Silent cabin servo environment management method and system based on full perception

Abstract

The application discloses a full-sensing-based mute cabin servo environment management method and system. Air quality indexes, environmental control equipment operation frequencies and acoustic spectrum data in the mute cabin are collected in real time; initial target powers of each device are calculated according to the deviation of the air quality indexes from target values, and acoustic superposition gain and frequency domain resonance probability under multi-device concurrent operation are predicted in combination with the operation frequencies and the acoustic spectrum data, so that an acoustic conflict index is quantitatively obtained; when the acoustic conflict index exceeds a preset mute boundary threshold value, an acoustic budget is constructed based on a maximum allowed noise value, an optimization algorithm is used to stagger the operation frequencies of each device within the acoustic budget to avoid resonance frequency bands, and control parameters are generated; each device is driven to operate according to the control parameters, and acoustic spectrum changes are continuously monitored to dynamically correct the control parameters. The application solves acoustic conflicts caused by multi-device concurrent operation in a high-enclosed mute cabin, and realizes accurate servo control of an air environment under mute boundary constraints.

Description

Technical Field

[0001] This invention relates to the field of soundproof cabin control technology, specifically to a soundproof cabin servo environment management method and system based on full perception. Background Technology

[0002] Soundproof cabins (also known as quiet rooms or soundproof enclosures) are increasingly widely used as highly soundproof, independent, and private modular spaces. To ensure the health and comfort of users within this highly enclosed and confined space, the cabin typically integrates multiple environmental control subsystems, including fresh air systems, air conditioning, air purification, and oxygenation. The fresh air system can adjust its frequency according to the concentration of carbon dioxide and PM2.5 within the cabin, ensuring fresh air while balancing energy consumption and noise. The air conditioning system, through a combination of temperature and humidity sensors and a human comfort model, can achieve adaptive adjustment of both temperature and humidity. The air purification system has real-time monitoring and linkage purification capabilities for multiple parameters such as PM2.5, formaldehyde, and TVOC, and can automatically switch operating modes based on changes in air quality. The oxygenation system precisely controls oxygen supply based on oxygen concentration monitoring.

[0003] Currently, environmental management in soundproof cabins mostly employs centralized control, typically based on multi-dimensional air quality indicators such as temperature, humidity, and carbon dioxide concentration, uniformly scheduling the aforementioned environmental control equipment. However, fresh air, air conditioning, purification, and oxygenation equipment all inherently contain physical noise sources such as motors, fans, or air pumps. When the operating power of multiple devices is increased simultaneously due to deviations in air quality indicators, the superposition effect of sound waves from multiple sources in a small, enclosed space becomes significant. If the operating frequencies of the various devices are similar or multiples of each other, it can trigger severe frequency resonance and beat frequency phenomena, causing the noise inside the cabin to instantly break through the quiet boundary and directly damage the core sound insulation function of the soundproof cabin.

[0004] Therefore, how to effectively resolve acoustic conflicts when multiple devices are running concurrently, and how to perform precise and decoupled servo control of the air environment within a preset quiet boundary, are key technical problems that urgently need to be solved in this field. Summary of the Invention

[0005] In view of the above-mentioned shortcomings mentioned in the background art, the purpose of this invention is to provide a method and system for managing the servo environment of a silent cabin based on full perception.

[0006] A first aspect of the present invention provides a method for managing the servo environment of a silent cabin based on full perception, the method comprising the following steps: The system collects real-time air quality indicators and the current operating frequencies of various environmental control devices within the soundproof cabin, and uses an acoustic sensor array to capture real-time acoustic spectrum data across the entire frequency band within the cabin; wherein, the environmental control devices include fresh air equipment, air conditioning, purification equipment, and oxygenation equipment; The initial target power of each environmental control device is calculated based on the deviation between the air quality index and the target value. Combined with the current operating frequency and acoustic spectrum data of each environmental control device, the acoustic superposition gain and frequency domain resonance probability under the concurrent operation of multiple devices are predicted, and the acoustic conflict index is quantified. When the acoustic conflict index exceeds the preset silence boundary threshold, an acoustic budget is constructed based on the maximum allowable noise value. An optimization algorithm is used to adjust the operating frequency of each environmental control device within the acoustic budget to avoid the resonance frequency band, and control parameters of each environmental control device are generated according to the adjusted operating frequency. The control parameters are used to drive the operation of each environmental control device and continuously monitor changes in the acoustic spectrum inside the cabin to dynamically correct the control parameters.

[0007] A second aspect of the present invention provides a servo environment management system for a silent cabin based on full perception, the system comprising: The full-sensing acquisition module is used to collect air quality indicators and the current operating frequency of each environmental control device in the soundproof cabin in real time, and to capture the acoustic spectrum data of the entire frequency band in the cabin in real time using an acoustic sensor group; wherein, the environmental control device includes fresh air equipment, air conditioning, purification equipment and oxygenation equipment; The acoustic conflict prediction module is used to calculate the initial target power of each environmental control device based on the deviation between the air quality index and the target value, and combined with the current operating frequency and acoustic spectrum data of each environmental control device, predict the acoustic superposition gain and frequency domain resonance probability under the concurrent operation of multiple devices, and quantify the acoustic conflict index. The acoustic budget and allocation module is used to construct an acoustic budget based on the maximum allowable noise value when the acoustic conflict index exceeds the preset silence boundary threshold. It uses an optimization algorithm to adjust the operating frequency of each environmental control device within the acoustic budget to avoid the resonance frequency band, and generates control parameters for each environmental control device based on the adjusted operating frequency. The servo control module is used to drive the operation of each environmental control device according to the control parameters, and continuously monitor the changes in the acoustic spectrum inside the cabin to dynamically correct the control parameters.

[0008] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention collects real-time air quality and acoustic spectrum data within the cabin, combines this data with the operating status of environmental control equipment, predicts acoustic conflicts under concurrent operation of multiple devices, and performs frequency staggered allocation to achieve coordinated operation of fresh air, air conditioning, purification, and oxygenation equipment. While ensuring air quality meets standards, it effectively avoids resonance and excessive noise from equipment within the cabin, making the cabin environment dynamic, adaptive, and efficient, thus improving user comfort and the overall management performance of the quiet cabin. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating a method for managing the servo environment of a silent cabin based on full perception, as disclosed in an embodiment of the present invention. Figure 2 This is a schematic diagram of a silent cabin servo environment management system based on full perception disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the acoustic conflict prediction module 20 disclosed in an embodiment of the present invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below in conjunction with the embodiments of this invention. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0011] This invention provides a servo environment management method for a soundproof cabin based on full perception. This solution is applied to a centralized control assembly, or a steward system, which uniformly manages the fresh air equipment, air conditioning, purification equipment, and oxygenation equipment deployed within the soundproof cabin. The steward system uses a soundproof boundary as its core constraint. By sensing the air quality and acoustic state within the cabin in real time, it dynamically coordinates the operating frequencies of various environmental control devices. This proactively resolves acoustic conflicts arising from the concurrent operation of multiple devices while ensuring air quality, achieving precise and decoupled servo control.

[0012] like Figure 1 As shown, the method includes the following steps: S1, real-time acquisition of air quality indicators and current operating frequencies of various environmental control devices in the soundproof cabin, and real-time capture of acoustic spectrum data across the entire frequency band within the cabin using an acoustic sensor array; wherein, the environmental control devices include fresh air equipment, air conditioning, purification equipment and oxygenation equipment; As a highly enclosed and independent space, the soundproof cabin's internal environment is managed by a centralized concierge system. To achieve precise servo control, a comprehensive understanding of the cabin's air quality, the real-time operational status of various environmental control devices, and the acoustic environment is required.

[0013] Specifically, a multi-dimensional air quality sensor array is installed inside the cabin. This array includes, but is not limited to, a carbon dioxide concentration sensor, a PM2.5 laser dust sensor, a temperature and humidity composite sensor, and an electrochemical oxygen concentration sensor. All of these sensors continuously collect data at a sampling frequency of no less than 1 Hz and report it to the management system in real time, forming an air quality index vector. Where CO2 is the carbon dioxide concentration, PM2.5 is the fine particulate matter concentration, T is the temperature, RH is the relative humidity, and O2 is the oxygen volume fraction.

[0014] Meanwhile, the system maintains a connection with the main control board or frequency converter of each environmental control device via a digital communication interface (such as CAN bus, Modbus, or proprietary protocol). For fresh air equipment, the current operating frequency of its supply fan is collected. This frequency directly determines the fan speed and ventilation volume; for air conditioning equipment, the current operating frequency of its compressor is collected. (Inverter air conditioners are read directly; fixed-frequency air conditioners are converted to equivalent frequency based on equivalent duty cycle); for purification equipment, the operating frequency of its internal fan motor is collected. For oxygenation equipment (such as molecular sieve oxygen generators or oxygen solenoid valves), collect the drive frequency of their air pumps or solenoid valves. The management system reads the current operating frequency of the above four types of devices at a frequency of no less than 0.5Hz, and forms a frequency vector. And store it in the local cache.

[0015] Furthermore, to perceive the acoustic environment within the cabin in real time, especially the acoustic conflicts that may arise from the concurrent operation of multiple devices, this embodiment symmetrically arranges two miniature electret microphones at ear height (approximately 10cm from the left and right ears of the seated user) to form an acoustic sensor group. For example, this sensor group has a frequency response range of 20Hz-20kHz, a dynamic range of 30-120dB SPL, and a sampling rate set to 48kHz. The signals from the two microphones are amplified by a preamplifier, filtered for anti-aliasing, and then synchronously sampled by a 24-bit high-precision analog-to-digital converter to form a dual-channel digital acoustic data stream.

[0016] The system processes the digital acoustic data stream in frames using continuous sliding time windows (e.g., window length 1 second, overlap rate 50%). For each frame, a Fast Fourier Transform (FFT) is performed to obtain the amplitude spectrum, which is then converted into a sound pressure level spectrum covering the entire frequency band from 20Hz to 20kHz after energy normalization. (Unit: dB SPL, frequency resolution) This data describes the sound pressure energy distribution at every frequency point inside the cabin, with no missing frequency bands.

[0017] Simultaneously, calculate the A-weighted equivalent continuous sound pressure level:

[0018] in, The instantaneous sound pressure after weighting by A. For reference sound pressure level, The integration time is the window length.

[0019] To facilitate subsequent psychoacoustic loudness analysis, the above full-frequency data were summarized into a 1 / 3 octave band spectrum. Its center frequency It covers the entire audible range from 25Hz to 20kHz, specifically with the following values: 25Hz, 31.5Hz, 40Hz, 50Hz, 63Hz, 80Hz, 100Hz, 125Hz, 160Hz, 200Hz, 250Hz, 315Hz, 400Hz, 500Hz, 630Hz, 800Hz, 1kHz, 1.25kHz, 1.6kHz, 2kHz, 2.5kHz, 3.15kHz, 4kHz, 5kHz, 6.3kHz, 8kHz, 10kHz, 12.5kHz, 16kHz, and 20kHz (a total of 30 frequency bands). The total energy value within each octave band fully preserves the acoustic characteristics of the entire frequency range.

[0020] The full-band sound pressure level spectrum obtained above A-weighted equivalent continuous sound pressure level And a 1 / 3 octave band spectrum covering 20kHz Together, they constitute the full-band acoustic spectrum data.

[0021] S2, calculate the initial target power of each environmental control device based on the deviation between the air quality index and the target value, and combine the current operating frequency and acoustic spectrum data of each environmental control device to predict the acoustic superposition gain and frequency domain resonance probability under the concurrent operation of multiple devices, and quantify the acoustic conflict index. In this step, the management system completes two tasks: First, based on the deviation between the current air quality and the set target, it calculates the initial target power of each device under the pure air quality control requirement; then, based on the operating frequency corresponding to these initial target powers and the current acoustic spectrum data, it predicts the acoustic superposition and resonance risks that may occur when multiple devices are running simultaneously, quantifies the acoustic conflict index, and uses it as the basis for whether to initiate acoustic budget allocation.

[0022] For the specific implementation process, please refer to steps S21-S25 below.

[0023] S21, obtain the difference between the measured value and the corresponding target value of each dimension of the air quality index, and construct a multi-dimensional deviation vector based on each difference; wherein, the air quality index includes at least one of carbon dioxide concentration, PM2.5 concentration, temperature, humidity and oxygen concentration; The system has preset target values ​​for various air quality dimensions. For example, the target concentration of carbon dioxide is set to 800 ppm, the target concentration of PM2.5 is set to 35 μg / m³, the target temperature is set to 24℃, the target relative humidity is set to 50%, and the target oxygen concentration is set to 20.9%.

[0024] Subtracting the aforementioned real-time measured values ​​from the target values ​​one by one yields the deviations for each dimension. These deviations are then arranged in a fixed order to form a multidimensional deviation vector. The positive or negative sign of the deviation indicates whether the corresponding indicator exceeds the standard or falls short.

[0025] S22, with the objective of minimizing the weighted sum of squares of the deviations in each dimension of the multidimensional deviation vector, multi-objective optimization is performed within the preset power constraint range of each environmental control device to obtain the initial target power of the fresh air device, air conditioner, purification device and oxygenation device; wherein, the weight of each deviation is dynamically adjusted according to the priority of each of the current air quality indicators in the cabin.

[0026] A pre-defined allowable normal fluctuation range is set for each air quality indicator. For example, the allowable fluctuation range for CO2 is ±200 ppm, for PM2.5 it is ±20 μg / m³, for temperature it is ±2℃, for humidity it is ±10%RH, and for oxygen concentration it is ±1%Vol. The normalized deviation is obtained by dividing the absolute deviation of each dimension by its corresponding allowable fluctuation range.

[0027] Considering that different air quality indicators have varying degrees of impact on user comfort and safety, and that their priorities change under different operating conditions (for example, ventilation should be prioritized when carbon dioxide accumulates rapidly, while air purification should be prioritized when PM2.5 levels are excessive), the concierge system assigns a dynamic weight to each dimension, forming a weight vector. The dynamic adjustment rule for the weights can be preset as follows: when the carbon dioxide concentration exceeds 1000 ppm and the rate of increase is greater than 50 ppm / min, the weight is increased. When PM2.5 concentration exceeds 75 μg / m³, increase When the temperature deviation exceeds 2℃, increase... Humidity is similar to oxygen concentration. All weights are normalized to satisfy... .

[0028] Define the optimization objective function as the weighted normalized sum of squared deviations across all dimensions:

[0029] The smaller the value of the objective function, the closer the overall air quality is to the target setting.

[0030] The system establishes approximate relationships between the power of various environmental control devices and the rates of change in various dimensions of air quality through offline identification or theoretical modeling. For example, increasing the power of fresh air equipment (increasing fan speed) can reduce carbon dioxide and PM2.5 concentrations, but will also slightly affect the temperature (bringing in warm outdoor air in summer and cold air in winter); increasing the power of air conditioning (increasing compressor frequency) mainly regulates temperature, but will also have some impact on humidity; increasing the power of purification equipment (increasing internal fan speed) mainly reduces PM2.5; increasing the power of oxygenation equipment (increasing air pump frequency) increases oxygen concentration. These relationships can be approximated as linear models, and their coefficients can be obtained through experimental calibration.

[0031] In each control cycle (e.g., every 5 seconds), the housekeeping system solves the following constrained optimization problem: finding a set of device power... , , , This minimizes the sum of squared weighted normalized deviations of air quality after a prediction step size (e.g., 30 seconds), which is the aforementioned optimization objective function. Simultaneously, the power of each device must meet pre-set lower limits (to avoid ineffective operation) and upper limits (to ensure equipment safety and lifespan). For example, the lower limit for fresh air fan power is set at 10W, and the upper limit at 100W; the lower limit for air conditioning power is set at 50W, and the upper limit at 500W; the lower limit for purification equipment power is set at 5W, and the upper limit at 60W; the lower limit for oxygenation equipment power is set at 2W, and the upper limit at 30W.

[0032] This optimization problem can be solved in real time using conventional quadratic programming algorithms or gradient descent methods, yielding a set of initial target powers, denoted as... , , , It should be understood that this power rating represents the driving force required by the butler system to eliminate air quality deviations as quickly as possible, without considering acoustic constraints at all.

[0033] S23, input the current operating frequency of each environmental control device into the pre-constructed cabin acoustic transmission model, and combine it with the current background noise spectrum in the acoustic spectrum data to calculate the sound pressure level superposition in the frequency band of interest when multiple devices are running concurrently, as the acoustic superposition gain. The system pre-stores an in-cabin acoustic transmission model obtained through offline calibration. The specific calibration method involves placing a reference microphone at the ear position within the soundproof cabin, and then individually operating the fresh air system, air conditioning, purification system, and oxygenation system at different frequencies within their permissible operating frequency ranges. The sound pressure level (in dB SPL) generated at the ear position is measured, and a frequency-sound pressure level lookup table or fitting function is generated for each device. .

[0034] In addition, it is necessary to measure the background noise spectrum inside the cabin when the equipment is turned off, and its equivalent sound pressure level is denoted as... (The A-weighted equivalent continuous sound pressure level can be obtained from step S1) Obtained from, or through, the full-band sound pressure level spectrum (Obtained by integration).

[0035] The current operating frequency of each device is denoted as , , , Substituting these frequencies into the corresponding lookup table or fitting function yields the sound pressure level for each device operating individually. , , , When multiple devices operate concurrently, the total sound pressure level at the ear position is calculated using the commonly used acoustic energy superposition formula:

[0036] Acoustic superposition gain Defined as the increase in sound pressure level when multiple devices are turned on compared to when only background noise is present:

[0037] The unit is dB. The larger the value, the more severe the degradation of the cabin acoustic environment caused by the concurrent operation of multiple devices. For example, if... This indicates that the noise energy has doubled; if This indicates that the subjective loudness has approximately doubled, which is unacceptable in a soundproof cabin.

[0038] S24, perform pairwise comparison and harmonic relationship analysis on the current operating frequency of each environmental control device, calculate the probability that the fundamental frequency and its lower harmonics of each environmental control device fall into the preset resonant danger frequency band, and use it as the frequency domain resonance probability. Besides the direct superposition of sound pressure levels, a more serious issue is that when the operating frequencies (or their lower harmonics) of multiple devices are close to or integer multiples of each other, resonance or beat frequency phenomena are easily triggered in a small, enclosed space, causing a sharp increase in instantaneous noise, even exceeding the simple superposition of device noise. The management system compares the current operating frequencies of each device pairwise and extends the analysis to harmonics at the second and third harmonics.

[0039] The specific judgment rules are as follows: The resonant danger frequency band is pre-set to ±5% of the fundamental frequency. For any two devices... and ( Check the following conditions: The base frequency is close: ; Second harmonic approximation: or ; The third harmonic is close to: or .

[0040] If any of the above conditions are met, the equipment is deemed to have a resonance risk. Since higher harmonic orders generally result in lower resonance intensity, different weights are assigned to the fundamental frequency, second harmonic, and third harmonic; for example, the fundamental frequency has a weight of 1.0, the second harmonic a weight of 0.6, and the third harmonic a weight of 0.3. This applies to all equipment pairs (total...). The process iterates through all pairs of devices, calculates the sum of weighted scores, and then divides by the maximum possible score (i.e., the score when all device pairs resonate at the fundamental frequency), yielding a frequency domain resonance probability between 0 and 1. . The closer it is to 1, the higher the probability that the current frequency combination will cause significant resonance.

[0041] For example, if the fundamental frequency difference between the fresh air equipment frequency and the air conditioning equipment frequency is less than 5%, 1.0 point is awarded; if the purification equipment frequency is exactly twice the oxygenation equipment frequency and the difference is less than 5%, 0.6 points are awarded, and so on.

[0042] S25, the acoustic superposition gain and the frequency domain resonance probability are weighted and fused to obtain the acoustic conflict index; wherein, the weight of the frequency domain resonance probability is higher than the weight of the acoustic superposition gain.

[0043] Considering that frequency domain resonance disrupts the silence boundary far more drastically and suddenly than simple sound pressure level superposition (resonance can cause noise to spike by more than 10 dB in a very short time, while the superposition gain is usually 1-3 dB), the weight of the resonance probability is set higher. An acoustic conflict index is defined. for:

[0044] in, This normalizes the acoustic superposition gain to the [0,1] interval (assuming a maximum allowable additional gain of 10dB; if it exceeds 10dB, it is set to 1). The weighting coefficients satisfy... ,and Preferably, take , . The value range is [0,1]. The larger the value, the easier it is for the current device frequency configuration to break through the silence boundary.

[0045] The butler system will calculate The noise level is compared with a preset silence threshold, which is preferably 0.6. If the current frequency combination is acceptable, the device will be driven directly at the initial target power; if... If the acoustic conflict exceeds the limit, step S3 is triggered, and frequency shifting under acoustic budget constraints is initiated.

[0046] S3, when the acoustic conflict index exceeds the preset silence boundary threshold, an acoustic budget is constructed based on the maximum allowable noise value, and an optimization algorithm is used to stagger the operating frequency of each environmental control device within the acoustic budget to avoid the resonance frequency band, and control parameters of each environmental control device are generated according to the adjusted operating frequency. When the acoustic conflict index When the preset silent boundary threshold is exceeded, it indicates that driving each device directly at the frequency corresponding to the initial target power will compromise the core sound insulation function of the soundproof cabin. At this point, the butler system initiates frequency staggered adjustment under acoustic budget constraints. While ensuring that the total noise does not exceed the maximum allowable value, it tries to approximate the air quality control effect corresponding to the initial target power as closely as possible, while avoiding the resonant frequency band.

[0047] For the specific implementation process, please refer to steps S31-S33 below.

[0048] S31, the difference between the maximum allowable noise value and the current background noise sound pressure level is used as the available acoustic budget, and the available acoustic budget is allocated to each environmental control device as a noise increment constraint when the operating frequency of each environmental control device is adjusted; The design specifications for a soundproof cabin define the maximum permissible noise level inside the cabin, denoted as . (e.g., 35 dB(A)). Current background noise sound pressure level. The equivalent sound pressure level can be measured in real time (i.e., without taking into account the noise of the equipment being adjusted). Then, an acoustic budget can be used. Defined as:

[0049] The unit is dB. If This indicates that the current background noise level has exceeded the standard. The management system should prioritize reducing the background noise (e.g., by forcibly reducing the power of existing equipment or issuing an alarm) before proceeding with this step.

[0050] Available acoustic budget The noise needs to be allocated to the four devices: fresh air system, air conditioning, purification system, and oxygenation system, as the maximum allowable noise increment when each device's operating frequency is adjusted. The allocation process must ensure that the sum of the noise energy contributed by each device does not exceed [a certain value]. At the same time, we should try to minimize the loss of air quality control efficiency caused by frequency adjustments.

[0051] In practice, the management system pre-establishes a set of candidate operating frequencies for each device (e.g., within the device's allowed frequency range, several selectable frequency points are discretized with a certain step size), and pre-calibrates the noise characteristics and performance costs of each candidate frequency. Based on this, the allowed frequency subset and corresponding noise increment quota for each device are determined through optimization. This includes the following sub-steps: S311, obtain the noise spectrum characteristics of each environmental control device under the preset candidate operating frequency set, and combine the frequency response function at the ear position in the cabin acoustic transmission model to predict the contribution of each environmental control device to the psychoacoustic loudness at each candidate operating frequency. The management system pre-establishes a candidate operating frequency-noise spectrum characteristic database for each device through offline calibration. The specific method is as follows: The silencing chamber is placed in a semi-anechoic environment. The device under test (e.g., a fresh air system) is installed in a standard position inside the chamber, with a calibrated microphone positioned at the ear level. Within the device's permissible operating frequency range (e.g., 20Hz-80Hz for a fresh air system), a series of candidate operating frequencies are selected in steps (e.g., 2Hz) to form a candidate operating frequency set. ( 1 is a fresh air system, 2 is an air conditioner, 3 is a purification system, and 4 is an oxygenation system.

[0052] For each candidate running frequency The device was driven to operate stably while a microphone was used to collect sound pressure signals at the ear position. The collected signals were analyzed using a 1 / 3 octave band method to obtain the 1 / 3 octave band sound pressure level spectrum. ,in Centered on a frequency, it covers 30 frequency bands from 25Hz to 20kHz. This 1 / 3 octave band sound pressure level spectrum represents the device's sound pressure level at that frequency. The noise spectrum characteristics described below represent the energy distribution of equipment noise across different frequency bands.

[0053] In the above processing, the microphone is placed directly at the ear position; therefore, the measured noise spectrum characteristics already include the influence of the propagation path from the device to the ear position. However, in actual acoustic budget allocation, the propagation of device noise within the cabin is also affected by the attenuation or enhancement of different frequencies by structures such as sound-absorbing cotton, sound insulation panels, and seats. This propagation characteristic is expressed by a frequency response function. describe. It is a real function (amplitude-frequency response), measured in dB, obtained through offline frequency sweep measurement. A standard sound source is placed at the ear position, and a reference microphone is positioned at the equipment installation location. The difference in sound pressure level spectra between the two locations is calculated. .

[0054] The housekeeping system is based on the candidate running frequency. The 1 / 3 octave band spectrum of the equipment at the sound source location (i.e., the equipment installation location) was obtained. , and then with By adding each frequency, we obtain the corrected 1 / 3 octave spectrum at the ear position:

[0055] The corrected 1 / 3 octave spectrum at the ear position Input the psychoacoustic loudness calculation module. This module uses the Zwicker loudness model to convert the physical sound pressure level spectrum into loudness values ​​(unit: sone) consistent with the subjective perception of the human ear. The core idea of ​​the Zwicker loudness model is that the basilar membrane of the human ear acts like a set of parallel filters, decomposing sound into several critical frequency bands (each with a bandwidth of approximately 1 bar), each frequency band independently contributing to loudness, and finally summing them to obtain the total loudness. The specific calculation process is as follows: 1) Convert the 1 / 3 octave band spectrum to the critical band (Bark scale). The 30 center frequencies of the 1 / 3 octave band spectrum cover the range of human hearing, while there are 24 critical bands (numbered...). arrive Bark), each bark corresponds to a frequency range (e.g., 0 bark corresponds to 0-50Hz, 1 bark corresponds to 50-100Hz, ..., 24 bark corresponds to approximately 15kHz and above). For each critical frequency band... Based on its frequency range, the overlapping 1 / 3 octave band is identified, and the sound pressure level of this critical band is obtained by energy-weighted averaging. (Unit: dB).

[0056] Then, considering the frequency response of the outer and middle ear, pressure gain curves from the free field to the eardrum are used. Make corrections: (Unit: dB). Corrected sound pressure level. ,in For reference sound pressure level.

[0057] 2) Calculate the excitation stage for each critical frequency band, taking into account the masking effect. Incentive level (Unit: dB) reflects the vibration intensity of the corresponding frequency band on the basilar membrane of the human ear, which is determined by the corrected sound pressure level. This is obtained by adding a frequency-related correction term. For pure tones or narrowband noise, we have... ,in The correction factor is 0-2dB. For broadband noise, a masking effect occurs between adjacent frequency bands, meaning that a strong sound in one frequency band will increase the effective excitation level of a neighboring frequency band.

[0058] The Zwicker model uses a diffusion function. To describe this effect, in which For the masked frequency band, To mask the frequency band. The diffused excitation stage is:

[0059] diffusion function It can be obtained by looking up a table, which is pre-stored in the housekeeping system.

[0060] 3) Calculate the characteristic loudness of each critical frequency band. Characteristic loudness (Unit: sone / bark) represents the contribution of each critical frequency band to the total loudness, which is related to the excitation level. There is a nonlinear relationship between them. The Zwicker model gives the following approximate formula: when hour, (Below the hearing threshold, no contribution); when When using simplified empirical formulas:

[0061] in, This is the hearing threshold excitation level (i.e., the minimum sound pressure level that the human ear can hear in this critical frequency band, which can be pre-calibrated). If the calculated result is negative, it is taken as 0. It should be understood that the characteristic loudness given by this formula increases approximately linearly at low excitation levels and slows down at high excitation levels, which is consistent with the nonlinear characteristics of the human ear.

[0062] 4) Integrate to obtain the total loudness The characteristic loudness of all critical bands is summed by integrating along the Bark scale. Since the width of each critical band... Bark, total loudness is:

[0063] Unit: sone. For reference, the loudness of typical ambient noise is: about 4 sone in a quiet library, about 16 sone for normal conversation, and about 2-4 sone for a target noise of 35 dB(A) in a soundproof cabin.

[0064] Output total loudness (Unit: sone), which refers to the device's performance at the candidate frequency. The psychoacoustic loudness contribution during operation. It should be understood that this value characterizes the subjective noise intensity perceived by the user at the ear position if the device were operating at this frequency.

[0065] The butler system embeds the above algorithm into embedded code, and inputs the corrected ear position 1 / 3 octave spectrum. Then, the psychoacoustic loudness contribution can be calculated within milliseconds. .

[0066] S312, calculate the attenuation of the control efficiency of each environmental control device on the associated air quality index when switching between the operating frequency corresponding to the initial target power and each candidate operating frequency, and use it as the efficiency cost of each candidate operating frequency. Initial target power It is obtained through step S22, and corresponds to the ideal target operating frequency. (This can be obtained by looking up the power-frequency characteristic curve of the equipment. For example, for a centrifugal fan, power is approximately proportional to the cube of the rotational speed, and rotational speed is proportional to the frequency. Therefore, a formula can be established.) (Relationship). If the equipment The frequency is adjusted to a candidate operating frequency. Its actual output power will become Because acoustic budget constraints typically require noise reduction, Often less than or equal to (Lowering the frequency can reduce noise, but it also weakens the air quality control capability.) If the candidate operating frequency is greater than... If the power loss is zero (overshoot is not allowed), then increasing the frequency will add extra noise and may exceed the budget.

[0067] For different types of equipment, the physical quantities of their regulation efficiency are different, and the efficiency cost needs to be defined separately: Fresh air systems primarily function to provide ventilation (unit: m³ / h) to dilute carbon dioxide and reduce PM2.5. Ventilation volume With fan speed It is directly proportional to the rotational speed and the driving frequency. Proportional, that is ,in This is a proportionality coefficient (obtained through airflow calibration tests). The performance cost is then defined as the relative airflow reduction:

[0068] That is, the air volume corresponding to the initial target frequency. Subtract the air volume corresponding to the candidate frequency Divide by the initial air volume .when hour, ;when far below hour, .

[0069] Air conditioning equipment: Its main performance indicator is cooling capacity or heating capacity (unit: W). For inverter air conditioners, cooling capacity... With compressor frequency The relationship is approximately linear: ,in For startup frequency, Let be the calibration coefficient. Then the performance cost is:

[0070] Purification equipment: Its main performance indicator is Clean Air Delivery Rate (CADR, unit: m³ / h). The CADR is approximately proportional to the internal fan speed (i.e., drive frequency). The performance cost is:

[0071] Oxygenation equipment: Its main efficiency is oxygen production rate (unit: L / min). For molecular sieve oxygen generators, the gas production rate is linearly related to the pump frequency. The performance cost is:

[0072] For all devices, when the candidate frequency is lower than the minimum allowed operating frequency... (Completely ineffective).

[0073] S313, with the sum of the psychoacoustic loudness contributions of each environmental control device not exceeding the available acoustic budget as a rigid constraint, and with the goal of minimizing the sum of the performance costs of each environmental control device, solve for the allowable subset of operating frequencies and the corresponding noise increment quota for each environmental control device, and complete the allocation.

[0074] After the aforementioned processing, the management system provides each device with... A candidate running frequency set was established. and for each candidate running frequency Related attributes, i.e., psychoacoustic loudness and efficiency cost .

[0075] Available acoustic budget While the noise level is measured in dB(A), psychoacoustic loudness is measured in sones, and the two cannot be directly converted. Therefore, this invention constructs a budget constraint from a loudness perspective and obtains the maximum permissible noise value for the soundproof chamber through offline calibration. The corresponding total loudness The specific calibration method is as follows: Playing music at the ear position inside the cabin Typical noise levels within the limit (such as broadband background noise of 35 dB(A)) are measured, and their 1 / 3 octave band spectrum is input into the psychoacoustic loudness calculation module to obtain the corresponding total loudness. (Unit: sone). Simultaneously, the management system measures the current background noise (excluding the equipment to be adjusted) in real time at 1 / 3 octave band, and inputs this data into the psychoacoustic loudness calculation module to obtain the current background loudness. The loudness budget can then be calculated as follows:

[0076] A rigid constraint stipulates that the sum of the psychoacoustic loudness contributions of all devices must not exceed the available loudness budget.

[0077] in, For equipment In candidate running frequency The psychological acoustic loudness contribution is determined by this constraint. It should be understood that this constraint is directly based on loudness units, without the need for empirical conversion between dB(A) and sone, thus ensuring the physical accuracy and consistency of the acoustic budget allocation.

[0078] The optimization objective is to minimize the total performance cost of all devices:

[0079] in, The performance priority weights for each device can be dynamically set based on the urgency of the current air quality deviation (e.g., increasing the weight of fresh air devices when carbon dioxide levels are severely exceeded; increasing the weight of air conditioning devices when temperature deviations are large). Due to the small number of devices (4 units), the size of each candidate operating frequency set is generally 10-20, and the total search space does not exceed 20. 4 =160000, the management system can solve this problem within tens of milliseconds using enumeration or branch-and-bound methods on an embedded controller. For each device, the optimal operating frequency obtained from the solution is denoted as . The corresponding psychoacoustic loudness This refers to the noise increment quota occupied by the equipment. .

[0080] Through the above processing, the acoustic budget is allocated to each device in a reasonable and precise manner, which not only ensures that the total noise does not exceed the standard, but also preserves the most critical control effectiveness for the current air quality to the maximum extent.

[0081] S32, with the goal of minimizing the sum of the power loss of each environmental control device relative to the initial target power corresponding to the adjusted operating frequency, and with the noise increment constraint and the preset allowable operating frequency range of each environmental control device as constraints, the operating frequency of each environmental control device is optimized and solved to obtain the adjusted operating frequency that avoids the resonance frequency band. After the aforementioned processing, the management system has assigned preliminary candidate operating frequencies to each device. and their corresponding noise increment quotas However, this allocation process is primarily based on the psychoacoustic loudness and performance cost of individual devices, without considering the frequency coupling relationships between devices, i.e., the risk of resonance. Therefore, this step, based on the above allocation results, further applies resonance avoidance constraints and performs local fine-tuning of the frequencies to ensure that the final frequency combination completely avoids the resonant frequency band.

[0082] Specifically, the decision variable is defined as the operating frequency of each device. ( ),in The value range is the set of candidate running frequencies. A subset, namely, close to Several neighborhood frequencies (e.g., 2-3 candidate points before and after) are used to reduce the search space and speed up the solution process.

[0083] The optimization objective is to minimize the weighted sum of the power losses of each device (relative to the initial target power):

[0084] in, For the initial target power, For frequency The corresponding actual power (which can be obtained from the device power-frequency response curve); The priority weights for each device can be dynamically set based on the urgency of deviations in various dimensions of current air quality (e.g., when carbon dioxide concentration is severely exceeded, the priority weights for fresh air devices will be increased). The target power is set relatively high to prioritize its effectiveness. Power loss is defined as the difference between the initial target power and the actual power, and if the actual power is higher than the initial target power, the loss is counted as 0 (increasing the frequency to increase noise is not allowed).

[0085] The constraints include: Noise increment constraint: Each device at frequency The resulting psychoacoustic loudness The quota allocated to the device must not be exceeded. The tolerance range (e.g., allowing ±5% fluctuation) and the sum of the loudness of all devices must still meet the requirements. (The overall budget will not be exceeded). This constraint can be written as:

[0086] in, This is the tolerance factor, for example, 0.05.

[0087] Resonance avoidance constraints: for any two devices Its fundamental frequency and its second and third harmonics must not fall within the resonance hazard band. The definition of the resonance hazard band is the same as above: a bandwidth of ±5% centered on each frequency. That is, it must simultaneously meet the following conditions:

[0088]

[0089]

[0090] The above inequalities must hold true for every pair of devices simultaneously. If a pair of devices cannot satisfy all the conditions, then at least the fundamental frequency should be kept away from each other (because fundamental frequency resonance is the most harmful). Harmonic conditions can be used as soft constraints.

[0091] Permissible operating frequency range: ,in for Central And close to A subset of candidate frequencies (e.g., two frequency points before and after).

[0092] Since there are only 4 devices, the candidate frequency subset size for each device is generally no more than 5, and the total search space is ≤5. 4 =625, the butler system can find the optimal solution within milliseconds using a complete enumeration method. If multiple solutions exist that result in the same objective function value, the combination with the smallest total loudness is preferred to allow for acoustic margin.

[0093] The solution yields a set of operating frequencies after allocation that satisfy all constraints, denoted as . , , , This set of frequencies, while strictly ensuring the quiet boundary (total noise not exceeding the standard) and completely avoiding resonance, approximates the air quality control requirements represented by the initial target power to the greatest extent possible.

[0094] S33, convert the adjusted operating frequency of each environmental control device into a corresponding voltage or current control signal, and generate control parameters for each environmental control device.

[0095] The management system converts the determined operating frequency into specific execution signals based on the physical interface and control protocol of each device's driver. The conversion methods for different types of devices are as follows: Fresh air systems typically use frequency converters to drive three-phase asynchronous motors or EC (electronically commutated) fans. The frequency converter accepts 0-10V analog voltage signals or 4-20mA current signals as frequency input. The system establishes a mapping relationship based on the given voltage. ,in The corresponding minimum frequency (e.g., 2V corresponds to 20Hz). This corresponds to the maximum frequency (e.g., 10V corresponds to 80Hz). The voltage is then output via a digital-to-analog converter. If PWM control is used, the frequency is converted to a duty cycle. It outputs a square wave with the corresponding duty cycle.

[0096] Air conditioning equipment: Inverter air conditioner compressors typically receive digital commands through the indoor unit's main control board, using communication protocols such as CAN bus, Modbus, or manufacturer-specific protocols. The management system will then set the target frequency. Encapsulated as a protocol frame (e.g., written to holding register address 0x01, with a value of...) The integer (the value) is sent to the air conditioning main control board via RS485 or CAN interface.

[0097] Air purification equipment: The internal fan motor of air purifiers is mostly a DC brushless motor, driven by PWM or voltage speed regulation. The butler system will... It is mapped to a 0-5V voltage or a 0-100% duty cycle and output to the motor control terminal via an isolation driver.

[0098] Oxygenation equipment: The air pump in oxygenation equipment (such as an oxygen concentrator) is driven by a brushless DC motor, and the speed regulation method is also PWM. Convert to the corresponding PWM duty cycle output.

[0099] The management system packages the aforementioned control signals or instructions into execution frames and sends them out sequentially according to a predetermined time sequence (e.g., adjusting fresh air and purification first, then adjusting air conditioning and oxygenation to avoid current surges). Simultaneously, it records information such as the currently adjusted frequency, power loss, and actual loudness to a local log for reference in the dynamic correction step S4.

[0100] S4. Drive each environmental control device to operate according to the control parameters, and continuously monitor the changes in the acoustic spectrum inside the cabin to dynamically correct the control parameters.

[0101] The management system converts the aforementioned frequency commands into voltage, current, or digital signals, and sends them sequentially to the fresh air equipment, air conditioner, purification equipment, and oxygenation equipment according to a preset timing sequence, driving them to operate at the adjusted frequency.

[0102] During equipment operation, the acoustic sensor array continuously acquires data in real time. The management system continuously calculates the A-weighted sound pressure level and 1 / 3 octave band spectrum using a sliding time window (e.g., 1 second), comparing them with the maximum allowable noise value and the resonant danger frequency band. If the measured noise exceeds the limit or a new resonance / beat frequency characteristic is detected, an event-driven correction is immediately triggered: starting from the current actual frequency, steps S2 and S3 are re-executed to generate and distribute new control parameters. Simultaneously, rolling corrections are performed at longer intervals (e.g., 30 seconds) to adapt to slow changes in air quality and background noise, forming a closed-loop servo control to ensure that the quiet boundary is never breached.

[0103] Please see Figure 2 This invention provides a fully perceptive silent cabin servo environment management system 200, the system comprising: The full-sensing acquisition module 10 is used to collect air quality indicators and the current operating frequency of each environmental control device in the soundproof cabin in real time, and to capture the acoustic spectrum data of the entire frequency band in the cabin in real time using an acoustic sensor group; wherein, the environmental control device includes fresh air equipment, air conditioning, purification equipment and oxygenation equipment; The acoustic conflict prediction module 20 is used to calculate the initial target power of each environmental control device based on the deviation between the air quality index and the target value, and to predict the acoustic superposition gain and frequency domain resonance probability under the concurrent operation of multiple devices by combining the current operating frequency and acoustic spectrum data of each environmental control device, and to quantify the acoustic conflict index. The acoustic budget and allocation module 30 is used to construct an acoustic budget based on the maximum allowable noise value when the acoustic conflict index exceeds the preset silence boundary threshold, use an optimization algorithm to allocate the operating frequency of each environmental control device within the acoustic budget to avoid the resonance frequency band, and generate control parameters for each environmental control device according to the allocated operating frequency. The servo control module 40 is used to drive the operation of each environmental control device according to the control parameters, and continuously monitor the changes in the acoustic spectrum inside the cabin to dynamically correct the control parameters.

[0104] As an example, such as Figure 3 As shown, the acoustic conflict prediction module 20 includes an initial power calculation unit 201, specifically used for: The difference between the measured value and the corresponding target value of each dimension of the air quality index is obtained, and a multidimensional deviation vector is constructed based on each difference; wherein, the air quality index includes at least one of carbon dioxide concentration, PM2.5 concentration, temperature, humidity and oxygen concentration; With the objective of minimizing the weighted sum of squares of the deviations in each dimension of the multidimensional deviation vector, a multi-objective optimization solution is performed within the preset power constraint range of each environmental control device to obtain the initial target power of the fresh air device, air conditioner, purification device and oxygenation device; wherein, the weight of each dimension of deviation is dynamically adjusted according to the priority of each of the current air quality indicators in the cabin.

[0105] As an example, the acoustic conflict prediction module 20 further includes an acoustic conflict quantization unit 202, specifically used for: The current operating frequency of each environmental control device is input into the pre-constructed cabin acoustic transmission model. Combined with the current background noise spectrum in the acoustic spectrum data, the sound pressure level superposition in the frequency band of interest when multiple devices are running concurrently is calculated as the acoustic superposition gain. The current operating frequencies of each environmental control device are compared pairwise and analyzed for harmonic relationships. The probability that the fundamental frequency and its lower harmonics of each environmental control device fall into the preset resonant danger bandwidth is calculated and used as the frequency domain resonance probability. The acoustic superposition gain and the frequency domain resonance probability are weighted and fused to obtain the acoustic conflict index; wherein the weight of the frequency domain resonance probability is higher than the weight of the acoustic superposition gain.

[0106] As an example, the acoustic budgeting and tuning module 30 is specifically used for: The difference between the maximum allowable noise value and the current background noise sound pressure level is used as the available acoustic budget, and the available acoustic budget is allocated to each environmental control device as a noise increment constraint when the operating frequency of each environmental control device is adjusted. With the objective of minimizing the sum of the power loss of each environmental control device relative to the initial target power corresponding to the adjusted operating frequency, and with the noise increment constraint and the preset allowable operating frequency range of each environmental control device as constraints, the operating frequency of each environmental control device is optimized and solved to obtain the adjusted operating frequency that avoids the resonance frequency band. The adjusted operating frequencies of each environmental control device are converted into corresponding voltage or current control signals to generate control parameters for each environmental control device.

[0107] As an example, the acoustic budgeting and tuning module 30 is also specifically used for: The noise spectrum characteristics of each environmental control device are obtained under a preset set of candidate operating frequencies. Combined with the frequency response function at the ear position in the cabin acoustic transfer model, the contribution of each environmental control device to the psychoacoustic loudness at each candidate operating frequency is predicted. The attenuation of the control efficiency of each environmental control device on the associated air quality index when switching between the operating frequency corresponding to the initial target power and each candidate operating frequency is calculated as the efficiency cost of each candidate operating frequency. The allocation is completed by taking the sum of the psychoacoustic loudness contributions of each environmental control device as a rigid constraint that does not exceed the available acoustic budget, and minimizing the sum of the performance costs of each environmental control device. The allocation is then completed by solving for the allowable subset of operating frequencies and the corresponding noise increment quotas for each environmental control device.

[0108] It should be understood that each module in the fully perceptive silent cabin servo environment management system corresponds one-to-one with each step in the above method embodiment. The specific implementation method and technical effect have been described in detail in the method embodiment, and will not be repeated here.

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

Claims

1. A method for managing the servo environment of a silent cabin based on full perception, characterized in that, Includes the following steps: The system collects real-time air quality indicators and the current operating frequencies of various environmental control devices within the soundproof cabin, and uses an acoustic sensor array to capture real-time acoustic spectrum data across the entire frequency band within the cabin; wherein, the environmental control devices include fresh air equipment, air conditioning, purification equipment, and oxygenation equipment; The initial target power of each environmental control device is calculated based on the deviation between the air quality index and the target value. Combined with the current operating frequency and acoustic spectrum data of each environmental control device, the acoustic superposition gain and frequency domain resonance probability under the concurrent operation of multiple devices are predicted, and the acoustic conflict index is quantified. When the acoustic conflict index exceeds the preset silence boundary threshold, an acoustic budget is constructed based on the maximum allowable noise value. An optimization algorithm is used to adjust the operating frequency of each environmental control device within the acoustic budget to avoid the resonance frequency band, and control parameters of each environmental control device are generated according to the adjusted operating frequency. The control parameters are used to drive the operation of each environmental control device and continuously monitor changes in the acoustic spectrum inside the cabin to dynamically correct the control parameters.

2. The method for managing the servo environment of a silent cabin based on full perception as described in claim 1, characterized in that: The initial target power of each environmental control device is calculated based on the deviation between the air quality index and the target value, including: The difference between the measured value and the corresponding target value of each dimension of the air quality index is obtained, and a multidimensional deviation vector is constructed based on each difference; wherein, the air quality index includes at least one of carbon dioxide concentration, PM2.5 concentration, temperature, humidity and oxygen concentration; With the objective of minimizing the weighted sum of squares of the deviations in each dimension of the multidimensional deviation vector, a multi-objective optimization solution is performed within the preset power constraint range of each environmental control device to obtain the initial target power of the fresh air device, air conditioner, purification device and oxygenation device; wherein, the weight of each dimension of deviation is dynamically adjusted according to the priority of each of the current air quality indicators in the cabin.

3. The method for managing the servo environment of a silent cabin based on full perception as described in claim 2, characterized in that: By combining the current operating frequency and acoustic spectrum data of each environmental control device, the acoustic superposition gain and frequency domain resonance probability under concurrent operation of multiple devices are predicted, and the acoustic conflict index is quantified, including: The current operating frequency of each environmental control device is input into the pre-constructed cabin acoustic transmission model. Combined with the current background noise spectrum in the acoustic spectrum data, the sound pressure level superposition in the frequency band of interest when multiple devices are running concurrently is calculated as the acoustic superposition gain. The current operating frequencies of each environmental control device are compared pairwise and analyzed for harmonic relationships. The probability that the fundamental frequency and its lower harmonics of each environmental control device fall into the preset resonant danger bandwidth is calculated and used as the frequency domain resonance probability. The acoustic superposition gain and the frequency domain resonance probability are weighted and fused to obtain the acoustic conflict index; wherein the weight of the frequency domain resonance probability is higher than the weight of the acoustic superposition gain.

4. The method for managing the servo environment of a silent cabin based on full perception as described in claim 1, characterized in that: An acoustic budget is constructed based on the maximum permissible noise value. An optimization algorithm is used to stagger the operating frequencies of each device within the acoustic budget to avoid resonant frequency bands. Control parameters for each environmental control device are then generated based on the adjusted operating frequencies, including: The difference between the maximum allowable noise value and the current background noise sound pressure level is used as the available acoustic budget, and the available acoustic budget is allocated to each environmental control device as a noise increment constraint when the operating frequency of each environmental control device is adjusted. With the objective of minimizing the sum of the power loss of each environmental control device relative to the initial target power corresponding to the adjusted operating frequency, and with the noise increment constraint and the preset allowable operating frequency range of each environmental control device as constraints, the operating frequency of each environmental control device is optimized and solved to obtain the adjusted operating frequency that avoids the resonance frequency band. The adjusted operating frequencies of each environmental control device are converted into corresponding voltage or current control signals to generate control parameters for each environmental control device.

5. The method for managing the servo environment of a silent cabin based on full perception as described in claim 4, characterized in that: Allocating the available acoustic budget to each environmental control device includes: The noise spectrum characteristics of each environmental control device are obtained under a preset set of candidate operating frequencies. Combined with the frequency response function at the ear position in the cabin acoustic transfer model, the contribution of each environmental control device to the psychoacoustic loudness at each candidate operating frequency is predicted. The attenuation of the control efficiency of each environmental control device on the associated air quality index when switching between the operating frequency corresponding to the initial target power and each candidate operating frequency is calculated as the efficiency cost of each candidate operating frequency. The allocation is completed by taking the sum of the psychoacoustic loudness contributions of each environmental control device as a rigid constraint that does not exceed the available acoustic budget, and minimizing the sum of the performance costs of each environmental control device. The allocation is then completed by solving for the allowable subset of operating frequencies and the corresponding noise increment quotas for each environmental control device.

6. A silent cabin servo environment management system based on full perception, characterized in that: The system includes: The full-sensing acquisition module is used to collect air quality indicators and the current operating frequency of each environmental control device in the soundproof cabin in real time, and to capture the acoustic spectrum data of the entire frequency band in the cabin in real time using an acoustic sensor group; wherein, the environmental control device includes fresh air equipment, air conditioning, purification equipment and oxygenation equipment; The acoustic conflict prediction module is used to calculate the initial target power of each environmental control device based on the deviation between the air quality index and the target value, and combined with the current operating frequency and acoustic spectrum data of each environmental control device, predict the acoustic superposition gain and frequency domain resonance probability under the concurrent operation of multiple devices, and quantify the acoustic conflict index. The acoustic budget and allocation module is used to construct an acoustic budget based on the maximum allowable noise value when the acoustic conflict index exceeds the preset silence boundary threshold. It uses an optimization algorithm to adjust the operating frequency of each environmental control device within the acoustic budget to avoid the resonance frequency band, and generates control parameters for each environmental control device based on the adjusted operating frequency. The servo control module is used to drive the operation of each environmental control device according to the control parameters, and continuously monitor the changes in the acoustic spectrum inside the cabin to dynamically correct the control parameters.

7. A silent cabin servo environment management system based on full perception as described in claim 6, characterized in that, The acoustic conflict prediction module includes an initial power calculation unit, specifically used for: The difference between the measured value and the corresponding target value of each dimension of the air quality index is obtained, and a multidimensional deviation vector is constructed based on each difference; wherein, the air quality index includes at least one of carbon dioxide concentration, PM2.5 concentration, temperature, humidity and oxygen concentration; With the objective of minimizing the weighted sum of squares of the deviations in each dimension of the multidimensional deviation vector, a multi-objective optimization solution is performed within the preset power constraint range of each environmental control device to obtain the initial target power of the fresh air device, air conditioner, purification device and oxygenation device; wherein, the weight of each dimension of deviation is dynamically adjusted according to the priority of each of the current air quality indicators in the cabin.

8. A silent cabin servo environment management system based on full perception as described in claim 7, characterized in that, The acoustic conflict prediction module also includes an acoustic conflict quantization unit, specifically used for: The current operating frequency of each environmental control device is input into the pre-constructed cabin acoustic transmission model. Combined with the current background noise spectrum in the acoustic spectrum data, the sound pressure level superposition in the frequency band of interest when multiple devices are running concurrently is calculated as the acoustic superposition gain. The current operating frequencies of each environmental control device are compared pairwise and analyzed for harmonic relationships. The probability that the fundamental frequency and its lower harmonics of each environmental control device fall into the preset resonant danger bandwidth is calculated and used as the frequency domain resonance probability. The acoustic superposition gain and the frequency domain resonance probability are weighted and fused to obtain the acoustic conflict index; wherein the weight of the frequency domain resonance probability is higher than the weight of the acoustic superposition gain.

9. A silent cabin servo environment management system based on full perception as described in claim 6, characterized in that, The acoustic budgeting and allocation module is specifically used for: The difference between the maximum allowable noise value and the current background noise sound pressure level is used as the available acoustic budget, and the available acoustic budget is allocated to each environmental control device as a noise increment constraint when the operating frequency of each environmental control device is adjusted. With the objective of minimizing the sum of the power loss of each environmental control device relative to the initial target power corresponding to the adjusted operating frequency, and with the noise increment constraint and the preset allowable operating frequency range of each environmental control device as constraints, the operating frequency of each environmental control device is optimized and solved to obtain the adjusted operating frequency that avoids the resonance frequency band. The adjusted operating frequencies of each environmental control device are converted into corresponding voltage or current control signals to generate control parameters for each environmental control device.

10. A silent cabin servo environment management system based on full perception as described in claim 9, characterized in that, The acoustic budgeting and allocation module is also specifically used for: The noise spectrum characteristics of each environmental control device are obtained under a preset set of candidate operating frequencies. Combined with the frequency response function at the ear position in the cabin acoustic transfer model, the contribution of each environmental control device to the psychoacoustic loudness at each candidate operating frequency is predicted. The attenuation of the control efficiency of each environmental control device on the associated air quality index when switching between the operating frequency corresponding to the initial target power and each candidate operating frequency is calculated as the efficiency cost of each candidate operating frequency. The allocation is completed by taking the sum of the psychoacoustic loudness contributions of each environmental control device as a rigid constraint that does not exceed the available acoustic budget, and minimizing the sum of the performance costs of each environmental control device. The allocation is then completed by solving for the allowable subset of operating frequencies and the corresponding noise increment quotas for each environmental control device.

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