A forest camellia pruning device with height convenient to adjust

Abstract

This invention discloses a height-adjustable camellia oleifera pruning device, belonging to the agricultural field, comprising a handle, a support rod, a pruning assembly, and a pushing assembly. The pruning assembly consists of a fixed first pruning shear and a sliding second pruning shear, with the pushing assembly driving the second pruning shear in linear motion to complete the pruning. The core innovation lies in the cutting speed control system. A data acquisition module acquires the blade status (blade angle, corrosion) and branch parameters (radius, moisture content, angle), while an evaluation module calculates the blade's basic coefficient and pruning condition coefficient, combining them with the angle index to generate a matching degree, and dynamically adjusting the cutting speed accordingly. This device can adapt to different branch conditions, improving pruning efficiency and accuracy, and extending blade life, making it particularly suitable for complex forest environments. This invention solves the problems of inconvenient adjustment and insufficient intelligence in traditional tools, providing reliable technical support for efficient camellia oleifera management.

Description

Technical Field

[0001] This invention belongs to the field of agricultural technology, and in particular relates to a woodland camellia oleifera pruning device that is easy to adjust in height. Background Technology

[0002] In camellia oleifera cultivation and management, regular pruning is a crucial step in improving yield and quality. Traditional camellia oleifera pruning equipment largely relies on manual operation, which suffers from problems such as inconvenient height adjustment and low efficiency. While existing electric pruning tools have partially solved the efficiency problem, they lack adaptability to complex forest environments. For example, they cannot dynamically adjust the cutting speed based on branch diameter, moisture content, and blade condition, leading to easy blade wear and inconsistent pruning results. Furthermore, the lack of an intelligent control system makes operation reliant on experience, making it difficult to guarantee pruning precision. Therefore, there is an urgent need for a new type of pruning equipment that can automatically adjust height and intelligently control cutting parameters to adapt to diverse pruning needs and extend tool life. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a woodland camellia oleifera pruning device with easily adjustable height, thus solving the aforementioned problems.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a height-adjustable camellia oleifera pruning device, comprising a handle and a support rod, wherein a second linear motion component with an output shaft fixedly connected to the support rod is embedded in the handle, and further comprising:

[0005] The trimming assembly includes a first trimmer and a second trimmer. The first trimmer is fixedly mounted on the upper side of the support rod, and the second trimmer is slidably engaged with the support rod.

[0006] A pusher assembly, mounted on a support rod, is used to push the second trimmer shears to move linearly along the length of the support rod so that the first trimmer shears can cooperate with the second trimmer shears to complete the trimming action.

[0007] The cutting speed control system is used to adjust the speed at which the second trimmer blade approaches the first trimmer blade, including:

[0008] The data acquisition module is used to collect basic data on the cutting tools and data on branch pruning.

[0009] The tool basic evaluation module is used to construct a tool basic evaluation model based on tool basic data and output tool basic evaluation coefficients.

[0010] The pruning condition evaluation module is used to construct a pruning condition evaluation model based on branch pruning data and output pruning condition evaluation coefficients.

[0011] The tool-pruning matching analysis module constructs a tool-pruning matching model based on the angle between the current branch and the horizontal direction, the basic evaluation coefficient of the tool, and the evaluation coefficient of the pruning conditions, and outputs the tool-pruning matching degree.

[0012] The cutting speed adjustment module constructs a cutting speed model based on the current tool-trimming matching degree and the standard cutting speed, and outputs the target cutting speed.

[0013] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0014] Further technical solutions: The basic data of the cutting tool includes the cutting edge angle and the rust area on the blade surface. The branch pruning data includes the branch radius, the distance from the pruning point to the root of the cutting tool, and the branch moisture content. The branch radius and the distance from the pruning point to the root of the cutting tool can be obtained by a laser rangefinder installed on the support rod and the second pruning shears. The branch moisture content can be obtained by a near-infrared sensor installed on the first pruning shears. The rust area of ​​the cutting tool can be obtained in advance by an image sensor.

[0015] Further technical solution: The working steps of the tool basic evaluation module are as follows:

[0016] The ratio of the rusted area on the blade surface to the maximum allowable rusted area is used to obtain the blade surface rust index;

[0017] A basic tool evaluation model is constructed based on the cutting edge index and the surface corrosion index of the cutting tool, and the basic tool evaluation coefficients are output.

[0018] The basic evaluation model for the cutting tool is expressed as follows: ;

[0019] in, This represents the basic evaluation coefficient of the cutting tool. Indicates the cutting edge angle. Indicates the ideal cutting edge angle. This indicates the rust index of the blade surface. Indicates the attenuation coefficient. , Represents weight and .

[0020] A further technical solution: The specific formula for calculating the penalty weight of the cutting edge deviation on the basic evaluation coefficient of the tool is as follows: , Indicates the ideal cutting edge angle. The larger the value, the faster the index decays, and the score drops rapidly. The smaller the value, the smoother the decay, allowing for greater deviation of the cutting edge angle.

[0021] Further technical solution: The working steps of the pruning condition evaluation module are as follows:

[0022] The branch radius is calculated by comparing the branch radius with the maximum allowable branch radius to obtain the branch radius index.

[0023] The distance index is obtained by comparing the distance from the cut point to the root of the shears with the maximum allowable distance.

[0024] The moisture content of the branches is compared with the maximum allowable moisture content of the branches to obtain the moisture content index;

[0025] A pruning condition evaluation model was constructed based on the branch radius index, distance index, and water content index. The branch radius index, distance index, and water content index were then imported, and the pruning condition evaluation coefficients were output.

[0026] The pruning condition evaluation model is expressed as follows:

[0027] ;

[0028] in, This represents the evaluation coefficient for pruning conditions. Indicates the branch radius index, Represents the distance index. Indicates the moisture content index. Represents weight and .

[0029] Further technical solution: The working steps of the tool-trimming matching analysis module are as follows:

[0030] The angle index is obtained by comparing the angle between the current branch and the horizontal direction with a right angle.

[0031] Based on the basic evaluation coefficient of the tool and the evaluation coefficient of the pruning condition under the current branch angle index, a tool-pruning matching model is constructed and the tool-pruning matching degree is output.

[0032] The tool-trimming matching model is represented as follows: ;

[0033] in, Indicates the tool-trimming match. This represents the basic evaluation coefficient of the cutting tool. This represents the evaluation coefficient for pruning conditions. This indicates the angle index.

[0034] Further technical solution: The specific steps of the cutting speed adjustment module are as follows:

[0035] A cutting speed model is constructed based on the current tool-trimming matching degree and the standard cutting speed. The current tool-trimming matching degree and the standard cutting speed are then imported to output the target cutting speed.

[0036] The cutting speed model is expressed as follows: ;

[0037] in, Indicates the target cutting speed. Indicates the standard cutting speed. Indicates the velocity attenuation coefficient. This indicates the tool-trimming match.

[0038] Further technical solutions: The Furthermore, a larger value indicates a better tool condition. Furthermore, a larger value indicates a higher difficulty in pruning. The larger the value, the better the match.

[0039] Further technical solution: The pushing component includes a limiting slider, a first linear motion component, and a guide rail groove. The limiting slider slides in cooperation with the guide rail groove formed on the support rod. The guide rail groove is embedded in the support rod and its output shaft is fixedly connected to the limiting slider. The limiting slider is fixedly connected to the second trimmer.

[0040] This invention provides a height-adjustable pruning device for camellia oleifera in forests, which has the following advantages compared with the prior art:

[0041] 1. The present invention offers convenient height adjustment. The extension and retraction of the first linear motion component enables rapid raising and lowering of the support rod, adapting to the pruning needs of branches at different heights. Based on the blade condition (blade angle, corrosion) and branch parameters (radius, moisture content, included angle), a mathematical model is constructed to dynamically adjust the cutting speed, which improves pruning efficiency and reduces blade wear. By comprehensively calculating the matching degree through the included angle index and evaluation coefficient, the shearing stability is optimized, reducing the risk of blade jamming caused by branch angle. Attached Figure Description

[0042] Figure 1 This is a three-dimensional structural diagram of the present invention.

[0043] Figure 2 This is a schematic diagram of the overall structure of the present invention.

[0044] Figure 3 This is a schematic diagram of the cutting speed control system of the present invention.

[0045] Figure label annotations: 1. Handle; 2. Bearing rod; 3. First trimmer; 4. Second trimmer; 5. Push assembly; 501. Limiting slider; 502. First linear motion component; 503. Guide rail groove; 6. Second linear motion component; 7. Power supply; 8. Cutting speed control system; 801. Data acquisition module; 802. Tool basic evaluation module; 803. Trimming condition evaluation module; 804. Tool-trimming matching analysis module; 805. Cutting speed adjustment module. Detailed Implementation

[0046] 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.

[0047] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0048] Please see Figure 1 According to one embodiment of the present invention, a height-adjustable camellia oleifera pruning device for forest land includes a handle 1 and a support rod 2. A second linear motion component 6, whose output shaft is fixedly connected to the support rod 2, is embedded in the handle 1. The device also includes:

[0049] The trimming assembly includes a first trimming shear 3 and a second trimming shear 4. The first trimming shear 3 is fixedly installed on the upper side of the support rod 2, and the second trimming shear 4 is slidably engaged with the support rod 2.

[0050] The push assembly 5 is installed on the support rod 2 and is used to push the second trimmer 4 to move linearly along the length of the support rod 2 so that the first trimmer 3 can cooperate with the second trimmer 4 to complete the trimming action.

[0051] The cutting speed control system 8 is used to adjust the speed of the second trimmer scissors 4 as it approaches the first trimmer scissors 3, including:

[0052] Data acquisition module 801 is used to collect basic data of the cutting tools and data on branch pruning;

[0053] The tool basic evaluation module 802 is used to construct a tool basic evaluation model based on tool basic data and output tool basic evaluation coefficients.

[0054] The pruning condition evaluation module 803 is used to construct a pruning condition evaluation model based on branch pruning data and output pruning condition evaluation coefficients.

[0055] The tool-pruning matching analysis module 804 constructs a tool-pruning matching model based on the angle between the current branch and the horizontal direction, the basic evaluation coefficient of the tool, and the evaluation coefficient of the pruning conditions, and outputs the tool-pruning matching degree.

[0056] The cutting speed adjustment module 805 constructs a cutting speed model based on the current tool-trimming matching degree and the standard cutting speed, and outputs the target cutting speed.

[0057] Specifically, during operation, the second linear motion component adjusts the length of the support rod according to the operating instructions, bringing the pruning assembly to the target height. A laser rangefinder measures the branch radius and the position of the pruning point in real time, while a near-infrared sensor detects the branch's moisture content. An image sensor acquires data on the blade's rust area, which, combined with the blade angle measurement, is input into the tool basic evaluation module to calculate the tool performance coefficient. The pruning condition evaluation module generates a work difficulty coefficient by integrating branch parameters. The matching analysis module incorporates the branch angle parameter, corrects the evaluation results using a cosine function, and outputs a comprehensive matching degree index. The speed adjustment module adjusts the output power of the drive assembly based on the matching degree. When the matching degree decreases, the cutting speed is reduced exponentially to prevent tool overload; when the matching degree increases, the speed is gradually increased to the standard value.

[0058] Compared to existing technologies, traditional equipment relies on a fixed speed mode and cannot cope with changes in tool wear and branch characteristics. This solution uses multi-sensor data fusion to construct a dynamic evaluation system, achieving real-time matching between cutting speed and operating conditions. Existing technologies do not involve matching degree calculation based on cosine correction of the cutting angle, making it impossible to optimize the cutting efficiency of branches at different angles. This application combines the penalty weight for blade angle deviation with the degree of corrosion to establish a two-factor tool evaluation model, effectively quantifying the performance degradation trend.

[0059] Through the above technical solutions, this application can automatically adapt to the pruning needs of branches at different heights, reducing the frequency of manual adjustments. The dynamic speed adjustment mechanism avoids the risk of breakage caused by high-speed operation of the blade in a rusted state, extending the tool's service life. Real-time monitoring of the branch radius and moisture content ensures that the shearing force matches the material strength, reducing blade jamming or incomplete cutting. The cosine correction term optimizes the cutting trajectory control of oblique branches, improving pruning accuracy in complex work scenarios.

[0060] Preferably, the pushing component 5 includes a limiting slider 501, a first linear motion component 502, and a guide rail groove 503. The limiting slider 501 is slidably engaged with the guide rail groove 503 formed on the support rod 2. The guide rail groove 503 is embedded in the support rod 2 and its output shaft is fixedly connected to the limiting slider 501. The limiting slider 501 is fixedly connected to the second pruning shears 4. The purpose of this arrangement is to use the first linear motion component 502 to push the limiting slider 501 to drive the 4 to move linearly along the length direction of the support rod 2, thereby prompting the second pruning shears 4 to cooperate with the first pruning shears 3 to cut the branches.

[0061] Specifically, during the trimming operation, the first linear motion component drives the limiting slider to move axially along the guide rail groove. Because the limiting slider is rigidly connected to the second trimmer shears, this linear motion is transmitted to the trimmer shears without backlash. The geometric precision of the guide rail groove constrains the slider's movement trajectory, preventing radial deviation of the blade during shearing. The guide rail groove layout embedded in the support rod optimizes the structural stress distribution, ensuring that the support rod does not undergo elastic deformation during reciprocating motion while maintaining the equipment's lightweight design. An interference-fit sliding bearing is used between the limiting slider and the guide rail groove to effectively absorb vibrations generated by the shearing reaction force.

[0062] Compared to existing technologies, traditional pruning equipment often uses open guide rails or simple sliding sleeve structures for their sliding mechanisms, resulting in large cumulative clearance errors and weak resistance to lateral loads. This solution, through the design of a closed guide rail groove and a limiting slider, increases the contact area of ​​the moving parts, significantly improving torsional resistance. Existing hinge-link transmission methods often produce transmission backlash, while this solution directly uses a rigid connection to transmit motion.

[0063] Through the above technical solution, this application achieves high-precision linear motion of the second trimmer shears in complex working environments, solving the problem of burrs on the shearing surface caused by instability of the motion mechanism. The cooperative design of the guide rail groove and the limit slider effectively suppresses equipment vibration caused by shearing reaction force, ensuring consistent action when pruning branches at different angles. The direct connection between the first linear motion component and the slider reduces power transmission loss. The overall structure extends the service life of key moving parts while maintaining the portability of the equipment.

[0064] Preferably, the basic data of the cutting tool includes the cutting edge angle and the rust area on the blade surface. The data of the branch pruning includes the branch radius, the distance from the point to be pruned to the root of the cutting tool, and the moisture content of the branch. The branch radius and the distance from the point to be pruned to the root of the cutting tool can be obtained by a laser rangefinder installed on the support rod 2 and the second pruning shears 4. The moisture content of the branch can be obtained by a near-infrared sensor installed on the first pruning shears 3. The rust area of ​​the cutting tool can be obtained in advance by an image sensor.

[0065] Specifically, a laser rangefinder measures the branch radius and the location of the pruning point, while a near-infrared sensor measures the branch moisture content. An image sensor automatically triggers blade surface image acquisition in standby mode, identifying the outline of the rusted area through grayscale threshold segmentation and generating a rust area percentage parameter. After preprocessing, the multi-source data is input into the evaluation model. The degree of deviation of the blade angle from the ideal value and the rusted area together constitute a quantitative indicator of the blade's condition. The branch radius, distance, and moisture content data form comprehensive pruning condition parameters, providing real-time input for subsequent speed control.

[0066] Compared to existing technologies, traditional pruning equipment relies on human experience to judge the condition of the blades and the characteristics of the branches, lacking the ability to simultaneously sense multiple parameters. Existing power tools are only equipped with a single distance sensor and do not consider the nonlinear effect of moisture content on shearing resistance. By using multi-sensor fusion and collaborative data processing, comprehensive dynamic monitoring of blade wear status, branch physical characteristics, and spatial position can be achieved, solving the problems of partial and lagging parameter acquisition in existing technologies.

[0067] Through the above technical solutions, this application achieves simultaneous and accurate measurement of tool cutting angle, corrosion degree, branch size, spatial position, and moisture content, establishing a complete data foundation for intelligent control of cutting speed. The dual-end arrangement of the laser rangefinder eliminates single-point measurement errors, ensuring accurate calculation of branch diameter. Near-infrared non-contact detection avoids damage to the branch tissue structure, ensuring the real-time nature and reliability of moisture content data. Offline detection using an image sensor reduces system power consumption while providing high-precision corrosion assessment. Multi-dimensional data forms a closed-loop feedback loop, effectively supporting the subsequent evaluation model's dynamic prediction of shear resistance, providing reliable input parameters for adaptive adjustment of cutting speed, and reducing the risk of abnormal tool wear.

[0068] Preferably, the working steps of the tool basic evaluation module 802 are as follows:

[0069] The ratio of the rusted area on the blade surface to the maximum allowable rusted area is used to obtain the blade surface rust index;

[0070] A basic tool evaluation model is constructed based on the cutting edge index and the surface corrosion index of the cutting tool, and the basic tool evaluation coefficients are output.

[0071] The basic evaluation model for the cutting tool is expressed as follows: ;

[0072] in, This represents the basic evaluation coefficient of the cutting tool. Indicates the cutting edge angle. Indicates the ideal cutting edge angle. This indicates the rust index of the blade surface. This represents the attenuation coefficient (used to quantify the impact of the cutting edge angle deviating from the ideal value on tool performance). , Represents weight and , A higher value indicates a better tool condition;

[0073] The specific formula for calculating the penalty weight of the cutting edge deviation on the basic evaluation coefficient of the tool is as follows: , Represents the ideal cutting edge angle, controlling the exponential function (cutting edge angle scoring item). The decay rate of ) The larger the value, the faster the index decays, and the score drops rapidly. The smaller the value, the smoother the decay, allowing for greater deviation of the cutting edge angle.

[0074] Using the above technical solution, data such as the cutting edge angle and rust area of ​​the cutting tool are collected. Combined with the preset ideal cutting edge angle and maximum allowable rust area, a mathematical model is constructed. Quantify the current state of the tool and output the basic evaluation coefficient of the tool. The closer this coefficient is to 1, the better the tool condition. This allows for real-time assessment of tool wear and corrosion, reminding users to maintain or replace the blades in a timely manner. This helps avoid decreased trimming efficiency or the risk of tool jamming due to tool aging. At the same time, by using attenuation coefficients and weights, the impact of blade angle deviation on performance is scientifically quantified, providing a precise basis for subsequent cutting speed adjustments.

[0075] Preferably, the working steps of the pruning condition evaluation module 803 are as follows:

[0076] The branch radius is calculated by comparing the branch radius with the maximum allowable branch radius to obtain the branch radius index.

[0077] The distance index is obtained by comparing the distance from the cut point to the root of the shears with the maximum allowable distance.

[0078] The moisture content of the branches is compared with the maximum allowable moisture content of the branches to obtain the moisture content index;

[0079] A pruning condition evaluation model was constructed based on the branch radius index, distance index, and water content index. The branch radius index, distance index, and water content index were then imported, and the pruning condition evaluation coefficients were output.

[0080] The pruning condition evaluation model is expressed as follows: ;

[0081] in, This represents the evaluation coefficient for pruning conditions. Indicates the branch radius index, Represents the distance index. Indicates the moisture content index. Represents weight and , The larger the value, the more difficult the pruning.

[0082] The above technical solution, based on data such as branch radius, distance from the pruning point to the root of the cutter ($d$), and moisture content, compares these values ​​with preset thresholds (such as maximum allowable radius, distance, and moisture content) to generate branch radius index, distance index, and moisture content index. Then through the model The output pruning condition evaluation coefficient indicates that the higher the pruning difficulty. This technical solution can accurately identify the physical characteristics of branches (such as thickness, location, and humidity), dynamically assess the pruning difficulty, and avoid insufficient shearing force or tool jamming caused by branches that are too thick or too wet. At the same time, by weighting, it prioritizes key factors (such as the radius having a greater impact on shearing force) to improve the scientific nature of the pruning strategy.

[0083] Preferably, the working steps of the tool-trimming matching analysis module 804 are as follows:

[0084] The angle index is obtained by comparing the angle between the current branch and the horizontal direction with a right angle.

[0085] Based on the basic evaluation coefficient of the tool and the evaluation coefficient of the pruning condition under the current branch angle index, a tool-pruning matching model is constructed and the tool-pruning matching degree is output.

[0086] The tool-trimming matching model is represented as follows: ;

[0087] in, Indicates the tool-trimming match. This represents the basic evaluation coefficient of the cutting tool. This represents the evaluation coefficient for pruning conditions. Indicates the angle index, A larger value indicates a better match. Converting pruning difficulty into a positive metric for matching accuracy. The larger the angle (closer to perpendicular), the lower the matching degree, due to the influence of gravity on shear stability.

[0088] The above technical solution constructs a matching model by comprehensively considering the basic evaluation coefficient of the cutting tool, the evaluation coefficient of the pruning condition, and the angle between the branch and the horizontal direction. The output tool-trimming matching degree is calculated. The closer the matching degree is to 1, the better the fit between the current tool state and the trimming conditions. This technical solution uses the cosine function of the included angle. The influence of branch angle on shear stability is quantified (low matching degree for vertical branches, high matching degree for horizontal branches), reducing the risk of shear displacement due to gravity, while the matching degree is modeled in relation to the cutting speed. The system dynamically adjusts the cutting speed in conjunction with the target material. For example, it increases the speed to improve efficiency when the target material is well-matched, and decreases the speed to ensure cutting accuracy and protect the cutting tool when the target material is poorly matched.

[0089] Preferably, the specific steps of the cutting speed adjustment module 805 are as follows:

[0090] A cutting speed model is constructed based on the current tool-trimming matching degree and the standard cutting speed. The current tool-trimming matching degree and the standard cutting speed are then imported to output the target cutting speed.

[0091] The cutting speed model is expressed as follows: ;

[0092] in, Indicates the target cutting speed. Indicates the standard cutting speed. Indicates the velocity attenuation coefficient. This indicates the tool-trimming match.

[0093] The above technical solution dynamically adjusts the target cutting speed based on the matching degree (using an exponential decay model). The lower the matching degree, the more significant the speed reduction. It can achieve intelligent speed adjustment, automatically reducing speed to protect the blades under complex conditions (such as blade aging or thick and hard branches), while maintaining efficient operation in high-matching scenarios. It balances pruning efficiency and equipment safety, reducing the need for manual intervention.

[0094] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0095] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A height-adjustable camellia oleifera pruning device, comprising a handle (1) and a support rod (2), wherein a second linear motion component (6) with an output shaft fixedly connected to the support rod (2) is embedded in the handle (1), characterized in that, Also includes: The trimming assembly includes a first trimmer (3) and a second trimmer (4). The first trimmer (3) is fixedly installed on the upper side of the support rod (2), and the second trimmer (4) is slidably engaged with the support rod (2). The push assembly (5) is installed on the support rod (2) and is used to push the second trimmer (4) to move linearly along the length of the support rod (2) so that the first trimmer (3) cooperates with the second trimmer (4) to complete the trimming action; The cutting speed control system (8) is used to adjust the speed of the second trimmer (4) as it approaches the first trimmer (3), including: The data acquisition module (801) is used to collect basic data of the cutting tools and data on branch pruning. The tool basic evaluation module (802) is used to construct a tool basic evaluation model based on tool basic data and output tool basic evaluation coefficients; The pruning condition evaluation module (803) is used to construct a pruning condition evaluation model based on branch pruning data and output pruning condition evaluation coefficients. The tool-pruning matching analysis module (804) constructs a tool-pruning matching model based on the angle between the current branch and the horizontal direction, the basic evaluation coefficient of the tool, and the evaluation coefficient of the pruning conditions, and outputs the tool-pruning matching degree. The cutting speed adjustment module (805) constructs a cutting speed model based on the current tool-trimming matching degree and the standard cutting speed, and outputs the target cutting speed. The basic data of the cutting tool includes the cutting edge angle and the rust area on the blade surface. The data of the branch pruning includes the branch radius, the distance from the point to be pruned to the root of the cutting tool, and the water content of the branch. The branch radius and the distance from the point to be pruned to the root of the cutting tool can be obtained by a laser rangefinder installed on the support rod (2) and the second pruning shears (4). The water content of the branch can be obtained by a near-infrared sensor installed on the first pruning shears (3). The rust area of ​​the cutting tool can be obtained in advance by an image sensor. The working steps of the tool-trimming matching analysis module (804) are as follows: The angle index is obtained by comparing the angle between the current branch and the horizontal direction with a right angle. Based on the basic evaluation coefficient of the tool and the evaluation coefficient of the pruning condition under the current branch angle index, a tool-pruning matching model is constructed and the tool-pruning matching degree is output. The tool-trimming matching model is represented as follows: ； in, Indicates the tool-trimming match. This represents the basic evaluation coefficient of the cutting tool. This represents the evaluation coefficient for pruning conditions. Indicates the angle index; The specific steps of the cutting speed adjustment module (805) are as follows: A cutting speed model is constructed based on the current tool-trimming matching degree and the standard cutting speed. The current tool-trimming matching degree and the standard cutting speed are then imported to output the target cutting speed. The cutting speed model is expressed as follows: ； in, Indicates the target cutting speed. Indicates the standard cutting speed. Indicates the velocity attenuation coefficient. This indicates the tool-trimming match.

2. The forest camellia oleifera pruning equipment with adjustable height according to claim 1, characterized in that, The working steps of the tool basic evaluation module (802) are as follows: The ratio of the rusted area on the blade surface to the maximum allowable rusted area is used to obtain the blade surface rust index; A basic tool evaluation model is constructed based on the cutting edge index and the surface corrosion index of the cutting tool, and the basic tool evaluation coefficients are output. The basic evaluation model for the cutting tool is expressed as follows: ； in, This represents the basic evaluation coefficient of the cutting tool. Indicates the cutting edge angle. Indicates the ideal cutting edge angle. This indicates the rust index of the blade surface. Indicates the attenuation coefficient. , Represents weight and .

3. The forest camellia oleifera pruning equipment with adjustable height according to claim 2, characterized in that, The specific formula for calculating the penalty weight of the cutting edge deviation on the basic evaluation coefficient of the tool is as follows: , Indicates the ideal cutting edge angle. The larger the value, the faster the index decays, and the score drops rapidly. The smaller the value, the smoother the decay, allowing for greater deviation of the cutting edge angle.

4. The forest camellia oleifera pruning equipment with adjustable height according to claim 3, characterized in that, The working steps of the pruning condition evaluation module (803) are as follows: The branch radius is calculated by comparing the branch radius with the maximum allowable branch radius to obtain the branch radius index. The distance index is obtained by comparing the distance from the cut point to the root of the shears with the maximum allowable distance. The moisture content of the branches is compared with the maximum allowable moisture content of the branches to obtain the moisture content index; A pruning condition evaluation model was constructed based on the branch radius index, distance index, and water content index. The branch radius index, distance index, and water content index were then imported, and the pruning condition evaluation coefficients were output. The pruning condition evaluation model is expressed as follows: ； in, This represents the evaluation coefficient for pruning conditions. Indicates the branch radius index, Represents the distance index. Indicates the moisture content index. Represents weight and .

5. The forest camellia oleifera pruning equipment with adjustable height according to claim 4, characterized in that, The Furthermore, a larger value indicates a better tool condition. Furthermore, a larger value indicates a higher difficulty in pruning. The larger the value, the better the match.

6. The height-adjustable camellia oleifera pruning equipment according to any one of claims 1-5, characterized in that, The pushing component (5) includes a limiting slider (501), a first linear motion component (502), and a guide rail groove (503). The limiting slider (501) is slidably engaged with the guide rail groove (503) opened on the support rod (2). The guide rail groove (503) is embedded in the support rod (2) and its output shaft is fixedly connected to the limiting slider (501). The limiting slider (501) is fixedly connected to the second trimmer (4).

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