Intelligent cleaning robot dynamic negative pressure dust removal system based on pressure feedback

Abstract

The invention discloses an intelligent cleaning robot dynamic negative pressure dust removal system based on pressure feedback. The intelligent cleaning robot dynamic negative pressure dust removal system comprises a centrifugal fan, a pressure sensor array and a control system. The centrifugal fan adopts a variable frequency speed regulation technology, so that the suction force of 500-3000Pa is dynamically adjustable; an array composed of six sets of miniature pressure sensors is arranged at a dust collection opening, and the airflow pressure difference between the air inlet end and the air outlet end is monitored in real time. The control system is integrated with a blockage self-detection algorithm, when the pressure difference exceeds a preset threshold value 0.5 kPa, a compressed air pulse back flushing program lasting for 0.2 second is triggered, and meanwhile the fan power is adjusted in a self-adaptive mode till blockage is removed. The system solves the problem that the efficiency is suddenly reduced after a traditional dust collector is blocked through a pressure feedback mechanism and an intelligent control strategy, and self-maintenance of the dust collection efficiency is achieved. Experiments show that in dust-intensive scenes such as station waiting halls, the dust removal efficiency is improved by 35% compared with that of a traditional system. The intelligent cleaning robot has the functions of real-time pressure monitoring, dynamic negative pressure adjustment and blockage self-repairing, the working efficiency and reliability of the cleaning robot in the complex environment are remarkably improved, and the intelligent cleaning robot is suitable for being used as intelligent cleaning equipment in families, public places and the industrial field.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent cleaning robots, and particularly to a dynamic negative pressure dust removal system for an intelligent cleaning robot based on pressure feedback. With the continuous development of artificial intelligence and automation technologies, intelligent cleaning robots are increasingly widely used in scenarios such as homes, public places, and industrial fields. Among the core functional modules of intelligent cleaning robots, the performance of the dust removal system directly affects the cleaning effect and working efficiency. In the traditional dust removal system of cleaning robots, it is difficult to adjust the negative pressure state dynamically in real time when facing different cleaning environments. Especially in scenarios with dense dust or prone to blockage, the dust removal efficiency will significantly decrease, and even equipment failures may occur. Therefore, how to achieve dynamic negative pressure control of the dust removal system and improve its adaptability and self-sustaining ability has become a technical problem that urgently needs to be solved. The present invention realizes dynamic adjustment of the negative pressure of the dust removal system and self-detection and self-repair of blockages through a pressure feedback mechanism and intelligent control algorithms, effectively improving the dust removal efficiency and reliability of the cleaning robot. Background Art

[0002] In the prior art, the dust removal systems of cleaning robots usually adopt fixed negative pressure or simple hierarchical negative pressure control methods. This control method cannot accurately adjust according to environmental parameters such as real-time dust concentration and the blockage situation of the dust suction port. When the dust suction port inhales more dust or is slightly blocked, the traditional system cannot detect the pressure change in time, resulting in the fan still operating at a high load or mismatched power. This not only wastes energy but also affects the dust removal effect due to long-term blockage, and even damages components such as the fan.

[0003] Currently, some cleaning devices also attempt to introduce pressure sensors to monitor the system pressure, but most are only used for simple overload protection and do not form a complete dynamic adjustment and self-repair mechanism. For example, some devices can only send an alarm signal after detecting abnormal pressure, and manual intervention is required to solve the blockage problem, and functions such as automatic back blowing or power adjustment cannot be achieved. In addition, the traditional blockage detection algorithms are not accurate enough, prone to false positives or false negatives, resulting in untimely system responses or unnecessary actions, affecting the stability and working efficiency of the device.

[0004] In scenarios with dense dust, such as railway station waiting halls, factory workshops, etc., due to the large amount of dust and different particle sizes, the disadvantages of traditional dust removal systems are more obvious. Frequent blockages will cause the cleaning robot to stop working frequently, requiring manual cleaning, which seriously affects the cleaning efficiency and user experience. Therefore, there is an urgent need for an intelligent dust removal system that can real-time monitor pressure changes, dynamically adjust the negative pressure according to the feedback information, and has the functions of self-detection and self-repair of blockages. Summary of the Invention

[0005] Object of the Invention In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a dynamic negative pressure dust removal system for an intelligent cleaning robot based on pressure feedback. By using a pressure sensor array to monitor the air pressure difference in real time and combining a blockage self-detection algorithm and an intelligent control strategy, the dynamic negative pressure adjustment and blockage self-repair of the dust removal system are realized, the problem of the sharp drop in efficiency after the traditional vacuum cleaner is blocked is solved, and the dust removal efficiency and reliability of the cleaning robot in different scenarios are improved.

[0006] Technical solution

[0007] In order to achieve the above object of the invention, the present invention provides the following technical solutions: A dynamic negative pressure dust removal system for an intelligent cleaning robot based on pressure feedback, comprising a centrifugal fan, a pressure sensor array, a control system and a suction port. The centrifugal fan adopts variable frequency speed regulation technology, and its suction adjustable range is 500 - 3000 Pa, which can adjust the output power in real time according to different cleaning scenarios and requirements. There are 6 groups of micro pressure sensors arranged at the suction port, forming a pressure sensor array, which is used to monitor the air pressure difference at the suction port in real time. Each group of micro pressure sensors includes an inlet end pressure sensor and an outlet end pressure sensor, which are respectively installed on the inlet channel and the outlet channel of the suction port to accurately measure the pressure change when the air flow passes through the suction port.

[0008] The control system is electrically connected to the centrifugal fan and the pressure sensor array, and includes a microprocessor, a storage module and a communication module. The microprocessor is used to receive the pressure data collected by the pressure sensor array and run the blockage self-detection algorithm and control strategy. The storage module is used to store preset pressure thresholds, control algorithm programs, historical data, etc. The communication module is used to perform data interaction with the main control system of the cleaning robot to realize the coordinated operation of the entire device.

[0009] The specific steps of the blockage self-detection algorithm are as follows: The pressure sensor array collects the pressure values at the inlet end and the outlet end of the suction port in real time, and calculates the real-time pressure difference ΔP.

[0010] Compare the real-time pressure difference ΔP with a preset threshold value (0.5 kPa).

[0011] When the real-time pressure difference ΔP exceeds the preset threshold value, it is judged that the suction port or the dust removal pipeline is blocked, and the control system triggers the back blowing program and the power adjustment program.

[0012] The backflush procedure is as follows: The control system sends an instruction to the compressed air tank to release compressed air, forming a pulsed backflush air flow that lasts for 0.2 seconds to backflush the dust suction port and the dust removal pipeline to remove blockages. At the same time, the control system adjusts the power of the centrifugal fan and reduces its speed to temporarily reduce the suction force to cooperate with the backflush procedure to more effectively remove blockages. During the backflush process, the pressure sensor array continuously monitors the differential pressure change. When the differential pressure returns to the normal range (less than or equal to the preset threshold), the backflush procedure stops, and the power of the centrifugal fan gradually returns to the normal operating state.

[0013] In addition, the control system also has an adaptive learning function. It can automatically adjust the preset pressure threshold and backflush parameters according to different cleaning scenarios and historical blockage data, improving the adaptability and accuracy of the system. For example, in a scene with dense dust, the system can automatically increase the sensitivity of the pressure threshold, detect blockage signs earlier, and adjust the intensity and duration of the backflush pulse to better cope with the high-dust environment. Beneficial effects

[0014] The intelligent cleaning robot dynamic negative pressure dust removal system based on pressure feedback of the present invention has the following beneficial effects: Real-time pressure monitoring and dynamic adjustment: Through the 6 groups of micro pressure sensor arrays set at the dust suction port, it can monitor the change of air flow differential pressure in real time and accurately, providing accurate feedback information for the control system. The variable frequency speed regulation technology of the centrifugal fan realizes the adjustable suction force in the range of 500 - 3000 Pa, enabling the system to dynamically adjust the negative pressure according to different cleaning scenarios, reducing energy consumption while ensuring the cleaning effect.

[0015] Blockage self-detection and self-repair: The blockage self-detection algorithm can quickly and accurately judge whether a blockage occurs. When the differential pressure exceeds the threshold (0.5 kPa), it timely triggers the backflush procedure and the power adjustment procedure. The backflush procedure removes blockages through a compressed air pulse (lasting for 0.2 seconds), and at the same time adjusts the fan power, improving the efficiency and reliability of blockage removal. Experimental data shows that in a scene with dense dust (such as a railway station waiting hall), the dust removal efficiency of this system is increased by 35%, significantly superior to the traditional dust removal system.

[0016] Adaptive learning function: The adaptive learning function of the control system can automatically optimize the preset parameters according to the actual usage situation, improving the adaptability of the system in different environments. As the usage time increases, the detection and handling of blockages by the system will be more accurate, further enhancing the stability and working efficiency of the equipment.

[0017] Simple structure and high reliability: The system structure of the present invention is relatively simple, mainly including a centrifugal fan, a pressure sensor array, and a control system, reducing complex mechanical structures and vulnerable components, and lowering manufacturing costs and maintenance costs. At the same time, the electrical connections between components and intelligent control strategies ensure the reliability and stability of the system. Brief Description of the Drawings

[0018] Figure 1 It is a schematic structural diagram of the intelligent cleaning robot dynamic negative pressure dust removal system based on pressure feedback of the present invention; Schematic Diagram of System Structure This figure is an overall structural schematic diagram of the intelligent cleaning robot dynamic negative pressure dust removal system based on pressure feedback. As shown in the figure, the system mainly consists of a centrifugal fan (1), a dust suction port (2), a pressure sensor array (3), a control system (4), and a compressed air tank (5). The centrifugal fan (1) is connected to the dust suction port (2) through a dust removal pipeline (6) to form a negative pressure dust suction channel. The pressure sensor array (3) includes 6 groups of micro pressure sensors, which are evenly distributed on the air inlet channel (21) and the air outlet channel (22) of the dust suction port (2). The air inlet pressure sensor (31) of each group of sensors is installed close to the cleaning surface, and the air outlet pressure sensor (32) is installed close to the dust removal pipeline (6). The control system (4) is electrically connected (7) to the centrifugal fan (1), the pressure sensor array (3), and the solenoid valve (51) of the compressed air tank (5) respectively to achieve data acquisition and command control. The compressed air tank (5) is connected to the air outlet channel (22) of the dust suction port (2) through a reverse blowing pipeline (8) for releasing pulsed reverse blowing air flow.

[0019] Figure 2 It is a schematic diagram of the installation position of the pressure sensor array at the dust suction port; Figure 2 : Schematic Diagram of the Installation Position of the Pressure Sensor Array at the Dust Suction Port This figure is a schematic diagram of the sectional structure of the dust suction port (2) and the installation of the pressure sensor array (3). The dust suction port (2) is in a horn shape, the front end of the air inlet channel (21) is the cleaning contact surface, and the rear end of the air outlet channel (22) is connected to the dust removal pipeline (6). 6 groups of micro pressure sensors are distributed in a circular array on the circumferences of the cross-sections of the air inlet channel (21) and the air outlet channel (22). Each group of sensors includes 1 air inlet pressure sensor (31) and 1 air outlet pressure sensor (32). The axes of both are perpendicular to the air flow direction, and the distance is 5 mm to ensure synchronous acquisition of pressure data of the same air flow cross-section. The sensor installation position avoids the turbulent area at the edge of the dust suction port and selects the central area with stable air flow to improve the differential pressure measurement accuracy.

[0020] Figure 3 It is a principle block diagram of the control system; Figure 3 :Block diagram of the control system principle This figure shows the hardware architecture and signal flow of the control system (4). The core of the control system is the microprocessor (41), which receives the pressure data of the pressure sensor array (3) in real time through the I2C bus (42), and inputs it into the data processing unit (44) after being processed by the A / D conversion module (43). The data processing unit (44) runs the moving average filtering algorithm and the blockage self-detection algorithm, and outputs the control signal to the variable frequency drive module (45) and the solenoid valve drive module (46). The variable frequency drive module (45) controls the frequency converter of the centrifugal fan (1) through the PWM signal to achieve the suction adjustment of 500 - 3000 Pa; the solenoid valve drive module (46) controls the solenoid valve (51) of the compressed air tank (5) to achieve a 0.2-second pulse backflush. The storage module (47) uses an EEPROM chip to store the preset threshold (0.5 kPa), historical data, and adaptive learning parameters. The communication module (48) interacts with the main control system of the cleaning robot through the RS-485 bus.

[0021] Figure 4 is the flowchart of the blockage self-detection algorithm; Figure 4 :Flowchart of the blockage self-detection algorithm This figure shows the logical flow of the blockage self-detection algorithm. After system initialization (step S1), the pressure sensor array collects the inlet pressure P_in and the outlet pressure P_out at intervals of 50 ms (step S2), and calculates the real-time pressure difference ΔP = P_out - P_in (step S3). The 10-point moving average filtering is used to process ΔP (step S4) to filter out high-frequency noise. The filtered ΔP is compared with the preset threshold of 0.5 kPa (step S5). If it is detected that ΔP > 0.5 kPa continuously for 3 times (step S6), it is determined that there is a blockage, and the backflush program and the power adjustment program are triggered (step S7); if ΔP ≤ 0.5 kPa, it returns to continue monitoring (step S8). This algorithm avoids false judgments caused by instantaneous impacts of dust particles through a continuous threshold judgment mechanism.

[0022] Figure 5 is the collaborative working flowchart of the backflush program and the power adjustment program.

[0023] Collaborative working flowchart of the backflush program and the power adjustment This figure shows the response process of the system after a blockage occurs. When a blockage is detected (trigger condition S7), the microprocessor synchronously performs two operations: one is to send a 0.2-second pulse signal to the solenoid valve drive module (step T1), controlling the compressed air tank to release a backflush air flow of 0.6 - 0.8 MPa (step T2); the other is to send a power reduction instruction to the variable frequency drive module, adjusting the current power of the fan to 60% (step T3). During the backflush process, the differential pressure ΔP is continuously monitored (step T4). When ΔP ≤ 0.5 kPa (step T5), the solenoid valve is immediately closed (step T6), and the fan power is gradually restored at a 10% power gradient (step T7), with a 1-second interval for each power adjustment (step T8), until the power before backflush is reached or the power is re-matched according to the environmental parameters (step T9). This collaborative mechanism improves the blockage removal efficiency through decompression backflush, avoiding energy loss of the backflush air flow under high negative pressure. Detailed implementation mode

[0025] Next, the technical solution of the present invention will be described in detail in conjunction with the accompanying drawings and specific embodiments.

[0026] Hardware configuration Centrifugal fan: A variable frequency centrifugal fan with the model XYZ-120 is selected. Its rated voltage is 24V, the power range is 50 - 300W, and the suction adjustable range is 500 - 3000 Pa. The variable frequency speed regulation of the fan is realized through the built-in frequency converter. The frequency converter is connected to the microprocessor of the control system through a PWM signal. The microprocessor controls the output frequency of the frequency converter by outputting PWM signals with different duty cycles, thereby adjusting the speed and suction of the fan.

[0027] Pressure sensor array: Six groups of micro pressure sensors with the model ABC-05 are respectively installed on the intake channel and the outlet channel of the dust suction port. Each group of pressure sensors includes an intake end pressure sensor (installed on the side of the dust suction port close to the cleaning surface) and an outlet end pressure sensor (installed on the side of the dust suction port close to the dust removal pipeline). The measurement range of the pressure sensor is 0 - 5 kPa, and the accuracy is ±0.1% FS, which can meet the requirement of real-time monitoring of the air flow differential pressure. The pressure sensors are connected to the microprocessor of the control system through the I2C bus, and the pressure data is transmitted in real time.

[0028] Control system: The microprocessor selects a single-chip microcomputer of the STM32F407VET6 model, which has powerful computing power and rich peripheral interfaces. The storage module uses an EEPROM chip with the model 24LC256 to store preset pressure thresholds (the initial value is 0.5 kPa), control algorithm programs, historical data, etc. The communication module uses an RS-485 bus interface to interact with the main control system of the cleaning robot, realizing the collaborative work of the dust removal system and other components (such as the drive system, navigation system, etc.).

[0029] Compressed air tank: A compressed air tank with a capacity of 0.5L is selected, and the working pressure is 0.6 - 0.8 MPa. The compressed air tank is connected to the dust suction port and the dust removal pipeline through a solenoid valve, and the solenoid valve is controlled by the microprocessor of the control system. When the backwashing program is triggered, the microprocessor sends an opening signal to the solenoid valve, and the compressed air tank releases compressed air to form a pulsed backwashing air flow with a duration of 0.2 seconds, which is precisely controlled by the timer of the microprocessor.

[0030] Software algorithm Pressure data acquisition and processing: The microprocessor collects the pressure data of the pressure sensor array every 50 ms, calculates the pressure difference of each group of pressure sensors (the pressure value at the air outlet end minus the pressure value at the air inlet end), and takes the average value of 6 groups of pressure differences as the current real-time pressure difference ΔP. In order to eliminate noise interference, a moving average filtering algorithm is used to process the pressure data, and the size of the filtering window is 10 sampling points.

[0031] Clogging self-detection algorithm: As shown in the appendix Figure 4 After the system is started, it first initializes and sets the preset pressure threshold P_threshold to 0.5 kPa. Then it enters the loop detection state, continuously collects pressure data and calculates the pressure difference ΔP. Compare ΔP with P_threshold. If ΔP > P_threshold, it is judged that clogging has occurred, and the backwashing program and the power adjustment program are triggered; if ΔP ≤ P_threshold, continue to monitor. When judging clogging, in order to avoid misjudgment, it is set that the corresponding program is triggered only when ΔP > P_threshold is detected continuously 3 times, which improves the detection accuracy.

[0032] Backwashing program and power adjustment program: When the backwashing program is triggered, as shown in the appendix Figure 5 The microprocessor first sends an opening signal to the solenoid valve to control the compressed air tank to release compressed air to form a pulsed backwashing air flow lasting for 0.2 seconds. At the same time, it sends an instruction to the frequency converter to reduce the speed of the fan and adjust the fan power to 60% of the current power (for example, if the current power is 200W, it is adjusted to 120W) to reduce the suction force and cooperate with the backwashing air flow to more effectively remove the clogging. During the backwashing process, continuously monitor the pressure difference ΔP. When ΔP ≤ P_threshold, immediately close the solenoid valve, stop the backwashing, and gradually restore the fan power, increasing by 10% of the current power each time, with an interval of 1 second, until the power before backwashing is restored or the power is readjusted according to the current cleaning scenario.

[0033] Adaptive learning function: The control system records the environmental parameters (such as dust concentration, cleaning time, working mode, etc.) and processing data (such as the number of backflushes, backflush time, power adjustment range, etc.) each time a blockage occurs. Through machine learning algorithms, it analyzes the correlation between historical data and the occurrence of blockages, and automatically adjusts the preset pressure threshold P_threshold and backflush parameters (such as backflush pulse intensity, duration, power adjustment range, etc.). For example, in a specific scenario with dense dust, if blockages occur multiple times, the system will automatically slightly reduce P_threshold (such as adjusting it to 0.45 kPa) to make the blockage detection more sensitive, trigger the backflush program in advance, and prevent the occurrence of serious blockages.

[0034] Workflow After the intelligent cleaning robot is started, the dust removal system enters the initialization state. The centrifugal fan operates at the default power (such as 150 W, corresponding to a suction force of about 1500 Pa), and the pressure sensor array starts to collect the pressure data of the dust suction port in real time.

[0035] The microprocessor of the control system processes and analyzes the pressure data, calculates the real-time pressure difference ΔP, and compares it with the preset pressure threshold P_threshold (0.5 kPa).

[0036] During normal cleaning, if there is no blockage at the dust suction port, ΔP ≤ P_threshold, and the fan operates at the current power to continuously perform dust removal work.

[0037] When the dust suction port inhales more dust or a slight blockage occurs, the pressure at the intake end will decrease, resulting in an increase in the pressure difference ΔP. When ΔP > P_threshold is detected continuously three times, the control system determines that a blockage has occurred and immediately triggers the backflush program and the power adjustment program.

[0038] After the backflush program is started, the compressed air tank releases a compressed air pulse (lasting for 0.2 seconds) to backflush the dust suction port and the dust removal pipeline. At the same time, the fan power is reduced to 60% of the current value to reduce the suction force and help remove the blockage.

[0039] During the backflush process, the pressure sensor array continues to monitor the change in the pressure difference. When ΔP returns to ≤ P_threshold, the backflush program stops, and the fan power gradually returns to the normal working state, and the system continues to perform dust removal operations.

[0040] During the cleaning process, the control system continuously learns and optimizes the preset parameters, and automatically adjusts the negative pressure and backflush strategies according to different cleaning scenarios to achieve the best dust removal effect and energy utilization efficiency.

[0041] Experimental verification To verify the effectiveness of the present invention, multiple experiments were conducted in the laboratory and actual scenarios.

[0042] Laboratory experiments Pressure monitoring accuracy experiment: Under different dust concentrations and blockage degrees, the differential pressure measured by the pressure sensor array of the present invention was compared with the measured value of a standard pressure gauge. The results showed that the error between the two was within ±0.05 kPa, meeting the accuracy requirements of the system for pressure monitoring.

[0043] Blockage detection and treatment time experiment: Different degrees of blockage were artificially set, and the time from the occurrence of blockage to the system detecting and triggering the backflush program, as well as the time for blockage removal, were recorded. The experimental results showed that the average detection time was 1.2 seconds, and the average blockage removal time was 2.5 seconds, capable of quickly and effectively handling blockage problems.

[0044] Dust removal efficiency experiment: In a standard dust test chamber, the dust removal efficiencies of a traditional dust removal system and the system of the present invention were respectively tested. In an environment with a dust concentration of 10 g / m³, the dust removal efficiency of the traditional system was 65%, while the dust removal efficiency of the system of the present invention reached 88%, an increase of 23 percentage points; in an environment with a higher dust concentration (20 g / m³), the dust removal efficiency of the system of the present invention was increased to 92%, a 35% increase compared to the traditional system, consistent with the data mentioned in the abstract.

[0045] Actual scenario experiment The waiting hall of a station was selected as the actual test scenario, where there is a large flow of people, dense dust, and various dusts and debris on the ground. A cleaning robot equipped with the dust removal system of the present invention was compared and tested with a traditional cleaning robot. After 8 consecutive hours of work, the following data were recorded: Number of blockages: The traditional robot had 12 blockages and required manual intervention for cleaning; the robot of the present invention had 3 blockages, all of which were cleared through the automatic backflush program without manual intervention.

[0046] Cleaning area: Due to blockage shutdown and manual cleaning, the actual effective working time of the traditional robot was 6 hours, and the cleaning area was 500 ㎡; the effective working time of the robot of the present invention was 7.8 hours, and the cleaning area was 800 ㎡, with significantly improved working efficiency.

[0047] Dust removal effect: The residual dust amount on the ground after cleaning was measured by a professional dust detection device. The residual dust amount after cleaning by the traditional robot was 15 mg / ㎡, while the residual dust amount after cleaning by the robot of the present invention was 8 mg / ㎡, with significantly better dust removal effect.

[0048] Industrial application The dynamic negative pressure dust removal system of the intelligent cleaning robot based on pressure feedback of the present invention is not only applicable to cleaning robots in families and public places, but also can be widely applied to cleaning equipment in the industrial field, such as floor sweeping robots in factory workshops, automatic cleaning equipment in warehousing and logistics, etc. In the industrial environment, the dust particles are of different sizes and there are more impurities, which requires higher requirements for the dust removal system. The dynamic negative pressure regulation and blockage self-repair functions of the present invention can better adapt to the complex working conditions of the industrial environment, improve the reliability and working efficiency of the equipment, reduce the manual maintenance cost, and have broad industrial application prospects.

[0049] In summary, through the pressure feedback mechanism, intelligent control algorithm and adaptive learning function, the present invention realizes the dynamic negative pressure regulation and blockage self-detection and self-repair of the dust removal system of the intelligent cleaning robot, effectively solves the problem of the sharp drop in efficiency after the traditional dust removal system is blocked, significantly improves the dust removal efficiency and equipment reliability, and has important practical application value and market promotion prospects.

Claims

1. An intelligent cleaning robot dynamic negative pressure dust removal system based on pressure feedback, characterized in that: It includes a centrifugal fan, a pressure sensor array, a control system and a dust suction port; the centrifugal fan adopts variable frequency speed regulation technology, and the suction adjustment range is 500 - 3000 Pa; the dust suction port is provided with 6 groups of micro pressure sensors to respectively collect the real-time pressure values at the air inlet end and the air outlet end to calculate the air flow pressure difference; the control system is electrically connected to the centrifugal fan and the pressure sensor array, and is built-in with a blockage self-detection algorithm. When the pressure difference exceeds the preset threshold of 0.5 kPa, it triggers the compressed air pulse backwashing program and adjusts the fan power until the blockage is removed.

2. The dynamic negative pressure dust removal system according to claim 1, wherein: The pressure sensor array is of a ring array structure. Each group of pressure sensors includes an air inlet end pressure sensor and an air outlet end pressure sensor, which are respectively installed on the clean contact surface side of the air inlet channel of the dust suction port and the dust removal pipeline side of the air outlet channel. The distance between the two is 5 - 10 mm and the axis is perpendicular to the air flow direction.

3. The dynamic negative pressure dust removal system according to claim 1, wherein: The control system includes a microprocessor, a storage module and a communication module; the microprocessor is used to run the blockage self-detection algorithm, the storage module stores the preset pressure threshold, backwashing parameters and historical data, and the communication module realizes data interaction with the main control system of the cleaning robot.

4. The dynamic negative pressure dust removal system according to claim 1, wherein: The pulse duration of the compressed air pulse backwashing program is 0.1 - 0.3 seconds, the backwashing air flow pressure is 0.6 - 0.8 MPa, and the backwashing pipeline is connected to the air outlet channel of the dust suction port.

5. The dynamic negative pressure dust removal system according to claim 1, wherein: The fan power adjustment strategy is: when blockage is detected, the current power is adjusted to 60% - 80% of the rated power. After the blockage is removed, it gradually recovers at a power gradient of 10% - 20%, and the adjustment interval for each time is 0.5 - 1.5 seconds.

6. The dynamic negative pressure dust removal system according to claim 1, characterized in that: The blockage self-detection algorithm includes a moving average filtering preprocessing step, which filters 10 groups of continuously collected pressure difference data, and sets the backwashing program to be triggered when the pressure difference exceeds the threshold for 2 - 3 consecutive times.

7. The dynamic negative pressure dust removal system according to claim 3, wherein: The control system has an adaptive learning function, and automatically adjusts the preset pressure threshold (0.4 - 0.6 kPa) and the backwashing pulse duration (0.15 - 0.25 seconds) based on historical blockage data and environmental parameters.

8. The dynamic negative pressure dust removal system according to claim 2, wherein: The measurement accuracy of the air inlet end pressure sensor and the air outlet end pressure sensor is ±0.1% FS, the measurement range is 0 - 5 kPa, and the installation position avoids the turbulent flow area at the edge of the dust suction port.

9. The dynamic negative pressure dust removal system according to claim 1, wherein: It also includes a compressed air tank and a solenoid valve. The compressed air tank is connected to the air outlet channel of the dust suction port through a backwashing pipeline, and the solenoid valve is controlled by the control system to open the time to realize pulse backwashing.

10. The dynamic negative pressure dust removal system according to any one of claims 1-9, characterized in that: The variable frequency speed regulation of the centrifugal fan is controlled by a PWM signal. The pressure sensor array communicates with the microprocessor through an I2C bus, and the control system and the main control system exchange data through an RS-485 bus.

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