Method for operating an intelligent mower and intelligent mower

Abstract

The application relates to a running control method of an intelligent mower and the intelligent mower, relates to the technical field of mowers, and is applied to the intelligent mower. The intelligent mower comprises a rack, a cutter head, a driving device, moving wheels and an adjusting device. The method comprises the following steps: collecting a cutter head height value, a vegetation detection image of the front end of the mower and a single-wheel height value of the moving wheels of the mower; generating a cutting height value according to the vegetation detection image; calculating the difference between the cutting height value and the cutter head height value and taking the difference as a height adjustment value; generating an angle adjustment value in combination with the single-wheel height value, the cutting height value and the cutter head height value; determining cutter head adjustment information according to the height adjustment value and the angle adjustment value, and outputting the cutter head adjustment information to the adjusting device to adjust the height and angle of the cutter head. The application has the effect of conveniently and quickly mowing the mowing area of complex terrain.

Description

Technical Field

[0001] This invention relates to the field of lawnmower technology, and in particular to an operation control method for an intelligent lawnmower and an intelligent lawnmower. Background Technology

[0002] A lawnmower, also known as a lawn mower, lawn trimmer, or lawn trimmer, is a mechanical tool specifically designed for trimming lawns, vegetation, and low shrubs.

[0003] A lawnmower typically includes a frame, a blade mounted on the frame for cutting, a drive unit mounted on the frame for rotating the blade, and casters mounted on the frame for moving the frame. Currently, when using a lawnmower, the operator usually pre-sets the blade height, the drive unit rotates the blade at high speed, and the operator manually pushes the machine along a planned route to mow the area.

[0004] Because the height of the blades on current lawnmowers is usually preset by the operator, the machine needs to be stopped and the height manually readjusted when the lawn is uneven, making it inconvenient to quickly mow grass in complex terrain. Summary of the Invention

[0005] To facilitate rapid mowing in complex terrain areas, this invention provides an operation control method for an intelligent lawnmower and an intelligent lawnmower.

[0006] In a first aspect, the present invention provides an operation control method for an intelligent lawnmower, employing the following technical solution: A method for controlling the operation of an intelligent lawnmower, applied to an intelligent lawnmower, the intelligent lawnmower comprising: a frame, a blade disc, a drive unit, wheels, and an adjustment device; The method includes: S1: Collect the blade height value, the vegetation detection image at the front of the lawnmower, and the single wheel height value of the moving wheel; S2: Generate the cutting height value based on vegetation detection images; S3: Calculate the difference between the cutting height value and the cutter head height value and use it as the height adjustment value; S4: Generates an angle adjustment value by combining the single wheel height value, cutting height value, and cutter head height value; S5: Determine the cutter head adjustment information based on the height adjustment value and the angle adjustment value, and output the cutter head adjustment information to the adjustment device to adjust the height and angle of the cutter head.

[0007] By adopting the above technical solution, the height adjustment value is calculated by collecting the blade height value, vegetation detection image and single wheel height value, and combined with the generated cutting height value. The angle adjustment value is also generated by combining the single wheel height and other factors. Finally, the blade adjustment information is determined and output to the adjustment device to adjust the height and angle of the blade. This enables dynamic and coordinated adjustment of the blade height and angle, allowing the blade to adapt to the vegetation conditions and terrain at the front end, improving cutting accuracy and adaptability, and facilitating rapid mowing in complex terrain areas.

[0008] Optionally, methods for generating the cut height value include: S21: Identify vegetation type information, vegetation diameter value, and vegetation density value based on vegetation detection images; S22: Retrieve the baseline height value and diameter range of the species based on vegetation species information; S23: Determine the vegetation age value based on the overlap between the vegetation diameter value and the species age diameter range; S24: Generate vegetation condition adjustment values ​​by combining vegetation age values ​​and vegetation density values; S25: Adjust the species baseline height value according to the vegetation condition adjustment value to obtain the vegetation age height value, and use the vegetation age height value as the cutting height value.

[0009] By adopting the above technical solution, vegetation type, diameter, and density are identified through vegetation detection images. The reference height of the type is retrieved, and the age value is determined by combining the diameter of the vegetation with the age range. Then, the age value and density value are used to generate an adjustment value to obtain the cutting height value. This makes the cutting height value match the vegetation type, growth age, and density, which is more in line with the needs of vegetation growth and improves the rationality of cutting.

[0010] Optionally, methods for generating vegetation condition adjustment values ​​include: S241: Determine the annual growth value and annual baseline density value based on the vegetation age value; S242: Calculate the difference between the vegetation density value and the annual baseline density value and use it as the density anomaly value; S243: Determine density growth adjustment values ​​based on density anomalies; S244: Calculate the sum between the annual growth value and the density growth adjustment value and use it as the growth estimate; S245: Determine the growth height adjustment value based on the growth forecast, and use the growth height adjustment value as the vegetation condition adjustment value.

[0011] By adopting the above technical solution, the annual growth value and annual baseline density value are determined by the vegetation age value, the density growth adjustment value is obtained by calculating the density anomaly value, and then the growth estimate is calculated to determine the growth height adjustment value and serve as the vegetation condition adjustment value. This allows the vegetation condition adjustment value to comprehensively reflect the vegetation age growth and density anomaly status, improve the accuracy of adjusting the species baseline height value, and ensure that the cutting height matches the actual growth status of the vegetation.

[0012] Optionally, after using the vegetation age height value as the cutting height value, the following may also be included: S251: Identifying vegetation bending based on vegetation detection images; S252: Retrieve the height and angle values ​​of the bending location based on the bending status of the vegetation; S253: Determine whether the height value at the bend location is greater than the height value based on the vegetation age; S254: If yes, continue to output the cutting height value; S255: If not, calculate the difference between the bending position height value and the vegetation age height value and use it as the bending height deviation value. S256: Generate tilt adjustment value by combining bending height deviation value and bending angle value; S257: Adjust and update the cutting height value based on the tilt adjustment value.

[0013] By adopting the above technical solution, after determining the height value of vegetation age, the cutting height value is updated by calculating the tilt adjustment value based on the bending position height and angle, combined with the bending condition of the vegetation. This can specifically adapt to bending vegetation, avoid cutting too high or too low due to vegetation bending, improve the cutting adaptability to complex vegetation shapes, and ensure the consistency of cutting results.

[0014] Optionally, methods for generating tilt adjustment values ​​include: S2561: The length of the bent portion is calculated based on the bending height deviation and bending angle values. S2562: Determine the bending reference adjustment value based on the length of the bent portion; S2563: Identifying vegetation height values ​​based on vegetation detection images; S2564: The proportion of the curved section is calculated based on the length of the curved section and the height of the vegetation. S2565: The proportion of the normal portion is calculated based on the height value of the bending location and the height value of the vegetation. S2566: Generate a proportional influence value by combining the proportional value of the curved part with the proportional value of the normal part; S2567: Calculate the product between the bending reference adjustment value and the proportional influence value and use it as the tilt adjustment value.

[0015] By adopting the above technical solution, the length of the curved part is calculated by the bending height deviation value and the bending angle value. The ratio of the curved part to the vegetation height and the ratio of the normal part to the vegetation height are combined to generate a proportional influence value, and then the tilt adjustment value is obtained. The tilt adjustment value quantifies the actual proportion and influence of the curved part, thereby improving the accuracy of adjusting the cutting height of the curved vegetation.

[0016] Optional methods for generating the proportional influence value include: S25661: Determine whether the proportion of the bent portion is greater than the proportion of the normal portion; S25662: If yes, then collect the current time point; S25663: Generate a baseline value for species ratio by combining the current time point with vegetation species information; S25664: Calculate the difference between the bending ratio value and the category ratio benchmark value and use it as the benchmark ratio deviation value; S25665: Determine the baseline deviation influence value based on the baseline proportional deviation value, and use the baseline deviation influence value as the proportional influence value; S25666: If not, calculate the difference between the normal part ratio value and the curved part ratio value and use it as the bending ratio deviation value. S25667: Determine the influence value of bending deviation based on the bending ratio deviation value, and use the influence value of bending deviation as the ratio influence value.

[0017] By adopting the above technical solution, the ratio of the curved part to the normal part is used to generate a benchmark value for the type ratio or to calculate the deviation value of the bending ratio based on the current time point and the type of vegetation. This determines the ratio influence value, making the ratio influence value suitable for different bending proportions and growth characteristics of vegetation, and further optimizing the rationality of the tilt adjustment value.

[0018] Optional methods for generating the category ratio baseline values ​​include: S256631: Retrieve the growth period of a plant species based on its species information; S256632: Determine the time period based on the current time point and the growth period of the species; S256633: Determine the current growth status based on the time period of the fall; S256634: Retrieve current weather conditions based on the current time. S256635: Determine the growth baseline value based on the current growth status and current weather conditions, and use the growth baseline value as the species ratio baseline value.

[0019] By adopting the above technical solution, the growth period of the vegetation is retrieved by the vegetation type, and the time period and growth status are determined by combining the current time point. Then, the species ratio benchmark value is determined by combining the current weather conditions. The benchmark value reflects the growth pattern of vegetation under specific time and weather conditions, improves the accuracy of the ratio influence value, and provides a reliable basis for adjusting the cutting height.

[0020] Optionally, methods for generating angle adjustment values ​​include: S41: Calculate the difference between the height values ​​of two single wheels and use it as the height deviation value of the two wheels; S42: Determine the reference range for cutter head height based on the cutter head height value; S43: Determine the reference range for cutting height based on the cutting height value; S44: Determine the dual-wheel comprehensive reference range by combining the cutter head height reference range and the cutting height reference range; S45: Combine the height deviation value of the two wheels with the comprehensive reference range of the two wheels to determine the deviation adjustment value of the two wheels, and use the deviation adjustment value of the two wheels as the angle adjustment value.

[0021] By adopting the above technical solution, the dual-wheel height deviation value is calculated, and the dual-wheel comprehensive reference range is determined by combining the cutter head height reference range and the cutting height reference range. Then, the angle adjustment value is obtained, which adapts the angle adjustment value to the terrain and cutting requirements, ensuring that the cutting angle of the cutter head is stable under different terrains and improving the cutting stability.

[0022] Optional methods for determining the dual-wheel integrated benchmark interval include: S441: Determine whether there is any overlap between the cutter head height reference range and the cutting height reference range; S442: If yes, then the overlapping range is determined by combining the cutter head height reference range and the cutting height reference range, and the overlapping range is used as the dual-wheel comprehensive reference range; S443: If not, then determine the midpoint value of the cutter head height range and the span value of the cutter head height range based on the cutter head height reference range; S444: Determine the cutter head height coefficient based on the range of cutter head height; S445: Calculate the product between the midpoint of the cutter head height range and the cutter head height coefficient, and use it as the end value of the cutter head height. S446: Determine the midpoint and span values ​​of the cutting height range based on the cutting height reference range; S447: Determine the cutting height coefficient based on the span value of the cutting height range; S448: Calculate the product between the midpoint of the cutting height range and the cutting height coefficient, and use it as the cutting height end value; S449: Determine the comprehensive height range based on the cutter head height end value and the cutting height end value, and use the comprehensive height range as the dual-wheel comprehensive reference range.

[0023] By adopting the above technical solution, the dual-wheel comprehensive reference range is determined by either the overlapping range or by calculating the cutter head and cutting height endpoints, to ensure that the comprehensive reference range reasonably covers the cutter head and cutting height requirements, thereby improving the reliability of the dual-wheel deviation adjustment value and adapting to different height parameter scenarios.

[0024] Secondly, the present invention provides an intelligent lawnmower, which adopts the following technical solution: A smart lawnmower includes: The acquisition module is used to acquire cutter head height values, vegetation detection images, single wheel height values, and the current time point; The memory stores a program for implementing an operation control method for an intelligent lawnmower as described in any one of the first aspects; The processor loads and executes programs stored in memory.

[0025] In summary, the present invention has at least one of the following beneficial technical effects: 1. By collecting the cutter head height value, vegetation detection image and single wheel height value, and combining the generated cutting height value to calculate the height adjustment value, and combining the single wheel height and other factors to generate the angle adjustment value, the cutter head adjustment information is finally determined and output to the adjustment device to adjust the height and angle of the cutter head. This enables dynamic and coordinated adjustment of the cutter head height and angle, allowing the cutter head to adapt to the vegetation conditions and terrain at the front end, improving cutting accuracy and adaptability, and facilitating rapid mowing in complex terrain areas. 2. By identifying vegetation type, diameter, and density through vegetation detection images, retrieve the reference height for the type, determine the age value by combining the diameter of the vegetation with the age range, and then generate an adjustment value by combining the age value and density value to obtain the cutting height value. This makes the cutting height value match the vegetation type, growth age, and density, which is more in line with the growth needs of the vegetation and improves the rationality of cutting. 3. By calculating the height deviation of the two wheels, and combining the cutter head height reference range with the cutting height reference range, the comprehensive reference range of the two wheels is determined, and then the angle adjustment value is obtained. This allows the angle adjustment value to adapt to the terrain and cutting requirements, ensuring that the cutting angle of the cutter head is stable under different terrains and improving cutting stability. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the operation and control methods of an intelligent lawnmower. Figure 2 This is a flowchart illustrating the method for generating the cutting height value; Figure 3This is a flowchart illustrating the method for generating angle adjustment values. Detailed Implementation

[0027] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0028] A method for controlling the operation of an intelligent lawnmower is disclosed. The intelligent lawnmower includes a frame, a blade disc, a drive unit, wheels, and an adjustment device. The blade disc is mounted on the frame and used for cutting. The drive unit is mounted on the frame and used to drive the blade disc. Wheels are mounted on both sides of the frame. The adjustment device is installed between the frame and the blade disc and used to adjust the height and angle of the blade disc. By collecting blade disc height values, vegetation detection images, and single-wheel height values, and combining the analysis and identification of vegetation type, diameter, and density to generate cutting height values, a height adjustment value is calculated. Furthermore, by combining the single-wheel height to calculate the height deviation value between the two wheels, an angle adjustment value is generated. Finally, the blade disc adjustment information is determined and output to the adjustment device for adjusting the height and angle of the blade disc. This enables dynamic and coordinated adjustment of the blade disc height and angle, allowing the blade disc to adapt to the vegetation conditions and terrain, conforming to the growth needs of the vegetation, improving cutting accuracy and adaptability, and facilitating rapid mowing in complex terrain areas.

[0029] Reference Figure 1 This invention discloses an operation control method for an intelligent lawnmower, comprising: S1: Collect the blade height value, vegetation detection image at the front of the lawnmower, and the height value of a single wheel of the moving wheel.

[0030] The blade height value refers to the vertical distance between the blade of the lawnmower used for cutting vegetation and the ground, and is a parameter reflecting the current cutting height of the blade. The blade height value is obtained by directly measuring the vertical distance between the blade and the ground using a distance sensor (such as an ultrasonic sensor or an infrared rangefinder) installed near the blade.

[0031] Vegetation detection images refer to images captured by image acquisition devices (such as cameras) mounted on the front of a lawnmower, containing images of the vegetation (such as grass, low shrubs, etc.) to be cut. Vegetation detection images are acquired through image acquisition devices mounted on the front of the lawnmower.

[0032] The single-wheel height value refers to the vertical distance between each moving wheel of the lawnmower (such as the left front wheel, right front wheel, etc.) and the ground. The single-wheel height value is obtained by detecting height sensors that are pre-installed on the axles or edges of each moving wheel.

[0033] S2: Generate the cutting height value based on the vegetation detection image.

[0034] The cutting height value refers to the target height that needs to be retained after the lawnmower cuts the vegetation.

[0035] By identifying and analyzing vegetation detection images, cutting height values ​​are generated for convenient subsequent use.

[0036] To further ensure the rationality of the cutting height value, it is necessary to perform a further separate analysis and calculation on the cutting height value, which will be explained in detail through the steps shown below.

[0037] Reference Figure 2 The method for generating the cutting height value includes the following steps: S21: Identify vegetation type information, vegetation diameter value and vegetation density value based on vegetation detection images.

[0038] Among these, vegetation type information refers to the season and specific category of the vegetation. Vegetation diameter refers to the cross-sectional diameter of a single vegetation stem. Vegetation density refers to the number of vegetation plants per unit area.

[0039] For example, vegetation type information could include cool-season tall fescue, warm-season bermudagrass, etc.

[0040] By comparing vegetation detection images with images of vegetation types in different seasons, vegetation type information is obtained. Contour recognition is performed on the vegetation stem areas in the vegetation detection images, and the maximum lateral pixel distance of the contour is calculated according to a preset pixel scale, which is then converted into an actual diameter value to obtain the vegetation diameter value. In addition, the number of vegetation stems in the image is counted, and the count result is compared with the corresponding reference area of ​​the image to obtain the vegetation density value for subsequent use.

[0041] S22: Retrieve the baseline height value and diameter range of the species based on the vegetation species information.

[0042] Among them, the species baseline height value refers to the basic stubble height preset for the maintenance standard of a specific vegetation species. The species age diameter range refers to the normal fluctuation range of stem diameter for the same vegetation species at different growth years.

[0043] The vegetation type information corresponds to different type baseline height values ​​and type age diameter ranges.

[0044] By inputting vegetation type information into a preset vegetation type database, the baseline height value and diameter range of the type's age can be obtained for easy subsequent use.

[0045] The vegetation type database pre-stores a table showing the information of different vegetation types and their corresponding reference height values ​​and diameter ranges for different age groups. The vegetation type database is obtained after the operator pre-inputs the information.

[0046] S23: Determine the vegetation age value based on the overlap between the vegetation diameter value and the species age diameter range.

[0047] Among them, the vegetation age value refers to the number of years the vegetation has grown from the beginning of its growth to the present.

[0048] By analyzing the vegetation diameter value and the species age diameter range, and taking the age corresponding to the vegetation diameter value falling into the species age diameter range as the vegetation age value, it is convenient for subsequent use.

[0049] S24: Generate vegetation condition adjustment values ​​by combining vegetation age values ​​and vegetation density values.

[0050] Among them, the vegetation condition adjustment value refers to the parameter used to correct the species baseline height value after taking into account the growth years and density of the vegetation.

[0051] By combining and analyzing vegetation age values ​​and vegetation density values, vegetation condition adjustment values ​​are generated for convenient subsequent use.

[0052] To further ensure the rationality of the vegetation condition adjustment values, it is necessary to perform further separate analysis and calculation on the vegetation condition adjustment values, which will be explained in detail through the steps shown below.

[0053] The method for generating vegetation condition adjustment values ​​includes the following steps: S241: Determine the annual growth value and annual baseline density value based on the annual vegetation value.

[0054] Among them, the annual growth value refers to the basic parameter determined based on the growth age of the vegetation, reflecting the adjustment of the cutting height by the vegetation's growth vitality at that age. The annual baseline density value refers to the standard density of a specific vegetation species under normal growth conditions at a certain growth age. Different vegetation age values ​​correspond to different annual growth values ​​and annual baseline density values.

[0055] By inputting the vegetation age value into a preset vegetation age database, the annual growth value and annual baseline density value are obtained for convenient subsequent use.

[0056] The vegetation age database has a pre-stored table of different vegetation age values ​​and corresponding annual growth values ​​and annual baseline density values. The vegetation age database is obtained after the operator pre-inputs the data.

[0057] S242: Calculate the difference between the vegetation density value and the annual baseline density value and use it as the density anomaly value.

[0058] Density anomalies refer to the deviation values ​​corresponding to the density of vegetation.

[0059] The difference between the vegetation density value and the annual baseline density value is calculated, and the calculation result is used as the density anomaly value for convenient subsequent use.

[0060] S243: Determine the density growth adjustment value based on the density anomaly value.

[0061] Among them, the density growth adjustment value refers to the adjustment parameter for the cutting height used to correct the deviation of vegetation density from the baseline state.

[0062] The product between the density anomaly value and the preset density anomaly coefficient is calculated, and the calculation result is used as the density growth adjustment value for convenient subsequent use.

[0063] The density anomaly coefficient is a coefficient used to convert density anomaly values ​​into density growth adjustment values. The density anomaly coefficient is obtained after being pre-input by the operator.

[0064] S244: Calculate the sum between the annual growth value and the density growth adjustment value and use it as the growth estimate.

[0065] Among them, the growth forecast refers to the baseline height for estimating vegetation growth under the influence of quantified density.

[0066] The sum of the annual growth value and the density growth adjustment value is calculated, and the calculation result is used as the growth estimate for convenient subsequent use.

[0067] S245: Determine the growth height adjustment value based on the growth forecast, and use the growth height adjustment value as the vegetation condition adjustment value.

[0068] Among them, the growth height adjustment value refers to the adjustment value determined based on the estimated height of the predicted vegetation growth, which is ultimately used to correct the species baseline height value.

[0069] By calculating the product between the estimated growth value and the preset growth adjustment coefficient, and using the calculation result as the growth height adjustment value, and then using the growth height adjustment value as the vegetation condition adjustment value, the accuracy of the obtained vegetation condition adjustment value is improved.

[0070] The growth adjustment factor is a coefficient used to convert the growth estimate into a growth height adjustment value. The growth adjustment factor is obtained after being pre-input by the operator.

[0071] S25: Adjust the species baseline height value according to the vegetation condition adjustment value to obtain the vegetation age height value, and use the vegetation age height value as the cutting height value.

[0072] Among them, the vegetation age height value refers to the height value corresponding to the adjustment of the vegetation cutting height based on the vegetation age.

[0073] The accuracy of the obtained cut height value is improved by calculating the sum between the vegetation condition adjustment value and the species baseline height value, and using the calculation result as the vegetation age height value, and then using the vegetation age height value as the cut height value.

[0074] To further ensure the rationality of using vegetation age height as the cutting height value, it is necessary to conduct a further separate analysis and calculation after using vegetation age height as the cutting height value, which will be explained in detail through the following steps.

[0075] After using the vegetation age height value as the cutting height value, the following steps are also included: S251: Identify vegetation bending based on vegetation detection images.

[0076] Among them, vegetation curvature refers to the morphological characteristics of vegetation stems deviating from the upright growth state. Vegetation curvature includes the height value of the curvature position and the curvature angle value.

[0077] The central axis of the vegetation stem is identified by detecting the vegetation image, the position of the inflection point on the axis is identified, and the straight line equation of the stem on both sides of the inflection point is calculated by using Hough transform to obtain the bending angle. Then, the position of the inflection point and the bending angle are combined to obtain the bending condition of the vegetation, which is convenient for subsequent use.

[0078] S252: Retrieve the height and angle values ​​of the bending location based on the bending status of the vegetation.

[0079] The bending height value refers to the specific location where the vegetation stem bends. The bending angle value refers to the bending angle corresponding to the bending of the vegetation stem.

[0080] The bending location height and bending angle values ​​can be retrieved by analyzing the bending of the vegetation, which will facilitate subsequent use.

[0081] S253: Determine whether the height value at the bend location is greater than the height value based on the vegetation age. If yes, proceed; if no, proceed to S255.

[0082] Specifically, by judging whether the height value of the bending position is greater than the height value of the vegetation age, it can be determined whether additional adjustments to the cutting height value are needed.

[0083] S254: Continue outputting the cutting height value.

[0084] When the height value of the bending position is greater than the height value of the vegetation age, it means that no additional adjustment is needed to the cutting height value. Therefore, the cutting height value continues to be output to improve the accuracy of the obtained cutting height value.

[0085] S255: Calculate the difference between the height value at the bending position and the height value based on the vegetation age, and use it as the bending height deviation value.

[0086] Among them, the bending height deviation value refers to the deviation value corresponding to the bending height deviation of the vegetation stem.

[0087] When the height value at the bending position is not greater than the height value of the vegetation age, it means that the cutting height value needs to be adjusted. Therefore, the difference between the height value at the bending position and the height value of the vegetation age is calculated, and the calculation result is used as the bending height deviation value for subsequent use.

[0088] S256: Generate tilt adjustment value by combining the bending height deviation value and the bending angle value.

[0089] The tilt adjustment value refers to the adjustment value used when adjusting the cutting height based on the tilt of the vegetation stem.

[0090] By combining and analyzing the bending height deviation value and the bending angle value, a tilt adjustment value is generated for convenient subsequent use.

[0091] To further ensure the rationality of the tilt adjustment value, it is necessary to perform a further separate analysis and calculation on the tilt adjustment value, which will be explained in detail through the steps shown below.

[0092] The method for generating tilt adjustment values ​​includes the following steps: S2561: The length of the bent section is calculated based on the bending height deviation and bending angle values.

[0093] The length of the bent portion refers to the actual length of the stem from the starting point of the bend to the position corresponding to the cutting height.

[0094] The angle between the axis corresponding to the bent part of the vegetation and the reference axis when the vegetation is not bent is determined by the bending angle value. Then, the length of the bent part is calculated by combining the bending height deviation value, which is convenient for subsequent use.

[0095] S2562: Determine the bending reference adjustment value based on the length of the bent portion.

[0096] Among them, the bending reference adjustment value refers to the reference adjustment value corresponding to the adjustment based on the length of the bent vegetation stem.

[0097] The product of the length of the bent portion and the preset bending adjustment coefficient is calculated, and the calculation result is used as the bending reference adjustment value for convenient subsequent use.

[0098] The bending section adjustment factor is a factor used to convert the bending section length value into a bending reference adjustment value. The bending section adjustment factor is obtained after being pre-input by the operator.

[0099] S2563: Identify vegetation height values ​​based on vegetation detection images.

[0100] Among them, the vegetation height value refers to the overall height value corresponding to the vegetation in the image.

[0101] By identifying individual vegetation stems in the vegetation detection image, and calculating the overall height of the outline based on the area corresponding to each individual vegetation stem and a preset pixel scale, the vegetation height value is obtained for subsequent use.

[0102] S2564: The proportion of the curved section is calculated based on the length of the curved section and the height of the vegetation.

[0103] The curvature ratio refers to the proportion of the length of the curved portion of the vegetation to the overall height of the vegetation.

[0104] The ratio between the length of the curved section and the height of the vegetation is calculated, and the calculation result is used as the ratio of the curved section for convenient subsequent use.

[0105] S2565: The proportion of the normal portion is calculated based on the height value of the bending position and the height value of the vegetation.

[0106] The normal part ratio refers to the proportion of the unbent part of the vegetation to the overall height of the vegetation.

[0107] The ratio between the height value at the bend and the height value of the vegetation is calculated, and the calculation result is used as the ratio value of the normal part for convenient subsequent use.

[0108] S2566: Generate a proportional influence value by combining the proportional value of the curved part with the proportional value of the normal part.

[0109] Among them, the proportional influence value refers to the influence value corresponding to the adjustment when the bending reference adjustment value is affected by the proportional situation.

[0110] By combining the proportional values ​​of the curved portion with those of the normal portion, a proportional influence value is generated for convenient subsequent use.

[0111] To further ensure the rationality of the proportional influence value, it is necessary to perform a further separate analysis and calculation of the proportional influence value, which will be explained in detail through the steps shown below.

[0112] The method for generating the proportional influence value includes the following steps: S25661: Determine whether the proportion of the bent portion is greater than the proportion of the normal portion. If yes, proceed to S25662; if no, proceed to S25666.

[0113] In this process, the determination of whether an analysis based on time is necessary is made by checking whether the proportion of the bent portion is greater than that of the normal portion.

[0114] S25662: Collect the current time point.

[0115] The current time point refers to the current point in time.

[0116] When the proportion of the bent portion is greater than that of the normal portion, it indicates that analysis based on the time context is required. Therefore, data is collected at the current time point for future use. S25663: Generate a baseline value for the proportion of different vegetation species by combining the current time point with information on vegetation species.

[0117] Among them, the species ratio benchmark value refers to the maximum ratio of the allowable bending part based on time and vegetation species.

[0118] By combining and analyzing the current time point with vegetation type information, a baseline value for the type ratio is generated for convenient subsequent use.

[0119] To further ensure the rationality of the species ratio benchmark value, it is necessary to conduct a further separate analysis and calculation of the species ratio benchmark value, which will be explained in detail through the following steps.

[0120] The method for generating the benchmark values ​​for species proportions includes the following steps: S256631: Retrieve the growth period of a plant species based on its species information.

[0121] Among them, the species growth period refers to the time intervals that are divided for a specific vegetation species to reflect different growth states (germination period, vigorous growth period, dormancy period, etc.).

[0122] By inputting vegetation type information into a preset vegetation type database, the growth period of each type can be matched for convenient subsequent use.

[0123] The vegetation type database also pre-stores a table showing the information of different vegetation types and their corresponding growth periods.

[0124] S256632: Determine the time period for falling into the species based on the current time point and the falling situation during the growth period.

[0125] The "falling time period" refers to the growth period of the species that fell into the field at the current time.

[0126] By comparing the current time point with the species growth period in sequence, the species growth period in which the current time point falls is taken as the falling time period, which is convenient for subsequent use.

[0127] S256633: Determine the current growth status based on the time period of the fall.

[0128] The current growth status refers to the growth status of the vegetation species at the time of the fall period.

[0129] By querying the growth status corresponding to the time period in question and using it as the current growth status, it is convenient for subsequent use.

[0130] S256634: Retrieve the current weather status based on the current time.

[0131] The current weather status refers to the weather type and status corresponding to the current time.

[0132] Check real-time weather websites to get the current weather status for future reference.

[0133] S256635: Determine the growth baseline value based on the current growth status and current weather conditions, and use the growth baseline value as the species ratio baseline value.

[0134] Among them, the growth benchmark value refers to the vegetation growth suitability parameter quantified by combining the vegetation growth status at the current time and the weather conditions.

[0135] A basic score is obtained by inputting the current growth status into a preset growth status scoring database for matching. Then, the current growth status is used to query a vegetation type database to match the suitable weather conditions corresponding to the species under different growth states. The suitable weather conditions are then compared with the current weather conditions for compatibility scoring. Finally, the basic score and compatibility score are weighted to obtain a growth baseline value, which is used as the species ratio baseline value, thereby improving the accuracy of the obtained species ratio baseline value.

[0136] The growth status score database pre-stores a table showing the correspondence between different current growth statuses and their corresponding baseline scores, while the vegetation type database pre-stores different current growth statuses and their corresponding suitable weather conditions. Both the growth status score database and the vegetation type database are obtained through pre-input by the operator. The coefficients used in the weighted calculation of the baseline score and the suitability score are pre-set by the operator according to actual needs.

[0137] S25664: Calculate the difference between the bending ratio value and the category ratio benchmark value and use it as the benchmark ratio deviation value.

[0138] The reference ratio deviation value refers to the difference between the ratio value of the curved part and the reference value of the type ratio.

[0139] Calculating the baseline ratio deviation value facilitates subsequent use.

[0140] S25665: Determine the influence value of the benchmark deviation based on the benchmark proportional deviation value, and use the influence value of the benchmark deviation as the proportional influence value.

[0141] Among them, the reference deviation influence value refers to the parameter used to correct the bending reference adjustment value based on the reference proportional deviation value.

[0142] By calculating the product between the benchmark proportional deviation value and the preset benchmark proportional coefficient, and using the calculation result as the benchmark deviation influence value, and then using the benchmark deviation influence value as the proportional influence value, the accuracy of the obtained proportional influence value is improved.

[0143] The benchmark proportionality coefficient is a coefficient used to convert the benchmark proportional deviation value into the benchmark deviation influence value. The benchmark proportionality coefficient is obtained after being pre-input by the operator.

[0144] S25666: Calculate the difference between the normal portion ratio and the curved portion ratio and use it as the curvature ratio deviation value.

[0145] Among them, the bending ratio deviation value refers to the difference between the ratio value of the normal part and the ratio value of the bent part.

[0146] When the proportion of the bent portion is not greater than the proportion of the normal portion, it means that there is no need to analyze based on the time situation. Therefore, the deviation value of the bending proportion is calculated to facilitate subsequent use.

[0147] S25667: Determine the influence value of bending deviation based on the bending ratio deviation value, and use the influence value of bending deviation as the ratio influence value.

[0148] Among them, the bending deviation influence value refers to the parameter used to correct the bending reference adjustment value based on the bending ratio deviation value.

[0149] By calculating the product of the bending ratio deviation value and the preset bending ratio coefficient, and using the calculation result as the bending deviation influence value, and then using the bending deviation influence value as the ratio influence value, the accuracy of the obtained ratio influence value is improved.

[0150] S2567: Calculate the product between the bending reference adjustment value and the proportional influence value and use it as the tilt adjustment value.

[0151] The tilt adjustment value refers to the adjustment value corresponding to the cutting height value when adjusting it according to the tilt situation.

[0152] The product of the bending reference adjustment value and the proportional influence value is calculated, and the calculation result is used as the tilt adjustment value for convenient subsequent use.

[0153] S257: Adjust and update the cutting height value based on the tilt adjustment value.

[0154] Specifically, by calculating the sum of the tilt adjustment value and the cutting height value, and then replacing and updating the cutting height value with the calculation result, the accuracy of the obtained cutting height value is improved.

[0155] S3: Calculate the difference between the cutting height value and the cutter head height value and use it as the height adjustment value.

[0156] The height adjustment value refers to the adjustment value corresponding to the adjustment of the running height of the cutter head.

[0157] The difference between the cutting height and the cutter head height is calculated, and the result is used as the height adjustment value for convenient subsequent use.

[0158] S4: Generates an angle adjustment value by combining the single wheel height value, cutting height value, and cutter head height value.

[0159] Among them, the angle adjustment value refers to the adjustment value corresponding to the adjustment of the running angle of the cutter head.

[0160] By combining and analyzing the single wheel height value, cutting height value, and cutter head height value, an angle adjustment value is generated for convenient subsequent use.

[0161] To further ensure the rationality of the angle adjustment value, it is necessary to perform a further separate analysis and calculation on the angle adjustment value, which will be explained in detail through the steps shown below.

[0162] Reference Figure 3 The method for generating angle adjustment values ​​includes the following steps: S41: Calculate the difference between the height values ​​of two single wheels and use it as the height deviation value of the two wheels.

[0163] The dual-wheel height deviation value refers to the difference between the height values ​​of the two single wheels.

[0164] Calculating the height deviation between the two wheels facilitates subsequent use.

[0165] S42: Determine the reference range of cutter head height based on the cutter head height value.

[0166] The cutter head height reference range refers to the normal range that the two single wheels need to be in when adapting to the current cutting requirements based on the cutter head height.

[0167] The cutter head height reference range is obtained by calculating the product between the cutter head height value and the preset range ratio coefficient, and then adjusting the cutter head range reference range according to the preset range fluctuation value, so as to facilitate subsequent use.

[0168] The interval scaling factor is a factor used to convert the height value into the midpoint of the interval, and the interval fluctuation value is the allowable fluctuation of the midpoint of the interval. Both the interval scaling factor and the interval fluctuation value are preset by the operator according to actual needs.

[0169] S43: Determine the reference range for cutting height based on the cutting height value.

[0170] The cutting height reference range refers to the normal range that two single wheels need to be in when adapting their cutting height to the current cutting requirements.

[0171] The cutting height reference range is obtained by calculating the product between the cutting height value and the preset interval ratio coefficient, and then adjusting the cutting height reference range according to the preset interval fluctuation value to obtain the cutting height reference range for subsequent use.

[0172] S44: Determine the dual-wheel comprehensive reference range by combining the cutter head height reference range and the cutting height reference range.

[0173] The dual-wheel integrated benchmark range refers to the normal range that the two single wheels need to be in when adapting to the current cutting requirements after integrating the cutter head height and cutting height.

[0174] By combining the analysis of the cutter head height reference range and the cutting height reference range, the dual-wheel comprehensive reference range is determined, which facilitates subsequent use.

[0175] To further ensure the rationality of the dual-wheel comprehensive benchmark interval, it is necessary to conduct further separate analysis and calculation of the dual-wheel comprehensive benchmark interval, which will be explained in detail through the following steps.

[0176] The method for determining the dual-wheel comprehensive benchmark interval includes the following steps: S441: Determine whether there is any overlap between the cutter head height reference range and the cutting height reference range. If yes, proceed to S442; if no, proceed to S443.

[0177] Specifically, by judging whether there is an overlap between the cutter head height reference range and the cutting height reference range, it can be determined whether the area corresponding to the overlap can be directly used.

[0178] S442: Combine the cutter head height reference range and the cutting height reference range to determine the overlapping range, and use the overlapping range as the dual-wheel comprehensive reference range.

[0179] The overlapping interval refers to the interval where there is an overlap between the cutter head height reference interval and the cutting height reference interval.

[0180] When the cutter head height reference range overlaps with the cutting height reference range, it means that the area corresponding to the overlap can be used directly. Therefore, by determining the area corresponding to the overlapping position of the cutter head height reference range and the cutting height reference range, and taking the overlapping area as the dual-wheel comprehensive reference range, the accuracy of the obtained dual-wheel comprehensive reference range can be improved.

[0181] S443: Determine the midpoint and span values ​​of the cutter head height range based on the cutter head height reference range.

[0182] The median value of the cutter head height range refers to the median value of the cutter head height reference range. The span value of the cutter head height range refers to the span between the two endpoints of the cutter head height reference range.

[0183] The average value of the cutter head height reference range is calculated by averaging the two endpoints of the range, thus obtaining the midpoint of the range. Then, the difference between the two endpoints of the range is calculated to obtain the span of the cutter head height range, which is convenient for subsequent use.

[0184] S444: Determine the cutter head height coefficient based on the range of cutter head height.

[0185] The cutter head height coefficient refers to the adjustment coefficient corresponding to the adjustment of the middle value of the cutter head height range.

[0186] The cutter head height range value is input into a preset cutter head height database to obtain the cutter head height coefficient, which facilitates subsequent use.

[0187] The cutter head height database has a pre-stored table of different cutter head height range values ​​and their corresponding cutter head height coefficients. The cutter head height database is obtained after the operator pre-inputs the values.

[0188] S445: Calculate the product between the midpoint of the cutter head height range and the cutter head height coefficient, and use it as the end value of the cutter head height.

[0189] Among them, the cutter head height end value refers to the interval end value set based on the middle value of the cutter head height range.

[0190] The product between the midpoint of the cutter head height range and the cutter head height coefficient is calculated, and the result is used as the cutter head height endpoint for convenient subsequent use.

[0191] S446: Determine the midpoint and span of the cutting height range based on the cutting height reference range.

[0192] The midpoint of the cutting height range refers to the midpoint of the reference cutting height range. The span of the cutting height range refers to the span between the two endpoints of the reference cutting height range.

[0193] The average value of the two endpoints of the cutting height reference interval is calculated to obtain the midpoint of the cutting height interval. Then, the difference between the two endpoints of the cutting height reference interval is calculated to obtain the span value of the cutting height interval, which is convenient for subsequent use.

[0194] S447: Determine the cutting height coefficient based on the span value of the cutting height range.

[0195] The cutting height coefficient refers to the adjustment coefficient corresponding to the adjustment of the middle value of the cutting height range.

[0196] The cutting height range span value is input into a preset cutting height database to obtain the cutting height coefficient, which facilitates subsequent use.

[0197] The cutting height database has a pre-stored table of different cutting height ranges and their corresponding cutting height coefficients. The cutting height database is obtained after the operator pre-inputs the values.

[0198] S448: Calculate the product between the midpoint of the cutting height range and the cutting height coefficient, and use it as the cutting height endpoint.

[0199] Among them, the cutting height end value refers to the interval end value set based on the middle value of the cutting height interval.

[0200] The product of the midpoint of the cutting height range and the cutting height coefficient is calculated, and the result is used as the cutting height endpoint for convenient subsequent use.

[0201] S449: Determine the comprehensive height range based on the cutter head height end value and the cutting height end value, and use the comprehensive height range as the dual-wheel comprehensive reference range.

[0202] The comprehensive height range refers to the range corresponding to the combination of the cutter head height and the cutting height.

[0203] By using the cutter head height end value and the cutting height end value as the two end values ​​of the interval, a comprehensive height interval is obtained. This comprehensive height interval is then used as the dual-wheel comprehensive reference interval, thereby improving the accuracy of the obtained dual-wheel comprehensive reference interval.

[0204] S45: Combine the height deviation value of the two wheels with the comprehensive reference range of the two wheels to determine the deviation adjustment value of the two wheels, and use the deviation adjustment value of the two wheels as the angle adjustment value.

[0205] Among them, the dual-wheel deviation adjustment value refers to the adjustment value corresponding to the angle adjustment based on the height deviation of the dual wheels.

[0206] By judging whether the height deviation value of the two wheels falls within the comprehensive reference range of the two wheels, when it falls within the range, a preset angle reference adjustment value is output as the two-wheel deviation adjustment value. When it does not fall within the range, the difference between the height deviation value of the two wheels and the comprehensive reference range of the two wheels is calculated as the height abnormal deviation value. Then, the product value between the height abnormal deviation value and the preset abnormal deviation angle coefficient is calculated, and the calculation result is used as the two-wheel deviation adjustment value. Finally, the two-wheel deviation adjustment value is used as the angle adjustment value, thereby improving the accuracy of the obtained angle adjustment value.

[0207] The angle reference adjustment value refers to the adjustment value when no angle adjustment is required. The angle reference adjustment value is obtained after being pre-input by the operator.

[0208] The abnormal deviation angle coefficient is a coefficient used to convert abnormal height deviation values ​​into dual-wheel deviation adjustment values. The abnormal deviation angle coefficient is preset by the operator according to actual needs.

[0209] S5: Determine the cutter head adjustment information based on the height adjustment value and the angle adjustment value, and output the cutter head adjustment information to the adjustment device to adjust the height and angle of the cutter head.

[0210] The cutter head adjustment information refers to the adjustment information corresponding to the height and angle adjustments of the cutter head. The adjustment device refers to the device used to adjust the height and angle of the cutter head, including a height adjustment mechanism for adjusting the height and an angle adjustment mechanism for adjusting the angle. Both the height adjustment mechanism and the angle adjustment mechanism can be stepper motor driven mechanisms or hydraulic cylinder driven mechanisms. In this embodiment, both the height adjustment mechanism and the angle adjustment mechanism are hydraulic cylinder driven mechanisms. The piston rod of the height adjustment mechanism is hinged to the middle position of the cutter head, thus facilitating height adjustment. Several angle adjustment mechanisms are provided, and the piston rods of the angle adjustment mechanisms are hinged at equal angles to the edge positions of the cutter head, thus facilitating angle adjustment.

[0211] By combining the height adjustment value and the angle adjustment value, and using the combined information as the cutter head adjustment information, the cutter head adjustment information is then output to the adjustment device to adjust the height and angle of the cutter head. This enables dynamic and coordinated adjustment of the cutter head height and angle, allowing the cutter head to adapt to the vegetation conditions and terrain at the front end, improving cutting accuracy and adaptability, and facilitating rapid mowing in complex terrain areas.

[0212] Based on the same inventive concept, embodiments of the present invention provide an intelligent lawnmower, comprising: The acquisition module is used to acquire cutter head height values, vegetation detection images, single wheel height values, and the current time point; The memory stores a program for implementing the operation control method of an intelligent lawnmower as described above; The processor loads and executes programs stored in memory.

[0213] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0214] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling the operation of an intelligent lawnmower, characterized in that, Applied to intelligent lawnmowers, the intelligent lawnmower includes: a frame, a blade disc, a drive unit, casters, and an adjustment device; The method includes: S1: Collect the blade height value, the vegetation detection image at the front of the lawnmower, and the single wheel height value of the moving wheel; S2: Generate the cutting height value based on vegetation detection images; S3: Calculate the difference between the cutting height value and the cutter head height value and use it as the height adjustment value; S4: Generates an angle adjustment value by combining the single wheel height value, cutting height value, and cutter head height value; S5: Determine the cutter head adjustment information based on the height adjustment value and the angle adjustment value, and output the cutter head adjustment information to the adjustment device to adjust the height and angle of the cutter head; Methods for generating cutting height values ​​include: S21: Identify vegetation type information, vegetation diameter value, and vegetation density value based on vegetation detection images; S22: Retrieve the baseline height value and diameter range of the species based on vegetation species information; S23: Determine the vegetation age value based on the overlap between the vegetation diameter value and the species age diameter range; S24: Generate vegetation condition adjustment values ​​by combining vegetation age values ​​and vegetation density values; S25: Adjust the species baseline height value according to the vegetation condition adjustment value to obtain the vegetation age height value, and use the vegetation age height value as the cutting height value. After using the vegetation age height value as the cutting height value, the following is also included: S251: Identifying vegetation bending based on vegetation detection images; S252: Retrieve the height and angle values ​​of the bending location based on the bending status of the vegetation; S253: Determine whether the height value at the bend location is greater than the height value based on the vegetation age; S254: If yes, continue to output the cutting height value; S255: If not, calculate the difference between the bending position height value and the vegetation age height value and use it as the bending height deviation value. S256: Generate tilt adjustment value by combining bending height deviation value and bending angle value; S257: Adjust and update the cutting height value based on the tilt adjustment value.

2. The operation control method for an intelligent lawnmower according to claim 1, characterized in that, The methods for generating vegetation condition adjustment values ​​include: S241: Determine the annual growth value and annual baseline density value based on the vegetation age value; S242: Calculate the difference between the vegetation density value and the annual baseline density value and use it as the density anomaly value; S243: Determine density growth adjustment values ​​based on density anomalies; S244: Calculate the sum between the annual growth value and the density growth adjustment value and use it as the growth estimate; S245: Determine the growth height adjustment value based on the growth forecast, and use the growth height adjustment value as the vegetation condition adjustment value.

3. The operation control method for an intelligent lawnmower according to claim 1, characterized in that, Methods for generating tilt adjustment values ​​include: S2561: The length of the bent portion is calculated based on the bending height deviation and bending angle values. S2562: Determine the bending reference adjustment value based on the length of the bent portion; S2563: Identifying vegetation height values ​​based on vegetation detection images; S2564: The proportion of the curved section is calculated based on the length of the curved section and the height of the vegetation. S2565: The proportion of the normal portion is calculated based on the height value of the bending location and the height value of the vegetation. S2566: Generate a proportional influence value by combining the proportional value of the curved part with the proportional value of the normal part; S2567: Calculate the product between the bending reference adjustment value and the proportional influence value and use it as the tilt adjustment value.

4. The operation control method for an intelligent lawnmower according to claim 3, characterized in that, Methods for generating proportional influence values ​​include: S25661: Determine whether the proportion of the bent portion is greater than the proportion of the normal portion; S25662: If yes, then collect the current time point; S25663: Generate a baseline value for species ratio by combining the current time point with vegetation species information; S25664: Calculate the difference between the bending ratio value and the category ratio benchmark value and use it as the benchmark ratio deviation value; S25665: Determine the baseline deviation influence value based on the baseline proportional deviation value, and use the baseline deviation influence value as the proportional influence value; S25666: If not, calculate the difference between the normal part ratio value and the curved part ratio value and use it as the bending ratio deviation value. S25667: Determine the influence value of bending deviation based on the bending ratio deviation value, and use the influence value of bending deviation as the ratio influence value.

5. The operation control method for an intelligent lawnmower according to claim 4, characterized in that, Methods for generating category ratio baseline values ​​include: S256631: Retrieve the growth period of a plant species based on its species information; S256632: Determine the time period based on the current time point and the growth period of the species; S256633: Determine the current growth status based on the time period of the fall; S256634: Retrieve current weather conditions based on the current time. S256635: Determine the growth baseline value based on the current growth status and current weather conditions, and use the growth baseline value as the species ratio baseline value.

6. The operation control method for an intelligent lawnmower according to claim 1, characterized in that, The methods for generating angle adjustment values ​​include: S41: Calculate the difference between the height values ​​of two single wheels and use it as the height deviation value of the two wheels; S42: Determine the reference range for cutter head height based on the cutter head height value; S43: Determine the reference range for cutting height based on the cutting height value; S44: Determine the dual-wheel comprehensive reference range by combining the cutter head height reference range and the cutting height reference range; S45: Combine the height deviation value of the two wheels with the comprehensive reference range of the two wheels to determine the deviation adjustment value of the two wheels, and use the deviation adjustment value of the two wheels as the angle adjustment value.

7. The operation control method for an intelligent lawnmower according to claim 6, characterized in that, The methods for determining the dual-wheel composite benchmark interval include: S441: Determine whether there is any overlap between the cutter head height reference range and the cutting height reference range; S442: If yes, then the overlapping range is determined by combining the cutter head height reference range and the cutting height reference range, and the overlapping range is used as the dual-wheel comprehensive reference range; S443: If not, then determine the midpoint value of the cutter head height range and the span value of the cutter head height range based on the cutter head height reference range; S444: Determine the cutter head height coefficient based on the range of cutter head height; S445: Calculate the product between the midpoint of the cutter head height range and the cutter head height coefficient, and use it as the end value of the cutter head height. S446: Determine the midpoint and span values ​​of the cutting height range based on the cutting height reference range; S447: Determine the cutting height coefficient based on the span value of the cutting height range; S448: Calculate the product between the midpoint of the cutting height range and the cutting height coefficient, and use it as the cutting height end value; S449: Determine the comprehensive height range based on the cutter head height end value and the cutting height end value, and use the comprehensive height range as the dual-wheel comprehensive reference range.

8. A smart lawnmower, characterized in that, include: The acquisition module is used to acquire cutter head height values, vegetation detection images, single wheel height values, and the current time point; The memory stores a program for implementing the operation control method of an intelligent lawnmower as described in any one of claims 1 to 7; The processor loads and executes programs stored in memory.

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