Method for monitoring the health status of a lawn

Abstract

The present disclosure provides a method for monitoring the health state of a lawn, which comprises: controlling an intelligent mower to move on the lawn and cut the grass on the lawn; wherein the intelligent mower comprises a mower body and a capacitive sensor installed on the mower body; the capacitive sensor is used to detect the grass on the lawn; obtaining the data of the grass detected by the capacitive sensor, and grading the quality of the grass according to the data to obtain the quality grade of the grass; and controlling the working state of the intelligent mower according to different quality grades of the grass.

Description

Technical Field

[0001] This disclosure relates to a method for monitoring the health status of a lawn. Background Technology

[0002] The health of a lawn is mainly related to its temperature and humidity, the water content of the grass blades, and its color. Currently, lawn health is generally detected through visual methods and spectral analysis. However, visual inspection of lawn health is a non-contact method, which is prone to misjudgment. Furthermore, even if poor-quality lawns are identified visually, there is still a certain degree of accuracy error when marking the area on a map.

[0003] There are patented technologies in the prior art that use capacitive sensors to detect the presence of grass. These technologies involve placing the capacitive sensor below the lawnmower body to detect whether there is grass underneath the lawnmower body.

[0004] However, existing lawnmowers cannot accurately determine the grass quality, and consequently, they cannot replan the path based on the current grass quality (whether there is grass, etc.), thus failing to effectively improve efficiency.

[0005] Moreover, existing lawnmowers fail to display the current grass distribution on the lawn, fail to effectively display abnormal grass cloud maps, are not intelligent enough, and cannot assist customers in maintaining their lawns; customers cannot remotely view any abnormalities in the current lawn. Summary of the Invention

[0006] To address one of the aforementioned technical problems, this disclosure provides a method for monitoring the health status of a lawn.

[0007] According to one aspect of this disclosure, a method for monitoring the health status of a lawn is provided, comprising:

[0008] The system controls an intelligent lawnmower to move across a lawn and cut the grass. The intelligent lawnmower includes a mower body and a capacitive sensor mounted on the mower body. The capacitive sensor is used to detect the grass on the lawn.

[0009] Acquire data on the grass detected by the capacitive sensor, and classify the quality of the grass based on this data to obtain the grass quality grade; and

[0010] The intelligent lawnmower controls its operation based on the different quality grades of the grass.

[0011] According to at least one embodiment of the method for monitoring the health status of a lawn, the data of the grass detected by the capacitive sensor is the capacitance value of the current collection point; when the capacitance value is greater than or equal to a first preset threshold, the quality level of the grass is determined to be a first level; when the capacitance value is less than the first preset threshold but greater than a second preset threshold, the quality level of the grass is determined to be a second level; when the capacitance value is less than or equal to the second preset threshold, the quality level of the grass is determined to be a third level; wherein, the first preset threshold is greater than the second preset threshold.

[0012] According to at least one embodiment of the lawn health monitoring method disclosed herein, when the smart lawnmower is cutting grass, the location of the smart lawnmower is acquired in real time; for the area at the current location, the smart lawnmower has already detected the quality of the grass and cut the grass in that area; when the time interval between the current cutting time and the last cutting time is less than or equal to a preset time threshold, the lawn health status map obtained during the last cutting is acquired, and the grass quality level of the current area is obtained based on the lawn health status map; when the grass quality level is level three, the smart lawnmower is controlled to maintain the current working state; when the grass quality level is level one or level two, the cutting speed of the smart lawnmower is increased, and the moving speed of the smart lawnmower is slowed down.

[0013] According to at least one embodiment of the lawn health monitoring method of the present disclosure, when the lawn grass is cut for the first time: when the grass quality grade is third grade, the intelligent lawnmower is controlled to repeatedly cut the grass of the third grade; when the grass quality grade is first grade or second grade, the intelligent lawnmower is controlled to maintain the current working state.

[0014] According to at least one embodiment of the lawn health monitoring method of this disclosure, when the time interval between the current cutting time of the smart lawnmower and the last cutting time is greater than a preset time threshold: when the quality grade of the grass is the third grade, the smart lawnmower is controlled to repeatedly cut the grass of the third grade; when the quality grade of the grass is the first grade or the second grade, the smart lawnmower is controlled to maintain the current working state.

[0015] According to at least one embodiment of the lawn health monitoring method of the present disclosure, after the intelligent lawnmower finishes cutting the grass, the quality grade of the grass is plotted on the lawn map to form a lawn health status map.

[0016] According to at least one embodiment of the lawn health monitoring method of this disclosure, when the quality level of grass in a certain area of ​​the lawn map is repeatedly judged as level three, the user is reminded to replenish grass seeds in that area.

[0017] According to at least one embodiment of the method for monitoring the health status of a lawn, the lawn is divided into multiple areas, the area of ​​which is more than 100 times the detection area of ​​a capacitive sensor. When the capacitive sensor detects the grass in the area, it obtains multiple capacitance values. The quality grade of the grass is determined based on the capacitance values. The number of different quality grades of grass in the area is counted, and the quality grade of grass with the most counts is taken as the quality grade of grass in the area.

[0018] According to at least one embodiment of the lawn health monitoring method of the present disclosure, the intelligent lawnmower further includes a vision sensor for taking pictures of the lawn and judging the quality grade of the grass based on the color of the grass in the picture.

[0019] According to at least one embodiment of the method for monitoring the health status of a lawn according to the present disclosure, the smart lawnmower further includes a positioning module to obtain the current position of the smart lawnmower. Attached Figure Description

[0020] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0021] Figure 1 This is a structural schematic diagram of a method for monitoring the health status of a lawn according to one embodiment of the present disclosure.

[0022] Figure 2 This is a schematic diagram of the health status of a lawn according to one embodiment of the present disclosure.

[0023] Figure 3 This is a schematic diagram showing the overlapping detection areas of a capacitive sensor according to one embodiment of the present disclosure.

[0024] Figure 4 This is a statistical chart of different quality grades of grass according to one embodiment of the present disclosure. Detailed Implementation

[0025] The present disclosure will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0026] It should be noted that, where there is no conflict, the embodiments and features described in this disclosure can be combined with each other. The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0027] Unless otherwise stated, the exemplary implementations / embodiments shown are to be understood as providing exemplary features of various details that provide ways in which the technical concepts of this disclosure can be implemented in practice. Therefore, unless otherwise stated, the features of various implementations / embodiments may be additionally combined, separated, interchanged and / or rearranged without departing from the technical concepts of this disclosure.

[0028] The use of crosshairs and / or shading in the accompanying drawings is generally used to clarify the boundaries between adjacent components. Thus, unless otherwise stated, the presence or absence of crosshairs or shading does not convey or indicate any preference or requirement for the specific material, material properties, dimensions, proportions, commonalities between the illustrated components, or any other characteristics, properties, etc., of the components. Furthermore, in the accompanying drawings, the dimensions and relative dimensions of components may be exaggerated for clarity and / or descriptive purposes. When exemplary embodiments can be implemented differently, a specific process sequence may be performed in a different order than that described. For example, two consecutively described processes may be performed substantially simultaneously or in the reverse order of their description. Furthermore, the same reference numerals denote the same components.

[0029] When a component is referred to as being "on" or "above" another component, "connected to," or "joined to" another component, the component may be directly on, directly connected to, or directly joined to the other component, or there may be intermediate components. However, when a component is referred to as being "directly on" another component, "directly connected to," or "directly joined to" another component, there are no intermediate components. Therefore, the term "connection" can refer to a physical connection, an electrical connection, etc., and may or may not have intermediate components.

[0030] For descriptive purposes, this disclosure may use spatial relative terms such as “below,” “under,” “below,” “down,” “above,” “above,” “higher,” and “side (e.g., in a “sidewall”)” to describe the relationship between one component and another component as shown in the accompanying drawings. In addition to the orientations depicted in the drawings, the spatial relative terms are also intended to encompass different orientations of the device during use, operation, and / or manufacture. For example, if the device in the drawings is flipped, a component described as “below” or “under” another component or feature would subsequently be positioned “above” said other component or feature. Thus, the exemplary term “below” can encompass both “above” and “below” orientations. Furthermore, the device may be otherwise positioned (e.g., rotated 90 degrees or in other orientations), thus interpreting the spatial relative descriptive terms used herein accordingly.

[0031] The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms “a” and “the” are intended to include the plural forms as well. Furthermore, when the terms “comprising” and / or “including” and variations thereof are used in this specification, it indicates the presence of the stated features, integrals, steps, operations, parts, components, and / or groups thereof, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, parts, components, and / or groups thereof. It should also be noted that, as used herein, the terms “substantially,” “about,” and other similar terms are used as approximate terms rather than as terms of degree, thus explaining the inherent biases in measurements, calculated values, and / or provided values ​​that would be recognized by one of ordinary skill in the art.

[0032] Figure 1 This is a structural schematic diagram of a method for monitoring the health status of a lawn according to one embodiment of the present disclosure.

[0033] like Figure 1 As shown, the method for monitoring the health status of lawns disclosed herein can be implemented using an intelligent lawnmower, that is, the intelligent lawnmower monitors the health status of the lawn while performing lawnmowing operations.

[0034] In this disclosure, the intelligent lawnmower includes a lawnmower body and a capacitive sensor installed on the lawnmower body. The lawnmower body is an autonomous motion platform, such as a mobile robot platform. A positioning module is provided on the lawnmower body, which can be a GPS module or a UWB module, thereby enabling the real-time location of the intelligent lawnmower. The capacitive sensor shown is used to detect the grass on the lawn and obtain the capacitance value of the grass.

[0035] In the method for monitoring the health status of a lawn disclosed herein, firstly, a smart lawnmower is controlled to move on the lawn and cut the grass; simultaneously, data of the grass detected by a capacitive sensor is acquired, and the quality of the grass is graded based on the data to obtain a grass quality grade; and the working state of the smart lawnmower is controlled according to the different grass quality grades.

[0036] In one embodiment, the data of grass detected by the capacitance sensor is the capacitance value of the current collection point; when the capacitance value is greater than or equal to a first preset threshold, the quality level of the grass is determined to be first level; that is, the quality of the grass is good (Good grass); correspondingly, when the capacitance value is less than the first preset threshold but greater than a second preset threshold, the quality level of the grass is determined to be second level; that is, the quality of the grass is average (Normal grass); when the capacitance value is less than or equal to the second preset threshold, the quality level of the grass is determined to be third level; that is, the quality of the grass is poor (Bad grass). The first preset threshold is greater than the second preset threshold.

[0037] In other words, according to this disclosure, the quality of the lawn is positively correlated with the water content and density of the grass blades. Moreover, when the water content of the grass is high (i.e., the grass does not wither) and the grass blades are dense (there is more grass), its capacitance value is also high. Therefore, the quality of the lawn grass is related to the overall capacitance of the lawn. Based on this, this disclosure monitors the health status of the lawn by measuring its capacitance value and analyzes and monitors the current health status of the lawn by measuring the differences in capacitance values.

[0038] In addition, the lawn health monitoring method disclosed herein can also control the working status of the intelligent lawnmower according to different grass quality grades.

[0039] There are two scenarios when controlling the operation of a smart lawnmower:

[0040] The first scenario: When the lawn is being cut for the first time, such as when a smart lawnmower is used for the first time, the smart lawnmower's map does not include the lawn's health status; or when the smart lawnmower is being used on the current lawn for the first time, meaning that although the smart lawnmower has been used before, the location information provided by the positioning module indicates that this is the first time the smart lawnmower has performed a cutting operation on the current lawn. "Controlling the smart lawnmower's working state according to different grass quality grades" includes: when the grass quality grade is level three, controlling the smart lawnmower to repeatedly cut the level three grass, thereby removing as much of the poor-quality grass as possible through multiple cuts. Correspondingly, when the grass quality grade is level one or two, controlling the smart lawnmower to maintain its current working state. Maintaining the current working state means not changing the smart lawnmower's cutting speed or movement speed.

[0041] The second scenario: When cutting the lawn grass for the nth time (n is greater than or equal to 2), the smart lawnmower's actions can be controlled by using the lawn health status obtained from the previous lawn mower cut.

[0042] Specifically, when the smart lawnmower is cutting grass, its location is monitored in real time. For the current area, the lawnmower has already checked the grass quality and cut the grass in that area. Furthermore, if the time interval between the current cutting and the last cutting is less than or equal to a preset time threshold (e.g., 5 days), it indicates that the grass change is within acceptable limits. At this point, the lawnmower can obtain a health status map of the lawn from the last cutting and determine the grass quality level for the current area based on that map. "Controlling the working state of the smart lawnmower according to different grass quality levels" includes: when the grass quality level is level three, since the grass in that area is within the preset time threshold and will not change significantly, the lawnmower can maintain its current working state or maintain its cutting speed while increasing its movement speed; when the grass quality level is level one or two, the cutting speed is increased, i.e., the rotation speed of the cutting blades is increased, while the movement speed of the lawnmower is slowed down, thus effectively cutting away newly grown grass in that area.

[0043] In other words, for lawns that have already been mowed a second or even multiple times by a smart lawnmower, when the grass quality grade is third, the grass in that area will not change significantly within a preset time, or even at all. Therefore, it is not necessary to focus on mowing that area. On the other hand, for first and second grade grass, significant changes may have occurred within the preset time threshold, such as changes in grass length. In such cases, it is advisable to increase the cutting speed of the smart lawnmower and decrease its moving speed to cleanly cut the first and second grade grass.

[0044] In this disclosure, for lawns that have been cut a second or even multiple times by a smart lawnmower, the quality grade of the grass is still detected by a capacitive sensor during the cutting operation. Furthermore, when the quality grade of the grass detected by the capacitive sensor is different from the quality grade of the grass recorded in the lawn health status map, the quality grade of the grass in that area in the lawn health status map is updated with the quality grade of the grass detected by the capacitive sensor.

[0045] In particular, when performing a second or even multiple lawn mowing operations, the frequency of capacitive sensor data collection can be reduced, for example, by increasing the sampling interval of the capacitive sensor, as long as sampling is performed in every area.

[0046] On the other hand, if the time interval between the current cutting and the last cutting by the intelligent lawnmower exceeds a preset time threshold, it is considered that the lawn grass has changed significantly. In this case, "controlling the working state of the intelligent lawnmower according to different grass quality grades" includes: when the grass quality grade is level three, controlling the intelligent lawnmower to repeatedly cut the grass of level three, thereby removing the poor-quality grass as cleanly as possible through multiple cuts. Correspondingly, when the grass quality grade is level one or level two, controlling the intelligent lawnmower to maintain the current working state.

[0047] In a preferred embodiment, after the intelligent lawnmower completes cutting the grass (i.e., after the daily work cycle T is completed), it plots the grass quality grade on a lawn map, forming a lawn health status map. In other words, each time the intelligent lawnmower completes cutting the grass, it generates a lawn health status map and saves it in the lawnmower's memory. After a preset time, the most recent lawn health status map is provided to the user for reference.

[0048] Specifically, the intelligent lawnmower includes an embedded controller that can generate a map of the lawn based on the movement trajectory of the intelligent lawnmower. Correspondingly, the embedded controller can also obtain the location of the intelligent lawnmower based on data sent to it by the positioning module, and obtain the grass quality level corresponding to the location of the intelligent lawnmower based on grass data detected by the capacitive sensor, and record the grass quality level at the corresponding location on the lawn map, thereby generating a lawn health status map.

[0049] In one scenario, when the grass quality level of a certain area on the lawn map is judged as level three multiple times (e.g., 3 times), the user is prompted to replenish grass seeds in that area, thereby improving the overall quality of the grass on the lawn.

[0050] In this disclosure, when the smart lawnmower detects that the daily work plan has been completed, for example, after the smart lawnmower detects that the grass on the lawn has been cut, the smart lawnmower counts the quality grade of the grass in each area for the day, and obtains the percentage of grass status for each grade based on the quality grade of the grass in each area for the day. It then compares the percentage of grass status for each grade for the day with the percentage of grass status for each grade for the previous time, and gives the percentage change of grass for each grade, so that the user can accurately know the overall change of grass on the lawn and further confirm the lawn maintenance method.

[0051] In this disclosure, because the capacitive sensor can achieve continuous or high-frequency detection, there will be overlapping areas between the two capacitance values ​​returned by the capacitive sensor. Based on this, in this disclosure, the lawn map can be divided into multiple regions, which are much larger than the contact area between the capacitive sensor and the grass on the lawn, that is, much larger than the detection area of ​​the capacitive sensor. For example, the area of ​​this region can be 100-200 times the detection area of ​​the capacitive sensor.

[0052] Within this area, the capacitive sensor can continuously or at high frequency detect the grass on the lawn and obtain multiple capacitance values.

[0053] The quality grade of the grass is determined based on the capacitance value; the number of different grass quality grades in the area is counted, and the quality grade of the grass with the most counts is taken as the quality grade of the grass in that area.

[0054] For example, within this area, 300 capacitance values ​​are obtained. Based on these 300 capacitance values, the quality level of the grass is determined and statistically analyzed. In one embodiment, among these 300 capacitance values, 100 correspond to the first-level grass, 80 correspond to the second-level grass, and 20 correspond to the third-level grass. Thus, the quality level of the grass in this area is set as the first level, thereby obtaining a comprehensive health status map of the grass. Moreover, obtaining the quality level of the grass in this area using statistical methods can overcome the fluctuations in capacitance values ​​detected by the capacitance sensor, making the health status map of the grass more accurate.

[0055] In this disclosure, the intelligent lawnmower also includes a vision sensor. The vision sensor is used to capture images of the lawn and determine the quality grade of the grass based on the color of the grass in the images, thereby confirming the quality grade of the grass detected by the capacitive sensor. In one embodiment, if the quality grade of the grass detected by the capacitive sensor is a first or second grade, there is no need to use the vision sensor to reconfirm the quality grade of the grass in that area. However, if the quality grade of the grass detected by the capacitive sensor is a third grade, then the vision sensor is needed for auxiliary judgment. When the quality grade of the grass detected by the vision sensor is the same as that detected by the capacitive sensor, the quality grade of the grass in that area is recorded on the lawn map. On the other hand, when the quality grade of the grass detected by the vision sensor is different from that detected by the capacitive sensor, the capacitive sensor is used again to detect the quality grade of the grass in that area, and the result of this detection of the quality grade of the grass in that area by the capacitive sensor is taken as the standard.

[0056] The intelligent lawnmower disclosed herein also includes a communication module, thereby enabling the intelligent lawnmower to connect directly or indirectly to a handheld terminal, such as a mobile phone, so as to send a health status map of the lawn to the handheld terminal and display it on the handheld terminal.

[0057] Furthermore, the lawn health monitoring method disclosed herein utilizes a capacitive sensor found in intelligent lawnmowers, and even if the intelligent lawnmower does not include this capacitive sensor, the cost of adding one is low. Additionally, the intelligent lawnmower disclosed herein includes a positioning module, which, combined with the high-precision positioning information provided, can provide customers with more information on lawn health and status.

[0058] The intelligent lawnmower disclosed herein can also work in conjunction with other robots that can perform functions such as seeding, watering, and fertilizing, and can automatically mow grass, replenish grass seeds, sow seeds, water, and fertilize.

[0059] Furthermore, the intelligent lawnmower disclosed herein can perform lawn mowing operations daily, thereby enabling users to receive a daily lawn health status map and obtain information on the grass change trends through these maps, thus allowing for more reasonable lawn management.

[0060] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment / mode or example is included in at least one embodiment / mode or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0061] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0062] Those skilled in the art should understand that the above embodiments are merely for illustrating the present disclosure and are not intended to limit the scope of the disclosure. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present disclosure.

Claims

1. A method for monitoring the health status of a lawn, characterized in that, include: The system controls an intelligent lawnmower to move across a lawn and cut the grass. The intelligent lawnmower includes a mower body and a capacitive sensor mounted on the mower body. The capacitive sensor is used to detect the grass on the lawn. The mower body is an autonomous motion platform. Acquire data on the grass detected by the capacitive sensor, and classify the quality of the grass based on this data to obtain the grass quality grade; and The intelligent lawnmower's operating status is controlled according to the different quality grades of the grass. The data of grass detected by the capacitance sensor is the capacitance value of the current collection point; when the capacitance value is greater than or equal to a first preset threshold, the quality level of the grass is determined to be the first level; when the capacitance value is less than the first preset threshold but greater than a second preset threshold, the quality level of the grass is determined to be the second level; when the capacitance value is less than or equal to the second preset threshold, the quality level of the grass is determined to be the third level; wherein, the first preset threshold is greater than the second preset threshold; Specifically, during the first cut of the lawn grass: when the grass quality grade is third, the intelligent lawnmower is controlled to repeatedly cut the grass of the third grade; when the grass quality grade is first or second, the intelligent lawnmower is controlled to maintain the current working state. The system includes the following steps: When the smart lawnmower is cutting grass, its location is monitored in real-time. For the current area, the lawnmower has already checked the grass quality and cut the grass in that area. If the time interval between the current cutting time and the previous cutting time is less than or equal to a preset time threshold, the system retrieves the lawn health status map obtained during the previous cutting and determines the grass quality level for the current area based on that map. When the grass quality level is level three, the system maintains the current operating state. When the grass quality level is level one or two, the cutting speed is increased, and the moving speed is decreased. When the time interval between the current cutting time and the previous cutting time is greater than a preset time threshold: if the grass quality level is level three, the system repeatedly cuts the level three grass; if the grass quality level is level one or two, the system maintains the current operating state. In this process, after the intelligent lawnmower finishes cutting the grass, it plots the quality grade of the grass on the lawn map to form a lawn health status map. When the quality grade of the grass detected by the capacitive sensor is different from the quality grade of the grass recorded in the lawn health status map, the quality grade of the grass in that area in the lawn health status map is updated with the quality grade of the grass detected by the capacitive sensor. Among these measures, the frequency of data collection by the capacitive sensor should be reduced when multiple grass-cutting operations are performed on the lawn.

2. The method for monitoring the health status of a lawn as described in claim 1, characterized in that, When the grass quality level of a certain area on the lawn map is repeatedly judged as level three, the user is reminded to replenish grass seeds in that area.

3. The method for monitoring the health status of a lawn as described in claim 1, characterized in that, The lawn is divided into multiple areas, each with an area more than 100 times the detection area of ​​a capacitive sensor. When the capacitive sensor detects the grass in the area, it obtains multiple capacitance values. The quality grade of the grass is determined based on these capacitance values. The number of different grass quality grades in the area is counted, and the quality grade with the highest number of grasses is taken as the quality grade of the grass in that area.

4. The method for monitoring the health status of a lawn as described in claim 1, characterized in that, The intelligent lawnmower also includes a vision sensor, which is used to take pictures of the lawn and determine the quality grade of the grass based on the color of the grass in the picture.

5. The method for monitoring the health status of a lawn as described in claim 1, characterized in that, The intelligent lawnmower also includes a positioning module to obtain the current location of the intelligent lawnmower.

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