falls within the ideal range of vertical load, and increases monotonically when it deviates from this range.

6. The leveling method for garden planting according to claim 1, characterized in that, The calculation method for the soil removal health coefficient is as follows: Obtain the adhesion contact area measured by the pressure sensor on the back of the scraper; The soil removal health coefficient is calculated based on the proportion of the adhesive contact area to the total monitored area. The soil removal health coefficient decreases monotonically as the proportion of the adhesive contact area increases.

7. The leveling method for garden planting according to claim 1, characterized in that, The calculation method for the geometric-mechanical confidence level is as follows: Obtain the fuselage pitch angle, the longitudinal slope angle of the ground in front, the scraper force balance coefficient, and the soil cutting resistance coefficient; Substitute the scraper force balance coefficient, soil cutting resistance coefficient, fuselage pitch angle, and longitudinal slope angle of the ground in front into the formula. Obtaining geometric-mechanical confidence scores ,in, For the fuselage pitch angle, This represents the longitudinal slope of the ground ahead. The soil cutting resistance coefficient, This is the scraper force balance coefficient.

8. The leveling method for garden planting according to claim 1, characterized in that, The calculation method for the target scraper's soil penetration angle is as follows: Obtain the back roughness of the flat surface, soil cutting resistance coefficient, soil removal health coefficient, and geometric-mechanical confidence level; The ratio of the current back roughness of the flat surface to the rated maximum allowable roughness is processed, and the upper limit of the ratio is truncated to 1. The complement of the ratio is taken as the surface quality factor. Using geometric-mechanical confidence as the adjustment strength coefficient, and combining the degree of soil deterioration reflected by the soil deterioration health coefficient and the degree of flatness deterioration reflected by the surface quality factor, an effective adjustment demand degree that monotonically increases with the accumulation of deterioration degree is generated. Using the soil cutting resistance coefficient as a decision-making trade-off parameter, the target scraper entry angle is determined between the scraper design limit angle of attack and the angle of attack modulated by the effective adjustment requirement. Among them, the larger the soil cutting resistance coefficient, the closer the target scraper entry angle is to the scraper's design limit angle of attack; the smaller the soil cutting resistance coefficient, the closer the target scraper entry angle is to being determined by the effective adjustment demand.

9. A leveling tool for garden planting, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the leveling method for garden planting as described in any one of claims 1 to 8.

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