Single-blade hedge trimmer blade retraction detection control method, system and equipment

Abstract

The present application relates to the field of hedge trimmer control technology, and specifically to a single-edged hedge trimmer blade retraction detection and control method, system, and device. The method comprises: obtaining the load current and speed at each moment during the operation of the hedge trimmer; recording multiple moments before each moment as a local time period of each moment; calculating a first characterization value and a second characterization value at each moment; determining a blade-stuck evaluation value at each moment and evaluating the blade-stuck state of the hedge trimmer; and controlling the hedge trimmer to retract the blade when the hedge trimmer is in the blade-stuck state. The present application can reduce the misjudgment of the blade-stuck state caused by foreign object collisions, improve the accuracy of the hedge trimmer blade-stuck state evaluation, and thereby enhance the accuracy of the hedge trimmer blade-retraction detection and the blade-retraction effect.

Description

Technical Field

[0001] The present application relates to the technical field of hedge trimmer control, and in particular to a method, system and device for detecting and controlling blade retraction of a single-edged hedge trimmer. Background Art

[0002] A hedge trimmer, also known as a hedge trimmer, is a specialized piece of machinery used in garden maintenance, primarily for trimming hedges, shrubs, and shaped plants. Its high-speed rotating cutting blades quickly trim plant branches, leaves, and trunks. However, due to the compression of branches during the trimming process, the blades can easily become stuck, affecting the consistency and efficiency of the trimming process.

[0003] When the existing technology automatically retracts the hedge trimmer, a fixed speed threshold is usually used to determine whether the hedge trimmer is in a stuck state, and retraction is achieved by reversing. However, in actual applications, the vegetation conditions faced by hedge trimmers are complex and diverse, and the resistance characteristics when the blade is stuck will vary significantly depending on the type of vegetation. When the blade collides with a foreign object, the blade will become stuck, causing the speed to decrease. If a fixed speed threshold is used to determine whether the blade is stuck, the judgment of the stuck phenomenon will be misjudged, reducing the accuracy of the judgment of the hedge trimmer's stuck state, affecting the accuracy of the hedge trimmer's retraction detection, and resulting in poor retraction effect. Summary of the Invention

[0004] In order to solve the above technical problems, a single-edged hedge trimmer blade retraction detection control method, system and device are provided to solve the existing problems.

[0005] The solution to the technical problem of this application is to provide a single-edged hedge trimmer blade retraction detection control method, system and device, including the following steps:

[0006] In a first aspect, an embodiment of the present application provides a method for detecting and controlling the blade retraction of a single-edged hedge trimmer, the method comprising the following steps:

[0007] Obtain the load current and speed at each moment during the operation of the hedge trimmer; record multiple moments before each moment as a local period of each moment;

[0008] Performing curve fitting on the rotation speed at different moments in the local time period, analyzing the deviation of the rotation speed at each moment, the change in the number of extreme points on the fitting curve, and the fitting degree of the fitting curve, and calculating the first characterization value at each moment;

[0009] Calculating a second characterization value at each moment according to a change trend of the load current at all moments in the local period and a degree of deviation of the load current;

[0010] Based on the first characterization value and the second characterization value, a knife-stuck evaluation value at each moment is determined, and the knife-stuck state of the hedge trimmer is evaluated; when the hedge trimmer is in the knife-stuck state, the hedge trimmer is controlled to retract the knife.

[0011] Preferably, the degree of fitting is measured by goodness of fit, specifically by calculating the goodness of fit of the fitting curve as the degree of fitting of the fitting curve.

[0012] Preferably, the calculating of the first characterization value at each moment includes:

[0013] Calculate the ratio of the speed at each moment to the preset rated no-load speed, and record it as the speed ratio;

[0014] Obtaining the number of extreme points of the fitting curve and performing positive mapping on the number; calculating the sum of the result of the positive mapping and the speed ratio;

[0015] The first characterization value is a ratio of the fitting degree to the sum value.

[0016] Preferably, the degree of deviation is measured by the mean absolute deviation, specifically by calculating the mean absolute deviation of the load current at all moments in the local period as the degree of deviation of the local period.

[0017] Preferably, the calculating of the second characterization value at each moment includes:

[0018] Performing a linear fit on the load current at all times within the local time period to obtain the slope of the fitted line; calculating the trend strength of the load current at all times within the local time period;

[0019] Calculating the product of the slope and the trend strength;

[0020] The second characterization value is a ratio of the product value to the offset degree.

[0021] Preferably, the knife evaluation value is a normalized result of the sum of the first characterization value and the second characterization value.

[0022] Preferably, the evaluating the stuck blade state of the hedge trimmer includes: if the stuck blade evaluation values at multiple consecutive moments are all greater than a preset segmentation threshold, the hedge trimmer is in the stuck blade state; otherwise, it is not in the stuck blade state.

[0023] Preferably, the controlling the hedge trimmer to retract includes: when the hedge trimmer is in a blade-stuck state, controlling the blade of the hedge trimmer to retract by a positive and negative pulse method.

[0024] In the second aspect, an embodiment of the present application also provides a single-edged hedge trimmer blade retraction detection and control system, the system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, it implements the steps of any one of the above-mentioned single-edged hedge trimmer blade retraction detection and control methods.

[0025] In a third aspect, an embodiment of the present application further provides a single-edged hedge trimmer blade retraction detection and control device, wherein the blade retraction detection and control process of the device is implemented by any one of the single-edged hedge trimmer blade retraction detection and control methods described above.

[0026] This application has at least the following beneficial effects:

[0027] The present application performs curve fitting on the rotation speed at different moments in a local time period, analyzes the change in the number of extreme points on the fitting curve, the fitting effect of the fitting curve, and the deviation of the rotation speed at each moment, and calculates the first characterization value at each moment. The beneficial effect is that it takes into account the process of smooth reduction of the rotation speed in the local time period when the blade is stuck. When colliding with foreign objects, the blade will bounce, and the rotation speed in the local time period will fluctuate and decrease. By analyzing the fluctuation of the rotation speed in the local time period and the degree of deviation of the rotation speed, the possibility of the hedge trimmer being stuck is preliminarily evaluated; further, the second characterization value at each moment is calculated through the change trend of the load current at different moments in the local time period and the degree of deviation. The characterization value has the beneficial effect of taking into account the changes in load current caused by knife jamming and collision with foreign objects, so as to further evaluate the possibility of knife jamming in the hedge trimmer, determine the knife jam evaluation value at each moment, and evaluate the knife jam state of the hedge trimmer; when the hedge trimmer is in the knife jam state, the hedge trimmer is controlled to retract the knife. The beneficial effect is that the knife jam state of the hedge trimmer is comprehensively evaluated through the changes in speed and load current, the misjudgment of the knife jam state caused by collision with foreign objects is reduced, and the accuracy of the assessment of the knife jam state of the hedge trimmer is improved, and then the knife retraction control is performed through the positive and negative pulse method, which improves the flexibility and practicality of the knife retraction control under complex conditions, thereby improving the knife retraction detection accuracy and knife retraction effect of the hedge trimmer. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The following is a detailed description of a single-edged hedge trimmer blade retraction detection and control method of the present application in conjunction with the accompanying drawings.

[0029] Figure 1 A flowchart of a method for detecting and controlling the blade retraction of a single-edged hedge trimmer provided in an embodiment of the present application;

[0030] Figure 2 A flowchart of the steps of a method for obtaining a first characterization value at each moment provided in an embodiment of the present application;

[0031] Figure 3A flowchart of the steps of the method for obtaining the second characterization value at each moment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions, and advantages of this application more clearly understood, the following, in conjunction with the accompanying drawings and implementation examples, further describes in detail a single-edged hedge trimmer blade retraction detection and control method, system, and device proposed in this application. It should be understood that the specific embodiments described herein are merely for the purpose of explaining this application and are not intended to limit this application.

[0033] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0034] See also Figure 1 , which shows a flowchart of a method for detecting and controlling the blade retraction of a single-edged hedge trimmer provided by one embodiment of the present application, the method comprising the following steps:

[0035] Step 1: Obtain the load current and speed at each moment during the operation of the hedge trimmer.

[0036] In order to obtain the operating status of the hedge trimmer equipment in real time, the intelligent sensor is installed on the brushless DC motor of the hedge trimmer. When the hedge trimmer equipment is running, the load current and speed at each moment are collected in real time.

[0037] In this embodiment, the collection time interval of the smart sensor is 20ms. As other implementation methods, the implementer can set it according to actual conditions. Secondly, since it often takes several seconds for the hedge trimmer equipment to start running and trim vegetation, the data collected within the first 2 seconds after the startup is not processed.

[0038] At this point, the load current and speed at each moment during the operation of the hedge trimmer are obtained.

[0039] Step 2: Perform curve fitting on the rotation speed at different moments in the local period, analyze the deviation of the rotation speed at each moment, the change in the number of extreme points on the fitting curve and the fitting degree of the fitting curve, and calculate the first characterization value at each moment.

[0040] During the pruning and cutting process, the blades on the hedge trimmer are easily squeezed by lateral branches, resulting in increased resistance or even stopping, and the blade becomes stuck. Therefore, a certain control method is required to enable the blade to withdraw from the stuck condition to ensure the smoothness of the hedge trimmer's pruning process.

[0041] In traditional blade retraction control algorithms, whether the motor is stuck is often judged based on a fixed motor speed threshold or load current threshold. However, during the operation of a hedge trimmer, different trees and vegetation often need to be trimmed. The toughness, hardness and thickness of different trees are different, which causes the motor speed and load current of the hedge trimmer to change. This will reduce the applicability of traditional blade sticking detection and affect the operating efficiency of the hedge trimmer equipment.

[0042] Secondly, when a hedge trimmer is trimming vegetation, the motor is connected to the blade through a connecting structure, and the rotation of the motor drives the blade to move back and forth, thereby trimming the vegetation. Usually, the output power of the motor is constant, that is, the speed of the motor is constant under no-load conditions. When the hedge trimmer is trimming vegetation, the motor speed is reduced due to the toughness and resistance of the vegetation branches. However, foreign objects such as stones and metals may appear in the branches during the cutting process. When the blade collides with foreign objects, it will also jam, causing the motor speed to decrease. As a result, the collision between the blade and the foreign object will be mistakenly judged as a knife jam in the hedge trimmer.

[0043] Based on the above analysis, the main occurrence of knife jams is when pruning tree branches, where the blade sidewalls are squeezed by the trees, increasing the blade resistance. When squeezed by trees, as the cutting depth of the blade deepens, the blade cutting resistance gradually increases, and the motor speed shows a gradually smooth decrease process. When the blade collides with a foreign object, it is affected by the high speed of the blade, causing the blade to bounce, and the motor speed will fluctuate and decrease. Therefore, by analyzing the changing trend of the speed, the first characterization value is calculated, wherein the step flow chart of the method for obtaining the first characterization value at each moment provided in the embodiment of the present application is as follows: Figure 2 As shown, specifically including:

[0044] Record multiple moments before each moment as local time periods;

[0045] In this embodiment, 50 moments before each moment are recorded as a local time period. As for other implementation methods, the implementer can set it according to actual conditions.

[0046] Performing curve fitting on the rotation speed at all moments in the local time period, and calculating the goodness of fit of the fitting curve;

[0047] In this embodiment, the least square method is used for curve fitting. The least square method and the calculation of goodness of fit are well-known techniques and will not be described in detail here.

[0048] Obtaining the number of extreme points of the fitting curve, and performing positive mapping on the number;

[0049] In this embodiment, the derivative method is used to obtain the extreme point, wherein the process of obtaining the extreme point by the derivative method is a well-known technology and will not be repeated here; secondly, the process of positive mapping is: the quantity is positively mapped by a logarithmic function, assuming that the quantity is recorded as M, The calculation result of is taken as the result of positive mapping, where is a logarithmic function with a natural constant as its base.

[0050] Calculate the ratio of the speed at each moment to the preset rated no-load speed, and record it as the speed ratio;

[0051] In this embodiment, the preset rated no-load speed is 6900 r / min. As other implementation methods, the implementer can set it according to the production data of the hedge trimmer equipment.

[0052] Calculating a sum of the forward mapping result and the speed ratio, and using a ratio of the goodness of fit to the sum as a first characterization value at each moment;

[0053] In this embodiment, the Taking the first characterization value at a moment as an example, the calculation process is:

[0054]

[0055] in, For the The first characterization value of the moment, For the The goodness of fit at time , For the The speed ratio at the moment, For the The quantity corresponding to the moment, is the result of the positive mapping, is a logarithmic function with a natural constant as its base.

[0056] It should be noted that if the hedge trimmer has a blade jam, the motor speed decreases smoothly and the number of extreme points is small, then the goodness of fit is high, the result of the positive mapping is small, and the speed is relatively small. The larger the obtained first characterization value, the greater the possibility of the hedge trimmer having a blade jam at this time; on the contrary, when the blade bounces due to collision with foreign objects, the motor speed fluctuates greatly, the obtained goodness of fit is small, and the degree of motor speed drop is small. The speed ratio is relatively high compared to when the blade is jammed. Due to the fluctuation when the speed drops, the number of extreme points is large, the result of the positive mapping is large, and the smaller the obtained first characterization value, the smaller the possibility of the hedge trimmer having a blade jam at this time.

[0057] At this point, the first characterization value at each moment is obtained.

[0058] Step 3: Calculate the second characterization value at each moment based on the load current change trend at all moments in the local period and the offset degree of the load current.

[0059] During the operation of a hedge trimmer, the battery in the hedge trimmer continuously outputs electrical energy. The motor converts this energy into kinetic energy through the action of a magnetic field, driving the blades to move and complete the pruning process. When the hedge trimmer is operating normally to trim vegetation, the toughness of the branches will create a certain amount of resistance on the blades, causing the motor load to fluctuate slightly, and so the motor's load current will also fluctuate slightly. When the hedge trimmer's blades are stuck by branches or other objects, the motor's speed decreases, causing the motor's load current to increase rapidly. If the motor speed stops completely, the motor becomes a resistive device, and the current reaches its maximum and tends to stabilize. When the hedge trimmer's blades are hit by foreign objects and cause them to jam and bounce, the collision time between the blades and the foreign objects is shorter, so the impact on the load current is smaller. The load current will not increase rapidly like when the blades are stuck, but will only increase the volatility of the load current.

[0060] Based on the above analysis, the second characterization value is calculated by the change of the load current in the local time period. The flowchart of the step of the method for obtaining the second characterization value at each moment provided in the embodiment of the present application is as follows: Figure 3 As shown, specifically including:

[0061] Performing linear fitting on the load current at all times within the local time period to obtain the slope of the fitted straight line;

[0062] In this embodiment, the least square method is used for linear fitting, wherein the least square method is a well-known technology and will not be described in detail here.

[0063] Calculating the trend intensity of the load current at all times within the local time period;

[0064] In this embodiment, the STL (Seasonal and Trend decomposition using Loess) trend decomposition algorithm is used to calculate the trend strength, wherein the calculation of the trend strength is a well-known technology and will not be repeated here; the specific process is: the load current at all times in the local period is decomposed into trend items using the STL trend decomposition algorithm , residual term , the calculation formula for trend strength is: ,in, is the trend strength, is the variance of the residual term, is the variance of the trend term and the residual term, To find the maximum function.

[0065] Calculating the average absolute deviation of the load current at all times within the local time period;

[0066] It should be noted that the calculation of the mean absolute deviation is a well-known technique, and the calculation formula is: ,in, For the The mean absolute deviation of the time, For the The first time in the local period of time The load current at the moment, For the The average value of the load current at all times in the local period at time, For the The number of all moments in the local period of time.

[0067] Calculating the product of the slope and the trend strength, and using the ratio of the product to the mean absolute deviation as the second characterization value at each moment;

[0068] It should be noted that when the hedge trimmer has a blade jam, the load current of its motor increases rapidly, and the resulting slope and the trend strength are relatively large. After the blade is completely jammed, the motor stops and loses the back electromotive force. The current is only determined by the resistance and input voltage, and the current stabilizes at the maximum value. The overall current deviation is relatively reduced, and the larger the second characterization value obtained, the more likely the hedge trimmer is to have a blade jam. On the contrary, when the blade bounces due to collision with foreign objects, the motor is affected by the load, causing the current to fluctuate greatly, resulting in a smaller overall trend, a smaller slope, and a larger average absolute deviation of the load current. The smaller the second characterization value obtained, the less likely the hedge trimmer is to have a blade jam.

[0069] At this point, the second characterization value at each moment is obtained.

[0070] Step 4: Based on the first characterization value and the second characterization value, determine the blade-stuck evaluation value at each moment, and evaluate the blade-stuck state of the hedge trimmer; when the hedge trimmer is in the blade-stuck state, control the hedge trimmer to retract the blade.

[0071] Further, based on the first characterization value and the second characterization value, a knife evaluation value is determined, specifically:

[0072] Normalizing the sum of the first characterization value and the second characterization value as the knife-stamping evaluation value at each moment;

[0073] In this embodiment, the sigmoid function is used for normalization processing, wherein the sigmoid function is a well-known technology and will not be described in detail here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as the softmax function, etc. This embodiment does not impose any special restrictions on this.

[0074] It should be noted that, the greater the blade jam evaluation value, the higher the possibility that the hedge trimmer will experience blade jam.

[0075] Secondly, to more accurately determine whether a hedge trimmer has experienced a blade jam, we collected data on the load current and speed of the hedge trimmer during multiple runs of the machine cutting different trees. This data set consisted of 20% of the data from blade jams, 20% of the data from foreign object collisions, and 60% of the data from normal cutting.

[0076] Calculate the stuck knife evaluation value corresponding to each run in the experimental data set, and obtain the optimal segmentation threshold of the stuck knife evaluation value of all runs. Among them, the segmentation threshold corresponding to the highest accuracy and recall rate of the stuck knife status evaluation is recorded as the optimal segmentation threshold, and the optimal segmentation threshold is used as the preset segmentation threshold. Therefore, the optimal segmentation threshold can effectively distinguish between the stuck knife situation, foreign object collision and normal cutting situation in all runs.

[0077] If the knife-stuck evaluation values at multiple consecutive moments are all greater than the preset segmentation threshold, the hedge trimmer is in the knife-stuck state; otherwise, it is not in the knife-stuck state;

[0078] In this embodiment, if the knife-stuck evaluation values are greater than the preset segmentation threshold for five consecutive moments, the hedge trimmer is in the knife-stuck state; as other implementation methods, the implementer can set it according to actual conditions.

[0079] When the hedge trimmer is judged to have a stuck blade, the blade's running resistance increases. By adjusting the motor's torque and controlling the motor to move back and forth, the blade can be withdrawn from the stuck position, thereby achieving the blade retraction operation. Specifically:

[0080] By controlling the voltage through the positive and negative pulse method and adjusting the pulse period and PWM duty cycle, the operating state of the motor can be changed. The optional interval of the pulse period is set to {2ms, 4ms, 6ms}, and the PWM duty cycle of the motor is {70%, 80%, 90%, 100%}. During the retraction process, in order to improve the flexibility and practicality of the retraction, the pulse period and PWM duty cycle of the motor drive pulse voltage are randomly selected and combined to realize the retraction process of the hedge trimmer. Among them, the process of controlling the retraction by the positive and negative pulse method is a well-known technology and will not be repeated here.

[0081] An embodiment of the present application also provides a single-edged hedge trimmer blade retraction detection and control system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned single-edged hedge trimmer blade retraction detection and control methods are implemented.

[0082] Based on the same inventive concept as the above method, an embodiment of the present application also provides a single-edged hedge trimmer blade retraction detection and control device, and the blade retraction detection and control process of the device is implemented by any of the above-mentioned single-edged hedge trimmer blade retraction detection and control methods.

[0083] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0084] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the present application. It should be noted that a person skilled in the art can make various modifications and improvements without departing from the spirit of the present application. Therefore, any simple modifications, equivalent variations, and modifications to the above embodiments made in accordance with the technical essence of the present application without departing from the content of the present application's technical solution fall within the scope of protection of the present application's technical solution.

Claims

1. A single-edged hedge trimmer blade retraction detection and control method, characterized in that: The method comprises the following steps: Obtain the load current and speed at each moment during the operation of the hedge trimmer; record multiple moments before each moment as a local period of each moment; Performing curve fitting on the rotation speed at different moments in the local time period, analyzing the deviation of the rotation speed at each moment, the change in the number of extreme points on the fitting curve, and the fitting degree of the fitting curve, and calculating the first characterization value at each moment; Calculating a second characterization value at each moment according to a change trend of the load current at all moments in the local period and a degree of deviation of the load current; Based on the first characterization value and the second characterization value, determining a blade-stuck evaluation value at each moment, and evaluating a blade-stuck state of the hedge trimmer; and when the hedge trimmer is in the blade-stuck state, controlling the hedge trimmer to retract the blade; The degree of fitting is measured by goodness of fit, specifically: calculating the goodness of fit of the fitting curve as the degree of fitting of the fitting curve; The calculating of the first characterization value at each moment includes: Calculate the ratio of the speed at each moment to the preset rated no-load speed, and record it as the speed ratio; Obtaining the number of extreme points of the fitting curve and performing positive mapping on the number; calculating the sum of the result of the positive mapping and the speed ratio; The first characterization value is a ratio of the degree of fit to the sum value; The calculating the second characterization value at each moment includes: Performing a linear fit on the load current at all times within the local time period to obtain the slope of the fitted line; calculating the trend strength of the load current at all times within the local time period; Calculating the product of the slope and the trend strength; The second characterization value is a ratio of the product value to the offset degree; The knife evaluation value is a normalized result of the sum of the first characterization value and the second characterization value; The hedge trimmer is evaluated on the blade-stuck state, including: if the blade-stuck evaluation values at multiple consecutive moments are all greater than a preset segmentation threshold, the hedge trimmer is in the blade-stuck state; otherwise, the hedge trimmer is not in the blade-stuck state.

2. A single-edged hedge trimmer blade retraction detection and control method according to claim 1, characterized in that: The degree of deviation is measured by the mean absolute deviation, specifically: the mean absolute deviation of the load current at all moments in the local period is calculated as the degree of deviation of the local period.

3. A single-edged hedge trimmer blade retraction detection and control method according to claim 1, characterized in that: The method of controlling the blade retraction of the hedge trimmer includes: when the hedge trimmer is in a blade-stuck state, controlling the blade retraction of the hedge trimmer by a positive and negative pulse method.

4. A single-edged hedge trimmer blade retraction detection and control system, characterized in that: The system includes a memory, a processor, and a computer program stored in the memory and running on the processor, and is characterized in that when the processor executes the computer program, it implements the steps of the single-edged hedge trimmer retraction detection and control method as described in any one of claims 1 to 3.

5. A single-edged hedge trimmer blade retraction detection and control device, characterized in that: The blade retraction detection and control process of the device is implemented by a single-edge hedge trimmer blade retraction detection and control method as described in any one of claims 1-3.

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