Server case self-cleaning system and method based on composite cleaning and intelligent decision

Abstract

This invention discloses a server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making, comprising: a composite cleaning execution unit, wherein the inner wall of the chassis and the surface of the heat dissipation components are covered with a nano-catalytic coating containing a metal-organic framework and a composite enzyme to decompose organic pollutants and release antibacterial ions; micro piezoelectric ceramic plates are embedded in the heat dissipation fins, which also have the functions of ultrasonic vibration dust removal and thermoelectric power generation, using the temperature difference between the fins and the air to power the system; the micro cleaning robotic arm brush head adopts a combination structure of shape memory alloy and graphene coating, which can flexibly adapt to gaps and self-repair wear. In this invention, the cleaning efficiency and comprehensiveness are significantly improved. Through the multi-dimensional synergy of the composite cleaning execution unit (decomposition of organic pollutants by nano-catalytic coating, peeling of dust by piezoelectric vibration, and directional removal of charged particles by electrohydraulic drive, etc.), full-particle-size coverage cleaning from nanoscale (≤50nm) to macroscopic particles is achieved.

Description

Technical Field

[0001] This invention relates to the field of server chassis cleaning technology, and particularly to a server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making. Background Technology

[0002] Server chassis cleaning primarily relies on mechanical cleaning, airflow purging, and filtration. Mechanical cleaning typically uses fixed brushes or telescopic scrapers to physically wipe the inner walls and heat dissipation components along a preset trajectory. Airflow purging utilizes high-pressure gas or fans to create directional airflow, expelling floating dust from the chassis. Filtration usually involves installing dust filters at the air inlets, using the filter's pore size to filter and block external particles. Additionally, some systems incorporate simple sensor controls, such as using dust concentration sensors to trigger cleaning actions or initiating maintenance programs at fixed intervals. These technologies are widely used in typical data center environments, meeting basic cleaning needs and mitigating the impact of accumulated dust on server heat dissipation performance to some extent, ensuring the basic operational stability of the equipment.

[0003] However, existing technologies have significant limitations in terms of cleaning efficiency, intelligent adaptation, and environmental adaptability. Firstly, they lack sufficient particle size coverage; mechanical sweeping and airflow purging are insufficient to remove nanoscale dust and stubborn dust buildup in the micropores of heat sink fins, and filtration is inefficient at blocking particles smaller than 50nm. Secondly, their decision-making mechanisms are simple, often relying on single concentration thresholds or timed triggers, without linkage to core operating parameters such as server CPU temperature and load rate. This can easily disrupt system performance under high load or waste resources under low load. Thirdly, they have high energy consumption and maintenance costs; traditional solutions rely on external energy sources and lack resource recycling design, requiring frequent manual intervention. Fourthly, they have poor adaptability to extreme environments; in dusty or high-humidity environments, simple physical cleaning is insufficient to handle contaminant intrusion, increasing the risk of equipment failure. These shortcomings make existing technologies unable to meet the development needs of high-density, unattended, and green data centers. Therefore, this paper proposes a server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making, which solves the problems of incomplete cleaning, lack of intelligent decision-making, high energy consumption, poor adaptability, and high maintenance costs in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A server chassis self-cleaning system based on composite cleaning and intelligent decision-making includes:

[0007] The composite cleaning execution unit features a nano-catalytic coating containing a metal-organic framework and composite enzymes covering the inner walls of the chassis and the surface of the heat dissipation components. This coating decomposes organic pollutants and releases antibacterial ions. The heat dissipation fins are embedded with micro-piezoelectric ceramic plates, combining ultrasonic vibration dust removal and thermoelectric power generation, using the temperature difference between the fins and the air to power the system. The micro-cleaning robotic arm brush head uses a combination of shape memory alloy and graphene coating, allowing for flexible adaptation to gaps and self-repairing wear. The motherboard, memory modules, and hard drive components inside the chassis are coated with an anti-static coating, and an adjustable electrostatic generator (output intensity dynamically adjusts with ambient temperature and humidity) actively adsorbs floating dust particles. An integrated micro-vacuuming device (including a high-efficiency, silent turbine fan) is paired with a hidden three-dimensional air duct (with an inner wall made of PTFE). The air duct inlets are located in areas prone to dust accumulation, such as the CPU heatsink and power module, while the outlet connects to a replaceable filter. The micro-cleaning robotic arm, vacuuming device, and electrostatic generator adopt a hot-swappable modular design, supporting quick replacement without disassembling the main server unit.

[0008] The intelligent decision control module integrates multi-chassis pollution data through a distributed learning network to dynamically optimize cleaning strategies while protecting data privacy. It employs an event-driven pulse neural network chip, combining server CPU temperature and load rate to simulate the pollution-heat dissipation-energy consumption relationship in real time, predicting dust accumulation risks and autonomously optimizing cleaning solutions. A digital twin engine couples nano- and micro-level physical models with macroscopic system simulations to dynamically plan the cleaning topology. The intelligent decision algorithm integrates multi-dimensional data such as server load, dust concentration, and ambient temperature and humidity to dynamically match cleaning modes: intermittent weak suction cleaning mode (running for 1 minute every 2 hours) is activated when the load is low and the dust concentration is low; a strong suction continuous cleaning mode is switched when the load is high and the dust concentration is high; and the intensity of the electrostatic generator is reduced when the humidity is too high (relative humidity > 60%). A new remote monitoring and early warning submodule uploads cleaning status, filter clogging level, and equipment fault data to a cloud management platform via IoT technology, supporting viewing on a mobile app / webpage. In case of faults, it pushes early warning notifications and solutions (e.g., "Filter clogging: Replace within 12 hours").

[0009] The pollution monitoring and tracing network deploys diamond color center quantum sensors in key locations to locate and identify dust particles with a size ≤50nm; microfluidic chip sensors analyze dust sources and link with the sodium alginate-based hydrogel protective layer at the air inlet of the chassis, which can be controlled to secrete to intercept specific pollutants; an intelligent sensor group is added, including: a dust concentration sensor (to detect dust concentration in different areas in real time), a temperature and humidity sensor (to provide a basis for the adjustment of the electrostatic device), and a pressure sensor (deployed at key nodes of the air duct to monitor air duct resistance and filter clogging status);

[0010] The multi-field collaborative fluid architecture integrates an electric field-modulated microfluidic channel with a liquid-cooled circuit, which removes stubborn dust through electrophoresis of suspended charged nanoparticles. The phase-change adaptive topology component is driven by cholesteric liquid crystal and can reconfigure microchannels, switching between negative pressure suction and flow guidance modes according to changes in the thermal resistance of the dust. The ion migration-assisted flow channel works in conjunction with the laminar flow channel to directionally remove conductive dust and suppress secondary pollution. The laminar flow channel, in conjunction with the hidden air duct and the micro-vacuuming device, forms a closed-loop air circulation path, ensuring that the vacuuming device efficiently collects the dust removed by piezoelectric vibration and suppresses secondary diffusion.

[0011] Preferably, the composite cleaning execution unit includes a self-healing cleaning component. When the robotic arm brush head wears down, it is electrically heated to restore the shape memory alloy to its original shape, and the graphene coating fills the wear gap and maintains the electrostatic adsorption performance. The vibration sensor monitors the brush head wear frequency and automatically completes calibration and predicts the replacement cycle when the server load rate is ≤20%. The hot-swappable modular design supports quick replacement without disassembling the server body when the brush head reaches the replacement cycle, and the replacement time is controlled within a few minutes.

[0012] Preferably, the composite cleaning execution unit includes an energy cycle symbiosis module, a thermoelectric generator to power the cleaning system, and collected carbon-containing dust is microwave-pyrolyzed into syngas, which is used to prepare cleaning consumables or nanoenzyme carriers, forming a pollution-energy-material closed loop; the hot-swappable modular design allows for quick replacement of the brush head without disassembling the server body when it reaches the replacement cycle, with the replacement time controlled within a few minutes.

[0013] Preferably, the intelligent decision control module includes a pollution source tracing and response mechanism. After locating the dust source through data analysis, it links with the data center management system to send a filter replacement warning and activates the temporary pre-filter layer at the air inlet. When PCB board oxidation particles are detected, local drying cleaning and moisture-proof control are triggered simultaneously, and the airflow cleaning module is activated first when the CPU temperature is ≥80℃. The degree of filter clogging is determined by combining pressure sensor data. When the clogging exceeds the threshold, a warning notification of "Filter clogging: Replacement recommended within 12 hours" is pushed through the cloud management platform, and a temporary bypass ventilation duct is opened to maintain basic heat dissipation.

[0014] Preferably, the intelligent decision control module includes an edge inference elastic expansion component, a clean resource pooled rack deployment of hot-swappable neuromorphic accelerator cards, dynamically enhancing real-time inference capabilities in high-pollution scenarios and avoiding single-node computing power bottlenecks; the remote monitoring and early warning submodule uploads the operating status and computing load data of the edge inference nodes to the cloud management platform, supporting maintenance personnel to remotely view and schedule accelerator card resources.

[0015] Preferably, the pollution monitoring and tracing network includes a quantum sensing-electromagnetic resonance composite removal unit. After locating nanoscale dust, a near-field electromagnetic resonance antenna array applies resonant energy waves to cause the particles to detach from the surface, and a magnetically controlled nanocollector simultaneously captures the charged dust. For carbon buildup in the micropores of circuit boards, a time-domain finite-difference optimized microwave emission source is used. W / m² thermoacoustic pulses are used to remove the carbonized layer; the dust concentration sensor in the intelligent sensor group monitors the changes in macroscopic dust concentration in real time, and merges it with the data from the quantum sensor to achieve full-scale dust state perception at the "nanoscale + macroscale" level.

[0016] Preferably, the multi-field collaborative fluid architecture includes a ternary composite flow channel of liquid-electricity-airflow-ion flow. The main liquid cooling circuit removes core heat, the micro-pressure airflow laminar flow channel maintains positive pressure filtration, and the ion migration channel drives salt deposited ions to migrate to the collector under the electric field gradient. The dielectric wetting valve dynamically switches the flow channel, closing the ion channel to save energy when demand is low and activating the collaborative path when there is high risk. The hidden air duct is seamlessly connected to the airflow laminar flow channel, and the fan power of the micro dust collection device is dynamically adjusted according to the airflow laminar flow intensity to ensure a balance between dust collection efficiency and airflow energy consumption.

[0017] A preferred server chassis self-cleaning method based on composite cleaning and intelligent decision-making includes the following steps:

[0018] (1) Real-time pollution perception and composition analysis: The particle size, composition and source of dust are identified by diamond color center quantum sensor and microfluidic chip. The quantum sensing accuracy is ≤50nm. Combined with image recognition, fiber, metal and salt spray dust types are classified. The intelligent sensor group collects dust concentration, ambient temperature and humidity and duct pressure data at the same time and uploads them to the intelligent decision control module.

[0019] (2) Distributed transfer learning decision-making: local data from multiple chassis is aggregated and trained into a global model with differential privacy protection to adapt to new pollution scenarios; a small sample predictor based on graph neural network and hierarchical reinforcement learning is used to generate a hierarchical cleaning path by combining CPU temperature and load rate; server load, dust concentration, temperature and humidity data are integrated to match the corresponding cleaning mode, with intermittent weak suction, continuous strong suction, and adjustment of electrostatic intensity.

[0020] (3) The composite cleaning strategy is implemented. Based on the cleaning intensity-heat dissipation efficiency-energy consumption surface of the digital twin simulation, the optimal parameters are determined through reinforcement learning. The liquid electro-piezoelectric and airflow modules switch operating modes according to the dust type. The electrostatic generator adjusts the output intensity according to the temperature and humidity data. The micro dust collection device is activated in conjunction with the hidden air duct to achieve a closed loop of adsorption-stripping-collection.

[0021] (4) Resource closed-loop management: The thermoelectric generator uses waste heat to power the catalytic decomposition module, and the electrolytic deposited salt crystallization is converted into a recyclable solution; carbon-based dust is converted into syngas for consumable production, and mycelium-based materials are used to make cleaning components to reduce carbon emissions; when the filter is saturated, it is prompted to be replaced by remote early warning, and modular components are hot-swappable for maintenance as needed;

[0022] (5) The system is self-evolving and fault-tolerant. The event-driven spiking neural network chip updates the contamination-policy mapping library based on cleaning feedback and optimizes parameters iteratively through genetic algorithm. When hardware failure occurs, the simulated annealing algorithm is used to search for alternative solutions and fall back to the policy backup tree. The parameter iteration is completed within 24-48 hours after cleaning. The remote monitoring submodule records the cleaning effect and equipment status data to provide a basis for policy iteration.

[0023] Preferably, in the real-time pollution sensing and component analysis step, after the quantum sensor locates the nanoscale dust, the near-field electromagnetic resonance antenna array applies resonant energy waves to cause the particles to detach from the surface, and the magnetically controlled nanocollector simultaneously captures them; for carbon buildup in the micropores of the circuit board, the output... W / m² microwave thermoacoustic pulse removes the carbonized layer; a dust concentration sensor monitors the macroscopic dust concentration in real time, and triggers the cleaning strategy execution step when the concentration exceeds the threshold.

[0024] Preferably, in the execution steps of the composite cleaning strategy, the electrohydraulic module applies a pulse voltage when the coolant flows through the dust accumulation fins, driving charged nanoparticles to electrophoretically peel off the stains; the cholesteric liquid crystal phase transition triggers the expansion of microchannels to form a negative pressure chamber to suck up dust in dead corners, or contracts into a guide channel to guide airflow to clear obstacles, and automatically reduces the operating noise of mechanical components when the server load rate is ≥80%; the electrostatic generator reduces its intensity when the humidity is too high (relative humidity >60%), the micro vacuum cleaner switches its operating power according to the cleaning mode, and the hidden air duct ensures that the airflow circulates and covers the entire area of ​​the chassis.

[0025] The technical effects and advantages of the server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making in this invention are as follows:

[0026] 1. This invention significantly improves cleaning efficiency and comprehensiveness. Through the multi-dimensional synergy of the composite cleaning execution unit (decomposition of organic pollutants by nano-catalytic coating, peeling of dust by piezoelectric vibration, and directional removal of charged particles by electrohydraulic drive), it achieves full-size coverage cleaning from nanoscale (≤50nm) to macroscopic particles. It effectively removes dead corners that are difficult to reach by traditional cleaning methods, such as gaps in heat sink fins and micropores in circuit boards, and solves the problem of reduced heat dissipation efficiency caused by dust accumulation.

[0027] 2. This invention enables intelligent decision-making to achieve dynamic adaptation and precise control. The intelligent decision-making control module combines core operating parameters such as server CPU temperature and load rate, and dynamically optimizes the cleaning strategy through distributed learning and spiking neural networks. It automatically reduces mechanical noise under high load scenarios and prioritizes deep cleaning and component self-repair during low load periods, avoiding interference with the server's core business and balancing cleaning effect and system performance.

[0028] 3. This invention optimizes energy consumption and recycles resources. The thermoelectric power generation and energy cycle symbiotic module utilizes server waste heat to power the clean system, reducing dependence on external energy sources. The collected carbon dust is processed and converted into syngas for consumable preparation, forming a closed loop of "pollution-energy-materials", reducing carbon emissions and consumable costs, and meeting the sustainable development needs of green data centers.

[0029] 4. This invention enhances the adaptability to extreme environments and the reliability of the system. The pollution monitoring and tracing network accurately identifies the source and composition of pollutants through quantum sensing and microfluidic chips, and links the protective adhesive layer and the directional removal unit to effectively intercept external pollutants in extreme environments such as sandstorms and high humidity, reducing the risk of circuit short circuit failures. The self-evolving fault tolerance mechanism improves the long-term stability of the system through strategy iteration and backup rollback.

[0030] 5. This invention reduces maintenance costs and improves operational efficiency. The self-healing cleaning component extends the lifespan of easily damaged parts of the brush head, and the edge inference elastic scaling supports efficient management of large-scale clusters, reducing the frequency of manual maintenance and downtime, significantly reducing operational costs. At the same time, intelligent early warning can help avoid hardware failures caused by dust accumulation, improving the continuous operation capability of the server cluster. Attached Figure Description

[0031] Figure 1 This is a system module block diagram of the server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making proposed in this invention;

[0032] Figure 2 This is a schematic diagram of the process flow of the server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making proposed in this invention. Detailed Implementation

[0033] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0034] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0035] Example 1

[0036] refer to Figure 1-2 This embodiment provides a server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making, for use in standard server room self-cleaning systems. Specific implementation details include:

[0037] Objective: To address the issue of reduced heat dissipation efficiency caused by dust accumulation in conventional server rooms, this project utilizes a composite cleaning unit (nanocatalysis + piezoelectric vibration + electrostatic adsorption + micro-dust collection) in conjunction with intelligent decision-making to achieve low-energy, automated internal cleaning of the server chassis, suitable for general computing scenarios with large fluctuations in CPU load.

[0038] System Architecture:

[0039] Composite cleaning execution unit:

[0040] The inner wall of the chassis is covered with a nano-catalytic coating containing a metal-organic framework and a composite enzyme (thickness 50-100μm), and the heat dissipation fins are embedded with piezoelectric ceramic sheets with a diameter of 5mm (resonant frequency 30-50kHz).

[0041] The motherboard and memory modules inside the chassis are covered with a 5μm thick anti-static coating and are equipped with an adjustable electrostatic generator (output voltage 5-15kV, which drops to 5-8kV when humidity is >60% and rises to 12-15kV when humidity is <30%).

[0042] It integrates a miniature vacuum cleaner (high-efficiency silent turbine fan, 5W power) and a hidden three-dimensional air duct (with PTFE inner wall material and air resistance ≤50Pa). The air duct inlets are located on the top of the CPU heatsink and power module, and the outlet is connected to a replaceable filter (filtration accuracy 0.3μm).

[0043] The brush head of the miniature robotic arm (8-12cm in length) uses a 0.1mm thick shape memory alloy substrate with a 5μm graphene coating. It adopts a hot-swappable modular design with the dust collection device and the electrostatic generator (replaceable via a dedicated interface on the side of the chassis, with a single module replacement time of ≤3 minutes).

[0044] Intelligent decision control module:

[0045] Equipped with an event-driven spiking neural network chip (1 TOPS / W computing power), it communicates with the server BMC via a PCIe interface to obtain CPU temperature (sampling frequency 1Hz) and load rate (sampling frequency 0.1Hz) in real time.

[0046] The digital twin engine is deployed on the edge server of the data center, achieving a simulation accuracy of 0.1mm.

[0047] A new remote monitoring and early warning sub-module has been added. Through IoT technology, the cleaning status, filter clogging level (based on pressure sensor data), and equipment fault information are uploaded to the cloud management platform. It supports viewing and early warning push on mobile APP (such as "Filter clogging: It is recommended to replace within 24 hours").

[0048] Pollution monitoring and source tracing network:

[0049] One diamond color center quantum sensor (detection range 0-1000 particles / cm³) is deployed at the air intake of the chassis and near the CPU heatsink fins.

[0050] Add a smart sensor group: 2 dust concentration sensors (monitoring CPU area and power supply area, range 0-500 particles / cm³), 1 temperature and humidity sensor (sampling frequency 1Hz), and 1 pressure sensor (air duct outlet, monitoring filter resistance, threshold 100Pa).

[0051] A sodium alginate-based hydrogel protective layer (10×10cm²) is installed on the inside of the air inlet, which is connected to a micro-pump-controlled secretion device.

[0052] Multi-field collaborative fluid architecture:

[0053] The liquid cooling circuit integrates a 0.5mm wide electric field modulation microchannel, with built-in... Graphene charged nanoparticles (concentration 0.1wt%);

[0054] The dielectric wetting valve (response time <10ms) controls the switching of the ion migration channel, and the laminar flow channel is linked with the concealed air duct and the miniature dust collection device to form a closed-loop air circulation.

[0055] Implementation steps:

[0056] (1) Pollution perception and analysis: Quantum sensors detect nanoscale dust (≤50nm) and concentration in real time, and microfluidic chips analyze components (such as fibers and metal debris); intelligent sensor groups simultaneously collect macroscopic dust concentration (such as CPU area >200 particles / cm³), ambient temperature and humidity (such as 25℃, 50% RH) and duct pressure data, and upload them to the intelligent decision-making module after fusing with image recognition results.

[0057] (2) Distributed decision generation: Integrate local pollution data from 10 servers of the same model in the computer room to train a global model; combine the current CPU temperature (e.g., 75℃) and load rate (60%) to generate a layered cleaning path through a graph neural network, and prioritize the activation of the piezoelectric vibration + electrostatic adsorption + dust collection collaborative mode.

[0058] (3) Composite cleaning process: The piezoelectric ceramic sheet vibrates at a frequency of 40kHz for 10 seconds to remove floating dust from the surface of the heat sink fins; the electrostatic generator is activated (outputting 10kV) to form an electrostatic field on the motherboard and memory surface to adsorb floating dust; the micro vacuum cleaner runs at a speed of 3000rpm for 5 seconds to suck the accumulated dust into the filter through the hidden air duct; the dielectric wetting valve closes the ion channel to save energy.

[0059] (4) Resource closed-loop management: The thermoelectric power generation module (output voltage 3.3V) powers the sensor and control circuit; the collected dust is temporarily stored in the filter screen and replaced after remote early warning prompts; the filter screen frame can be recycled.

[0060] (5) System self-evolution: Within 24 hours after cleaning, the neural network chip updates the strategy parameters according to the change in heat dissipation efficiency (such as the CPU temperature dropping to 68℃) and establishes a linkage mapping relationship of "vibration frequency - electrostatic intensity - dust collection power".

[0061] Implementation results: After 30 days of continuous operation, the removal rate of nano-level dust inside the chassis is >90%, and the removal rate of macro-level dust (50nm-10μm) reaches 95%; the average CPU temperature is reduced by 10℃, the cooling fan speed is reduced by 20%, and the total energy consumption is reduced by 8%; remote monitoring makes the filter replacement early warning accuracy 100%; the modular design reduces the maintenance time of a single server from 30 minutes to 5 minutes, and the operation and maintenance efficiency is improved by 80%.

[0062] Example 2

[0063] This embodiment provides a server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making, for use in a data center enhancement system (dust-resistant version) in desert areas. Specific implementation details include:

[0064] Objective: To enhance pollution source tracing and proactive interception capabilities in desert environments with high dust levels (PM10 concentration often > 500 μg / m³), this project aims to address the risk of short circuits caused by silicate particle deposition through enhanced electrostatic adsorption, high negative pressure dust collection, and linked early warning systems, thereby ensuring stable server operation in extreme environments.

[0065] System Architecture:

[0066] Composite cleaning execution unit:

[0067] The area of ​​the protective adhesive layer at the air inlet has been increased to 20×20cm², and 0.5wt% nano-calcium carbonate has been added to enhance adhesion;

[0068] The antistatic coating is enhanced with 0.3wt% nano-calcium carbonate particles to improve its adsorption capacity for silicate dust; the output voltage of the electrostatic generator is increased to 15-20kV.

[0069] The thickness of the graphene coating on the robotic arm brush head has been increased to 10μm, the miniature vacuum cleaner has been upgraded to a high negative pressure model (10W power, 200Pa air pressure), the hidden air duct inlet has been equipped with a stainless steel dustproof grille (1mm aperture), and the filter replacement reminder is linked to the cloud platform.

[0070] Pollution monitoring and source tracing network:

[0071] Three new microfluidic chip sensors (deployed in the chassis air inlet, power module, and memory slot) have been added, which can identify characteristic components of silicate and iron oxide dust; the detection accuracy of the quantum sensor has been improved to ≤30nm.

[0072] In the intelligent sensor group, the dust concentration sensor is linked with the microfluidic chip. When the proportion of silicate particles is >30% and the concentration is >500 particles / cm³, a dual warning is triggered (local + cloud).

[0073] Intelligent decision control module:

[0074] An integrated algorithm model specifically designed for sand and dust pollution is used. When the proportion of silicate particles is detected to be greater than 30%, a filter replacement warning is sent to the data center management system via the SNMP protocol.

[0075] Implementation steps:

[0076] (1) Pollution perception and analysis: Quantum sensors locate nano-sized silicate particles, and microfluidic chips confirm that the source of dust is the external environment; the intelligent sensor group detects dust concentration > 800 particles / cm³, humidity 20%, and image recognition shows that the dust accumulation thickness in the gaps of the heat dissipation fins reaches 50μm.

[0077] (2) Distributed decision generation: Call the historical migration model of the data center in the desert area, and combine it with the current CPU temperature (82℃) to decide to prioritize the activation of the air inlet adhesive layer secretion + electrostatic adsorption + high negative pressure dust collection collaborative mode.

[0078] (3) Composite cleaning execution: The adhesive layer secretion device pumps in 0.5ml of hydrogel to form a temporary high-viscosity interception layer; the electrostatic generator operates at a high intensity of 20kV to adsorb tiny dust particles that penetrate the adhesive layer; the micro-vacuuming device operates at a high speed of 5000rpm for 10 seconds to remove dust accumulated in gaps by strengthening the negative pressure of the air duct; the laminar flow channel works with the air duct to form a closed loop circulation to avoid secondary diffusion; for carbon buildup in the micropores of the circuit board, the output... W / m² Thermoacoustic pulse removal.

[0079] (4) Closed-loop management of resources: The collected sand and dust are pyrolyzed by microwave plasma (power 500W), and the converted syngas is temporarily stored in a high-pressure tank for the preparation of nanoenzyme carriers.

[0080] (5) System self-evolution: The neural network adjusts the secretion amount according to the sand and dust interception efficiency (colloidal capture rate > 80%), and optimizes the correspondence between "sand and dust concentration - electrostatic intensity - dust suction power".

[0081] Implementation results: In dusty environments, the amount of dust accumulated inside the chassis is reduced by 70% compared to traditional solutions, and the short circuit failure rate is 0 (traditional solutions average 1-2 times per month); the combination of electrostatic adsorption and dust collection increases the dust removal rate by 40%, the remote early warning response time is less than 5 seconds, and high-risk areas can be detected 24 hours earlier than manual inspections; the maintenance cycle is extended to 4 months, and the replacement time of modular filters is shortened to 2 minutes.

[0082] Example 3

[0083] This embodiment provides a server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making, for use in financial-grade high-load server systems. Specific implementation details include:

[0084] Purpose of implementation: To adapt to high-load scenarios in financial transactions (CPU load rate often > 90%), and to reduce operating noise while ensuring cleaning effectiveness by linking silent composite cleaning (low-noise electrostatic + vacuuming + hydraulic electrostatic) with the trading system, avoiding interference with precision electronic components and ensuring business continuity.

[0085] System Architecture:

[0086] Composite cleaning execution unit:

[0087] The robotic arm drive motor has been replaced with a brushless silent model (noise ≤30dB), and 0.1wt% nickel has been added to the shape memory alloy of the brush head to enhance its flexibility;

[0088] The electrostatic generator is equipped with a "silent mode": when the load rate is ≥80%, the output voltage automatically drops to 5-8kV to reduce electromagnetic noise;

[0089] The miniature vacuum cleaner integrates sound-absorbing cotton (15dB noise reduction), and the air duct adopts an arc transition design (to reduce turbulence noise), with operating noise ≤30dB.

[0090] Intelligent decision control module:

[0091] A new noise sensor (detection range 30-80dB) is added, which is linked to the CPU load rate: when the load rate is ≥80%, the total noise must be ≤35dB;

[0092] The remote monitoring and early warning submodule is linked with the financial transaction system through the API interface, and sends a "low impact mode" warning 10 seconds before the cleaning action.

[0093] Implementation steps:

[0094] (1) Pollution perception and analysis: Dust accumulation on the CPU heat sink fins was detected, causing the temperature to rise to 85℃, with a load rate of 95% and a current noise sensor reading of 40dB; the intelligent sensor group showed a dust concentration of >300 particles / cm³ and a humidity of 45%.

[0095] (2) Distributed decision generation: The decision model determines the high load state, prioritizes the activation of electrohydraulic cleaning + silent mode electrostatic adsorption + low wind speed dust collection, and disables high frequency vibration of the robotic arm.

[0096] (3) Composite cleaning execution: The liquid cooling circuit applies a 10V pulse voltage to drive charged nanoparticles to quickly peel off the accumulated dust; the electrostatic generator operates in "silent mode" (5kV) to assist in adsorbing dust without generating high-frequency noise; the micro vacuum cleaner operates at a low wind speed (1m / s) and smoothly collects the accumulated dust through the hidden air duct; the robotic arm rotates at a low speed (5rpm) to clean only in the gaps, and the total noise is controlled at 32dB.

[0097] (4) Resource closed-loop management: Utilize the off-peak trading hours (2-4 am, load rate <20%) to start thermoelectric power generation + dust recovery to reduce the impact on the main system's energy consumption.

[0098] (5) System self-evolution: Record cleaning parameters (voltage, wind speed, electrostatic intensity) under high load and optimize the three-dimensional mapping table of "load rate - noise - cleaning intensity".

[0099] Implementation results: Cleaning noise ≤35dB under high load (traditional mechanical cleaning is about 55dB), CPU temperature stabilizes below 75℃; remote linkage mechanism ensures that the cleaning process does not interfere with financial transactions, transaction latency fluctuation <1ms; impact on system performance <1%, fully meeting financial-grade real-time requirements.

[0100] Example 4

[0101] This embodiment provides a server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making, for use in a large-scale cluster edge inference system (elastic expansion version). Specific implementation details include:

[0102] Implementation objective: To solve the problem of cleaning strategy inference latency in a cluster of tens of thousands of servers, and to achieve real-time processing and strategy generation of large-scale pollution data through elastic scaling of edge inference (hot-swappable accelerator cards) and fusion of cluster-level sensor data, adapting to cloud computing data center scenarios.

[0103] System Architecture:

[0104] Intelligent decision control module:

[0105] The data center is equipped with 10 clean resource pooled racks, each containing 8 hot-swappable neuromorphic accelerator cards (2 TOPS computing power per card); dynamic resource scheduling is achieved through Kubernetes, and the accelerator card load rate is linked to the remote monitoring platform (sampling frequency 1Hz).

[0106] Within the cluster, every 100 servers share one distributed learning node, and a federated learning framework is used to aggregate local data (privacy protection level: differential privacy ε=10) and fuse macroscopic dust data from the intelligent sensor group.

[0107] Pollution monitoring and source tracing network:

[0108] A cluster-level deployment of a centralized temperature and humidity sensor network (one node for every 50 sensors) provides a unified environmental parameter benchmark for the electrostatic devices of each server.

[0109] Implementation steps:

[0110] (1) Pollution perception and analysis: 1,000 servers in the cluster simultaneously upload dust data (sampling frequency 0.5Hz), and the total data volume of quantum sensors reaches 10GB / h; the intelligent sensor group summarizes the cluster's average dust concentration and filter resistance data.

[0111] (2) Distributed decision generation: When a sudden increase in dust data is detected (such as a computer room air conditioning failure), three idle acceleration cards are automatically called to enhance inference; within 10 seconds, the federated learning nodes aggregate the global model and, combined with the filter resistance data, prioritize the allocation of computing power to high-risk nodes.

[0112] (3) Composite cleaning execution: cleaning is performed according to the importance of the server: core database servers are given priority to start the hydraulic + airflow + electrostatic collaborative mode, while non-core storage servers are delayed until the load is low.

[0113] (4) Resource closed-loop management: The energy consumption of the accelerator card is powered by the waste heat recovery system in the computer room, accounting for 30% of the total energy consumption.

[0114] (5) System self-evolution: When the accelerator card load rate is >80%, the new card hot-swap expansion is automatically triggered, and the inference latency is controlled to <50ms.

[0115] Implementation results: Cluster cleaning policy generation time was reduced from 50 seconds to 8 seconds, and inference latency was reduced by 84%; it supports concurrent cleaning decisions for 10,000 servers, and resource pooling increases accelerator card utilization to 70% (compared to only 30% in the traditional fixed allocation mode); the remote platform enables cluster status visualization, improving operation and maintenance efficiency by 60%.

[0116] Example 5

[0117] This embodiment provides a server chassis self-cleaning system and method based on composite cleaning and intelligent decision-making for use in a green data center closed-loop system (environmentally friendly version). Specific implementation details include:

[0118] Implementation objective: To achieve a closed loop of "pollution-energy-materials", reduce the carbon footprint of data centers and meet the carbon neutrality target by utilizing dust resources (carbon-based dust → syngas) and clean energy (thermal energy conversion), and adopting biodegradable components (mycelium-based brush head + filter screen).

[0119] System Architecture:

[0120] Composite cleaning execution unit:

[0121] The robotic arm brush head uses mycelium-based biocomposite material (replacing the traditional plastic substrate) and is covered with a biodegradable antistatic coating (chitosan-graphene composite) with a degradation rate of up to 90%.

[0122] The filter frame of the mini vacuum cleaner is made of mycelium material, which degrades simultaneously with the brush head.

[0123] Energy Cycle Symbiosis Module:

[0124] The power of the thermoelectric generator has been increased to 5W, and it is equipped with a microwave plasma pyrolysis furnace (processing capacity of 100g / day) and a mycelium incubator (temperature 25℃, humidity 60%).

[0125] Non-carbon-based dust collected by the vacuum cleaner is marked as "recyclable" via a remote platform, which then links to the resource recycling system.

[0126] Implementation steps:

[0127] (1) Pollution perception and analysis: Focus on monitoring carbon-based dust (such as condensed particles of volatile organic compounds from PCB boards), and quantum sensors identify that its proportion is >40%; intelligent sensor groups simultaneously monitor temperature and humidity to provide parameters for mycelial culture.

[0128] (2) Distributed decision generation: The decision model prioritizes the resource recycling process, linking cleaning with material regeneration.

[0129] (3) Composite cleaning execution: The robotic arm collects carbon-based dust into a special storage box, and the piezoelectric vibration removes dust while providing power to the thermoelectric generator (power generation efficiency 20%); the carbon-based dust adsorbed by the electrostatic device is collected by the dust collection device and then directly introduced into the microwave pyrolysis furnace.

[0130] (4) Resource closed-loop management: Carbon-based dust is pyrolyzed by microwave to generate syngas ( =1:2), a nanoenzyme carrier for 3D printing mycelial brush heads; thermoelectric power generation meets 70% of the sensor's energy consumption requirements.

[0131] (5) System self-evolution: The pyrolysis parameters (power, time) are optimized by genetic algorithm to increase the syngas conversion rate from 60% to 85%; the mycelium culture parameters are optimized based on sensor data to shorten the brush head production cycle by 10%.

[0132] Implementation results: Data center carbon emissions were reduced by 35%, and the cost of cleaning consumables was reduced by 60%; the service life of mycelium brush heads reached 80% of that of traditional brush heads, and they naturally degraded within 3 months after disposal; the resource recycling marking function of the remote platform increased the dust resource utilization rate to 90%.

[0133] Comparative Example 1

[0134] This comparison provides a traditional passive cleaning solution.

[0135] System Architecture:

[0136] It uses a single-layer dustproof net (50μm aperture) + monthly manual compressed air purging (pressure 0.6MPa), is equipped with only one infrared dust sensor (detection limit 500nm), and has no intelligent decision-making module.

[0137] Implementation steps:

[0138] An alarm will sound when the dust sensor detects a concentration >1000 particles / cm³, and manual intervention will be required to stop the machine for cleaning.

[0139] Cleaning is performed by simply blowing compressed air in all directions, without targeted removal or component differentiation;

[0140] There is no resource recycling mechanism, and dust is directly emitted into the computer room environment.

[0141] Implementation results:

[0142] There are many blind spots in cleaning (such as carbon buildup in the micropores of the circuit board that cannot be removed), and after 30 days, the CPU temperature rises by 15°C and the heat dissipation efficiency decreases by 30%.

[0143] It requires 2 hours of downtime per month, resulting in high labor costs, and the compressed air energy consumption is 5 times that of this invention;

[0144] The inability to clean under high load (to avoid downtime affecting business) has led to an average of 2 hardware failures per year due to dust accumulation.

[0145] The comparison between Examples 1-5 and Comparative Example 1 is summarized as follows:

[0146] In terms of cleaning efficiency, Examples 1-5 achieved full-size dust removal through a composite cleaning unit (nanocatalytic coating, piezoelectric vibration, and electrohydraulic synergy), with a removal rate of over 90% for nanoscale dust (≤50nm) and over 85% for microporous carbon deposits; Comparative Example 1 could only remove particles larger than 500nm and had a large number of cleaning blind spots, with a 30% decrease in heat dissipation efficiency after 30 days.

[0147] In terms of intelligent decision-making, Examples 1-5 combine multi-dimensional data such as CPU temperature and load rate to dynamically adjust strategies. For example, Example 3 automatically reduces noise to ≤35dB when the load rate is ≥80%, and Example 4 reduces the cluster decision latency from 50 seconds to 8 seconds through edge elastic scaling. Comparative Example 1 relies on a single dust concentration threshold, has no load linkage, and cannot clean under high load, resulting in frequent hardware failures.

[0148] In terms of energy consumption and environmental protection, Examples 1-5 achieve a 60% reduction in energy consumption and a 30%-35% reduction in carbon footprint through thermoelectric power generation and resource closed-loop (such as the conversion of carbon-based dust into syngas in Example 5); Comparative Example 1 has five times the energy consumption of Examples 1, no recycling mechanism, and direct dust emissions pollute the environment.

[0149] In terms of adaptability and reliability, Examples 1-5 are suitable for deserts with high dust levels (Example 2 reduces dust accumulation by 70%) and financial high-load scenarios, with a failure rate of almost 0; Comparative Example 1 experiences an average of 1-2 failures per month in extreme environments, with a maintenance cycle of only 1-3 months.

[0150] In terms of maintenance costs, Examples 1-5 reduce costs by 50%-60% through self-healing components (extending brush head life by 3 times) and intelligent early warning (extending maintenance cycle to 4-6 months); Comparative Example 1 requires monthly manual intervention and 2 hours of downtime per month, with a comprehensive cost more than 3 times that of Examples 1.

[0151] The above embodiments can be implemented in whole or in part by software, hardware, firmware or other arbitrary combinations. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0152] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0153] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0154] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0155] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, substitutions, or improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A server chassis self-cleaning system based on composite cleaning and intelligent decision-making, characterized in that, include: The composite cleaning execution unit has a nano-catalytic coating containing a metal-organic framework and composite enzymes covering the inner wall of the chassis and the surface of the heat dissipation components. This coating decomposes organic pollutants and releases antibacterial ions. The heat dissipation fins are embedded with micro piezoelectric ceramic plates, which also have the functions of ultrasonic vibration dust removal and thermoelectric power generation. The system is powered by the temperature difference between the fins and the air. The brush head of the micro cleaning robotic arm adopts a combination structure of shape memory alloy and graphene coating, which can flexibly adapt to gaps and self-repair wear. The motherboard, memory modules, and hard drive components inside the chassis are coated with an anti-static coating and work with an adjustable electrostatic generator to actively attract floating dust particles. A miniature dust collection device is integrated with a hidden three-dimensional air duct. The air duct inlets are located in areas prone to dust accumulation, such as the CPU heatsink and power module, while the outlet is connected to a replaceable filter. The miniature cleaning robot arm, dust collection device, and electrostatic generator adopt a hot-swappable modular design, supporting quick replacement without disassembling the server body. The intelligent decision control module integrates multi-chassis pollution data through a distributed learning network to dynamically optimize cleaning strategies while protecting data privacy. It adopts an event-driven spiking neural network chip to simulate the correlation between pollution, heat dissipation, and energy consumption in real time by combining server CPU temperature and load rate, predicting dust accumulation risks and autonomously optimizing cleaning solutions. The digital twin engine couples nano- and micro-level physical models with macroscopic system simulations to dynamically plan the cleaning topology. The intelligent decision-making algorithm integrates multi-dimensional data such as server load, dust concentration, and ambient temperature and humidity to dynamically match the cleaning mode: when the load is low and the dust concentration is low, an intermittent weak suction cleaning mode is activated; when the load is high and the dust concentration is high, a strong suction continuous cleaning mode is switched; when the humidity is too high, the intensity of the static electricity generator is reduced; a new remote monitoring and early warning sub-module is added, which uploads cleaning status, filter clogging degree, and equipment failure data to the cloud management platform through IoT technology, which supports viewing on mobile APP / web page, and pushes early warning notifications and solutions when failure occurs; The pollution monitoring and tracing network deploys diamond color center quantum sensors in key areas to locate and identify dust particles with a size ≤50nm; microfluidic chip sensors analyze the source of dust and link the sodium alginate-based hydrogel protective layer at the air inlet of the chassis, which can be secreted in a controlled manner to intercept specific pollutants. Add a smart sensor group, including: dust concentration sensor, temperature and humidity sensor, and pressure sensor; The multi-field collaborative fluid architecture integrates an electric field-modulated microfluidic channel with a liquid-cooled circuit, which removes stubborn dust through electrophoresis of suspended charged nanoparticles. The phase-change adaptive topology component is driven by cholesteric liquid crystal and can reconfigure microchannels, switching between negative pressure suction and flow guidance modes according to changes in the thermal resistance of the dust. The ion migration-assisted flow channel works in conjunction with the laminar flow channel to directionally remove conductive dust and suppress secondary pollution. The laminar flow channel, in conjunction with the hidden air duct and the micro-vacuuming device, forms a closed-loop air circulation path, ensuring that the vacuuming device efficiently collects the dust removed by piezoelectric vibration and suppresses secondary diffusion.

2. The server chassis self-cleaning system based on composite cleaning and intelligent decision-making as described in claim 1, characterized in that, The composite cleaning execution unit includes a self-healing cleaning component. When the robotic arm brush head wears down, it is electrically heated to restore the shape memory alloy to its original shape. The graphene coating fills the wear gap and maintains the electrostatic adsorption performance. The vibration sensor monitors the brush head wear frequency and automatically completes calibration and predicts the replacement cycle when the server load rate is ≤20%. The hot-swappable modular design allows for quick replacement of the brush head without disassembling the server body when it reaches the replacement cycle, with the replacement time controlled within a few minutes.

3. The server chassis self-cleaning system based on composite cleaning and intelligent decision-making as described in claim 1, characterized in that, The composite cleaning execution unit includes an energy cycle symbiosis module, a thermoelectric generator to power the cleaning system, and collected carbon-containing dust is cracked into syngas by microwave plasma, which is used to prepare cleaning consumables or nanoenzyme carriers, forming a pollution-energy-material closed loop; the hot-swappable modular design allows for quick replacement without disassembling the server body when the brush head reaches its replacement cycle, with the replacement time controlled within a few minutes.

4. The server chassis self-cleaning system based on composite cleaning and intelligent decision-making as described in claim 1, characterized in that, The intelligent decision control module includes a pollution source tracing and response mechanism. After locating the dust source through data analysis, it links with the data center management system to send a filter replacement warning and activates the temporary pre-filter layer at the air inlet. When PCB board oxidation particles are detected, local drying cleaning and moisture control are triggered simultaneously, and the airflow cleaning module is activated first when the CPU temperature is ≥80℃. The degree of filter clogging is determined by combining pressure sensor data. When the clogging exceeds the threshold, a warning notification of "Filter clogging: Replacement recommended within 12 hours" is pushed through the cloud management platform, and a temporary bypass ventilation duct is opened to maintain basic heat dissipation.

5. The server chassis self-cleaning system based on composite cleaning and intelligent decision-making as described in claim 1, characterized in that, The intelligent decision control module includes an edge inference elastic expansion component and a clean resource pooled rack deployment of hot-swappable neuromorphic accelerator cards, which dynamically enhances real-time inference capabilities in high-pollution scenarios and avoids single-node computing power bottlenecks. The remote monitoring and early warning submodule uploads the operating status and computing load data of the edge inference nodes to the cloud management platform, allowing maintenance personnel to remotely view and schedule accelerator card resources.

6. The server chassis self-cleaning system based on composite cleaning and intelligent decision-making as described in claim 1, characterized in that, The pollution monitoring and tracing network includes a quantum sensing-electromagnetic resonance composite removal unit. After locating nanoscale dust, a near-field electromagnetic resonance antenna array applies resonant energy waves to cause the particles to detach from the surface, while a magnetically controlled nanocollector simultaneously captures the charged dust. For carbon buildup in the micropores of circuit boards, a time-domain finite-difference optimized microwave emission source is used. W / m² thermoacoustic pulses are used to remove the carbonized layer; the dust concentration sensor in the intelligent sensor group monitors the changes in macroscopic dust concentration in real time, and the data is fused with the quantum sensor data to achieve full-scale dust state perception at the nanometer and macroscopic levels.

7. The server chassis self-cleaning system based on composite cleaning and intelligent decision-making as described in claim 1, characterized in that, The multi-field collaborative fluid architecture includes a ternary composite flow channel of liquid-electricity-airflow-ion flow. The main liquid cooling circuit removes core heat, the micro-pressure airflow laminar flow channel maintains positive pressure filtration, and the ion migration channel drives salt deposited ions to migrate to the collector under the electric field gradient. The dielectric wetting valve dynamically switches the flow channel, closing the ion channel to save energy when demand is low and activating the collaborative path when there is high risk. The hidden air duct is seamlessly connected to the airflow laminar flow channel, and the fan power of the micro dust collection device is dynamically adjusted according to the airflow laminar flow intensity to ensure a balance between dust collection efficiency and airflow energy consumption.

8. A server chassis self-cleaning method based on composite cleaning and intelligent decision-making, characterized in that, Includes the following steps: (1) Real-time pollution perception and composition analysis: The particle size, composition and source of dust are identified by diamond color center quantum sensor and microfluidic chip. The quantum sensing accuracy is ≤50nm. Combined with image recognition, fiber, metal and salt spray dust types are classified. The intelligent sensor group collects dust concentration, ambient temperature and humidity and duct pressure data at the same time and uploads them to the intelligent decision control module. (2) Distributed transfer learning decision-making: local data from multiple chassis is aggregated with differential privacy protection to train a global model and adapt it to new pollution scenarios; a small sample predictor based on graph neural network and hierarchical reinforcement learning is used to generate hierarchical cleaning paths by combining CPU temperature and load rate. It integrates server load, dust concentration, temperature and humidity data to match the corresponding cleaning mode, with intermittent weak suction, continuous strong suction, and adjustable electrostatic intensity; (3) The composite cleaning strategy is implemented. Based on the cleaning intensity-heat dissipation efficiency-energy consumption surface of the digital twin simulation, the optimal parameters are determined through reinforcement learning. The liquid electro-piezoelectric and airflow modules switch operating modes according to the dust type. The electrostatic generator adjusts the output intensity according to the temperature and humidity data. The micro dust collection device is activated in conjunction with the hidden air duct to achieve a closed loop of adsorption-stripping-collection. (4) Resource closed-loop management: The thermoelectric generator uses waste heat to power the catalytic decomposition module, and the electrolytic deposited salt crystallization is converted into a recyclable solution; carbon-based dust is converted into syngas for consumable production, and mycelium-based materials are used to make cleaning components to reduce carbon emissions; when the filter is saturated, it is prompted to be replaced by remote early warning, and modular components are hot-swappable for maintenance as needed; (5) The system is self-evolving and fault-tolerant. The event-driven spiking neural network chip updates the contamination-policy mapping library according to the cleaning feedback, optimizes the parameters iteratively through the genetic algorithm, and uses the simulated annealing algorithm to search for alternative solutions and fall back to the policy backup tree when hardware failure occurs. The parameter iteration is completed within 24-48 hours after cleaning. The remote monitoring submodule records cleaning results and equipment status data, providing a basis for strategy iteration.

9. The server chassis self-cleaning method based on composite cleaning and intelligent decision-making as described in claim 8, characterized in that, In the real-time pollution sensing and component analysis step, after the quantum sensor locates nanoscale dust, a near-field electromagnetic resonance antenna array applies resonant energy waves to cause the particles to detach from the surface, and a magnetically controlled nanocollector simultaneously captures them; for carbon buildup in the micropores of circuit boards, the output... W / m² microwave thermoacoustic pulse removes the carbonized layer; a dust concentration sensor monitors the macroscopic dust concentration in real time, and triggers the cleaning strategy execution step when the concentration exceeds the threshold.

10. The server chassis self-cleaning method based on composite cleaning and intelligent decision-making as described in claim 8, characterized in that, In the execution steps of the composite cleaning strategy, the electrohydraulic module applies a pulse voltage when the coolant flows through the dust accumulation fins, driving charged nanoparticles to electrophoretically peel off the stains; the cholesteric liquid crystal phase transition triggers the expansion of microchannels to form a negative pressure chamber to suck up dust accumulated in dead corners, or contracts into a guide channel to guide airflow to clear obstacles, and automatically reduces the operating noise of mechanical components when the server load rate is ≥80%; the electrostatic generator reduces its intensity when the humidity is too high, the micro-vacuuming device switches its operating power according to the cleaning mode, and the hidden air duct ensures that the airflow circulates and covers the entire area of ​​the chassis.

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