An adaptive air pressure balance sliding door control system

Abstract

The application discloses a kind of self-adapting air pressure balance sliding door control systems, it is related to sliding door control technical field.The sensor layer, PID control module, execution layer and prediction maintenance module are included;Sensor layer gathers pressure difference, sealed pressure, vibration, door position, temperature and humidity and other data, PID control module uses the improved algorithm with nonlinear correction and output amplitude limiting, the pressure difference is stabilized at 2Pa target value, simultaneously through closed loop PID, hydraulic sealed pressure is stabilized at 1.0MPa, and anti-integral saturation and output amplitude limiting are configured to ensure operation safety;Execution layer includes servo motor drive sliding door, hydraulic sealing system, emergency pressure relief valve and audible and visual alarm device;Prediction maintenance module is based on random forest regression model, according to the characteristics such as sealed pressure drop rate, valve response time, motor current variance, vibration level, operation frequency, remaining service life is predicted, and maintenance suggestion is output according to critical, early warning, normal classification.

Description

Technical Field

[0001] This invention relates to the field of sliding door control technology, and more specifically to an adaptive air pressure balance sliding door control system. Background Technology

[0002] Existing sliding door control systems generally suffer from low control accuracy and poor stability in air pressure balance regulation. They are prone to overshoot and difficulty in eliminating static error when pressure differential fluctuates, failing to achieve stable and precise adaptive air pressure regulation. Simultaneously, the lack of closed-loop optimization strategies in sealing pressure control makes them susceptible to pressure instability and seal failure, affecting door sealing performance and operational safety. Furthermore, traditional maintenance methods rely on regular manual inspections, which cannot intelligently predict equipment operating status and remaining lifespan, resulting in insufficient fault warning capabilities. This leads to high equipment failure rates, high maintenance costs, and limited service life, making it difficult to meet the demands for high safety, high reliability, and intelligent operation.

[0003] Therefore, how to propose an adaptive air pressure balance sliding door control system to overcome the shortcomings of existing technologies is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides an adaptive air pressure balance sliding door control system, overcoming the shortcomings of existing sliding door air pressure balance control such as insufficient accuracy, unstable adjustment, and easy overshoot, and achieving high-precision, adaptive, and zero-static-error differential pressure stability control. The hydraulic sealing system pressure control strategy is optimized to ensure constant and reliable sealing pressure, improving the door's airtightness and structural safety. An intelligent predictive maintenance mechanism is introduced to accurately predict the remaining service life of the equipment and provide early warning of faults, transforming passive maintenance into proactive maintenance. To achieve the above objectives, the present invention adopts the following technical solution: An adaptive air pressure balance sliding door control system includes: Sensor layer, PID control module, execution layer, and predictive maintenance module; The sensor layer is used to collect data and transmit it to the PID control module; The PID control module is used to output control commands based on the collected data; The execution layer is used to receive control commands and control the operation of the components. The predictive maintenance module is used to collect and process operational data and output maintenance suggestions.

[0005] Optionally, the sensor layer includes: a differential pressure sensor, a vibration sensor, a sealing pressure sensor, a door position sensor, and a temperature and humidity sensor.

[0006] Optionally, the execution layer includes: a servo motor-driven sliding door, a hydraulic sealing system, an emergency pressure relief valve, and an audible and visual alarm device, wherein the servo motor-driven sliding door is sealed and connected to the hydraulic sealing system and the emergency pressure relief valve, respectively.

[0007] Optionally, the PID control module includes: pressure balance regulation based on PID control, combined with nonlinear correction and output limiting, with the following control formula: ; Where u is the valve opening degree. This represents the error between the current differential pressure and the target differential pressure. This represents the rate of change of pressure difference; =0.05, =0.001, =0.1 is the PID coefficient; Nonlinear correction factor: ; =0.2 is the maximum valve opening limit.

[0008] Optionally, the specific steps of the PID control include: (1) Initial generation: Set PID parameters , , Target pressure difference Δ ptarget =2Pa, maximum opening u max =0.2; Initialize the integral term I=0; (2) Balanced sequence execution: 1) Obtain the current differential pressure and confirm the system status; 2) Pre-open the valve with a very small increase in elevation; 3) Dynamic adjustment: a. Sample the current pressure difference e and rate of change at a fixed period Δt. ; b. Update the integral term: I←I+e△t; c. Calculate the proportional term p = ; like If p < 20, then p ← p × 0.3; Calculate the integral term = ×I; Calculate the differential term d= ; Combined to get u pid =p+ +d; Limiting: u=min(u pid u max ); d. Output the opening command u to the servo motor to drive the sliding door; e. Determine whether the equilibrium condition has been met. If e is less than the threshold, repeat a to e if not. 4) Make fine adjustments as you approach the target to eliminate steady-state error and achieve balance; 5) After balancing is complete, release the seal and end the process; (3) Error handling If any step fails, the error is recorded and the process terminates, iterating until the preset number of steps is reached or until success is achieved.

[0009] Optionally, the hydraulic sealing system further includes sealing pressure control; (1) Read the current pressure; (2) Calculate the pressure error; (3) Update the integral term, perform accumulation and anti-integral saturation; (4) Calculate the differential term; (5) Calculate the PID output; (6) Output limiting, constraining the output to the range [0, 1]; (7) Execute control and output the valve opening command; (8) Update the status and save the current error for use in the next cycle of differential calculation; The sealing pressure is stabilized at the target value of 1.0 MPa through closed-loop feedback control, and the system is limited by anti-integral saturation and output limiting.

[0010] Optionally, the sealing pressure control specifically includes: (1) Error calculation ; in, =1.0MPa, This represents the current measured pressure. (2) Integral term ; The discrete form is: ; The integral value is subject to anti-integral saturation limiting: ; (3) Differential term: ; The discrete form is: ; (4) PID output ; in, , , The preset proportional, integral, and differential coefficients; (5) Output limiting The valve opening command is limited to the range [0, 1]. ; The output value is used to drive the valve actuator of the hydraulic sealing system.

[0011] Optionally, the predictive maintenance module includes: Receive raw data containing time series data from multiple sensors; Calculate the feature values ​​from the original data to form the feature vector x; Input the feature vector x into the random forest regression model to obtain the predicted remaining useful life; The lifespan prediction values ​​are categorized into states, and maintenance recommendations are output.

[0012] Optionally, the predictive maintenance module specifically includes: The feature vector is calculated from the raw sensor data: ; in, For the rate of decrease in sealing pressure, Valve response time Let Variance be the motor current. For vibration level, Number of operations; The trained random forest regression model F is used to predict the feature vectors: RUL=F( ); Random forests consist of multiple decision trees {T1, T2, ..., T} 100 The output is the average of all tree predictions, composed of [predictions]. ; The state is divided according to the predicted RUL.

[0013] Optionally, the partitioning state includes: ; The corresponding outputs suggest immediate maintenance, planned maintenance, and normal maintenance.

[0014] As can be seen from the above technical solution, compared with the prior art, the present invention discloses an adaptive air pressure balance sliding door control system, which has the following beneficial effects: This invention proposes an adaptive air pressure balance sliding door control system, comprising: a sensor layer, a PID control module, an execution layer, and a predictive maintenance module. The sensor layer is used to collect data and transmit it to the PID control module. The PID control module is used to output control commands based on the collected data. The execution layer is used to receive control commands and control the operation of the components. The predictive maintenance module is used to collect and process operating data and output maintenance suggestions. This invention employs a PID control algorithm with nonlinear correction and output limiting, which can quickly stabilize the pressure difference at the target value of 2 Pa, with gentle adjustment within a small error range, no overshoot, no steady-state error, and a stable and reliable balancing effect. Through closed-loop PID and anti-integral saturation and output limiting strategies, the sealing pressure is stabilized at 1.0 MPa, ensuring safe operation of the actuator and long-lasting sealing performance. Based on a random forest model, intelligent prediction of remaining service life is achieved, and maintenance suggestions are output in three levels: critical, early warning, and normal, which can predict faults in advance and significantly reduce downtime risks and maintenance costs. Through real-time monitoring by multi-source sensors, emergency pressure relief valves, and audible and visual alarm linkage, abnormal states can be handled quickly, improving the door's operational safety and environmental adaptability. By optimizing the control logic, mechanical shock and component wear are reduced, the overall durability of the system is improved, and the service life of the sliding door and its drive and sealing mechanisms is extended. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 The present invention provides a structural framework diagram of an adaptive air pressure balance sliding door control system. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention discloses an adaptive air pressure balance sliding door control system, such as... Figure 1 As shown, it includes: Sensor layer, PID control module, execution layer, and predictive maintenance module; The sensor layer is used to collect data and transmit it to the PID control module; The PID control module is used to output control commands based on the collected data; The execution layer is used to receive control commands and control the operation of the components. The predictive maintenance module is used to collect and process operational data and output maintenance suggestions.

[0019] Furthermore, the sensor layer includes: a differential pressure sensor, a vibration sensor, a sealing pressure sensor, a gate position sensor, and a temperature and humidity sensor. Specifically, it includes a high-precision differential pressure sensor (MS5611-01BA03, accuracy ±0.2Pa), a vibration sensor (ADXL1002, bandwidth 11kHz), a sealing pressure sensor (Honeywell 40PC100G2A), a gate position sensor (SICKWL12-3P2431), and a temperature and humidity sensor (SHT35-DIS-B).

[0020] Furthermore, the execution layer includes: a servo motor driven sliding door (Panasonic A6 series), a hydraulic sealing system (Enerpac RR-2012), an emergency pressure relief valve (automatically opens at a set value of 500Pa), and an audible and visual alarm device. The servo motor driven sliding door is sealed and connected to the hydraulic sealing system and the emergency pressure relief valve, respectively.

[0021] Furthermore, the PID control module includes: based on PID control, combined with nonlinear correction and output limiting, performing air pressure balance regulation, with the following control formula: ; Where u is the valve opening degree. This represents the error between the current differential pressure and the target differential pressure. This represents the rate of change of pressure difference; =0.05, =0.001, =0.1 is the PID coefficient; Nonlinear correction factor: ; =0.2 is the maximum valve opening limit.

[0022] This formula calculates the valve opening based on the real-time differential pressure and its rate of change, and eliminates steady-state error through the integral term to ensure that the system smoothly reaches a balanced state.

[0023] Furthermore, the specific steps of the PID control include: (1) Initial generation: Set PID parameters , , Target pressure difference Δ ptarget=2Pa, maximum opening u max =0.2; Initialize the integral term I=0; (2) Balanced sequence execution: 1) Obtain the current differential pressure and confirm the system status; 2) Pre-open the valve with a very small increase in elevation; 3) Dynamic adjustment: a. Sample the current pressure difference e and rate of change at a fixed period Δt. ; b. Update the integral term: I←I+e△t; c. Calculate the proportional term p = ; like If p < 20, then p ← p × 0.3; Calculate the integral term = ×I; Calculate the differential term d= ; Combined to get u pid =p+ +d; Limiting: u=min(u pid u max ); d. Output the opening command u to the servo motor to drive the sliding door; e. Determine whether the equilibrium condition has been met. If e is less than the threshold, repeat a to e if not. 4) Make fine adjustments as you approach the target to eliminate steady-state error and achieve balance; 5) After balancing is complete, release the seal and end the process; (3) Error handling If any step fails, the error is recorded and the process terminates, iterating until the preset number of steps is reached or until success is achieved.

[0024] This algorithm achieves stable and precise control of air pressure through piecewise nonlinear adjustment and output limiting.

[0025] In a specific embodiment, the door opening sequence (fully automatic) control process includes: 1. System self-test (3 seconds): ① Sensor status confirmation ② Power supply voltage check ③ Emergency stop status 2. Pressure equilibrium phase (30-120 seconds): ① Read the internal and external pressure difference (ΔP) ② If |ΔP| < 5Pa: skip balancing ③ If |ΔP| 5-100Pa: slow balancing (valve opening 5%) ④ If |ΔP| > 100Pa: multi-stage balancing (valve opening 10% → 5% → 2%) ⑤ Monitor vibration in real time and dynamically adjust the venting speed; 3. Sealing and releasing phase (5 seconds): ① Step-by-step pressure relief: 100%→70%→40%→10%→0% ② Confirm that the seal is completely detached; 4. Door movement phase (10 seconds): ① Acceleration curve: 0→0.1m / s²→0.05m / s²→0 ② Real-time vibration monitoring, stop immediately if limits are exceeded; 5. Completed status: ① Locking device released ② Status indicator light switched.

[0026] Furthermore, the hydraulic sealing system also includes sealing pressure control; (1) Read the current pressure; (2) Calculate the pressure error; (3) Update the integral term, perform accumulation and anti-integral saturation; (4) Calculate the differential term; (5) Calculate the PID output; (6) Output limiting, constraining the output to the range [0, 1]; (7) Execute control and output the valve opening command; (8) Update the status and save the current error for use in the next cycle of differential calculation; The sealing pressure is stabilized at the target value of 1.0 MPa through closed-loop feedback control, and the system is limited by anti-integral saturation and output limiting.

[0027] Furthermore, the sealing pressure control specifically includes: (1) Error calculation ; in, =1.0MPa, This represents the current measured pressure. (2) Integral term ; The discrete form is: ; The integral value is subject to anti-integral saturation limiting: ; (3) Differential term: ; The discrete form is: ; (4) PID output ; in, , , The preset proportional, integral, and differential coefficients; (5) Output limiting The valve opening command is limited to the range [0, 1]. ; The output value is used to drive the valve actuator of the hydraulic sealing system.

[0028] Furthermore, the predictive maintenance module includes: Receive raw data containing time series data from multiple sensors; Calculate the feature values ​​from the original data to form the feature vector x; Input the feature vector x into the random forest regression model to obtain the predicted remaining useful life; The lifespan prediction values ​​are categorized into states, and maintenance recommendations are output.

[0029] Furthermore, the predictive maintenance module specifically includes: The feature vector is calculated from the raw sensor data: ; in, For the rate of decrease in sealing pressure, Valve response time Let Variance be the motor current. For vibration level, Number of operations; The trained random forest regression model F is used to predict the feature vectors: RUL=F( ); Random forests consist of multiple decision trees {T1, T2, ..., T} 100 The output is the average of all tree predictions, composed of [predictions]. ; The state is divided according to the predicted RUL.

[0030] Furthermore, the division state includes: ; The corresponding outputs suggest immediate maintenance, planned maintenance, and normal maintenance.

[0031] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0032] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An adaptive air pressure balance sliding door control system, characterized in that, include: Sensor layer, PID control module, execution layer, and predictive maintenance module; The sensor layer is used to collect data and transmit it to the PID control module; The PID control module is used to output control commands based on the collected data; The execution layer is used to receive control commands and control the operation of the components. The predictive maintenance module is used to collect and process operational data and output maintenance suggestions.

2. The adaptive air pressure balance sliding door control system according to claim 1, characterized in that, The sensor layer includes: a differential pressure sensor, a vibration sensor, a sealing pressure sensor, a door position sensor, and a temperature and humidity sensor.

3. The adaptive air pressure balance sliding door control system according to claim 1, characterized in that, The execution layer includes: a servo motor-driven sliding door, a hydraulic sealing system, an emergency pressure relief valve, and an audible and visual alarm device. The servo motor-driven sliding door is sealed and connected to the hydraulic sealing system and the emergency pressure relief valve, respectively.

4. The adaptive air pressure balance sliding door control system according to claim 1, characterized in that, The PID control module includes: pressure balance regulation based on PID control, combined with nonlinear correction and output limiting, with the following control formula: ； Where u is the valve opening degree. This represents the error between the current differential pressure and the target differential pressure. This represents the rate of change of pressure difference; =0.05, =0.001, =0.1 is the PID coefficient; Nonlinear correction factor: ; =0.2 is the maximum valve opening limit.

5. The adaptive air pressure balance sliding door control system according to claim 4, characterized in that, The specific steps of the PID control include: (1) Initial generation: Set PID parameters , , Target pressure difference Δ ptarget =2Pa, maximum opening u max =0.2; Initialize the integral term I=0; (2) Balanced sequence execution: 1) Obtain the current differential pressure and confirm the system status; 2) Pre-open the valve with a very small increase in elevation; 3) Dynamic adjustment: a. Sample the current pressure difference e and rate of change at a fixed period Δt. ; b. Update the integral term: I←I+e△t; c. Calculate the proportional term p = ; like If p < 20, then p ← p × 0.3; Calculate the integral term = ×I; Calculate the differential term d= ; Combined to get u pid =p+ +d; Limit width: u = min(u pid ,u max ); d. Output the opening command u to the servo motor to drive the sliding door; e. Determine whether the equilibrium condition has been met. If not, repeat steps a through e. 4) Make fine adjustments as you approach the target to eliminate steady-state error and achieve balance; 5) After balancing is complete, release the seal and end the process; (3) Error handling If any step fails, the error is recorded and the process terminates, iterating until the preset number of steps is reached or until success is achieved.

6. The adaptive air pressure balance sliding door control system according to claim 3, characterized in that, The hydraulic sealing system also includes sealing pressure control; (1) Read the current pressure; (2) Calculate the pressure error; (3) Update the integral term, perform accumulation and anti-integral saturation; (4) Calculate the differential term; (5) Calculate the PID output; (6) Output limiting, constraining the output to the range [0, 1]; (7) Execute control and output the valve opening command; (8) Update the status and save the current error for use in the next cycle of differential calculation; The sealing pressure is stabilized at the target value of 1.0 MPa through closed-loop feedback control, and the system is limited by anti-integral saturation and output limiting.

7. The adaptive air pressure balance sliding door control system according to claim 6, characterized in that, The sealing pressure control specifically includes: (1) Error calculation ； in, =1.0MPa, This represents the current measured pressure. (2) Integral term ； The discrete form is: ; The integral value is subject to anti-integral saturation limiting: ； (3) Differential term: ； The discrete form is: ; (4) PID output ； in, , , The preset proportional, integral, and differential coefficients; (5) Output limiting The valve opening command is limited to the range [0, 1]. ； The output value is used to drive the valve actuator of the hydraulic sealing system.

8. The adaptive air pressure balance sliding door control system according to claim 1, characterized in that, The predictive maintenance module includes: Receive raw data containing time series data from multiple sensors; Calculate the feature values ​​from the original data to form the feature vector x; Input the feature vector x into the random forest regression model to obtain the predicted remaining useful life; The lifespan prediction values ​​are categorized into states, and maintenance recommendations are output.

9. The adaptive air pressure balance sliding door control system according to claim 1, characterized in that, The predictive maintenance module specifically includes: The feature vector is calculated from the raw sensor data: ； in, For the rate of decrease in sealing pressure, Valve response time Let Variance be the motor current. For vibration level, Number of operations; The trained random forest regression model F is used to predict the feature vectors: RUL=F( ); Random forests consist of multiple decision trees {T1, T2, ..., T} 100 The output is the average of all tree predictions, composed of [predictions]. ； The state is divided according to the predicted RUL.

10. The adaptive air pressure balance sliding door control system according to claim 9, characterized in that, The partitioning states include: ； The corresponding outputs suggest immediate maintenance, planned maintenance, and normal maintenance.

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