A state monitoring system and device for a sweeping robot

Abstract

This invention relates to the field of smart home technology and provides a robotic vacuum cleaner status monitoring system and device. The robotic vacuum cleaner status monitoring system includes the following modules: a zone division module for dividing the floor to be cleaned into several cleaning zones; a first estimation module for acquiring the robot's current operating status data, evaluating the robot's actual cleaning capacity based on the operating status data, and outputting a first estimation index for the next cleaning zone; and a second estimation module for acquiring the environmental data of the next cleaning zone and outputting a second estimation index for that next cleaning zone. In this invention, according to the range of adjustment compensation, three-level adjustment strategies are triggered sequentially: speed reduction only, speed reduction + speed increase, and speed reduction + speed increase + pressure boost. This ensures sufficient cleaning intensity in high-difficulty areas while avoiding excessive energy consumption and mechanical wear in low-difficulty areas.

Description

Technical Field

[0001] This invention belongs to the field of smart home technology, and in particular relates to a status monitoring system and device for a robotic vacuum cleaner. Background Technology

[0002] As a core cleaning device in modern smart home systems, the stability and adaptability of robotic vacuum cleaners depend heavily on their ability to accurately perceive and adaptively adjust the dynamic changes in complex ground environments and their own operating status.

[0003] Currently, most mainstream robotic vacuum cleaners employ static preset parameter strategies during cleaning operations. Existing technologies lack an effective mechanism for predicting cleaning capacity, leading to an irreversible decline in the robot's actual suction and cleaning efficiency as battery energy reserves are continuously depleted, the fan intake channels become progressively clogged, and the performance of the filter components degrades. However, the control system continues to operate according to the initial calibration parameters. This not only results in significant energy waste on smooth, hard surfaces or in low-pollution areas but also causes severe stain residue problems in high-resistance scenarios (such as long-pile carpets or damp surfaces) due to insufficient supply capacity. Furthermore, the system lacks a quantitative indicator that integrates fan speed, duct aerodynamic resistance, and battery health status from multiple dimensions, failing to objectively represent the crucial parameter of "the robot's current effective cleaning power."

[0004] Furthermore, the methods for assessing floor cleaning resistance are too simplistic. Most products only roughly infer the roughness of the floor texture based on the drive wheel current intensity or inertial measurement unit data, completely ignoring the combined effects of initial dust accumulation density and ambient relative humidity on cleaning difficulty.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this invention is to provide a status monitoring system and device for a sweeping robot, in order to solve the above-mentioned problems.

[0007] This invention is implemented as follows: a status monitoring system for a robotic vacuum cleaner, comprising the following modules:

[0008] Zone division module: used to divide the floor to be cleaned into several cleaning zones;

[0009] First estimation module: used to acquire the robot's current operating status data, evaluate the robot's actual cleaning supply capacity based on the above operating status data, and output the first estimation index for the next cleaning area;

[0010] The second prediction module is used to acquire on-site environmental data of the next cleaning area, assess the degree of resistance of the ground to cleaning based on the above on-site environmental data, and output a second prediction index for the next cleaning area.

[0011] Cleaning effect prediction module: used to combine the first prediction index and the second prediction index to construct a cleaning effect prediction index for the next cleaning area;

[0012] Residue assessment module: used to acquire data on the state of stains on the ground in the previous cleaning area before and after cleaning, and calculate the stain residue assessment ratio of the previous cleaning area; where, for the first cleaning area, the stain residue assessment ratio is a preset initial value.

[0013] Compensation amount construction module: used to determine whether the cleaning effect prediction index of the next cleaning area is lower than the preset minimum tolerance threshold; if so, then based on the difference between the cleaning effect prediction index and the minimum tolerance threshold, and combined with the stain residue evaluation ratio of the previous cleaning area, an adjustment compensation amount is constructed.

[0014] Multi-level adjustment module: used to perform multi-level time-coupling adjustment of multiple cleaning execution parameters when the robot enters the next cleaning area to perform operations, according to the numerical range of the adjustment compensation amount and in the order of energy consumption and physical intervention degree from low to high.

[0015] A further technical solution involves pre-mapping any specific parameter with physical units obtained before performing exponent calculations in the aforementioned modules using the following dimensionless normalization formula:

[0016] The normalized value is equal to the original parameter value minus the allowed physical lower boundary value of the parameter, and then divided by the difference between the physical upper boundary value and the lower boundary value, so that all parameters are mapped to the interval between 0 and 1;

[0017] The current operating status data, on-site environmental data, and ground stain status data mentioned in the above modules all refer to the dimensionless normalized values ​​after the above processing.

[0018] A further technical solution is that the current operating status data includes the actual speed of the wind turbine, the aerodynamic impedance of the duct, and the battery health support level; in the first estimation module, the calculation formula for the first estimation index is:

[0019] The first prediction index is equal to the state matching coefficient multiplied by the square of the normalized value of the actual speed of the fan, divided by the normalized value of the aerodynamic impedance of the duct, and then multiplied by the battery health.

[0020] The battery health status is equal to the current battery usage time divided by the battery life, and the state matching coefficient is a constant greater than 0 and less than or equal to 1.

[0021] A further technical solution is that the on-site environmental data includes initial dust concentration, ground roughness, and relative humidity; in the second estimation module, the calculation formula for the second estimation index is:

[0022] The second estimated index is equal to the normalized value of the initial dust concentration in the next cleaning area multiplied by (1 plus the surface friction influence coefficient multiplied by the normalized value of the ground roughness), and then multiplied by the humidity viscosity enhancement coefficient multiplied by the normalized value of the ambient relative humidity, which is the exponential function value.

[0023] The surface friction influence coefficient ranges from 0.1 to 0.5, and the humidity viscosity enhancement coefficient ranges from 0.5 to 2. Furthermore, a saturation range is added to the exponential function term. When the normalized value of the ambient relative humidity exceeds a preset humidity saturation threshold, the calculation is performed according to the preset humidity saturation threshold to avoid excessive amplification of the exponent under high humidity. The humidity saturation threshold ranges from 0.85 to 0.95, and when the threshold is exceeded, the humidity viscosity enhancement coefficient automatically switches to 0.6 to 0.8 times the original value.

[0024] In a further technical solution, the formula for calculating the cleaning effect prediction index in the cleaning effect prediction module is as follows:

[0025] The cleaning effect prediction index is equal to the natural logarithm function (1 plus the supply and demand coupling constant multiplied by the ratio of the first prediction index to the second prediction index).

[0026] The supply and demand coupling constant ranges from 1 to 5; and the cleaning effect prediction index is protected by an upper limit function before being input into the compensation amount construction module, that is, the smaller value between the cleaning effect prediction index and the preset effective upper limit is taken, and the effective upper limit ranges from 2 to 4.

[0027] A further technical solution is that the ground stain status data includes the stain concentration in the area before cleaning and the stain concentration in the area after cleaning; in the residue assessment module, the formula for calculating the stain residue assessment ratio is:

[0028] The residual stain assessment ratio of the previous cleaned area is equal to the stain concentration after cleaning divided by the stain concentration before cleaning.

[0029] For the first cleaning area, the initial setting value for the stain residue assessment ratio is between 0.05 and 0.15.

[0030] A further technical solution is that the multiple cleaning execution parameters include three items: the robot's cruising speed, the fan's target rotation speed, and the main brush's downward pressure; in the compensation amount construction module, the calculation formula for constructing the adjustment compensation amount is:

[0031] The adjustment compensation amount is equal to the basic proportional gain coefficient multiplied by (the minimum tolerance threshold minus the cleaning effect prediction index) and then multiplied by (1 plus the historical feedback weight coefficient multiplied by the stain residue assessment ratio of the previous cleaning area); where the basic proportional gain coefficient ranges from 0.1 to 1, and the historical feedback weight coefficient ranges from 0.5 to 3; there are preset first-level intervention thresholds and second-level intervention thresholds for the adjustment compensation amount, and the first-level intervention threshold is less than the second-level intervention threshold. The first-level intervention threshold ranges from 0.1 to 0.5, and the second-level intervention threshold ranges from 0.6 to 1.5, and the second-level intervention threshold is not less than twice the first-level intervention threshold.

[0032] In a further technical solution, the multi-level adjustment module performs multi-level time-coupling adjustment in order of energy consumption and physical intervention degree from low to high. The specific adjustment method and sequence are as follows:

[0033] (1) When the adjustment compensation amount is greater than 0 and does not exceed the first-level intervention threshold, the first-level adjustment is triggered: only the robot's cruise movement speed is dynamically reduced, and the target speed of the fan and the pressure of the main brush remain unchanged; the updated cruise movement speed is equal to the original set speed divided by (1 plus the adjustment compensation amount).

[0034] (2) When the adjustment compensation amount is greater than the first-level intervention threshold but not more than the second-level intervention threshold, the second-level adjustment is triggered: the cruise speed is locked to the minimum allowable value of the first-level adjustment stage (i.e., the original set speed divided by (1 plus the first-level intervention threshold)), and the target speed of the wind turbine is dynamically adjusted to increase speed, while the pressure under the main brush remains unchanged; the updated target speed of the wind turbine is equal to the original set speed multiplied by (1 plus the preset wind turbine speed-up compensation coefficient multiplied by (adjustment compensation amount minus the first-level intervention threshold)).

[0035] (3) When the adjustment compensation amount is greater than the secondary intervention threshold, the tertiary adjustment is triggered: the cruise speed is locked at the minimum allowable value mentioned above, and the target speed of the wind turbine is locked at the maximum allowable value of the secondary adjustment stage (i.e., the original set speed multiplied by (1 plus the wind turbine speed-up compensation coefficient multiplied by (secondary intervention threshold minus primary intervention threshold))), while the main brush downpressure is dynamically boosted and adjusted; the updated main brush downpressure is equal to the original set downpressure plus the preset main brush boosting compensation coefficient multiplied by (adjustment compensation amount minus secondary intervention threshold).

[0036] The initial values ​​of the originally set cruise speed, target fan speed, and main brush pressure are obtained through factory calibration, self-learning cleaning, or manual user setting.

[0037] A robotic vacuum cleaner status monitoring device, applied to any of the above-described systems, includes a memory and a processor. The memory stores a computer program, and the processor executes the program to implement the functions of each module in the system.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] In this invention, by acquiring operational status data such as the actual rotation speed of the fan, the aerodynamic impedance of the air duct, and the health support of the battery, and constructing a first prediction index, the effective cleaning power that the robot can currently provide can be quantitatively characterized. This overcomes the shortcomings of traditional methods where the actual dust collection efficiency decreases due to battery degradation, air duct blockage, etc., but the robot still operates according to fixed parameters, thus achieving dynamic and accurate perception of cleaning supply capacity.

[0040] In this invention, a second prediction index is constructed by combining the initial dust concentration, ground roughness, and ambient relative humidity. The surface friction influence coefficient and humidity viscosity enhancement coefficient are introduced, and a saturation range is set for high humidity environments. This avoids the problem of distortion in the assessment of complex ground resistance by a single sensor or simple model, enabling the system to accurately identify high resistance scenarios such as carpets and damp surfaces, and providing a reliable basis for parameter adjustment.

[0041] In this invention, by obtaining the stain concentration before and after cleaning the previous cleaning area, calculating the stain residue assessment ratio, and using it as a correction factor to adjust the compensation amount, the system can dynamically adjust the compensation intensity of subsequent areas based on the actual cleaning effect. This breaks the limitations of pure feedforward prediction and realizes closed-loop control of "prediction-execution-feedback-compensation", effectively suppressing the accumulation and residue of stains.

[0042] In this invention, according to the range of the adjustment compensation amount, three-level adjustment strategies are triggered sequentially: only speed reduction, speed reduction + speed increase, and speed reduction + speed increase + pressure boost. The low-energy-consumption movement speed adjustment is given priority, and the fan speed increase and main brush pressure boost are only gradually intervened when necessary. This ensures that sufficient cleaning intensity is achieved in high-difficulty areas, while avoiding excessive energy consumption and mechanical wear in low-difficulty areas, thus achieving an optimized balance between cleaning effect and operating economy. Attached Figure Description

[0043] Figure 1 A diagram showing the overall data flow of the system modules;

[0044] Figure 2 A logic diagram is constructed to predict the cleaning effect index;

[0045] Figure 3 The logic block diagram for adjusting the compensation amount;

[0046] Figure 4 This is a block diagram of the multi-level timing coupling adjustment execution logic. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0049] like Figures 1-4 As shown, a robotic vacuum cleaner status monitoring system according to an embodiment of the present invention includes the following modules:

[0050] Zone division module: used to divide the floor to be cleaned into several cleaning zones;

[0051] First estimation module: used to acquire the robot's current operating status data, evaluate the robot's actual cleaning supply capacity based on the above operating status data, and output the first estimation index for the next cleaning area;

[0052] The second prediction module is used to acquire on-site environmental data of the next cleaning area, assess the degree of resistance of the ground to cleaning based on the above on-site environmental data, and output a second prediction index for the next cleaning area.

[0053] Cleaning effect prediction module: used to combine the first prediction index and the second prediction index to construct a cleaning effect prediction index for the next cleaning area;

[0054] Residue assessment module: used to acquire data on the state of stains on the ground in the previous cleaning area before and after cleaning, and calculate the stain residue assessment ratio of the previous cleaning area; where, for the first cleaning area, the stain residue assessment ratio is a preset initial value.

[0055] Compensation amount construction module: used to determine whether the cleaning effect prediction index of the next cleaning area is lower than the preset minimum tolerance threshold; if so, then based on the difference between the cleaning effect prediction index and the minimum tolerance threshold, and combined with the stain residue evaluation ratio of the previous cleaning area, an adjustment compensation amount is constructed.

[0056] Multi-level adjustment module: used to perform multi-level time-coupling adjustment of multiple cleaning execution parameters when the robot enters the next cleaning area to perform operations, according to the numerical range of the adjustment compensation amount and in the order of energy consumption and physical intervention degree from low to high.

[0057] In this embodiment,

[0058] In a preferred embodiment of the present invention, before the above modules perform exponent calculations, any specific parameter with physical units obtained is pre-mapped using the following dimensionless normalization formula:

[0059]

[0060] in The parameter values ​​after dimensionless normalization satisfy... ; These are the raw values ​​of the parameters actually collected by the sensor; This is the physical lower limit boundary value allowed for this parameter under the robot system calibration; This represents the physical upper limit boundary value allowed for this parameter under the robot system calibration; the current operating status data, on-site environmental data, and ground stain status data involved in the above modules all refer to the dimensionless normalized values ​​processed by the above formula.

[0061] In this embodiment, This represents the parameter value after normalization, and its value range is limited to between 0 and 1, which intuitively reflects the relative position of the original parameter within the preset physical boundary. It refers to the raw physical quantities actually collected by the sensor, such as fan speed and dust concentration. and These represent the lower and upper physical limit boundary values ​​of the parameter under the robot system calibration, respectively. These boundary values ​​can be limitations of the sensor's measurement range or effective working ranges set by the system to ensure stable operation or cleaning effect. For example, for fan speed, It can be the lowest speed at which the fan can start stably, and That is the highest safe operating speed designed for the fan. Regarding the concentration of dirt on the ground, It can be a benchmark value for clean floors. This represents the highest stain concentration the system can handle. By subtracting the lower limit and dividing by the total range, the original parameter is transformed into a relative proportional value, thus achieving dimensionless elimination and numerical standardization. The current operating status data, on-site environmental data, and ground stain status data involved in the above modules all refer to the dimensionless normalized values ​​processed by the above formula, ensuring that all input data are comparable when participating in subsequent complex calculations.

[0062] In a preferred embodiment of the present invention, the current operating status data includes the actual speed of the fan, the aerodynamic impedance of the duct, and the battery health support level; in the first estimation module, the calculation formula for the first estimation index is:

[0063]

[0064] in, The first predicted index, This is the normalized value of the actual fan speed. This is the normalized value of the aerodynamic impedance of the air duct. For battery health, It equals the current battery usage time divided by the battery life (in cleaning scenarios, battery health is approximately linearly related to usage time, so the ratio of usage time is used as a measure of health). For state matching coefficients, .

[0065] In this embodiment, the current operating status data refers to the real-time working status information of each key component inside the robot vacuum cleaner during operation. This data is the basis for evaluating the robot's cleaning capabilities. It can be obtained in real time through various sensors integrated within the robot, such as obtaining fan speed through Hall effect sensors or encoders, obtaining duct aerodynamic impedance through differential pressure sensors or flow sensors, and obtaining battery health through the battery management system (BMS).

[0066] The actual fan speed refers to the rotational speed of the fan motor inside the robotic vacuum cleaner that generates suction during actual operation. This parameter directly reflects the fan's workload and suction power potential. The actual fan speed can be obtained by installing a Hall effect sensor or photoelectric encoder on the fan motor to monitor the rotor's rotational frequency in real time and convert it into a speed value. Alternatively, it can be estimated by monitoring the fan motor's drive current and voltage, combined with the motor's characteristic curve, to obtain a normalized value for the actual fan speed. The actual rotational speed of the fan is calculated using the dimensionless normalization formula described above.

[0067] The aerodynamic impedance of a robotic vacuum cleaner refers to the degree of obstruction to airflow within its internal suction duct. The magnitude of this impedance is affected by factors such as filter blockage, a full dustbin, and changes in the duct structure, directly impacting the effectiveness of the suction force generated by the fan on the floor. The aerodynamic impedance can be obtained by installing differential pressure sensors at the duct inlet and outlet to measure the pressure difference before and after airflow through the duct, and then calculating it in conjunction with airflow data. Alternatively, it can be estimated by monitoring the fan's current changes at specific speeds, combining this with the fan performance curve and a pre-defined duct model to obtain a normalized value for the aerodynamic impedance. The aerodynamic impedance of the air duct is calculated using the dimensionless normalization formula described above.

[0068] Battery health support refers to the ability of a robotic vacuum cleaner's battery system to continuously provide stable cleaning power under current conditions. It reflects the impact of the battery's physical aging and remaining lifespan on the robot's performance. Battery health support can be comprehensively evaluated by real-time monitoring of parameters such as battery cycle count, internal resistance, and maximum charge / discharge capacity decay through the battery management system (BMS), combined with a battery life model. For example, it can be defined as the ratio of the current maximum usable battery capacity to the initial design capacity, or, as described in this application, as the current battery usage time divided by the battery lifespan.

[0069] The first prediction module is a functional unit in the robotic vacuum cleaner's status monitoring system. Its role is to receive and process current operating status data, and then assess the robot's current cleaning capacity. This module can be implemented by specific software algorithms on the robot's main control chip, and is responsible for data acquisition, normalization processing, and calculation of the first prediction index.

[0070] The formula for calculating the first forecast index This formula is the core mathematical model for quantifying the cleaning supply capacity of the robot. It normalizes the actual rotational speed of the fan. Normalized value of air duct aerodynamic impedance and battery health These three key parameters are coupled and comprehensively reflect the robot's suction generation potential, suction transmission efficiency, and continuous power supply capability through a multiplicative relationship. Among them, the square term of the normalized value of the actual fan speed... It more accurately characterizes the dynamic pressure or suction intensity generated by the fan, while dividing by This demonstrates how the resistance of the air duct weakens the actual suction effect.

[0071] State matching coefficient This is a constant between 0 and 1 used to calibrate and adjust the first estimated index. This coefficient allows the system to fine-tune the assessment results of cleaning delivery capacity based on different robot models, design characteristics, or user preferences. For example, different robot models may have different actual cleaning efficiencies under the same physical parameters; this can be achieved by adjusting... This allows the index to more accurately reflect the robot's true performance. The coefficient can be calibrated at the factory or optimized during initial user use or system self-learning.

[0072] The solution proposed in this application organically integrates multiple key operating parameters within the robot vacuum cleaner to construct a quantitative first prediction index, thereby solving the problem of fuzzy assessment of cleaning supply capacity in traditional methods.

[0073] In a preferred embodiment of the present invention, the on-site environmental data includes initial dust concentration, ground roughness, and relative humidity; in the second estimation module, the calculation formula for the second estimation index is:

[0074]

[0075] in, This is the second estimated index; This is the normalized value of the initial dust concentration for the next cleaning area; This represents the normalized value for surface roughness. This is the normalized value of ambient relative humidity; The surface friction coefficient is the coefficient of influence. ; The viscosity enhancement coefficient is the humidity. ;

[0076] Furthermore, the humidity viscosity enhancement term Add a saturation range when the ambient relative humidity is normalized. Exceeding the preset humidity saturation threshold At that time, according to Calculations are performed to avoid excessive amplification of the exponential term under high humidity conditions; ,and exist Automatically switch to , The value is the original 0.6-0.8 times.

[0077] In this embodiment, initial dust concentration refers to the amount or density of dust accumulated on the ground in the area to be cleaned before the cleaning operation begins. This parameter reflects the basic difficulty of the cleaning task. It can be obtained through real-time detection by dust sensors on the robot (e.g., PM2.5 / PM10 sensors based on the principle of optical scattering), or estimated by analyzing the dust coverage in ground images using image recognition technology. Ground roughness refers to the unevenness or texture characteristics of the ground surface. This parameter affects the friction between the cleaning tools (such as the main brush and side brushes) and the ground, as well as the adhesion of dust. Ground roughness can be measured by scanning with laser sensors or ultrasonic sensors on the bottom of the robot, or indirectly assessed by analyzing changes in the current of the drive wheels and vibration sensor data. Ambient relative humidity refers to the ratio of water vapor content in the air to saturated water vapor content. High humidity environments significantly increase dust adhesion, making cleaning more difficult. Ambient relative humidity can be collected in real-time by temperature and humidity sensors built into the robot.

[0078] Second forecast index This is a comprehensive indicator used to quantify the resistance of the ground to cleaning. A higher value indicates greater cleaning difficulty and requires a more powerful cleaning robot. (Initial dust concentration normalized value) This is a dimensionless normalized value derived from the raw dust concentration data, used to represent the relative level of dust concentration and serving as the basis for cleaning resistance. Ground roughness normalized value. This is a dimensionless normalized value derived from the raw ground roughness data, used to reflect the relative magnitude of the physical frictional resistance of the ground. Normalized value of ambient relative humidity. It is a value obtained by dimensionlessly normalizing the original relative humidity data and is used to characterize the degree of influence of environmental humidity on cleaning resistance.

[0079] Surface friction influence coefficient It is an adjustment parameter used to adjust the weight of the influence of ground roughness on cleaning resistance. Its value is limited to between 0.1 and 0.5 to ensure that the roughness has an impact on resistance within a reasonable range.

[0080] Humidity viscosity enhancement coefficient It is an adjustable parameter used to control the intensity of the effect of ambient relative humidity on the enhanced cleaning resistance. Its value is limited to between 0.5 and 2 to reflect the nonlinear effect of humidity on dust adhesion.

[0081] Humidity saturation threshold A preset critical humidity value, when the ambient relative humidity is normalized. When this threshold is exceeded, the system will no longer operate according to the actual... Instead of calculating the humidity viscosity enhancement term, it is restricted to... The corresponding level is set to prevent the exponential term from being excessively amplified in extreme high humidity conditions, which could lead to an abnormally high estimated cleaning resistance. This threshold is typically set between 0.85 and 0.95 to cover most high humidity environments.

[0082] The normalized value of ambient relative humidity Exceeding the humidity saturation threshold At that time, the humidity viscosity enhancement coefficient is automatically switched. Its value is the original value. The value is 0.6-0.8 times higher, which is intended to further reduce the excessive influence of humidity on cleaning resistance under extreme high humidity conditions, making the prediction results more robust.

[0083] The proposed solution constructs a multi-factor coupled second prediction index by introducing three key dimensions: initial dust concentration, ground roughness, and ambient relative humidity. This allows for precise quantification of cleaning resistance. Specifically, the system first obtains the normalized value of the initial dust concentration in the next cleaning area. Surface roughness normalized value Normalized value of ambient relative humidity Then, As a basic item for cleaning resistance, and combined with Through linear correction terms This reflects the influence of ground physical friction resistance. Based on this, a humidity viscosity enhancement term is introduced. An exponential function is used to simulate the nonlinear enhancement effect of humidity on dust adhesion. During the calculation, to avoid the influence of the humidity viscosity enhancement term in extremely high humidity environments... Uncontrolled nonlinear growth occurred, so a saturation range was added to the system. When the normalized value of the ambient relative humidity... Exceeding the preset humidity saturation threshold At that time, the system will proceed according to... Calculate and increase the humidity viscosity coefficient. Automatically switch to This ensures the second forecast index Fluctuations within a physically reasonable range prevent unnecessary energy consumption or mechanical wear due to over-response. Through the fusion of these multi-dimensional parameters and nonlinear correction, the system can more accurately perceive cleaning resistance under different environments, providing a scientific basis for subsequent cleaning parameter adjustments. This second predictive index... The first predicted index output by the first prediction module The data are input into the cleaning effect prediction module to comprehensively evaluate the robot's cleaning effect on the next cleaning area, enabling the entire system to more accurately adjust the cleaning parameters.

[0084] In a preferred embodiment of the present invention, the calculation formula for constructing the cleaning effect prediction index in the cleaning effect prediction module is as follows:

[0085]

[0086] in, This is an index used to predict the cleaning effect for the next cleaning area; The first estimated index; This is the second estimated index; Let be the supply and demand coupling constant. ;

[0087] Furthermore, the cleaning effect prediction index Before the input compensation amount construction module participates in the difference calculation, it is first protected by an upper limit function to avoid the second estimated index. Too small The abnormal increase weakens the adjustment trigger condition; the upper limit limit formula is:

[0088]

[0089] in, The effective upper limit of the preset cleaning effect prediction index. ;when At that time, take directly Participate in subsequent calculations.

[0090] In this embodiment, the cleaning effect prediction index This is a quantitative assessment of the degree of matching between the robot's cleaning ability and environmental resistance. A higher value generally indicates a better cleaning effect or more abundant cleaning ability. First Prediction Index This characterizes the robot's current cleaning capabilities, such as suction power and battery life. Second Predictive Index It indicates the cleaning resistance level of the next cleaning area, such as dust concentration, surface roughness, and humidity.

[0091] Supply and demand coupling constant Used to adjust the first estimated index Compared with the second forecast index Ratio as a predictive index for cleaning effectiveness The influence weight can be adjusted based on the robot's design performance, cleaning mode preferences, or user-defined settings. For example, it can be preset according to the suction level or brush head type of the robot model, or automatically adjusted when the user selects "powerful cleaning" or "energy-saving cleaning" mode in the mobile application. The value of .

[0092] Effective upper limit of cleaning effect prediction index This is a predictive index for cleaning effectiveness. A maximum reasonable value is set to prevent it from increasing abnormally under specific conditions. This upper limit can be derived from statistical analysis of a large amount of experimental data to reflect the highest effect achievable under optimal cleaning conditions, or it can be calibrated by the manufacturer based on the performance parameters of different models.

[0093] This application's solution, by introducing a logarithmic function mapping and an upper limit mechanism, achieves nonlinear smoothing and boundary constraints on the cleaning effect prediction index, thereby ensuring the stability of the monitoring system under different environments. Specifically, using a logarithmic function to construct the cleaning effect prediction index can compress and map the ratio of supply capacity to resistance, making the trend of the prediction index more gradual and avoiding overreaction of the system due to drastic fluctuations in the ratio. By introducing a supply-demand coupling constant, the sensitivity of the prediction index can be flexibly adjusted according to the performance characteristics of different models, making the calculation results more consistent with actual cleaning scenarios. Based on this, for the second prediction index... To prevent numerical anomalies caused by excessively low values, an upper limit function is introduced for protection. This is achieved by setting an effective upper limit for the cleaning effect prediction index. The system forcibly truncates estimated values ​​that exceed a reasonable range to within a preset threshold. The core of this approach is that when environmental resistance is extremely low, the system no longer blindly pursues an infinite increase in the estimated index, but instead locks it at a preset upper limit. This ensures that the subsequent compensation module receives stable and reasonable input signals, effectively preventing adjustment trigger conditions from failing or being misjudged due to calculation anomalies, and ensuring that the robot vacuum maintains reasonable parameter adjustment logic in various floor environments.

[0094] In a preferred embodiment of the present invention, the ground stain status data includes the stain concentration in the area before cleaning and the stain concentration in the area after cleaning; the formula for calculating the stain residue assessment ratio in the residue assessment module is as follows:

[0095]

[0096] in, This is the assessment ratio of stain residue in the previously cleaned area; The concentration of stains in the area after the previous cleaning; The initial setting for the stain concentration of the area before cleaning the previous area; and the initial setting for the stain residue assessment ratio of the first area to be cleaned. .

[0097] In this embodiment, the ground stain status data refers to a set of data used to characterize the degree of ground cleanliness. This data may include, but is not limited to, ground stain information collected through various means such as optical sensors, acoustic sensors, image recognition systems, or chemical sensors. This data forms the basis for quantifying the cleaning effect and reflects the actual changes in ground stains before and after cleaning.

[0098] Pre-cleaning stain concentration refers to the quantitative degree of stains on the floor in a specific area before the robot begins cleaning. It can be obtained through methods such as analyzing floor images using dust sensors on the robot, visual recognition systems, or detecting the concentration of airborne dust particles using laser scattering principles. For example, stain concentration can be indirectly assessed by detecting changes in floor reflectivity using infrared sensors, or by identifying stain coverage using miniature cameras combined with image processing algorithms.

[0099] Post-cleaning stain concentration refers to the quantified degree of stain residue remaining on the floor in a specific area after the robot has completed its cleaning operation. It is obtained similarly to pre-cleaning stain concentration; typically, after completing the cleaning path, the robot scans or samples the area again to obtain the actual stain residue level. For example, the cleaning effect can be monitored in real time using a particle counter at the suction port, or a visual sensor can be used to perform a secondary inspection of the cleaned area after cleaning to assess residual stains.

[0100] The residue assessment module is a functional unit within the system. Its function is to calculate and output a quantified residue assessment ratio based on the stain status data before and after cleaning. This module can be a standalone software module running on the robot's main control chip, or a cloud service module that receives data uploaded by the robot and performs calculations. Its core function is to transform the raw stain data into an indicator that can be used for subsequent decision-making.

[0101] Stain Residue Assessment Ratio This is a dimensionless metric used to quantify the degree of stain residue in the previously cleaned area. The ratio directly reflects the cleaning efficiency; a smaller ratio indicates better cleaning and less stain residue, while a larger ratio indicates poorer cleaning and more stain residue. This ratio eliminates differences in initial stain levels between different areas, providing a relatively objective evaluation of cleaning effectiveness. The previous cleaned area refers to the area that was cleaned before the area the robot is currently cleaning or about to clean. The system evaluates the cleaning effect of the previous area and obtains historical feedback data to guide parameter adjustments for subsequent cleaning areas.

[0102] The first cleaning area refers to the area that the robot cleans first in this cleaning task. Since there is no historical data for the "previous cleaning area" for this area, an initial stain residue assessment ratio needs to be set for it.

[0103] Initial settings This is the preset stain residue assessment ratio for the first cleaning area. This value is typically set based on experience, factory calibration, or user preference, and ranges from 0.05 to 0.15. Setting an initial value ensures the system has a baseline feedback information at the start of the cleaning task, allowing the compensation module to function properly and preventing adjustment mechanisms from failing due to a lack of historical data. For example, it can be set based on the cleaning residue rate under standard conditions using the robot's default cleaning mode, or it can be adjusted by the user according to their desired cleaning results.

[0104] This application's solution addresses the issues of lacking closed-loop control and difficulty in handling varying levels of dirt between different areas by introducing a quantitative feedback mechanism for historical cleaning quality. Specifically, the residue assessment module obtains the dirt concentration in the area before and after robot cleaning, and uses the formula for calculating the residue assessment ratio to quantify the actual cleaning effect of the previous cleaning area into an operable numerical indicator. This feedback based on actual cleaning results overcomes the limitations of relying solely on environmental predictions while ignoring actual cleaning performance. When the robot enters a new cleaning area, the compensation quantity construction module considers not only the current area's cleaning effect prediction index but also the residue assessment ratio of the previous cleaning area. If the residue ratio of the previous area is high, it indicates that the robot's cleaning ability may be insufficient or the environmental resistance may exceed expectations. The system will then appropriately enhance the adjustment needs of the current area when constructing the adjustment compensation quantity, thereby prompting the multi-level adjustment module to adopt a more aggressive cleaning strategy. For the first cleaning area,

[0105] In a preferred embodiment of the present invention, the multiple cleaning execution parameters include three items: the robot's cruising speed, the fan's target rotation speed, and the main brush's downward pressure; in the compensation amount construction module, the calculation formula for constructing the adjustment compensation amount is:

[0106]

[0107] in, To adjust the compensation amount, This is the preset minimum tolerance threshold. To predict the cleaning effect index, This is the ratio of stain residue assessment for the previous cleaned area. The basic proportional gain coefficient, ; Historical feedback weighting coefficients Regarding the aforementioned adjustment compensation amount There is a preset first-level intervention threshold. With secondary intervention threshold ,and ;

[0108] in, , ,and The system allows users to select cleaning preferences in advanced settings. , Adjust the gears; each gear corresponds to a fixed set. , Numerical value.

[0109] In this embodiment, the aforementioned cleaning execution parameters are the core control variables for the robot during cleaning operations. Specifically, the cruising speed refers to the speed at which the robot moves along a preset path during cleaning; its speed directly affects cleaning efficiency and the time it takes to act on dirt on the floor. The target fan speed refers to the target rotational speed that drives the robot's internal fan, determining the suction power generated by the suction module and thus affecting its ability to pick up dust and particles. The main brush downward pressure refers to the vertical pressure applied when the main brush contacts the floor; this pressure affects the brushing and scraping effect of the main brush on dirt. Proper adjustment of these parameters is key to achieving efficient cleaning and optimized energy consumption.

[0110] Adjustment compensation amount It is a comprehensive quantitative indicator used to reflect the gap between current cleaning capabilities and target requirements, and is adjusted based on historical cleaning performance. This compensation amount serves as the input for subsequent multi-level adjustment strategies, and its magnitude directly determines the intensity and level of adjustment. A preset minimum tolerance threshold is also included. This is a baseline value set internally by the system, representing the minimum acceptable cleaning performance standard. When the estimated cleaning performance falls below this threshold, the system will activate an adjustment mechanism. Cleaning Performance Prediction Index This is a prediction of the cleaning effect for the next area to be cleaned, taking into account both the robot's cleaning capacity and the floor's cleaning resistance. It also includes an assessment ratio of stain residue from the previous cleaned area. This provides feedback on historical cleaning quality, used to correct the adjustment strategy for the current area, ensuring the system can learn from past experience and make adaptive adjustments. Basic proportional gain coefficient. This is used to adjust the degree of influence of cleaning effect deviation on the adjustment compensation amount. It can be set according to actual needs to balance the system's response sensitivity. Historical feedback weighting coefficient. This is used to control the weight of the historical residual ratio in the calculation of the adjustment compensation amount, so that the system can adjust the current strategy according to the degree of importance attached to historical experience.

[0111] Level 1 intervention threshold and secondary intervention threshold These are two preset key dividing points that will adjust the compensation amount. The numerical range is divided into different levels. When When the cleaning falls within different zones, the system will trigger different levels of cleaning parameter adjustment strategies, thus achieving fine-grained multi-level adjustments. This hierarchical mechanism allows the system to achieve optimal cleaning results with minimal energy consumption and physical intervention, based on the actual needs of the cleaning task. The system allows users to adjust cleaning parameters according to their preferences in advanced settings. , Adjusting the power level means users can choose different modes based on their cleaning habits, floor type, or energy consumption sensitivity. Each power level corresponds to a fixed set of settings. , This numerical value provides the system with greater environmental adaptability and personalized service capabilities.

[0112] This application's solution achieves a leap from static parameter control to dynamic closed-loop adjustment by introducing a historical cleaning feedback mechanism. By constructing a compensation calculation formula that includes basic proportional gain, cleaning effect deviation, and historical residue ratio, the system can use the cleaning quality of the previous area as a correction factor for the parameter adjustment of the current area, effectively compensating for the limitations of a single prediction model when facing continuous cleaning tasks. Specifically, by using the difference between the cleaning effect prediction index and the minimum tolerance threshold as the core adjustment driving force, the gap between the current cleaning capacity and the target requirements can be accurately quantified. Based on this, the introduction of the stain residue assessment ratio of the previous area ensures that the adjustment compensation amount not only reflects the predicted difficulty of the current area but also incorporates historical cleaning performance, ensuring the continuity and targeting of adjustment instructions. By setting first-level and second-level intervention thresholds, the compensation amount is divided into different intervals, providing a clear trigger basis for subsequent multi-level time-series adjustments. This hierarchical mechanism allows the system to accurately match corresponding execution strategies according to different levels of compensation, avoiding energy waste or incomplete cleaning caused by excessive or insufficient adjustment. In addition, allowing users to adjust the intervention threshold according to their cleaning preferences gives the system stronger environmental adaptability and personalized service capabilities, enabling the monitoring system to flexibly respond to the differentiated cleaning needs in different household environments.

[0113] In a preferred embodiment of the present invention, the multi-level adjustment module performs multi-level time-coupling adjustment in order of energy consumption and physical intervention degree from low to high. The specific adjustment method and sequence are as follows:

[0114] (1) When At this time, a first-level adjustment is triggered: the system only dynamically reduces the robot's cruising speed, while the target speed of the fan and the downward pressure of the main brush remain unchanged at their current settings; the updated formula for calculating the cruising speed is:

[0115]

[0116] in, For the updated cruise speed, The system's default cruising speed;

[0117] (2) When At this time, the secondary adjustment is triggered: the system locks the robot's cruising speed to the minimum allowable value corresponding to the primary adjustment stage, i.e. The system then begins to dynamically adjust the target speed of the wind turbine while maintaining constant pressure under the main brush. The updated formula for calculating the target speed of the wind turbine is as follows:

[0118]

[0119] in, The updated target speed for the wind turbine. The original target speed of the fan was set by the system. This is the preset fan speed compensation coefficient;

[0120] (3) When At that time, a three-level adjustment is triggered: the system maintains the cruise speed locked at the aforementioned minimum allowable value. The target speed of the wind turbine is locked at the highest allowable value corresponding to the second-stage regulation phase, i.e. Simultaneously, the main brush downpressure is dynamically adjusted; the updated main brush downpressure calculation formula is:

[0121]

[0122] in, To reduce pressure on the updated main refresh, The original primary brush pressure set by the system. The preset main brush boost compensation coefficient;

[0123] And the system's original cruise speed The originally set target speed of the fan Original setting of main brush pressure The initial value is obtained through one of the following methods:

[0124] (a) During the factory calibration stage, on a standard clean surface (normalized surface roughness value) Normalized initial dust concentration value for the next cleaning area ≤0.1), operate the robot with its nominal rated parameters and record them as the initial settings;

[0125] (b) After the first power-on or factory reset, perform a short-distance self-learning cleanup and take the average value of the parameters corresponding to the "good" cleaning effect as the initial setting value;

[0126] (c) Users can manually set default values ​​through a mobile application or the default values ​​can be pushed by the cloud based on the device model and common ground types.

[0127] In this embodiment, the multi-level adjustment module is a functional module whose function is to adjust the received adjustment compensation amount. This module performs graded, sequential, and coupled adjustments to multiple cleaning parameters of the robotic vacuum cleaner (cruising speed, target fan speed, and main brush pressure). This adjustment can be implemented by integrating software algorithms into the robot's internal main control chip, or by a separate parameter adjustment unit combined with firmware logic. Its core function is to dynamically change the robot's cleaning behavior based on preset thresholds and adjustment strategies to adapt to different cleaning needs.

[0128] Adjustment compensation amount This is a quantitative indicator calculated by the compensation module based on the difference between the estimated cleaning effect index and the minimum tolerance threshold, combined with the stain residue assessment ratio of the previous cleaned area. It reflects the difficulty of the current cleaning task or the degree of inadequacy in the cleaning effect; a higher value indicates greater cleaning difficulty or less ideal results, requiring stronger cleaning intervention. Level 1 Intervention Threshold With secondary intervention threshold It is a preset value used to divide the adjustment compensation amount. The key parameters in the numerical range define the adjustment trigger conditions for different levels. and The values ​​can be set according to the robot's design performance, energy consumption strategy, and user preferences. For example, they can be tested experimentally in different cleaning scenarios. The typical value distribution is used to determine the cleaning mode, or it can be optimized based on historical cleaning data using machine learning algorithms. Users can also select different cleaning modes in the mobile application, each mode corresponding to a set of preset values. and This allows for personalized adjustment strategies.

[0129] Cruise speed / This refers to the speed at which the robot vacuum moves along a preset path during cleaning. `vold` is the initial cruising speed set by the system. This is the updated cruising speed after the first-level adjustment. The purpose of dynamic speed reduction adjustment is to improve cleaning performance without increasing energy consumption by extending the contact time between the cleaning tool and the dirt on the ground. This speed adjustment can be achieved by controlling the motor speed of the robot's drive wheels, for example, by adjusting the motor drive voltage or current through a PWM signal.

[0130] Target speed of the wind turbine / This refers to the expected rotational speed of the vacuum cleaner's internal suction fan. It is the initial target speed of the fan originally set by the system. This is the updated target fan speed after secondary adjustment. The purpose of dynamic speed increase adjustment is to increase suction power by increasing the fan speed, thereby more effectively removing dirt from the floor. The fan speed can be adjusted by controlling the voltage or frequency of the fan drive circuit, for example, by using variable frequency drive technology or a DC brushless motor drive scheme.

[0131] Main brush pressure / This refers to the vertical pressure exerted on the floor by the main brush of a robotic vacuum cleaner. This is the initial primary flash pressure originally set by the system. This refers to the updated main brush downforce after three levels of adjustment. The purpose of dynamic pressure boosting is to increase the contact pressure between the main brush and the ground, thereby enhancing the physical scraping and patting ability against stubborn stains and improving cleaning efficiency. The adjustment of the main brush downforce can be achieved by controlling the actuator of the main brush suspension mechanism, for example, by adjusting the vertical position of the main brush module or changing its spring preload.

[0132] Fan speed compensation coefficient Compensation coefficient for main brush boost These are proportional factors used to quantify the adjustment range of fan speed and main brush pressure. They determine the adjustment compensation amount in the secondary and tertiary adjustment stages. For each additional unit, the fan speed and main brush downforce increase by how much? These coefficients can be calibrated and optimized through extensive experimental data to ensure smooth and effective adjustment, avoiding over- or under-adjustment. Initial parameters can be obtained in various ways. Factory calibration can be performed during the robot manufacturing stage, allowing the robot to operate on a standardized clean surface with its nominal rated parameters, recording the cruising speed, target fan speed, and main brush downforce as initial settings. Self-learning cleaning allows the robot to perform a short-distance self-learning cleaning task upon first power-on or after a factory reset, selecting the average parameter value corresponding to a "good" cleaning effect as the initial settings. User settings / cloud push provide flexibility and intelligence. Users can manually input or select cleaning preferences through a mobile application, or the cloud server can push a set of optimized default initial parameters to the robot based on the robot model, common floor types in the user's area, and historical cleaning data.

[0133] The robotic vacuum cleaner status monitoring system of this application receives the adjustment compensation amount output by the compensation amount construction module. Then, the multi-level adjustment module will activate its core function, which is to perform multi-level time-coupled adjustment of multiple cleaning execution parameters of the robot in order of increasing energy consumption and physical intervention. The operating logic of this mechanism is that it first adjusts the compensation amount... Compared with the preset first-level intervention threshold and secondary intervention threshold By comparing different levels of cleaning needs, the compensation amount can be adjusted. At a low level (i.e., 0 < ≤ When the system detects a low speed (indicated by a value indicating a relatively easy cleaning task or a minor deficiency in cleaning effectiveness), it triggers a primary adjustment, dynamically reducing the robot's cruising speed. By decreasing the speed, the contact time between the cleaning tool and the dirt on the floor is extended, thereby improving the cleaning effect without increasing the energy consumption of the fan and main brush. The target rotation speed of the fan and the downward pressure of the main brush remain unchanged, reflecting a strategy that prioritizes low-energy consumption and low-physical-intervention methods.

[0134] As the difficulty or inadequacy of cleaning increases, the compensation amount is adjusted. Exceeding the first-level intervention threshold But still below or equal to the secondary intervention threshold (Right now < ≤ When the speed reaches its limit, the system triggers secondary adjustment. At this point, a simple speed reduction adjustment is insufficient to meet cleaning needs, so the system locks the cruising speed at the minimum permissible value achievable in the primary adjustment phase to ensure maximum cleaning contact time. Based on this, the system begins dynamically adjusting the target fan speed to increase suction power and further improve cleaning efficiency. The main brush downforce remains unchanged during this stage, reflecting a strategy of prioritizing increased suction power—a moderate-energy-consumption, moderate-physical-intervention strategy—after the speed adjustment reaches its limit.

[0135] When the difficulty or inadequacy of cleaning reaches a high level, adjust the compensation amount. Exceeding the threshold for secondary intervention (Right now > When the system triggers Level 3 adjustment, the cruising speed is locked at its minimum, and the fan's target speed has been increased to the maximum allowable value for Level 2 adjustment. To handle more stubborn or heavy stains, the system dynamically increases the pressure of the main brush. By increasing the physical force of the main brush on the ground, the system enhances its ability to remove deep or sticky stains. This adjustment strategy reflects the ultimate guarantee strategy of using high-energy-consuming and high-physical-intervention methods after speed and suction adjustments have reached a high level. The entire adjustment process is time-coupled, meaning that the adjustment parameters at different levels are interconnected and progressively triggered. For example, Level 2 adjustment is based on Level 1 adjustment, and Level 3 adjustment is based on Level 2 adjustment. This hierarchical time-coupled adjustment mechanism, in conjunction with the compensation amount construction module, cleaning effect prediction module, and residue assessment module, enables the system to dynamically and precisely adjust cleaning parameters based on the cleaning effect prediction for the next cleaning area and the stain residue feedback from the previous cleaning area. It avoids the traditional "one-size-fits-all" adjustment method, effectively balancing cleaning performance, energy consumption, and mechanical wear, ensuring that the robot can operate in an optimized manner in various complex cleaning environments. In addition, the system's initial parameters are obtained in various ways, whether through factory calibration, self-learning cleaning, or user settings / cloud push, all of which provide a stable and reasonable benchmark for subsequent dynamic adjustments, ensuring the reliability and adaptability of the entire adjustment system.

[0136] A robotic vacuum cleaner status monitoring device, applied to any of the above-described systems, includes a memory and a processor. The memory stores a computer program, and the processor executes the program to implement the functions of each module in the system.

[0137] The device is integrated inside the robot vacuum cleaner and includes non-volatile memory (such as Flash ROM) and an embedded microprocessor (such as ARM Cortex-M series). The memory is pre-programmed with computer program code implementing the area division module, first estimation module, second estimation module, cleaning effect estimation module, residue assessment module, compensation amount construction module, and multi-level adjustment module described in claim 1. When the robot vacuum cleaner is powered on and begins its cleaning task, the microprocessor reads the program from the memory and executes it line by line. During execution, the processor collects data such as fan speed, duct impedance, battery health, dust concentration, floor roughness, and ambient humidity through various sensor interfaces. Based on the dimensionless normalization formula, various index calculation formulas, and hierarchical adjustment logic preset in the program, it calculates the first estimation index, second estimation index, cleaning effect estimation index, stain residue assessment ratio, and adjustment compensation amount in real time, and outputs corresponding cruise speed, fan speed, and main brush pressure control signals. This device can independently complete all monitoring and adjustment functions without external computing equipment, realizing closed-loop adaptive control of the robot vacuum cleaner's cleaning ability.

[0138] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A status monitoring system for a sweeping robot, characterized in that, Includes the following modules: Zone division module: used to divide the floor to be cleaned into several cleaning zones; First estimation module: used to acquire the robot's current operating status data, evaluate the robot's actual cleaning supply capacity based on the above operating status data, and output the first estimation index for the next cleaning area; The second prediction module is used to acquire on-site environmental data of the next cleaning area, assess the degree of resistance of the ground to cleaning based on the above on-site environmental data, and output a second prediction index for the next cleaning area. Cleaning effect prediction module: used to combine the first prediction index and the second prediction index to construct a cleaning effect prediction index for the next cleaning area; Residue assessment module: used to acquire data on the state of stains on the ground in the previous cleaning area before and after cleaning, and calculate the stain residue assessment ratio of the previous cleaning area; where, for the first cleaning area, the stain residue assessment ratio is a preset initial value. Compensation amount construction module: used to determine whether the cleaning effect prediction index of the next cleaning area is lower than the preset minimum tolerance threshold; if so, then based on the difference between the cleaning effect prediction index and the minimum tolerance threshold, and combined with the stain residue evaluation ratio of the previous cleaning area, an adjustment compensation amount is constructed. Multi-level adjustment module: used to perform multi-level time-coupling adjustment of multiple cleaning execution parameters when the robot enters the next cleaning area to perform operations, according to the numerical range of the adjustment compensation amount and in the order of energy consumption and physical intervention degree from low to high.

2. The system according to claim 1, characterized in that, Before performing exponent calculations in the above modules, any specific parameter with physical units is pre-mapped using the following dimensionless normalization formula: The normalized value is equal to the original parameter value minus the allowed physical lower boundary value of the parameter, and then divided by the difference between the physical upper boundary value and the lower boundary value, so that all parameters are mapped to the interval between 0 and 1; The current operating status data, on-site environmental data, and ground stain status data mentioned in the above modules all refer to the dimensionless normalized values ​​after the above processing.

3. The system according to claim 2, characterized in that, The current operating status data includes the actual speed of the wind turbine, the aerodynamic impedance of the duct, and the battery health support level; in the first estimation module, the calculation formula for the first estimation index is: The first prediction index is equal to the state matching coefficient multiplied by the square of the normalized value of the actual fan speed, divided by the normalized value of the duct aerodynamic impedance, and then multiplied by the battery health.

4. The system according to claim 3, characterized in that, The on-site environmental data includes initial dust concentration, ground roughness, and relative humidity; in the second estimation module, the calculation formula for the second estimation index is: The second estimated index is equal to the normalized value of the initial dust concentration in the next cleaning area multiplied by (1 plus the surface friction influence coefficient multiplied by the normalized value of the ground roughness), and then multiplied by the humidity viscosity enhancement coefficient multiplied by the normalized value of the ambient relative humidity, which is the exponential function value. The exponential function term is equipped with a saturation range. When the normalized value of the ambient relative humidity exceeds the preset humidity saturation threshold, the calculation is performed according to the preset humidity saturation threshold.

5. The system according to claim 4, characterized in that, In the cleaning effect prediction module, the calculation formula for constructing the cleaning effect prediction index is as follows: The cleaning effect prediction index is equal to the natural logarithm function (1 plus the supply and demand coupling constant multiplied by the ratio of the first prediction index to the second prediction index). Before the cleaning effect prediction index is input into the compensation amount construction module, it is first protected by the upper limit function, that is, the smaller value between the cleaning effect prediction index and the preset effective upper limit is taken.

6. The system according to claim 5, characterized in that, The ground stain status data includes the stain concentration in the area before cleaning and the stain concentration in the area after cleaning; the formula for calculating the stain residue assessment ratio in the residue assessment module is as follows: The stain residue assessment ratio of the previous cleaning area is equal to the stain concentration after cleaning divided by the stain concentration before cleaning.

7. The system according to claim 6, characterized in that, The multiple cleaning execution parameters include three items: the robot's cruising speed, the target rotation speed of the fan, and the downward pressure of the main brush; in the compensation amount construction module, the calculation formula for constructing the adjustment compensation amount is: The adjustment compensation amount is equal to the base proportional gain coefficient multiplied by (the minimum tolerance threshold minus the cleaning effect prediction index) and then multiplied by (1 plus the historical feedback weight coefficient multiplied by the previous cleaning area's stain residue assessment ratio). There are preset primary intervention thresholds and secondary intervention thresholds for the adjustment compensation amount, and the primary intervention threshold is less than the secondary intervention threshold.

8. The system according to claim 7, characterized in that, In the multi-level adjustment module, multi-level time-coupled adjustment is performed in order of energy consumption and physical intervention degree from low to high. The specific adjustment method and sequence are as follows: (1) When the adjustment compensation amount is greater than 0 and does not exceed the first-level intervention threshold, the first-level adjustment is triggered: only the robot's cruise movement speed is dynamically reduced, and the target speed of the fan and the pressure of the main brush remain unchanged; the updated cruise movement speed is equal to the original set speed divided by (1 plus the adjustment compensation amount). (2) When the adjustment compensation amount is greater than the first-level intervention threshold but not more than the second-level intervention threshold, the second-level adjustment is triggered: the cruise speed is locked to the minimum allowable value of the first-level adjustment stage, and the target speed of the wind turbine is dynamically increased, while the pressure under the main brush remains unchanged; The updated target speed of the fan is equal to the original set speed multiplied by (1 plus the preset fan speed increase compensation coefficient multiplied by (adjustment compensation amount minus the first-level intervention threshold)). (3) When the adjustment compensation amount is greater than the secondary intervention threshold, the tertiary adjustment is triggered: the cruise speed is locked at the minimum allowable value mentioned above, and the target speed of the wind turbine is locked at the maximum allowable value of the secondary adjustment stage. At the same time, the pressure of the main brush is dynamically increased and adjusted. The updated main brush downforce is equal to the original downforce plus the preset main brush boost compensation coefficient multiplied by (adjustment compensation amount minus secondary intervention threshold).

9. A status monitoring device for a sweeping robot, applied to the system described in any one of claims 1-8, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the functions of each module in the system.

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