Multifunctional embedded intelligent cleaning control system

Abstract

The invention relates to the technical field of intelligent control systems, and discloses a multifunctional embedded intelligent cleaning control system which comprises a control execution layer, an internet-of-things transmission layer and a decision analysis layer. The control execution layer comprises an intelligent variable-frequency scraping device, a wind-water sweeping device and the like and executes sweeping and dust removal operation; the Internet of Things transmission layer realizes data interconnection and transmission through an edge computing node, a protocol conversion gateway and the like; and the decision analysis layer adopts a time sequence data analysis and convolutional neural network algorithm, dynamically optimizes the cleaning operation model, constructs a multi-dimensional linkage cleaning control strategy model, and realizes cleaning intensity adjustment, air and water parameter cooperative control and dust removal efficiency dynamic optimization. According to the system, through multi-technology fusion and collaborative optimization, the intelligent, precise and efficient level of sweeping operation is improved, the system is suitable for a conveying belt sweeping scene, the sweeping efficiency can be improved, equipment loss and dust emission can be reduced, and remarkable economic benefits and environmental protection benefits are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control systems, in particular to a multifunctional embedded intelligent cleaning control system. Background Art

[0002] In industrial production, conveyor belts are key equipment for material transportation. The efficiency and effectiveness of their surface cleaning and dust removal directly impact production continuity, safety, and environmental performance. Traditional conveyor belt cleaning control systems suffer from low automation, poor adaptability, and a single control strategy, making them unable to meet the precise cleaning requirements of complex operating conditions.

[0003] From the perspective of cleaning operations, traditional devices mostly use mechanical scrapers with fixed parameters and a single dust removal method. For example, traditional scraping devices usually rely on a fixed pressure mechanical structure to operate, and are unable to dynamically adjust the scraping force according to changes in the adhesion strength of the material on the conveyor belt surface. This not only leads to incomplete material cleaning, but also may cause excessive wear of the blades due to excessive pressure and even damage the conveyor belt. In terms of dust removal, traditional wind and water cleaning and dust removal equipment often use fixed wind and water mixing ratios and dust removal parameters. They are difficult to adapt to changes in complex working conditions such as different dust particle sizes, ambient wind speeds, and conveyor belt surface humidity. This leads to low dust removal efficiency, dust emissions that easily exceed standards, and non-compliance with environmental protection requirements.

[0004] At the data transmission and control level, traditional systems lack data interconnection capabilities. Different devices often use different communication protocols, preventing effective data intercommunication and creating "information silos." The transmission of control commands lacks dynamic routing and priority management, making it difficult to achieve collaborative operation among multiple devices. Furthermore, traditional systems make insufficient use of equipment operating data, unable to optimize cleaning operation models based on historical and real-time operating data. This prevents control strategies from dynamically adjusting to changing operating conditions, making intelligent cleaning control difficult to achieve.

[0005] From the perspective of model building and optimization, traditional cleaning control systems lack the ability to integrate and analyze multidimensional data and dynamically model it. They are unable to effectively link the physical state of the equipment with the control logic space, making it difficult to transform actual cleaning operation data into a high-precision digital control space. Regarding model training and optimization, traditional methods typically employ simple mathematical models or empirical formulas, failing to leverage advanced machine learning algorithms to precisely adjust and optimize cleaning intensity, air flow parameters, and dust removal efficiency, resulting in a low level of intelligent cleaning operations.

[0006] Furthermore, the advancement of intelligent industry is placing higher demands on the intelligence, automation, and integration of cleaning control systems. Traditional systems struggle to meet the demands of large-scale equipment status monitoring, anomaly warnings, and remote control, and are unable to monitor and manage the entire cleaning process. Therefore, there is an urgent need for a multifunctional, embedded, intelligent cleaning control system that can adapt to complex operating conditions, achieve precise control, and possess intelligent optimization capabilities. This can improve the efficiency and quality of conveyor belt cleaning operations, reduce equipment wear and operating costs, and meet the demands of intelligent and environmentally friendly industrial production. Summary of the Invention

[0007] The purpose of the present invention is to provide a multifunctional embedded intelligent cleaning control system to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solutions: a multifunctional embedded intelligent cleaning control system, the system comprising:

[0009] Control execution layer, IoT transmission layer and decision analysis layer;

[0010] The control execution layer includes an intelligent variable frequency scraping device, a wind and water cleaning device, a return scraping device and a micro-powered dust collector, which is used to perform scraping and cleaning of the conveyor belt surface, wind and water combined dust removal and removal of residual materials on the return belt surface according to control instructions;

[0011] The IoT transport layer includes edge computing nodes, protocol conversion gateways, and cloud server clusters, and deploys a multi-protocol adaptation engine and load balancing mechanism to establish data interconnection channels between devices, enabling the synchronous transmission of cleaning device status monitoring data and operating instructions. It uses a dynamic routing scheduling strategy to prioritize and distribute task instructions in parallel based on data traffic and device response speed, and implements bidirectional interaction between the physical device layer and the control strategy layer through multi-source heterogeneous communication protocols.

[0012] The decision analysis layer is used to dynamically optimize the cleaning operation model by using time series data analysis technology in combination with the equipment operation history library and the real-time working condition data set, and then build a multi-dimensional linkage cleaning control strategy model. Based on the strategy model, a convolutional neural network algorithm is used to adjust the cleaning intensity, coordinate the control of wind and water parameters, and dynamically optimize the dust removal efficiency.

[0013] Preferably, the intelligent variable frequency scraping device includes at least a hydraulic drive mechanism, a pressure sensor, an angle adjuster and a blade wear detection module, which are used to collect the material adhesion strength characteristics on the conveyor belt surface; the wind and water cleaning device includes at least a high-pressure air unit, an atomizing nozzle array, a flow regulating valve and a water quality filtration unit, which are used to perform wind and water mixed cleaning operations under different working conditions; the micro-power dust collector includes at least a negative pressure air induction module, a cartridge dust collection box, a pulse backblowing device and a dust concentration sensor, which are used to complete the directional capture of dust around the conveyor belt.

[0014] Preferably, in the IoT transmission layer, the working condition data collected by the control execution layer is transmitted from the edge computing node to the protocol conversion gateway via the industrial bus. The protocol conversion gateway then standardizes the data format and transmits it together with the historical operation records stored in the cloud server cluster to the decision analysis layer for model training. The model training adopts multi-dimensional feature fusion technology, and the collected cleaning operation parameters are input into the feature space constructed by different neural network architectures for joint learning.

[0015] The bidirectional interaction between the physical device layer and the control strategy layer is achieved through multi-source heterogeneous communication protocols, including: using Modbus-TCP, OPC-UA and MQTT protocols to establish a data communication channel between the device layer and the strategy layer, exchanging real-time operating parameters of the equipment, and collaboratively processing the analyzed operation characteristics, while achieving dynamic matching of control parameters, completing command issuance, status feedback and abnormal warning, and outputting variable frequency speed regulation parameters to the control execution layer.

[0016] Preferably, the construction of a multi-dimensional linkage cleaning control strategy model includes:

[0017] Establish the parameter association relationship between the physical state of the equipment and the control logic space and a two-way calibration mechanism;

[0018] The actual cleaning operation data is extracted and pattern recognized through feature extraction. Based on the material adhesion strength obtained by the pressure sensor and the dust distribution data monitored by the dust concentration sensor, a baseline feature library for conveyor belt cleanliness and an abnormal operating condition template library are constructed. The model weights are updated and the operation trend is predicted based on the real-time operation data, thus transforming the physical operation scene into a high-precision digital control space.

[0019] The parameters of the cleaning control strategy model are calibrated. The real-time collected operation data is input into the established strategy model. The output results of the model are iteratively corrected using the gradient descent algorithm to obtain the optimized cleaning control strategy model.

[0020] The cleaning control strategy model includes a physical operation space, a control logic space, a feature database, and interaction protocols between various levels;

[0021] The physical operation space is the data input source of the strategy model, which includes conveyor belt operating parameters and cleaning status characteristics; the control logic space forms a mapping relationship with the physical operation space, and mathematically represents the cleaning operation characteristics through multi-dimensional parametric modeling; the feature database integrates equipment historical data and real-time monitoring records, and provides a benchmark data set including a working mode library, an abnormal event library, and an equipment performance library; the interactive protocol realizes data communication between various levels, and the physical operation space and the feature database realize real-time collection of feature parameters and model update through a standardized interface, the physical operation space and the control logic space transfer parameters through a data bus, and the control logic space and the feature database realize information synchronization through the middleware.

[0022] Preferably, the sweeping intensity adjustment based on the strategy model using a convolutional neural network algorithm includes:

[0023] Based on the cleaning control strategy model, historical conveyor belt speed data, material transportation volume records, and blade wear characteristics are obtained to construct a working condition sample set;

[0024] After normalizing the working condition sample set, divide it into training set and test set;

[0025] Establish a CNN-LSTM-attention mechanism hybrid model architecture, initialize model hyperparameters, input the training set into the hybrid model for end-to-end training, extract spatial features through convolutional layers, capture temporal dependencies through LSTM networks, focus on key feature dimensions through the attention mechanism, and optimize the gradient propagation path using residual connections until the model converges or reaches the preset number of training times;

[0026] The test set is input into the trained hybrid model to evaluate the model prediction accuracy and select the optimal sweeping intensity adjustment model;

[0027] The dynamic pressure setting value of the scraper device is output based on the optimal adjustment model, and the inverter control instruction is generated in combination with the real-time speed data of the conveyor belt.

[0028] Preferably, the collaborative control of Feng Shui parameters using a convolutional neural network algorithm based on the strategy model includes:

[0029] Based on the cleaning control strategy model, the surface humidity distribution data of the conveyor belt, dust particle size characteristics, and ambient wind speed parameters are extracted to construct a wind and water operation characteristic matrix;

[0030] A feature pyramid network is used to perform cross-scale fusion processing on multi-dimensional features to obtain an enhanced feature map;

[0031] Establish a Feng Shui control model based on the U-Net architecture, integrating shallow detail features with deep semantic information through skip connections;

[0032] The fused feature map is input into the regression prediction layer to output the coordinated control quantities of the high-pressure fan speed and atomizing water pressure parameters.

[0033] Preferably, the dynamic optimization of dust removal efficiency using a convolutional neural network algorithm based on the strategy model includes:

[0034] Based on the cleaning control strategy model, filter cartridge pressure difference data, pulse backflushing cycle records, and dust accumulation rate parameters are collected to build a dust removal efficiency feature library;

[0035] The data in the dust removal efficiency feature library is segmented into sliding time windows to generate a sample sequence of continuous operation periods;

[0036] Establish a temporal convolutional network model, set the dilated convolution kernel size and receptive field range, and extract temporal features through causal convolution operations;

[0037] The time series features are input into the multi-layer perceptron for efficiency level classification, and the matching result between the working status of the dust removal system and the optimal backflushing strategy is output.

[0038] Preferably, the construction of a multi-dimensional linkage cleaning control strategy model further includes:

[0039] Adopt overlapping segmented sampling mechanism to slice continuous operation data, and each data slice is independently feature-encoded;

[0040] Establish a correlation matrix between data slice characteristics and equipment energy consumption, and record the energy consumption distribution patterns corresponding to different cleaning modes;

[0041] The control model is continuously optimized through an online distillation algorithm, and the model structure adaptive reconstruction mechanism is triggered when a new operating mode is detected.

[0042] Preferably, the method for generating the abnormal operating condition processing strategy includes:

[0043] Establish a mapping relationship table between fault types and treatment measures, including the treatment methods for blade jam corresponding to reverse drive and filter cartridge blockage corresponding to enhanced backflushing;

[0044] A Bayesian optimization algorithm was used to search for the best combination of treatment parameters, including hydraulic driving pressure, reverse driving duration, and backwash airflow intensity;

[0045] The processing effect and equipment loss are comprehensively measured through a multi-objective evaluation function, and the parameter reconfiguration process is triggered when the processing effect does not meet the standards.

[0046] Preferably, the training process of the hybrid model architecture includes: using a data enhancement method to generate a diverse set of training samples, and monitoring the risk of model overfitting through an early stopping method.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] In terms of cleaning operations, the intelligent variable frequency scraper device at the control execution layer, through the coordinated action of a hydraulic drive mechanism, pressure sensor, angle adjuster, and blade wear detection module, can collect the adhesion strength characteristics of materials on the conveyor belt surface in real time. It dynamically adjusts the scraping force and angle according to control instructions, achieving efficient scraping of materials from the conveyor belt surface while avoiding excessive blade wear and conveyor belt damage. The wind and water cleaning device, through a combination of a high-pressure air unit, an atomizing nozzle array, a flow control valve, and a water quality filtration unit, can flexibly adjust the wind and water mixing ratio and cleaning parameters according to different operating conditions, achieving precise cleaning of the conveyor belt surface and effectively improving cleaning effectiveness and adaptability. The micro-powered dust collector, through the coordinated action of a negative pressure ventilation module, a cartridge dust collection box, a pulse backflush device, and a dust concentration sensor, can target and capture dust around the conveyor belt. Combined with a dynamically optimized dust removal strategy, it significantly improves dust removal efficiency, reduces dust emissions, and meets environmental requirements.

[0049] In terms of data transmission and control, the IoT transport layer establishes an efficient data interconnection channel between devices through the deployment of edge computing nodes, protocol conversion gateways, and cloud server clusters, as well as the application of multi-protocol adaptation engines and load balancing mechanisms. This enables the synchronous transmission of cleaning device status monitoring data and operating instructions. Task instructions are prioritized and distributed in parallel through dynamic routing scheduling strategies, ensuring the efficiency and reliability of multi-device collaborative operations. The application of multi-source heterogeneous communication protocols enables two-way interaction between the physical device layer and the control strategy layer, allowing the real-time operating parameters of the equipment to be uploaded to the decision analysis layer for processing in a timely manner. At the same time, the control parameters can be accurately sent to the control execution layer, realizing real-time monitoring and precise control of cleaning operations.

[0050] In terms of model construction and optimization, the decision analysis layer uses time-series data analysis techniques combined with a historical database of equipment operations and real-time operating condition data to dynamically optimize the cleaning operation model, constructing a multi-dimensional, interconnected cleaning control strategy model. This model establishes a parameter correlation between the equipment's physical state and the control logic space, using a bidirectional calibration mechanism, to transform actual cleaning operation data into a high-precision digital control space, enabling intelligent modeling of the cleaning operation. Cleaning intensity adjustment, coordinated control of air and water parameters, and dynamic optimization of dust removal efficiency based on convolutional neural network algorithms automatically adjust cleaning parameters based on varying operating conditions, improving cleaning accuracy and efficiency. For example, cleaning intensity adjustment using a hybrid CNN-LSTM-attention mechanism model effectively leverages historical and real-time operating condition data to precisely set the dynamic pressure of the scraper mechanism. Coordinated control of air and water parameters using a U-Net architecture enables optimal matching of high-pressure blower speed and atomizing water pressure. Dynamic optimization of dust removal efficiency using a time-series convolutional network model accurately matches the dust removal system's operating state with the optimal backflush strategy.

[0051] In terms of system intelligence and adaptability, the application of technologies such as overlapping segmented sampling, an online distillation algorithm, and a model structure adaptive reorganization mechanism enables the sweeping control strategy model to continuously learn and adapt to new operating modes, achieving continuous model optimization and upgrading. The abnormal operating condition handling strategy establishes a mapping table between fault types and treatment measures and uses a Bayesian optimization algorithm to search for the optimal treatment parameter combination, enabling rapid response to equipment failures and improving system reliability and stability. Furthermore, the application of data augmentation and early stopping methods improves the quality and efficiency of model training and reduces the risk of model overfitting. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a working principle diagram of the multifunctional embedded intelligent cleaning control system of the present invention;

[0053] Figure 2 A flow chart for controlling the functions of each device in the execution layer;

[0054] Figure 3 Flowchart constructed for the multi-dimensional linkage cleaning control strategy model;

[0055] Figure 4 Flowchart of sweeping intensity adjustment based on convolutional neural network. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] See also Figure 1-Figure 4 The present invention relates to a multifunctional embedded intelligent cleaning control system, which includes a control execution layer, an Internet of Things transmission layer, and a decision analysis layer. Each layer works together to achieve efficient and intelligent cleaning control. The specific implementation steps are as follows:

[0058] The control execution layer includes an intelligent variable frequency scraper, a wind and water cleaning device, a return scraper, and a micro-powered dust collector. The intelligent variable frequency scraper scrapes and cleans the conveyor belt surface according to control commands. The wind and water cleaning device performs a combined wind and water dust removal operation. The return scraper removes residual material from the return belt surface. The micro-powered dust collector performs targeted capture of dust around the conveyor belt.

[0059] The IoT transport layer comprises edge computing nodes, a protocol conversion gateway, and a cloud server cluster, deploying a multi-protocol adaptation engine and load balancing mechanism. Its function is to establish data interconnection channels between devices, enabling the synchronous transmission of cleaning device status monitoring data and operating instructions. Specifically, the operating condition data collected by the control execution layer is transmitted by the edge computing nodes via the industrial bus to the protocol conversion gateway. The protocol conversion gateway standardizes the data format and then transmits it, along with historical operation records stored in the cloud server cluster, to the decision analysis layer for model training. Simultaneously, the IoT transport layer uses dynamic routing scheduling strategies to prioritize and parallelize task instructions based on data flow and device response speed. It also enables bidirectional interaction between the physical device layer and the control strategy layer through multi-source heterogeneous communication protocols. For example, Modbus-TCP, OPC-UA, and MQTT protocols are used to establish a data channel between the device layer and the strategy layer, enabling functions such as command issuance, status feedback, and abnormality warnings. The layer also outputs variable frequency speed control parameters to the control execution layer.

[0060] The decision-making analysis layer uses time-series data analysis technology combined with a historical database of equipment operations and real-time operating condition datasets to dynamically optimize the cleaning operation model and construct a multi-dimensional, interconnected cleaning control strategy model. This model comprises a physical operating space, a control logic space, a feature database, and interaction protocols between various layers. The physical operating space serves as the data input source for the strategy model and contains conveyor belt operating parameters and cleaning status characteristics. The control logic space forms a mapping relationship with the physical operating space, mathematically characterizing cleaning operation characteristics through multi-dimensional parametric modeling. The feature database integrates historical equipment data and real-time monitoring records, providing a benchmark dataset including an operating mode library, an abnormal event library, and an equipment performance library. The interaction protocol enables data integration between various layers. Based on this strategy model, the decision-making analysis layer employs a convolutional neural network algorithm to adjust cleaning intensity, coordinate wind and water parameters, and dynamically optimize dust removal efficiency.

[0061] The present invention will be further described below in conjunction with Examples 1 to 5:

[0062] Example 1:

[0063] As core components of the control execution layer, the intelligent variable frequency scraper, wind and water cleaning device, and micro-powered dust collector achieve precise execution of conveyor belt cleaning operations through the coordinated design of hardware structure and control logic. The following describes the implementation of each device in detail based on specific application scenarios:

[0064] The hardware architecture of the intelligent variable frequency scraper is based on hydraulic drive and integrates multiple types of sensors and adjustment mechanisms. The hydraulic drive mechanism adopts a closed-loop control system, including a hydraulic pump, a proportional reversing valve and a hydraulic cylinder, in which the hydraulic cylinder is hinged to the support arm of the scraper blade. For example, when the conveyor belt is transporting sticky materials (such as wet coal slime), the pressure sensor (model can be Huba511 series) , a torque sensor can also be used to indirectly determine the pressure value ) collects the normal pressure data of the contact surface between the blade and the conveyor belt in real time, and the data is transmitted to the local controller of the control execution layer through a 4-20mA analog signal. The angle adjuster is an electric push rod structure, and its stroke range corresponds to the angle adjustment range between the blade and the conveyor belt surface (such as 5°-45°). When the system recognizes that the material adhesion strength is high (such as the pressure value exceeds the preset threshold of 20kPa), the local controller sends a command to the angle adjuster to adjust the blade angle from the initial 25° to 15° to increase the scraper cutting depth. The blade wear detection module uses a laser displacement sensor (such as KeyenceLK-G80) to measure the change in blade edge thickness through non-contact. When the wear is detected to be more than 3mm, it triggers a warning signal and synchronously adjusts the hydraulic drive pressure to compensate for the scraping force attenuation caused by wear.

[0065] In actual operation, the device's control logic is divided into automatic and manual modes. For example, in the case of ore conveyor belt cleaning, in automatic mode, the system dynamically adjusts the hydraulic pump output power (adjustable range: 0-15MPa) based on real-time data from the pressure sensor. When the conveyor belt speed is 2m / s and the material accumulation thickness reaches 10mm, the hydraulic drive pressure automatically rises to 12MPa, ensuring that the blades scrape the material at a constant pressure. Manual mode allows the operator to directly set parameters such as blade angle and hydraulic pressure through the local human-machine interface (HMI), making it suitable for commissioning or handling special working conditions.

[0066] The wind and water cleaning device adopts modular design, and the high-pressure air unit is connected to the atomizing nozzle array through an air-water mixing pipeline. The high-pressure air unit uses a centrifugal fan (such as the model of Tuthill Vacuum & Blower Systems) with a rated air volume of 1500m 3 / h, and the wind pressure can be adjusted within the range of 5-30kPa. The atomizing nozzle array is arranged equidistantly along the width of the conveyor belt (spacing 200mm). A single nozzle uses air-assisted atomization technology, which can produce droplets with an average particle size of 50μm at a water pressure of 2MPa and an air pressure of 5kPa. The flow control valve is an electric three-way valve, which adjusts the water flow (0-50L / min) and air flow (0-2000m 3 / h). For example, when treating fine dust particles smaller than 10μm, the air-to-water ratio is set to 40:1, creating a high-concentration mist curtain to absorb dust. The water filtration unit comprises a three-stage filtration structure: a primary filter (1mm pore size) intercepts large particles, a secondary filter (50μm precision) filters suspended solids, and a reverse osmosis membrane (300Da molecular weight cut-off) removes soluble salts, ensuring long-term, clog-free operation of the nozzles.

[0067] Taking cement powder transportation as an example, when the dust concentration sensor (such as TSI9306) detects that the dust concentration in the working area exceeds 80mg / m 3 When the system is in operation, the wind and water cleaning device automatically activates: the high-pressure blower output pressure rises to 25kPa, the atomizing water pressure is adjusted to 3MPa, and the water flow rate is set to 30L / min. This creates a mixed air-water flow that flushes the conveyor belt surface. At the same time, the mist droplets combine with the dust to form gravity sedimentation, reducing the amount of suspended dust in the air. The system monitors the operating parameters of each component in real time via the Modbus protocol. If a nozzle flow abnormality (deviation of ±10% from the set value) is detected in a particular branch, the branch is automatically closed and a fault notification is issued.

[0068] The micro-power dust collector adopts the working mechanism of combining negative pressure drainage and pulse cleaning. The negative pressure ventilation module is composed of a low-noise fan (power 5kW, air volume 800m 3 / h) and air duct, with a guide plate at the duct entrance to guide the dust-laden air around the conveyor belt to flow toward the cartridge dust box. The cartridge dust box has multiple sets of pleated filter cartridges (filter area 20m 2 / piece, filtration accuracy 1μm), when the dust-laden airflow passes through the filter cartridge, the dust is intercepted on the surface of the filter material, and the purified airflow is discharged through the fan. The pulse back-blowing device includes an air bag, an electromagnetic pulse valve and a blowpipe. When the filter cartridge pressure differential sensor (such as Setra267) detects that the pressure difference exceeds 1200Pa, the pulse back-blowing program is triggered: the electromagnetic pulse valve opens, and compressed air (pressure 0.4-0.6MPa) is sprayed into the filter cartridge through the blowpipe, causing the filter cartridge to expand and shake instantaneously, peeling off the surface dust. The dust concentration sensor (installed at the air outlet of the dust collector) monitors the emission concentration in real time. When the concentration exceeds 10mg / m 3 When the backflush cycle is extended or the backflush pressure is increased, the system automatically extends the backflush cycle or increases the backflush pressure.

[0069] In the grain processing and transportation scenario, for light and fine dust such as flour, the negative pressure ventilation module of the micro-powered dust collector keeps running continuously, and the fan speed is adjusted by the frequency converter (frequency 20-50Hz) to adapt to the changes in dust volume under different conveying speeds. When the conveyor belt transportation volume suddenly increases, resulting in a surge in dust, the system automatically increases the fan frequency from 30Hz to 45Hz, and shortens the pulse backflushing cycle (from 5min to 3min) to ensure that the filter cartridge always maintains a high-efficiency filtration state. If an abnormally high pressure difference is detected in a filter cartridge (such as exceeding 1500Pa), the system determines that the filter cartridge may be clogged, immediately starts the spare filter cartridge group and issues a replacement prompt.

[0070] Each of these devices is connected to the edge computing nodes of the IoT transport layer via an industrial bus (such as Profinet), uploading real-time operational data (such as pressure, flow, and speed) and receiving control commands. For example, when an intelligent variable-frequency scraper detects that blade wear has reached a threshold, the edge computing node integrates this information with historical wear data and transmits it to the decision-making analysis layer via a protocol conversion gateway for optimization of the scraper pressure prediction model. Furthermore, each device's local controller possesses independent computing capabilities, enabling it to execute pre-set local control strategies in the event of a network outage, ensuring uninterrupted cleaning operations.

[0071] Through the refined design of hardware components and the hierarchical coordination of control logic, the control execution layer achieves adaptive response to different material properties and environmental conditions, providing a reliable physical execution foundation for the efficient operation of the entire intelligent cleaning control system.

[0072] Example 2:

[0073] The IoT transport layer serves as the core hub connecting the control execution layer and the decision analysis layer. Through a hierarchical architecture consisting of edge computing nodes, protocol conversion gateways, and cloud server clusters, combined with a multi-protocol adaptation engine and dynamic routing mechanism, it enables efficient device data collection, transmission, processing, and command distribution. The following describes its implementation in detail from three dimensions: data transmission process, model training mechanism, and protocol interaction logic.

[0074] The data transmission process is designed based on the industrial bus and cloud-edge collaborative architecture. The control execution layer devices (such as the pressure sensor of the intelligent variable frequency scraper device and the flow control valve of the wind and water cleaning device) gather real-time working condition data to the edge computing node (Advantech UNO series embedded controller is selected) through the field bus (such as CANopen or ModbusRTU). Taking a mine conveyor belt scenario as an example, the edge computing node collects 100 sets of data per minute, including blade pressure (accuracy ±0.1kPa), fan speed (accuracy ±1r / min), dust concentration (accuracy ±1mg / m 3 ) and other parameters are transmitted to the protocol conversion gateway via Industrial Ethernet (Profinet protocol). The protocol conversion gateway deploys a multi-protocol adaptation engine that supports real-time conversion of protocols such as Modbus-TCP, OPC-UA, and MQTT. For example, it parses Modbus RTU data frames from the control execution layer into JSON format and adds metadata such as timestamps and device IDs to the data to ensure standardized data format. The standardized data is transmitted to a cloud server cluster (using a distributed storage architecture such as Hadoop HDFS) via 4G / 5G or a wired network. Together with historical job records (storage period ≥ 36 months), it forms the data source for the decision analysis layer.

[0075] The model training mechanism utilizes multi-dimensional feature fusion and a distributed computing architecture. After receiving standardized data from the IoT transmission layer, the decision analysis layer first uses a data cleaning module to remove outliers (such as sudden negative pressure sensor values). Key features are then extracted using a feature engineering module. Taking cleaning operation parameters as an example, extracted features include conveyor speed (m / s), material stacking thickness (mm), blade angle (°), air-water mixture ratio (air-water volume ratio), and filter cartridge pressure differential (Pa). Multi-dimensional feature fusion technology is implemented using a parallel neural network architecture. For example, pressure data is fed into a one-dimensional convolutional neural network (CNN) to extract spatial features, while time series data is fed into a long short-term memory network (LSTM) to capture temporal dependencies. Joint learning of the feature space is then achieved through fully connected layers. The cloud server cluster distributes computing tasks using load balancing mechanisms (such as round-robin algorithms). When processing training data for 100 devices simultaneously, the task can be split among 10 computing nodes for parallel processing, with each node responsible for feature extraction and model training for 10 devices, significantly improving training efficiency.

[0076] The protocol interaction logic enables two-way real-time communication between the physical device layer and the control strategy layer. The IoT transport layer establishes a real-time data channel between the device layer and the strategy layer through the Modbus-TCP protocol. For example, the hydraulic drive pressure setting value of the intelligent variable frequency scraper device is updated 10 times per second through this protocol to ensure the real-time nature of the control instructions. The OPC-UA protocol is used to transmit structured data, such as equipment ledger information, maintenance records, etc. It supports cross-platform access and data encryption (AES-256 algorithm) to meet the security requirements of industrial scenarios. The MQTT protocol is used for lightweight data transmission, such as dust concentration over-limit warning signals, which achieve second-level responses through the publish-subscribe model. When the dust concentration in a certain area exceeds the threshold, the warning information can be transmitted to the decision analysis layer and the local monitoring terminal within 200ms.

[0077] During the two-way interaction process, the dynamic routing scheduling strategy optimizes the transmission path based on data traffic and device response speed. For example, when the cloud server cluster detects that the network delay in a certain area exceeds 500ms, it automatically switches to a backup route (such as a satellite communication link) to transmit key control instructions to ensure uninterrupted device control. At the same time, the load balancing mechanism dynamically allocates model training tasks based on the CPU utilization (threshold ≤ 80%) and memory usage (threshold ≤ 70%) of the computing node to avoid overloading a single node. Taking the peak data traffic scenario as an example, when 500 devices are connected at the same time, the load balancer can complete route switching and task allocation within 50ms, ensuring that the data transmission delay is ≤ 100ms.

[0078] The IoT transport layer also features fault self-healing capabilities. When an edge computing node detects a communication interruption on the industrial bus, it automatically switches to local caching mode, temporarily storing real-time data on a built-in SSD (capacity ≥ 256GB). Once communication is restored, data is uploaded in batches to the protocol conversion gateway to ensure data integrity. The protocol conversion gateway has built-in dual power modules (AC220V and DC24V). If the primary power supply fails, the backup power supply seamlessly switches within 10ms, ensuring continuous system operation.

[0079] Example 3:

[0080] The multi-dimensional, linked cleaning control strategy model achieves dynamic modeling and intelligent optimization of cleaning operations by constructing an organic whole of physical workspace, control logic space, feature database, and interaction protocol. The following describes its implementation in detail, including the model construction process, parameter association mechanism, feature database establishment method, and model calibration algorithm:

[0081] The model building process is data-driven, and first establishes a parameter association between the physical state of the equipment and the control logic space. The physical operation space includes conveyor belt operating parameters (such as speed v, material transportation volume q) and cleaning status characteristics (such as material adhesion strength F, dust concentration C). These parameters are collected in real time by sensors at the control execution layer. The control logic space realizes the mapping of physical parameters through mathematical modeling. For example, the association function between scraper pressure P and material adhesion strength F and conveyor belt speed v is defined as P = f(F, v), where f is a nonlinear mapping relationship obtained through historical data training. The two-way calibration mechanism is implemented by comparing the actual measured value of the physical equipment with the predicted value of the model. For example, when the deviation between the actual scraper pressure and the model output value exceeds 5%, the parameter calibration process is triggered, and the coefficient of the function f is adjusted until the deviation converges.

[0082] The feature library establishment method is based on multi-source data fusion technology. Based on the material adhesion strength F obtained by the pressure sensor and the dust distribution data C(x, y) monitored by the dust concentration sensor (where x and y are the coordinates of the conveyor plane), the feature vector is extracted using a sliding window algorithm (the window size is 5 minutes of operation data). The baseline feature library contains the feature mean μ under normal working conditions. F , standard deviation σ F , dust concentration spatial distribution matrix M C For example, under normal working conditions, the mean value of F is 15kPa, the standard deviation is 2kPa, and the dust concentration in the center area C(0,0) of the conveyor belt is 30mg / m 3 , the edge area C (±L, 0) is 10 mg / m 3 (L is the half-width of the conveyor belt.) The abnormal operating condition template library stores typical fault characteristics, such as a sudden increase in F to 30kPa and a duration of more than 10 seconds when a blade becomes stuck, and a sudden increase in dust concentration C accompanied by an abnormal increase in the negative pressure ventilation module current when the filter cartridge is clogged. Real-time operating data is reduced in dimension using a feature extraction algorithm (such as principal component analysis (PCA)) and then matched with the baseline feature library using cosine similarity. When the similarity falls below a threshold (such as 0.8), it is determined to be an abnormal operating condition and an early warning is triggered.

[0083] The model parameter correction process uses the gradient descent algorithm to iteratively optimize the model output. Assume that the scraping pressure predicted by the model is The actual measured value is P real , define the loss function as:

[0084]

[0085] Where N is the number of samples and i is the sample index. The gradient of the loss function with respect to the model parameter θ is calculated by the back propagation algorithm. And follow the update formula Adjust the parameters, where α is the learning rate (range 0.001-0.1). Taking a coal conveyor belt at a port as an example, the initial model predicted pressure with a deviation of 8 kPa from the actual value. After 50 iterations of training, the deviation was reduced to 1.5 kPa, meeting the control accuracy requirements.

[0086] The interactive protocol design realizes data communication between various levels. The physical operation space and the feature database are connected through a standardized interface (such as REST API). The real-time feature parameters F(t) and C(t) are transmitted to the database in JSON format every second. The historical data query delay is ≤200ms. The physical operation space and the control logic space transmit parameters through a data bus (such as EtherCAT). The control instructions P cmd The release cycle is 100ms, ensuring real-time response. The control logic space and feature database are synchronized through middleware (such as Apache Kafka). Parameter updates after model training are pushed to the control logic space through the message queue, with an update delay of ≤500ms.

[0087] Under complex working conditions, the model adapts to environmental changes through a real-time weight update mechanism. For example, when the conveyor belt switches from transporting ore to coal powder, the dust particle size distribution characteristics change significantly. The model automatically adjusts the weight of the wind and water parameter collaborative control branch, increasing the atomizing water pressure from 2MPa to 3MPa, and at the same time increasing the high-pressure fan pressure by 10% to meet the adsorption requirements of fine dust. The operation trend prediction module is based on the time series difference model (TD model). Based on the F and v data of the last 30 minutes, it predicts the change in material adhesion strength in the next 10 minutes, and adjusts the blade angle and hydraulic pressure in advance to avoid insufficient cleaning or excessive wear.

[0088] Through the aforementioned implementation, the cleaning control strategy model transforms the physical work scenario into a computable digital space, achieving closed-loop control from data acquisition, feature modeling, parameter optimization, and command execution. The model's multi-dimensional linkage features: physical data drives logical decision-making, logical outputs are fed back to physical equipment, and historical data optimizes model parameters, forming a self-learning and self-adjusting intelligent control system. This system is suitable for dusty, high-load industrial cleaning scenarios such as coal mines, power plants, and building materials.

[0089] Example 4:

[0090] The application of a convolutional neural network algorithm based on a cleaning control strategy model in cleaning intensity regulation achieves adaptive control of the scraper device through a full-process design of working condition data modeling, hybrid model training, and dynamic parameter output. The following describes its implementation in detail, using the steel plant sintered ore conveyor belt scenario:

[0091] The working condition sample set is constructed by integrating multi-source historical data. The system extracts the conveyor belt operation data of the past 6 months from the cloud server cluster, including the operating speed v (range 0.8-2.5m / s), the material transportation volume q (collected by weighing sensor, accuracy ±1%), and the blade wear degree w (the blade thickness change is measured by laser displacement sensor, unit mm). Each complete cleaning operation cycle (about 30 minutes) is taken as a sample unit, and a total of 2000 samples are obtained. For example, a sample record is: v = 1.8m / s, q = 500t / h, w = 1.2mm, and the actual pressure setting value of the corresponding scraper device is 10MPa. All sample data are mapped to the [0, 1] interval through normalization processing to eliminate dimensional differences, such as dividing the pressure value by the maximum set pressure of 15MPa for normalization.

[0092] The hybrid model architecture is built and trained using a three-layer structure consisting of CNN, LSTM, and attention mechanism. The convolutional layer contains two one-dimensional convolution kernels (size 5) to extract spatial features, such as the correlation pattern between material transport volume and blade wear. The LSTM layer has 64 memory cells to capture temporal dependencies, such as the delayed effect of conveyor belt speed changes on material adhesion strength (with a lag time of approximately 5 minutes). The attention mechanism module weights the hidden states of the LSTM output, focusing on key feature dimensions (such as blade wear). End-to-end training is performed with a batch size of 32 and 20 training cycles. Residual connections are used during training to mitigate the vanishing gradient problem. For example, in the early stages of training, the model's pressure prediction deviation for high-wear conditions (w>2mm) was large. By directly passing the original input to subsequent layers through residual connections, the model's prediction error was significantly reduced after the 10th cycle.

[0093] Model evaluation and optimization divides the sample set into training set and test set in a ratio of 7:3. The test set contains 600 samples, and the model performance is evaluated by the root mean square error (RMSE) and mean absolute error (MAE). In a certain training, the RMSE of the test set was 0.8MPa and the MAE was 0.6MPa, which met the industrial control accuracy requirements (allowable error ≤1MPa). If the evaluation result does not meet the standard, the system automatically adjusts the hyperparameters (such as increasing the number of LSTM layers to 2 layers and adjusting the learning rate from 0.001 to 0.0005) and retrains the model. The optimal model finally screened out can output a dynamic pressure set value in the range of 0-15MPa based on the input operating parameters, and the error is controlled within ±0.9MPa.

[0094] Dynamic control command generation is combined with real-time conveyor belt data to achieve closed-loop control. When the system detects the current conveyor belt speed v = 2.2m / s and the material transport volume q = 600t / h, the data is input into the optimal adjustment model through the edge computing node, and the output dynamic pressure setting value of the scraper device is 12MPa. At the same time, the local controller generates inverter control instructions based on the real-time signal of the conveyor belt speed sensor (accuracy ±0.05m / s): If the speed exceeds 2m / s, the inverter increases the motor frequency from 50Hz to 55Hz, improving the operating efficiency of the scraper device; if the speed is less than 1m / s, the frequency is reduced to 40Hz to reduce equipment wear.

[0095] The abnormal operating condition handling mechanism integrates wear warning and parameter compensation logic. The blade wear detection module monitors w in real time. When w ≥ 2.5mm, the system automatically increases the pressure setting value output by the model by 15% as compensation (for example, the original output of 10MPa is adjusted to 11.5MPa), and simultaneously triggers a blade replacement reminder. If the pressure is still unable to effectively remove material after compensation (for example, the pressure setting value reaches the upper limit of 15MPa for three consecutive cycles), the system determines that the blade has failed, forces the machine to shut down, and issues a fault alarm.

[0096] The engineering application scenario has been expanded to the grain storage conveyor belt scenario. For granular materials such as wheat and corn, the model input parameter increases the material humidity h (measured by a capacitive sensor, ranging from 5% to 15%). Since humidity affects the viscosity of the material, when h>12%, the model automatically reduces the pressure setting value by 10%-20% to avoid excessive scraping and material breakage. At the same time, according to seasonal changes (such as high temperature and high humidity in summer), the system automatically adjusts the training set weights to increase the learning proportion of humidity features, making the model more adaptable to periodic working condition changes.

[0097] Through the above implementation, the convolutional neural network algorithm is deeply integrated with the cleaning control strategy model, achieving an intelligent upgrade from historical data learning to real-time control. When deployed on industrial sites, this solution can dynamically adjust the cleaning intensity based on different material characteristics (such as particle size, moisture, and viscosity) and equipment status (such as blade wear). This not only avoids conveyor belt deviation and material residue caused by insufficient cleaning, but also prevents equipment loss and energy waste caused by excessive cleaning, providing a highly efficient solution for intelligent cleaning in complex working conditions.

[0098] Example 5:

[0099] The convolutional neural network algorithm based on the cleaning control strategy model is applied to the coordinated control of Feng Shui parameters and the dynamic optimization of dust removal efficiency. Through the full-process collaboration of feature matrix construction, model architecture design, and strategy matching, multi-parameter linkage control of cleaning operations is achieved. The following describes its implementation in detail, combining the fly ash conveyor belt scenario of a thermal power plant:

[0100] The implementation process for coordinated control of Feng Shui parameters begins with multidimensional feature acquisition. The system uses a humidity sensor (accuracy ±2% RH) to obtain conveyor belt surface humidity distribution data H(x, y) (where x and y are the coordinates of the conveyor belt plane). A laser particle size analyzer (such as the Malvern Mastersizer 3000) measures the dust particle size distribution D(d) (d is the particle size in μm) online. An anemometer (accuracy ±0.1 m / s) collects ambient wind speed u. For example, in fly ash conveying, when dust particles with a size d < 10 μm account for 60%, the ambient wind speed u = 3 m / s, and the conveyor belt surface humidity H = 10% RH, a Feng Shui operation feature matrix containing 12 feature dimensions is constructed, such as the mean humidity, median particle size, and wind speed vector.

[0101] The Feature Pyramid Network (FPN) fuses multidimensional features across scales. Low-level features retain detailed information about humidity distribution (e.g., lower humidity at the edges of conveyor belts), while high-level features extract semantic information about dust particle size distribution (e.g., a high proportion of fine dust). Through upsampling and lateral concatenation, an enhanced feature map containing information at different scales is generated. For example, by fusing a particle size feature map (resolution 16×16) with a humidity feature map (resolution 64×64), a feature map is obtained that simultaneously characterizes fine dust distribution and localized dry areas, providing more comprehensive input for subsequent control models.

[0102] The Fengshui control model based on the U-Net architecture fuses shallow and deep features through skip connections. The encoder consists of four convolutional blocks (each containing two 3×3 convolutional layers and one maximum pooling layer), which gradually extracts the associated features of dust, humidity, and wind speed. The decoder restores spatial resolution through deconvolution and skip connections, ultimately outputting the coordinated control variables of high-pressure fan speed n (in r / min) and atomizing water pressure p (in MPa). In the fly ash scenario, the model outputs n = 2800 r / min and p = 4 MPa, forming a Fengshui mixed flow with a wind speed of 12 m / s and a droplet size of 30 μm, effectively adsorbing fine dust and suppressing secondary dust.

[0103] The implementation process of dynamic dust removal efficiency optimization centers around filter cartridge pressure differential monitoring. The system collects real-time filter cartridge pressure differential data (ΔP) (accuracy ±5 Pa), pulse backwash cycle T (in minutes), and dust accumulation rate r (calculated by measuring the weight change of the dust collection box using a load cell, in kg / h). When the fly ash flow rate increases, resulting in r = 15 kg / h and ΔP = 1100 Pa, a dust removal efficiency feature library containing the past two hours of data is constructed. Using a sliding time window (10-minute window length), a sample sequence of 12 consecutive operating periods is generated. Each sequence includes time series features such as a pressure differential curve and backwash cycle records.

[0104] The Temporal Convolutional Network (TCN) model extracts unidirectional temporal dependencies through causal convolution. The dilated convolution kernel size is set to 3 and the receptive field range is set to 20 minutes. The first causal convolution layer captures the pressure difference fluctuation trend over the past 10 minutes, and the second layer captures the impact of the backflush cycle over the past 20 minutes. For example, if ΔP is detected to have increased at a rate of 50 Pa / min over the past 15 minutes without triggering backflush, the model determines that the risk of filter cartridge clogging has increased. The multi-layer perceptron (MLP) outputs the dust removal system operating status as "inefficient operation" and matches the optimal backflush strategy: increasing the backflush pressure from 0.4 MPa to 0.5 MPa and extending the backflush duration from 5 seconds to 8 seconds.

[0105] The model collaborative optimization mechanism is achieved through overlapping segmented sampling and online distillation. Continuous operation data is overlapped and sampled at intervals of 5 minutes (overlap rate 50%), and each data slice is independently feature-encoded to avoid interference of abnormal data at a single time point on the model. For example, a slice contains interference data of a sudden change in conveyor belt speed. By averaging the features of adjacent slices, the impact of the abnormal value can be reduced. The correlation matrix between data slice features and equipment energy consumption (such as fan power and water pump power) records the energy consumption distribution of different cleaning modes. For example, the energy consumption in the "strong wind and strong water" mode is 25% higher than that in the "standard mode", providing a basis for the system to select energy-saving strategies.

[0106] When a new operating condition is detected (such as a sudden increase in fly ash humidity causing dust agglomeration), the online distillation algorithm triggers the adaptive reorganization of the model structure. For example, the original model did not learn enough about the pressure difference mutation characteristics of agglomerated dust. By distilling the knowledge of the teacher model (pre-trained wide neural network), the student model (lightweight TCN) quickly adjusts the convolution kernel parameters to enhance the ability to capture step-type features. The abnormal operating condition handling strategy is implemented by combining the fault mapping table with Bayesian optimization: if the filter cartridge pressure difference exceeds 1500Pa and does not drop after backflushing, it is determined that the filter cartridge is blocked, and it automatically switches to the spare filter cartridge group and issues a replacement prompt; the Bayesian optimization algorithm searches for the optimal combination in the parameter space of hydraulic drive pressure (8-15MPa) and reverse drive duration (10-30s). For example, for the blade jam fault, after 5 iterations, the optimal parameters are determined to be 12MPa pressure and 20s duration, taking into account both the cleaning effect and the equipment load.

[0107] Cross-scenario application expansion In the cement raw material transportation scenario, the dust particle size distribution is mainly 50-100μm, and the ambient wind speed often reaches 5m / s. The wind and water control model outputs n=3200r / min and p=5MPa based on the characteristic matrix (humidity 8% RH, median particle size 70μm, wind speed 5m / s), forming a mixed flow of high-speed airflow and large-particle droplets, effectively impacting the residual bulk materials. The dust removal system uses a time-series convolutional network to identify the sinusoidal fluctuation characteristics of the dust accumulation rate under high wind speed (a period of about 15 minutes), and automatically adjusts the backflush period to 12 minutes to avoid overloading the filter cartridge due to periodic dust peaks.

[0108] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0109] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multifunctional embedded intelligent cleaning control system, characterized in that: It includes: Control execution layer, IoT transmission layer and decision analysis layer; The control execution layer includes an intelligent variable frequency scraping device, a wind and water cleaning device, a return scraping device and a micro-powered dust collector, which is used to perform scraping and cleaning of the conveyor belt surface, wind and water combined dust removal and removal of residual materials on the return belt surface according to control instructions; The IoT transport layer includes edge computing nodes, protocol conversion gateways, and cloud server clusters, and deploys a multi-protocol adaptation engine and load balancing mechanism to establish data interconnection channels between devices, enabling the synchronous transmission of cleaning device status monitoring data and operating instructions. It uses a dynamic routing scheduling strategy to prioritize and distribute task instructions in parallel based on data traffic and device response speed, and implements bidirectional interaction between the physical device layer and the control strategy layer through multi-source heterogeneous communication protocols. The decision analysis layer is used to dynamically optimize the cleaning operation model by using time series data analysis technology in combination with the equipment operation history library and the real-time working condition data set, and then build a multi-dimensional linkage cleaning control strategy model. Based on the strategy model, a convolutional neural network algorithm is used to adjust the cleaning intensity, coordinate the control of wind and water parameters, and dynamically optimize the dust removal efficiency.

2. A multifunctional embedded intelligent cleaning control system according to claim 1, characterized in that: The intelligent variable frequency scraping device includes at least a hydraulic drive mechanism, a pressure sensor, an angle adjuster and a blade wear detection module, which are used to collect the material adhesion strength characteristics on the conveyor belt surface; the wind and water cleaning device includes at least a high-pressure air unit, an atomizing nozzle array, a flow regulating valve and a water quality filtration unit, which are used to perform wind and water mixed cleaning operations under different working conditions; the micro-power dust collector includes at least a negative pressure air induction module, a cartridge dust collection box, a pulse backblowing device and a dust concentration sensor, which are used to complete the directional capture of dust around the conveyor belt.

3. A multifunctional embedded intelligent cleaning control system according to claim 1, characterized in that: In the IoT transmission layer, the working condition data collected by the control execution layer is transmitted from the edge computing node to the protocol conversion gateway via the industrial bus. The protocol conversion gateway then standardizes the data format and transmits it together with the historical operation records stored in the cloud server cluster to the decision analysis layer for model training. The model training uses multi-dimensional feature fusion technology to input the collected cleaning operation parameters into the feature space constructed by different neural network architectures for joint learning. The bidirectional interaction between the physical device layer and the control strategy layer is achieved through multi-source heterogeneous communication protocols, including: using Modbus-TCP, OPC-UA and MQTT protocols to establish a data communication channel between the device layer and the strategy layer, exchanging real-time operating parameters of the equipment, and collaboratively processing the analyzed operation characteristics, while achieving dynamic matching of control parameters, completing command issuance, status feedback and abnormal warning, and outputting variable frequency speed regulation parameters to the control execution layer.

4. A multifunctional embedded intelligent cleaning control system according to claim 1, characterized in that: The construction of a multi-dimensional linkage cleaning control strategy model includes: Establish the parameter association relationship between the physical state of the equipment and the control logic space and a two-way calibration mechanism; The actual cleaning operation data is extracted and pattern recognized through feature extraction. Based on the material adhesion strength obtained by the pressure sensor and the dust distribution data monitored by the dust concentration sensor, a baseline feature library for conveyor belt cleanliness and an abnormal operating condition template library are constructed. The model weights are updated and the operation trend is predicted based on the real-time operation data, thus transforming the physical operation scene into a high-precision digital control space. The parameters of the cleaning control strategy model are calibrated. The real-time collected operation data is input into the established strategy model. The output results of the model are iteratively corrected using the gradient descent algorithm to obtain the optimized cleaning control strategy model. The cleaning control strategy model includes a physical operation space, a control logic space, a feature database, and interaction protocols between various levels; The physical operation space is the data input source of the strategy model, which includes conveyor belt operating parameters and cleaning status characteristics; the control logic space forms a mapping relationship with the physical operation space, and mathematically represents the cleaning operation characteristics through multi-dimensional parametric modeling; the feature database integrates equipment historical data and real-time monitoring records, and provides a benchmark data set including a working mode library, an abnormal event library, and an equipment performance library; the interactive protocol realizes data communication between various levels, and the physical operation space and the feature database realize real-time collection of feature parameters and model update through a standardized interface, the physical operation space and the control logic space transfer parameters through a data bus, and the control logic space and the feature database realize information synchronization through the middleware.

5. The multifunctional embedded intelligent cleaning control system according to claim 1, characterized in that: The convolutional neural network algorithm is used to adjust the sweeping intensity based on the strategy model, including: Based on the cleaning control strategy model, historical conveyor belt speed data, material transportation volume records, and blade wear characteristics are obtained to construct a working condition sample set; After normalizing the working condition sample set, divide it into training set and test set; Establish a CNN-LSTM-attention mechanism hybrid model architecture, initialize model hyperparameters, input the training set into the hybrid model for end-to-end training, extract spatial features through convolutional layers, capture temporal dependencies through LSTM networks, focus on key feature dimensions through the attention mechanism, and optimize the gradient propagation path using residual connections until the model converges or reaches the preset number of training times; The test set is input into the trained hybrid model to evaluate the model prediction accuracy and select the optimal sweeping intensity adjustment model; The dynamic pressure setting value of the scraper device is output based on the optimal adjustment model, and the inverter control instruction is generated in combination with the real-time speed data of the conveyor belt.

6. A multifunctional embedded intelligent cleaning control system according to claim 1, characterized in that: The method of using a convolutional neural network algorithm to coordinate feng shui parameters based on the strategy model includes: Based on the cleaning control strategy model, the surface humidity distribution data of the conveyor belt, dust particle size characteristics, and ambient wind speed parameters are extracted to construct a wind and water operation characteristic matrix; A feature pyramid network is used to perform cross-scale fusion processing on multi-dimensional features to obtain enhanced feature maps; Establish a Feng Shui control model based on the U-Net architecture, integrating shallow detail features with deep semantic information through skip connections; The fused feature map is input into the regression prediction layer to output the coordinated control quantities of the high-pressure fan speed and atomizing water pressure parameters.

7. The multifunctional embedded intelligent cleaning control system according to claim 1, characterized in that: The dynamic optimization of dust removal efficiency using a convolutional neural network algorithm based on the strategy model includes: Based on the cleaning control strategy model, filter cartridge pressure difference data, pulse backflushing cycle records, and dust accumulation rate parameters are collected to build a dust removal efficiency feature library; The data in the dust removal efficiency feature library is segmented into sliding time windows to generate a sample sequence of continuous operation periods; Establish a temporal convolutional network model, set the dilated convolution kernel size and receptive field range, and extract temporal features through causal convolution operations; The time series features are input into the multi-layer perceptron for efficiency level classification, and the matching result between the working status of the dust removal system and the optimal backflushing strategy is output.

8. The multifunctional embedded intelligent cleaning control system according to claim 1, characterized in that: The construction of the multi-dimensional linkage cleaning control strategy model also includes: Adopt overlapping segmented sampling mechanism to slice continuous operation data, and each data slice is independently feature-encoded; Establish a correlation matrix between data slice characteristics and equipment energy consumption, and record the energy consumption distribution patterns corresponding to different cleaning modes; The control model is continuously optimized through an online distillation algorithm, and the model structure adaptive reconstruction mechanism is triggered when a new operating mode is detected.

9. The multifunctional embedded intelligent cleaning control system according to claim 1, characterized in that: The method for generating the abnormal operating condition processing strategy includes: Establish a mapping relationship table between fault types and treatment measures, including the treatment methods for blade jam corresponding to reverse drive and filter cartridge blockage corresponding to enhanced backflushing; A Bayesian optimization algorithm was used to search for the best combination of treatment parameters, including hydraulic driving pressure, reverse driving duration, and backwash airflow intensity; The processing effect and equipment loss are comprehensively measured through a multi-objective evaluation function, and the parameter reconfiguration process is triggered when the processing effect does not meet the standards.

10. The multifunctional embedded intelligent cleaning control system according to claim 1, characterized in that: The training process of the hybrid model architecture includes: using data augmentation methods to generate a diverse set of training samples, and monitoring the risk of model overfitting through early stopping.

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