Indoor multi-floor intelligent smoke control and exhaust test method, system, product and medium

Abstract

The invention discloses an indoor multi-floor intelligent smoke prevention and exhaust test method, system, product and medium, and relates to the field of ventilation system control. The method comprises the following steps: injecting an excitation signal into an air supply network in a non-fire period; differential pressure time sequence data are collected, and pressure response component amplitudes and phases are processed and extracted; substituting the excitation signal and the amplitude of the pressure response component into a pipe network fluid resistance transfer function model to obtain a real-time leakage coefficient and a real-time natural wind pressure value; according to the real-time parameters and a preset target pressure difference value, the initial target rotating speed needed by fan starting under the fire working condition is calculated, and a feedforward control instruction is generated; when a fire alarm signal is received, the feedforward control instruction is executed, and the fan is controlled to be started at the initial target rotating speed; and when the fluctuation amplitude of the real-time differential pressure value in the continuous preset time period is smaller than a preset threshold value, switching to a PID closed-loop feedback control mode. By implementing the technical scheme provided by the invention, the pressure control stability at the fire starting moment is improved.

Description

Technical Field

[0001] This application relates to the field of ventilation system control, and in particular to an indoor multi-story intelligent smoke control and exhaust testing method, system, product, and medium. Background Technology

[0002] Currently, with the acceleration of urbanization and the continuous increase in building height, super high-rise buildings have become an important symbol of modern cities. In these buildings, in order to ensure the safe evacuation of people in the event of a fire, the mechanical pressurized air supply system, as a core component of the smoke control system, delivers air to the smoke-proof stairwells and their anterooms, creating a positive pressure environment in the evacuation routes that is higher than that of the fire area, thereby blocking the intrusion of smoke and ensuring the unobstructed flow of life-saving passages.

[0003] In related technologies, pressurized air supply systems for super high-rise buildings typically employ a closed-loop feedback control strategy to maintain the target pressure difference. The system is equipped with variable frequency fans, pressure sensors, and PID controllers. When a fire alarm signal triggers the system, the fans begin operation, and pressure sensors distributed throughout the stairwells collect the current pressure values ​​in real time and feed them back to the controller. The controller compares the collected pressure values ​​with a preset target value (e.g., 50 Pa) and calculates the deviation. Subsequently, the PID algorithm dynamically adjusts the output frequency of the frequency converter based on the proportional, integral, and derivative characteristics of this deviation: when the measured pressure is lower than the target value, the fan is instructed to accelerate; conversely, when it is higher, the fan is instructed to decelerate, and this cycle continues until the pressure stabilizes within the set range.

[0004] However, in practical applications of super high-rise buildings, due to the presence of huge vertical air columns inside, seasonal temperature differences can create a chimney effect, and the airtightness of the building envelope decreases with age. In the initial stage of a fire, the start-up of fans may be subject to natural wind pressure or unknown leakage conditions. PID control, which inherently relies on lag adjustment after deviations occur, and whose control parameters are usually fixed based on static testing environments, cannot immediately match the complex fluid resistance characteristics at startup. This can easily lead to significant overshoot oscillations in the initial pressure build-up phase, causing excessively high instantaneous pressure in stairwells, making it difficult to open evacuation doors, or causing excessively slow pressure build-up, allowing smoke to seep into the passages within the golden evacuation time. Consequently, pressure control stability is poor in the initial stage of a fire. Summary of the Invention

[0005] This application provides an indoor multi-story intelligent smoke control testing method, system, product, and medium to improve pressure control stability at the moment of fire initiation.

[0006] The first aspect of this application provides a method for testing intelligent smoke control and exhaust systems on multiple floors indoors, the method comprising: During non-fire periods, the pressurized air supply fan is controlled to operate at a preset low speed, superimposed with a sinusoidal speed fluctuation signal of a preset specific frequency, which is injected into the air supply network as an excitation signal. Differential pressure time-series data between the stairwell and the vestibule is collected, and the differential pressure time-series data is processed by Fast Fourier Transform to extract the amplitude and phase of the pressure response component with the same frequency as the excitation signal. The amplitude of the excitation signal and the amplitude of the pressure response component are substituted into the pipeline fluid resistance transfer function model to calculate the real-time leakage coefficient and real-time natural wind pressure. Based on the real-time leakage coefficient and real-time natural wind pressure, combined with the preset target differential pressure value, the initial target speed is calculated, and a feedforward control command containing the initial target speed is generated. When a fire alarm signal is received, the feedforward control command is executed, controlling the pressurized air supply fan to start at the initial target speed. The real-time differential pressure value between the stairwell and the vestibule is monitored. When the fluctuation amplitude of the real-time differential pressure value is less than a preset amplitude threshold within a continuous preset time period, the system switches to PID closed-loop feedback control mode.

[0007] In the above embodiments, excitation signals are continuously injected and responses are analyzed during non-fire periods. The leakage coefficient and natural wind pressure of the building are identified and updated in real time. The accurate initial target rotation speed at the time of fire is calculated in advance. This allows the fan to start at a rotation speed that closely matches the actual needs when a fire alarm signal is received. This avoids pressure difference fluctuations or insufficiency at the initial start-up due to parameter lag or inaccuracy, thereby improving the stability of pressure control at the moment of fire start-up and ensuring the rapid and effective establishment of the smoke barrier.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, based on the real-time leakage coefficient and the real-time natural wind pressure value, combined with a preset target pressure difference value, an initial target rotational speed is calculated, and a feedforward control command containing the initial target rotational speed is generated, specifically including: The system collects the indoor and outdoor reference temperature difference during the excitation signal injection period; calculates the ratio of the real-time natural wind pressure value to the reference temperature difference value to obtain the thermo-pressure coupling sensitivity coefficient; constructs a dynamic feedforward mapping function with the indoor and outdoor temperature difference at the moment of fire start-up as the independent variable and the initial target speed of the fan as the dependent variable; when a fire alarm signal is received, the system collects the real-time indoor and outdoor temperature difference value; inputs the real-time indoor and outdoor temperature difference value into the dynamic feedforward mapping function to obtain the instantaneous initial target speed; and generates a feedforward control command containing the instantaneous initial target speed.

[0009] In the above embodiments, the initial target rotation speed for compensating for the thermal pressure effect is instantaneously calculated by the dynamic mapping function, so that the smoke control system can effectively offset or utilize the thermal pressure generated by the fire at the moment of startup, avoiding pressure imbalance caused by ignoring the influence of thermal pressure, improving the accuracy and stability of pressure difference control in the early stage of fire, and ensuring the reliable establishment of the smoke barrier.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, after inputting the real-time indoor-outdoor temperature difference value into the dynamic feedforward mapping function to obtain the instantaneous initial target rotational speed, the method further includes: Acquire real-time operating status data of the elevator control system within the building. In the case of an elevator car in a high-speed forced landing state, obtain the real-time downward speed of the elevator car. Calculate the transient aerodynamic pressure wave amplitude generated by the elevator car on the stairwell vestibule at the real-time downward speed. Calculate the speed correction coefficient based on the transient aerodynamic pressure wave amplitude using the fan similarity law. Multiply the instantaneous initial target speed by the speed correction coefficient to obtain the final execution target speed. Replace the instantaneous initial target speed in the feedforward control command with the final execution target speed.

[0011] In the above embodiments, the elevator's operating status is acquired in real time, especially during high-speed emergency landing. The amplitude of the transient aerodynamic pressure wave is calculated, and a speed correction coefficient is dynamically generated using the fan similarity law. This coefficient is then applied to the initial target speed to obtain the final target speed that fully considers elevator disturbances. This allows the smoke control system to proactively predict and effectively compensate for the instantaneous and severe fluctuations in pressure difference in the stairwell anteroom caused by the high-speed operation of the elevator. This avoids the risk of pressure differential instability or smoke backflow caused by the elevator piston effect and improves the pressure differential control accuracy and stability of the smoke control system in complex dynamic disturbance environments.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the amplitude of the excitation signal and the amplitude of the pressure response component are substituted into the pipeline fluid resistance transfer function model to calculate the real-time leakage coefficient and the real-time natural wind pressure value in reverse, specifically including: Acquire layered differential pressure response data from multiple pressure sensors installed at different floor heights in a building; construct a vertical multi-node pipe network fluid network model of the building, where each floor node contains an independent local leakage coefficient variable, and the vertical multi-node pipe network fluid network model includes a natural wind pressure variable; use the layered differential pressure response data and the amplitude of the excitation signal to perform multivariate joint identification and solution on the vertical multi-node pipe network fluid network model of the building, to obtain the layered leakage coefficient vector and the real-time natural wind pressure value; calculate the weighted average of the layered leakage coefficient vector as the real-time leakage coefficient.

[0013] In the above embodiments, by deploying pressure sensors on different floors and combining them with excitation signal responses, a refined multivariate joint identification of the vertical multi-node pipe network fluid network model of the building can be achieved. This enables the real-time acquisition of the independent leakage coefficient of each floor and the comprehensive calculation of a more representative real-time leakage coefficient, which more accurately reflects the actual air leakage characteristics at different building heights. It also captures the differences in the vertical direction of factors such as building structure and door and window sealing, improving the accuracy and adaptability of subsequent differential pressure control strategies and providing a more reliable operating basis for the smoke control system, thereby improving the control accuracy and reliability of the overall smoke control system.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, based on the real-time leakage coefficient and the real-time natural wind pressure value, combined with a preset target pressure difference value, an initial target rotational speed is calculated, and a feedforward control command containing the initial target rotational speed is generated, specifically including: The system analyzes the received fire alarm signal and extracts the floor location information of the fire. It calls the pre-built building layer leakage distribution database and retrieves the local leakage coefficient of the corresponding fire floor based on the floor location information. The local leakage coefficient is used to replace the real-time leakage coefficient, and combined with the real-time natural wind pressure value and the preset target pressure difference value, it is substituted into the fluid control equation to calculate the accurate start-up speed. The system generates a feedforward control command containing the accurate start-up speed.

[0015] In the above embodiments, after receiving a fire alarm signal, the local leakage coefficient of the fire floor can be retrieved based on the specific floor where the fire occurred. Combined with parameters such as real-time natural wind pressure, the starting speed is calculated by substituting them into the fluid control equation. This allows the smoke control system to match the actual air leakage of the fire floor when it starts up, avoiding control deviations caused by using non-fire floors or average leakage coefficients. It also establishes and maintains the pressure difference in the fire area, improving the accuracy and reliability of smoke control.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, based on the real-time leakage coefficient and the real-time natural wind pressure value, combined with a preset target pressure difference value, an initial target rotational speed is calculated, and a feedforward control command containing the initial target rotational speed is generated, specifically including: The phase difference between the pressure response component phase and the excitation signal phase is extracted, and the aerodynamic lag time constant is calculated based on the phase difference. The steady-state target speed is calculated based on the real-time leakage coefficient and the real-time natural wind pressure value. A flexible start-up acceleration curve is constructed with the steady-state target speed as the endpoint and the aerodynamic lag time constant as the acceleration limiting factor. The flexible start-up acceleration curve is discretized into a time-varying fan speed control sequence. A dynamic trajectory feedforward control command containing the fan speed control sequence is generated to replace the feedforward control command.

[0017] In the above embodiments, by analyzing the phase difference between the pressure response and the excitation signal, the aerodynamic lag time constant is calculated, and a flexible start-up acceleration curve is constructed. This allows the fan speed increase process to fully adapt to the dynamic changes in the airflow inside the building, avoiding the drastic pressure fluctuations, overshoot, or undershoot that may result from rigid start-up. The target pressure difference can be established and maintained quickly, smoothly, and accurately in the early stages of start-up, improving the stability and response efficiency of pressure difference control.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the amplitude of the excitation signal and the amplitude of the pressure response component are substituted into the pipeline fluid resistance transfer function model to calculate the real-time leakage coefficient and the real-time natural wind pressure value in reverse, specifically including: The fundamental frequency amplitude and the second harmonic frequency amplitude are extracted from the fast Fourier transform results of the differential pressure time series data; the ratio of the second harmonic frequency amplitude to the fundamental frequency amplitude is calculated to obtain the aeroelastic deformation coefficient; the basic linear leakage coefficient is calculated using the fundamental frequency amplitude, and the high-pressure corrected leakage coefficient is calculated; the basic leakage coefficient is mapped to the high-pressure corrected leakage coefficient, and the high-pressure corrected leakage coefficient is used as the real-time leakage coefficient.

[0019] In the above embodiments, by performing Fourier transform on the differential pressure time series data and analyzing the amplitudes of the fundamental and second harmonic waves, the aeroelastic deformation characteristics of the building envelope under different pressure differentials are identified and quantified. A corrected leakage coefficient, considering the influence of material deformation under high pressure, is calculated, reflecting the nonlinear leakage behavior of the building under actual working conditions, especially under high pressure differentials. Therefore, the improved accuracy of identifying building leakage characteristics allows subsequent differential pressure control strategies to more accurately adapt to complex and changing actual environments, thereby enabling the stable and reliable establishment and maintenance of an effective smoke control differential in critical moments such as fires.

[0020] In a second aspect, embodiments of this application provide an indoor multi-story intelligent smoke control and exhaust testing system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the indoor multi-story intelligent smoke control and exhaust testing system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an indoor multi-story intelligent smoke control and exhaust testing system, cause the indoor multi-story intelligent smoke control and exhaust testing system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an indoor multi-story intelligent smoke control and exhaust testing system, cause the indoor multi-story intelligent smoke control and exhaust testing system to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the indoor multi-story intelligent smoke control and exhaust testing system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the indoor multi-story intelligent smoke control and exhaust testing method provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application continuously injects excitation signals and analyzes responses during non-fire periods, identifies and updates the building's leakage coefficient and natural wind pressure in real time, and pre-calculates the accurate initial target speed when a fire occurs. This allows the fan to start at a speed that closely matches the actual needs when a fire alarm signal is received, avoiding pressure fluctuations or insufficiency in the initial startup caused by parameter lag or inaccuracy. This improves the stability of pressure control at the moment of fire startup and ensures the rapid and effective establishment of smoke barriers.

[0025] 2. This application uses a dynamic mapping function to instantaneously calculate the initial target rotation speed to compensate for the thermal pressure effect, so that the smoke control system can effectively offset or utilize the thermal pressure generated by the fire at the moment of startup, avoiding pressure imbalance caused by ignoring the influence of thermal pressure, improving the accuracy and stability of pressure difference control in the early stage of a fire, and ensuring the reliable establishment of the smoke barrier.

[0026] 3. This application acquires the elevator's operating status in real time, especially during high-speed emergency landings, calculates the amplitude of the transient aerodynamic pressure wave, dynamically generates a speed correction coefficient using the fan similarity law, and applies it to the initial target speed, thereby obtaining the final execution target speed that fully considers elevator disturbances. This enables the smoke control system to actively predict and effectively compensate for the instantaneous and severe fluctuations in pressure difference in the stairwell anteroom caused by the high-speed operation of the elevator, avoiding the risk of pressure instability or smoke backflow caused by the elevator piston effect, and improving the pressure difference control accuracy and stability of the smoke control system in complex dynamic disturbance environments. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating an indoor multi-story intelligent smoke control and exhaust testing method in this application embodiment; Figure 2 This is another flowchart illustrating the indoor multi-story intelligent smoke control and exhaust testing method in this application embodiment; Figure 3 This is an exemplary hardware structure diagram of an indoor multi-story intelligent smoke control and exhaust testing system in this application embodiment. Detailed Implementation

[0028] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0030] In related technologies, pressurized air supply systems for super high-rise buildings typically employ a PID closed-loop feedback control strategy to maintain the target pressure difference between stairwells and vestibules. When a fire alarm signal triggers the system, the fan starts operating, and pressure sensors collect the differential pressure value in real time and feed it back to the controller. The controller compares the measured pressure with the preset target value, calculates the deviation, and dynamically adjusts the fan speed according to the PID algorithm to stabilize the pressure within the set range. However, this control method, which relies on lag adjustment after the deviation occurs, has shortcomings in the complex dynamic environment of super high-rise buildings. Due to the chimney effect caused by the huge vertical air column inside the building due to temperature differences, and the decay of the airtightness of the building envelope over time, the leakage characteristics of the pipeline network and the natural wind pressure value constantly change. PID control parameters are usually fixed based on the static debugging environment, making it difficult to match the current complex fluid resistance characteristics at the moment the fan starts. This can easily lead to large overshoot oscillations in the early stage of pressure establishment, causing difficulties in opening evacuation doors, or slow pressure establishment leading to smoke infiltration, thus affecting the stability of pressure control in the early stages of a fire.

[0031] In this embodiment, during non-fire periods, a sinusoidal speed fluctuation signal of a preset specific frequency is superimposed on the pressurized air supply fan as excitation, and the differential pressure time-series data between the stairwell and the vestibule is collected and processed by Fast Fourier Transform. This allows for real-time identification of the real-time leakage coefficient and real-time natural wind pressure value of the pipeline network. Based on these dynamically updated parameters, an initial target speed that closely matches the current actual needs can be pre-calculated before a fire occurs, and feedforward control commands can be generated. When a fire alarm signal is received, the fan starts directly at this initial target speed, avoiding large pressure fluctuations caused by parameter lag or inaccuracy in the initial startup phase, ensuring the rapid and stable establishment of the smoke barrier. After the pressure fluctuation amplitude stabilizes, the system switches to PID closed-loop feedback control mode for adjustment, thereby improving the pressure control stability and response efficiency of the smoke control system at the moment of fire initiation.

[0032] Figure 1 This is a flowchart illustrating the indoor multi-story intelligent smoke control and exhaust testing method used in the embodiments of this application, including the following steps: S101. During non-fire periods, control the pressurized air supply fan to operate at a preset low speed and superimpose a sinusoidal speed fluctuation signal of a preset specific frequency as an excitation signal into the air supply duct network.

[0033] Among them, non-fire periods refer to the time periods when no fire occurs in the building and the smoke control and exhaust system is in standby, routine operation, or maintenance status; pressurized air supply fans refer to the mechanical equipment in the smoke control and exhaust system used to supply air to smoke-proof stairwells or anterooms to establish a positive pressure difference; preset low-speed operation state refers to the fan operating at a specific low speed below its rated speed. This low speed is usually preset based on the system's identified needs, the noise and energy consumption limitations of the building's daily operation, and through engineering experience or prior testing; preset sinusoidal speed fluctuation signal of a specific frequency refers to a periodic, fixed-frequency speed change pattern superimposed on the fan's low-speed operation state. The frequency and amplitude are usually preset based on the theoretical requirements identified by the system (such as avoiding resonance and covering the frequency range of the system's main dynamic characteristics) and actual engineering experience; excitation signal refers to an input signal actively applied to elicit a response and used to analyze system characteristics; air supply duct network refers to the duct system connecting the pressurized air supply fans to various smoke-proof areas (such as stairwells and anterooms).

[0034] By controlling the frequency converter of the pressurized air supply fan, a sinusoidal speed command with a specific frequency and amplitude is superimposed while maintaining a stable, low base speed (e.g., 10%-30% of the fan's rated speed). This superimposed sinusoidal speed fluctuation serves as an active excitation, generating corresponding airflow and pressure fluctuations within the air supply duct network through the periodic speed changes of the fan impeller.

[0035] Choosing low-speed operation is to ensure that the system operates within a relatively linear range without causing excessive noise, consuming too much energy, or interfering with the normal use of the building, so as to facilitate subsequent system identification.

[0036] Sine wave excitation signals are easy to analyze due to their concentrated spectral characteristics and ability to effectively excite the dynamic response of a system at a specific frequency, thus facilitating the extraction of effective system response information from complex background noise. This excitation signal injects kinetic energy into the air supply network via a fan, causing periodic changes in the airflow and pressure within the network, laying the foundation for subsequent acquisition of system response data.

[0037] In some embodiments, the control of a pressurized air supply fan under a preset low-speed operating state, superimposed with a sinusoidal speed fluctuation signal of a preset specific frequency, can be achieved in various ways: Optionally, this can be achieved through a PLC or DCS-based control system: A basic low-speed command value is pre-programmed into the programmable logic controller (PLC) or distributed control system (DCS); simultaneously, a digital sine wave signal with a specific frequency and amplitude is generated; this sine wave signal is superimposed on the low-speed command value to form a composite speed command, which is then sent to the wind turbine's frequency converter via an analog output module. It is understandable that other methods can be used to inject the excitation signal, and no specific method is specified here.

[0038] S102. Collect differential pressure time-series data between the stairwell and the vestibule, and perform fast Fourier transform on the differential pressure time-series data to extract the amplitude and phase of the pressure response component that is consistent with the frequency of the excitation signal.

[0039] The differential pressure time-series data between the stairwell and the vestibule refers to the sequence of pressure difference values ​​between the stairwell and the vestibule obtained by continuous measurement by pressure sensors over a period of time. Fast Fourier Transform (FFT) processing is an efficient algorithm for calculating the Discrete Fourier Transform, used to convert time-domain signals into frequency-domain signals, thereby revealing the frequency components contained in the signal and their corresponding amplitude and phase information. The excitation signal frequency refers to the periodic frequency of the sinusoidal speed fluctuation signal applied to the fan in step S101. The pressure response component amplitude refers to the intensity of the pressure fluctuation component corresponding to the excitation signal frequency in the FFT processing result. The phase refers to the relative position of the pressure fluctuation component corresponding to the excitation signal frequency relative to the start time or reference point of the excitation signal in the FFT processing result.

[0040] First, differential pressure sensors installed in the stairwell and anteroom continuously collect the pressure difference between them in real time, forming discrete data points arranged in chronological order, i.e., differential pressure time-series data. This data reflects the dynamic changes in pressure within the smoke-proof area under the excitation of the fan.

[0041] Subsequently, the acquired differential pressure time-series data is input into a data processing unit (such as an industrial computer or embedded controller) for frequency domain analysis using the Fast Fourier Transform (FFT) algorithm. FFT processing can decompose complex time-domain pressure fluctuations into sinusoidal components of different frequencies. Since a sinusoidal excitation signal of a specific frequency is injected in step S101, significant pressure response components will appear at frequency points consistent with the frequency of this excitation signal in the FFT results.

[0042] By analyzing the FFT results at this frequency point, the amplitude and phase information of the pressure response component can be extracted. The amplitude represents the intensity of the pressure fluctuation at this frequency, while the phase represents its degree of lag or lead relative to the fan speed excitation signal.

[0043] S103. Substitute the amplitude of the excitation signal and the amplitude of the pressure response component into the pipeline fluid resistance transfer function model, and calculate the real-time leakage coefficient and real-time natural wind pressure value in reverse.

[0044] Among them, the amplitude of the excitation signal refers to the intensity of the sinusoidal speed fluctuation signal applied to the fan in step S101; the amplitude of the pressure response component refers to the intensity of the pressure fluctuation component with the same frequency as the excitation signal, extracted from the FFT processing result of the differential pressure time series data in step S102; the pipeline fluid resistance transfer function model refers to the mathematical model describing the dynamic relationship between airflow and pressure in the air supply pipeline network, which links the fan speed excitation with the differential pressure response of the stairwell-anteroom, and includes parameters reflecting the building's airtightness (leakage coefficient) and the influence of the external environment (natural wind pressure); the leakage coefficient variable refers to the parameter in the model used to quantify the resistance of the building envelope (such as door and window gaps) to airflow leakage, and the larger the value, the smaller the leakage; the natural wind pressure variable refers to the parameter in the model used to characterize the pressure difference between the stairwell and the anteroom caused by natural factors such as the chimney effect and external wind pressure, without the action of a fan; the real-time leakage coefficient and the real-time natural wind pressure value are dynamic parameters that reflect the building's airtightness and the influence of the natural environment at the current moment, obtained through reverse calculation.

[0045] The amplitude of the excitation signal applied in S101 and the amplitude of the pressure response component extracted in S102 are used as known inputs and outputs, respectively, and substituted into a pre-established pipeline fluid resistance transfer function model. This model is typically constructed based on fluid mechanics principles and building thermal engineering theory, and can describe how factors such as fan speed, pipeline resistance, leakage characteristics, and natural wind pressure jointly determine the pressure difference between the stairwell and the vestibule. The model includes leakage coefficient variables and natural wind pressure variables to be identified. Since the excitation signal is a sine wave and the response is also a sine wave of the same frequency, frequency domain analysis methods can be used to substitute the amplitudes of the excitation signal and the response signal into the frequency domain expression of the model. By solving this model, for example using the least squares method, Kalman filtering, or an optimization-based parameter identification algorithm, the leakage coefficient and natural wind pressure values ​​that best explain these observations at the current moment can be derived "backwards" from the known input and output data.

[0046] These real-time parameters reflect the current airtightness of the building (such as aging doors and windows, failure of sealing strips, etc.) and the degree to which it is affected by natural factors such as the chimney effect and external wind pressure.

[0047] In some embodiments, when calculating the real-time leakage coefficient, the nonlinear leakage characteristics of the pipeline network can be more accurately identified by analyzing the harmonic components of the pressure response, thereby improving the accuracy of the leakage coefficient and the robustness of system control.

[0048] First, the fundamental frequency amplitude and second harmonic frequency amplitude are extracted from the Fast Fourier Transform (FFT) results of the differential pressure time series data. In step S102, the differential pressure time series data has already undergone FFT processing. The fundamental frequency amplitude refers to the amplitude of the pressure response component in the FFT result that has the same frequency as the excitation signal, representing the system's main linear response to the excitation signal. The second harmonic frequency amplitude refers to the amplitude of the pressure response component in the FFT result whose frequency is twice that of the excitation signal, typically reflecting the system's nonlinear characteristics (such as aeroelastic deformation at a leak). By extracting these two amplitudes, the nonlinear response characteristics of the pipeline network under excitation can be captured.

[0049] Next, the ratio of the second harmonic frequency amplitude to the fundamental frequency amplitude is calculated to obtain the aeroelastic deformation coefficient. The aeroelastic deformation coefficient, which is the ratio of the second harmonic frequency amplitude to the fundamental frequency amplitude, quantifies the degree of elastic deformation at the leak point in the pipeline network under the action of airflow. A larger ratio indicates a more significant nonlinear deformation effect at the leak point, enabling dynamic assessment of the nonlinearity of the pipeline network leak.

[0050] Subsequently, the fundamental linear leakage coefficient is calculated using the fundamental frequency amplitude. The fundamental linear leakage coefficient refers to the leakage coefficient calculated based on the fundamental frequency amplitude (representing the main linear response) under the assumption that pipeline leakage is linear (i.e., the leakage rate is proportional to the pressure difference). This calculation is typically based on the linear part of the pipeline fluid resistance model, by substituting the excitation signal amplitude and the fundamental frequency amplitude into the model for inverse solving.

[0051] Based on this, the high-pressure corrected leakage coefficient is calculated. The high-pressure corrected leakage coefficient is the leakage coefficient obtained by correcting the basic linear leakage coefficient after considering the aeroelastic deformation effect at the pipeline leak point. It accurately reflects the actual leakage situation of the pipeline under high pressure differential. The high-pressure corrected leakage coefficient is the basic linear leakage coefficient multiplied by a correction factor. The correction factor is used to adjust the multiplier of the basic linear leakage coefficient, incorporating nonlinear effects. The correction factor is the sum of 1 multiplied by the aeroelastic deformation coefficient and the preset elastic weighting factor. The preset elastic weighting factor is a pre-set constant used to adjust the influence weight of the aeroelastic deformation coefficient on the correction factor, usually preset based on experimental data or engineering experience. The introduction of this correction factor allows the basic linear leakage coefficient to be dynamically adjusted according to the actual nonlinear deformation degree of the pipeline, thus obtaining a high-pressure corrected leakage coefficient that better reflects actual operating conditions.

[0052] Finally, the baseline leakage coefficient is mapped to a high-pressure corrected leakage coefficient, which is then used as the real-time leakage coefficient. Here, mapping means using the high-pressure corrected leakage coefficient calculated above as the final, more accurate real-time leakage coefficient.

[0053] The above technical steps, by analyzing the harmonic components of the pressure response and introducing an aeroelastic deformation coefficient to correct the leakage coefficient, can more accurately identify the nonlinear leakage characteristics of the pipeline network. This allows the calculated real-time leakage coefficient to better reflect the actual situation of the pipeline network under high pressure differentials, thereby improving the accuracy of feedforward control. In fire conditions, it can more effectively maintain the target pressure differential between the stairwell and the vestibule, avoiding insufficient or excessive pressure differentials due to inaccurate leakage coefficient estimation, thus enhancing the reliability and safety of the smoke control system.

[0054] S104. Based on the real-time leakage coefficient and real-time natural wind pressure value, combined with the preset target pressure difference value, calculate the initial target speed and generate a feedforward control command containing the initial target speed.

[0055] Among them, the preset target pressure difference value refers to the ideal pressure difference between the stairwell and the vestibule that the smoke control system needs to maintain when a fire occurs. It is usually preset according to fire protection codes and engineering design requirements. The initial target speed refers to the speed at which the pressurized air supply fan should start immediately and reach after the fire alarm signal is triggered, so as to establish the preset target pressure difference in the shortest possible time. The feedforward control command refers to the control signal that is pre-calculated before the fire occurs and sent directly to the fan frequency converter when the fire alarm is triggered, in order to guide the fan to start at the initial target speed.

[0056] Using the real-time leakage coefficient and real-time natural wind pressure value identified in S103, combined with the preset target pressure difference value of fire protection code or design requirements, the system calculates the required fan speed under the current building conditions in order to quickly and stably reach and maintain the target pressure difference through pre-established fluid control equations or models.

[0057] This calculation process considers the actual airtightness of the building (leakage coefficient) and the influence of environmental factors (natural wind pressure) on pressure differential establishment, thus avoiding overshoot or lag caused by unknown disturbances in the initial startup phase of traditional PID control. The calculated initial target speed is the startup command, which is encapsulated as a feedforward control command. This command does not depend on real-time deviation but is ready before a fire occurs. Once the fire alarm signal is triggered, it can be executed immediately to achieve pressure establishment.

[0058] In some embodiments, the calculation of the initial target rotational speed and the generation of feedforward control commands can be achieved in a variety of ways: Optionally, this can be achieved through direct solution based on a physical model: establish a physical model describing the relationship between steady-state pressure difference and fan speed, leakage coefficient, and natural wind pressure (e.g., based on fan performance curves, pipeline resistance equations, and pressure difference balance equations); substitute the real-time leakage coefficient, real-time natural wind pressure value, and preset target pressure difference value obtained in S103 into the physical model; solve the equation to directly calculate the initial target fan speed required to meet the target pressure difference, and encapsulate it as a feedforward control command in digital or analog form.

[0059] It is understandable that other methods can be used to calculate the initial target speed and generate feedforward control commands, which are not limited here.

[0060] In some embodiments, when calculating the initial target speed, the influence of indoor and outdoor temperature difference and elevator operating status on differential pressure control can also be comprehensively considered to further improve the accuracy of fan speed at the moment of fire start-up and the robustness of the control system.

[0061] First, during non-fire periods, the indoor and outdoor reference temperature difference values ​​are collected during the excitation signal injection. The reference temperature difference value is a record of the ambient temperature during system identification and is used for subsequent calculation of the thermo-pressure coupling sensitivity. The indoor and outdoor reference temperature difference value refers to the temperature difference between indoor and outdoor temperature sensors measured during system identification, typically obtained from real-time measurement data. Next, the ratio of the real-time natural wind pressure value to the reference temperature difference value is calculated to obtain the thermo-pressure coupling sensitivity coefficient. The thermo-pressure coupling sensitivity coefficient refers to the amount of change in natural wind pressure caused by a unit change in temperature difference, quantifying the intensity of the building's chimney effect. This coefficient is calculated based on the real-time identified natural wind pressure and reference temperature difference, reflecting the building's sensitivity to temperature changes under its current condition.

[0062] Subsequently, a dynamic feedforward mapping function is constructed, with the indoor-outdoor temperature difference at the moment of fire initiation as the independent variable and the initial target speed of the fan as the dependent variable. The dynamic feedforward mapping function is a mathematical model or algorithm; the input is the indoor-outdoor temperature difference, and the output is the initial target speed of the fan, capable of dynamically adjusting the fan's starting speed based on the real-time temperature difference. Fixed model parameters refer to the fact that, after the mapping function is constructed, its key internal parameters (such as coefficients and weights) are determined based on the previously identified results (real-time leakage coefficient and thermo-pressure coupling sensitivity coefficient). These parameters remain unchanged before the fire occurs, but the output of the mapping function changes dynamically with the input temperature difference. The real-time leakage coefficient and thermo-pressure coupling sensitivity coefficient, as fixed model parameters of this mapping function, ensure that the function can reflect the actual airtightness and chimney effect characteristics of the building.

[0063] Upon receiving a fire alarm signal, the real-time indoor and outdoor temperature difference is collected. The real-time indoor and outdoor temperature difference refers to the actual temperature difference between the indoor and outdoor temperatures measured by the indoor and outdoor temperature sensors at the time of the fire alarm. Then, the real-time indoor and outdoor temperature difference is input into a dynamic feedforward mapping function to obtain the instantaneous initial target rotational speed.

[0064] Building upon this, to further address potential transient aerodynamic disturbances during elevator operation, real-time operational status data of the building's elevator control system is obtained. This real-time operational status data refers to information acquired from the elevator control system regarding the elevator car's current position, speed, and direction of travel, typically through a Building Automation System (BAS) or a dedicated interface. In cases where the elevator car is in a high-speed emergency landing, the real-time downward speed of the elevator car is acquired. A high-speed emergency landing refers to an elevator descending at a speed exceeding its normal operating speed in an emergency (such as a fire) to evacuate passengers to safe floors as quickly as possible. The real-time downward speed refers to the actual speed of the elevator car during the emergency landing process, acquired through the elevator control system. During high-speed operation, especially during an emergency landing, the elevator generates a piston effect within the shaft, causing significant transient disturbances to the pressure difference between the stairwell and the vestibule, potentially leading to momentarily excessively high or low pressure differentials.

[0065] Next, the amplitude of the transient aerodynamic pressure wave generated by the elevator car at its real-time downward speed on the stairwell lobby is calculated. The transient aerodynamic pressure wave amplitude refers to the intensity of the instantaneous pressure fluctuation generated in the stairwell or lobby due to the piston effect when the elevator car is running at high speed (especially during a forced landing), and is typically based on a fluid dynamics model and elevator operating parameters. This calculation quantifies the instantaneous impact of elevator operation on the pressure difference. Then, using the fan similarity law, a speed correction coefficient is calculated based on the transient aerodynamic pressure wave amplitude. The fan similarity law is a physical law describing the relationship between fan performance parameters (such as flow rate, head, power, and speed), allowing the calculation of the required speed change based on pressure changes. The speed correction coefficient is a dimensionless multiplier used to adjust the initial target speed of the fan to counteract the influence of the transient aerodynamic pressure wave generated by elevator operation. The speed correction coefficient is equal to the square root of the ratio of the preset target pressure difference value minus the transient aerodynamic pressure wave amplitude to the preset target pressure difference value. This formula is derived based on the similarity law of fans, converting the pressure disturbance generated by the elevator into a correction for the fan speed, ensuring that the stairwell can maintain the target pressure difference even under the disturbance of elevator operation. The preset target pressure difference value is used as the benchmark for the correction calculation here.

[0066] Finally, the instantaneous initial target speed is multiplied by a speed correction factor to obtain the final execution target speed. This final execution target speed is the optimized starting speed after comprehensively considering temperature difference and elevator disturbance. The instantaneous initial target speed in the feedforward control command is replaced with the final execution target speed, and a feedforward control command containing the final execution target speed is generated. This command will be used to start the fan in case of a fire alarm.

[0067] Through the above steps, the fan starting speed is dynamically adjusted based on the real-time temperature difference and elevator operation status at the moment the fire starts, thereby improving the accuracy and robustness of differential pressure control, effectively compensating for the chimney effect, and avoiding differential pressure deviation caused by temperature changes. Meanwhile, the real-time monitoring and speed correction of the elevator operation status can effectively offset the transient pressure disturbance caused by the piston effect, so that the fan starting speed can more accurately match the complex and ever-changing actual working conditions. This ensures that the differential pressure between the stairwell and the vestibule can be quickly and stably established and maintained within the target range during the golden evacuation time in the early stage of a fire, thereby maximizing the safety of personnel evacuation.

[0068] In some embodiments, when calculating the initial target speed, the dynamic response characteristics of the system can be further considered to construct a flexible start-up acceleration curve, so as to achieve a smooth transition during the wind turbine start-up process and a rapid and stable establishment of the pressure difference.

[0069] First, the phase difference between the pressure response component and the excitation signal is extracted. In step S102, the phase information of the pressure response component is obtained through Fast Fourier Transform, while the phase of the excitation signal is known. The phase difference refers to the difference between the two, directly reflecting the degree of lag in the response of the air supply network to changes in fan speed.

[0070] Next, the aerodynamic lag time constant is calculated based on the phase difference. The aerodynamic lag time constant is a physical quantity that characterizes the response speed of airflow and pressure in the air supply duct network to changes in fan speed, quantifying the time delay required for the system to generate a corresponding pressure response from receiving the excitation. This time constant is usually calculated by dividing the phase difference (in radians) by the angular frequency of the excitation signal (in radians per second).

[0071] Subsequently, the steady-state target speed is calculated based on the real-time leakage coefficient and real-time natural wind pressure. The steady-state target speed refers to the fan speed required to reach a stable state and maintain a preset target pressure difference after a fire occurs. The calculation utilizes the real-time leakage coefficient and real-time natural wind pressure identified in S103, combined with the preset target pressure difference (an ideal pressure difference preset according to fire protection codes or design requirements, such as 50 Pa), and is obtained through inverse solving using a pipeline fluid resistance model. The steady-state speed is the final speed the fan needs to reach and represents the endpoint of the flexible start-up curve.

[0072] Then, a flexible start-up acceleration curve is constructed, using the steady-state target speed as the endpoint and the aerodynamic lag time constant as the acceleration limiting factor. The flexible start-up acceleration curve describes the smooth change of the fan speed over time, guiding the fan to gradually accelerate from a stationary or low-speed state to the steady-state target speed. The acceleration limiting factor is a parameter used to constrain the rate of change of the fan speed; here, the aerodynamic lag time constant is used as this factor, meaning that the fan's acceleration process will fully consider the aerodynamic inertia of the pipeline network, avoiding excessive acceleration that could lead to system instability or overshoot. The purpose of constructing this curve is to prevent the fan from instantly reaching high speeds during a fire alarm, which could cause huge airflow impacts, pipeline vibrations, excessive noise, or even severe pressure differential overshoot, thus hindering personnel evacuation. By introducing the aerodynamic lag time constant as a limiting factor, it is ensured that the rate of increase of the fan speed matches the actual response capability of the pipeline network, achieving a smooth, shock-free start-up process while ensuring that the pressure differential can quickly and stably reach the target value. This curve can typically be an S-curve, an exponential curve, or a polynomial curve, with its shape and parameters jointly determined by the steady-state target speed and the aerodynamic lag time constant.

[0073] Next, the flexible start-up acceleration curve is discretized into a time-varying turbine speed control sequence. This sequence involves sampling the continuous flexible start-up acceleration curve along the time axis to obtain a series of turbine speed command values ​​at different time points. Since the actual control system is digital, the continuous curve needs to be converted into a discrete control command sequence so that the controller can send the corresponding speed command to the turbine inverter in each control cycle. This sequence is the set of speed commands that the turbine should reach at each moment during startup. Finally, a dynamic trajectory feedforward control command containing the turbine speed control sequence is generated to replace the original feedforward control command. This dynamic trajectory feedforward control command is a package of commands containing the entire turbine speed control sequence, guiding the turbine to operate along a predetermined smooth acceleration path, rather than simply jumping to the initial target speed. This command replaces the single initial target speed command in the original S104 because it provides a more detailed and smoother speed change path.

[0074] The above technical steps achieve a smooth transition of fan speed based on the actual dynamic response characteristics of the pipeline network, avoiding the pressure differential overshoot, system oscillation or airflow impact that may occur at the moment of startup in traditional feedforward control. This ensures that the pressure differential can be established quickly, stably and without impact in the early stage of a fire, improving the safety and comfort of the smoke control system and providing a more reliable guarantee for personnel evacuation.

[0075] S105. When a fire alarm signal is received, execute the feedforward control command to control the pressurized air supply fan to start at the initial target speed.

[0076] Among them, a fire alarm signal refers to a signal indicating that a fire has occurred in the building, triggered by a fire detector (such as a smoke detector or heat detector) or a manual alarm button and issued by the fire alarm controller; a pressurized air supply fan refers to a mechanical device in the smoke control system used to supply air to the smoke-proof stairwell or vestibule; and the initial target speed refers to the speed that the fan should immediately start and reach when a fire alarm is triggered, calculated in step S104.

[0077] Once the fire alarm controller detects a fire and issues an alarm signal, this signal is transmitted to the control unit of the smoke control system. Upon receiving the fire alarm signal, the control unit no longer waits for pressure deviation feedback but immediately invokes the feedforward control command pre-generated in step S104. This command is sent directly to the frequency converter of the pressurized air supply fan, instructing the fan to start rapidly at the initial target speed specified in the command. This feedforward starting method, based on a prediction of the current building conditions (real-time leakage coefficient and natural wind pressure), directly provides the most suitable starting speed, thus avoiding the slow pressure build-up or significant overshoot caused by the lag in traditional PID control during the initial startup phase. This ensures that during the golden evacuation time in the early stages of a fire, stairwells and vestibules can quickly and stably establish positive pressure that meets regulatory requirements, effectively preventing smoke intrusion.

[0078] S106. Monitor the real-time differential pressure between the stairwell and the vestibule.

[0079] The real-time differential pressure value between the stairwell and the vestibule refers to the pressure difference between the stairwell and the vestibule at the current moment, which is measured in real time by a pressure sensor.

[0080] After a fire breaks out, the smoke control system is in operation, and its control effectiveness needs continuous evaluation. Differential pressure sensors installed in the stairwell and vestibule continuously and frequently collect the pressure difference between them. These sensors are typically high-precision micro-differential pressure sensors, capable of sensitively detecting minute pressure changes. The collected real-time differential pressure data is transmitted as digital signals to the control unit of the smoke control system via a data acquisition module or intelligent transmitter. The purpose of monitoring real-time differential pressure values ​​is to continuously evaluate the actual effect of the feedforward control commands, i.e., whether the pressure difference between the stairwell and vestibule quickly reaches the preset target value after the fan starts, and whether it remains stable.

[0081] S107. When the fluctuation range of the real-time differential pressure value is less than the preset amplitude threshold within a continuous preset time period, switch to PID closed-loop feedback control mode.

[0082] Among them, the continuous preset time period refers to the pre-set time length used to evaluate system stability. This time period is usually preset based on system response speed, control accuracy requirements, and engineering experience. The fluctuation range of the real-time differential pressure value refers to the maximum range or standard deviation of the real-time differential pressure value fluctuating around the average value within the continuous preset time period. The preset amplitude threshold refers to the pre-set upper limit of pressure fluctuation used to determine whether the system has reached a stable state. This threshold is usually preset based on the requirements of fire protection codes for differential pressure stability and the allowable error range of the system. The PID closed-loop feedback control mode is a classic control strategy that uses three links—proportional (P), integral (I), and derivative (D)—to dynamically adjust the fan speed based on the real-time deviation (the difference between the actual differential pressure and the target differential pressure) to eliminate the deviation and maintain the system stable at the target value.

[0083] After the fan starts at the initial target speed, the control unit continuously monitors the real-time differential pressure between the stairwell and the anteroom. It calculates the fluctuation of this real-time differential pressure over a continuous preset time period (e.g., 5 seconds, 10 seconds). If the fluctuation amplitude of the differential pressure (e.g., the difference between the maximum and minimum values, or the standard deviation) is less than a preset amplitude threshold (e.g., ±5 Pa) during this period, it indicates that the feedforward control has rapidly established and stabilized the system differential pressure.

[0084] At this point, the control unit determines that the system has entered a relatively stable operating state and smoothly switches to the PID closed-loop feedback control mode. The PID controller will take over the speed regulation of the fan, continuously compare the deviation between the real-time differential pressure value and the preset target differential pressure value, and output adjustment commands to the frequency converter according to the PID algorithm, thereby achieving precise and continuous stable control of the differential pressure to cope with possible minor disturbances or slow changes in system parameters.

[0085] In the above embodiments, by continuously injecting excitation signals and analyzing the response during non-fire periods, the leakage coefficient and natural wind pressure of the building are identified and updated in real time, and the accurate initial target speed at the time of fire is calculated in advance. This enables the fan to start at a speed that closely matches the actual needs when a fire alarm signal is received, avoiding pressure fluctuations or insufficiency in the initial startup caused by parameter lag or inaccuracy. This improves the stability of pressure control at the moment of fire startup and ensures the rapid and effective establishment of the smoke barrier.

[0086] In other embodiments of this application, when a fire occurs in a multi-story building and the leakage characteristics and external wind pressure vary significantly between floors, using a single leakage coefficient may fail to accurately establish and maintain the target pressure difference on the fire-affected floor, thus affecting the smoke control effect. The indoor multi-story intelligent smoke control and exhaust testing method provided in this application can achieve accurate control of fan speed and effective smoke control through modeling, layer identification, and dynamic adjustment of the leakage coefficient based on the fire-affected floor.

[0087] like Figure 2 The diagram shown is another flowchart illustrating the indoor multi-story intelligent smoke control and exhaust testing method provided in this application embodiment, including the following steps: S201. During non-fire periods, the pressurized air supply fan is controlled to operate at a preset low speed, and a sinusoidal speed fluctuation signal of a preset specific frequency is superimposed on it as an excitation signal and injected into the air supply duct network.

[0088] S202. Collect differential pressure time-series data between the stairwell and the vestibule, and perform fast Fourier transform on the differential pressure time-series data to extract the amplitude and phase of the pressure response component that is consistent with the frequency of the excitation signal.

[0089] Steps S201-S202 and Figure 1 Steps S101-S102 in the illustrated embodiment are similar and can be found in the descriptions of steps S101-S102, which will not be repeated here.

[0090] S203. Acquire the differential pressure response data of multiple pressure sensors installed at different floor heights in the building.

[0091] Among them, multiple pressure sensors at different floor heights refer to devices installed on different floors of a building (e.g., every few floors or every floor) to measure air pressure. They are usually installed in smoke-proof stairwells, vestibules, and adjacent non-smoke-proof areas to monitor pressure changes in these areas in real time. Layered differential pressure response data refers to the data measured by these pressure sensors installed at different floor heights when an excitation signal is applied to the smoke control system, reflecting the pressure difference between floors or between each floor and the outside over time. Layered representation data is divided by floor, reflecting the pressure conditions at different heights. Differential pressure refers to the pressure difference between two measurement points, such as the pressure difference between a stairwell and a corridor. Response data refers to the pressure change information generated by the system after being subjected to external excitation (such as the operation of a fan).

[0092] The smoke control system applies a known excitation signal (e.g., periodically varying rotational speed or flow rate) to a fan, while multiple differential pressure sensors installed on different floors of the building (e.g., between stairwells and adjacent areas) synchronously collect differential pressure values ​​at their respective locations. These sensors continuously record differential pressure changes over a period of time, forming time-series data.

[0093] By collecting data from different floors, a comprehensive understanding of the building's vertical pressure gradient and distribution can be obtained.

[0094] S204. Construct a vertical multi-node pipe network fluid network model for a building, wherein each floor node contains an independent local leakage coefficient variable, and the vertical multi-node pipe network fluid network model for a building includes a natural wind pressure variable.

[0095] Among them, the vertical multi-node pipe network fluid network model refers to the mathematical model that abstracts the smoke control system and surrounding environment of a high-rise building into a mathematical model composed of multiple nodes and pipes connecting these nodes; the vertical representation model mainly focuses on the airflow and pressure distribution of the building in the vertical direction; the multi-node representation model regards each floor or specific area of ​​the building as an independent node, and these nodes are interconnected through simulated airflow channels (such as stairwells, elevator shafts, leak outlets, etc.); the pipe network fluid network model uses fluid dynamics principles (such as mass conservation, energy conservation, and momentum conservation) to describe the flow and pressure changes of air in these nodes and channels; floor nodes in the model represent a specific floor or Its key areas (such as stairwells and anterooms) are the basic units for pressure and flow calculations in the model; the independent local leakage coefficient variable refers to the unique and variable parameters that each floor node in the model has, used to quantify the degree of air leakage between that floor and the outside or adjacent areas; the local leakage coefficient reflects the sealing performance of doors, windows, gaps, etc. on that floor, and independence means that the leakage characteristics of different floors can be different, and the value represented by the variable can be dynamically determined by the system identification; the natural wind pressure variable refers to the parameter in the model used to characterize the pressure influence on the building's outer surface caused by outdoor wind speed and direction, and it is a variable quantity that reflects the real-time changes in external environmental wind pressure.

[0096] First, key areas of the building, such as smoke-proof stairwells, vestibules, corridors on each floor, and the outdoor environment, are abstracted as nodes in the model.

[0097] Then, the air circulation paths between these nodes (such as stairwell shafts, door gaps, window gaps, elevator shafts, etc.) are abstracted into pipes or connections in the model. For each floor node, the model introduces an independent local leakage coefficient variable to capture the leakage characteristics unique to that floor. For example, the lower floors of a high-rise building may have greater leakage due to frequent personnel entry and exit, while the upper floors may have different leakage due to wind pressure.

[0098] In addition, the model will also introduce natural wind pressure variables to simulate the impact of external wind pressure on the pressure difference between different floors of the building, because wind pressure varies with building height and external environment.

[0099] The core of the model is based on fluid mechanics principles such as the law of conservation of mass and Bernoulli's equation, establishing a series of algebraic or differential equations to describe the flow balance and pressure loss relationships between nodes. For example, the flow balance equation for each node considers the supply air volume, the leakage through the leak outlet, and the flow through the internal channels. In this way, the model can simulate the pressure distribution and airflow organization inside a building under different fan operating conditions, different external wind pressures, and different floor leakage conditions.

[0100] In some embodiments, a building's vertical multi-node pipe network fluid network model can be constructed in various ways: Optionally, based on the lumped parameter method: each floor (or several floors as a region) of the building is abstracted as a "control body" or "node" with specific volume and leakage characteristics; the connections between nodes are defined, such as stairwell shafts, elevator shafts, and leakage outlets of doors and windows on each floor, and these connections are assigned corresponding fluid resistance or leakage characteristics; according to the law of conservation of mass, a flow balance equation is established for each node, that is, the flow rate entering the node is equal to the flow rate leaving the node; according to Bernoulli's equation or empirical formula, pressure-flow relationship equations are established between nodes and between nodes and the external environment, which include local leakage coefficient variables and natural wind pressure variables.

[0101] It is understandable that other methods can be used to construct the vertical multi-node pipe network fluid network model of a building, and no limitation is made here.

[0102] S205. Using the amplitude of the layered differential pressure response data and the excitation signal, the multivariate joint identification and solution of the vertical multi-node pipe network fluid network model of the building is performed to obtain the layered leakage coefficient vector and the real-time natural wind pressure value.

[0103] Among them, multivariate joint identification and solution refers to a system identification method that simultaneously estimates multiple unknown parameters in the model (i.e., multiple local leakage coefficients and natural wind pressure values). By comparing the differences between the model output and the actual measurement data, these parameters are continuously adjusted until the model output highly matches the actual measurement data. Multivariate emphasizes the simultaneous identification of multiple parameters. The layered leakage coefficient vector refers to a mathematical set (e.g., a list or array) containing the independent local leakage coefficients of all floor nodes, where each element corresponds to the leakage coefficient of a floor. The real-time natural wind pressure value refers to the value obtained through the identification process that reflects the impact of the external environmental wind pressure on the system at the current moment.

[0104] First, the layered differential pressure response data obtained in S203 is used as the target output for identification, and the amplitude of the excitation signal applied in S201 / S202 is used as the known input for identification.

[0105] Then, the input and output data are substituted into the building's vertical multi-node pipe network fluid network model constructed in S204. Since the model contains multiple unknown local leakage coefficient variables (one for each floor) and natural wind pressure variables, a multivariate joint identification and solution algorithm is required. This algorithm continuously adjusts these unknown parameters in the model through an iterative optimization process, minimizing the error between the model's predicted differential pressure response under a given excitation signal amplitude and the actually measured layered differential pressure response data.

[0106] For example, least squares, Kalman filtering, or more complex optimization algorithms can be used. Through this joint solution, the system can simultaneously obtain the layered leakage coefficient vector (i.e., the local leakage coefficient of each floor) and the current real-time natural wind pressure value for each floor.

[0107] S206. Calculate the weighted average of the layered leakage coefficient vector as the real-time leakage coefficient.

[0108] Among them, the layered leakage coefficient vector refers to the set of independent local leakage coefficients of each floor of the building obtained by multivariate joint identification in step S205; the weight of the weighted average is preset and is usually set according to factors such as the importance of each floor in the smoke control system, area size, expected leakage contribution or fire risk, for example, it can be weighted according to the floor area size or set according to empirical values; the real-time leakage coefficient refers to a single value that can represent the overall leakage characteristics of the entire building or smoke control system after being calculated by weighted average.

[0109] Obtain the layered leakage coefficient vector identified in S205, which contains the local leakage coefficient of each floor of the building.

[0110] Then, these local leakage coefficients are weighted according to preset weights. For example, if a floor has a larger area or is of greater importance in the fire evacuation route, it can be assigned a higher weight. These weights can be preset based on building design parameters, fire protection code requirements, or historical experience data. The calculation formula is usually: Real-time leakage coefficient = (Σ(local leakage coefficient_i × weight_i)) / (Σ weight_i). By weighted averaging, a real-time leakage coefficient that comprehensively considers the leakage characteristics of each floor can be obtained.

[0111] S207. Analyze the received fire alarm signal and extract the floor location information where the fire occurred.

[0112] Among them, parsing refers to decoding and processing the received fire alarm signal to identify and extract useful information; the floor location information of the fire refers to the data obtained by parsing the fire alarm signal, which indicates which floor of the building the fire occurred on, such as floor number, area code or detector address.

[0113] The smoke control system communicates with the building's automatic fire alarm system (FAS) (e.g., via hard-wired interface, RS485, BACnet, Modbus, etc.) to receive fire alarm signals from the FAS. This signal is typically not a simple switch signal, but a message or coded information containing structured data.

[0114] The parsing module analyzes the received signals according to preset communication protocols and data formats. For example, if the alarm signal is a data frame, the parsing module will read the fields in the data frame that indicate the area where the fire occurred or the address of the detector.

[0115] Then, this address information is mapped to the specific floor location where the fire occurred. For example, by querying a preset address-floor mapping table, the detector address is converted into the corresponding floor number.

[0116] S208. Call the pre-built building layered leakage distribution database and retrieve the local leakage coefficient of the corresponding fire floor based on the floor location information.

[0117] The pre-built building layered leakage distribution database refers to the data set established and stored in the memory of the smoke control system or related servers during the system design or commissioning phase. This database contains detailed leakage coefficient information for each floor or each smoke control zone of the building. Pre-built means that the database is established and filled before the system is put into operation through system identification steps such as S205 or based on building design drawings, experience data, etc.

[0118] The smoke control system uses the floor location information of the fire extracted from S207 as a query key to access a pre-built building floor leakage distribution database. This database may exist in the form of tables, arrays, or more complex database structures, storing the local leakage coefficient of each floor (or smoke control zone) under normal or specific operating conditions.

[0119] Based on the floor number affected by the fire, a search is performed in the database to find records matching the floor's location information, and the corresponding local leakage coefficient is extracted from these records. This retrieved local leakage coefficient is a specific parameter for the floor affected by the fire, reflecting the actual leakage situation on that floor at the time of the fire.

[0120] S209. Replace the real-time leakage coefficient with the local leakage coefficient, and combine the real-time natural wind pressure value with the preset target pressure difference value, and substitute them into the fluid control equation to calculate the accurate starting speed.

[0121] Among them, the preset target pressure difference value refers to the minimum pressure difference that should be maintained between the smoke-proof stairwell or vestibule and the adjacent area, which is set in advance according to fire protection codes, building design requirements or safety standards. For example, it is preset to 50Pa, which is usually set according to national or local fire protection regulations, engineering design specifications or through expert experience; the fluid control equation refers to the mathematical formula describing the relationship between air flow and pressure balance inside the smoke control system, which usually includes fan performance curves, pipeline resistance equations and leakage equations, etc.

[0122] First, a replacement operation is performed: the local leakage coefficient (for the fire floor) retrieved in S208 is replaced with the real-time leakage coefficient (overall average value) calculated in S206. This means that when calculating the fire start-up speed, the specific leakage situation of the fire floor will be given priority.

[0123] Then, the updated leakage coefficient, along with the real-time natural wind pressure value identified in S205 and the preset target pressure difference value (e.g., 50 Pa as required by fire protection regulations), are substituted into the fluid control equation. The fluid control equation is typically a complex set of nonlinear equations describing the balance between the pressure provided by the fan, the resistance loss of the piping network, the pressure loss caused by leakage, and the influence of natural wind pressure. For example, the equation might be expressed as: Fan head = Target pressure difference + Natural wind pressure influence + Leakage loss head. Here, the fan head is a function of the fan speed, and the leakage loss head is a function of the leakage coefficient, flow rate, and pressure difference. By solving this equation, the accurate starting speed required to meet the target pressure difference can be calculated in reverse.

[0124] In some embodiments, the accurate starting speed can be calculated by substituting into the fluid control equations in various ways: Optionally, based on analytical solutions or numerical iteration methods: The fan performance curve (head-flow-speed relationship), pipeline resistance model, and leakage model (flow-pressure difference-leakage coefficient relationship) are integrated into a comprehensive fluid control equation. The local leakage coefficient, real-time natural wind pressure value, and preset target pressure difference value are substituted into the equation. If the equation form allows, the equation is solved directly to obtain the accurate start-up speed. If the equation is complex or nonlinear, numerical iterative methods (such as the Newton-Raphson method or the bisection method) are used to solve it until the speed that satisfies the equation conditions is found.

[0125] It is understandable that other methods can be used to accurately calculate the starting speed, and no specific method is specified here.

[0126] S210, Generate feedforward control commands containing accurate start-up speed.

[0127] Obtain the accurate starting speed calculated in S209. Then, according to the communication protocol and interface requirements of the wind turbine inverter or motor controller, encapsulate this speed value into a feedforward control command. For example, if the wind turbine is controlled by an inverter, the command might be a 0-10V analog voltage signal, a 4-20mA analog current signal, or a speed setpoint sent via digital communication protocols such as Modbus or BACnet. This command is immediately sent to the wind turbine inverter, which, upon receiving the command, drives the wind turbine motor to operate at that accurate starting speed.

[0128] S211. When a fire alarm signal is received, execute the feedforward control command to control the pressurized air supply fan to start at the initial target speed.

[0129] S212. Monitor the real-time differential pressure between the stairwell and the vestibule.

[0130] S213. When the fluctuation range of the real-time differential pressure value is less than the preset amplitude threshold within a continuous preset time period, switch to PID closed-loop feedback control mode.

[0131] Steps S211-S213 and Figure 1 Steps S105-S107 in the illustrated embodiment are similar and can be found in the descriptions of steps S105-S107, which will not be repeated here.

[0132] In the above embodiments, by acquiring the floor-by-floor leakage coefficients and real-time natural wind pressure values ​​of each floor of the building, and upon fire alarm, calling the local leakage coefficient of the specific floor where the fire occurs, the calculation of the fan starting speed no longer depends on the overall average value, but rather matches the actual leakage situation of the fire area. Therefore, the smoke control system can accurately establish and maintain a preset target pressure difference in the early stages of a fire, avoiding insufficient or excessive pressure difference caused by inaccurate parameters in traditional methods, thus improving the smoke control effect and reliability of the smoke control system.

[0133] The following describes an exemplary indoor multi-story intelligent smoke control and exhaust testing system 300 provided in an embodiment of this application. Figure 3 This is an exemplary hardware structure diagram of the indoor multi-story intelligent smoke control and exhaust testing system 300 provided in this application embodiment.

[0134] In some embodiments, the indoor multi-story intelligent smoke control and exhaust testing system 300 is a computer device or includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0135] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0136] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0137] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0138] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0139] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for testing intelligent smoke control and exhaust systems on multiple floors indoors, characterized in that, include: During non-fire periods, the pressurized air supply fan is controlled to operate at a preset low speed, and a sinusoidal speed fluctuation signal of a preset specific frequency is superimposed on it as an excitation signal and injected into the air supply network. Collect differential pressure time-series data between the stairwell and the vestibule, and perform fast Fourier transform on the differential pressure time-series data to extract the amplitude and phase of the pressure response component that is consistent with the frequency of the excitation signal. The amplitude of the excitation signal and the amplitude of the pressure response component are substituted into the pipeline fluid resistance transfer function model to calculate the real-time leakage coefficient and the real-time natural wind pressure value in reverse. The pipeline fluid resistance transfer function model includes leakage coefficient variables and natural wind pressure variables. Based on the real-time leakage coefficient and the real-time natural wind pressure value, combined with the preset target pressure difference value, the initial target rotational speed is calculated, and a feedforward control command containing the initial target rotational speed is generated. When a fire alarm signal is received, the feedforward control command is executed to control the pressurized air supply fan to start at the initial target speed; Monitor the real-time differential pressure between the stairwell and the vestibule; When the fluctuation range of the real-time differential pressure value is less than the preset amplitude threshold within a continuous preset time period, the system switches to PID closed-loop feedback control mode.

2. The method according to claim 1, characterized in that, The step involves calculating the initial target rotational speed based on the real-time leakage coefficient and the real-time natural wind pressure, combined with a preset target pressure difference value, and generating a feedforward control command containing the initial target rotational speed. Specifically, this includes: Collect the indoor and outdoor reference temperature difference during the injection of the excitation signal; The ratio of the real-time natural wind pressure value to the reference temperature difference value is calculated to obtain the thermo-pressure coupling sensitivity coefficient; A dynamic feedforward mapping function is constructed with the indoor-outdoor temperature difference at the moment of fire initiation as the independent variable and the initial target speed of the fan as the dependent variable; the fixed model parameters of the dynamic feedforward mapping function are the real-time leakage coefficient and the thermo-pressure coupling sensitivity coefficient. When a fire alarm signal is received, the real-time indoor and outdoor temperature difference value is collected. The real-time indoor-outdoor temperature difference value is input into the dynamic feedforward mapping function to obtain the instantaneous initial target rotational speed; Generate a feedforward control command that includes the instantaneous initial target rotational speed.

3. The method according to claim 2, characterized in that, After inputting the real-time indoor-outdoor temperature difference value into the dynamic feedforward mapping function to obtain the instantaneous initial target rotational speed, the method further includes: The system acquires real-time operating status data of the elevator control system within the building, and in the case of an elevator car in a high-speed forced landing state, acquires the real-time downward speed of the elevator car. Calculate the transient aerodynamic pressure wave amplitude generated by the elevator car on the stairwell vestibule at the real-time downward speed; Using the similarity law of wind turbines, a speed correction coefficient is calculated based on the transient aerodynamic pressure wave amplitude. The speed correction coefficient is equal to the square root of the ratio of the difference between the preset target pressure difference value and the transient aerodynamic pressure wave amplitude value to the preset target pressure difference value. Multiply the instantaneous initial target speed by the speed correction coefficient to obtain the final execution target speed; Replace the instantaneous initial target speed in the feedforward control command with the final execution target speed.

4. The method according to claim 1, characterized in that, The step of substituting the amplitude of the excitation signal and the amplitude of the pressure response component into the pipeline fluid resistance transfer function model to calculate the real-time leakage coefficient and the real-time natural wind pressure specifically includes: Acquire stratified differential pressure response data from multiple pressure sensors installed at different floor levels in a building; A vertical multi-node pipe network fluid network model of a building is constructed, wherein each floor node contains an independent local leakage coefficient variable, and the vertical multi-node pipe network fluid network model of the building includes a natural wind pressure variable; Using the layered differential pressure response data and the amplitude of the excitation signal, the building's vertical multi-node pipe network fluid network model is subjected to multivariate joint identification and solution to obtain the layered leakage coefficient vector and real-time natural wind pressure value. The weighted average of the layered leakage coefficient vector is calculated as the real-time leakage coefficient.

5. The method according to claim 1, characterized in that, The step involves calculating the initial target rotational speed based on the real-time leakage coefficient and the real-time natural wind pressure, combined with a preset target pressure difference value, and generating a feedforward control command containing the initial target rotational speed. Specifically, this includes: Analyze the received fire alarm signal and extract the floor location information where the fire occurred; The pre-built building layered leakage distribution database is invoked, and the local leakage coefficient of the corresponding fire floor is retrieved based on the floor location information; The local leakage coefficient is used to replace the real-time leakage coefficient, and combined with the real-time natural wind pressure value and the preset target pressure difference value, the accurate starting speed is calculated by substituting them into the fluid control equation. Generate feedforward control commands that include the accurate start-up speed.

6. The method according to claim 1, characterized in that, The step involves calculating the initial target rotational speed based on the real-time leakage coefficient and the real-time natural wind pressure, combined with a preset target pressure difference value, and generating a feedforward control command containing the initial target rotational speed. Specifically, this includes: Extract the phase difference between the pressure response component phase and the excitation signal phase, and calculate the aerodynamic hysteresis time constant based on the phase difference; The steady-state target rotational speed is calculated based on the real-time leakage coefficient and the real-time natural wind pressure value. A flexible start-up acceleration curve is constructed with the steady-state target rotational speed as the endpoint and the aerodynamic lag time constant as the acceleration limiting factor. The flexible start-up acceleration curve is discretized into a time-varying fan speed control sequence; A dynamic trajectory feedforward control command containing the wind turbine speed control sequence is generated to replace the feedforward control command.

7. The method according to claim 1, characterized in that, The step of substituting the amplitude of the excitation signal and the amplitude of the pressure response component into the pipeline fluid resistance transfer function model to calculate the real-time leakage coefficient and the real-time natural wind pressure specifically includes: The fundamental frequency amplitude and the second harmonic frequency amplitude are extracted from the fast Fourier transform results of the differential pressure time series data; The aeroelastic deformation coefficient is obtained by calculating the ratio of the amplitude of the second harmonic frequency to the amplitude of the fundamental frequency. The fundamental frequency amplitude is used to calculate the basic linear leakage coefficient, and the high-pressure corrected leakage coefficient is also calculated. The high-pressure corrected leakage coefficient is the basic linear leakage coefficient multiplied by a correction factor. The correction factor is the sum of the products of 1, the aeroelastic deformation coefficient, and a preset elastic weighting factor. The basic leakage coefficient is mapped to a high-pressure corrected leakage coefficient, and the high-pressure corrected leakage coefficient is used as the real-time leakage coefficient.

8. An indoor multi-story intelligent smoke control and exhaust testing system, characterized in that, The indoor multi-story intelligent smoke control and exhaust testing system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the indoor multi-story intelligent smoke control and exhaust testing system to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on the indoor multi-story intelligent smoke control and exhaust testing system, the indoor multi-story intelligent smoke control and exhaust testing system performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the indoor multi-story intelligent smoke control and exhaust testing system, the indoor multi-story intelligent smoke control and exhaust testing system performs the method as described in any one of claims 1-7.

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