Window cleaning robot water temperature control method, device, medium, and program product

Abstract

The embodiment of the application provides a window cleaning robot water temperature control method, device, medium and program product, and relates to the technical field of window cleaning robots. The method comprises the following steps: obtaining an initial temperature of cleaning water and an ambient temperature of the window cleaning robot; adjusting the heating power of an instant heating module of the window cleaning robot according to the initial temperature and the ambient temperature; and controlling the temperature of the cleaning water through the instant heating module according to the heating power. Based on the method, the heating power of the instant heating module of the window cleaning robot can be adjusted in a timely manner according to the initial temperature and the ambient temperature, so that the heating power can be dynamically adapted to the current initial temperature and ambient temperature, the cleaning power of the water mist is improved, and the cleaning efficiency of the window cleaning robot is improved.

Description

Technical Field

[0001] This application relates to the field of window cleaning robot technology, and in particular to a method, device, medium and program product for controlling water temperature in a window cleaning robot. Background Technology

[0002] In modern architecture, glass windows and curtain walls are widely used due to their aesthetic appeal and ability to allow light to pass through. However, they are difficult to clean, and existing manual cleaning methods pose safety risks and are inefficient. Window cleaning robots utilize suction negative pressure technology to perform high-altitude operations. However, current window cleaning robots generally only use room temperature water for cleaning, making it difficult to handle stubborn stains, oil stains, and other complex dirt.

[0003] For example, in cold regions (such as northern winters), high-altitude areas, or high-rise buildings, the ambient temperature may be below 10°C, causing ice to easily form or frost to adhere to the glass surface, significantly limiting the cleaning effect of room temperature water. Furthermore, users' expectations for cleaning performance have evolved from basic cleaning to deep cleaning and even sterilization functions, but existing window cleaning robots lack the ability to dynamically control the water temperature, resulting in low cleaning efficiency. Summary of the Invention

[0004] This application provides a method, device, medium, and program product for controlling the water temperature of a window cleaning robot, which is used to control the water temperature of the cleaning water used by the window cleaning robot and improve the cleaning efficiency of the window cleaning robot.

[0005] In a first aspect, embodiments of this application provide a method for controlling the water temperature of a window cleaning robot, the method comprising:

[0006] Obtain the initial temperature of the cleaning water and the ambient temperature of the window cleaning robot;

[0007] Adjust the heating power of the instant heating module of the window cleaning robot according to the initial temperature and ambient temperature;

[0008] The temperature of the cleaning water is controlled by an instant heating module based on the heating power.

[0009] In one possible implementation, adjusting the heating power of the instant heating module of the window cleaning robot based on the initial temperature and ambient temperature includes:

[0010] The target temperature is determined based on the ambient temperature and is used to indicate the target water temperature to which the cleaning water is heated.

[0011] Based on the difference between the initial temperature and the target temperature, multiple adjustment ranges of the heating power are set in segments;

[0012] Within any of the multiple adjustment ranges, the heating power of the instant heating module of the window cleaning robot is dynamically adjusted using a proportional-integral-differential algorithm.

[0013] In one possible implementation, before dynamically adjusting the heating power of the instant heating module of the window cleaning robot using a proportional-integral-differential algorithm, the method further includes:

[0014] Calculate the difference between the ambient temperature and the initial temperature to obtain the ambient water temperature difference;

[0015] Based on the difference in ambient water temperature, the feedforward compensation power increment is calculated. The feedforward compensation power increment is used to characterize the amount of adjustment of heating power in advance to cope with the impact of changes in ambient temperature on heating demand.

[0016] In one possible implementation, the heating power of the instantaneous heating module of the window cleaning robot is dynamically adjusted using a proportional-integral-differential algorithm, including:

[0017] Determine the temperature change trend of the cleaning water based on its real-time temperature.

[0018] Based on the temperature change trend of the clean water, the output amplitude of the heating power is adjusted in stages, and the output amplitude is used to characterize the amount of change in heating power.

[0019] In one possible implementation, before adjusting the heating power of the instant heating module of the window cleaning robot based on the initial temperature and ambient temperature, the method further includes:

[0020] Determine the cleaning task type for the current cleaning task of the window cleaning robot;

[0021] Depending on the type of cleaning task, switch the heating mode of the window cleaning robot to either enhanced mode or energy-saving mode.

[0022] In one possible implementation, determining the cleaning task type of the window cleaning robot's current cleaning task includes:

[0023] Acquire image data of the area where cleaning water is sprayed;

[0024] Based on image data, the type of stains in the area where the cleaning water was sprayed is analyzed using image recognition algorithms;

[0025] The cleaning task type of the window cleaning robot is determined based on the type of stain.

[0026] In one possible implementation, the method further includes:

[0027] Based on multiple historical ambient temperatures within a historical period, the environmental dynamic prediction model is used to predict the trend of ambient temperature changes in future periods.

[0028] Among them, the historical period is the period corresponding to the first preset time before the moment when the ambient temperature of the window cleaning robot is obtained, the future period is the period corresponding to the second preset time after the moment when the ambient temperature of the window cleaning robot is obtained, and the environmental dynamic prediction model is a pre-trained neural network model used to predict temperature changes.

[0029] Based on the initial temperature and ambient temperature, adjust the heating power of the instant heating module of the window cleaning robot, including:

[0030] The heating power of the instant heating module of the window cleaning robot is adjusted according to the initial temperature, ambient temperature, and the trend of ambient temperature changes.

[0031] Secondly, embodiments of this application provide a water temperature control device for a window cleaning robot, the device comprising:

[0032] The acquisition module is used to acquire the initial temperature of the cleaning water and the ambient temperature of the window cleaning robot.

[0033] The adjustment module is used to adjust the heating power of the instant heating module of the window cleaning robot according to the initial temperature and the ambient temperature.

[0034] The control module is used to control the temperature of the cleaning water according to the heating power through the instant heating module.

[0035] In one possible implementation, the adjustment module is specifically used for:

[0036] The target temperature is determined based on the ambient temperature and is used to indicate the target water temperature to which the cleaning water is heated.

[0037] Based on the difference between the initial temperature and the target temperature, multiple adjustment ranges of the heating power are set in segments;

[0038] Within any of the multiple adjustment ranges, the heating power of the instant heating module of the window cleaning robot is dynamically adjusted using a proportional-integral-differential algorithm.

[0039] In one possible implementation, the adjustment module is also used for:

[0040] Calculate the difference between the ambient temperature and the initial temperature to obtain the ambient water temperature difference;

[0041] Based on the difference in ambient water temperature, the feedforward compensation power increment is calculated. The feedforward compensation power increment is used to characterize the amount of adjustment of heating power in advance to cope with the impact of changes in ambient temperature on heating demand.

[0042] In one possible implementation, the adjustment module is specifically used for:

[0043] Determine the temperature change trend of the cleaning water based on its real-time temperature.

[0044] Based on the temperature change trend of the clean water, the output amplitude of the heating power is adjusted in stages, and the output amplitude is used to characterize the amount of change in heating power.

[0045] In one possible implementation, the adjustment module is also used for:

[0046] Determine the cleaning task type for the current cleaning task of the window cleaning robot;

[0047] Depending on the type of cleaning task, switch the heating mode of the window cleaning robot to either enhanced mode or energy-saving mode.

[0048] In one possible implementation, the adjustment module is specifically used for:

[0049] Acquire image data of the area where cleaning water is sprayed;

[0050] Based on image data, the type of stains in the area where the cleaning water was sprayed is analyzed using image recognition algorithms;

[0051] The cleaning task type of the window cleaning robot is determined based on the type of stain.

[0052] In one possible implementation, the device further includes a prediction module, which is used to:

[0053] Based on multiple historical ambient temperatures within a historical period, the environmental dynamic prediction model is used to predict the trend of ambient temperature changes in future periods.

[0054] Among them, the historical period is the period corresponding to the first preset time before the moment when the ambient temperature of the window cleaning robot is obtained, the future period is the period corresponding to the second preset time after the moment when the ambient temperature of the window cleaning robot is obtained, and the environmental dynamic prediction model is a pre-trained neural network model used to predict temperature changes.

[0055] The adjustment module is specifically used to adjust the heating power of the instant heating module of the window cleaning robot according to the initial temperature, ambient temperature, and the trend of ambient temperature changes.

[0056] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0057] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0058] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0059] The window cleaning robot water temperature control method, device, medium, and program products provided in this application embodiment obtain the initial temperature of the cleaning water and the ambient temperature of the window cleaning robot. Based on these initial and ambient temperatures, the heating power of the instant heating module of the window cleaning robot can be adjusted dynamically to adapt to the current initial and ambient temperatures. When the window cleaning robot is in a low-temperature working environment, the heating module can quickly control the temperature of the cleaning water in real time according to the determined heating power, ensuring that the temperature of the sprayed water mist can effectively resist the low ambient temperature, enhancing the cleaning power of the water mist and improving the cleaning efficiency of the window cleaning robot. Attached Figure Description

[0060] 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.

[0061] Figure 1 A flowchart illustrating the water temperature control method for the window cleaning robot provided in this application embodiment. Figure 1 ;

[0062] Figure 2 A flowchart illustrating the water temperature control method for the window cleaning robot provided in this application embodiment. Figure 2 ;

[0063] Figure 3 A flowchart illustrating the water temperature control method for the window cleaning robot provided in this application embodiment. Figure 3 ;

[0064] Figure 4 This is a schematic diagram of the water temperature control device for the window cleaning robot provided in an embodiment of this application;

[0065] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0066] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0067] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0068] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize or refuse.

[0069] Furthermore, the technical solution involved in this application, which involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) and the use of artificial intelligence technology for automated decision-making, and makes decisions that have a significant impact on personal rights based on the results of automated decision-making, provides users with corresponding operation entry points for users to choose to agree to or reject the results of automated decision-making; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0070] In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0071] In the embodiments of this application, the use of terms such as "first" and "second" is to distinguish between identical or similar items that have essentially the same function and effect. For example, "first electronic device" and "second electronic device" are merely used to distinguish different electronic devices and do not limit their order of execution. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply that they are different.

[0072] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0073] Existing window cleaning robots generally use room temperature water cleaning systems, which rely solely on room temperature water (20℃-30℃) for cleaning. This results in limited effectiveness against stubborn stains and grease, failing to achieve deep cleaning. Because existing window cleaning robots lack active control over water temperature, their cleaning performance is limited by the physical properties of room temperature water, making it difficult to break down the molecular structure of stubborn stains. This leads to poor cleaning results, requiring rework or manual wiping, thus reducing cleaning efficiency.

[0074] Due to the lack of heating functionality, existing window cleaning robots do not integrate a heating module, making it impossible to enhance cleaning effectiveness or achieve sterilization through hot water. Even if a heating module from an existing heating appliance is simply added to the window cleaning robot itself, without considering the special characteristics of high-altitude working environments (such as high-speed centrifugal fan heat dissipation and low-temperature environments), the heating module will be unable to maintain a stable water temperature, causing the sprayed water mist temperature to drop rapidly and significantly reducing the cleaning effect.

[0075] For example, in low-temperature scenarios of 0-10℃, existing heating modules, under the combined conditions of high altitude, low temperature, outdoor environment, and high-speed negative pressure fan heat dissipation, will be unable to quickly raise the water temperature to the target temperature due to large thermal inertia and rapid heat dissipation (such as high-speed fan heat dissipation and low external temperature environment). If they are directly applied to window cleaning robots, the temperature of the sprayed water mist will drop rapidly, and the cleaning effect will be significantly reduced.

[0076] Existing technologies mainly rely on open-loop regulation or conventional proportional-integral-derivative (PID) algorithms. If the heating power is not dynamically adjusted in conjunction with the inlet water temperature and ambient temperature, it will result in large fluctuations in the outlet water temperature (e.g., ±5℃ or more), low temperature control accuracy, and an inability to meet the deep cleaning needs of window cleaning robots.

[0077] Furthermore, if energy efficiency control is too rudimentary and lacks intelligent temperature control algorithms, the heating power will not be able to dynamically adjust according to actual needs, resulting in energy waste due to continuous full-power heating. If closed-loop temperature control logic cannot be adopted, relying on open-loop adjustment or simple feedback will lead to large fluctuations in the outlet water temperature (usually ±5℃ or more), resulting in poor temperature stability and inability to meet the needs of precision cleaning.

[0078] Furthermore, if the heat dissipation protection and insulation design of the heating module are not optimized for high-altitude operation scenarios, the efficiency of the heating module will drop sharply in low-temperature environments. Continuous full-power heating will lead to energy waste, and in low-temperature environments, it is prone to causing safety hazards due to dry burning and localized boiling, resulting in a serious lack of energy efficiency optimization.

[0079] The inventors conducted research on the heating requirements and working environment characteristics of window cleaning robots. Addressing the technical problems faced by window cleaning robots during heating operations, such as poor low-temperature adaptability and low temperature control accuracy, they proposed setting up an instant heating module in the window cleaning robot. Through an intelligent temperature control algorithm, the instant heating module is used to control the temperature of the cleaning water. This allows the window cleaning robot to quickly raise the water temperature to the target temperature when it is in a low-temperature working environment, avoiding the rapid drop in temperature of the sprayed water mist due to pre-cooling, and improving the cleaning effect of the window cleaning robot.

[0080] Instantaneous heating modules can achieve rapid heating using heating ceramic plates or high-power-density thick-film heaters (such as stainless steel thick-film heating tubes). They have low thermal inertia, are suitable for continuous water output scenarios, and reduce cold air intrusion. Through intelligent temperature control algorithms, multiple negative temperature coefficient (NTC) sensors (for inlet / outlet water temperature monitoring) and flow sensors are introduced. Combined with a segmented variable-parameter PID algorithm, the heating power is dynamically adjusted, and feedforward compensation logic is added to predict the impact of ambient temperature changes on heating, achieving closed-loop temperature control.

[0081] In some implementations, through coordinated optimization of hardware and algorithms, high-density insulation layers (greater than 15mm) can be used to reduce heat loss. Combined with pulse width modulation (PWM) duty cycle control, precise adjustment of heating power can be achieved. Multiple safety protections (such as over-temperature and low-flow protection) can also be implemented to cope with the complex working environment of high-altitude operations. Furthermore, energy efficiency optimization strategies are employed, avoiding the continuous heat preservation energy consumption of water-storage heating through an instant-on mode, and reducing overall energy consumption through segmented power control (such as reducing power when approaching the target temperature).

[0082] In view of this, this application provides a water temperature control method for a window cleaning robot. This method solves the technical problem of the window cleaning robot's inability to stably achieve the target water temperature in specific working environments (such as low temperature, high altitude, and strong heat dissipation scenarios) by coordinating an instantaneous high-power-density heating module with an intelligent temperature control algorithm. The instantaneous heating module rapidly raises the water temperature to the target temperature, while temperature sensors monitor the inlet / outlet water temperature in real time. A piecewise variable-parameter PID algorithm dynamically adjusts the heating power, and feedforward compensation logic is introduced to predict the impact of ambient temperature changes on heating. This achieves rapid response, stable output, and energy efficiency optimization of the water temperature in high-altitude and low-temperature environments.

[0083] The water temperature control method for window cleaning robots provided in this application can be applied to automated cleaning scenarios in high-rise buildings, and is suitable for operational needs in specific working environments such as high altitude, low temperature, and strong wind. For example, the water temperature control method for window cleaning robots provided in this application can be used in high-altitude glass curtain wall cleaning scenarios, and is especially suitable for operations in cold regions (such as northern winters), high-altitude regions, or super high-rise buildings.

[0084] The window cleaning robot is fixed to the glass surface using adsorption negative pressure technology. The cleaning system needs to achieve rapid heating of the sprayed water mist (target temperature 60-80℃) under conditions of high-speed centrifugal fan heat dissipation, low external temperature (e.g., 0-10℃), and high altitude and low pressure. In this scenario, existing water temperature control methods are difficult to stabilize due to rapid heat dissipation and high thermal inertia. However, the method provided in this application combines instant heating with intelligent temperature control algorithms to solve the problems of insufficient heating efficiency, large temperature fluctuations, and excessive energy consumption in low-temperature environments.

[0085] The technical solutions of this application will be described in detail below with reference to specific embodiments. The specific embodiments described below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0086] Figure 1 A flowchart illustrating the water temperature control method for the window cleaning robot provided in this application embodiment. Figure 1 The execution entity of this method can be an electronic device with corresponding data storage and computing capabilities, such as the controller of a window cleaning robot. Figure 1 As shown, the method includes steps S101, S102 and S103.

[0087] S101, obtains the initial temperature of the cleaning water and the ambient temperature of the window cleaning robot.

[0088] For example, cleaning water can be understood as any liquid used for cleaning. Cleaning water can be water or a mixture of water and cleaning agent. The cleaning water sprayed by window cleaning robots is usually tap water or filtered tap water, and its temperature directly affects the cleaning effect. For example, in low-temperature environments (such as 0℃-10℃), the initial temperature of the cleaning water may be lower than the target temperature (such as 60℃ or 80℃).

[0089] The initial temperature of the cleaning water can be understood as the temperature of the cleaning water before it is heated, or as the water temperature when it enters the heating chamber. The ambient temperature of the window cleaning robot can include the ambient temperature around the outside of the robot body, the ambient temperature around the water tank or heating chamber inside the robot, or the ambient temperature around the water spray outlet.

[0090] For example, by using multiple NTC sensors to collect the inlet water temperature of the heating chamber and the ambient temperature outside the window cleaning robot in real time, the initial temperature of the cleaning water and the ambient temperature of the window cleaning robot can be obtained. Real-time acquisition of the initial temperature of the cleaning water and the ambient temperature can be used as input parameters for subsequent dynamic adjustment of the heating power.

[0091] When acquiring temperature values ​​using an NTC sensor, the sensor's resistance value can be converted to a corresponding temperature value using the B-value formula. An NTC sensor is a negative temperature coefficient thermistor, meaning its resistance decreases as temperature increases. The B-value formula, a first-order approximation of the Steinhart-Hart equation, can be used to convert NTC resistance values ​​to temperature values. By monitoring temperature changes in real time using an NTC sensor and utilizing the B-value formula to eliminate the non-linear relationship between resistance and temperature, accurate temperature input data is provided for subsequent PID algorithms, ensuring the accuracy of heating power regulation.

[0092] S102, adjusts the heating power of the instant heating module of the window cleaning robot according to the initial temperature and ambient temperature.

[0093] For example, an instant heating module can be understood as an integrated heating component that can quickly respond to heating commands, does not require pre-stored cleaning water, can directly and instantly heat circulating cleaning water, and has adjustable power. The core components of an instant heating module include, but are not limited to, a heating element (such as a ceramic heating element or a metal heating tube), a power adjustment unit, and a temperature detection unit. The instant heating module can adjust its heating power in real time according to the input power control signal, thereby achieving precise control of the cleaning water temperature. Instant heating modules are characterized by fast heating response, controllable energy consumption, and compact size, making them suitable for the water heating needs of window cleaning robots.

[0094] Instantaneous heating modules can use heating ceramic plates or thick-film heaters (such as stainless steel thick-film heating tubes), enabling rapid heating with low thermal inertia. For example, an instantaneous heating module can quickly raise the temperature when the water temperature is lower than the target temperature, avoiding insufficient heating efficiency caused by low-temperature environments.

[0095] Heating power can be understood as the intensity of heat energy applied to the cleaning water by the instantaneous heating module per unit time, and the unit can be watts (W). The range of heating power values ​​can be dynamically adjusted according to the target temperature of the cleaning water, the water flow rate, and the inlet water temperature (initial temperature). Heating power is the core parameter that determines the rate of change of the cleaning water temperature and the final stable temperature. The heating power can be adjusted by changing the input voltage and current of the heating module, and its adjustment accuracy directly affects the control accuracy of the cleaning water temperature.

[0096] For example, based on the difference between the initial temperature and the ambient temperature, the instantaneous heating module can dynamically adjust the heating power through algorithms (such as PID control). For instance, in a low-temperature environment, when the initial temperature is much lower than the target temperature, the heating power is increased to the maximum value to rapidly raise the temperature; when the initial temperature approaches the target temperature, the power is gradually reduced to avoid overshoot.

[0097] Furthermore, the heating chamber of the instant heating module can also be wrapped with an insulation layer. This insulation layer can be made of high-density insulation material, which reduces heat loss. High-density insulation material can be understood as insulation material with low thermal conductivity (such as insulation cotton ≥15mm thick), used to reduce heat loss. After wrapping the heating module with high-density insulation material, it can effectively block the heat dissipation effect of the high-speed centrifugal fan inside the window cleaning robot. For example, when the internal temperature drops sharply due to the operation of the high-speed centrifugal fan, the insulation material can maintain the heat output of the heating module, ensuring a stable temperature of the cleaning water.

[0098] Figure 2 A flowchart illustrating the water temperature control method for the window cleaning robot provided in this application embodiment. Figure 2 ,like Figure 2 As shown, the heating power of the instant heating module of the window cleaning robot is adjusted according to the initial temperature and the ambient temperature, including steps S201, S202 and S203.

[0099] S201, determine the target temperature based on the ambient temperature. The target temperature is used to indicate the target water temperature to which the cleaning water is heated.

[0100] For example, the target temperature is used to indicate the target water temperature to which the cleaning water is heated. When determining the target temperature based on the ambient temperature, it can be done, for example, through a preset mapping table between ambient temperature and target temperature.

[0101] For example, the mapping table includes multiple ambient temperatures and their corresponding target temperatures. By iterating through the mapping table based on the obtained ambient temperatures, the target temperature can be determined. Other methods can also be used to determine the target temperature, which are not limited in this embodiment.

[0102] S202, based on the difference between the initial temperature and the target temperature, sets multiple adjustment ranges for the heating power in segments.

[0103] For example, the adjustment range can be understood as multiple temperature ranges divided according to the difference between the initial temperature and the target temperature, with each range corresponding to a different heating power adjustment strategy. When dividing into multiple adjustment ranges, the ranges can be divided according to a preset number, or they can be adaptively divided according to the magnitude of the difference.

[0104] For example, if the preset number of segments is n, then the difference can be divided into equal segments to obtain n adjustment intervals, where n can be any positive integer. Alternatively, adaptive segmentation can be performed based on the magnitude of the difference; for instance, when the difference is small, it can be adaptively segmented into fewer adjustment intervals, and when the difference is large, it can be adaptively segmented into more adjustment intervals.

[0105] S203: Within any of the multiple adjustment ranges, the heating power of the instant heating module of the window cleaning robot is dynamically adjusted using a proportional-integral-differential algorithm.

[0106] For example, the Proportional-Integral-Derivative (PID) algorithm is a closed-loop control algorithm that adjusts the heating power through a combination of a proportional (P), integral (I), and derivative (D) term. For instance, when the initial temperature is lower than the target temperature, the proportional term rapidly increases the heating power, the integral term eliminates static errors, and the derivative term suppresses overshoot.

[0107] For example, by setting segmented adjustment ranges, the difference between the initial temperature and the target temperature can be divided into multiple adjustment ranges, each corresponding to a different heating power adjustment strategy. For instance, in a low temperature difference range (e.g., the initial temperature is 20°C lower than the target temperature), a high-power mode is entered to rapidly raise the temperature; in a small temperature difference range (e.g., the initial temperature is close to the target temperature), a low-power mode is switched to avoid overshoot. The PID algorithm dynamically adjusts the heating power within the adjustment range. The proportional term responds quickly to the temperature difference, the integral term eliminates long-term errors, and the derivative term suppresses the rate of temperature change, thereby achieving precise control of the heating power.

[0108] In this embodiment, the dynamic adaptability of heating power is further improved by combining segmented adjustment intervals with a PID algorithm. The segmented adjustment intervals ensure reasonable power allocation under different temperature difference scenarios, avoiding response lag or overshoot caused by a single power strategy. The PID algorithm, through the synergistic effect of proportional-integral-derivative operations, achieves rapid elimination of temperature errors and enhanced stability, thereby significantly improving the response speed and stability of water temperature in low-temperature environments while reducing energy consumption.

[0109] S103 controls the temperature of the cleaning water according to the heating power through an instant heating module.

[0110] For example, controlling the temperature of the cleaning water can be understood as controlling the dynamic process of the cleaning water transitioning from its initial inlet temperature to a preset target temperature as it flows through the instantaneous heating module. This includes two core dimensions: the rate of temperature increase and the stable temperature value. In this step, the temperature control of the cleaning water must meet a preset control accuracy (typically a temperature difference not exceeding ±2℃) to ensure that the temperature of the cleaning water meets the real-time requirements of the cleaning operation, avoiding damage to the cleaning object due to excessively high temperatures or affecting the cleaning effect due to excessively low temperatures.

[0111] When the temperature of cleaning water is controlled by an instant heating module based on the heating power, for example, the controller of the window cleaning robot converts the determined heating power into a control command and transmits it to the power regulation unit (such as a silicon controlled rectifier or a semiconductor field-effect transistor) of the instant heating module. The power regulation unit adjusts the heating power precisely by changing the voltage or current input to the heating element (ceramic heating element or metal heating tube) according to the control command.

[0112] The cleaning water comes into full contact with the heating element inside the heating chamber, absorbing heat and gradually increasing in temperature. A temperature sensor continuously monitors the real-time temperature of the cleaning water at the outlet of the heating chamber and feeds this signal back to the controller. The controller compares the real-time temperature of the cleaning water with a preset target temperature to determine if the real-time temperature falls within the preset temperature control accuracy threshold range.

[0113] For example, if the real-time temperature is lower than the lower limit of the target temperature (target temperature - 2℃), the control unit issues a command to increase the heating power of the power regulation unit, accelerating the rate at which the temperature of the clean water rises. If the real-time temperature is higher than the upper limit of the target temperature (target temperature + 2℃), the control unit issues a command to decrease the heating power of the power regulation unit, slowing down the rate at which the temperature of the clean water rises. If the real-time temperature is within the preset accuracy threshold range, the control unit controls the power regulation unit to maintain the current heating power, ensuring that the temperature of the clean water remains stable near the target temperature.

[0114] The water temperature control method for window cleaning robots provided in this application obtains the initial temperature of the cleaning water and the ambient temperature of the window cleaning robot. Based on these initial and ambient temperatures, the heating power of the instant heating module of the window cleaning robot can be adjusted in real time, allowing the heating power to dynamically adapt to the current initial and ambient temperatures. When the window cleaning robot is in a low-temperature working environment, the heating module can quickly control the temperature of the cleaning water in real time according to the determined heating power, ensuring that the temperature of the sprayed water mist can effectively resist the low ambient temperature, enhancing the cleaning power of the water mist and improving the cleaning efficiency of the window cleaning robot.

[0115] For example, existing temperature control algorithms (such as simple PID or open-loop control) cannot dynamically adapt to fluctuations in the initial temperature of the cleaning water and the ambient temperature of the window cleaning robot, resulting in large fluctuations in the water temperature output by the window cleaning robot (e.g., ±5℃ or more), which cannot meet the requirements of precision cleaning. To address this, the method in this application introduces feedforward compensation logic to adjust the heating power output in advance.

[0116] In one possible implementation, before dynamically adjusting the heating power of the instant heating module of the window cleaning robot using a proportional-integral-differential algorithm, the method further includes: calculating the difference between the ambient temperature and the initial temperature to obtain the ambient water temperature difference; and calculating the feedforward compensation power increment based on the ambient water temperature difference, wherein the feedforward compensation power increment is used to characterize the amount of adjustment of the heating power in advance to cope with the impact of changes in ambient temperature on heating demand.

[0117] For example, the feedforward compensation power increment can be understood as the amount of adjustment used to proactively adjust the heating power in response to changes in ambient temperature on heating demand. For instance, when the ambient temperature suddenly drops to -5°C, the feedforward compensation power increment can be calculated to be 20%, meaning that the feedforward compensation power increment is a 20% increase in heating power based on the current heating power, in order to offset the heat loss caused by the low temperature.

[0118] For example, before adjusting the heating power using the PID algorithm, the changing trend of heating demand is predicted by the difference between the ambient temperature and the initial temperature, and the feedforward compensation power increment is calculated. For instance, in a low-temperature environment where the difference between the ambient temperature and the initial temperature is large, the feedforward compensation power increment is calculated first, and the heating power is increased in advance to compensate for heat loss. This step reduces the temperature lag effect through predictive adjustment, ensuring that the PID algorithm can quickly respond to actual needs during dynamic adjustment.

[0119] When calculating the feedforward compensation power increment based on the ambient water temperature difference, it can be calculated by multiplying the ambient water temperature difference by a preset compensation coefficient. For example, the preset compensation coefficient can be any positive number, such as 2. If the ambient water temperature difference is 20℃, then 20 × 2 = 40. This can be understood as the feedforward compensation power increment being 40% when the ambient temperature is 20℃ lower than the initial temperature. Based on this feedforward compensation power increment, the heating power can be increased by 40% from the currently determined heating power to offset the risk of heat loss caused by the low temperature environment.

[0120] By setting multiple adjustment ranges for heating power in segments, segmented variable-parameter PID algorithm control can be achieved. By differentiating between different adjustment ranges (e.g., enhancing the proportional term response speed in the low-temperature range, and increasing the integral term to reduce overshoot when approaching the target temperature), the contradiction between fast response and stability in simple PID algorithms under a single parameter is resolved. Feedforward compensation logic can predict changes in ambient temperature difference in real time and adjust the heating power in advance, reducing temperature hysteresis. Through algorithm-level optimization, this solution significantly reduces the fluctuation range of outlet water temperature, improves temperature control accuracy, and ensures that the sprayed water mist can stably maintain the target temperature even in complex environments (such as high-altitude strong heat dissipation), thus meeting the high temperature stability requirements for cleaning stubborn stains.

[0121] In this embodiment, the hysteresis of temperature control is further reduced by introducing feedforward compensation power increment. Feedforward compensation predicts the impact of ambient temperature changes on heating demand and adjusts the heating power in advance, which can reduce temperature fluctuations caused by hysteresis response of PID algorithm, improve the real-time stability of water temperature in low-temperature environments, optimize heating efficiency, and reduce the risk of equipment failure due to temperature fluctuations.

[0122] For example, in low-temperature environments, the heating module may waste energy and pose safety hazards due to continuous full-power heating. To address this, an instant-on heating mode and a segmented power control strategy can be implemented, activating the heating module only when water is used and gradually reducing the power as the water temperature approaches the target temperature, thus avoiding continuous high-power operation.

[0123] In one possible implementation, the heating power of the instant heating module of the window cleaning robot is dynamically adjusted using a proportional-integral-differential algorithm, including: determining the temperature change trend of the cleaning water based on the real-time temperature of the cleaning water; and adjusting the output amplitude of the heating power in segments according to the temperature change trend of the cleaning water, wherein the output amplitude is used to characterize the amount of change in the heating power.

[0124] For example, the real-time temperature of the cleaning water can be understood as the instantaneous temperature of the cleaning water during the heating process. Based on multiple consecutive real-time temperatures of the cleaning water, it can be determined whether the temperature of the cleaning water is gradually increasing, remaining constant, or gradually decreasing, thus determining the temperature change trend of the cleaning water.

[0125] Segmented adjustment can be understood as dynamically adjusting the output of heating power based on the temperature change trend of the clean water (such as the heating phase and the stabilization phase). For example, during the heating phase of the clean water, the heating power output is 100%; during the stabilization phase, the output gradually decreases to 50%.

[0126] For example, when adjusting heating power using a PID algorithm, the controller can further adjust the output amplitude in segments based on the temperature change trend of the clean water (such as the heating rate and steady state). For instance, during the heating phase, the controller maintains a high power output to quickly increase the temperature; during the temperature stabilization phase, the power is gradually reduced to avoid overshoot. Segmented control can optimize heating efficiency and reduce energy waste caused by continuous high-power operation.

[0127] Based on the temperature change trend of the cleaning water, the output range of the heating power can be adjusted in stages to achieve an instant-heating mode. Through a two-stage control of "preheating + fine heating" (e.g., preheating to 25°C first, then precisely heating to the target temperature), cold air intrusion can be reduced and energy consumption lowered. The staged power control strategy gradually reduces power as the water temperature approaches the target value, avoiding the risk of dry burning or localized boiling caused by continuous full-power heating. The method in this application embodiment achieves a balance between energy efficiency optimization and safety protection by dynamically adjusting the heating power. In low-temperature environments, it can quickly raise the water temperature while avoiding equipment failure or safety hazards caused by high-power operation, significantly extending the equipment's service life.

[0128] In this embodiment, by adjusting the output range of heating power in stages, energy efficiency and temperature stability can be further optimized. The staged control strategy ensures that the heating power matches the actual demand at different stages, avoiding energy waste caused by continuous high-power operation. At the same time, by gradually reducing the power output, the risk of temperature overshoot is reduced, thereby achieving a balance between rapid and stable output of clean water temperature and energy-saving effect in low-temperature environments.

[0129] For example, if the target temperature cannot be dynamically adjusted according to the type of cleaning task (such as stubborn stains or daily cleaning) during heating, it will lead to an imbalance between energy efficiency and cleaning effect. To address this, a dynamic heating mode switching logic based on the cleaning task can be used to automatically switch between "enhanced mode" (target temperature 80℃) and "energy-saving mode" (target temperature 60℃) through user presets or image recognition algorithms (such as a stain recognition module), thereby improving the balance between energy efficiency and cleaning effect.

[0130] Figure 3 A flowchart illustrating the water temperature control method for the window cleaning robot provided in this application embodiment. Figure 3 ,like Figure 3 As shown, before adjusting the heating power of the instant heating module of the window cleaning robot according to the initial temperature and the ambient temperature, the method further includes steps S301 and S302.

[0131] S301, Determine the cleaning task type for the current cleaning task of the window cleaning robot.

[0132] For example, cleaning task types can be understood as classifications of cleaning tasks based on different levels of dirt. Cleaning task types may include, for instance, cleaning tasks for stubborn stains and cleaning tasks for non-stubborn stains. The cleaning task type for the current cleaning task of the window cleaning robot can be determined in various ways, such as by accepting user specifications or by the window cleaning robot's intelligent algorithm determining it autonomously.

[0133] S302, depending on the type of cleaning task, switches the heating mode of the window cleaning robot to either enhanced mode or energy-saving mode.

[0134] For example, an enhanced mode can be understood as a heating mode set for cleaning stubborn stains, such as one with a higher target temperature (e.g., 80°C). For instance, when oil stains are detected on a glass surface, the system automatically switches to enhanced mode.

[0135] Energy-saving mode can be understood as a heating mode set for cleaning tasks with non-stubborn stains, such as those with a lower target temperature (e.g., 60°C). For example, when only ordinary dust is detected on the glass surface, it automatically switches to energy-saving mode.

[0136] The dynamic heating mode switching logic drives the temperature setting based on the cleaning task type (e.g., switching to enhanced mode when oil stains are detected, and switching to energy-saving mode when cleaning dust), optimizing energy consumption while ensuring cleaning effectiveness.

[0137] For example, when determining the cleaning task type of a window cleaning robot, it can either retrieve the user-defined cleaning task type or analyze the type of stains in the current cleaning area in real time using image recognition algorithms to determine the cleaning task, avoiding overheating or underheating, thereby achieving an optimal match between cleaning efficiency and energy efficiency. Through cleaning task type perception and adaptive heating mode, the problem of energy efficiency and cleaning effect imbalance in different cleaning scenarios with a fixed target temperature setting can be solved, improving the intelligence of window cleaning robots.

[0138] For example, before adjusting the heating power, the enhanced mode or energy-saving mode can be dynamically switched through image recognition algorithms or user-preset cleaning task types.

[0139] In one possible implementation, determining the cleaning task type of the current cleaning task of the window cleaning robot includes: acquiring image data of the area where the cleaning water is sprayed; analyzing the type of stains in the area where the cleaning water is sprayed using an image recognition algorithm based on the image data; and determining the cleaning task type of the current cleaning task of the window cleaning robot based on the type of stains.

[0140] For example, an image recognition algorithm can be understood as an algorithm that analyzes image features using computer vision technology to identify target objects. For instance, an image recognition algorithm can analyze images of glass surfaces to identify types of stains such as oil and dust.

[0141] For example, window cleaning robots use image recognition algorithms to analyze the type of stains in the area where cleaning water is sprayed, and automatically select the corresponding heating mode based on the stain type. For instance, when oil stains are detected, it switches to enhanced mode; when dust is detected, it switches to energy-saving mode. This step optimizes the dynamic adjustment of the heating mode through intelligent stain recognition.

[0142] For example, during the window cleaning process, a camera can capture images of the area to be cleaned, and image recognition and analysis algorithms can be used to determine whether there are stubborn stains (such as oil stains) in the current cleaning area. If so, the current cleaning task can be classified as a stubborn stain cleaning task. At this time, the window cleaning robot can switch to an enhanced mode to increase the target temperature and improve the cleaning effect by using higher-temperature cleaning water.

[0143] After cleaning the areas with stubborn stains, when cleaning another area, if image recognition determines that there are no stubborn stains in that area, the current cleaning task type can be determined as a non-stubborn stain cleaning task. At this point, the window cleaning robot can switch to energy-saving mode to clean at a lower target temperature, reducing the robot's overall energy consumption. This method, which drives the heating mode switching based on the cleaning task type, optimizes the balance between energy efficiency and cleaning effectiveness.

[0144] In this embodiment, image data and image recognition algorithms can be used to quickly and accurately determine the cleaning task type, enabling task-type-driven heating mode switching and improving the balance between cleaning efficiency and energy efficiency. For example, when oil stains are detected on the glass surface, the system can automatically switch to an enhanced mode to rapidly increase the water temperature and improve the cleaning effect; while when cleaning ordinary dust, the energy-saving mode can reduce heating power and energy consumption. The dynamic mode switching logic, combined with task requirements, achieves the optimal match between cleaning effect and energy efficiency, avoiding overheating or underheating.

[0145] The introduction of image recognition algorithms further enhances the intelligence of mode switching. These algorithms can accurately analyze stain types, avoiding misjudgments caused by manual presets, thus ensuring a precise match between the heating mode and cleaning needs. Based on this, a better balance between cleaning efficiency and energy efficiency can be achieved, reducing energy waste or insufficient cleaning results caused by mode misjudgments.

[0146] In this embodiment, the optimal match between cleaning effect and energy efficiency is further achieved by switching heating modes driven by cleaning task type. The enhanced mode increases the target temperature to improve the removal of stubborn stains, while the energy-saving mode reduces energy consumption by lowering the target temperature. This approach achieves adaptive heating mode based on cleaning task type perception, improving the user's cleaning experience and extending the lifespan of components.

[0147] In one possible implementation, the method further includes: predicting the trend of environmental temperature change in a future period based on multiple historical environmental temperatures within a historical period using an environmental dynamic prediction model; wherein the historical period is the period corresponding to a first preset time length before the moment the environmental temperature of the window cleaning robot is acquired, the future period is the period corresponding to a second preset time length after the moment the environmental temperature of the window cleaning robot is acquired, and the environmental dynamic prediction model is a pre-trained neural network model used to predict temperature changes; adjusting the heating power of the instant heating module of the window cleaning robot according to the initial temperature and the environmental temperature, including: adjusting the heating power of the instant heating module of the window cleaning robot according to the initial temperature, the environmental temperature, and the trend of environmental temperature change.

[0148] For example, the environmental dynamic prediction model can be understood as a neural network model obtained through pre-training, used to predict the trend of environmental temperature changes when a window cleaning robot performs cleaning tasks. For instance, it could be a model based on a Long Short-Term Memory (LSTM) network. Supervised learning, semi-supervised learning, or other methods can be used for pre-training.

[0149] The historical time period can be understood as the time period corresponding to the first preset duration before the moment the ambient temperature of the window cleaning robot is acquired, and the future time period can be understood as the time period corresponding to the second preset duration after the moment the ambient temperature of the window cleaning robot is acquired. The duration values ​​of the first preset duration and the second preset duration can be the same or different.

[0150] For example, if both the first and second preset durations are set to 10 seconds, it can be understood that: at the moment t when the ambient temperature of the window cleaning robot is obtained, the ambient temperature within 10 seconds after the moment t is predicted based on the ambient temperature within 10 seconds before the moment t, so that the trend of ambient temperature change over a period of time can be obtained.

[0151] When adjusting the heating power of the instant heating module of the window cleaning robot based on the initial temperature, ambient temperature, and the trend of ambient temperature changes, one could compare the ambient temperature trend with the ambient temperature to determine if there are moments in the future when the ambient temperature will change drastically. If such moments exist, the heating power can be adjusted in advance, such as increasing or decreasing the heating power beforehand.

[0152] To determine if there are moments with significant abrupt changes in ambient temperature, the difference between the temperature values ​​at various points in the temperature change trend and the current ambient temperature can be calculated. These differences are then compared to a preset abrupt change threshold. If a difference is greater than or equal to the threshold, the moment corresponding to that difference is identified as the moment with the significant ambient temperature change. Furthermore, the magnitude of the temperature change can be used to determine the adjustment range of the heating power.

[0153] By introducing a dynamic environmental prediction model and combining it with historical environmental temperature data, the trend of environmental temperature changes within a certain period of time (e.g., 5-10 seconds) can be predicted. Based on the prediction results, the heating power allocation strategy can be dynamically adjusted, prioritizing the increase or decrease of heating power before a sudden drop or rise in environmental temperature, thereby reducing the impact of temperature fluctuations.

[0154] In this embodiment, by predicting the trend of ambient temperature changes, the heating power of the heating module can be adjusted in advance, reducing the hysteresis response of the outlet water temperature. This allows for dynamic optimization of the heating power allocation, reducing the risk of temperature runaway due to sudden environmental changes and improving temperature stability in low or high temperature environments. For example, when a window cleaning robot experiences a sudden drop in internal temperature due to high-speed operation, the heating power can be increased in advance to prevent a sudden drop in outlet water temperature.

[0155] For example, when acquiring environmental and cleaning water parameters, the glass surface temperature can be monitored by an infrared temperature sensor and / or the flow rate of the cleaning water can be monitored by a pressure sensor.

[0156] For example, an infrared temperature sensor can be understood as a sensor that measures the surface temperature of an object through infrared radiation, without requiring contact with the target object. For instance, an infrared temperature sensor can monitor the surface temperature of glass in real time to predict the required temperature for clean water. A pressure sensor can be understood as a sensor that measures flow rate by detecting changes in fluid pressure. For example, a pressure sensor can monitor the flow rate of clean water to dynamically adjust heating power to adapt to flow fluctuations. By jointly detecting data from multiple sensors, environmental awareness can be improved, and the dynamic adjustment capability of heating strategies can be enhanced.

[0157] The introduction of infrared temperature and pressure sensors further enhances the comprehensiveness of environmental perception. The infrared temperature sensor monitors the glass surface temperature in real time, preventing a decrease in cleaning effectiveness due to sudden drops in glass temperature; the pressure sensor dynamically adjusts the heating power to adapt to flow fluctuations, preventing safety hazards caused by localized boiling. Based on this, the window cleaning robot's adaptability in complex working environments can be improved, ensuring a stable output of cleaning water temperature.

[0158] Existing technologies do not address the issue of fan heat dissipation combined with the low external temperature environment in high-altitude operations of window cleaning robots, which leads to a sharp drop in heating module efficiency and difficulty in maintaining water temperature.

[0159] In the method provided in this application embodiment, an infrared temperature sensor (monitoring glass surface temperature) and a pressure sensor (monitoring water flow rate changes) can be integrated on the basis of an NTC sensor to construct a multi-sensor fusion environmental sensing system. By comprehensively analyzing the inlet water temperature, ambient temperature, glass surface temperature, and water flow rate data through data fusion algorithms (such as Kalman filtering), the heating strategy can be dynamically adjusted. For example, when the glass surface temperature drops sharply due to environmental changes, the system can prioritize increasing the heating power to maintain the cleaning effect of the sprayed water mist.

[0160] The multi-sensor fusion system achieves accurate perception of complex environments by real-time monitoring of ambient temperature (e.g., around the water spray outlet), glass surface temperature, and water flow rate changes, combined with Kalman filtering algorithms to eliminate sensor noise. Dynamically adjusting the heating strategy automatically optimizes power distribution based on fan heat dissipation intensity (e.g., during high-speed operation) or external low-temperature environments (e.g., high-altitude areas), ensuring the heating module maintains stable water temperature even under extreme conditions. Based on this, environmental adaptive logic overcomes the temperature runaway problem caused by multiple factors in high-altitude operations under single-sensor control, improving the reliability of the window cleaning robot in complex scenarios.

[0161] This solution enhances the adaptability of window cleaning robots to complex environments through multi-sensor data fusion. For example, in high-altitude areas where water flow fluctuates due to air pressure changes, pressure sensors can provide real-time feedback on flow anomalies, allowing the robot to automatically adjust heating power to prevent localized boiling. Infrared temperature sensors monitor changes in glass surface temperature, dynamically optimizing spray water temperature to ensure cleaning effectiveness is unaffected by environmental factors. The collaborative work of multiple sensors enables more comprehensive environmental perception and adaptive control.

[0162] In one possible implementation, a phase change material (PCM) can be integrated around the heating module to maintain water temperature in low-temperature environments, utilizing its endothermic-exothermic properties. For example, when the heating module is operating, the PCM absorbs and stores excess heat; when the ambient temperature drops sharply, the PCM releases the stored heat to compensate for the water temperature loss. Simultaneously, a control algorithm dynamically adjusts the matching relationship between the heating power and the PCM's heat release rate to avoid temperature overshoot or undershoot.

[0163] For example, a phase change material can be understood as a material that absorbs or releases heat during a phase change process, such as paraffin or hydrated salts. For instance, a phase change material can absorb excess heat when the heating module is operating and release heat to maintain the water temperature when the ambient temperature drops sharply.

[0164] For example, after the heating module is wrapped with high-density insulation material, phase change materials further assist in maintaining the temperature. For instance, when the heating module is operating, the phase change material absorbs and stores excess heat; when the ambient temperature drops sharply, the phase change material releases heat to compensate for water temperature loss. Through the heat storage and release functions of the phase change material, the continuous high-power operation requirement of the heating module can be reduced.

[0165] Based on this, the introduction of phase change materials further reduces the reliance on heating modules. Phase change materials can release heat to maintain water temperature when ambient temperature drops sharply, reducing the need for continuous high-power operation of heating modules. Furthermore, it can extend the service life of heating modules and other equipment, while improving the stability of cleaning water temperature and cleaning effect in low-temperature environments, reducing energy consumption and equipment failure risks.

[0166] Based on any of the embodiments described above, segmented PID parameter optimization with multi-level thermal inertia compensation can also be implemented. For example, a multi-level thermal inertia compensation mechanism can be introduced based on the segmented PID parameter adjustment. According to the thermal inertia characteristics of the heating module (such as the heating rate and cooling rate of the thick film heater), the temperature control range is divided into multiple sub-ranges (such as 0-15℃, 15-30℃, 30-50℃), and an independent PID parameter set is designed for each sub-range. At the same time, a thermal inertia compensation factor is added for each sub-range to dynamically correct the PID output to adapt to the thermal response characteristics of different temperature ranges.

[0167] Based on this, by refining the temperature range division and thermal inertia compensation, the adaptability of the PID algorithm to different temperature ranges can be improved. For example, in the low-temperature range (0-15℃), the proportional term (P) is enhanced to respond quickly to temperature changes, while when approaching the target temperature (e.g., 55-60℃), the integral term (I) is increased to reduce overshoot. The thermal inertia compensation factor further corrects the PID output, reducing temperature oscillations caused by differences in the thermal inertia of the heating module, and achieving a smoother temperature regulation process.

[0168] Figure 4 This is a schematic diagram of the water temperature control device for the window cleaning robot provided in the embodiments of this application, as shown below. Figure 4 As shown in the figure, this application embodiment provides a water temperature control device for a window cleaning robot, the device comprising:

[0169] The acquisition module 401 is used to acquire the initial temperature of the cleaning water and the ambient temperature of the window cleaning robot;

[0170] Adjustment module 402 is used to adjust the heating power of the instant heating module of the window cleaning robot according to the initial temperature and ambient temperature.

[0171] The control module 403 is used to control the temperature of the cleaning water according to the heating power through the instant heating module.

[0172] In one possible implementation, the adjustment module 402 is specifically used for:

[0173] The target temperature is determined based on the ambient temperature and is used to indicate the target water temperature to which the cleaning water is heated.

[0174] Based on the difference between the initial temperature and the target temperature, multiple adjustment ranges of the heating power are set in segments;

[0175] Within any of the multiple adjustment ranges, the heating power of the instant heating module of the window cleaning robot is dynamically adjusted using a proportional-integral-differential algorithm.

[0176] In one possible implementation, the adjustment module 402 is further configured to:

[0177] Calculate the difference between the ambient temperature and the initial temperature to obtain the ambient water temperature difference;

[0178] Based on the difference in ambient water temperature, the feedforward compensation power increment is calculated. The feedforward compensation power increment is used to characterize the amount of adjustment of heating power in advance to cope with the impact of changes in ambient temperature on heating demand.

[0179] In one possible implementation, the adjustment module 402 is specifically used for:

[0180] Determine the temperature change trend of the cleaning water based on its real-time temperature.

[0181] Based on the temperature change trend of the clean water, the output amplitude of the heating power is adjusted in stages, and the output amplitude is used to characterize the amount of change in heating power.

[0182] In one possible implementation, the adjustment module 402 is further configured to:

[0183] Determine the cleaning task type for the current cleaning task of the window cleaning robot;

[0184] Depending on the type of cleaning task, switch the heating mode of the window cleaning robot to either enhanced mode or energy-saving mode.

[0185] In one possible implementation, the adjustment module 402 is specifically used for:

[0186] Acquire image data of the area where cleaning water is sprayed;

[0187] Based on image data, the type of stains in the area where the cleaning water was sprayed is analyzed using image recognition algorithms;

[0188] The cleaning task type of the window cleaning robot is determined based on the type of stain.

[0189] In one possible implementation, the device further includes a prediction module, which is used to:

[0190] Based on multiple historical ambient temperatures within a historical period, the environmental dynamic prediction model is used to predict the trend of ambient temperature changes in future periods.

[0191] Among them, the historical period is the period corresponding to the first preset time before the moment when the ambient temperature of the window cleaning robot is obtained, the future period is the period corresponding to the second preset time after the moment when the ambient temperature of the window cleaning robot is obtained, and the environmental dynamic prediction model is a pre-trained neural network model used to predict temperature changes.

[0192] The adjustment module 402 is specifically used to adjust the heating power of the instant heating module of the window cleaning robot according to the initial temperature, ambient temperature and the trend of ambient temperature change.

[0193] The window cleaning robot water temperature control device provided in this application embodiment can be used to execute the technical solution of the window cleaning robot water temperature control method in any of the above embodiments of this application. Its implementation principle and technical effect are similar, and will not be described again here.

[0194] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 5 As shown, the electronic device of this embodiment may include: at least one processor 501; and a memory 502 communicatively connected to the at least one processor; wherein the memory 502 stores instructions executable by the at least one processor 501, the instructions being executed by the at least one processor 501 to cause the electronic device to perform the method as described in any of the above embodiments.

[0195] Optionally, the memory 502 can be either standalone or integrated with the processor 501.

[0196] The implementation principle and technical effects of the electronic device provided in this embodiment can be found in the foregoing embodiments, and will not be repeated here.

[0197] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method of any of the foregoing embodiments.

[0198] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method of any of the foregoing embodiments.

[0199] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0200] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0201] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU) or other general-purpose processors. The processor can also be a Digital Signal Processor (DSP) or an Application Specific Integrated Circuit (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0202] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device, and may also be various media that can store program code, such as USB flash drives, portable hard drives, read-only memory (ROM), disks or optical discs.

[0203] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Examples of storage media include Static Random-Access Memory (SRAM) or Electrically Erasable Programmable Read Only Memory (EEPROM).

[0204] Storage media can be, for example, erasable programmable read-only memory (EPROM) or programmable read-only memory (PROM). Storage media can also be read-only memory (ROM), magnetic storage, flash memory, magnetic disks, or optical disks. Storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0205] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside within an application-specific integrated circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components within an electronic device or host device.

[0206] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0207] The sequence numbers of the embodiments in this application are merely for description and do not represent the superiority or inferiority of the embodiments. Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0208] Based on this understanding, the technical solution of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0209] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0210] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0211] It should be further noted that although the steps in the flowchart are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise explicitly stated in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders.

[0212] Furthermore, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0213] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0214] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0215] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for controlling the water temperature of a window cleaning robot, characterized in that, The method includes: The initial temperature of the cleaning water and the ambient temperature of the window cleaning robot are obtained. The heating power of the instant heating module of the window cleaning robot is adjusted according to the initial temperature and the ambient temperature. The instant heating module controls the temperature of the cleaning water according to the heating power.

2. The method according to claim 1, characterized in that, The step of adjusting the heating power of the instant heating module of the window cleaning robot according to the initial temperature and the ambient temperature includes: The target temperature is determined based on the ambient temperature, and the target temperature is used to indicate the target water temperature to which the cleaning water is heated. Based on the difference between the initial temperature and the target temperature, multiple adjustment ranges of the heating power are set in segments; Within any of the multiple adjustment ranges, the heating power of the instant heating module of the window cleaning robot is dynamically adjusted using a proportional-integral-differential algorithm.

3. The method according to claim 2, characterized in that, Before dynamically adjusting the heating power of the instant heating module of the window cleaning robot using the proportional-integral-differential algorithm, the method further includes: Calculate the difference between the ambient temperature and the initial temperature to obtain the ambient water temperature difference; Based on the ambient water temperature difference, the feedforward compensation power increment is calculated. The feedforward compensation power increment is used to characterize the amount of adjustment of heating power in advance to cope with the impact of changes in ambient temperature on heating demand.

4. The method according to claim 2, characterized in that, The method of dynamically adjusting the heating power of the instant heating module of the window cleaning robot using a proportional-integral-differential algorithm includes: Based on the real-time temperature of the cleaning water, determine the temperature change trend of the cleaning water; Based on the temperature change trend of the clean water, the output amplitude of the heating power is adjusted in segments, and the output amplitude is used to characterize the amount of change in the heating power.

5. The method according to any one of claims 1-4, characterized in that, Before adjusting the heating power of the instant heating module of the window cleaning robot based on the initial temperature and the ambient temperature, the method further includes: Determine the cleaning task type of the window cleaning robot's current cleaning task; Depending on the type of cleaning task, the heating mode of the window cleaning robot can be switched to either enhanced mode or energy-saving mode.

6. The method according to claim 5, characterized in that, Determining the cleaning task type of the current cleaning task of the window cleaning robot includes: Acquire image data of the area where the cleaning water is sprayed; Based on the image data, the type of stains in the area where the cleaning water was sprayed is analyzed using an image recognition algorithm; The cleaning task type of the window cleaning robot is determined based on the type of stain.

7. The method according to any one of claims 1-4, characterized in that, The method further includes: Based on multiple historical ambient temperatures within a historical period, the environmental dynamic prediction model is used to predict the trend of ambient temperature changes in future periods. Wherein, the historical time period is the time period corresponding to the first preset time before the moment when the ambient temperature of the window cleaning robot is obtained, the future time period is the time period corresponding to the second preset time after the moment when the ambient temperature of the window cleaning robot is obtained, and the environmental dynamic prediction model is a pre-trained neural network model used to predict temperature changes. The step of adjusting the heating power of the instant heating module of the window cleaning robot according to the initial temperature and the ambient temperature includes: The heating power of the instant heating module of the window cleaning robot is adjusted according to the initial temperature, the ambient temperature, and the trend of ambient temperature change.

8. An electronic device, characterized in that, include: Memory and processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-7.

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