Cleaning mechanism easy to disassemble and used for cooling fan

Abstract

The invention relates to the field of fan self-cleaning, in particular to an easy-to-disassemble cleaning mechanism for a cooling fan, which is characterized in that a quick buckle is arranged on a shell of a detachable fan assembly, so that the quick buckle is convenient to disassemble and assemble during maintenance or cleaning; the sensor module comprises at least one particulate matter concentration sensor and a temperature and humidity sensor and is used for collecting dust content, temperature and humidity data in the cooling fan and in the surrounding environment. A first data processing sub-module, a second data processing sub-module and a third linkage optimization sub-module are arranged in the control module, and the execution mechanism is controlled by obtaining and analyzing data collected by the sensor module; the executing mechanism comprises a cleaning liquid spraying unit and a fan rotating speed adjusting unit which are used for spraying cleaning liquid to the cooling fan blades and the ventilation channel and adjusting the rotating speed of a fan to be matched with the cleaning process. The first data processing submodule comprises a pollution identification algorithm; the second data processing sub-module comprises an airflow regulation and control algorithm; and the third linkage optimization sub-module comprises a comprehensive optimization algorithm.

Description

Technical Field

[0001] The present invention relates to the field of self - cleaning of fans, and specifically to a cleaning mechanism for a cold fan that is easy to disassemble. Background Art

[0002] With the extensive use of air conditioners, cold fans, and various ventilation devices, the dust and bacteria accumulated inside them often affect the refrigeration effect and air hygiene. After a period of use, ordinary cold fans need to be disassembled and cleaned. However, existing cold fans usually adopt a fixed shell or a design that is inconvenient to disassemble and assemble, resulting in difficulties for users or maintenance personnel to clean. In addition, most current fans can only be maintained by simple manual cleaning methods, and it is difficult to make refined cleaning control according to different environmental temperature and humidity and dust concentration. Traditional regular cleaning methods may cause the following problems: It is impossible to flexibly adjust the cleaning frequency and cleaning method according to the real - time dust accumulation situation, often resulting in either excessive cleaning and wasting resources or insufficient cleaning and serious dust accumulation; There is a lack of linkage and optimization between the fan operation and the cleaning process. It may still operate at a constant air volume in a high - pollution environment, causing unnecessary energy consumption; The manual cleaning method is highly subjective, unable to effectively monitor the residual dust on the fan after cleaning, and it is also difficult to perform intelligent scheduling of the spraying amount or the fan speed in real - time.

[0003] Based on the above problems, it is necessary to design a cleaning mechanism for a cold fan that is easy to disassemble and integrates sensor detection, automatic control, and linkage optimization algorithms to adaptively identify dust and perform cleaning operations in different environments and usage scenarios, improving the convenience of equipment maintenance and the cleaning efficiency. Summary of the Invention

[0004] The purpose of the present invention is to provide a cleaning mechanism for a cold fan that is easy to disassemble to solve the technical problems raised in the above - mentioned background art.

[0005] Based on the above ideas, the present invention provides the following technical solutions: A cleaning mechanism for a cold fan that is easy to disassemble, comprising: A detachable fan assembly, a sensor module, a control module, and an actuator; Quick - release buckles are provided on the shell of the detachable fan assembly for easy disassembly and assembly during maintenance or cleaning; The sensor module includes at least one particulate matter concentration sensor and a temperature - humidity sensor, which are used to collect data on the dust content, temperature, and humidity inside and around the cold fan; The control module is built - in with a first data - processing sub - module, a second data - processing sub - module, and a third linkage - optimization sub - module, which controls the actuator by obtaining and analyzing the data collected by the sensor module; The actuator includes a cleaning liquid spraying unit and a fan speed regulating unit, which are respectively used to spray cleaning liquid on the cooling fan blades and the ventilation channel, and to regulate the fan speed to cooperate with the cleaning process; The first data processing submodule includes a pollution identification algorithm for preprocessing the output data of the particle concentration sensor and identifying the pollution degree; The second data processing submodule includes an airflow control algorithm, which outputs a specific fan speed, cleaning liquid spraying amount or spraying time according to the temperature and humidity and the preliminary identification results; The third linkage optimization submodule includes a comprehensive optimization algorithm, which integrates the output results of the pollution identification algorithm and the airflow control algorithm, and dynamically adjusts the fan cleaning process according to actual operating parameters and real-time monitoring data.

[0006] Through the organic combination of detachable fan components, sensor modules, control modules and actuators, dual innovations in hardware structure and software algorithms are achieved. First, the detachable fan components adopt structures such as quick buckles, which can be quickly disassembled and assembled during daily maintenance and deep cleaning, significantly reducing the difficulty of operation, and can also be replaced or repaired more conveniently when the shell or blades fail. Secondly, the sensor module includes at least particulate matter concentration and temperature and humidity sensors, which can monitor the dust content, temperature and humidity inside and around the fan in real time to form a complete data input source; these sensor data are processed in layers through the first data processing submodule (pollution identification algorithm), the second data processing submodule (airflow control algorithm) and the third linkage optimization submodule (comprehensive optimization algorithm) in the control module, and the dust pollution degree and temperature and humidity coupling are respectively focused on at different stages. Finally, the actuator coordinates with the fan speed adjustment unit through the cleaning liquid spraying unit. Under the guidance of the sensor monitoring results, targeted cleaning of the fan blades and ventilation channels and dynamic adjustment of the fan speed can be achieved, thereby reducing dust adhesion and improving cleaning efficiency while taking into account energy consumption and noise control. This overall technical solution can not only effectively solve the problems of traditional cooling fans that are inconvenient to disassemble and assemble, have a single cleaning method, and have high energy consumption, but also provide flexible and adjustable control strategies for different usage scenarios through multi-algorithm linkage, with significant technological advancement and practical value.

[0007] Preferably, the pollution identification algorithm specifically includes the following steps: A1. Obtain the raw data of the particle concentration sensor, remove abnormal values ​​and perform noise filtering; A2. Calculate the accumulated dust concentration of the fan blades and their surrounding environment based on the processed valid data; A3. Compare the calculated accumulated dust concentration with the pre-set multi-level pollution threshold to generate a pollution level indication; A4. Output the preliminary recognition results including information such as pollution level and dust concentration estimation.

[0008] First, the outlier rejection and noise filtering in step A1 can effectively remove instantaneous interference and measurement noise, laying an accurate data foundation for subsequent calculations. Second, in step A2, calculating the cumulative dust concentration in the environment around the fan blades can abstract a value representing the overall pollution level from the original readings of multiple sensors, providing a quantitative basis for subsequent control links. In step A3, comparing the measured concentration with the pre-set multi-level thresholds can obtain a more intuitive pollution level indication, greatly improving the refinement of the cleaning strategy. Finally, in step A4, outputting the preliminary recognition results including information such as pollution level and dust concentration estimation not only provides a key input for the subsequent air flow regulation algorithm but also enables the system to have the ability to make judgments for different dust concentrations at the algorithm level. Through this process design, those skilled in the art can easily reproduce and use this algorithm to achieve accurate fan dust recognition, reducing blind cleaning while improving the cleaning effect.

[0009] Preferably, the pollution recognition algorithm quantifies the pollution degree through the following pollution index formula, and the pollution index formula is defined as follows:

[0010] Wherein, D I is the pollution index; P i is the effective concentration value output by the i-th particulate matter concentration sensor after preprocessing; α i is the weight coefficient related to the position and sensitivity of the i-th sensor; H is the environmental humidity, and β is the coefficient of the influence of humidity on dust aggregation; T is the environmental temperature, and γ is the correction coefficient of the influence of temperature on dust aggregation; n is the number of sensors; Through the calculation of the pollution index, the quantitative evaluation of the dust concentration inside and around the fan is realized, the pollution recognition accuracy is improved, and a precise quantitative basis is provided for the subsequent control strategy of the cleaning mechanism.

[0011] The pollution index formula D I is introduced to quantitatively evaluate the dust concentration inside and around the cooling fan. The core is to incorporate factors such as sensor position weights, environmental humidity correction, and temperature compensation into the same calculation framework. By applying the weight α i related to position and sensitivity to the effective concentration value P i, it can more accurately reflect the true situation of the locally dust - prone areas of the cooling fan and the overall air duct; at the same time, by adding the humidity influence coefficient β and the temperature correction coefficient γ, it can capture the promoting effect of humidity on dust condensation or adhesion, as well as the correction effect of temperature on air flow and dust diffusion. Since D I It is presented in the form of weighted summation and correction terms. Those skilled in the art can flexibly adjust and calibrate the n sensors and their weight coefficients according to the actual fan structure and usage environment, with high scalability and adaptability. With the help of this formula, the system can quickly obtain the pollution index of the current environment where the fan is located, thereby evaluating the dust degree inside the fan in a more quantitative way, and providing a highly accurate decision - making reference in subsequent air - flow regulation and comprehensive optimization links, greatly improving the scientific nature and feasibility of the cleaning strategy.

[0012] Preferably, the air - flow regulation algorithm specifically includes the following steps: B1. Obtain real - time temperature and humidity information from the temperature - humidity sensor; B2. Calculate the fan speed regulation parameter and the cleaning liquid spraying amount according to the pollution index and the real - time temperature - humidity data; B3. Convert the regulation parameter into an execution instruction, and send a control signal to the fan speed regulation unit and the cleaning liquid spraying unit in the execution mechanism; B4. Monitor the response data during the execution process, and the response data includes the actual fan speed and the spraying pressure.

[0013] Combining the temperature - humidity data with the pollution index to generate regulation instructions for the fan speed and the spraying amount, and monitoring the execution process in real - time. The beneficial effects of this process are as follows: First, in step B1, the real - time temperature and humidity are obtained through the sensor, enabling the algorithm to automatically match the optimal fan speed and spraying scheme under different environmental parameters, avoiding energy consumption waste or insufficient cleaning caused by simply fixed fan speeds; in step B2, through in - depth coupling calculation of the pollution index and the temperature - humidity, the regulation parameters suitable for the current cleaning target are obtained. It can not only dynamically change the fan speed to enhance or weaken the air flow to drive dust, but also make the cleaning liquid fully cover the blade and air - duct surfaces through precise allocation of the spraying amount; step B3 then converts the above calculation results into executable hardware instructions and responds in a timely manner through the fan speed regulation unit and the spraying unit; finally, the execution monitoring described in step B4 ensures that in case of accidents or sudden changes in environmental parameters, the system can re - evaluate and adjust the strategy according to the actual feedback, forming a closed - loop control. This not only ensures the cleaning effect but also takes into account energy consumption and equipment life, with remarkable practicality and high efficiency.

[0014] Preferably, the air - flow regulation algorithm uses the following fan regulation formula to calculate the fan speed and the cleaning liquid spraying amount, and the fan regulation formula is defined as follows:

[0015]

[0016] Among them, V f Indicates the fan speed that needs to be set; Q s Indicates the amount of cleaning fluid sprayed; D I is the calculated pollution index; T and H are temperature and humidity, respectively; k1, k2, k3, M1, M2, and δ are parameters obtained in advance in the experiment, which are used to balance the effects of temperature, humidity, and dust concentration on the fan speed and spraying amount.

[0017] k1 is used to determine the pollution index D I Fan speed V f When the dust in the environment accumulates more (D I When the fan speed is high, in order to enhance the cleaning effect, it is necessary to increase the fan speed appropriately to drive the dust attached to the blades or in the air duct to fall off or to cooperate with the spray liquid to achieve flushing. The larger the k1, the more sensitive it is to dust concentration. Under different dust concentrations (and the temperature and humidity are relatively fixed), by adjusting the fan speed multiple times and evaluating the cleaning efficiency (or dust residue), find the most appropriate speed change range, and use curve fitting or linear / nonlinear regression to determine k1.

[0018] k2 is the temperature T that determines the effect of the fan speed V f When the temperature is high, the cooling fan usually needs a faster airflow to remove the heat. At the same time, humidity changes will also affect the evaporation rate and efficiency of the cleaning liquid. When k2 is large, it means that when the temperature rises, the fan speed will be increased accordingly to take into account both cooling and cleaning effects. Under different temperature environments (dust concentration and humidity are roughly the same), measure the corresponding relationship between speed, cleaning effect and energy consumption. By regressing or modeling the test data, the optimal k2 is obtained to keep the system reasonably cooled and self-cleaning.

[0019] k3 is the value for determining the effect of humidity H on fan speed V f The higher the humidity, the easier it is for dust to condense and adhere to the blades or air ducts. At the same time, the evaporation rate of the cleaning fluid in a high humidity environment is reduced. In order to allow the cleaning fluid to fully cover and flush the dust, it may also be necessary to adjust the fan speed within a specific humidity range. Experiments are conducted under different humidity environments to compare the fan speed and its efficiency in dust removal, while measuring energy consumption, airflow effects, etc., and finally obtain the empirical coefficient of k3.

[0020] M1 is used to determine the pollution index D I The amount of cleaning fluid sprayed Q sThe influence range. When the dust concentration is high, more cleaning fluid is required for flushing; while in scenarios with less severe dust, excessive spraying not only wastes water resources but may also increase the motor load. The larger M1 is, the more sensitive it is to the pollution index, and the greater the increase in the spraying volume when the pollution is high. In the case of pollution level D I In multiple scenario tests from low to high, observe the influence of changes in the spraying volume on indicators such as cleaning speed, dust residue, and water consumption, and select the optimal M1 that can balance cleaning efficiency and resource conservation.

[0021] M2 is the coefficient for further correcting the cleaning fluid spraying volume Q s due to the fact that temperature and humidity conditions can affect the evaporation, adhesion, and flushing effect of the cleaning fluid. Therefore, under the same pollution index, different temperature and humidity environments may require different spraying volumes. When M2 is larger, it indicates that the spraying volume will increase significantly in a high-temperature environment. Under different temperature and humidity environments (but with the same dust concentration), gradually adjust the spraying volume, test the actual cleaning effect and water consumption / energy consumption, to obtain an optimal response curve, and then determine M2 by regression or numerical fitting methods.

[0022] δ often appears as an "exponential amplification" or "power function amplification" coefficient in the formula, used to describe the non-linear sensitivity of the cleaning fluid spraying volume Q s to the pollution index D I At a relatively high pollution level, through multiple experiments, observe the curve changes of the spraying volume, cleaning efficiency, and resource consumption, and combine the equipment's limit tolerance and cleaning requirements to select the corresponding δ value. If it is desired to quickly increase the spraying to wash away stubborn dust after D I increases, a relatively large δ can be taken; if the system hopes to grow more flexibly, then δ takes a relatively small value.

[0023] Further introduce the fan control formula, with V f representing the fan speed and Q s representing the cleaning fluid spraying volume, and clearly present the relationship between environmental variables such as the pollution index, temperature, and humidity and the fan speed and spraying volume in a mathematical form. The mechanism of this formula is to use non-linear mapping methods such as logarithmic functions and square root functions to map D IThe change significantly affects the fan speed and the spraying volume, thereby achieving a differential response to different pollution levels. In addition, experimental calibration parameters such as k1, k2, k3, M1, M2, and δ are set in the formula, enabling those skilled in the art to obtain the parameter values most suitable for a specific fan structure, environmental temperature and humidity range, and expected cleaning effect through multiple experiments, so as to balance the cleaning efficiency and energy consumption under low or high temperature, high or low humidity conditions. This can avoid the problem that a simple linear formula cannot fully reflect the differences in actual working conditions, and also endows the algorithm with an adaptive ability within a certain range, thereby ensuring that the cold fan can perform effective cleaning operations in different scenarios and minimizing water resource waste and power consumption.

[0024] Preferably, the control module further includes a comprehensive optimization algorithm, which is used to perform linkage adjustment and optimization on the output results based on the pollution recognition algorithm and the air flow regulation algorithm. The implementation steps include: C1. Obtain the pollution index, fan speed, and cleaning liquid spraying volume; C2. Update the pollution index according to the real-time sensor data, and combine the actual execution effect feedback by the actuator; C3. Generate a new comprehensive index for evaluating the fan cleaning effect and the fan energy consumption situation; C4. According to the comprehensive index, adaptively adjust the fan speed and the cleaning liquid spraying mode to form a closed-loop control process, so as to ensure the best cleaning effect and energy consumption level under different environments and usage scenarios.

[0025] Based on the pollution recognition algorithm and the air flow regulation algorithm, linkage adjustment and overall optimization are carried out, forming a global and dynamic management system for the fan cleaning process. Through the four steps C1 to C4, this algorithm realizes a closed-loop control from obtaining the pollution index and the fan speed / spraying volume to generating a new comprehensive index, and then to adaptively adjusting the fan and the spraying mode. Since real-time sensor data and the actual feedback of the actuator are integrated in this process, the system can iteratively update and optimize the decision according to the latest DustIndex correction result and the energy consumption change situation, so as to reduce resource waste while ensuring the cleanliness. For example, if it is found that the current spraying volume is too large, resulting in a sharp increase in energy consumption, or the fan speed is too low, causing a decrease in cleaning efficiency, then the comprehensive optimization algorithm can intervene in time and dynamically adjust the corresponding parameters. Through this hierarchical linkage method, the system can not only quickly adapt to environmental fluctuations or working condition changes, but also deploy efficient and flexible cleaning strategies in multiple modes such as high pollution and normal pollution, significantly improving the robustness and usage value of the cold fan cleaning mechanism.

[0026] Preferably, the comprehensive optimization algorithm uses the following comprehensive optimization formula to output the results for linkage optimization: Op = ω1×D Inew + ω2×f(V f , Q s ) − ω3×E; Wherein, D Inew = α u × D I + (1 − α u ) × D Ipast ; f(V f , Q s ) = δ1×V f + δ2×Q s ; E = η1×(V f ) 2 + η2×Q s ; Wherein: O p is a comprehensive optimization index used to evaluate the cleaning effect and energy consumption level; D Inew is a pollution index iteratively corrected based on real-time monitoring and historical data; D Ipast is the historical pollution index; V f and Q s are respectively the fan speed and the cleaning liquid spraying amount in the second algorithm; E represents the energy consumption evaluation function; ω1, ω2, ω3, αupd, δ1, δ2, η1, η2 are all weight coefficients obtained during model construction and system calibration; When O p reaches the preset threshold or extreme value, the current fan cleaning optimization process is completed; if not up to the standard, continue to iteratively adjust the fan speed and the cleaning liquid spraying amount to achieve adaptive control of the system operation state.

[0027] Integrate the iterative correction D Inew of the pollution index, the fan speed and the spraying amount function f(V f , Q s ), the energy consumption E and other key factors into the unified optimization index O p , and cooperate with the weight coefficients ω1, ω2, ω3 and the update parameters α upd , δ1, δ2, η1, η2, etc. for multi-dimensional modeling and balancing. The principle is that: on the one hand, D Inew is gradually corrected over time or during the iteration process, taking into account the historical dust accumulation data (D Ipast), and the current sensor measurement values, thus smoothing the noise and having real-time performance; on the other hand, the influence of the fan speed and the spraying amount on the cleaning effect and energy consumption is also quantified through f(V f , Q s ), and E, enabling the system to identify which combination can best balance cleanliness and low energy consumption under specific conditions. When the comprehensive optimization index Op reaches the preset threshold or extreme value, it indicates that the control parameters such as the fan speed and the spraying amount have entered the optimal range, and further adjustment can be stopped; if not up to the standard, continue to iterate and update, so as to achieve adaptive closed-loop control in a complex environment. This mechanism overcomes the limitations of traditional single-speed regulation or single-spraying amount control. Through multiple iterative optimizations of key parameters, a reliable and efficient cleaning effect is finally obtained, significantly improving the overall working performance and practical value of the fan equipment.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: The pollution recognition algorithm forms a quantitative pollution index by collecting, preprocessing, and cumulatively analyzing the output data of multiple particulate matter concentration sensors in real time, and divides the pollution level according to multi-level pollution thresholds. By introducing technical means such as noise filtering, outlier removal, and multi-sensor fusion, false alarms caused by the inaccuracy of a single sensor or sudden environmental interference can be effectively reduced, so as to accurately reflect the true dust accumulation degree around the fan blades and in the ventilation channels. The algorithm can update the pollution index DI at a high frequency, and capture the tiny dust accumulation changes generated during the operation of the fan at any time, making the subsequent regulation and cleaning decisions timely and targeted. For different sensor layouts and diverse environments (high temperature, high humidity, low temperature, etc.), it can be adapted by adjusting the weight coefficients or threshold intervals, providing flexible and calibratable inputs for the subsequent linkage algorithm.

[0029] Starting from the pollution index output by the pollution recognition algorithm and real-time environmental data such as temperature and humidity, the air flow regulation algorithm calculates the fan speed and the spraying amount or spraying duration of the cleaning liquid through a pre-calibrated parameter formula, realizing automatic and refined adjustment of the cold fan. With the design of non-linear functions such as logarithm and square root, the response to the pollution degree is more flexible. When the dust concentration is high, the fan speed and the spraying amount can be significantly increased, while when the pollution degree is low, unnecessary resource consumption can be reduced, balancing the relationship between the cleaning effect and energy consumption. By integrating the two key environmental parameters of temperature and humidity into the algorithm, the limitation of simply relying on the dust concentration and ignoring physical effects such as evaporation and condensation is avoided; in a high-humidity or high-temperature environment, the spraying amount can be adjusted accordingly to improve the utilization efficiency of the cleaning liquid. The air flow regulation algorithm sends control instructions to the fan speed regulation unit and the cleaning liquid spraying unit respectively, and further corrects the regulation amount through the feedback monitoring of the execution process, so that the entire cleaning process always remains at a relatively optimal level.

[0030] The comprehensive optimization algorithm is based on the output results of the pollution identification and air flow regulation algorithms. By continuously integrating real-time monitoring data and the feedback information of the actuator, a comprehensive optimization index is constructed for multiple rounds of iterative optimization. Compared with the single-dimensional control of only dust concentration or fan speed, this algorithm considers multiple indicators such as pollution degree, fan energy consumption, spraying amount, and cleaning efficiency, balances the requirements of all parties in the same optimization formula, and realizes the globally optimal cleaning solution. By introducing an iteratively updated pollution index (combining historical data and current measurements), and an energy consumption evaluation function, the operating state of the system at different time points can be evaluated and dynamically corrected multiple times, enabling the fan cleaning process to have the ability to adapt to environmental changes and continuously optimize. If insufficient cleanliness or high energy consumption is found during the cleaning process or when the environment changes, the system can adjust the fan speed and spraying amount in real time. At the same time, as the comprehensive optimization index gradually converges, a stable and efficient operating state is finally achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a module schematic diagram of a detachable cleaning mechanism for a cold fan according to the present invention.

[0032] Figure 2 It is a working flow chart of a detachable cleaning mechanism for a cold fan according to the present invention DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] A detachable cleaning mechanism for a cold fan includes: A detachable fan assembly, a sensor module, a control module, and an actuator; Quick buckles are provided on the outer shell of the detachable fan assembly for easy disassembly and assembly during maintenance or cleaning; The sensor module includes at least one particulate matter concentration sensor and a temperature and humidity sensor, which are used to collect dust content, temperature, and humidity data in the cold fan and its surrounding environment; The control module is built-in with a first data processing sub-module, a second data processing sub-module, and a third linkage optimization sub-module, and controls the actuator by acquiring and analyzing the data collected by the sensor module; The actuator includes a cleaning liquid spraying unit and a fan speed adjustment unit, which are respectively used to spray cleaning liquid on the cold fan blades and the ventilation channel, and adjust the fan speed to cooperate with the cleaning process; The first data processing sub-module includes a pollution identification algorithm, which is used to preprocess the output data of the particulate matter concentration sensor and identify the pollution degree; The second data processing sub-module includes an air flow regulation algorithm, which outputs a specific fan speed, cleaning liquid spraying amount, or spraying time according to the temperature, humidity, and preliminary identification results; The third linkage optimization sub-module includes a comprehensive optimization algorithm, which integrates the output results of the pollution identification algorithm and the air flow regulation algorithm, and dynamically adjusts the fan cleaning process according to the actual operating parameters and real-time monitoring data.

[0034] The present invention can meet the detection requirements by using commercially available particulate matter sensors (such as laser PM2.5 / PM10 modules) and temperature and humidity sensors (digital type); The cleaning liquid spraying unit can select common micro water pumps and nozzle assemblies, and the fan speed regulation unit can adopt frequency converters or PWM controllers; Through the electronic control circuit or industrial PLC, the sensor data can be obtained and the three major algorithms can be executed.

[0035] The software implementation is to write corresponding C / C++ or Python programs on a single-chip microcomputer (such as the STM32 series) or an embedded Linux platform (such as ARM), including functions or tasks of the pollution identification algorithm, the air flow regulation algorithm, and the comprehensive optimization algorithm; Use the interrupt or timer mechanism to collect data periodically, and output execution instructions after completing the operation.

[0036] The cleaning mechanism for the detachable cold fan of the present invention mainly includes: The housing of the detachable fan assembly uses several quick buckles or threaded connectors, which can be quickly opened when maintenance or deep cleaning is required; The fan blades can also be disassembled by buckles or central screws, which is convenient for manual or automatic cleaning; The fan motor is relatively sealed with the housing to ensure the safety of the motor during the spraying of the cleaning liquid.

[0037] The sensor module includes a particulate matter sensor group and a temperature and humidity sensor; the particulate matter sensor group can be placed at several positions near the air inlet, air outlet, and blades to capture the dust distribution; The temperature and humidity sensor can be installed inside or outside the fan to obtain real-time environmental parameters.

[0038] The control module contains three core sub-modules: The first data processing sub-module: executes the pollution identification algorithm; The second data processing sub-module: executes the air flow regulation algorithm; The third linkage optimization sub-module: executes the comprehensive optimization algorithm; The three sub-modules interact through a data bus or the internal shared memory of the CPU; The control module can be implemented based on hardware platforms such as MCU, embedded Linux, or PLC, and the program is run by firmware or software stored in Flash or SD card.

[0039] The actuator includes a cleaning liquid spraying unit; including a controllable water pump or air pump and a plurality of nozzles; and can spray the cleaning liquid quantitatively to the blades or the air duct; Fan speed regulation unit; for example, the speed of the fan motor is controlled by a frequency converter or a PWM speed regulator to meet the real-time speed regulation needs.

[0040] Through the organic combination of detachable fan components, sensor modules, control modules and actuators, dual innovations in hardware structure and software algorithms are achieved. First, the detachable fan components adopt structures such as quick buckles, which can be quickly disassembled and assembled during daily maintenance and deep cleaning, significantly reducing the difficulty of operation, and can also be replaced or repaired more conveniently when the shell or blades fail. Secondly, the sensor module includes at least particulate matter concentration and temperature and humidity sensors, which can monitor the dust content, temperature and humidity inside and around the fan in real time to form a complete data input source; these sensor data are processed in layers through the first data processing submodule (pollution identification algorithm), the second data processing submodule (airflow control algorithm) and the third linkage optimization submodule (comprehensive optimization algorithm) in the control module, and the dust pollution degree and temperature and humidity coupling are respectively focused on at different stages. Finally, the actuator coordinates with the fan speed adjustment unit through the cleaning liquid spraying unit. Under the guidance of the sensor monitoring results, targeted cleaning of the fan blades and ventilation channels and dynamic adjustment of the fan speed can be achieved, thereby reducing dust adhesion and improving cleaning efficiency while taking into account energy consumption and noise control. This overall technical solution can not only effectively solve the problems of traditional cooling fans that are inconvenient to disassemble and assemble, have a single cleaning method, and have high energy consumption, but also provide flexible and adjustable control strategies for different usage scenarios through multi-algorithm linkage, with significant technological advancement and practical value.

[0041] Specifically, the pollution identification algorithm includes the following steps: A1. Obtain the raw data of the particle concentration sensor, remove abnormal values ​​and perform noise filtering; A2. Calculate the accumulated dust concentration of the fan blades and their surrounding environment based on the processed valid data; A3. Compare the calculated accumulated dust concentration with the pre-set multi-level pollution threshold to generate a pollution level indication; A4. Output preliminary identification results including pollution level, dust concentration estimation and other information.

[0042] First, the outlier rejection and noise filtering in step A1 can effectively remove instantaneous interference and measurement noise, laying an accurate data foundation for subsequent calculations; second, calculating the cumulative dust concentration in the environment around the fan blade in step A2 can abstract a value representing the overall pollution level from the original readings of multiple sensors, providing a quantitative basis for subsequent control; comparing the measured concentration with the pre-set multi-level thresholds in step A3 can obtain a more intuitive pollution level indication, significantly improving the refinement of the cleaning strategy; finally, outputting the preliminary recognition results including pollution level, dust concentration estimation, etc. in step A4 not only provides key inputs for subsequent air flow regulation algorithms but also enables the system to make judgments for different dust concentrations at the algorithm level. Through this process design, those skilled in the art can easily reproduce and utilize this algorithm to achieve accurate fan dust recognition, reducing blind cleaning while improving the cleaning effect.

[0043] Specifically, the pollution recognition algorithm quantifies the pollution degree through the following pollution index formula, and the pollution index formula is defined as follows:

[0044] Among them, D I is the pollution index; P i is the effective concentration value output by the i-th particulate matter concentration sensor after preprocessing; α i is the weight coefficient related to the position and sensitivity of the i-th sensor; H is the environmental humidity, and β is the coefficient of the influence of humidity on dust aggregation; T is the environmental temperature, and γ is the correction coefficient of the influence of temperature on dust aggregation; n is the number of sensors; Through the calculation of the pollution index, the quantitative evaluation of the dust concentration inside and around the fan is realized, the pollution recognition accuracy is improved, and a precise quantitative basis is provided for the subsequent control strategy of the cleaning mechanism.

[0045] Determination of α i Under different sensor arrangements, determine the contribution degree of each sensor to the pollution index through experimental measurement or empirical methods; Determination of β, γ: Under different temperature and humidity conditions, according to the experimental results of dust being easily attached or easily dispersed, fit the increasing and decreasing influence of humidity and temperature on the pollution index.

[0046] The pollution index formula D is introduced I, used to quantitatively evaluate the dust concentration inside and around the air cooler. The core lies in incorporating factors such as sensor position weights, environmental humidity correction, and temperature compensation into the same calculation framework. By applying weights α related to position and sensitivity to the effective concentration values P output by each sensor i , the true situation of the local dust-prone areas and the overall air duct of the air cooler can be more accurately reflected; at the same time, by adding the humidity influence coefficient β and the temperature correction coefficient γ, the promotion effect of humidity on dust condensation or adhesion and the correction effect of temperature on air flow and dust diffusion can be captured. Since D i is presented in the form of weighted summation and correction terms, those skilled in the art can flexibly adjust and calibrate the n sensors and their weight coefficients according to the actual fan structure and usage environment, which has high scalability and adaptability. With the help of this formula, the system can quickly obtain the pollution index of the current environment where the fan is located, thereby evaluating the dust degree inside the fan in a more quantitative manner and providing a high-accuracy decision-making reference in subsequent air flow regulation and comprehensive optimization links, greatly improving the scientificity and executability of the cleaning strategy. I Specifically, the air flow regulation algorithm specifically includes the following steps:

[0047] B1. Obtain real-time temperature and humidity information from the temperature and humidity sensors; B2. Calculate the fan speed regulation parameter and the cleaning liquid spraying amount according to the pollution index and the real-time temperature and humidity data; B3. Convert the regulation parameter into an execution instruction and send a control signal to the fan speed regulation unit and the cleaning liquid spraying unit in the actuator; B4. Monitor the response data during the execution process, and the response data includes the actual fan speed and the spraying pressure.

[0048] ​Combine the temperature and humidity data with the pollution index to generate control instructions for the fan speed and spraying volume, and monitor the execution process in real time. The beneficial effects of this process are as follows: First, in step B1, the real-time temperature and humidity are obtained through sensors, enabling the algorithm to automatically match the optimal fan speed and spraying plan under different environmental parameters, avoiding energy consumption waste or insufficient cleaning caused by simply fixed speeds; in step B2, through in-depth coupling calculation of the pollution index and temperature and humidity, the control parameters suitable for the current cleaning target are obtained. This can not only dynamically change the fan speed to enhance or weaken the airflow to drive dust, but also make the cleaning liquid fully cover the blade and duct surfaces through precise adjustment of the spraying volume; step B3 then converts the above calculation results into executable hardware instructions, which are promptly responded to by the fan speed adjustment unit and the spraying unit; finally, the execution monitoring described in step B4 ensures that in case of accidents or sudden changes in environmental parameters, the system can re-evaluate and adjust the strategy based on the actual feedback, forming a closed-loop control. This not only ensures the cleaning effect but also takes into account energy consumption and equipment life, with remarkable practicality and efficiency.

[0049] Specifically, the airflow control algorithm uses the following fan control formula to calculate the fan speed and the cleaning liquid spraying volume, and the fan control formula is defined as follows:

[0050]

[0051] Among them, V f represents the fan speed to be set; Q s represents the cleaning liquid spraying volume; D I is the calculated pollution index; T and H are the temperature and humidity respectively; k1, k2, k3, M1, M2, δ are parameters obtained in advance in experiments, which are used to balance the influence of temperature, humidity and dust concentration on the fan speed and spraying volume.

[0052] According to the calculation results, convert them into a fan speed control signal (such as PWM duty cycle or frequency conversion instruction) and a spraying unit switch quantity or flow control instruction; Monitor the actual execution situation (fan speed, current, spraying pressure, etc.), and record the feedback value for the use of the third algorithm.

[0053] Under different temperature, humidity and DustIndex conditions, measure the optimal fan speed and spraying volume, the corresponding cleaning effect score and energy consumption value respectively; k1 is the influence weight of the determined pollution index D I on the fan speed V f . When there is more accumulated dust in the environment (D IWhen the dust concentration is relatively high (higher), in order to enhance the cleaning effect, it is necessary to moderately increase the fan speed to drive the dust attached to the blades or in the air duct to fall off or cooperate with the spraying liquid to achieve flushing. The larger k1 is, the more sensitive it is to the dust concentration. Under different dust concentrations (with relatively fixed temperature and humidity), by adjusting the fan speed multiple times and evaluating the cleaning efficiency (or dust residue), find the most appropriate speed change range, and use curve fitting or linear / nonlinear regression to determine k1.

[0054] k2 is used to determine the influence weight of temperature T on the fan speed V f When the temperature is high, the cold fan usually requires faster air flow to take away heat. At the same time, humidity changes will also affect the evaporation rate and usage efficiency of the cleaning liquid. When k2 is larger, it means that when the temperature rises, the fan speed will be increased accordingly to balance the cooling and cleaning effects. Under different temperature environments (with roughly the same dust concentration and humidity), measure the corresponding relationship between the rotation speed, cleaning effect and energy consumption. Through regression or modeling of the test data, obtain the optimal k2 that enables the system to maintain reasonable cooling and self-cleaning at the same time.

[0055] k3 is used to determine the influence weight of humidity H on the fan speed V f The higher the humidity, the easier it is for dust to condense and adhere to the blades or air ducts. At the same time, the evaporation rate of the cleaning liquid decreases in a high-humidity environment. In order to make the cleaning liquid fully cover and wash away the dust, it may also be necessary to adjust the fan speed within a specific humidity range. Conduct experiments in different humidity environments, compare the fan speed and its efficiency in cleaning dust, and at the same time measure the energy consumption, air flow effect, etc. Finally, obtain the empirical coefficient of k3.

[0056] M1 is used to determine the influence range of the pollution index D I on the spraying amount Q of the cleaning liquid s When the dust concentration is relatively high, more cleaning liquid is needed to cooperate with the flushing; while in the scenario where the dust is not serious, excessive spraying not only wastes water resources but may also increase the motor load. The larger M1 is, the more sensitive it is to the pollution index, and the greater the increase in the spraying amount when the pollution is high. In multiple scenario tests where the pollution degree D I ranges from low to high, observe the influence of the change in the spraying amount on indicators such as the cleaning speed, dust residue, and water consumption, and select the optimal M1 that can balance the cleaning efficiency and resource conservation.

[0057] M2 is used to combine the temperature T and determine the spraying amount Q of the cleaning liquid sCoefficient for further correction. Since temperature and humidity conditions can affect the evaporation, adhesion, and rinsing effect of the cleaning liquid, different spraying amounts may be required in different temperature and humidity environments under the same pollution index. When M2 is larger, it indicates that the spraying amount will increase significantly in a high-temperature environment. In different temperature and humidity environments (but with the same dust concentration), gradually adjust the spraying amount, and test the actual cleaning effect and water consumption / energy consumption to obtain an optimal response curve, and then determine M2 by regression or numerical fitting methods.

[0058] δ often appears as an "exponential amplification" or "power function amplification" coefficient in the formula, which is used to describe the cleaning liquid spraying amount Q s For the pollution index D I of the non-linear sensitivity. At a relatively high pollution level, through multiple experiments, observe the curve changes of the spraying amount, cleaning efficiency, and resource consumption, and combine the ultimate bearing capacity and cleaning requirements of the equipment to select the corresponding δ value. If it is desired to quickly increase the spraying amount to wash away stubborn dust after D I increases, a relatively large δ can be taken; if the system hopes to grow more flexibly, then δ takes a relatively small value.

[0059] Further introduce the fan control formula, with V f representing the fan speed and Q s representing the cleaning liquid spraying amount, and clearly present the relationship between environmental variables such as the pollution index, temperature, and humidity and the fan speed and spraying amount in a mathematical form. The mechanism of this formula is to non-linearly map through logarithmic functions, square root functions, etc., so that the change of D I significantly affects the fan speed and spraying amount, thereby realizing a differential response to different pollution levels. In addition, experimental calibration parameters such as k1, k2, k3, M1, M2, and δ are set in the formula, enabling those skilled in the art to obtain the parameter values most suitable for a specific fan structure, environmental temperature and humidity range, and expected cleaning effect through multiple experiments, so as to balance the cleaning efficiency and energy consumption under low or high temperature, high or low humidity conditions. In this way, the problem that a simple linear formula cannot fully reflect the differences in actual working conditions can be avoided, and the algorithm also has an adaptive ability within a certain range, thereby ensuring that the cold fan can perform effective cleaning operations in different scenarios and minimizing water resource waste and power consumption.

[0060] Specifically, the control module further includes a comprehensive optimization algorithm, which is used to perform linkage adjustment and optimization on the output results based on the pollution recognition algorithm and the air flow control algorithm. Its implementation steps include: C1. Obtain the pollution index, fan speed, and cleaning liquid spraying amount; C2. Update the pollution index according to the real-time sensor data, and combine the actual execution effect feedback by the actuator; C3. Generate a new comprehensive index to evaluate the fan cleaning effect and the energy consumption of the fan; C4. According to the comprehensive index, adaptively adjust the fan speed and the cleaning liquid spraying mode to form a closed-loop control process, so as to ensure the best cleaning effect and energy consumption level under different environments and usage scenarios.

[0061] Based on the pollution identification algorithm and the air flow regulation algorithm, linkage adjustment and overall optimization are carried out to form a global and dynamic management system for the fan cleaning process. Through four steps C1 to C4, the algorithm realizes the closed-loop control from obtaining the pollution index and the fan speed / spraying amount to generating a new comprehensive index and then to adaptively adjusting the fan and the spraying mode. Since real-time sensor data and the actual feedback of the actuator are integrated in this process, the system can iteratively update and optimize the decision according to the latest DustIndex correction result and the energy consumption change situation, so as to reduce resource waste while ensuring the cleanliness. For example, if it is found that the current spraying amount is too large, resulting in a sharp increase in energy consumption, or the fan speed is too low, causing a decrease in cleaning efficiency, then the comprehensive optimization algorithm can intervene in time and dynamically adjust the corresponding parameters. Through such a hierarchical linkage method, the system can not only quickly adapt to environmental fluctuations or working condition changes, but also deploy efficient and flexible cleaning strategies in multiple modes such as high pollution and conventional pollution, significantly improving the robustness and usability of the cold fan cleaning mechanism.

[0062] Specifically, the comprehensive optimization algorithm uses the following comprehensive optimization formula to output the result for linkage optimization: O p = ω1×D Inew + ω2×f(V f , Q s ) - ω3×E; Among them, D Inew = α u ×D I +(1 - α u )×D Ipast ; f(V f , Q s ) = δ1×V f + δ2×Q s ; E = η1×(V f ) 2 + η2×Q s ; Among them: O p is the comprehensive optimization index, used to evaluate the cleaning effect and the energy consumption level; D Inew is the pollution index iteratively corrected according to real-time monitoring and historical data; D Ipast is the historical pollution index; V f and Q s are the fan speed and the cleaning liquid spraying volume in the second algorithm respectively; E represents the energy consumption evaluation function; ω1, ω2, ω3, αupd, δ1, δ2, η1, η2 are all weight coefficients obtained during model construction and system calibration; When O p reaches the preset threshold or extreme value, the current fan cleaning optimization process is completed; if not up to the standard, continue to iteratively adjust the fan speed and the cleaning liquid spraying volume to achieve adaptive control of the system operating state.

[0063] ω1, ω2, ω3 and the update parameter α upd , δ1, δ2, η1, η2 are adjusted for weights through simulation; When O p is the maximum or exceeds the threshold, it can be determined that the cleaning of this round reaches the optimal effect, and the system can enter the stable or low-power mode; Integrate the iterative correction D Inew of the pollution index, the fan speed and the spraying volume function f(V f , Q s ), the energy consumption E and other key factors into the unified optimization index O p , and cooperate with the weight coefficients ω1, ω2, ω3 and the update parameter α upd , δ1, δ2, η1, η2, etc. for multi-dimensional modeling and balancing. The principle is as follows: on the one hand, D Inew is gradually corrected over time or during the iterative process, which can take into account the historical dust accumulation data (D Ipast ) and the current sensor measurement values, so as to smooth the noise and have real-time performance; on the other hand, the influence of the fan speed and the spraying volume on the cleaning effect and energy consumption is also quantified through f(V f , Q s ) and E, enabling the system to identify which combination can best balance cleanliness and low energy consumption under specific conditions. When the comprehensive optimization index Op reaches the preset threshold or extreme value, it means that the control parameters such as the fan speed and the spraying volume have entered the optimal range, and further adjustment can be stopped; if not up to the standard, continue to iterate and update, so as to achieve adaptive closed-loop control in a complex environment. This mechanism overcomes the limitations of traditional single-speed regulation or single spraying volume control. Through multiple iterative optimizations of key parameters, a reliable and efficient cleaning effect is finally obtained, significantly improving the overall working performance and practical value of the fan equipment.

Claims

1. A detachable cleaning mechanism for a cold fan, characterized in that, include: Removable fan assembly, sensor module, control module, actuator; The housing of the detachable fan assembly is provided with a quick buckle to facilitate disassembly and assembly during maintenance or cleaning; The sensor module includes at least one particle concentration sensor and a temperature and humidity sensor for collecting dust content, temperature and humidity data inside the cooling fan and in the surrounding environment; The control module has a built-in first data processing submodule, a second data processing submodule and a third linkage optimization submodule, and controls the actuator by acquiring and analyzing the data collected by the sensor module; The actuator includes a cleaning liquid spraying unit and a fan speed regulating unit, which are respectively used to spray cleaning liquid on the cooling fan blades and the ventilation channel, and to regulate the fan speed to cooperate with the cleaning process; The first data processing submodule includes a pollution identification algorithm for preprocessing the output data of the particle concentration sensor and identifying the pollution degree; The second data processing submodule includes an airflow control algorithm, which outputs a specific fan speed, cleaning liquid spraying amount or spraying time according to the temperature and humidity and the preliminary identification results; The third linkage optimization submodule includes a comprehensive optimization algorithm, which integrates the output results of the pollution identification algorithm and the airflow control algorithm, and dynamically adjusts the fan cleaning process according to actual operating parameters and real-time monitoring data.

2. The easily detachable cooling fan cleaning mechanism according to claim 1, characterized in that: The pollution identification algorithm specifically includes the following steps: A1. Obtain the raw data of the particle concentration sensor, remove abnormal values ​​and perform noise filtering; A2. Calculate the accumulated dust concentration of the fan blades and their surrounding environment based on the processed valid data; A3. Compare the calculated accumulated dust concentration with the pre-set multi-level pollution threshold to generate a pollution level indication; A4. Output preliminary identification results including pollution level, dust concentration estimation and other information.

3. The easily detachable cooling fan cleaning mechanism according to claim 2, characterized in that: The pollution identification algorithm quantifies the degree of pollution through a pollution index formula, which is: ； in, D I is the pollution index; P i is the effective concentration value output by the i-th particulate matter concentration sensor after pretreatment; α i is the weight coefficient related to the position and sensitivity of the i-th sensor; H is the ambient humidity, β is the coefficient of humidity on dust accumulation; T is the ambient temperature, γ is the correction coefficient of the effect of temperature on dust accumulation; n is the number of sensors.

4. The easily detachable cooling fan cleaning mechanism according to claim 3, characterized in that: The airflow control algorithm specifically includes the following steps: B1. Obtain real-time temperature and humidity information from the temperature and humidity sensor; B2. Calculate the fan speed control parameters and the cleaning liquid spraying amount according to the pollution index and the real-time temperature and humidity data; B3, converting the control parameters into execution instructions, and sending control signals to the fan speed adjustment unit and the cleaning liquid spraying unit in the actuator; B4. Monitor the response data during the execution process, wherein the response data includes the actual fan speed and spraying pressure.

5. The easily detachable cooling fan cleaning mechanism according to claim 4, characterized in that: The airflow control algorithm calculates the fan speed and the cleaning liquid spraying amount using the fan control formula, and the fan control formula is: ； ； Among them, V f represents the rotational speed of the fan to be set; Q s represents the cleaning liquid spraying amount; D I is the calculated pollution index; T and H are the temperature and humidity respectively; k1, k2, k3, M1, M2, and δ are parameters obtained in advance in the experiment, which are used to balance the influence of temperature, humidity, and dust concentration on the fan speed and spraying amount.

6. The cleaning mechanism for a detachable cooling fan according to claim 5, wherein The comprehensive optimization algorithm is used to perform linkage adjustment and optimization on the output results based on the pollution recognition algorithm and the airflow control algorithm, and its implementation steps include: C1. Obtain the pollution index, the fan speed, and the cleaning liquid spraying amount; C2. Update the pollution index according to the real-time sensor data, and combine the actual execution effect feedback by the actuator; C3. Generate a new comprehensive index for evaluating the fan cleaning effect and the fan energy consumption situation; C4. According to the comprehensive index, adaptively adjust the fan speed and the cleaning liquid spraying mode to form a closed-loop control process, so as to ensure the best cleaning effect and energy consumption level under different environments and usage scenarios.

7. The cleaning mechanism for a detachable cooling fan according to claim 6, wherein The comprehensive optimization algorithm uses the comprehensive optimization formula to output the results for linkage optimization, and the comprehensive optimization formula is: O p = ω1 × D Inew + ω2 × f(V f , Q s ) − ω3 × E; Among them, D Inew = α u × D I + (1 - α u ) × D Ipast ; f(V f ,Q s ) = δ1×V f + δ2×Q s ; E = η1×(V f ) 2 + η2×Q s ; Where: O p is a comprehensive optimization index used to evaluate the cleaning effect and energy consumption level; D Inew is the pollution index iteratively corrected based on real-time monitoring and historical data; D Ipast is the historical pollution index; V f and Q s are the fan speed and the cleaning liquid spraying amount in the second algorithm, respectively; E represents the energy consumption evaluation function; ω1, ω2, ω3, αupd, δ1, δ2, η1, η2 are all weight coefficients obtained during model construction and system calibration; When O p reaches the preset threshold or extreme value, the current fan cleaning and optimization process is completed; if not up to the standard, continue to iteratively adjust the fan speed and the cleaning liquid spraying amount to achieve adaptive control of the system operating state.

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