Control method and device of window cleaning robot, window cleaning robot, medium and electronic equipment

Abstract

The application provides a window cleaning robot control method and device, a window cleaning robot, a medium and an electronic device. The method comprises: periodically acquiring a first operating characteristic value of a heating assembly of the window cleaning robot; determining a risk level of the window cleaning robot according to the first operating characteristic value of the heating assembly; determining a control action of the heating assembly according to a preset mapping relationship between the risk level and the control action; and adjusting a heating parameter of the heating assembly according to the control action. The application overcomes the problem of the existing control mode of extensive response by executing a differentiated heating parameter adjustment strategy according to the heating assembly abnormal type and severity.

Description

Technical Field

[0001] This application relates to the field of window cleaning robot technology, and more specifically, to a control method, device, window cleaning robot, medium, and electronic equipment for a window cleaning robot. Background Technology

[0002] With the rapid development of the smart home industry, window cleaning robots have become mainstream smart home cleaning equipment.

[0003] In related technologies, the control method of the heating component of window cleaning robots usually only performs a single shutdown protection action when an abnormality is detected. It lacks the ability to identify and classify the type and severity of abnormalities, resulting in a crude control strategy. When an abnormality occurs, the protection action is often delayed, and the abnormal state cannot be suppressed in a timely and effective manner, which can easily cause the heating component to overheat and be damaged, affecting the operational reliability of the window cleaning robot.

[0004] Therefore, there is an urgent need to propose an optimized control scheme for heating components that can accurately identify the severity of abnormalities based on the operating status of the heating components themselves, and implement a graded control strategy for the heating components accordingly to solve the above problems. Summary of the Invention

[0005] The purpose of this application is to provide a control method, device, robot, medium, and electronic equipment for a window cleaning robot, which can execute differentiated heating parameter adjustment strategies according to the type and severity of heating component malfunctions, overcoming the problem of coarse response in existing control methods. The specific solution is as follows: According to a specific embodiment of this application, in a first aspect, this application provides a method comprising: The first operational characteristic value of the heating component of the window cleaning robot is periodically acquired; The risk level of the window cleaning robot is determined based on the first operating characteristic value of the heating component; The control actions of the heating component are determined based on the preset mapping relationship between risk level and control action; The heating parameters of the heating component are adjusted according to the control action.

[0006] In some possible embodiments, the method further includes: The second operational characteristic value of each functional component of the window cleaning robot, excluding the heating component, is periodically acquired, wherein the functional component includes at least one of the adsorption component, the water spraying component, and the driving component; Determining the risk level of the window cleaning robot includes: The risk level of the window cleaning robot is determined based on the first and second operating characteristic values ​​of the heating component.

[0007] In some possible embodiments, determining the risk level of the window cleaning robot based on the first operational characteristic value and the second operational characteristic value includes: Based on the first operating characteristic value, the abnormality level of the heating component is determined; and based on the second operating characteristic, the abnormality level of the functional component is determined. The risk level of the window cleaning robot is determined based on the highest anomaly level among all anomaly levels.

[0008] In some possible embodiments, determining the anomaly level of the heating component based on the first operational characteristic value includes: Based on the first operating characteristic value, determine at least two of the following: the degree of deviation of the first operating characteristic value relative to the first preset reference value, the first deviation duration of the first operating characteristic value relative to the first preset reference value, and the first rate of change of the first operating characteristic value; Based on preset weighting coefficients, at least two of the first offset degree, the first offset duration, and the first rate of change are fuzzily weighted and fused to determine the abnormality level of the heating component.

[0009] In some possible embodiments, the weighting coefficients of the first rate of change, the first degree of offset, and the first duration of offset are determined according to the operating phase of the heating component; at least one weighting coefficient has a different value in different cleaning phases.

[0010] In some possible embodiments, the at least one weighting coefficient takes different values ​​at different cleaning stages, including: During the start-up and cooling phases, the weighting coefficient of the first rate of change is higher than the weighting coefficient of the first degree of deviation and the weighting coefficient of the first duration of deviation. During the isothermal phase, the weighting coefficient for the degree of the first offset is higher than the weighting coefficient for the duration of the first offset and the weighting coefficient for the rate of change.

[0011] In some possible embodiments, determining the risk level of the window cleaning robot based on the highest anomaly level among all anomaly levels includes: When there are at least two highest anomaly levels, the final anomaly level is determined based on a preset anomaly level amplification mapping table. The risk level of the window cleaning robot is determined based on the final anomaly level.

[0012] In some possible embodiments, determining the final anomaly level based on a preset anomaly level amplification mapping table includes: Based on a preset anomaly level amplification mapping table, the final anomaly level is determined according to the highest anomaly level and the corresponding component type.

[0013] In some possible embodiments, the final anomaly level determined when the component type corresponding to the highest anomaly level includes a heating component and / or an adsorption component is higher than the final anomaly level determined when the component type corresponding to the highest anomaly level does not include a heating component and an adsorption component.

[0014] In some possible embodiments, the risk levels include: high risk level, medium risk level, and low risk level; The step of determining the control action of the heating component based on a preset mapping relationship between risk level and control action includes: When the risk level is high, the control action is to turn off the heating component; When the risk level is medium risk, the control action is to reduce the heating level of the heating component and / or reduce the target temperature; When the risk level is low, the control action is to reduce the maximum allowable heating level of the heating component.

[0015] In some possible embodiments, after adjusting the heating parameters of the heating component according to the control action, the method further includes: When the risk level is medium risk and the heating component is in the on state, monitor the temperature drop rate of the heating component; When the temperature drop rate is less than a first preset threshold, the number of reduction levels of the heating setting is increased based on the temperature drop rate.

[0016] In some possible embodiments, the method further includes: When the temperature drop rate is less than the second preset rate threshold, the heating component is turned off; wherein the first preset rate threshold is greater than the second preset rate threshold.

[0017] According to a specific embodiment of this application, in a second aspect, this application also provides a control device for a window cleaning robot, the device comprising: The data acquisition module is used to periodically acquire the first operating characteristic values ​​of the heating component of the window cleaning robot; The risk rating module is used to determine the risk level of the window cleaning robot based on the first operating characteristic value of the heating component. The action decision module is used to determine the control action of the heating component based on a preset mapping relationship between risk level and control action; The temperature control execution module is used to adjust the heating parameters of the heating component according to the control action.

[0018] According to a specific embodiment of this application, in a third aspect, this application also provides a window cleaning robot, which is used to implement the method described in any of the preceding claims.

[0019] According to a specific embodiment of this application, in a fourth aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the preceding claims.

[0020] According to a specific embodiment of this application, in a fifth aspect, this application also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to perform the method as described in any of the preceding claims.

[0021] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects: First, the initial operational characteristic value of the heating component is obtained, and this value is used to quantify the risk level of the window cleaning robot. Second, after determining the risk level of the window cleaning robot, the control actions for the heating component are determined based on the preset mapping relationship between the risk level and control actions. This allows the heating parameters of the heating component to be adjusted in response to abnormal states of the heating component itself. Thus, when the window cleaning robot is in a high-risk state, equipment safety can be prioritized to prevent the heating component from operating under continuous high load, which could exacerbate malfunctions or cause secondary hazards; when in a low-risk state, necessary heating functions can be maintained to ensure the continuity and effectiveness of cleaning operations. This solution effectively solves the problem of how to adaptively adjust the heating parameters of the heating component based on its operational risk, avoiding safety hazards caused by abnormal operation of the heating component. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 The flowchart of the control method for the window cleaning robot shown in the embodiments of this application Figure 1 ; Figure 2 The flowchart of the control method for the window cleaning robot shown in the embodiments of this application Figure 2 ; Figure 3 The flowchart of the control method for the window cleaning robot shown in the embodiments of this application Figure 3 ; Figure 4 The structural frame of the control device for the window cleaning robot shown in the embodiments of this application. Figure 1 ; Figure 5 The structural frame of the control device for the window cleaning robot shown in the embodiments of this application. Figure 2 ; Figure 6 This is a schematic diagram of the electronic device structure shown in an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail 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 in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0025] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0026] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0027] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0028] The optional embodiments of this application are described in detail below with reference to the accompanying drawings.

[0029] In existing technologies, cleaning equipment such as window cleaning robots and floor sweeping robots are generally equipped with a cleaning fluid heating function. The conventional solution is to install a heating element in the cleaning fluid delivery pipeline, which heats the cleaning fluid flowing through the pipeline. The heated cleaning fluid is more likely to dissolve and remove stains, effectively improving the cleaning effect.

[0030] To ensure the safe operation of heating components, existing technologies typically employ real-time monitoring of their operating characteristics (such as temperature, current, and voltage). Based on these characteristics, it is determined whether the heating component is in an abnormal state, such as dry burning, overheating, thermostat open circuit, or power outage. Once an abnormality is detected, the component is shut down for protection.

[0031] However, current methods for monitoring the safe operation of heating components typically only execute a single shutdown protection action when an anomaly is detected. They lack the ability to assess the severity of the anomaly and cannot adjust heating parameters according to the real-time operating status of the heating component. Minor anomalies may lead to overreaction and interruption of normal operation, while severe anomalies may result in insufficient response and inability to effectively mitigate risks. The former causes unnecessary work interruptions and energy waste, while the latter may cause damage to the heating component due to the continued deterioration of the abnormal state, affecting the operational reliability and service life of the window cleaning robot.

[0032] To address the shortcomings of existing technologies, such as the coarse control strategies for heating components and the inability to adaptively adjust heating parameters based on the severity of anomalies, this new approach periodically acquires the first operational characteristic value of the heating component and quantifies its risk level. Then, it matches corresponding control actions to adjust the heating parameters based on the risk level. This enables tiered control based on the severity of heating component anomalies, overcoming the limitations of existing solutions that lack coarse response and differentiated handling capabilities.

[0033] Figure 1 The flowchart of the control method for the window cleaning robot shown in the embodiments of this application Figure 1 ,like Figure 1 As shown in this embodiment, a control method for a window cleaning robot includes the following steps: S101. Periodically acquire the first operating characteristic value of the heating component of the window cleaning robot; For example, the main body executing the control method of the window cleaning robot can be the main control system of the window cleaning robot.

[0034] Among them, the operating characteristic values ​​are real-time data that can be monitored during the operation of the functional component. This real-time data can characterize whether the functional component is in a normal or abnormal state. For example, if the heating component fluctuates within a preset target temperature range (e.g., 65℃±3℃), it is determined to be in a normal operating state. If the temperature remains below 50℃ and cannot rise, or if the temperature exceeds 80℃ and shows no downward trend, it is determined to be in an abnormal state.

[0035] S102. Determine the risk level of the window cleaning robot based on the first operating characteristic value of the heating component.

[0036] Understandably, when the heating component malfunctions, the malfunction level can be classified according to its severity. The malfunction level of the heating component corresponds to the risk level of the window cleaning robot.

[0037] S103. Determine the control action of the heating component based on the preset mapping relationship between risk level and control action.

[0038] The mapping between risk levels and control actions is established using a pre-built rule table that stores the corresponding risk levels and control actions. This rule table categorizes the overall safety risk level into four levels: safe, low-risk, medium-risk, and high-risk. Each risk level corresponds to a specific control action; for example, high risk corresponds to shutting down the heating element; low risk corresponds to limiting the maximum allowable heating level (heating power); and a safe level corresponds to maintaining the heating parameters of the heating element. This rule table is typically stored in the main control system's memory for later retrieval.

[0039] S104. Adjust the heating parameters of the heating component according to the control action.

[0040] The heating parameters can include the target temperature (the desired temperature value), heating level (heating power), and on / off status (on or off). The control action is that the main control system sends instructions to the control circuit of the heating component to change its target temperature, heating level, and maximum allowable heating level (the upper limit of heating power), etc.

[0041] In this embodiment, firstly, a first operational characteristic value of the heating component is obtained, and this value is used to quantify the risk level of the window cleaning robot. Secondly, after determining the risk level of the window cleaning robot, a control action for the heating component is determined based on a preset mapping relationship between the risk level and control actions. This allows the heating parameters of the heating component to be adjusted in response to abnormal states of the heating component itself. Thus, when the window cleaning robot is in a high-risk state, equipment safety can be prioritized to prevent the heating component from operating under continuous high load, which could exacerbate malfunctions or cause secondary hazards; when in a low-risk state, necessary heating functions can be maintained to ensure the continuity and effectiveness of cleaning operations. This solution effectively solves the problem of how to adaptively adjust the heating parameters of the heating component based on its operational risk, avoiding safety hazards caused by abnormal operation of the heating component.

[0042] The following are several scenario examples where the heating component is determined to be in an abnormal state based on its first operating characteristic value. In these scenario examples, it is necessary to take control actions to shut down the heating component.

[0043] Abnormal dry burning state of heating component: Within a first preset time period, the current temperature of the heating component relative to the start-up temperature rises above the first preset temperature; for example, the temperature rise of the heating component is ≥50℃ within 15s.

[0044] Temperature controller open circuit abnormality: Within the second preset time period, the temperature rise of the heating component relative to the start-up temperature is less than the second preset temperature.

[0045] NTC (temperature sensor) short circuit abnormality: The current temperature of the heating element reaches the upper or lower limit of the range of the temperature sensor of the heating element; for example, if the detected temperature reaches the maximum or minimum value in the resistance temperature correspondence table.

[0046] Over-temperature abnormality: The temperature of the heating component exceeds the preset safety protection threshold and the duration exceeds the preset third preset duration.

[0047] Power outage abnormality: Within the fourth preset time period, the current of the heating component remains less than the preset current threshold.

[0048] It should be noted that the judgment thresholds for each abnormal state of the heating components mentioned above are not fixed values, but can be adapted to the inherent characteristic parameters of different heating components to ensure the accuracy of the judgment results.

[0049] Figure 2 The flowchart of the control method for the window cleaning robot shown in the embodiments of this application Figure 2 ,like Figure 2 As shown, this application provides a control method for a window cleaning robot, which includes the following steps: S201. Periodically acquire the first operating characteristic value of the heating component of the window cleaning robot and the second operating characteristic value of each functional component other than the heating component; wherein, the functional component includes at least one of the adsorption component, the water spraying component and the driving component.

[0050] The number of second operating characteristic values ​​corresponds to the number of functional components. For example, if a functional component includes an adsorption component, then the second operating characteristic value is the operating characteristic value of the adsorption component. Similarly, if a functional component includes an adsorption component and a water spray component, then the second operating characteristic value is the operating characteristic value of both the adsorption component and the water spray component. The same applies if a functional component includes an adsorption component, a water spray component, and a drive component.

[0051] Furthermore, determining the risk level of the window cleaning robot in this embodiment may include: The risk level of the window cleaning robot is determined based on the first and second operating characteristic values ​​of the heating components.

[0052] For example, the operating characteristic of the heating component is the real-time temperature; The operating characteristic of the water spray assembly is the real-time water spray volume; The operating characteristic of the adsorption component is the vacuum level; The operating characteristics of the drive components are the drive current and / or the moving speed of the window cleaning robot.

[0053] In principle, when selecting the operating characteristic values ​​of each functional component, the following requirements should be met: The values ​​should be able to characterize the abnormal state of the functional component, and if this abnormal state persists, continuing to control the heating component according to the current parameters will affect the performance of the heating component and the working safety of the window cleaning robot. Furthermore, these abnormal states can be effectively weakened or eliminated by adjusting the temperature parameters of the heating component.

[0054] Understandably, the heating element needs to monitor its real-time temperature through a temperature sensor to determine its operating status and ensure that it operates within a safe range.

[0055] The adsorption component of the window cleaning robot relies on the vacuum chamber to adhere to the surface to be cleaned. A higher vacuum level results in stronger adsorption, reducing the risk of falling. Conversely, a low vacuum level increases the risk of the device falling. In such cases, it's crucial to limit the real-time temperature of the heating component, as the heat from the heating component can cause the vacuum chamber to expand due to air heating and soften the seals, further reducing the adsorption force and increasing the risk of falling. Therefore, it's essential to monitor the vacuum level of the adsorption component and adjust the real-time temperature of the heating component accordingly. For example, if the vacuum level in the vacuum chamber is less than 1 kPa when the window cleaning robot is working against the surface to be cleaned, the adsorption component is considered to be malfunctioning.

[0056] When the water spray system fails to spray water normally or the spray volume falls below a preset threshold (e.g., the water tank is empty, the water pump is clogged, or the spray volume drops sharply), if the heating element continues to heat according to the original parameters, the heat that would otherwise be carried away by the water mist cannot be effectively dissipated. This can cause a sharp increase in the local temperature of the heating element, resulting in wasted energy, potential damage to surrounding components, and even safety risks. Therefore, abnormal water spray volume should be considered a crucial control condition for the heating element's parameters. For example, when no water is detected or the spray volume is significantly lower than the preset value, even if the heating element's own temperature has not yet reached the abnormal threshold, the heating power should be proactively reduced, the target temperature limited, or heating should be shut off to avoid ineffective heating and energy waste, while also protecting equipment safety.

[0057] When the drive component malfunctions (such as motor stall or slippage), a stall fault causes a significant increase in drive current, while the movement speed approaches zero. At this time, the motor generates strong electromagnetic interference, which may lead to inaccurate temperature readings from the heating component's temperature sensor. Combined with the motor's own heating, this creates a double risk of thermal runaway. A slippage fault causes a decrease or fluctuation in drive current, while the movement speed decreases significantly or becomes unstable. In this situation, the robot cannot move normally. If the heating component continues to heat without changing its heating parameters, it may cause localized overheating of the surface to be cleaned. For example, if the drive component receives a movement command but the motor current is close to 0A, it is considered an abnormal state.

[0058] In this embodiment, the real-time temperature of the heating component can effectively reflect anomalies such as heating runaway, overheating, and heating failure. The real-time water spray volume of the water spray component can effectively reflect anomalies such as water shortage. The vacuum degree of the adsorption component can effectively reflect anomalies such as insufficient adsorption and risk of detachment. The drive current and moving speed of the drive component can effectively reflect anomalies such as motor stall, slippage, or abnormal speed. Therefore, there is a clear and direct correspondence between the operating characteristic values ​​of this embodiment and the anomalies of each functional component.

[0059] S202. Determine the abnormality level of the heating component based on the first operating characteristic value; and determine the abnormality level of the functional component based on the second operating characteristic.

[0060] It is understandable that the abnormality level of the heating component is determined by the first operating characteristic value, the abnormality level of the functional component is determined by the second operating characteristic value, and then the risk level of the window cleaning robot is determined based on the determined abnormality level.

[0061] For example, determining the anomaly level of the heating component based on the first operating characteristic value may include: Based on the first operating characteristic value, determine at least two of the following: the degree of deviation of the first operating characteristic value relative to the first preset reference value, the first deviation duration of the first operating characteristic value relative to the first preset reference value, and the first rate of change of the first operating characteristic value; Based on preset weighting coefficients, at least two of the first offset degree, first offset duration, and first rate of change are fuzzily weighted and fused to determine the anomaly level of each functional component.

[0062] Of course, the specific steps for determining the anomaly level of a functional component based on the second operational characteristic value are the same as the calculation model for determining the anomaly level of a heating component. The difference lies in the fact that the data substituted is the data of the functional component, which may include: Based on the second operating characteristic value, determine at least two of the following: the degree of deviation of the second operating characteristic value relative to the second preset reference value, the second deviation duration of the second operating characteristic value relative to the second preset reference value, and the second rate of change of the second operating characteristic value; Based on preset weighting coefficients, at least two of the second offset degree, second offset duration, and second rate of change are fuzzily weighted and fused to determine the anomaly level of each functional component.

[0063] It is worth noting that the anomaly level is calculated separately for each functional component.

[0064] Among them, the preset benchmark values ​​(first preset benchmark value, second preset benchmark value) are the normal working range or target values ​​of each operating characteristic value (first operating characteristic value, second operating characteristic value). These can be a range or a fixed value, such as a target temperature of 60±5℃ for the heating element and a real-time water spray rate of 10mL / s. The degree of deviation (first deviation degree, second deviation degree) is the extent to which the current operating characteristic value deviates from the preset benchmark value, reflecting the severity of the anomaly. The duration of deviation (first deviation duration, second deviation duration) is the duration for which the operating characteristic value continuously deviates from the benchmark value, reflecting how long the anomaly has lasted. The rate of change (first rate of change, second rate of change) is the rate of change of the operating characteristic value per unit time, reflecting whether the anomaly is worsening or improving.

[0065] When determining the anomaly level, the input data (degree of deviation, duration of deviation and / or rate of change) is input into a preset fuzzy weighted fusion model. The fuzzy weighted fusion model has preset weighting coefficients corresponding to the input data. The anomaly level is output through fuzzy weighted fusion calculation.

[0066] For heating components, the trend and rate of temperature change need to be monitored to determine if heating is functioning correctly. Therefore, calculating the anomaly level requires three input data points: deviation degree (the degree to which the temperature deviates from the target value), deviation duration (the duration of continuous temperature deviation), and rate of change (the rate of temperature rise or fall). Anomalies in the water spray component mainly manifest as insufficient or no water. This can be determined using the deviation degree and deviation duration, without needing a rate of change. Therefore, the water spray component only requires two input data points: deviation degree (the degree to which the spray volume deviates from the target value) and deviation duration (the duration of continuous spray volume deviation). Anomalies in the adsorption component mainly manifest as a decrease in vacuum. This can be determined using the deviation degree and deviation duration, without needing a rate of change. Therefore, the adsorption component only requires two input data points: deviation degree (the degree to which the vacuum deviates from the target value) and deviation duration (the duration of continuous vacuum deviation). The main abnormalities of the drive component are stalling or slipping, which can be identified by the degree and duration of deviation of current and speed. Therefore, the drive component only needs two input data: the degree of deviation (the degree to which the drive current or moving speed deviates from the target value) and the duration of deviation (the duration of continuous deviation of current or speed).

[0067] This embodiment considers operational data from at least two dimensions: offset degree, offset duration, and rate of change. This allows for the differentiation between transient disturbances and persistent anomalies, reducing false alarm and false negative rates. Furthermore, by comprehensively considering the importance of operational data from different functional components and configuring weighting coefficients, the varying importance of each dimension of operational data is reflected, making the evaluation results more realistic. In addition, the rate of change can predict anomaly development trends, allowing more response time for risk control and effectively improving the safety and reliability of the window cleaning robot.

[0068] For example, the determination of the abnormality level of a heating component will be used as an example for specific explanation.

[0069] First, using a pre-defined membership function, the precise values ​​of each input data point (first offset degree, first offset duration, first rate of change) are converted into membership degrees of multiple semantic levels. For example, the membership function is a triangular membership function, dividing the offset degree into three semantic levels: low, medium, and high. When the first offset degree is 0.65, its membership degree is 0 in "low," 0.75 in "medium," and 0.25 in "high." Similarly, the offset duration and rate of change are also converted into membership degrees of their corresponding semantic levels using their respective membership functions.

[0070] Secondly, the membership degree of each input data is converted into a semantic score according to a preset weighting coefficient. Specifically, each semantic level has a preset semantic score (e.g., "low" is 0.2, "medium" is 0.6, and "high" is 0.9). The score of each input data is calculated using a weighted average method: Input data score = Σ(membership degree × semantic score) / Σmembership degree. For example, if the membership degree of the offset is {low: 0, medium: 0.75, high: 0.25}, then the offset score = (0.75 × 0.6 + 0.25 × 0.9) / (0.75 + 0.25) = (0.45 + 0.225) / 1 = 0.675.

[0071] Then, the scores of the input data are weighted and summed according to preset weighting coefficients to obtain the fusion score. For example, if the preset weighting for offset degree is 0.5, offset duration is 0.3, and change rate is 0.2, then the fusion score = offset degree score × 0.5 + offset duration score × 0.3 + change rate score × 0.2.

[0072] Finally, the fusion score is converted into anomaly levels using a deblurring method. A predefined correspondence between anomaly levels and score ranges is established, for example: there are 8 anomaly levels, with the degree of anomaly increasing progressively. The score range for level 1 is 0 to 0.1; level 2 is 0.1 to 0.2; level 3 is 0.2 to 0.3; level 4 is 0.3 to 0.4; level 5 is 0.4 to 0.5; level 6 is 0.5 to 0.6; level 7 is 0.6 to 0.7; and level 8 is 0.7 to 1.0. When the fusion score is 0.65, it falls within the 0.6 to 0.7 range, corresponding to an anomaly level of 7. When the fusion score is 0.85, it falls within the 0.7 to 1.0 range, corresponding to an anomaly level of 8.

[0073] Furthermore, the weighting coefficients of the first rate of change, the first degree of offset, and the first duration of offset of the heating component are determined according to the operating stage of the heating component; at least one weighting coefficient has a different value in different cleaning stages.

[0074] In this embodiment, the weighting coefficients are dynamically adjusted according to the operating stage of the heating component, making the anomaly level determination more closely reflect the actual working characteristics of the heating component at each stage. For example, the heating stage focuses on the rate of change to promptly detect heating faults; the constant temperature stage focuses on the balance assessment; and the cooling stage focuses on the degree of deviation to ensure that the temperature meets the target. This improves the accuracy of anomaly determination and the safety of heating control.

[0075] For example, at least one weighting coefficient has different values ​​at different cleaning stages, including: During the start-up and cooling phases, the weighting coefficient for the first rate of change is higher than the weighting coefficient for the first degree of deviation and the weighting coefficient for the duration of deviation. During the isothermal phase, the weighting coefficient for the first degree of deviation is higher than the weighting coefficient for the first deviation duration and the weighting coefficient for the first rate of change.

[0076] The entire working cycle of the heating element is divided into three stages: the start-up stage, the constant temperature stage, and the cooling stage. The start-up stage is when the heating element begins heating until the target temperature is reached. The constant temperature stage is when the heating element maintains the target temperature to coordinate with the cleaning work of the cloth assembly. The cooling stage is when the cleaning work is finished, and the heating element no longer needs to maintain the target temperature. During the constant temperature stage, the weighting coefficients of the deviation degree, deviation duration, and change rate can be balanced, without any one factor being emphasized.

[0077] It can be understood that during the start-up and cooling phases, the temperature of the heating element rises from the start-up temperature to the target temperature in one phase and falls from the target temperature to a lower temperature in the other. The rate of temperature rise (rate of change) directly reflects whether the heating is working properly, therefore the rate of change has the highest weighting coefficient. For example, the weighting coefficient for the rate of change is 0.5, the deviation degree is 0.3, and the deviation duration is 0.2. In the isothermal phase, the heating element has approached and maintained the target temperature. The focus shifts from whether the temperature can rise further to whether the temperature is stable near the target value. Therefore, the deviation degree has the highest weighting coefficient.

[0078] S203. Determine the risk level of the window cleaning robot based on the highest anomaly level among all anomaly levels.

[0079] When there is a highest level of abnormality, that highest level of abnormality will be used directly as the basis for determining the risk level of the window cleaning robot.

[0080] It is important to note that the "highest anomaly level" here is not the highest anomaly level in a pre-defined anomaly level sequence, but rather the highest anomaly level among the heating components and / or functional components in an abnormal state. For example, if there are 7 anomaly levels in a pre-defined anomaly level sequence, level 7 is the highest anomaly level in that sequence. If one heating component and the other adsorption component have an anomaly level of 3 and 4 respectively, then level 4 is the "highest anomaly level".

[0081] The anomaly level is used to characterize the severity of the anomaly in the heating component and each functional component. The higher the anomaly level value, the more severe the anomaly. When any component (heating component, functional component) is in a normal state, its anomaly level is defined as 0. When it is in an anomaly, the anomaly level value is a non-zero value (e.g., 1, 2, 3...). In particular, in this embodiment, the risk level of the window cleaning robot is not determined based on the anomaly of the heating component. When the heating component is operating normally and at least one functional component is in an anomaly, the anomaly level of 0 for the heating component and the non-zero anomaly level value of the abnormal functional component are used together to determine the subsequent risk level.

[0082] For example, the following are examples of scenarios in which a functional component is determined to be in an abnormal state based on the second operating characteristic value of different functional components. In these scenarios, it is necessary to take control actions to shut down the heating component.

[0083] Fall prevention abnormality: The current vacuum level of the window cleaning robot is lower than the preset vacuum safety threshold.

[0084] Water tank water shortage abnormality: The real-time flow rate through the water pipe detected by the water flow sensor is lower than the preset lower flow limit threshold, and the water pump is in operation.

[0085] Drive wheel stalling abnormal state: Within the fifth preset time period, the real-time drive current of the drive wheel continues to be greater than the preset current stalling threshold, and the actual speed of the drive wheel continues to be less than the preset speed lower limit.

[0086] Drive wheel slippage abnormality: Within the sixth preset time period, the difference between the target speed and the actual speed of the drive wheel continues to be greater than the preset speed difference threshold, and the body posture change rate of the window cleaning robot is less than the preset inertia threshold.

[0087] It should be noted that the judgment thresholds for the abnormal states of the above-mentioned functional components are not fixed values, but can be adapted to the inherent characteristic parameters of different functional components to ensure the accuracy of the judgment results.

[0088] This embodiment uses the worst-case scenario principle to quickly assess the risk level of the window cleaning robot, which can significantly reduce the computational overhead of the main control system. This is particularly suitable for window cleaning robot main control systems with limited computing power and strict resource constraints. This method can quickly determine the risk level, allowing the heating components to adjust heating parameters in a timely manner, thereby enhancing the real-time safety of the window cleaning robot under abnormal conditions.

[0089] S204. Determine the control action of the heating component based on the preset mapping relationship between risk level and control action.

[0090] The system pre-defines the mapping relationship between the final anomaly level and the risk level. The risk level characterizes the overall risk level of the window cleaning robot and serves as the direct basis for selecting subsequent control actions. For example, the risk level includes four levels, in ascending order of risk: safe level, low risk level, medium risk level, and high risk level.

[0091] The final anomaly level corresponding to the low-risk level is level 1 or 2. At this level, the window cleaning robot has a slight anomaly, but it will not cause it to fall or be damaged.

[0092] The medium-risk level corresponds to an anomaly level of 3 to 6. At this level, the window cleaning robot exhibits significant anomalies, which may affect adsorption stability or operational effectiveness.

[0093] The highest risk level corresponds to an anomaly level of 7 or above. At this level, the robot exhibits a serious anomaly and poses a risk of falling from a great height.

[0094] For example, the risk levels include: high risk level, medium risk level, and low risk level; Based on the preset mapping relationship between risk levels and control actions, the control actions of the heating component are determined, including: When the risk level is high, the control action is to shut down the heating component; When the risk level is medium risk, the control action is to reduce the heating level of the heating component and / or reduce the target temperature; When the risk level is low, the control action is to reduce the maximum allowable heating level of the heating component.

[0095] Understandably, at high-risk levels, the most urgent control action is to immediately shut down the heating element to prevent falls or equipment damage caused by abnormal heating. At medium-risk levels, the heating element's heating level is reduced and / or the target temperature is lowered, while ensuring the reduced level is not lower than the lowest level to achieve safe cooling operation. At low-risk levels, the maximum permissible heating level of the heating element is reduced, limiting the maximum temperature of the heating element to prevent further escalation of risk. Thus, through a tiered control strategy, differentiated and precise control of the heating element can be implemented according to different risk levels, avoiding safety issues caused by underestimating risk and preventing the impact of over-control on normal cleaning operations. This allows for differentiated control strategies based on the degree of risk, avoiding a one-size-fits-all approach of over-control or under-control. At lower risks, a slight reduction in the heating level is used to maintain the normal operating capacity of the heating element to the maximum extent and reduce the impact on operational effectiveness (such as cleaning efficiency); at higher risks, a significant reduction in the heating level is used to prioritize equipment safety. Meanwhile, the number of risk sub-levels and the downgrade range corresponding to each level can be flexibly calibrated and adjusted according to specific equipment parameters, application scenarios, or safety requirements, providing a high degree of flexibility.

[0096] In medium-risk scenarios, the heating element's operating level and the system's target temperature can be lowered simultaneously, or only one of these can be executed. The heating level represents the power output level of the heating element, and the levels are divided according to conventional hierarchical settings (e.g., level 1 is the lowest power, level 8 is the highest power). The higher the level, the greater the heating output power and the faster the heating rate. The target temperature is the preset standard operating temperature of the heating system that needs to be maintained for stable operation. By adjusting the heating level in real time, the actual operating temperature of the heating element is made to approach and stabilize within the target temperature range.

[0097] Furthermore, the medium-risk level can be further divided into several sub-risk levels, and each sub-risk level is matched with a corresponding heating level down (or heating level after down) and a target temperature down (or target temperature after cooling).

[0098] The risk sub-levels are determined based on the actual severity of the abnormality of the functional components. The higher the abnormality level of the functional component (i.e., the more severe the abnormality), the more heating levels need to be lowered, and the greater the reduction in the target operating temperature. The number of risk sub-levels, as well as the corresponding reduction in heating levels and temperature for each risk sub-level, can be flexibly set and modified according to actual usage scenarios and safety operation specifications.

[0099] S205. Adjust the heating parameters of the heating component according to the control action.

[0100] Based on the determined control actions, the operating parameters of the heating element are adjusted by methods such as lowering the heating level, reducing the set temperature, or shutting down the heating element. For example, the heating level is adjusted from level 6 to level 5, and the target temperature is reduced from 45℃ to 42℃ to ensure the stable and safe operation of the heating element.

[0101] S206. When the risk level is medium risk and the heating element is in the on state, monitor the temperature drop rate of the heating element.

[0102] Understandably, after performing control actions on the heating component, it is necessary to monitor the temperature change of the heating component to determine whether the control actions are effective and whether the heating component is operating normally.

[0103] S207. When the temperature drop rate is less than the first preset rate threshold, increase the number of heating gear reduction levels based on the temperature drop rate.

[0104] Different risk levels can correspond to different first preset rate thresholds, which are not fixed.

[0105] Understandably, if the temperature drop rate of the heating component reaches or exceeds the first preset threshold after the heating level and / or target temperature is lowered, it indicates that the control action is effective and the cooling is achieved, and the current heating parameters can be maintained. If the cooling rate does not reach the first preset threshold, it indicates that the expected cooling effect has not been achieved, which is likely due to insufficient control action or abnormal component, requiring further use of stronger cooling control actions.

[0106] If the temperature drop rate reaches or exceeds the preset threshold after the heating element lowers the heating level or the target temperature, it indicates that the control action is effective and the cooling meets expectations, and the current control action can be maintained. If the temperature drop rate is lower than the preset threshold, it indicates that the control action has failed to produce the expected cooling effect, which may be due to insufficient control strength, poor environmental heat dissipation, or a malfunction in the heating element. In this case, further enhanced control actions are required. Furthermore, the preset threshold is used here to determine whether the actual cooling effect of the heating element meets expectations, and its determination method is directly related to the current risk level. Different risk levels correspond to different cooling requirements; therefore, the preset threshold is not a fixed value but is dynamically determined based on the risk level.

[0107] In this way, the temperature drop rate is monitored in real time, and when the temperature drops too slowly (below the first preset threshold), the number of downshifts is increased to quickly bring the temperature back to the safe range. This avoids insufficient downshifting leading to cooling failure, and also prevents excessive downshifting (such as shutting down directly) from affecting normal cleaning operations.

[0108] S208. When the temperature drop rate is less than the second preset rate threshold, turn off the heating component; wherein the first preset rate threshold is greater than the second preset rate threshold.

[0109] It can be understood that when the temperature drop rate is lower than the second preset threshold, it indicates that even if a downshifting operation has been performed, the temperature can still hardly be lowered effectively. At this time, continuing to rely on downshifting adjustment has no practical significance, and the heating assembly must be directly turned off.

[0110] In this way, the first preset threshold triggers downshifting adjustment, and the second preset threshold triggers emergency protection of turning off the heating assembly, forming a progressive protection logic that effectively prevents further damage to the heating assembly.

[0111] Of course, in some scenarios, when monitoring that the temperature of the heating assembly rises instead of falling, the control action of turning off the heating assembly is directly executed.

[0112] In this embodiment, first, the operating characteristic values of the heating assembly and other functional assemblies are obtained synchronously, which can quantify the real-time status of the window cleaning robot from the dimension of the whole machine, avoid the limitation of judging the operation risk only based on single data of the heating assembly, and improve the comprehensiveness and accuracy of the status evaluation of the window cleaning robot. Second, after determining the risk level of the window cleaning robot, the control action for the heating assembly is determined according to the preset mapping relationship between risk levels and control actions. This enables the adjustment of the heating parameters of the heating assembly to not only respond to the abnormal state of the heating assembly itself, but also timely respond to the whole-machine risk caused by the abnormal state of other functional assemblies when the heating assembly continues to heat with the current heating parameters, thereby realizing multi-association linkage safety protection. In this way, when the window cleaning robot is in a high-risk state, the safety of the device can be prioritized to prevent the continuous high-load operation of the heating assembly from aggravating faults or causing secondary hazards; when it is in a low-risk state, the necessary heating function can be maintained to ensure the continuity of cleaning operation and the cleaning effect. This solution effectively solves the problem that the heating parameters of the heating assembly cannot be adaptively adjusted combined with the comprehensive operation risk of multiple components of the window cleaning robot, and avoids potential safety hazards caused by insufficient risk judgment based on a single functional component.

[0113] Figure 3 Flowchart of the control method for a window cleaning robot shown in the embodiment of the present application Figure 3 , as Figure 3 shows, a control method for a window cleaning robot according to this embodiment includes the following steps: The method comprises the following steps: S301: Periodically acquiring a first operating characteristic value of a heating assembly of a window cleaning robot and a second operating characteristic value of each functional assembly other than the heating assembly; wherein the functional assemblies include at least one of an adsorption assembly, a water spraying assembly and a driving assembly.

[0114] S302: Determining an abnormality level of the heating assembly according to the first operating characteristic value; and determining an abnormality level of the functional assembly according to the second operating characteristic value.

[0115] Step S301 is similar to step S201 above, and step S302 is similar to step S202, so they will not be described again here.

[0116] S303. When there are at least two highest anomaly levels, the final anomaly level is determined based on the preset anomaly level amplification mapping table.

[0117] Here, "when there are at least two highest anomaly levels" means that at least two components have the same anomaly level, both being the "highest anomaly level," regardless of the anomaly levels of other components. For example, if the heating component, adsorption component, and drive component are all in an abnormal state, with the heating and adsorption components at an anomaly level of 6 and the drive component at an anomaly level of 3, then the final anomaly level will be determined based on the 6th anomaly level of the heating and adsorption components, without considering the 3rd anomaly level of the drive component. As another example, if the heating and adsorption components are both at an anomaly level of 4, while the water spray component and drive component are in normal operation (with an anomaly level value of 0), then the final anomaly level will be determined based on the 4th anomaly level of the heating and adsorption components.

[0118] Understandably, if two or more components malfunction, they may couple and exacerbate each other, resulting in an overall risk higher than any single malfunction. Therefore, by using a pre-defined malfunction level amplification mapping table, the "highest malfunction level" is taken as input, and a higher final malfunction level is output. Thus, when multiple functional components malfunction simultaneously, their cumulative effect is often greater than that of a single malfunction. For example, if heating and adsorption malfunctions occur simultaneously, the risk level of the window cleaning robot is higher than if either occurs alone. A final malfunction level greater than the highest malfunction level more accurately represents this 1+1>2 risk state.

[0119] For example, Table 1 is an amplified mapping table for anomaly level 1, and Table 2 is an amplified mapping table for anomaly level 3. Tables 1 and 2 can be data relationships stored in the main control system of the window cleaning robot. They can be a single data table or multiple data tables, as long as they can indicate the mapping relationship between the combination of functional components and the final anomaly level. The corresponding final anomaly levels for other anomaly levels are not listed.

[0120] Table 1. Anomaly Level Amplification Mapping Table for Anomaly Level 1 Table 2. Anomaly Level Amplification Mapping Table for Anomaly Level 3 For example, based on a preset anomaly level amplification mapping table, the final anomaly level is determined according to the highest anomaly level and the type of the corresponding functional component.

[0121] It is understandable that anomalies between different functional components have different coupling relationships, and the resulting risks and consequences are also different. For example, when a heating component malfunction (such as heating runaway) and a water spray component malfunction (such as a clogged water spray nozzle) occur simultaneously, heat may not be able to be dissipated through the water spray, causing a sharp rise in local temperature and posing a risk of damaging the glass. The combined risk is far greater than the combination of heating anomalies and drive anomalies.

[0122] In this embodiment, by introducing the functional component type as an amplification factor, it is possible to identify which dangerous combinations need to be upgraded, thus avoiding a one-size-fits-all approach to all multiple abnormal situations.

[0123] Furthermore, in the anomaly level amplification mapping table, when the anomaly levels of two component combinations are the same, the final anomaly level corresponding to the component combination containing the heating component and / or the adsorption component is higher than the final anomaly level corresponding to the component combination not containing the heating component and / or the adsorption component.

[0124] Understandably, the heating and adsorption components are crucial to the window cleaning robot's adsorption safety and operational stability, respectively; malfunctions in either could lead to falls or equipment damage. Assigning a higher final malfunction level to combinations containing these two types of functional components is equivalent to assigning a higher risk weight to the heating and adsorption components. Thus, a high final malfunction level corresponds to emergency response measures (such as shutting down the heating component), while a low final malfunction level corresponds to standard procedures, achieving precise allocation of emergency measures and ensuring both safe operation and effective cleaning.

[0125] S304. Determine the risk level of the window cleaning robot based on the final anomaly level.

[0126] In this step, the "final anomaly level" is equivalent to (replaces) the "highest anomaly level" determined in step S203. The other parts are similar to step S203 and will not be described again here.

[0127] S305. Determine the control action of the heating component based on the preset mapping relationship between risk level and control action.

[0128] S306. Adjust the heating parameters of the heating component according to the control action.

[0129] Step S305 is similar to step S204 above, and will not be described again here.

[0130] In some embodiments, after adjusting the heating parameters of the heating component according to the control action, the method further includes: Once the window cleaning robot is safe, after a preset delay time, the target temperature of the heating component will be restored to the value before this adjustment was performed.

[0131] In this embodiment, the delayed recovery mechanism can avoid frequent switching of heating parameters due to the short duration of the risk state, thereby improving the stability of the main control system and extending the life of the heating components.

[0132] In one scenario, when the heating element is in the off state, the corresponding initial heating level is determined from multiple preset heating levels based on the current temperature of the heating element; The heating element is started at the initial heating level and controlled to run continuously for a first preset time. After running for a first preset duration, based on the comparison between the current temperature of the heating element and the preset target temperature, the heating level is adjusted stepwise at preset time intervals to increase, decrease, or maintain the heating level, so that the temperature of the heating element is maintained at the target temperature before this adjustment.

[0133] This scenario describes the restart process of the heating component. During restart, the starting temperature of the heating component is the first real-time temperature detected after restarting. The heating component has multiple heating levels, each with a different heating intensity. The initial heating level is the level assigned when the heating component starts. The initial heating level is matched according to the starting temperature of the heating component. The first preset duration is the preset duration for the heating component to maintain continuous operation at the current heating level after starting at the initial heating level. After continuous operation for the first preset duration, and when the heating component meets the preset safe heating conditions, based on the comparison between the current temperature of the heating component and the preset target temperature, the heating level is adjusted stepwise at preset step adjustment time intervals to increase, decrease, or maintain the level, so that the heating component temperature is maintained at the target temperature. The safe heating condition is: within the first preset duration, the detected temperature of the heating component is always lower than the preset safe temperature threshold. The step adjustment time interval is the minimum maintenance duration of the heating level of the heating component, and also serves as the determination period for whether the heating level needs to be switched. Whenever the timing reaches a step adjustment interval, it determines whether a gear switch is needed. If so, it controls the heating component to increase or decrease the gear; otherwise, it keeps the gear unchanged.

[0134] In one scenario, when the heating element is turned on, the heating level is adjusted step by step at preset time intervals based on the comparison between the current temperature of the heating element and the preset target temperature, either by increasing the heating level, decreasing the heating level, or maintaining the heating level, so that the temperature of the heating element is maintained at the target temperature before the adjustment was performed.

[0135] This scenario involves adjusting the heating level to raise the temperature of the heating element to the target temperature before adjustment, representing a temperature recovery process. In this scenario, there is no need to determine the initial heating level based on the temperature of the heating element. Since the heating element is in the on state, the initial heating level is the current heating level. The subsequent heating control process is the same; it's just that the initial heating level is the current heating level.

[0136] Understandably, compared to fixed power control, the step-by-step temperature regulation and stabilization method makes the temperature regulation and stabilization process of the heating element more stable and controllable. This effectively avoids temperature overshoot and reduces the frequency and abrupt switching between high and low power, thus helping to reduce energy consumption and extend the lifespan of the heating element. Compared to PID control, it fundamentally simplifies the temperature control logic and computational load, saving computing resources and hardware costs while reducing the difficulty of subsequent algorithm iteration and maintenance.

[0137] This application also provides apparatus embodiments that follow the above embodiments, for implementing the method steps of the above embodiments. The interpretation of the same names is the same as that of the above embodiments, and they have the same technical effects as those of the above embodiments, so they will not be repeated here.

[0138] Figure 4 The structural frame of the control device for the window cleaning robot shown in the embodiments of this application. Figure 1 ,like Figure 4 As shown in this embodiment, a control device for a window cleaning robot includes: The data acquisition module 401 is used to periodically acquire the first operating characteristic values ​​of the heating component of the window cleaning robot; Risk rating module 402 is used to determine the risk level of the window cleaning robot based on the first operating characteristic value of the heating component; Action decision module 403 is used to determine the control action of the heating component based on the preset mapping relationship between risk level and control action; The temperature control execution module is used to adjust the heating parameters of the heating component according to the control action.

[0139] In some embodiments, the data acquisition module 401 is further configured to: The second operational characteristic value of each functional component of the window cleaning robot, excluding the heating component, is periodically acquired, wherein the functional component includes at least one of the adsorption component, the water spraying component, and the driving component; Risk rating module 402 is also used for: The risk level of the window cleaning robot is determined based on the first and second operating characteristic values ​​of the heating component.

[0140] In some embodiments, the risk rating module 402 is further configured to: Based on the first operating characteristic value, the abnormality level of the heating component is determined; and based on the second operating characteristic, the abnormality level of the functional component is determined. The risk level of the window cleaning robot is determined based on the highest anomaly level among all anomaly levels.

[0141] In some embodiments, the risk rating module 402 is further configured to: Based on the first operating characteristic value, determine at least two of the following: the degree of deviation of the first operating characteristic value relative to the first preset reference value, the first deviation duration of the first operating characteristic value relative to the first preset reference value, and the first rate of change of the first operating characteristic value; Based on preset weighting coefficients, at least two of the first offset degree, the first offset duration, and the first rate of change are fuzzily weighted and fused to determine the abnormality level of the heating component.

[0142] In some embodiments, the weighting coefficients of the first rate of change, the first degree of offset, and the first duration of offset are determined according to the operating phase of the heating component; at least one weighting coefficient has a different value in different cleaning phases.

[0143] In some embodiments, during the start-up and cooling phases, the weighting coefficient of the first rate of change is higher than the weighting coefficient of the first degree of deviation and the weighting coefficient of the first deviation duration; during the isothermal phase, the weighting coefficient of the first degree of deviation is higher than the weighting coefficient of the first deviation duration and the weighting coefficient of the first rate of change.

[0144] In some embodiments, the risk rating module 402 is further configured to determine the final anomaly level based on a preset anomaly level amplification mapping table when there are at least two highest anomaly levels; and to determine the risk level of the window cleaning robot based on the final anomaly level.

[0145] In some embodiments, the risk rating module 402 is further configured to: Based on a preset anomaly level amplification mapping table, the final anomaly level is determined according to the highest anomaly level and the type of the corresponding functional component.

[0146] In some embodiments, when the component type corresponding to the highest anomaly level includes a heating component and / or an adsorption component, the determined final anomaly level is higher than the determined final anomaly level when the component type corresponding to the highest anomaly level does not include a heating component and an adsorption component.

[0147] In some embodiments, the risk levels include: high risk level, medium risk level, and low risk level; Action decision module 403 is also used for: When the risk level is high, the control action is to shut down the heating component; When the risk level is medium risk, the control action is to reduce the heating level of the heating component and / or reduce the target temperature; When the risk level is low, the control action is to reduce the maximum allowable heating level of the heating component.

[0148] In some embodiments, the temperature monitoring and control module 501 is further configured to: When the risk level is medium risk and the heating element is on, monitor the temperature drop rate of the heating element; when the temperature drop rate is less than the first preset threshold, increase the number of heating levels that are reduced based on the temperature drop rate.

[0149] In some embodiments, the temperature monitoring and control module 501 is further configured to: When the temperature drop rate is less than the second preset rate threshold, the heating component is turned off; wherein, the first preset rate threshold is greater than the second preset rate threshold.

[0150] This application also provides apparatus embodiments that follow the above embodiments, for implementing the method steps of the above embodiments. The interpretation of the same names is the same as that of the above embodiments, and they have the same technical effects as those of the above embodiments, so they will not be repeated here.

[0151] This application provides a window cleaning robot for performing the method steps described in the above embodiments.

[0152] like Figure 6 As shown, this embodiment provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by a processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method steps of the above embodiment.

[0153] This application provides a non-volatile computer storage medium storing computer-executable instructions that can execute the method steps of the above embodiments.

[0154] The following is for reference. Figure 6 The diagram illustrates a structural schematic of an electronic device suitable for implementing embodiments of this application. The terminal devices in the embodiments of this application may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0155] like Figure 6 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the electronic device. The processing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0156] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0157] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, it performs the functions defined in the methods of the embodiments of this application.

[0158] It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (R-heating assembly M), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (spray assembly drive assembly-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0159] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0160] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as J_heating_component_v_heating_component, S_m_heating_component_llt_heating_component_lk, water spraying_component_++—and conventional procedural programming languages—such as the "water spraying_component" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (L_heating_component_N) or a wide area network (W_heating_component_N)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0161] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0162] The units described in the embodiments of this application can be implemented in software or hardware. The names of the units are not, in some cases, limiting the scope of the unit itself.

Claims

1. A control method for a window cleaning robot, characterized in that, The method includes: The first operational characteristic value of the heating component of the window cleaning robot is periodically acquired; The risk level of the window cleaning robot is determined based on the first operating characteristic value of the heating component; The control actions of the heating component are determined based on the preset mapping relationship between risk level and control action; The heating parameters of the heating component are adjusted according to the control action.

2. The method according to claim 1, characterized in that, The method further includes: The second operational characteristic value of each functional component of the window cleaning robot, excluding the heating component, is periodically acquired, wherein the functional component includes at least one of the adsorption component, the water spraying component, and the driving component; Determining the risk level of the window cleaning robot includes: The risk level of the window cleaning robot is determined based on the first and second operating characteristic values ​​of the heating component.

3. The method according to claim 2, characterized in that, Determining the risk level of the window cleaning robot based on the first operational characteristic value and the second operational characteristic value includes: Based on the first operating characteristic value, the abnormality level of the heating component is determined; and based on the second operating characteristic, the abnormality level of the functional component is determined. The risk level of the window cleaning robot is determined based on the highest anomaly level among all anomaly levels.

4. The method according to claim 3, characterized in that, Determining the abnormality level of the heating component based on the first operational characteristic value includes: Based on the first operating characteristic value, determine at least two of the following: the degree of deviation of the first operating characteristic value relative to the first preset reference value, the first deviation duration of the first operating characteristic value relative to the first preset reference value, and the first rate of change of the first operating characteristic value; Based on preset weighting coefficients, at least two of the first offset degree, the first offset duration, and the first rate of change are fuzzily weighted and fused to determine the abnormality level of the heating component.

5. The method according to claim 4, characterized in that, in, The weighting coefficients for the first rate of change, the first degree of offset, and the first offset duration are determined according to the operating stage of the heating component; at least one weighting coefficient has a different value in different cleaning stages.

6. The method according to claim 5, characterized in that, The at least one weighting coefficient takes different values ​​at different cleaning stages, including: During the start-up and cooling phases, the weighting coefficient of the first rate of change is higher than the weighting coefficient of the first degree of deviation and the weighting coefficient of the first duration of deviation. During the isothermal phase, the weighting coefficient for the degree of the first offset is higher than the weighting coefficient for the duration of the first offset and the weighting coefficient for the rate of change.

7. The method according to claim 3, characterized in that, Determining the risk level of the window cleaning robot based on the highest anomaly level among all anomaly levels includes: When there are at least two highest anomaly levels, the final anomaly level is determined based on a preset anomaly level amplification mapping table. The risk level of the window cleaning robot is determined based on the final anomaly level.

8. The method according to claim 7, characterized in that, The determination of the final anomaly level based on the preset anomaly level amplification mapping table includes: Based on a preset anomaly level amplification mapping table, the final anomaly level is determined according to the highest anomaly level and the corresponding component type.

9. The method according to claim 8, characterized in that, in, When the component type corresponding to the highest anomaly level includes a heating component and / or an adsorption component, the determined final anomaly level is higher than the determined final anomaly level when the component type corresponding to the highest anomaly level does not include a heating component and an adsorption component.

10. The method according to any one of claims 1 to 9, characterized in that, in, The risk levels include: high risk level, medium risk level, and low risk level; The step of determining the control action of the heating component based on a preset mapping relationship between risk level and control action includes: When the risk level is high, the control action is to turn off the heating component; When the risk level is medium risk, the control action is to reduce the heating level of the heating component and / or reduce the target temperature; When the risk level is low, the control action is to reduce the maximum allowable heating level of the heating component.

11. The method according to claim 10, characterized in that, After adjusting the heating parameters of the heating component according to the control action, the method further includes: When the risk level is medium risk and the heating component is in the on state, monitor the temperature drop rate of the heating component; When the temperature drop rate is less than a first preset threshold, the number of reduction levels of the heating setting is increased based on the temperature drop rate.

12. The method according to claim 11, characterized in that, The method further includes: When the temperature drop rate is less than the second preset rate threshold, the heating component is turned off; wherein the first preset rate threshold is greater than the second preset rate threshold.

13. A control device for a window cleaning robot, characterized in that, The device includes: The data acquisition module is used to periodically acquire the first operating characteristic values ​​of the heating component of the window cleaning robot; The risk rating module is used to determine the risk level of the window cleaning robot based on the first operating characteristic value of the heating component. The action decision module is used to determine the control action of the heating component based on a preset mapping relationship between risk level and control action; The temperature control execution module is used to adjust the heating parameters of the heating component according to the control action.

14. A window cleaning robot, characterized in that, The window cleaning robot is used to implement the method as described in any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 12.

16. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 12.

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