An intelligent thermal management system for a cleaning robot

Abstract

The present application relates to the technical field of power equipment, and more particularly to an intelligent thermal management system for a cleaning robot, comprising a state sensing unit, a risk assessment unit, a thermal state calculation unit, a data decision unit, an execution unit and a feedback correction unit, the state sensing unit collects internal temperature, humidity and key component temperature; the risk assessment unit calculates dew condensation trend and overheating trend representation values accordingly; the thermal state calculation unit quantifies the overall thermal state of the system; the data decision unit determines the actual adjustment amount of heating and cooling power based on the above parameters; the execution unit includes heating, cooling and load management modules, and adjusts the output power accordingly; the feedback correction unit corrects the thermal state according to the operation deviation. Through multi-parameter fusion decision and dynamic power adjustment, the present application realizes the collaborative management of heat dissipation and dew condensation prevention, and improves the working reliability of the cleaning robot in harsh environments.

Description

Technical Field

[0001] This invention relates to the field of power equipment technology, and in particular to an intelligent thermal management system for cleaning robots. Background Technology

[0002] High-voltage transmission lines are often deployed in complex outdoor environments, exposing silicone rubber insulators to the atmosphere for extended periods. Dust and other contaminants easily accumulate on their surfaces, forming a contamination layer. Under dry conditions, the contamination layer has high resistance and limited impact on insulation performance. However, under humid conditions such as fog, dew, light rain, or snow, the electrolytes in the contamination layer dissolve due to moisture, leading to a significant increase in surface conductivity and a decline in insulation performance. Simultaneously, the ash in the contamination layer further promotes wetting due to its water-holding capacity, triggering partial discharge and even flashover. Flashover has become a major cause of power line tripping, and in severe cases, it can cause line breaks, threatening power supply safety. Therefore, regular cleaning and maintenance of insulators are necessary. Early cleaning methods were mostly implemented under power outage conditions, but these operations were time-consuming and affected power supply continuity. To overcome this limitation, the industry has gradually developed live-line cleaning technology for insulators, resulting in various live-line cleaning devices. Currently, these cleaners have been applied in practice, but their automation level remains insufficient, especially in terms of thermal management.

[0003] Currently, live-line insulator cleaning devices face a dual challenge in thermal management: on the one hand, the fully enclosed structure accumulates internal heat during continuous operation, which, without an efficient heat dissipation mechanism, can easily lead to overheating of electronic components, affecting equipment lifespan and stability; on the other hand, in humid environments, condensation easily forms inside the casing due to fluctuations in external temperature and humidity. This condensation can cause short circuits or deterioration of insulation performance, further amplifying the risk of failure. Existing cleaning equipment often lacks integrated thermal management design, failing to simultaneously achieve coordinated control of heat dissipation and anti-condensation, making it difficult to adapt to the demands of long-term operation under variable weather conditions in the field. Furthermore, existing devices are slow to respond to dynamic temperature and humidity adjustments and lack intelligent environmental adaptability, resulting in unsatisfactory reliability and safety in practical applications. Therefore, designing a thermal management system that balances efficient heat dissipation and effective anti-condensation, while possessing environmental adaptability, has become a key technical challenge for improving the automation level and operational safety of live-line insulator cleaning devices. Summary of the Invention

[0004] Therefore, the present invention provides an intelligent thermal management system for cleaning robots to overcome the problem that it is difficult to balance heat dissipation and condensation prevention in existing technologies for cleaning robots.

[0005] To achieve the above objectives, the present invention provides an intelligent thermal management system for a cleaning robot, comprising:

[0006] The state sensing unit includes a first sensor for collecting the ambient temperature inside the cleaning robot, a second sensor for collecting the ambient humidity inside the cleaning robot, a first temperature measuring module for collecting the temperature of the drive motor, and a second temperature measuring module for collecting the temperature of the electronic control unit.

[0007] A risk assessment unit, connected to the state sensing unit, is used to determine the condensation critical temperature based on the ambient temperature and the ambient humidity, determine the condensation trend characterization value based on the rate of change of the condensation critical temperature in a preset detection period, and determine the overheating trend characterization value based on the rate of change of the drive motor temperature and the rate of change of the electronic control unit temperature in a preset detection period.

[0008] A thermal state calculation unit is connected to the state sensing unit and the risk assessment unit respectively, and calculates a thermal state characterization value to quantify the overall thermal state of the system based on the temperature of the electronic control unit, the temperature of the drive motor and the condensation critical temperature.

[0009] The data decision unit is connected to the thermal state calculation unit and the risk assessment unit respectively. It is used to determine the basic cooling power adjustment amount and the basic heating power adjustment amount of the execution unit according to the thermal state characterization value and the condensation critical temperature, and to correct the basic power adjustment amount according to the condensation trend characterization value and the overheating trend characterization value to obtain the actual cooling power adjustment amount and the actual heating power adjustment amount of the execution unit.

[0010] An execution unit, connected to the data decision unit, includes a heating execution module, a cooling execution module, and a load management module. The heating execution unit adjusts its heating output power according to the actual heating power adjustment amount, the cooling execution module adjusts its cooling output power according to the actual cooling power adjustment amount, and the load management module adjusts the maximum output power of the drive motor according to the overheating trend characterization value and the state characterization value.

[0011] The feedback correction unit is connected to the risk assessment unit and the thermal state calculation unit respectively, and is used to correct the thermal state characterization value based on the deviation between the overheating trend characterization value and the expected overheating data in adjacent preset detection cycles.

[0012] Furthermore, the thermal state calculation unit obtains a preliminary thermal state value by weighting the temperature of the electronic control unit and the temperature of the drive motor; the preliminary thermal state value is corrected based on the condensation risk deviation determined by the difference between the condensation critical temperature and the ambient temperature to obtain the thermal state characterization value; wherein, if the difference between the condensation critical temperature and the ambient temperature is less than or equal to a first judgment threshold, the thermal state characterization value is increased to preferentially trigger anti-condensation protection.

[0013] Furthermore, the data decision unit corrects the base power adjustment amount based on the condensation trend characterization value and the thermal state characterization value;

[0014] If the condensation trend indicator value is greater than the first preset threshold, then the basic cooling power adjustment amount is reduced;

[0015] If the thermal state characterization value is greater than the second preset threshold, then the basic heating power adjustment amount is increased.

[0016] Furthermore, the data decision unit corrects the base power adjustment based on the synergistic relationship between the condensation trend characterization value and the overheating trend characterization value;

[0017] If the condensation trend indicator value increases and the overheating trend indicator value decreases, then the basic cooling power adjustment amount is reduced.

[0018] If the overheating trend characterization value increases and the condensation trend characterization value decreases, then the basic cooling power adjustment amount is increased;

[0019] If the condensation trend characterization value and the overheating trend characterization value increase simultaneously, the basic cooling power adjustment amount is adjusted based on the difference between the condensation trend characterization value and the overheating trend characterization value.

[0020] Furthermore, based on the judgment that the difference between the condensation trend characterization value and the overheating trend characterization value is greater than zero, the data decision unit reduces the basic cooling power adjustment amount;

[0021] Based on the judgment result that the difference between the condensation trend characterization value and the overheating trend characterization value is less than zero, the adjustment amount of the basic cooling power is reduced.

[0022] Furthermore, the load management module limits the maximum output power of the drive motor based on the weighted sum of the overheating trend characterization value and the thermal state characterization value; wherein, the weighting coefficient of the overheating trend characterization value in the weighted sum increases as the overheating trend characterization value increases.

[0023] Furthermore, the feedback correction unit calculates a feedback correction coefficient based on the deviation between the overheating trend characterization value and the preset overheating trend safety threshold within adjacent preset detection cycles, and multiplies the feedback correction coefficient by the thermal state characterization value to correct the thermal state characterization value in real time.

[0024] Furthermore, the risk assessment unit also modifies the condensation trend characterization value based on the degree of proximity between the condensation critical temperature and the ambient temperature; wherein, if the difference between the condensation critical temperature and the ambient temperature is less than a second judgment threshold, the condensation trend characterization value is increased.

[0025] Furthermore, the cooling execution module in the execution unit adjusts its cooling intensity according to the magnitude of the actual cooling power adjustment amount; wherein, if the actual cooling power adjustment amount is less than the third judgment threshold, the first level of cooling intensity is adopted; if the actual cooling power adjustment amount is greater than or equal to the third judgment threshold, the second level of cooling intensity is adopted.

[0026] Furthermore, the heating execution module in the execution unit adjusts the heating response characteristics according to the changing trend of the actual heating power adjustment amount; wherein, if it is detected that the actual heating power adjustment amount continues to increase within a series of preset detection cycles, the heating response speed is increased.

[0027] Compared with the prior art, the beneficial effects of the present invention are that, through multi-parameter fusion decision-making and dynamic power adjustment, the present invention realizes the coordinated management of heat dissipation and anti-condensation of the cleaning robot, effectively improving the working reliability and equipment stability in harsh environments such as humidity and high temperature, overcoming the problems of slow thermal management response and difficulty in taking into account heat dissipation and anti-condensation in the prior art, thereby extending the service life of the equipment and ensuring the safety of continuous operation.

[0028] Furthermore, this invention dynamically calculates the characterization values ​​of condensation trend and overheating trend through a risk assessment unit, thereby achieving a forward-looking assessment of condensation risk and overheating risk. This provides a reliable basis for the system to take preventive measures and enhances the risk response capability in complex environments.

[0029] Furthermore, this invention integrates the temperature of key components and the risk of condensation through a thermal state calculation unit, quantifies the overall thermal state of the system, and prioritizes triggering anti-condensation protection when the risk of condensation increases, thus ensuring the safety orientation and adaptability of the thermal management strategy.

[0030] Furthermore, this invention integrates thermal state characterization values, condensation trends, and overheating trends through a data decision unit, and dynamically corrects the adjustment of heating and cooling power, thereby achieving intelligent coordinated control of heat dissipation and anti-condensation, and improving energy efficiency and system response accuracy.

[0031] Furthermore, the present invention optimizes the output characteristics based on the actual power adjustment amount by implementing hierarchical cooling control and adaptive heating response of the execution unit, thereby achieving efficient and energy-saving thermal management execution, while avoiding secondary risks caused by excessive cooling or heating.

[0032] Furthermore, the present invention dynamically limits the power of the drive motor based on the overheating trend and thermal state through the load management module, thereby controlling the generation of system heat load from the source. This works in conjunction with heat dissipation and anti-condensation measures to further ensure the safe operation of the system.

[0033] Furthermore, this invention uses a feedback correction unit to correct the thermal state characterization value in real time based on the overheating trend deviation, thereby eliminating the influence of model errors and environmental fluctuations, ensuring the accuracy of thermal management decisions and the long-term stable operation of the system. Attached Figure Description

[0034] Figure 1 This is a connection block diagram of an intelligent thermal management system for a cleaning robot according to an embodiment of the present invention;

[0035] Figure 2 This is a logical block diagram of the raw data acquisition and risk assessment indicators of an intelligent thermal management system for a cleaning robot according to an embodiment of the present invention.

[0036] Figure 3 This is a logical block diagram of data decision-making and power allocation for an intelligent thermal management system for a cleaning robot according to an embodiment of the present invention;

[0037] Figure 4 This is a logic block diagram of feedback correction for an intelligent thermal management system for a cleaning robot, according to an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0039] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0040] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or module must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0041] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two modules. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0042] Please see Figure 1 As shown, it is a connection block diagram of an intelligent thermal management system for a cleaning robot according to an embodiment of the present invention.

[0043] This invention provides an intelligent thermal management system for a cleaning robot, comprising:

[0044] The state sensing unit includes a first sensor for collecting the ambient temperature inside the cleaning robot, a second sensor for collecting the ambient humidity inside the cleaning robot, a first temperature measuring module for collecting the temperature of the drive motor, and a second temperature measuring module for collecting the temperature of the electronic control unit.

[0045] In one specific embodiment, the state perception unit employs the following mature existing technology: a digital temperature and humidity integrated sensor (such as the SHT3x series) is used as the first and second sensors, installed at the center of the robot's housing for synchronously collecting ambient temperature and humidity; a surface-mount NTC thermistor is used as the first temperature sensing module, tightly attached to the thermally conductive silicone pad on the drive motor housing for detecting the drive motor's operating temperature; and a temperature sensing diode integrated into the main control chip of the electronic control unit is used as the second temperature sensing module for monitoring the chip temperature. Preferably, the data sampling period of all sensors is uniformly set to 100 milliseconds, analog signal acquisition uses a 12-bit ADC for digital conversion, and communication with the main controller is achieved via an I2C bus.

[0046] A risk assessment unit, connected to the state sensing unit, is used to determine the condensation critical temperature based on the ambient temperature and the ambient humidity, determine the condensation trend characterization value based on the rate of change of the condensation critical temperature in a preset detection period, and determine the overheating trend characterization value based on the rate of change of the drive motor temperature and the rate of change of the electronic control unit temperature in a preset detection period.

[0047] Specifically, the risk assessment unit further modifies the condensation trend characterization value based on the proximity of the condensation critical temperature to the ambient temperature; wherein, if the difference between the condensation critical temperature and the ambient temperature is less than a second judgment threshold, the condensation trend characterization value is increased.

[0048] In one specific embodiment, the risk assessment unit implements its function by executing a preset algorithm through a microcontroller. First, based on the currently collected ambient temperature and humidity, the critical condensation temperature is calculated using the Magnus formula. The method for determining the condensation trend characterization value is as follows: calculate the rate of change of the critical condensation temperature between the current detection cycle and the previous detection cycle, multiply this rate of change by a trend coefficient to obtain the basic condensation trend value; simultaneously, compare the difference between the critical condensation temperature and the ambient temperature with a second judgment threshold. When the difference is less than the threshold, the basic condensation trend value is multiplied by a correction coefficient greater than 1. The method for determining the overheating trend characterization value is as follows: calculate the rate of change of the drive motor temperature and the electronic control unit temperature within the preset detection cycle, and take the larger of the two as the basic overheating trend value. Preferably, the detection cycle is set to 10 seconds, the second judgment threshold is set to 3°C, the trend coefficient is 0.8, and the correction coefficient is 1.5.

[0049] Understandably, the calculation of the dew condensation critical temperature uses the well-established Magnus formula from meteorology, which accurately reflects the temperature conditions under certain ambient humidity at which dew begins to appear. The rate of change parameter in the dew condensation trend characterization value reflects the speed at which the risk of dew condensation changes, while the correction mechanism based on the temperature difference threshold considers the risk amplification effect of actual conditions approaching dew condensation. The overheating trend characterization value selects the larger of the temperature change rates of two key components, based on a conservative design principle of system safety, ensuring that rapid temperature rises in any component can be identified in a timely manner. These parameters and coefficients are set based on optimization using a large amount of experimental data, achieving a good balance between early warning sensitivity and system stability.

[0050] Understandably, this implementation method achieves a proactive assessment of condensation risk by dynamically monitoring the changing trend of the critical condensation temperature and combining this with the proximity of the actual temperature to the condensation point. This dual assessment mechanism not only focuses on the absolute value of the condensation risk but, more importantly, captures its dynamic changing characteristics, enabling the system to take preventative measures before condensation actually occurs. Simultaneously, by monitoring the temperature change trends of the two core heat-generating components, the system can promptly detect potential overheating risks, providing accurate early warning information for subsequent thermal management decisions. This risk assessment method, based on trend prediction rather than simple threshold judgment, significantly improves the system's ability to identify potential thermal risks and its response speed.

[0051] Please see Figure 3 As shown, it is a logical block diagram of data decision-making and power allocation for an intelligent thermal management system for a cleaning robot according to an embodiment of the present invention.

[0052] A thermal state calculation unit is connected to the state sensing unit and the risk assessment unit respectively, and calculates a thermal state characterization value to quantify the overall thermal state of the system based on the temperature of the electronic control unit, the temperature of the drive motor and the condensation critical temperature.

[0053] Specifically, the thermal state calculation unit obtains a preliminary thermal state value by weighting the temperature of the electronic control unit and the temperature of the drive motor; the preliminary thermal state value is corrected based on the condensation risk deviation determined by the difference between the condensation critical temperature and the ambient temperature to obtain the thermal state characterization value; wherein, if the difference between the condensation critical temperature and the ambient temperature is less than or equal to a first judgment threshold, the thermal state characterization value is increased to preferentially trigger anti-condensation protection.

[0054] In one specific embodiment, the thermal state calculation unit performs the following calculation process via an embedded processor: First, the temperature of the electronic control unit and the temperature of the drive motor are weighted and calculated in a 4:6 ratio to obtain a preliminary thermal state value; then, the difference between the condensation critical temperature and the ambient temperature is compared with a first judgment threshold. When the difference is less than or equal to the first judgment threshold, the preliminary thermal state value is multiplied by a correction coefficient greater than 1 to obtain the final thermal state characterization value; when the difference is greater than the first judgment threshold, the thermal state characterization value is equal to the preliminary thermal state value. Preferably, the first judgment threshold is set to 2°C, and the correction coefficient is set to 1.3.

[0055] Understandably, the aforementioned weighting ratios are based on the reality that the drive motor, as the primary heat source, contributes more significantly to the overall thermal state of the system due to its temperature rise. While the electronic control unit generates relatively less heat, it is more sensitive to overheating, hence its 40% weighting. The correction coefficient is set to proactively raise the system's thermal state level when the risk of condensation increases, prioritizing the triggering of anti-condensation protection. The first judgment threshold of 2°C is an empirical value, designed to intervene promptly when risks emerge, striking a balance between safety and avoiding excessive intervention.

[0056] Understandably, the principle behind this implementation lies in integrating temperature parameters reflecting equipment heating status and condensation risk parameters reflecting environmental safety into a unified quantitative index through a modifiable weighted calculation model. The model first calculates the basic equipment heat load value by weighted averaging the temperatures of the two key heat sources. Subsequently, the model introduces a correction mechanism based on condensation critical conditions: when the measured ambient temperature approaches the condensation critical temperature, indicating a real risk of condensation, the condensation threat must be addressed first, regardless of the equipment's own heating status. At this point, the system increases the thermal state characterization value to trigger anti-condensation measures such as dehumidification or heating in advance, thus prioritizing condensation prevention over simple heat dissipation in thermal management decisions. This design allows a single thermal state value to simultaneously carry information on both equipment operating status and external environmental risk, providing a precise basis for the coordinated control of subsequent execution units.

[0057] The data decision unit is connected to the thermal state calculation unit and the risk assessment unit respectively. It is used to determine the basic cooling power adjustment amount and the basic heating power adjustment amount of the execution unit according to the thermal state characterization value and the condensation critical temperature, and to correct the basic power adjustment amount according to the condensation trend characterization value and the overheating trend characterization value to obtain the actual cooling power adjustment amount and the actual heating power adjustment amount of the execution unit.

[0058] Specifically, the data decision unit corrects the base power adjustment based on the condensation trend characterization value and the thermal state characterization value;

[0059] If the condensation trend indicator value is greater than the first preset threshold, then the basic cooling power adjustment amount is reduced;

[0060] If the thermal state characterization value is greater than the second preset threshold, the basic heating power adjustment amount is increased.

[0061] In one specific embodiment, the data decision unit implements power decisions through a microcontroller control algorithm. This unit first establishes a direct proportional relationship between the thermal state characterization value and the basic cooling power adjustment, and an inverse proportional relationship between the condensation critical temperature and the basic heating power adjustment. Then, trend correction is performed: when the condensation trend characterization value is greater than a first preset threshold, the basic cooling power adjustment is multiplied by a cooling attenuation coefficient between zero and one; when the thermal state characterization value is greater than a second preset threshold, the basic heating power adjustment is multiplied by a heating enhancement coefficient greater than one. Preferably, the first preset threshold is 0.8, and the cooling attenuation coefficient is 0.6; the second preset threshold is 75, and the heating enhancement coefficient is 1.2.

[0062] Understandably, the direct proportionality between base cooling power and thermal state characterization reflects the fundamental principle that higher temperatures necessitate enhanced heat dissipation; conversely, the inverse proportionality between base heating power and the critical condensation temperature reflects the control logic that higher condensation risk necessitates preheating to prevent condensation. The cooling attenuation coefficient is set based on the safety consideration that excessive cooling would exacerbate the condensation risk when the condensation trend is significant; the heating enhancement coefficient is based on the design principle that anti-condensation effects must still be guaranteed when the system's heat load is high. The specific values ​​of these coefficients were determined through system thermal simulation and environmental testing optimization.

[0063] Understandably, when the system detects a rapidly increasing risk of condensation, it will limit cooling power to prevent accelerated condensation due to excessive cooling, even if the current temperature has not yet exceeded the limit. Conversely, when the overall system heat load is high but condensation risk exists, heating power will be appropriately increased to ensure anti-condensation effectiveness. This dual decision-making mechanism, based on both the current state and the changing trend, effectively coordinates the potentially conflicting control objectives of heat dissipation and condensation prevention, achieving more intelligent thermal management.

[0064] Specifically, the data decision unit corrects the base power adjustment based on the synergistic relationship between the condensation trend characterization value and the overheating trend characterization value;

[0065] If the condensation trend indicator value increases and the overheating trend indicator value decreases, then the basic cooling power adjustment amount is reduced.

[0066] If the overheating trend characterization value increases and the condensation trend characterization value decreases, then the basic cooling power adjustment amount is increased;

[0067] If the condensation trend characterization value and the overheating trend characterization value increase simultaneously, the basic cooling power adjustment amount is adjusted based on the difference between the condensation trend characterization value and the overheating trend characterization value.

[0068] Specifically, the data decision unit reduces the basic cooling power adjustment based on the judgment that the difference between the condensation trend characterization value and the overheating trend characterization value is greater than zero.

[0069] Based on the judgment result that the difference between the condensation trend characterization value and the overheating trend characterization value is less than zero, the adjustment amount of the basic cooling power is reduced.

[0070] In one specific embodiment, the data decision unit implements coordinated correction of the base power adjustment amount through a decision algorithm running in the microcontroller. This algorithm first establishes a coordinated relationship model between the condensation trend characterization value and the overheating trend characterization value: when the condensation trend characterization value increases and the overheating trend characterization value decreases, the base cooling power adjustment amount is multiplied by a cooling attenuation coefficient of 0.6; when the overheating trend characterization value increases and the condensation trend characterization value decreases, the base cooling power adjustment amount is multiplied by a cooling enhancement coefficient of 1.3; when both trend characterization values ​​increase simultaneously, the normalized difference between the two is calculated, and the base cooling power adjustment amount is multiplied by a linear adjustment coefficient based on this difference, the coefficient ranging from 0.7 to 1.2. Each trend characterization value needs to be normalized before comparison, ensuring its value range is uniformly between 0 and 1.

[0071] Understandably, the cooling attenuation coefficient of 0.6 is set based on the design principle that cooling power should be significantly suppressed to prevent exacerbating condensation when the risk of condensation is significant but the risk of overheating is relatively small. The cooling enhancement coefficient of 1.3, on the other hand, is set based on the consideration of strengthening heat dissipation when the risk of overheating becomes the primary concern. The range of values ​​for the linear adjustment coefficient ensures a smooth power transition when both risks coexist, avoiding control oscillations. These parameters were determined based on system thermal balance simulations and optimization using test data from various operating conditions.

[0072] Understandably, the principle behind this implementation lies in establishing a dynamic power allocation mechanism based on risk situation assessment. This mechanism not only considers changes in individual risk factors but, more importantly, identifies the primary contradiction facing the system by analyzing the synergistic evolution between condensation and overheating trends. Specifically, when the two risks exhibit a trade-off, the system explicitly prioritizes addressing the primary risk; when both risks intensify simultaneously, the system achieves fine-tuning of cooling power by quantitatively comparing their relative severity. This risk situation analysis-based decision-making method effectively resolves the inherent contradiction between the two control objectives of heat dissipation and condensation prevention. It prevents condensation risks caused by excessive cooling while avoiding the negative impact on heat dissipation due to overemphasis on condensation risks, thereby improving the system's adaptability and reliability under different operating conditions.

[0073] An execution unit, connected to the data decision unit, includes a heating execution module, a cooling execution module, and a load management module. The heating execution unit adjusts its heating output power according to the actual heating power adjustment amount, the cooling execution module adjusts its cooling output power according to the actual cooling power adjustment amount, and the load management module adjusts the maximum output power of the drive motor according to the overheating trend characterization value and the state characterization value.

[0074] Specifically, the cooling execution module in the execution unit adjusts its cooling intensity according to the magnitude of the actual cooling power adjustment; wherein, if the actual cooling power adjustment is less than the third judgment threshold, the first level of cooling intensity is adopted; if the actual cooling power adjustment is greater than or equal to the third judgment threshold, the second level of cooling intensity is adopted.

[0075] Specifically, the heating execution module in the execution unit adjusts the heating response characteristics according to the changing trend of the actual heating power adjustment amount; wherein, if it is detected that the actual heating power adjustment amount continues to increase within a series of preset detection cycles, the heating response speed is increased.

[0076] In one specific embodiment, the cooling execution module in the execution unit uses an axial fan assembly, with its speed controlled by a PWM signal; the heating execution module uses surface-mount ceramic heating elements, with its heating power controlled by adjusting the PWM duty cycle; and the load management module adjusts power through the current limiting function of the motor driver. The cooling execution module employs tiered control based on the actual cooling power adjustment: when the adjustment is less than a third threshold, the fan operates at 50% of its rated speed; when the adjustment is greater than or equal to the third threshold, the fan operates at 100% of its rated speed. The heating execution module adjusts its response characteristics based on the trend of the actual heating power adjustment: if the adjustment is detected to continuously increase within three consecutive preset detection cycles, the PWM control frequency is increased from 1kHz to 5kHz to improve the dynamic response speed. Preferably, the third threshold is set to 40% of the rated cooling power, and the preset detection cycle is set to 5 seconds.

[0077] Understandably, setting the tiered control threshold of the cooling execution module to 40% is based on a balance between system thermal inertia and energy consumption optimization, ensuring basic heat dissipation requirements while avoiding frequent start-stop of high-power fans. The design of increasing the PWM frequency of the heating execution module from 1kHz to 5kHz is based on the thermal response characteristics of the ceramic heating element and the requirements for control precision; a higher switching frequency allows for finer power regulation. These parameter settings have all been verified through thermodynamic simulation and actual operating condition testing, and will not be elaborated further here.

[0078] Understandably, the cooling system employs a tiered control strategy, ensuring both economic efficiency under normal operating conditions and sufficient heat dissipation capacity under high heat loads. The heating system adaptively adjusts its response speed based on changing demand trends, enabling more timely temperature regulation when the risk of condensation rises rapidly, effectively preventing condensation formation. This differentiated execution strategy allows the system to achieve reliable thermal management with superior energy consumption, ensuring safe operation of equipment in harsh environments while also maintaining system energy efficiency.

[0079] Specifically, the load management module limits the maximum output power of the drive motor based on the weighted sum of the overheating trend characterization value and the thermal state characterization value; wherein, the weighting coefficient of the overheating trend characterization value in the weighted sum increases as the overheating trend characterization value increases.

[0080] In one specific embodiment, the load management module implements output control through the power limiting function of the motor driver. First, the overheating trend characterization value and the thermal state characterization value are normalized, and then a weighted sum of the two is calculated. In implementation, the weighting coefficient of the overheating trend characterization value adopts a dynamic adjustment strategy: when the characterization value is less than 0.5, the weighting coefficient is 0.7; when the characterization value is between 0.5 and 0.8, the weighting coefficient linearly increases to 0.9; when the characterization value is greater than 0.8, the weighting coefficient is 0.9. Based on the calculated weighted sum, the maximum output power of the drive motor is limited to a corresponding percentage of the rated power. Preferably, the correspondence between the weighted sum and the power limit is as follows: when the weighted sum is less than 0.4, the power limit is 100%; between 0.4 and 0.6, it is linearly limited to 80%; between 0.6 and 0.8, it is linearly limited to 60%; and when it is greater than 0.8, it is limited to 50%.

[0081] Understandably, the dynamic adjustment mechanism of the weighting coefficients is designed based on the urgency of the overheating trend. When the overheating trend is relatively mild, more attention is paid to the overall thermal state, while when the overheating trend intensifies, more attention is paid to its rate of change. The segmented setting of power limits considers the balance between system safety and operational efficiency, maintaining full power operation when the risk is low, and gradually limiting power to ensure safety when the risk increases. The weighting coefficients are optimized and calibrated based on system thermal characteristic tests and load experiments, which will not be elaborated further here.

[0082] Understandably, when a rapid temperature rise is detected, the system takes stricter power limiting measures in advance to effectively reduce the heat load before overheating occurs; while when the thermal state is stable or improving, the limits are appropriately relaxed to ensure operational efficiency. This risk-prediction-based load management strategy can control system heat generation at its source, working in synergy with subsequent heat dissipation and heating measures to ensure stable system operation within a safe temperature range.

[0083] Please see Figure 4 As shown, it is a logic block diagram of feedback correction for an intelligent thermal management system for a cleaning robot according to an embodiment of the present invention.

[0084] The feedback correction unit is connected to the risk assessment unit and the thermal state calculation unit respectively, and is used to correct the thermal state characterization value based on the deviation between the overheating trend characterization value and the expected overheating data in adjacent preset detection cycles.

[0085] Specifically, the feedback correction unit calculates a feedback correction coefficient based on the deviation between the overheating trend characterization value and the preset overheating trend safety threshold within adjacent preset detection cycles, and multiplies the feedback correction coefficient by the thermal state characterization value to correct the thermal state characterization value in real time.

[0086] In one specific embodiment, the feedback correction unit implements its function through a closed-loop control algorithm running within the microcontroller. Within each preset detection cycle, it calculates the deviation between the current overheating trend characterization value and a preset overheating trend safety threshold. This deviation is then multiplied by a proportionality coefficient to obtain a feedback correction coefficient. Finally, this coefficient is multiplied by the thermal state characterization value output by the thermal state calculation unit to complete the real-time correction of the thermal state characterization value. Preferably, the preset detection cycle is set to 10 seconds, the overheating trend safety threshold is set to 0.75, the proportionality coefficient is 0.1, and the effective range of the feedback correction coefficient is limited to between 0.9 and 1.1.

[0087] Understandably, the overheating trend safety threshold of 0.75 is a safety boundary value derived from long-term system operation data statistics, representing the upper limit of acceptable overheating risk for the system. The proportional gain of 0.1 is set to ensure that the correction process has sufficient sensitivity while avoiding system oscillations caused by over-correction. The range limitation of the feedback correction coefficient is to prevent excessively large single corrections and maintain system stability.

[0088] Understandably, when the actual overheating trend deviates from the preset safety threshold, the system automatically identifies this deviation and adjusts the thermal state characterization value through corresponding correction coefficients, making the system's thermal state assessment closer to the actual operating conditions. This dynamic correction mechanism effectively eliminates assessment biases caused by model errors or environmental changes, ensuring the accuracy and timeliness of thermal management decisions. Through continuous feedback and correction, the system can adaptively optimize thermal management strategies, improving the overall control accuracy and reliability.

[0089] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all be within the scope of protection of the present invention.

Claims

1. An intelligent thermal management system for a cleaning robot, characterized in that, include: The state sensing unit includes a first sensor for collecting the ambient temperature inside the cleaning robot, a second sensor for collecting the ambient humidity inside the cleaning robot, a first temperature measuring module for collecting the temperature of the drive motor, and a second temperature measuring module for collecting the temperature of the electronic control unit. A risk assessment unit, connected to the state sensing unit, is used to determine the condensation critical temperature based on the ambient temperature and the ambient humidity, determine the condensation trend characterization value based on the rate of change of the condensation critical temperature in a preset detection period, and determine the overheating trend characterization value based on the rate of change of the drive motor temperature and the rate of change of the electronic control unit temperature in a preset detection period. A thermal state calculation unit is connected to the state sensing unit and the risk assessment unit respectively, and calculates a thermal state characterization value to quantify the overall thermal state of the system based on the temperature of the electronic control unit, the temperature of the drive motor and the condensation critical temperature. A data decision unit, connected to both the thermal state calculation unit and the risk assessment unit, determines the basic cooling power adjustment and basic heating power adjustment of the execution unit based on the thermal state characterization value and the condensation critical temperature. It then corrects these adjustments based on the condensation trend characterization value and the overheating trend characterization value to obtain the actual cooling power adjustment and actual heating power adjustment of the execution unit. Specifically, if the condensation trend characterization value increases and the overheating trend characterization value decreases, the basic cooling power adjustment is reduced; if the overheating trend characterization value increases and the condensation trend characterization value decreases, the basic cooling power adjustment is increased; and if both the condensation trend characterization value and the overheating trend characterization value increase simultaneously, the basic cooling power adjustment is adjusted based on the difference between them. An execution unit, connected to the data decision unit, includes a heating execution module, a cooling execution module, and a load management module. The heating execution module adjusts its heating output power according to the actual heating power adjustment amount, the cooling execution module adjusts its cooling output power according to the actual cooling power adjustment amount, and the load management module adjusts the maximum output power of the drive motor according to the overheating trend characterization value and the state characterization value. The feedback correction unit is connected to the risk assessment unit and the thermal state calculation unit respectively, and is used to correct the thermal state characterization value based on the deviation between the overheating trend characterization value and the expected overheating data in adjacent preset detection cycles.

2. The intelligent thermal management system for a cleaning robot according to claim 1, characterized in that, The thermal state calculation unit obtains a preliminary thermal state value by weighting the temperature of the electronic control unit and the temperature of the drive motor. The preliminary thermal state value is corrected based on the condensation risk deviation determined by the difference between the critical condensation temperature and the ambient temperature to obtain the thermal state characterization value; wherein, if the difference between the critical condensation temperature and the ambient temperature is less than or equal to a first judgment threshold, the thermal state characterization value is increased to preferentially trigger anti-condensation protection.

3. The intelligent thermal management system for a cleaning robot according to claim 2, characterized in that, The data decision unit corrects the basic cooling power adjustment and the basic heating power adjustment based on the condensation trend characterization value and the thermal state characterization value. If the condensation trend indicator value is greater than the first preset threshold, then the basic cooling power adjustment amount is reduced; If the thermal state characterization value is greater than the second preset threshold, then the basic heating power adjustment amount is increased.

4. The intelligent thermal management system for a cleaning robot according to claim 3, characterized in that, The data decision unit reduces the basic cooling power adjustment based on the judgment that the difference between the condensation trend characterization value and the overheating trend characterization value is greater than zero. Based on the judgment result that the difference between the condensation trend characterization value and the overheating trend characterization value is less than zero, the adjustment amount of the basic cooling power is reduced.

5. The intelligent thermal management system for a cleaning robot according to claim 4, characterized in that, The load management module limits the maximum output power of the drive motor based on the weighted sum of the overheating trend characterization value and the thermal state characterization value; wherein, the weighting coefficient of the overheating trend characterization value in the weighted sum increases as the overheating trend characterization value increases.

6. The intelligent thermal management system for a cleaning robot according to claim 5, characterized in that, The feedback correction unit calculates a feedback correction coefficient based on the deviation between the overheating trend characterization value and the preset overheating trend safety threshold within adjacent preset detection cycles, and multiplies the feedback correction coefficient by the thermal state characterization value to correct the thermal state characterization value in real time.

7. The intelligent thermal management system for a cleaning robot according to claim 6, characterized in that, The risk assessment unit further modifies the condensation trend characterization value based on the proximity of the condensation critical temperature to the ambient temperature; wherein, if the difference between the condensation critical temperature and the ambient temperature is less than a second judgment threshold, the condensation trend characterization value is increased.

8. The intelligent thermal management system for a cleaning robot according to claim 7, characterized in that, The cooling execution module in the execution unit adjusts its cooling intensity according to the magnitude of the actual cooling power adjustment amount; wherein, if the actual cooling power adjustment amount is less than the third judgment threshold, the first level of cooling intensity is adopted; if the actual cooling power adjustment amount is greater than or equal to the third judgment threshold, the second level of cooling intensity is adopted.

9. The intelligent thermal management system for a cleaning robot according to claim 8, characterized in that, The heating execution module in the execution unit adjusts the heating response characteristics according to the changing trend of the actual heating power adjustment amount; wherein, if it is detected that the actual heating power adjustment amount continues to increase within a series of preset detection cycles, the heating response speed is increased.

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