Lawn maintenance decision method, mowing robot control system, and mowing robot

Abstract

The application relates to the technical field of intelligent mowing robots, and provides a lawn maintenance decision method, a mowing robot control system and a mowing robot.The method comprises the following steps: acquiring lawn basic data, mowing equipment operation data and environment data; predicting a grass height curve and a risk curve in a target time period based on the grass type, the historical mowing height, current meteorological data and predicted meteorological data; predicting a lawn health state in the target time period according to the grass height curve and the risk curve; and planning a first maintenance task based on the lawn health state.Based on this, the lawn maintenance task can be automatically planned, the rationality of task planning can be improved by combining the lawn basic data, equipment data and environment data in the task planning process, and the dependence on user experience can be reduced.

Description

Technical Field

[0001] This application relates to the field of intelligent lawn mowing robot control technology, and in particular to lawn maintenance decision-making methods, lawn mowing robot control systems, and lawn mowing robots. Background Technology

[0002] With the development of automation technology and artificial intelligence, intelligent lawn mowing robots have been widely used in home gardens, public green spaces, and other scenarios, becoming an efficient tool to replace traditional manual mowing. In existing technologies, the task execution of intelligent lawn mowing robots typically relies on two triggering modes: user-triggered mode: users manually start the device through mobile terminal applications, physical buttons, or voice commands, and the robot only performs a single mowing task after receiving the command; fixed-period triggering mode: the robot automatically starts according to preset time intervals (such as daily or weekly) and completes the mowing task in a periodic cycle.

[0003] However, the above-mentioned modes have significant limitations in practical applications. For example, the temporary trigger mode suffers from insufficient timeliness and strong operational dependence, while the fixed-cycle trigger mode suffers from poor environmental adaptability and low energy efficiency.

[0004] Specifically, users need to actively monitor the lawn's growth status and manually trigger tasks. Failure to do so in a timely manner can easily lead to excessive lawn growth (such as weed spread and uneven height), affecting aesthetics and subsequent mowing efficiency. Furthermore, users need to continuously monitor the equipment status, especially during the rainy season or rapid growth period, which requires frequent intervention and violates the "maintenance-free" design intention of automated equipment.

[0005] Lawn growth rates are significantly affected by climate (temperature, humidity, light), season, and local microenvironments (such as differences between shaded and sunny areas). Fixed-cycle patterns cannot dynamically match actual needs, leading to over-mowing (damaging the turf) or under-mowing (proliferating weeds). During periods of slow lawn growth (such as winter), equipment continues to operate on a fixed cycle, resulting in unnecessary energy consumption; conversely, during rapid growth periods (such as spring), excessively long cycles may cause the lawn to become uncontrollable.

[0006] To address the aforementioned issues, there is an urgent need for a control method for intelligent lawn mowing robots to improve mowing efficiency, reduce resource consumption, and minimize the need for user intervention. Summary of the Invention

[0007] To address the aforementioned technical problems, this application provides a lawn maintenance decision-making method applied to a lawnmower robot control system. The method includes: acquiring basic lawn data, lawnmower operating data, and environmental data, wherein the basic lawn data includes grass species type, the lawnmower operating data includes historical mowing height, and the environmental data includes current weather data and predicted weather data for a target time period; predicting a grass height curve and a risk curve for the target time period based on the grass species type, the historical mowing height, the current weather data, and the predicted weather data; predicting the lawn health status for the target time period based on the grass height curve and the risk curve; and planning a first maintenance task based on the lawn health status.

[0008] In one implementation, the method for predicting the grass height curve and risk curve within the target time period includes: constructing a water budget model input layer based on the current meteorological data and the predicted meteorological data, and calculating based on the water budget model to obtain the soil moisture content curve corresponding to the target time period; matching the corresponding grass species parameter model in a preset grass species model library based on the grass species type to obtain the recommended mowing height range, growth rate range, and growth sensitivity parameter corresponding to the grass species type; constructing a growth model input layer based on the current meteorological data, the predicted meteorological data, the moisture content curve, the recommended mowing height range, the growth rate range, and the growth sensitivity parameter, and calculating based on the growth model to obtain the grass height curve and risk curve corresponding to the target time period.

[0009] In one implementation, the method for predicting the lawn health status within the target time period includes: determining grass height data and growth status within the target time period based on the grass height curve; determining humidity conditions, physiological stress, disease risk, and lawn structural integrity within the target time period based on the risk curve; and calculating the lawn health status corresponding to the target time period based on the grass height data, growth status, humidity conditions, physiological stress, disease risk, and lawn structural integrity.

[0010] In one implementation, the method for calculating the lawn health status corresponding to the target time period based on the grass height data, the growth status, the humidity conditions, the physiological stress, the disease risk, and the lawn structural integrity includes: obtaining a growth score based on the grass height data and the growth status assessment; obtaining a physiological score based on the humidity conditions and the physiological stress assessment; obtaining a risk score based on the disease risk assessment; obtaining a structural score based on the lawn structural integrity assessment; and calculating the lawn health status based on the growth score, the physiological score, the risk score, the structural score, and the corresponding weights.

[0011] In one implementation, the method for planning a first maintenance task based on the lawn health status includes: determining a first time within the target time period that is suitable for mowing the lawn and a second time that is not suitable for mowing the lawn based on the lawn health status; and planning a first maintenance task based on the first time and the second time.

[0012] In one implementation, the first maintenance task includes a first sub-task and a second sub-task. The method for planning the first maintenance task based on the first time and the second time includes: calculating the time difference between each first time and the adjacent second time; if the time difference is less than a preset time difference threshold, discarding it; generating the first sub-task based on the remaining first time to instruct the lawn mowing equipment to start mowing the lawn at the first time; and generating the second sub-task based on the second time to prevent the lawn mowing equipment from mowing the lawn at the second time.

[0013] In one implementation, the method further includes: generating a task execution instruction based on the first maintenance task and sending it to the mowing equipment; and updating the grass seed model based on the task execution result.

[0014] In one implementation, the method further includes generating a second maintenance task based on the first maintenance task, wherein the second maintenance task is used to initiate execution when the first maintenance task fails.

[0015] Based on the same inventive concept, this application also provides a lawn mowing robot control system, which makes lawn maintenance decisions based on the above method.

[0016] In addition, this application also provides a lawn mowing robot, including a controller and a lawn mowing component. The controller receives a task execution instruction sent by the aforementioned system, and determines whether to change the operating state of the lawn mowing component based on the task execution time and local time carried in the task execution instruction. If the determination is yes, a control instruction is sent to the lawn mowing component to change the lawn mowing component to the target state indicated by the task execution instruction.

[0017] Based on the above technical solution, this application predicts grass height curves and risk curves by acquiring basic lawn data, mowing equipment operation data, and environmental data, thereby predicting the lawn health status and planning mowing tasks accordingly. By dynamically predicting the lawn health status and planning mowing tasks, it can adapt to different grass species and environmental changes, improve the accuracy and efficiency of lawn maintenance, reduce lawn damage, and enhance overall health. At the same time, it also reduces the dependence of maintenance management on users. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the control system structure of a lawnmower robot provided in one embodiment of this application.

[0020] Figure 2 This is a flowchart of a lawn maintenance decision-making method provided in an embodiment of this application.

[0021] Figure 3 This is a flowchart illustrating the method for predicting grass height curves and risk curves within a target time period, as described in this application embodiment.

[0022] Figure 4 This is a flowchart of a method for predicting the health status of a lawn within a target time period, as described in this application.

[0023] Figure 5 This is a flowchart illustrating the method for planning the first maintenance task based on the health status of the lawn in this application embodiment. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this invention.

[0025] In the description of this application, it should be noted that the terms "first" and "second" are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] The lawn maintenance decision-making method provided in this application can automatically generate lawn maintenance tasks. In the process of generating tasks, it integrates meteorological data, lawn data and historical mowing data for evaluation and calculation. Through multi-source data fusion and intelligent algorithms, it achieves adaptive optimization of task triggering, thereby improving mowing efficiency, reducing resource consumption and reducing the need for user intervention.

[0027] The lawn maintenance decision-making method provided in this application can be applied to the control system of a lawnmower robot. In implementation, please refer to... Figure 1 The lawnmower control system includes a cloud server 10, a lawnmower 20, and a client 30, which communicate with each other.

[0028] In this embodiment, the lawn maintenance decision-making method is executed by the cloud server 10, that is, the corresponding software program is deployed and runs on the cloud server 10; in other embodiments, the lawn maintenance decision-making method can also be executed by the lawn mowing robot 20, that is, the corresponding software program is deployed and runs on the lawn mowing robot 20.

[0029] Please refer to Figure 2 The lawn maintenance decision-making method provided in this application specifically includes the following steps.

[0030] S101, in response to the task generation command, acquires basic lawn data, lawn mowing equipment operation data, and environmental data.

[0031] In practice, task generation instructions can be automatically triggered based on preset timed tasks, issued by users through the client, or generated and issued by the lawnmower robot based on abnormal task execution status.

[0032] The basic lawn data includes, but is not limited to, grass species, lawn area, lawn location or climate zone, and soil type. This data can be collected and stored in a configuration file based on the actual condition of the user's lawn. Accordingly, the basic lawn data can be obtained by reading the configuration file.

[0033] The operating data of the lawn mowing equipment includes historical mowing height, duration of a single mowing session, mowing frequency per unit time, and mowing coverage area. This data is uploaded and stored in a data table in real time by the lawn mowing robot. Accordingly, the operating data of the lawn mowing equipment can be obtained by reading the data table.

[0034] Environmental data includes current meteorological data and forecast meteorological data for the target time period. The meteorological data includes one or more of the following: ambient temperature, rainfall, humidity, and light intensity. Specific data can be obtained through third-party meteorological data platform interfaces.

[0035] S102, based on grass species type, historical mowing height, current meteorological data and predicted meteorological data, predicts grass height curves and risk curves for the target time period.

[0036] The target time period can be pre-configured by the system or set by the user, such as 7 days, 15 days, 30 days, etc.

[0037] A grass height curve is used to represent the predicted trend of grass height change over time within a target period. This curve can reflect the grass's growth rate and potential growth peaks or troughs.

[0038] A risk curve is used to represent the predicted trend of potential risks to a lawn (such as pest and disease risk, physiological stress risk, drought risk, etc.) over time within a target period. This curve is used to assess the vulnerability and health threats to a lawn.

[0039] During implementation, please refer to Figure 3 The method for predicting the grass height curve and risk curve within a target time period includes the following steps.

[0040] S201, based on current meteorological data and predicted meteorological data, constructs the input layer of the water budget model, and calculates the soil moisture content curve corresponding to the target time period based on the water budget model.

[0041] Specifically, when constructing the input layer of the water budget model, real-time meteorological data such as temperature, rainfall, humidity, wind speed, and sunshine duration are first collected, along with meteorological forecast data for a future period (i.e., the target time period), such as predicted temperature and predicted rainfall. These data serve as the initial and driving parameters for the water budget model. Furthermore, the current and predicted meteorological data can be preprocessed, such as performing time series alignment, missing value imputation, and outlier handling, before being formatted into the specific input format required by the water budget model, such as daily average temperature, daily total rainfall, and daily evapotranspiration, to complete the construction of the input layer.

[0042] When calculating soil moisture content curves for a target time period based on a water budget model, one example uses a common soil moisture balance model, such as the Penman-Monteith model combined with a soil reservoir model. This model comprehensively considers multiple factors, including precipitation, evapotranspiration, runoff, and deep infiltration, to calculate soil moisture content at different time points, thus obtaining the soil moisture content curve. Another example uses numerical integration or iterative algorithms to calculate soil moisture content daily or hourly within the target time period based on the physical equations of the water budget model, generating a curve that changes over time to dynamically reflect the availability of water in the soil.

[0043] S202, based on the grass species type, match the corresponding grass species parameter model in the preset grass species model library to obtain the recommended mowing height range, growth rate range and growth sensitivity parameters corresponding to the grass species type.

[0044] Specifically, a database or model library containing detailed parameters for various common grass species (such as tall fescue, Kentucky bluegrass, and ryegrass) can be pre-established. When a specific grass species type is input, the system performs an exact match in this database, extracting all the preset parameters corresponding to that grass species. Furthermore, if the grass species type is complex or not precisely matched, fuzzy matching or rule-based matching methods can be used to select the closest or most suitable parameter set from the model library based on the grass species' family, genus, growth habits, and other characteristics.

[0045] The grass species parameter model includes recommended mowing height ranges, growth rate ranges, and growth sensitivity parameters corresponding to different grass species. The recommended mowing height range defines the minimum and maximum suitable mowing height for the grass species under healthy conditions; for example, tall fescue might be recommended at 5-8 cm, while greengrass might be recommended at 0.5-1.5 cm. The growth rate range describes the range of growth rate of the grass species under ideal conditions, such as 0.5-1.5 cm per day, which is affected by factors such as temperature and light. Growth sensitivity parameters include response coefficients to environmental factors such as temperature, light, water, and nutrients. For example, the temperature sensitivity coefficient determines the increase or decrease in growth rate at different temperatures, and the water sensitivity coefficient determines the degree to which growth is inhibited when water is scarce. These parameters can be calibrated and standardized through long-term field trials, laboratory studies, literature, or expert experience, and stored in a model library for easy access.

[0046] S203 is an input layer for a growth model, which is constructed based on current meteorological data, predicted meteorological data, moisture content curves, recommended mowing height ranges, growth rate ranges, and growth-sensitive parameters. Based on the growth model, calculations are performed to obtain the grass height curve and risk curve corresponding to the target time period.

[0047] In practice, the growth model can be a turfgrass growth model based on physiological and ecological processes, such as a dynamic model based on mechanisms such as photosynthesis, respiration, transpiration, and biomass allocation, to simulate the growth process of turfgrass under different environmental conditions.

[0048] When constructing the input layer of the growth model, the aforementioned data, including real-time meteorological data, predicted meteorological data, soil moisture content curves, and matched grass species parameters (recommended mowing height range, growth rate range, and growth-sensitive parameters), undergo a unified data format conversion and time series alignment to form a multi-dimensional input vector or matrix, which serves as the input to the growth model. Simultaneously, feature engineering can be performed on the original data, such as calculating derived features like daily average temperature, cumulative rainfall, effective accumulated temperature, and water stress index, to better capture the impact of environmental factors on lawn growth and use them as input to the growth model.

[0049] The grass height curve can be obtained based on the correspondence between grass height and time output by the generative model.

[0050] The risk curve can be obtained by evaluating factors such as grass height, growth rate, water stress level, and temperature stress level output by the growth model, combined with preset risk thresholds and rules. For example, the risk value will increase when grass height is too low or too high, the water stress index exceeds the threshold, or extreme weather is predicted. The risk curve can be represented as a comprehensive index or probability of different risk factors.

[0051] S103 predicts the health status of the lawn within a target time period based on the grass height curve and risk curve.

[0052] Among them, lawn health status is a quantitative assessment of the overall health level of lawn, which takes into account multiple dimensions such as lawn growth, physiological stress, disease risk and structural integrity.

[0053] In implementation, please refer to Figure 4 The method for predicting the health status of lawns within a target time period includes the following steps.

[0054] S301 determines grass height data and growth status within a target time period based on the grass height curve.

[0055] Specifically, a grass height curve is a dynamic trend chart predicting the change in grass height over time within a target period. Determining grass height data and growth status aims to extract specific quantitative information from this dynamic curve.

[0056] In one example, a fixed time interval can be set, such as every 24 hours or less, to sample the grass height curve, thereby obtaining discrete grass height data points. Simultaneously, by comparing the amount and rate of change in grass height between adjacent sampling points, the growth status of the lawn can be determined. For example, if the grass height continues to grow rapidly, it is determined to be in a vigorous growth period; if the grass height growth is slow or stagnant, it is determined to be in a stable or dormant period.

[0057] In another example, mathematical models, such as polynomial fitting and spline interpolation, can be used to model the grass height curve, thereby enabling accurate calculation of grass height data at any time within the target time period. The growth status can then be determined by differentiating this mathematical model to obtain the instantaneous growth rate, and based on a preset growth rate threshold range, the lawn's growth status can be categorized into different levels, such as fast growth, medium growth, and slow growth.

[0058] S302 determines humidity conditions, physiological stress, disease risk, and lawn structural integrity within a target time period based on risk curves.

[0059] Specifically, a risk curve is a comprehensive or component trend chart predicting various adverse factors that a lawn may face during a target time period. It identifies humidity conditions, physiological stress, disease risk, and lawn structural integrity, aiming to deduce specific risk factors and their severity from the risk curve. In one example, the risk curve can be a comprehensive risk index, the calculation of which integrates multiple environmental and biological factors. The system can preset a series of thresholds; when the comprehensive risk index or specific components within it exceed these thresholds, it can identify the corresponding humidity conditions (e.g., excessively wet or dry), physiological stress (e.g., high-temperature stress, nutrient deficiency), disease risk (e.g., increased risk of fungal infection), and lawn structural integrity (e.g., risk of physical damage due to environmental stress). In another example, the risk curve can consist of multiple independent sub-curves, each representing a specific risk type; for example, a humidity risk curve, a physiological stress curve, a disease risk curve, and a structural integrity risk curve. The system can directly read or calculate the corresponding humidity conditions, physiological stress levels, disease risk levels, and lawn structural integrity assessment results for the target time period from these independent curves.

[0060] S303 calculates the lawn health status for a target time period based on grass height data, growth status, humidity conditions, physiological stress, disease risk, and lawn structural integrity.

[0061] In implementation, to obtain the lawn health status at each target time point within the target time period, calculations can be performed based on grass height data, growth status, humidity conditions, physiological stress, disease risk, and lawn structural integrity at each target time point. The target time points can be determined directly based on preset time intervals, for example, every 24 hours can be designated as a target time point.

[0062] In one example, a multi-index weighted scoring model can be used to determine lawn health status. This involves pre-assigning different weights to various indicators such as grass height, growth status, humidity conditions, physiological stress, disease risk, and lawn structural integrity, and then standardizing each indicator to obtain a score. The final lawn health status score is obtained by summing the standardized scores of each indicator multiplied by their corresponding weights. The weights can be dynamically adjusted based on grass species characteristics, seasonal changes, or expert experience.

[0063] For example, a growth score P1 can be obtained first based on grass height data and growth status assessment; a physiological score P2 can be obtained based on humidity conditions and the aforementioned physiological stress assessment; a risk score P3 can be obtained based on disease risk assessment; and a structure score P4 can be obtained based on turf structure integrity assessment. Then, based on the growth score P1, physiological score P2, risk score P3, structure score P4, and corresponding weights w1, w2, w3, and w4, the turf health status H is calculated using the following formula: H = P1w1 + P2w2 + P3w3 + P4w4 The growth score P1 can be calculated based on grass height data and growth status. For example, firstly, based on the difference between the predicted grass height and the mowing trigger threshold, a corresponding height score table is matched according to the difference. The larger the difference, the higher the height score. The mowing trigger threshold refers to the grass height exceeding this value, at which point mowing is required. Then, the height score is adjusted based on the growth status. For example, when the growth rate at the target time point is greater than the first rate threshold, the adjustment coefficient corresponding to the growth status is set to a value greater than 1. When the growth rate at the target time point is less than the second rate threshold, the adjustment coefficient corresponding to the growth status is set to a value less than 1. When the growth rate is less than or equal to the first rate threshold and greater than or equal to the second rate threshold, the adjustment coefficient is set to 1. The first and second rate thresholds are determined based on the average growth rate of the grass species. Finally, the product of the adjustment coefficient and the height score is determined as the growth score P1.

[0064] The physiological score P2 can be determined based on humidity conditions and physiological stress. Specifically, the humidity conditions at the target time point can be determined based on meteorological forecast data. If the humidity conditions trigger grass growth sensitive parameters (such as high temperature and high humidity), which will have a negative impact on the growth rate, the score will be reduced.

[0065] The risk score P3 can be determined based on the disease risk curve. Specifically, the probability of occurrence of a specific disease can be calculated based on meteorological forecast data; the higher the probability, the lower the score.

[0066] The structural score P4 can be determined based on the structural integrity of the turf. Specifically, it can be calculated using a water budget model to determine the soil moisture content curve. When the soil moisture approaches saturation, leading to a decrease in bearing capacity, the score tends to approach 0.

[0067] The sum of all weights w1, w2, w3, and w4 is 1. The value of each weight can be dynamically adjusted according to the actual operating data. Under different grass species, different seasons, and different time periods, each weight can be dynamically adjusted to adapt to different actual conditions.

[0068] In another example, machine learning algorithms such as decision trees, random forests, support vector machines, or neural networks are used. Grass height data, growth status, humidity conditions, physiological stress, disease risk, and turf structure integrity are used as input features. A pre-trained model predicts the health status of the turf within a target time period, which can be a discrete level (e.g., "Excellent," "Good," "Medium," "Poor") or a continuous health index. The model's training data can come from historical turf maintenance data, environmental monitoring data, and turf health records assessed manually or by experts.

[0069] S104, the first maintenance task is planned based on the health status of the lawn.

[0070] During implementation, please refer to Figure 5 The method for planning the first maintenance task based on the health status of the lawn specifically includes the following steps.

[0071] S401, based on the lawn's health status, determines the first suitable time for mowing the lawn and the second unsuitable time for mowing the lawn within the target time period.

[0072] In one example, the first and second times of abnormal condition can be determined based on the relationship between the lawn's health status and the normal state range. For instance, in the lawn health status scoring example above, when the lawn health status score at the target time point is greater than the upper limit of the set normal state range (e.g., H ≥ 0.8), the first time point is determined based on that target time point. Conversely, when the score is less than the lower limit (e.g., H ≤ 0.4), the second time point is determined based on that target time point. It's worth noting that the first and second times can be the corresponding target time points, or a time period centered on the target time point. For example, if the target time point is 12:00, then 11:00-13:00 would be the first time point, allowing for flexible task execution.

[0073] In some implementations, key parameters can be further combined to determine the first and second time points, thereby avoiding the omission of key task time points due to algorithmic deficiencies. These key parameters include one or more of the following: grass height data, growth status, humidity conditions, physiological stress, disease risk, and turf structure integrity. In implementation, task triggering conditions can be set for each key parameter. When a key parameter meets the task triggering condition, the corresponding target time point is determined as either the first or second time point.

[0074] S402, the first maintenance task is planned based on the first and second time periods.

[0075] In implementation, the first maintenance task includes a first sub-task and a second sub-task. The method for planning the first maintenance task includes: calculating the time difference between each first time and the adjacent second time; if the time difference is less than a preset time difference threshold, it is removed; generating a first sub-task based on the remaining first time to instruct the lawn mowing equipment to start mowing the lawn at the first time; and generating a second sub-task based on the second time to prevent the lawn mowing equipment from mowing the lawn at the second time.

[0076] The preset time difference threshold is a pre-defined time interval value used to determine whether a first time and a second time are too close together. This threshold can be set based on factors such as the growth characteristics of the lawn, the operating efficiency of the mowing equipment, or the precision of the maintenance strategy. For example, it can be set to 24 hours or 48 hours. The removal operation refers to removing a task from the list of tasks to be planned when the time difference between the first time and the adjacent second time is less than the preset time difference threshold, in order to avoid unnecessary mowing or potential damage.

[0077] Based on this, the problem of unreasonable lawn mowing task planning caused by the first and second time points being too close together can be effectively solved. Generating the first sub-task based on the optimized remaining first time point allows for precise instruction of the lawn mowing equipment to perform mowing operations at the most suitable time, significantly improving the reliability of task execution and the precision of lawn maintenance. Simultaneously, generating the second sub-task based on the second time point proactively prevents the lawn mowing equipment from operating at unsuitable times, effectively avoiding damage to the lawn under adverse conditions. Overall, these measures work synergistically to achieve refined and intelligent lawn mowing task planning, resolve time conflict issues, and significantly improve the efficiency of lawn maintenance and the health of the lawn.

[0078] S105, generate task execution instructions based on the first maintenance task and send them to the lawn mowing equipment.

[0079] In implementation, the task execution instruction includes the task execution time and task content. The task content includes the task type and corresponding parameters. For example, in the task execution instruction corresponding to the first subtask, the task content is to mow the lawn and the target mowing height. The target mowing height is determined based on the recommended mowing height range. In the task execution instruction corresponding to the second subtask, the task content is to prohibit lawn mowing and the corresponding time window. Within the time window, the prohibition instruction is valid.

[0080] The task execution command can be sent all at once or in multiple installments. This application is not limited to this.

[0081] S106, Update the grass seed model based on the task execution results.

[0082] During implementation, after the lawn mowing equipment completes the corresponding maintenance task according to the received task execution instructions, it can synchronize the corresponding task execution feedback data to the cloud server to evaluate the execution effect of the lawn maintenance decision parameters. Based on the evaluation results, the grass species parameter model is updated, including adjusting the lawn growth rate parameters and recommended mowing interval parameters, for subsequent lawn maintenance decisions.

[0083] The aforementioned method comprehensively acquires basic lawn data, mowing equipment operation data, and environmental data. Based on this multi-dimensional information, it dynamically predicts grass height and risk curves, thereby assessing the lawn's health status and ultimately planning mowing tasks. This solves the problem of unreasonable lawn maintenance caused by fixed mowing parameters in existing technologies, enabling dynamic responses to lawn growth status, assisting users in making refined maintenance decisions, and forming long-term optimized lawn maintenance strategies, thereby improving the overall health of the lawn.

[0084] The grass height curve provides a dynamic trend of lawn height changes. By determining grass height data and growth status based on the grass height curve, the actual growth condition of the lawn can be quantified, avoiding the limitations of judging solely based on single height data. Simultaneously, based on the risk curve, humidity conditions, physiological stress, disease risk, and lawn structural integrity are determined, enabling the system to comprehensively identify and quantify the potential threats of environmental factors to the lawn, ensuring that key risk dimensions are not overlooked in the health assessment. Finally, by comprehensively calculating based on grass height data, growth status, humidity conditions, physiological stress, disease risk, and lawn structural integrity, this application can integrate multi-dimensional data from both growth and risk perspectives to generate more reliable and refined lawn health status assessment results. This provides a more accurate and comprehensive basis for subsequent mowing task planning, thereby optimizing the adaptability and effectiveness of overall lawn maintenance decisions, enabling mowing tasks to better respond to the actual needs of the lawn and environmental changes.

[0085] In some embodiments of this application, after generating the first maintenance task, a second maintenance task can also be generated based on the first maintenance task. The second maintenance task is used to start execution when the first maintenance task fails.

[0086] Specifically, in some application scenarios, the execution time of the first maintenance task corresponds to a preset target time period. Within the preset target time period, some sub-tasks may fail to be executed smoothly due to reasons such as equipment failure, human interference, or environmental anomalies, which reduces the rationality of subsequent unexecuted sub-tasks.

[0087] Based on this, in one implementation, a target subtask can be determined in advance according to the task content and time relationship of each subtask, the impact of the failure of the target subtask on subsequent subtasks can be simulated, and the subsequent subtasks can be adjusted accordingly to generate a second maintenance task, which is used to start execution when the target subtask is not executed successfully.

[0088] In one example, a first subtask that is the second subtask adjacent to an adjacent first subtask can be selected from all subtasks and identified as the target subtask. For example, if a first subtask is adjacent to a second subtask, it is identified as the target subtask. Then, based on whether the time interval between the target subtask and the next subtask is greater than the target interval, it is determined whether to insert a first subtask before the target subtask. If it is greater than the target interval, insertion is confirmed. If it is less than or equal to the target interval, it is further determined whether to insert a first subtask after the target subtask based on whether the time interval between the target subtask and the next subtask is greater than the target interval. If it is greater than the target interval, addition is confirmed. If it is less than or equal to the target interval, the process continues until the insertion of the first subtask is completed, or the preset target time period or preset time distance threshold is exceeded (i.e., the difference between the insertion time of the task and the execution time of the target subtask exceeds the preset time distance threshold). In this way, all subsequent tasks adjusted after the simulated target subtask fails can be used as the second maintenance task. It is understandable that when there are multiple target subtasks, there will also be multiple corresponding second maintenance tasks. Consequently, different target subtasks will result in different second maintenance tasks being executed after they fail.

[0089] The target interval is determined based on the predicted growth rate for the corresponding time period. Specifically, it can be calculated based on the growth rate for that time period and the difference between the recommended mowing height and the mowing trigger threshold. For example, the time required for the grass height to increase by the height difference can be predicted based on the height difference and growth rate, and then multiplied by a target multiple to determine the corresponding target interval. The target multiple is greater than 1 and can be set based on experience. In this way, it can be ensured to a certain extent that after inserting the first subtask, the mowing task that should be completed can be completed as soon as possible, and the subsequent first subtasks will not be completely useless, thus avoiding energy waste.

[0090] Of the two implementation methods described above, the first implementation method is more suitable for scenarios where the communication stability between the device and the cloud server is poor.

[0091] Similarly, after generating the second maintenance task, a corresponding task execution instruction is generated and sent to the lawn mowing equipment. It is worth noting that, unlike the first maintenance task, the task execution instruction of the second maintenance task also includes a task trigger condition, so as to automatically trigger the execution of the second maintenance task when the target sub-task fails.

[0092] In this way, by selecting target subtasks and inserting them into the first subtask at appropriate execution times, the pruning tasks that should be executed can be completed in a timely manner.

[0093] In other embodiments of this application, when the cloud server determines, based on data interaction with the device, that a sub-task has not been successfully executed, it can directly generate a new first maintenance task based on the current time and relevant collected data using the method described above, and send it to the lawnmower for execution. This allows for dynamic updates of the first maintenance task based on real-time task execution. Especially in the early stages of application, when incomplete data may lead to unreasonable task generation, a new first maintenance task can be generated based on execution feedback and newly collected data when it is detected that the unexecuted sub-task was forcibly canceled by the user multiple times. This achieves automatic data updates and dynamic task adjustments, ensuring that the generated task continuously aligns with user expectations.

[0094] Accordingly, this application also provides a lawn mowing robot, including a controller and a lawn mowing component, wherein the controller receives and stores task execution instructions sent by the system, and is able to complete the corresponding task according to the task execution instructions.

[0095] Specifically, the controller can set a timed task according to the task execution time in the task execution instruction. When the local time reaches the task execution time, the task is triggered. Then, based on the task content, it determines whether to change the running state of the lawn mowing component. If it is determined to be yes, a control instruction is sent to the lawn mowing component to change the lawn mowing component to the target state indicated by the task content and complete the corresponding task content. If it is determined to be no, the running state of the lawn mowing component is continuously monitored to determine the completion status of the task content.

[0096] In implementation, the method for determining whether to change the operating state of the lawn mowing component includes: obtaining the control parameters of the lawn mowing component, and determining whether the execution result corresponding to the control parameters completely covers the task content. For example, the current mowing height meets the mowing height required in the task content, the current state is standby, and the task content is prohibiting mowing operations. If it is determined that the task content is completely covered, it is determined as no; if it is determined that the task content is not completely covered, i.e., partially covered, or completely not covered, it is determined as yes.

[0097] If the determination is yes, the specific control parameters that need to be issued can be determined based on the coverage of the task content, and a control command containing the control parameters can be sent to the lawn mowing component to adjust the running state of the lawn mowing component to the target state indicated by the task content.

[0098] In this way, the corresponding adjustment strategy can be adaptively generated by combining the real-time operating status of the lawn mowing component, thereby avoiding unnecessary repetitive work caused by ignoring the current actual working conditions.

[0099] In some implementations, when the task is to mow the lawn, but the mowing assembly is not currently operating, the controller can further determine whether the mowing conditions are met. These mowing conditions are crucial for ensuring the safe and effective execution of the mowing operation. Specifically, meeting the mowing conditions can include various scenarios: On one hand, mowing conditions can involve environmental factors. For example, the controller can acquire current weather data (such as whether it is raining, wind speed) and soil moisture data to determine if it is suitable to perform mowing operations, avoiding mowing in inclement weather or on muddy ground. On the other hand, mowing conditions can also involve the equipment's own status or the operating area's conditions. For example, the controller can check whether the mowing equipment's battery is sufficiently charged, whether the mowing assembly is in normal working order, detect obstacles or safety hazards in the mowing area using sensors, or whether the interval between the last time the mowing task was manually stopped and the current time exceeds a time threshold (e.g., mowing tasks cannot be restarted within one hour).

[0100] Through one or more of the above methods, the controller can comprehensively assess whether the necessary conditions for performing the lawn mowing task are currently met.

[0101] The controller sends control commands to the mowing assembly only when the mowing conditions are met. These commands change the operating state of the mowing assembly, such as starting the mowing motor or adjusting the blade height. This conditional command sending mechanism ensures that the mowing task is executed only when all necessary conditions are met, thus avoiding blindly executing the task under unsuitable conditions.

[0102] Through the above technical solution, this application effectively solves the problem of potentially ignoring actual conditions when responding to lawn mowing tasks. The controller no longer simply changes the operating state of the mowing components based on preset times or task instructions, but introduces a dynamic "mowing condition" judgment. This judgment mechanism enables the lawn mowing robot to make intelligent decisions based on real-time environmental data, equipment status, and lawn health (such as humidity conditions, physiological stress, and disease risk, which may be obtained through lawn health assessment). For example, when rain is predicted or lawn humidity is too high, the system can intelligently delay or cancel the mowing task even at the scheduled mowing time, thus avoiding damage to the lawn caused by mowing under unsuitable conditions, such as cutting wet grass leading to disease spread, or equipment slipping and being damaged on muddy ground. Simultaneously, by checking the equipment's own status, such as battery level or component health, it also avoids ineffective operations due to equipment malfunction or insufficient power. Therefore, this solution significantly improves the adaptability, safety, and reliability of mowing decisions, ensuring that mowing tasks are executed at the optimal time and under optimal conditions, thereby more effectively protecting lawn health and extending the service life of the mowing equipment.

[0103] The controller determines the task execution status by collecting device monitoring data and records and provides feedback.

[0104] In some implementations, the controller receives multiple sets of task execution instructions, namely, multiple sets of task execution instructions generated based on a first maintenance task and a second maintenance task. The controller writes the instruction corresponding to the first maintenance task into a task list to await execution, and caches the instruction corresponding to the second maintenance task. During the execution of instructions in the task list, if it is determined that a task execution has failed, the controller can determine whether a task has been triggered based on the task triggering condition corresponding to the second maintenance task. If a task has been triggered, the corresponding instruction of the second maintenance task is written into the task list, overwriting the original instruction content. At the same time, the timed task generated based on the original instruction is deleted, and a timed task is generated based on the new instruction in the task list to achieve dynamic adjustment of tasks.

[0105] The above description is merely a preferred embodiment of this application and is not intended to limit this application in any way. Although this application has been disclosed above with reference to preferred embodiments, it is not intended to limit this application. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the technical solution of this application. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of this application without departing from the scope of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A lawn maintenance decision-making method, characterized in that, The method is applied to the control system of a lawnmower robot, including: Acquire basic lawn data, lawn mowing equipment operation data, and environmental data. The basic lawn data includes grass species type, the lawn mowing equipment operation data includes historical mowing height, and the environmental data includes current meteorological data and predicted meteorological data for the target time period. Based on the grass species type, the historical mowing height, the current meteorological data, and the predicted meteorological data, predict the grass height curve and risk curve for the target time period; Predict the lawn health status within the target time period based on the grass height curve and the risk curve; The first maintenance task is planned based on the described lawn health status.

2. The lawn maintenance decision-making method according to claim 1, characterized in that, The method for predicting the grass height curve and risk curve within the target time period includes: Based on the current meteorological data and the predicted meteorological data, a water balance model input layer is constructed, and calculations are performed based on the water balance model to obtain the soil moisture content curve corresponding to the target time period. Based on the grass species type, the corresponding grass species parameter model is matched in the preset grass species model library to obtain the recommended mowing height range, growth rate range and growth sensitivity parameters corresponding to the grass species type. The input layer of the growth model is constructed based on the current meteorological data, the predicted meteorological data, the moisture content curve, the recommended mowing height range, the growth rate range, and the growth-sensitive parameters. The grass height curve and risk curve corresponding to the target time period are obtained by calculation based on the growth model.

3. The lawn maintenance decision-making method according to claim 1, characterized in that, The method for predicting the lawn health status during the target time period includes: Based on the grass height curve, determine the grass height data and growth status within the target time period; Based on the risk curve, determine the humidity conditions, physiological stress, disease risk, and lawn structural integrity within the target time period; The lawn health status corresponding to the target time period is calculated based on the grass height data, growth status, humidity conditions, physiological stress, disease risk, and lawn structural integrity.

4. The method according to claim 3, characterized in that, The method for calculating the lawn health status corresponding to the target time period based on the grass height data, growth status, humidity conditions, physiological stress, disease risk, and lawn structural integrity includes: A growth score is obtained based on the grass height data and the growth status assessment. A physiological score is obtained based on the humidity conditions and the physiological stress assessment. A risk score is obtained based on the aforementioned disease risk assessment; A structural score is obtained based on the lawn structural integrity assessment. The health status of the lawn is calculated based on the growth score, the physiological score, the risk score, the structural score, and the corresponding weights.

5. The lawn maintenance decision-making method according to claim 1, characterized in that, The method for planning the first maintenance task based on the lawn's health status includes: Based on the lawn's health status, a first time suitable for mowing the lawn and a second time unsuitable for mowing the lawn are determined within the target time period. The first maintenance task is planned based on the first time and the second time.

6. The lawn maintenance decision-making method according to claim 5, characterized in that, The first maintenance task includes a first sub-task and a second sub-task, and the method for planning the first maintenance task based on the first time and the second time includes: The time difference between each first time and the adjacent second time is calculated. If the time difference is less than a preset time difference threshold, it is discarded. The first subtask is generated based on the remaining first time to instruct the lawn mowing equipment to start mowing the lawn at the first time. The second subtask is generated based on the second time to prevent the lawn mowing equipment from trimming the lawn during the second time.

7. The lawn maintenance decision-making method according to claim 1, characterized in that, The method further includes: Based on the first maintenance task, a task execution instruction is generated and sent to the mowing equipment. Based on the task execution result, the grass seed model is updated.

8. The lawn maintenance decision-making method according to claim 1, characterized in that, The method further includes generating a second maintenance task based on the first maintenance task, wherein the second maintenance task is used to initiate execution when the first maintenance task fails.

9. A lawnmower robot control system, characterized in that, The system makes lawn maintenance decisions based on the method described in claims 1 to 8.

10. A lawnmower robot, characterized in that, The system includes a controller and a lawn mowing component. The controller receives a task execution instruction sent by the system of claim 9. Based on the task execution time and local time carried in the task execution instruction, the controller determines whether to change the operating state of the lawn mowing component. If the determination is yes, the controller sends a control instruction to the lawn mowing component to change the lawn mowing component to the target state indicated by the task execution instruction.

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