Civil air defense building ventilation opening protective door equipment with efficient purification and self-cleaning functions

Abstract

The invention relates to the technical field of civil air defense building ventilation equipment, in particular to efficient-purification and self-cleaning civil air defense building ventilation opening protective door equipment which comprises a door frame, a door body, a driving unit, a sensing system, a purification device, a self-cleaning execution unit and a control unit. The sensing system comprehensively collects environment and equipment data, and the control unit adopts a weighted fusion algorithm to calculate a comprehensive pollution index and determine a pollution level; the protection and ventilation efficiency coefficients under different door body opening and closing angles are calculated based on fluid analogue simulation; dynamically solving the optimal opening and closing angle of the door body and the optimal operation parameters of the purification device through a multi-objective optimization algorithm; and the self-cleaning effect is intelligently triggered and verified according to the filter screen state and the pollution level. Self-adaptive intelligent regulation and control of the protective door in a complex pollution environment are achieved, the safety protection and ventilation efficiency is effectively balanced, and the purification capacity and the automatic maintenance level are improved.

Description

Technical Field

[0001] The present invention relates to the field of civil air defense engineering and air purification technology, and in particular to a highly efficient and self-cleaning air vent protective door device for civil air defense buildings. Background Art

[0002] The ventilation vent protection door equipment of civil air defense buildings is a comprehensive protection device that adjusts the opening and closing angle of the door body, purifies the air and realizes the self-cleaning function. It is mainly composed of a door frame, a door body, a drive unit, a sensor system, a purification device and a self-cleaning execution unit. The various components work together to realize functions such as air purification, pollution protection and equipment maintenance.

[0003] The utility model with announcement number: CN220955362U discloses a rail transit clean ventilation protection closed door, including a door frame, a civil defense door body placed in the door frame, a ventilation opening on the civil defense door body, an installation cavity and the ventilation opening connected, an air duct connected to the outside is provided on the top of the installation cavity, and multiple ventilation fans are symmetrically installed on the inside of the duct, and a ventilation component for controlling the opening and closing state of the ventilation opening is connected to the civil defense door body. When the ventilation fan works, the outside air is transported to the ventilation opening through the duct, and the internal air is output to the outside of the duct through the ventilation fan in the opposite direction, so as to ventilate the interior of the space. At the same time, the rotating wheel drives the linkage gear group composed of a connecting gear and a tooth plate to move, and the tooth plate follows the rotation displacement of the connecting gear, thereby driving the sealing plate to move left and right, thereby controlling the opening and closing state of the ventilation opening position, and the tooth plate is connected to the installation cavity through a guide seat and a slide groove to increase the smoothness of the tooth plate during movement.

[0004] The utility model with announcement number: CN219119101U discloses a clean ventilation protection civil airtight door, including a door frame and a sealed door body, and a heat dissipation mechanism is provided on the outside of the sealed door body; the sealed door body is composed of a fireproof board, a heat insulation layer, a sound insulation layer and an insulating layer, and the number of the fireproof boards is two, and the two fireproof boards are respectively located on the side where the heat insulation layer and the insulating layer are separated; the heat dissipation mechanism includes an installation box fixedly installed on the outside of the sealed door body, a mounting frame fixedly installed on the inside of the installation box, and a cooling fan fixedly installed on the outside of the mounting frame.

[0005] The equipment currently used to protect vents is usually equipped with basic filtering devices to achieve air purification functions. However, the environmental data collection dimension is single and fails to be deeply coupled with the control algorithm, resulting in limited dynamic adjustment capabilities of the purification device operating parameters and certain limitations in purification efficiency under different pollution levels. In addition, the cleaning of filters in existing equipment mostly relies on manual operation or a simple timed cleaning mechanism. The cleaning trigger logic fails to introduce external environmental pollution variables, and lacks quantifiable verification and closed-loop adjustment of the cleaning effect. The cleaning effect is often less than ideal. In terms of door opening and closing angle adjustment, there is a lack of quantitative evaluation methods based on real-time flow field simulation, and the safety protection level has not been embedded as a rigid constraint in the optimization decision-making process, which limits the adaptability of the equipment in complex environments.

[0006] In response to the above problems, the present invention provides a highly efficient and self-cleaning air vent protection door device for civil air defense buildings to solve the above problems. Summary of the Invention

[0007] The purpose of the present invention is to provide a highly efficient and self-cleaning air vent protection door device for civil air defense buildings to solve the above problems.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0009] A highly efficient and self-cleaning air vent protection door device for civil air defense buildings, comprising a door frame, a door body embedded in the door frame, and a drive unit connected to the door body;

[0010] Also includes:

[0011] A sensor system for collecting environmental data and equipment status data; the environmental data includes toxic gas concentration, particulate matter concentration, temperature and humidity, and ventilation flow rate inside the vent; the equipment status data includes door opening and closing angle, drive unit operating current, pressure difference across the purification device filter, cumulative purification device operating time, and self-cleaning medium remaining;

[0012] A purification device, arranged on the ventilation path, for filtering and purifying the air;

[0013] A self-cleaning execution unit, configured to perform a cleaning action on the purification device;

[0014] a control unit, electrically connected to the sensing system, the driving unit, the purification device, and the self-cleaning execution unit, respectively;

[0015] The control unit is configured to:

[0016] Step A: Based on the environmental data, a comprehensive pollution index is calculated using a weighted fusion algorithm, and the pollution level, purification priority and protection level are determined accordingly;

[0017] Step B: Based on the pollution level, the ventilation flow rate, and the door structure parameters, calculate the protection effect coefficient and the ventilation efficiency coefficient under different door opening angles through fluid simulation simulation;

[0018] Step C: Based on the protection effect coefficient, the ventilation efficiency coefficient, the protection level, and the driving unit operating current, calculate the optimal opening angle of the door, and control the driving unit to drive the door to the optimal opening angle;

[0019] Step D: Based on the purification priority, the pollutant concentration inside and outside the ventilation port, calculate the optimal operating parameters of the purification device through an optimization algorithm, and control the purification device to work according to the optimal operating parameters;

[0020] Step E: Based on the pressure difference across the filter screen, the cumulative operating time, the purification efficiency, the pollution level, and the amount of self-cleaning medium, determine the self-cleaning requirement, and control the self-cleaning execution unit to perform the corresponding cleaning action.

[0021] Further, the weighting fusion algorithm in step A includes: calculating the gas pollution sub-index, the particulate matter pollution sub-index, the temperature and humidity deviation index, and the flow rate deviation index respectively; using a preset weight to fuse each sub-index to obtain the comprehensive pollution index; the preset weight is determined by training a plurality of sets of historical environmental data.

[0022] Further, the fluid simulation simulation process in step B includes: establishing a three-dimensional model of the door and the door frame; using the ventilation flow rate and the pollutant concentration corresponding to the pollution level as boundary conditions; by solving the fluid dynamics equation, the flow field and the pollutant concentration distribution under different door opening angles are obtained, and the protection effect coefficient and the ventilation efficiency coefficient are calculated accordingly.

[0023] Further, the control unit is further configured to periodically execute steps A and B, dynamically adjust the boundary conditions according to the updated pollution level, and recalculate the protection effect coefficient and the ventilation efficiency coefficient to achieve adaptive adjustment of the door opening angle.

[0024] Further, the process of calculating the optimal opening angle in step C includes:

[0025] Construct a multi-objective optimization function with the protection effect coefficient, the ventilation efficiency coefficient, and an energy consumption coefficient calculated based on the driving current as the target; find the angle that makes the optimization function value maximum as the preliminary optimal angle by traversing the door opening angle; then correct the preliminary optimal angle according to the protection level to obtain the final optimal opening angle.

[0026] Further, the optimization algorithm in step D is a particle swarm optimization algorithm, and the control unit is configured to dynamically adjust an inertia weight of the particle swarm optimization algorithm according to the level of the purification priority, wherein the higher the purification priority, the smaller the inertia weight, so as to accelerate the convergence speed.

[0027] Further, the process of judging the self-cleaning demand in step E includes:

[0028] determining a cleaning demand correction coefficient according to the pollution level; calculating a filter screen pollution index based on the pressure difference between the two sides of the filter screen, the cumulative running time and the purification efficiency; multiplying the filter screen pollution index by the cleaning demand correction coefficient to obtain a self-cleaning demand degree; and when the self-cleaning demand degree exceeds a threshold value, selecting a self-cleaning mode according to the remaining amount of the self-cleaning medium.

[0029] Further, the control unit is further configured to form a self-cleaning closed-loop control: after the self-cleaning execution unit completes the cleaning action, verifying the cleaning effect; if the decrease of the pressure difference between the two sides of the filter screen after cleaning does not reach a first preset proportion, or the recovery of the purification efficiency does not reach a second preset proportion, then controlling the self-cleaning execution unit to upgrade the cleaning mode or prolong the cleaning time before performing the cleaning action again.

[0030] Further, the control unit is configured to form a door body adjustment closed-loop control: after controlling the driving unit to act, receiving the real-time opening and closing angle of the door body and the working current of the driving unit fed back by the sensing system; if the deviation of the real-time opening and closing angle of the door body from the target angle exceeds an angle tolerance, then adjusting the output of the driving unit to correct the deviation; if the working current of the driving unit exceeds a safe current threshold value, then immediately stopping driving and triggering an alarm signal.

[0031] Further, the fluid simulation model in step B is calibrated by experimental data, and the calibration process includes: comparing the simulation data and the measured data under different door body opening and closing angles and ventilation flow rates, and adjusting the fluid physical property parameters in the simulation model until the error between the simulation data and the measured data is less than a preset error threshold.

[0032] Compared with the prior art, the present application has the following beneficial effects:

[0033] The present application solves the problem of limited environmental data collection range of traditional equipment by comprehensively collecting environmental data through a sensing system and calculating a comprehensive pollution index in combination with a weighted fusion algorithm. The protective effect coefficient and ventilation efficiency coefficient under different door opening angles are calculated through fluid simulation, and the comprehensive optimization of the door opening angle is realized. At the same time, a self-cleaning closed-loop control mechanism is introduced, which determines whether to upgrade the cleaning method or extend the cleaning time after the self-cleaning execution unit completes the cleaning action according to the cleaning effect, solving the problem of unsatisfactory cleaning effect of traditional equipment. The operating parameters of the purification device are dynamically adjusted through the particle swarm optimization algorithm, improving the response speed and processing capacity of the equipment under the condition of sudden high concentration pollution. The door adjustment closed-loop control mechanism of the present application can monitor the door opening angle and driving unit working current in real time, ensure that the door is accurately adjusted to the target angle, and avoid safety hazards caused by overload. The purification device of the present application adopts a multi-layer filter screen structure, which significantly improves the air purification efficiency, and through the buckle structure, the filter screen can be conveniently disassembled and replaced, reducing the maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0035] Figure 1 The present application is a system architecture diagram.

[0036] Figure 2 The present application is a control unit logic flow chart.

[0037] Reference signs:

[0038] 101 door frame, 102 door body, 103 driving unit, 104 control unit, 105 self-cleaning execution unit, 106 sensing system, 107 purification device. DETAILED DESCRIPTION

[0039] In the following, only some exemplary embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the embodiments of the present application. Therefore, the drawings and the description are considered to be exemplary in nature rather than limiting. The embodiments of the present application are described in detail below with reference to the drawings.

[0040] Example 1:

[0041] Reference Figure 1 and Figure 2The utility model provides a high -efficient purifying and self -cleaning civil air defense building ventilation opening protective door equipment, including door frame 101, the door body 102 of embedding in door frame 101 and with door body 102 transmission connection drive unit 103,

[0042] Further comprising:

[0043] Sensing system 106 for collecting environmental data and equipment state data, the environmental data includes the concentration of toxic gases, particulate matter concentration, temperature and humidity and ventilation flow rate inside the ventilation opening, the equipment state data includes the opening angle of the door body 102, the working current of the drive unit 103, the pressure difference on both sides of the filter screen of the purification device, the cumulative running time of the purification device and the remaining amount of self-cleaning medium;

[0044] Purification device arranged on the ventilation path for filtering and purifying air;

[0045] Self-cleaning execution unit 105 for performing cleaning action on the purification device;

[0046] Control unit 104 is electrically connected with the sensing system 106, the drive unit 103, the purification device and the self-cleaning execution unit 105 respectively;

[0047] The control unit 104 is configured to:

[0048] Step A: based on the environmental data, the comprehensive pollution index is calculated by weighted fusion algorithm, and the pollution grade, purification priority and protection level are determined accordingly;

[0049] Step B: based on the pollution grade, the ventilation flow rate and the structure parameters of the door body 102, the protection effect coefficient and the ventilation efficiency coefficient under different opening angles of the door body 102 are calculated by fluid simulation simulation;

[0050] Step C: based on the protection effect coefficient, the ventilation efficiency coefficient, the protection level and the working current of the drive unit 103, the optimal opening angle of the door body 102 is calculated, and the drive unit 103 is controlled to drive the door body 102 to the optimal opening angle;

[0051] Step D: based on the purification priority, the concentration of pollutants inside and outside the ventilation opening, the optimal operating parameters of the purification device are calculated by optimization algorithm, and the purification device is controlled to work according to the optimal operating parameters;

[0052] Step E: based on the pressure difference on both sides of the filter screen, the cumulative running time, the purification efficiency, the pollution grade and the remaining amount of self-cleaning medium, the self-cleaning demand is judged, and the self-cleaning execution unit 105 is controlled to perform the corresponding cleaning action.

[0053] Furthermore, the weighted fusion algorithm in step A includes: respectively calculating the gas pollution sub-index, the particulate matter pollution sub-index, the temperature and humidity deviation index, and the flow rate deviation index; using preset weights to fuse each sub-index to obtain the comprehensive pollution index; the preset weights are determined by training with multiple sets of historical environmental data.

[0054] Furthermore, the fluid simulation process in step B includes: establishing a three-dimensional model of the door body 102 and the door frame 101; using the ventilation flow rate and the pollutant concentration corresponding to the pollution level as boundary conditions; obtaining the flow field and pollutant concentration distribution under different opening and closing angles of the door body 102 by solving the fluid dynamics equation, and calculating the protection effect coefficient and the ventilation efficiency coefficient accordingly.

[0055] Furthermore, the control unit 104 is further configured to: periodically execute steps A and B, dynamically adjust the boundary conditions according to the updated pollution level, and recalculate the protection effect coefficient and the ventilation efficiency coefficient to achieve adaptive adjustment of the opening and closing angle of the door body 102.

[0056] Furthermore, the process of calculating the optimal opening and closing angle in step C includes:

[0057] A multi-objective optimization function is constructed with the protection effect coefficient, the ventilation efficiency coefficient and an energy consumption coefficient calculated based on the driving current as the targets; by traversing the opening and closing angles of the door body 102, the angle that maximizes the value of the optimization function is found as the preliminary optimal angle; and the preliminary optimal angle is then corrected according to the protection level to obtain the final optimal opening and closing angle.

[0058] Furthermore, the optimization algorithm in step D is a particle swarm optimization algorithm, and the control unit 104 is configured to dynamically adjust the inertia weight of the particle swarm optimization algorithm according to the level of the purification priority, wherein the higher the purification priority, the smaller the inertia weight, so as to speed up the convergence speed.

[0059] Furthermore, the process of determining the self-cleaning requirement in step E includes:

[0060] A cleaning requirement correction coefficient is determined according to the pollution level; a filter pollution index is calculated based on the pressure difference on both sides of the filter, the cumulative operating time and the purification efficiency; the filter pollution index is multiplied by the cleaning requirement correction coefficient to obtain a self-cleaning requirement degree; when the self-cleaning requirement degree exceeds a threshold, a self-cleaning mode is selected according to the remaining amount of the self-cleaning medium.

[0061] Furthermore, the control unit 104 is further configured to form a self-cleaning closed-loop control: after the self-cleaning execution unit 105 completes the cleaning action, the cleaning effect is verified; if the drop in the pressure difference on both sides of the filter after cleaning does not reach a first preset ratio, or the recovery of the purification efficiency does not reach a second preset ratio, the self-cleaning execution unit 105 is controlled to upgrade the cleaning mode or extend the cleaning time and then perform the cleaning action again.

[0062] Furthermore, the control unit 104 is configured to form a closed-loop control for adjusting the door body 102: after controlling the action of the drive unit 103, it receives the real-time opening and closing angle of the door body 102 and the working current of the drive unit 103 fed back by the sensing system 106; if the deviation between the real-time opening and closing angle of the door body 102 and the target angle exceeds the angle tolerance, the output of the drive unit 103 is adjusted to correct the deviation; if the working current of the drive unit 103 exceeds the safety current threshold, the drive is stopped immediately and an alarm signal is triggered.

[0063] Furthermore, the fluid simulation model in step B is calibrated with experimental data. The calibration process includes: comparing the simulation data with the measured data under different door body 102 opening and closing angles and ventilation flow rates, and adjusting the fluid physical parameters in the simulation model until the error between the simulation data and the measured data is less than a preset error threshold.

[0064] Among them, in step A, in the weighted fusion algorithm, the training method of the preset weights is a multivariate linear regression or neural network algorithm, which is obtained by supervised learning based on historical environmental data and pollution level labels annotated by experts.

[0065] In step B, the fluid simulation is performed using CFD software (such as ANSYS Fluent or OpenFOAM), and the mapping relationship between the pollutant concentration and the pollution level in the boundary conditions is determined by a table lookup method, as shown below: Pollution level PM2.5 concentration (μg / m³) CO concentration (ppm) 1 ≤35 ≤10 2 35~75 10~20 3 75~150 20~50 4 >150 >50

[0066] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is further supplemented below with reference to a specific application scenario.

[0067] The embodiment discloses a high-efficiency purification and self-cleaning civil air defense building ventilation opening protective door equipment, which comprises a door frame 101, a door body 102, a purification device 107, a self-cleaning execution unit 105, a sensing system 106 and a driving unit 103. The door frame 101 is a basic frame of an integral structure, the door body 102 is installed at the upper and lower ends of the inner side of the door frame 101 through a rotating shaft, the rotating shaft is connected with the driving unit 103 through a transmission mechanism, the transmission mechanism is composed of a worm and a worm wheel, wherein the worm is fixedly connected with the output shaft of a speed reducer in a coaxial mode, and the worm wheel is fixedly connected with the rotating shaft of the door body 102. The driving unit 103 comprises a motor and a speed reducer, the motor is connected with the input shaft of the speed reducer through a shaft coupling, and the output shaft of the speed reducer is connected with the transmission mechanism through a gear pair, so that the opening and closing action of the door body 102 is realized.

[0068] The purification device 107 is arranged on the ventilation path and located in the ventilation channel between the door frame 101 and the door body 102, close to the inner side of the ventilation opening. The purification device 107 adopts a multi-layer filter screen structure and comprises a primary filter screen, a high-efficiency filter screen and an activated carbon filter screen, and each layer of filter screen is fixed in the frame of the purification device 107 through a buckle structure. The primary filter screen is used for intercepting large particles, the high-efficiency filter screen is used for filtering fine particles, and the activated carbon filter screen is used for adsorbing toxic gases. The design of the buckle structure makes the filter screen convenient to disassemble and replace, thereby reducing the maintenance cost. Differential pressure sensors are arranged on the two sides of the purification device 107 respectively, for detecting the differential pressure change on the two sides of the filter screen.

[0069] The self-cleaning execution unit 105 comprises a spraying assembly and a recovery assembly, the spraying assembly is connected with a liquid storage tank through a flexible pipeline, a one-way valve is arranged in the flexible pipeline to prevent the backflow of the cleaning medium. The spraying assembly is installed on one side of the purification device 107 and is used for spraying the cleaning medium to the surface of the filter screen, and the cleaning medium can be water or special cleaning liquid. The recovery assembly is located below the purification device 107 and is used for collecting the waste generated in the cleaning process, and the waste is discharged to an external collecting device through a pipeline.

[0070] The sensing system 106 comprises a plurality of sensor modules, and each sensor module is connected with a control unit 104 through a wireless communication protocol. The shell of the sensor module is made of shielding material to reduce the influence of electromagnetic interference on signal transmission. The sensing system 106 is used for collecting environmental data and equipment state data, wherein the environmental data comprises the concentration of toxic gases, the concentration of particles, the temperature and humidity and the ventilation flow rate on the inner side of the ventilation opening, and the equipment state data comprises the opening angle of the door body 102, the working current of the driving unit 103, the differential pressure on the two sides of the filter screen of the purification device 107, the cumulative running time of the purification device 107 and the remaining amount of the self-cleaning medium. The sensor modules are distributed at key positions of the equipment, for example, the gas sensor and the particle sensor are installed on the inner side of the ventilation opening close to the purification device 107, the temperature and humidity sensor is installed on the outer side of the ventilation opening, and the differential pressure sensors are installed on the two sides of the purification device 107.

[0071] The control unit 104 is configured as a core processing module and is electrically connected to the sensor system 106, the drive unit 103, the purification device 107 and the self-cleaning execution unit 105. The control unit 104 performs the following steps based on the collected data:

[0072] First, a weighted fusion algorithm is used to calculate a comprehensive pollution index based on the environmental data collected by sensor system 106. This index is then used to determine the pollution level, purification priority, and protection level. The weighted fusion algorithm separately calculates the gas pollution sub-index, the particulate matter pollution sub-index, the temperature and humidity deviation index, and the flow rate deviation index. These sub-indexes are then fused using preset weights, determined through training with multiple sets of historical environmental data, to create a comprehensive pollution index.

[0073] Next, based on the pollution level, ventilation flow rate, and the structural parameters of the door 102, a fluid simulation was performed to calculate the protection effectiveness coefficient and ventilation efficiency coefficient at different door 102 opening and closing angles. The fluid simulation model was calibrated using experimental data. This calibration process involved comparing the simulated data with the measured data at different door 102 opening and closing angles and ventilation flow rates, and adjusting the fluid physical properties in the simulation model until the error between the simulated and measured data was less than a preset error threshold.

[0074] Next, the optimal opening and closing angle of the door body 102 is calculated based on the protection effect coefficient, ventilation efficiency coefficient, protection level, and the operating current of the drive unit 103, and the door body 102 is adjusted to this angle via the drive unit 103. A multi-objective optimization function is constructed with the protection effect coefficient, ventilation efficiency coefficient, and an energy consumption coefficient calculated based on the drive current as its objectives. By traversing the opening and closing angles of the door body 102, the angle that maximizes the optimization function value is found as the preliminary optimal angle. This preliminary optimal angle is then modified based on the protection level to obtain the final optimal opening and closing angle.

[0075] Control unit 104 also uses an optimization algorithm to calculate the optimal operating parameters for purification device 107 based on the purification priority and the pollutant concentrations inside and outside the vents, and controls purification device 107 to operate according to these parameters. The optimization algorithm is a particle swarm optimization (PSO) algorithm. Control unit 104 dynamically adjusts the inertia weight of the PSO algorithm based on the purification priority. The higher the purification priority, the smaller the inertia weight, which accelerates convergence.

[0076] In addition, the control unit 104 determines the self-cleaning demand based on the pressure difference across the filter screen, the cumulative running time, the purification efficiency, the pollution level, and the remaining amount of self-cleaning medium, and controls the self-cleaning execution unit 105 to perform the corresponding cleaning action. The cleaning demand correction coefficient is determined according to the pollution level, the filter screen pollution index is calculated based on the pressure difference across the filter screen, the cumulative running time, and the purification efficiency, the self-cleaning demand degree is obtained by multiplying the filter screen pollution index by the cleaning demand correction coefficient, and when the self-cleaning demand degree exceeds the threshold value, the self-cleaning mode is selected according to the remaining amount of self-cleaning medium.

[0077] The control unit 104 forms a self-cleaning closed-loop control, and verifies the cleaning effect after the self-cleaning execution unit 105 completes the cleaning action. If the decrease in the pressure difference across the filter screen after cleaning does not reach the first preset proportion, or the recovery of the purification efficiency does not reach the second preset proportion, the self-cleaning execution unit 105 is controlled to upgrade the cleaning mode or prolong the cleaning time before performing the cleaning action again. The control unit 104 also forms a door body 102 adjustment closed-loop control, and receives the real-time opening and closing angle of the door body 102 and the working current of the driving unit 103 fed back by the sensing system 106 after the driving unit 103 is actuated. If the deviation of the real-time opening and closing angle of the door body 102 from the target angle exceeds the angle tolerance, the output of the driving unit 103 is adjusted to correct the deviation; if the working current of the driving unit 103 exceeds the safe current threshold, the driving is immediately stopped and an alarm signal is triggered.

[0078] A sealing strip is arranged between the door frame 101 and the door body 102, and is made of multiple layers of composite materials. The outer layer is corrosion-resistant rubber, and the inner layer is elastic foam material, so as to improve the sealing performance and reduce vibration noise. The sealing strip is installed on the inner side edge of the door frame 101 and tightly adheres to the outer surface of the door body 102. The motor of the driving unit 103 is installed on the top of the door frame 101, the reducer is connected with the motor through a shaft coupling, the output shaft of the reducer is engaged with the transmission mechanism through a gear pair, and the worm and the worm wheel of the transmission mechanism cooperate to achieve smooth opening and closing of the door body 102. The sensor modules of the sensing system 106 are distributed at key positions of the equipment, such as the gas sensor and the particulate matter sensor which are installed inside the air vent near the purification device 107, the temperature and humidity sensor which is installed outside the air vent, and the pressure difference sensor which is installed on both sides of the purification device 107.

[0079] The process of the control unit 104 collecting data through the sensing system 106 and calculating the optimal opening and closing angle and the purification parameters. The control unit 104 periodically performs a weighted fusion algorithm and a fluid simulation simulation, dynamically adjusts the boundary conditions of the fluid simulation simulation according to the updated pollution level, and recalculates the protection effect coefficient and the ventilation efficiency coefficient to achieve adaptive adjustment of the opening and closing angle of the door body 102.

[0080] The spray assembly and recovery assembly of the self-cleaning actuator 105 work in tandem. The spray assembly is connected to the liquid storage tank via a flexible pipe. A one-way valve within the flexible pipe ensures unidirectional flow of the cleaning medium, preventing backflow. The recovery assembly discharges waste generated during the cleaning process through the pipe to an external collection device, ensuring an environmentally friendly and efficient cleaning process.

[0081] The sealing strip design between the door frame 101 and the door body 102 improves the overall sealing performance of the equipment, reduces the infiltration of external pollutants, and reduces the vibration and noise during the operation of the equipment. The multi-layer filter structure of the purification device 107 significantly improves the air purification efficiency. Each layer of filter is fixed to the frame of the purification device 107 by a snap-on structure, which is easy to disassemble and replace, reducing maintenance costs. The wireless communication protocol design of the sensor system 106 reduces the complexity of wiring and improves the installation flexibility and reliability of the equipment. The closed-loop control mechanism of the control unit 104 ensures the accuracy and safety of the equipment during operation, and solves the problem of insufficient response speed and processing capacity of traditional equipment under sudden high-concentration pollution conditions.

[0082] First, when the device starts up, the multiple sensor modules in the sensing system 106 begin collecting environmental and device status data. Gas and particulate matter sensors are installed inside the vent, near the purification device 107, to monitor toxic gas and particulate matter concentrations. Temperature and humidity sensors are installed outside the vent to detect changes in temperature and humidity. Pressure differential sensors are located on both sides of the purification device 107, measuring the pressure differential across the filter in real time. This data is transmitted to the control unit 104 via a wireless communication protocol, ensuring signal transmission stability and reducing wiring complexity. Based on the collected data, the control unit 104 executes a weighted fusion algorithm to calculate the gas pollution sub-index, particulate matter pollution sub-index, temperature and humidity deviation index, and flow rate deviation index. These sub-indexes are then combined using preset weights to generate a comprehensive pollution index. This comprehensive pollution index is used to determine the current pollution level, purification priority, and protection level, providing a basis for subsequent device operation.

[0083] Next, control unit 104 uses a fluid simulation model to perform simulations based on the pollution level, ventilation velocity, and the structural parameters of door 102. Using ventilation velocity and pollutant concentration as boundary conditions, the fluid simulation model solves fluid dynamics equations to determine the flow field distribution and pollutant concentration changes at different door 102 opening and closing angles.

[0084] On this basis, the control unit 104 calculates the protection effect coefficient and the ventilation efficiency coefficient corresponding to each opening and closing angle.

[0085] To ensure the accuracy of the simulation results, the fluid simulation model is calibrated with experimental data. The calibration process includes comparing the simulation data with the measured data under different door body 102 opening and closing angles and ventilation flow rates, and adjusting the fluid physical properties in the simulation model until the error is less than a preset threshold.

[0086] Subsequently, control unit 104 constructs a multi-objective optimization function based on the protection effectiveness coefficient, ventilation efficiency coefficient, and energy consumption coefficient. By traversing the opening and closing angles of door body 102, it finds the angle that maximizes the optimization function value as the preliminary optimal angle. This angle is then modified based on the protection level, ultimately determining the optimal opening and closing angle of door body 102. Based on the instructions from control unit 104, drive unit 103 adjusts door body 102 to the target angle via a reducer and transmission mechanism, achieving a comprehensive optimization of protection effectiveness and ventilation efficiency.

[0087] Thirdly, the control unit 104 calculates the optimal operating parameters of the purification device 107 using a particle swarm optimization algorithm based on the purification priority and the pollutant concentrations inside and outside the vent.

[0088] Under high pollution levels, control unit 104 dynamically reduces the inertia weight of the particle swarm optimization algorithm to accelerate convergence and quickly adjust the operating mode of purification device 107. The multi-layer filter structure of purification device 107 includes a primary filter, a high-efficiency filter, and an activated carbon filter. Each layer of filter is fixed to the frame by a snap-fit ​​structure, making it easy to remove and replace.

[0089] The primary filter intercepts large particles, the high-efficiency filter filters fine particles, and the activated carbon filter absorbs toxic gases, significantly improving air purification efficiency. The control unit 104 monitors the pressure difference between the two sides of the filter through a pressure differential sensor and determines the degree of contamination of the filter based on the accumulated operating time and purification efficiency.

[0090] When the self-cleaning demand obtained by multiplying the filter pollution index and the cleaning demand correction coefficient exceeds the preset threshold, the control unit 104 starts the self-cleaning execution unit 105. The spray assembly draws the cleaning medium from the liquid storage tank through a flexible pipe and sprays the cleaning liquid onto the surface of the filter. The one-way valve in the flexible pipe ensures the one-way flow of the cleaning medium to avoid backflow. The recovery component is located below the purification device 107 and is used to collect waste generated during the cleaning process and discharge it to an external collection device through a pipe to ensure the environmental protection and high efficiency of the cleaning process. After the self-cleaning is completed, the control unit 104 verifies the cleaning effect. If the pressure difference on both sides of the filter does not drop to the first preset ratio, or the purification efficiency recovery does not reach the second preset ratio, the control unit 104 upgrades the cleaning method or extends the cleaning time and then performs the cleaning action again to form a closed-loop control.

[0091] Furthermore, control unit 104 forms a closed-loop control mechanism for regulating door 102. After drive unit 103 activates, control unit 104 receives feedback from sensor system 106 on the door 102's real-time opening and closing angle and the drive unit's operating current. If the deviation between the door 102's real-time opening and closing angle and the target angle exceeds the angle tolerance, control unit 104 adjusts the drive unit's 103 output to correct the deviation. If the drive unit's operating current exceeds the safety current threshold, drive unit 103 immediately stops and triggers an alarm signal to ensure safe operation of the device.

[0092] The sealing strip design between the door frame 101 and the door body 102 further enhances the overall sealing performance of the equipment. Made of a multi-layer composite material, with a corrosion-resistant rubber outer layer and an elastic foam inner layer, this strip not only reduces the infiltration of external contaminants but also reduces vibration and noise during operation. The motor of the drive unit 103 is connected to the reducer via a coupling. The reducer's output shaft meshes with the transmission mechanism via a gear pair. The worm and worm gear of the transmission mechanism cooperate to achieve smooth opening and closing of the door body 102, ensuring reliable operation of the equipment.

[0093] This invention achieves efficient purification and self-cleaning capabilities for protective doors used in civil air defense building vents through comprehensive data collection from the sensor system 106, precise calculations using fluid simulations, dynamic adjustments using a particle swarm optimization algorithm, and effective verification of self-cleaning closed-loop control. These components work together to address existing technical issues, including limited environmental data collection, inflexible filter cleaning mechanisms, a lack of comprehensive optimization for adjusting the door's opening and closing angle, and insufficient response capabilities to sudden high-concentration pollution. This significantly improves the device's adaptability and protective effectiveness.

[0094] This invention establishes an integrated intelligent control system combining full-dimensional data collection, intelligent algorithm optimization, closed-loop functional control, and coordinated structural adaptation. This system addresses the problems of traditional civil air defense building vent protection door equipment (door frame 101, door body 102) with limited environmental data collection, difficulty balancing protection and ventilation efficiency, rigid purification parameter control, unreliable self-cleaning effectiveness, high maintenance costs, and insufficient safety. This system offers a multi-dimensional breakthrough. Breaking through the limitations of traditional single-indicator monitoring, the system utilizes a sensor system 106 to provide comprehensive coverage of both environmental and equipment data. Environmental data encompasses toxic gas concentrations, particulate matter concentrations, temperature and humidity, and ventilation flow rate within the vent. Equipment status data includes the opening and closing angle of the door body 102, the operating current of the drive unit 103, the pressure differential across the filter screen of the purification device 107, the accumulated operating time, and the remaining self-cleaning medium in the self-cleaning execution unit 105. Combined with a weighted fusion algorithm whose weights are trained using multiple sets of historical environmental data, the system accurately calculates a comprehensive pollution index. This index is then used to scientifically categorize pollution levels, purification priorities, and protection levels, providing precise data support for subsequent control.

[0095] The present invention adopts a fluid simulation model calibrated by experiments. By comparing the simulation and measured data under different door body 102 opening and closing angles and ventilation flow rates, the fluid physical parameters are adjusted to an error less than a preset threshold value, and the protection effect coefficient and ventilation efficiency coefficient corresponding to different door body 102 opening and closing angles are calculated. At the same time, a multi-objective optimization function is constructed that integrates the protection effect coefficient, ventilation efficiency coefficient and energy consumption coefficient based on the current of the drive unit 103. The angles are first traversed to determine the preliminary optimal angle, and then the final optimal opening and closing angle is obtained based on the correction of the protection level. The data collection and simulation calculation of the sensor system 106 are periodically executed, and the boundary conditions are dynamically adjusted to realize adaptive optimization of the door body 102 angle, effectively balancing safety protection and ventilation efficiency.

[0096] To address the control difficulties of purification device 107, the present invention adopts a particle swarm optimization algorithm to dynamically adjust the inertia weight according to the purification priority. The higher the priority, the smaller the inertia weight, which accelerates the convergence speed. The optimal operating parameters of purification device 107 are quickly solved based on the pollutant concentrations inside and outside the ventilation port, thereby improving the response and processing capabilities in sudden high-concentration pollution scenarios.

[0097] The self-cleaning demand determination and closed-loop control mechanism of the self-cleaning execution unit 105 of the present invention determines the cleaning demand correction coefficient based on the pollution level, and calculates the filter pollution index in combination with the pressure difference on both sides of the filter of the purification device 107, the cumulative operating time and the purification efficiency. The two are multiplied to obtain the self-cleaning demand degree. When the threshold is exceeded, the cleaning method is selected according to the self-cleaning medium residue of the self-cleaning execution unit 105, and the pressure difference decrease ratio and the purification efficiency recovery ratio of the purification device 107 filter are verified after cleaning. If the preset standard is not met, the cleaning method is upgraded or the time is extended, thereby solving the problem of poor cleaning effect of traditional timing / single triggering.

[0098] The present invention constructs a closed-loop control for adjusting the door body 102, and compares the actual opening and closing angle of the door body 102 with the target angle in real time through the control unit 104. When the deviation exceeds the tolerance limit, the output of the drive unit 103 is adjusted for correction. At the same time, the current of the drive unit 103 is monitored through the sensor system 106. When the safety threshold is exceeded, the control unit 104 immediately stops the drive of the drive unit 103 and alarms, taking into account both control accuracy and equipment safety.

[0099] Example 2:

[0100] This embodiment is basically the same as the first embodiment, except that, in this embodiment, the control unit is configured as follows:

[0101] Step A: Based on the environmental data, a comprehensive pollution index is calculated using a weighted fusion algorithm, and the pollution level, purification priority and protection level are determined accordingly;

[0102] Step B: driving a fluid simulation model calibrated by experimental data with the pollution concentration corresponding to the pollution level and the ventilation flow rate as dynamic boundary conditions to calculate the protection effect coefficient and ventilation efficiency coefficient under different door opening angles;

[0103] Step C: constructing a multi-objective optimization function with the protection effect coefficient, ventilation efficiency coefficient and energy consumption coefficient as targets to obtain a preliminary optimal opening angle; then, according to the protection level, the preliminary result is forcibly corrected to obtain the final optimal opening angle of the door, and the driving unit is controlled to drive the door to the optimal opening angle;

[0104] The multi-objective optimization function is as follows:

[0105]

[0106] represents the comprehensive performance evaluation value when the door opening angle is is the protection effect coefficient, is the ventilation efficiency coefficient, is the energy consumption coefficient, is the weight coefficient, the value range is [0.1], and is determined by the analytic hierarchy process (AHP) combined with expert scoring.

[0107] Step D: based on the purification priority, the pollutant concentrations inside and outside the ventilation port, the optimal operating parameters of the purification device are calculated by a particle swarm optimization algorithm with dynamic adjustment of inertia weight, and the purification device is controlled to work according to the optimal operating parameters; wherein the higher the purification priority, the smaller the inertia weight value, and the faster the algorithm converges;

[0108] Step E: according to the pollution level, a cleaning demand correction coefficient is determined; based on the pressure difference across the filter screen, the cumulative running time and the purification efficiency, a filter screen pollution index is calculated; the two are multiplied to obtain the self-cleaning demand degree; when the self-cleaning demand degree exceeds the threshold value, the self-cleaning mode is selected according to the self-cleaning medium remaining amount.

[0109] The threshold value is a dynamic threshold value, which is jointly calibrated according to historical self-cleaning effect data and pollution levels, as follows: when the pollution level is 1, 2 or 3, the self-cleaning demand degree threshold value is set to 80, 70 or 60.

[0110] ​​​​​Furthermore, the control unit is further configured to form a self-cleaning closed-loop control: after the self-cleaning execution unit completes the cleaning action, the cleaning effect is verified; the verification indicators include whether the drop in the pressure difference on both sides of the filter after cleaning reaches a first preset ratio, and whether the recovery of the purification efficiency reaches a second preset ratio; if the standards are not met, the self-cleaning execution unit is controlled to upgrade the cleaning method or extend the cleaning time and then perform the cleaning action again until the verification indicators are met.

[0111] Furthermore, the control unit is configured to form a closed-loop control of the door body adjustment: the control includes a dual-loop structure: a precision control loop, which is used to adjust the output of the drive unit according to the deviation between the real-time opening and closing angle of the door body and the target angle to correct the deviation; and a safety monitoring loop, which is used to immediately stop driving and trigger an alarm signal when the operating current of the drive unit exceeds the safety current threshold.

[0112] In specific implementation:

[0113] The control unit 104 performs the following steps based on the collected data:

[0114] First, a weighted fusion algorithm is used to calculate a comprehensive pollution index based on the environmental data collected by sensor system 106. This index is then used to determine the pollution level, purification priority, and protection level. The weighted fusion algorithm separately calculates the gas pollution sub-index, the particulate matter pollution sub-index, the temperature and humidity deviation index, and the flow rate deviation index. These sub-indexes are then fused using preset weights, determined through training with multiple sets of historical environmental data, to create a comprehensive pollution index.

[0115] Next, using the pollutant concentrations corresponding to the real-time collected ventilation flow rates and pollution levels as dynamically changing boundary conditions, the control unit drives a pre-installed fluid simulation model calibrated with experimental data to calculate the protection effectiveness coefficient and ventilation efficiency coefficient at different door opening and closing angles. During calibration, the simulated data for different door opening and closing angles and ventilation flow rates are compared with the measured data, and the fluid physical property parameters in the simulation model are adjusted until the error is less than a preset threshold.

[0116] Next, a multi-objective optimization function is constructed, with the protection effectiveness coefficient, ventilation efficiency coefficient, and an energy consumption coefficient calculated based on the drive current as its objectives. This function is solved by traversing the door opening and closing angles, and a preliminary optimal angle that maximizes the function value is obtained. Subsequently, an independently determined protection level is introduced as a rigid constraint, and a mandatory correction is made to this preliminary optimal angle to obtain the final safe and efficient optimal opening and closing angle. The door 102 is then adjusted to this angle via the drive unit 103.

[0117] Control unit 104 also uses a particle swarm optimization (PSO) algorithm to calculate the optimal operating parameters for purification device 107 based on the purification priority and pollutant concentrations inside and outside the vents. This innovation lies in dynamically mapping the purification priority, a business logic metric, to the PSO algorithm's inertia weight parameter: the higher the purification priority, the smaller the inertia weight. This accelerates the algorithm's convergence, allowing for rapid response to high-risk pollution situations and controlling purification device 107 to operate according to optimal parameters.

[0118] Furthermore, control unit 104 innovatively couples the external environment with internal conditions to determine self-cleaning requirements: a cleaning requirement correction factor is determined based on the pollution level; a filter contamination index is calculated based on the pressure difference across the filter, accumulated operating time, and purification efficiency; and the two are multiplied together to determine the self-cleaning requirement. If the self-cleaning requirement exceeds a threshold, a self-cleaning mode is selected based on the remaining self-cleaning medium.

[0119] Control unit 104 further implements a self-cleaning closed-loop control system with verifiable results and scalable strategies. After self-cleaning execution unit 105 completes a cleaning operation, the system does not automatically assume that the cleaning is effective. Instead, it verifies two quantifiable performance indicators: whether the pressure difference across the filter has dropped by a first preset ratio, and whether the purification efficiency has recovered by a second preset ratio. If either indicator fails to be verified, self-cleaning execution unit 105 is controlled to upgrade the cleaning method (e.g., increase the spray pressure) or extend the cleaning time before re-running the cleaning operation and re-verifying the results until the desired results are achieved.

[0120] The control unit 104 also forms a dual-loop door control architecture that integrates precision and safety monitoring. After the drive unit 103 is activated, the precision control loop continuously receives feedback from the sensor system 106 on the real-time opening and closing angle of the door 102, and compares it with the target angle. If the deviation exceeds the angle tolerance, the output of the drive unit 103 is adjusted to correct the deviation. The safety monitoring loop continuously monitors the operating current of the drive unit 103. If the current exceeds the safety current threshold, the drive is immediately stopped and an alarm signal is triggered. These two loops operate in parallel to ensure that the door adjustment process is both precise and safe.

[0121] This invention comprehensively collects environmental data through the sensor system 106 and calculates a comprehensive pollution index using a weighted fusion algorithm, addressing the limited scope of environmental data collection in traditional equipment. By introducing an experimentally calibrated fluid simulation model and using real-time data to drive the calculation of the protection effectiveness coefficient and ventilation efficiency coefficient at different door opening and closing angles, scientific, quantitative, and dynamic optimization of door opening and closing angles is achieved.

[0122] At the same time, a self-cleaning demand model based on pollution level correction and a closed-loop control mechanism for cleaning effect verification and upgrade execution are introduced to solve the problem of rigid cleaning trigger logic and unguaranteed cleaning effect of traditional equipment. By dynamically mapping the purification priority to the optimization algorithm parameters, the response speed and processing capacity of the equipment under sudden high-concentration pollution conditions are improved. The door adjustment of the present invention adopts a precision and safety dual-loop closed-loop control mechanism, which can monitor the door opening and closing angle and the operating current of the drive unit in real time to ensure that the door is accurately adjusted to the target angle while avoiding safety hazards caused by overload.

[0123] Example 3:

[0124] This embodiment is further optimized based on implementation 1 or embodiment 2. In this embodiment, the control unit is connected to a digital twin cloud platform;

[0125] The digital twin cloud platform builds an ultra-high-precision virtual model of the protective door equipment, which is continuously trained and calibrated through real-time and historical data uploaded by the sensor system;

[0126] The control unit is further configured to:

[0127] Step F: Synchronizing the real-time data collected by the sensing system to the digital twin cloud platform;

[0128] Step G: receiving an instruction issued by the digital twin cloud platform, the instruction including: a remaining useful life (RUL) of the filter calculated based on the virtual model prediction;

[0129] Optimizing the boundary conditions and physical parameters of the fluid simulation model based on historical data and real-time flow field simulation;

[0130] Equipment maintenance strategy library generated based on multi-scenario rehearsals.

[0131] Among them, the equipment maintenance strategy library in step G includes the optimal purification device start-stop timing for different pollutants, the drive unit torque compensation parameters for different wear levels, and the optimal triggering time of the self-cleaning execution unit based on predictive RUL.

[0132] In this embodiment, the control unit 104 establishes a low-latency, high-bandwidth connection with the digital twin cloud platform through a 5G module.

[0133] The digital twin cloud platform uses ANSYS Twin Builder or an equivalent tool to construct an ultra-high-precision virtual model of multiple physical fields, encompassing door dynamics, fluid dynamics, and filter adsorption dynamics. The virtual model's initial parameters are derived from the equipment design drawings. During operation, the virtual model continuously receives full data uploaded by the sensor system 106 (once per second) for self-calibration, ensuring a synchronization error rate of less than 3% with the physical entity.

[0134] In specific implementation, the control unit 104 performs the following steps in addition to the original steps AE:

[0135] Step F: Package and encrypt the real-time collected environment and device status data, and synchronize them to the digital twin cloud platform.

[0136] Step G: Receive and execute the optimization instructions issued by the cloud platform.

[0137] For example, the cloud platform predicts that the current filter RUL is only 48 hours based on the filter pressure differential growth rate, pollutant adsorption model, and historical cleaning performance data. Control unit 104 pre-schedules self-cleaning execution unit 105 accordingly and issues a filter replacement warning to maintenance personnel 12 hours before the RUL is exhausted, thus avoiding sudden failure.

[0138] The cloud platform leverages its powerful computing power to mine massive amounts of historical data and discovers that when particulate matter concentration suddenly increases, prioritizing the high-efficiency filter to run at maximum power for 10 seconds before activating the activated carbon filter can reduce energy consumption by 15% compared to starting both simultaneously. This optimized start-stop sequence is then sent to control unit 104, which updates the execution logic for step D.

[0139] A cloud platform simulation revealed that after 100,000 cycles, wear and tear on the worm gear mechanism of drive unit 103 would cause a 5% drop in transmission efficiency. Therefore, it issued a set of torque compensation parameters. When the control unit 104 detected that the number of actuations was approaching this threshold, it automatically increased the upper limit of the drive current by 5%, compensating for the torque loss caused by wear and ensuring consistent door angle control accuracy.

[0140] The introduction of digital twins has transformed the system from a post-event response to a pre-event prediction. Comparative laboratory data shows that the accuracy of filter replacement warnings has increased to 95%, preventing purification failures caused by filter breakdown. Through continuous cloud-based optimization of operating strategies, overall equipment energy consumption has been reduced by 10-15%. Preventive maintenance of key mechanical components is expected to increase the mean time between failures by 20%.

[0141] Furthermore, in some preferred embodiments, the sensing system 106 further includes a MEMS ultrasonic array sensor, and a transmitting end and a receiving end of the MEMS ultrasonic array sensor are non-contactly arranged on both sides of the filter screen of the purification device 107;

[0142] The control unit (104) is further configured to:

[0143] Step H: controlling the MEMS ultrasonic array sensor to transmit ultrasonic waves of a specific frequency toward the filter and receiving a penetration signal;

[0144] Step I: Analyze the sound velocity attenuation, spectrum change, and phase offset of the penetrating ultrasound wave and input them into a trained deep learning model to directly output the filter's pollutant load and pollutant component identification results, such as oily particles, water-based particles, and fibers;

[0145] When determining the need for self-cleaning in step E, the filter contamination index is calculated based on the pollutant load and pollutant composition identification results. The deep learning model in step I is a convolutional neural network (CNN). The CNN training data is derived from a dataset of correspondences between ultrasonic penetration signals at different levels of contamination and pollutant composition, and actual values ​​obtained by standard weighing and chemical analysis methods.

[0146] In actual use, a MEMS ultrasonic array sensor is added near the primary filter and the high-efficiency filter of the purification device 107, and the model can be TDK's CH101.

[0147] In specific implementation, the control unit 104 performs steps H and I regularly (e.g., every 30 minutes):

[0148] Ultrasonic pulses with a frequency of 1 MHz are transmitted through the filter.

[0149] The signal obtained by the receiving end is compared with the signal of the transmitting end, such as:

[0150] The calculated sound velocity has attenuated by 15%, and the center frequency has shifted by 5 kHz. These characteristic values ​​are input into a trained CNN model, the model file of which is stored in the flash memory of control unit 104. The model quickly outputs that the current pollutant load on the filter is 250 g / m², with oily particulate matter accounting for over 70%.

[0151] Based on this component identification result, the control unit 104 makes an intelligent decision in step E: since the pollutants are mainly oily, the effect will be greatly reduced if only conventional water washing is used. Therefore, it controls the self-cleaning execution unit 105 to:

[0152] First, spray the special emulsified cleaner and let it react with the oil for 60 seconds.

[0153] Rinse again using high pressure water mist.

[0154] After cleaning, the ultrasonic sensor is used again to verify that the rate of sound velocity attenuation has returned to within ±2% of the clean state.

[0155] Compared with the traditional differential pressure sensor, the measurement accuracy is higher, the qualitative identification of the pollutant composition is realized, the scientific basis for the selection of the self-cleaning mode is provided, and the waste or incomplete cleaning caused by the misuse of the cleaning medium is avoided. At the same time, the non-contact measurement completely solves the industry pain point that the sensor itself is polluted and fails.

[0156] Although the preferred embodiments of the present application have been described, those skilled in the art who understand the basic inventive concept can make further changes and modifications to these embodiments. Therefore, the appended claims are intended to include the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0157] The above is only a preferred embodiment of the present application, and is not intended to limit the present application. It should be noted that any modification, equivalent replacement and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A highly efficient and self-cleaning protective door system for air vents in air-defense buildings, comprising a purification device, a self-cleaning actuator, a door frame, a door body embedded in the door frame, and a drive unit connected to the door body. The purification device is positioned in the ventilation path for filtering and purifying air; the self-cleaning actuator is configured to clean the purification device. Its characteristics are: It also includes a control unit and a sensor system, wherein the control unit is electrically connected to the sensor system, the drive unit, the purification device and the self-cleaning execution unit respectively; The sensing system is used to collect environmental data and equipment status data; the control unit is configured to: Step A: Based on environmental data, a weighted fusion algorithm is used to calculate the comprehensive pollution index, and the pollution level, purification priority, and protection level are determined accordingly; Step B: Based on the pollution level, ventilation flow rate and door structure parameters, calculate the protection effect coefficient and ventilation efficiency coefficient at different door opening and closing angles through fluid simulation; Step C: Calculating the optimal opening and closing angle of the door based on the protection effect coefficient, ventilation efficiency coefficient, protection level, and operating current of the drive unit, and controlling the drive unit to drive the door to the optimal opening and closing angle; Step D: Based on the purification priority and the pollutant concentrations inside and outside the vents, the optimal operating parameters of the purification device are calculated through an optimization algorithm, and the purification device is controlled to operate according to the optimal operating parameters; Step E: Based on the pressure difference on both sides of the filter, the accumulated operating time, the purification efficiency, the pollution level and the remaining amount of the self-cleaning medium, the self-cleaning demand is determined, and the self-cleaning execution unit is controlled to perform the corresponding cleaning action.

2. A highly efficient and self-cleaning air vent protection door device for civil air defense buildings according to claim 1, characterized in that: The weighted fusion algorithm in step A includes: The gas pollution sub-index, particulate matter pollution sub-index, temperature and humidity deviation index, and flow rate deviation index are calculated separately; the sub-indices are fused using preset weights to obtain a comprehensive pollution index; the preset weights are determined through training with multiple sets of historical environmental data.

3. According to claim 1, a highly efficient and self-cleaning air vent protection door device for civil air defense buildings, characterized in that: The fluid simulation process in step B includes: A three-dimensional model of the door body and door frame was established; the ventilation flow rate and pollutant concentration corresponding to the pollution level were used as boundary conditions; the flow field and pollutant concentration distribution at different door opening and closing angles were obtained by solving the fluid dynamics equations, and the protection effect coefficient and ventilation efficiency coefficient were calculated accordingly.

4. A highly efficient and self-cleaning air vent protection door device for air-raid buildings according to claim 3, characterized in that: The control unit is further configured to: Periodically execute steps A and B, dynamically adjust the boundary conditions according to the updated pollution level, and recalculate the protection effect coefficient and ventilation efficiency coefficient to achieve adaptive adjustment of the door opening and closing angle.

5. According to claim 1, a highly efficient and self-cleaning air defense building ventilation door device, characterized in that: The process of calculating the optimal opening and closing angle in step C includes: A multi-objective optimization function is constructed with the protection effect coefficient, ventilation efficiency coefficient and an energy consumption coefficient calculated based on the driving current as the targets. The angle that maximizes the optimization function value is found by traversing the door opening and closing angles as the preliminary optimal angle. The preliminary optimal angle is then corrected according to the protection level to obtain the final optimal opening and closing angle.

6. According to claim 1, a highly efficient and self-cleaning air-defense building ventilation door device, characterized in that: The optimization algorithm in step D is a particle swarm optimization algorithm, and the control unit is configured to dynamically adjust the inertia weight of the particle swarm optimization algorithm according to the level of purification priority, wherein the higher the purification priority, the smaller the inertia weight, so as to speed up the convergence speed.

7. According to claim 1, a highly efficient and self-cleaning air vent protection door device for civil air defense buildings, characterized in that: The process of determining the self-cleaning requirement in step E includes: A cleaning requirement correction coefficient is determined based on the pollution level; a filter pollution index is calculated based on the pressure difference on both sides of the filter, the cumulative operating time and the purification efficiency; the filter pollution index is multiplied by the cleaning requirement correction coefficient to obtain the self-cleaning requirement degree; when the self-cleaning requirement degree exceeds the threshold, the self-cleaning method is selected based on the remaining self-cleaning medium.

8. A highly efficient and self-cleaning air vent protection door device for civil air defense buildings according to any one of claims 1 to 7, characterized in that: The control unit is further configured to form a self-cleaning closed-loop control: after the self-cleaning execution unit completes the cleaning action, the cleaning effect is verified; if the drop in the pressure difference on both sides of the filter after cleaning does not reach a first preset ratio, or the recovery of the purification efficiency does not reach a second preset ratio, the self-cleaning execution unit is controlled to upgrade the cleaning method or extend the cleaning time and then perform the cleaning action again.

9. According to claim 1, a highly efficient and self-cleaning air vent protection door device for civil air defense buildings, characterized in that: The control unit is configured to form a closed-loop control of door adjustment: after controlling the action of the drive unit, it receives feedback from the sensing system on the real-time opening and closing angle of the door and the operating current of the drive unit; if the deviation between the real-time opening and closing angle of the door and the target angle exceeds the angle tolerance, the output of the drive unit is adjusted to correct the deviation; if the operating current of the drive unit exceeds the safety current threshold, the drive is stopped immediately and an alarm signal is triggered.

10. According to claim 1, a highly efficient and self-cleaning air vent protection door device for civil air defense buildings, characterized in that: The fluid simulation model in step B is calibrated with experimental data. The calibration process includes: comparing the simulation data with the measured data under different door opening and closing angles and ventilation flow rates, and adjusting the fluid physical properties in the simulation model until the error between the simulation data and the measured data is less than a preset error threshold.

Images