Anesthesia equipment centralized management method and system based on Internet of Things

Abstract

This invention relates to the field of Internet of Things (IoT) and communication sensing technology for medical devices, and particularly to a centralized management method and system for anesthesia equipment based on IoT. The method includes the following steps: S1: Frequency division multiplexing of high-frequency communication and low-frequency acoustic envelopes to control the array to transmit a composite optical signal; S2: Identifying the end-expiratory phase based on mechanical vibration rhythm to generate a timing gating; S3: Responding to the gating to open the photoacoustic resonant cavity to receive the optical signal; S4: Filtering and separating demodulated data and photoacoustic pressure, extracting base station coordinates and inverting leakage concentration; S5: Optimizing beamforming based on pressure amplitude and bit error rate, and generating a ledger by combining coordinates and concentration. In this invention, by frequency division multiplexing and fusing the high-frequency communication envelope and the low-frequency acoustic excitation envelope, and utilizing a light-emitting diode array to simultaneously achieve downlink communication and photoacoustic sensing excitation, the problem of electromagnetic interference easily caused by traditional RF IoT solutions in the operating room environment is solved.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology for medical devices and communication sensing technology, and in particular to a centralized management method and system for anesthesia equipment based on IoT. Background Technology

[0002] With the large-scale application of IoT technology in medical settings, the centralized management and operational status monitoring of anesthesia equipment in operating rooms increasingly rely on wireless networks and device-side sensor nodes. Currently, mainstream medical IoT data dissemination and ledger location management mostly utilize radio frequency (RF) communication technology. However, the electromagnetic radiation generated by RF signals in enclosed spaces can easily cause electromagnetic interference to surrounding highly sensitive life support equipment and monitoring instruments, posing a significant safety hazard in the operating room environment, which demands high electromagnetic compatibility.

[0003] Aging tubing and gas leaks in anesthesia equipment during service are critical monitoring indicators for centralized management. The industry commonly uses photoacoustic spectroscopy for trace gas detection. Existing photoacoustic sensing devices require dedicated excitation light source systems to excite specific gas molecules. In conventional equipment upgrades, technicians typically overlay existing RF communication modules with independent photoacoustic sensing modules on the outside of the anesthesia device. This physical splicing results in communication and sensing operating on two separate hardware chains, preventing hardware resource reuse at the underlying physical level. This not only fails to solve the fundamental electromagnetic interference problem but also inevitably increases the physical footprint of the receiving node and the overall system power consumption due to the need to drive the dedicated excitation light source for extended periods, making it difficult to meet the practical engineering requirements of miniaturization and low power consumption in modern medical equipment. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a centralized management method and system for anesthesia equipment based on the Internet of Things, aiming to improve the problem of electromagnetic interference in the operating room environment.

[0005] In a first aspect, the present invention provides the following technical solution: a centralized management method for anesthesia equipment based on the Internet of Things, comprising the following steps: Step S1: Obtain device instructions, and use a frequency division multiplexing algorithm to fuse the high-frequency communication envelope carrying the device instructions with the low-frequency acoustic excitation envelope with locked resonance characteristics, and control the light-emitting diode array to emit composite optical signals; Step S2: Collect mechanical vibration rhythm data, identify the end-expiratory phase in the mechanical vibration rhythm data through a feature extraction algorithm, and generate a timing gating opening signal in the interval corresponding to the end-expiratory phase; Step S3: In response to the timing gate opening signal, open the air intake valve of the photoacoustic resonant cavity and the sound acquisition circuit to receive the composite optical signal; Step S4: Separate the composite optical signal into communication demodulation data and photoacoustic pressure signal using a filtering algorithm, extract the base station coordinates from the communication demodulation data, and run a concentration inversion algorithm to calculate the leakage concentration based on the photoacoustic pressure signal; Step S5: Input the amplitude of the photoacoustic pressure signal and the bit error rate of the communication demodulation data into the beamforming optimization algorithm, adjust the emission weight and phase of the light-emitting diode array, combine the base station coordinates and the leakage concentration to calculate the spatial location and generate a ledger.

[0006] Preferably, in step S1, the step of fusing the high-frequency communication envelope carrying the device command with the low-frequency acoustic excitation envelope with locked resonance characteristics using a frequency division multiplexing algorithm to control the light-emitting diode array to emit a composite optical signal includes: The frequency value of the low-frequency acoustic excitation envelope is strictly matched to the inherent acoustic resonant frequency of the underlying photoacoustic resonant cavity. The DC lighting bias data, the high-frequency communication envelope, and the low-frequency acoustic excitation envelope are superimposed in the frequency domain to calculate the driving data, which is then broadcast to the LED array.

[0007] Preferably, in step S2, the step of collecting mechanical vibration rhythm data and identifying the end-expiratory phase in the mechanical vibration rhythm data through a feature extraction algorithm includes: The mechanical vibration rhythm data of the bellows is collected by calling the microelectromechanical sensor, and the time peak of the mechanical vibration rhythm data is extracted by the zero-crossing detection algorithm; The maximum pressure range corresponding to the time peak is defined as the end-expiratory phase.

[0008] Preferably, in step S3, the step of opening the air intake valve of the photoacoustic resonant cavity and the sound acquisition circuit in response to the timing gate opening signal to receive the composite optical signal includes: When the timing gate opening signal in a high-level state is detected, a drive command is sent to open the air inlet valve of the photoacoustic resonant cavity to allow surrounding gas to flow in; Synchronous power supply activates the photodetector to receive the composite optical signal and wakes up the microphone to listen to the sound generated inside the photoacoustic resonant cavity.

[0009] Preferably, in step S4, the step of separating the composite optical signal into communication demodulation data and photoacoustic pressure signal using a filtering algorithm, and extracting the base station coordinates from the communication demodulation data, includes: The composite optical signal is converted into an electrical signal using a photodetector, and the low-frequency components in the electrical signal are filtered out using a high-pass filtering algorithm to obtain the communication demodulation data. The frame structure of the communication demodulated data is parsed to extract the coordinates of the base station.

[0010] Preferably, in step S4, the step of running the concentration inversion algorithm to calculate the leakage concentration based on the photoacoustic pressure signal includes: The change in sound pressure generated by the expansion of gas flowing into the photoacoustic resonant cavity after absorbing low-frequency acoustic excitation envelope light energy is obtained, and the change in sound pressure is converted into the photoacoustic pressure signal. The amplitude characteristics of the photoacoustic pressure signal are extracted, and the leakage concentration is derived by combining the cavity structure constant, gas light absorption coefficient and effective excitation light power parameters through the concentration inversion algorithm.

[0011] Preferably, in step S5, the step of inputting the amplitude of the photoacoustic pressure signal and the bit error rate of the communication demodulation data into the beamforming optimization algorithm to adjust the emission weight and phase of the light-emitting diode array includes: The beamforming optimization algorithm uses the maximum amplitude of the photoacoustic pressure signal and the minimum bit error rate of the communication demodulated data as the joint optimization function. The spatial projection angle of the emitted beam is controlled by iteratively calculating and redistributing the emission weight and phase of each light-emitting unit in the LED array.

[0012] Preferably, in step S5, the step of calculating the spatial location and generating a ledger by combining the base station coordinates and the leakage concentration includes: Based on the base station coordinates extracted from multiple light-emitting diode arrays, the three-dimensional spatial position coordinates of the anesthesia device are calculated using a time difference of arrival (TDOA) positioning algorithm. The three-dimensional spatial coordinates are bound to the corresponding leakage concentration and stored in the database to generate the ledger.

[0013] Preferably, the step of generating the ledger further includes: The leakage concentrations at different time points are aggregated to form a time series set, and the concentration change gradient of the time series set with the service life of the anesthesia equipment is calculated. When the concentration change gradient exceeds the safety benchmark threshold, the corresponding equipment is marked as having a risk of pipeline aging in the ledger, and an early warning command is output.

[0014] Secondly, the present invention provides the following technical solution: a centralized management system for anesthesia equipment based on the Internet of Things, the system comprising: The signal transmission module acquires device commands and uses a frequency division multiplexing algorithm to fuse the high-frequency communication envelope carrying the device commands with the low-frequency acoustic excitation envelope with locked resonance characteristics, thereby controlling the light-emitting diode array to emit a composite optical signal. The rhythm sensing module collects mechanical vibration rhythm data, identifies the end-expiratory phase in the mechanical vibration rhythm data through a feature extraction algorithm, and generates a timing gating opening signal in the interval corresponding to the end-expiratory phase. The gating receiving module responds to the timing gating opening signal by opening the air intake valve of the photoacoustic resonant cavity and the sound acquisition circuit to receive the composite optical signal. The data demodulation module separates the composite optical signal into communication demodulation data and photoacoustic pressure signal through a filtering algorithm, extracts the base station coordinates from the communication demodulation data, and runs a concentration inversion algorithm to calculate the leakage concentration based on the photoacoustic pressure signal. The optimization management module inputs the amplitude of the photoacoustic pressure signal and the bit error rate of the communication demodulation data into the beamforming optimization algorithm, adjusts the emission weight and phase of the light-emitting diode array, and calculates the spatial location by combining the base station coordinates and the leakage concentration to generate a ledger.

[0015] The present invention has the following beneficial effects: 1. This invention integrates high-frequency communication envelope and low-frequency acoustic excitation envelope through frequency division multiplexing, and uses a light-emitting diode array to simultaneously realize downlink communication and photoacoustic sensing excitation. Without adding an additional dedicated excitation light source, it solves the problem of electromagnetic interference caused by traditional radio frequency IoT solutions in the operating room environment, while reducing the physical size of the receiving node and the system's operating power consumption.

[0016] 2. This invention adopts a time-gated acquisition mechanism based on mechanical vibration data. Through feature extraction algorithm, the opening time of the photoacoustic resonant cavity is synchronized with the end-expiratory phase of the anesthesia device. This allows the physical sensing window to accurately cover the stage where the gas pressure in the device is the highest and gas leakage is most likely to occur. It effectively eliminates environmental background noise during irrelevant periods and improves the accuracy of identifying early micro-tube aging leaks.

[0017] 3. This invention establishes a beamforming dynamic adjustment closed loop with joint feedback of photoacoustic pressure amplitude and communication bit error rate. When the position of the anesthesia device moves, causing the light energy of the receiving surface to attenuate, the centralized management system can reallocate the emission weight and phase of each light-emitting unit in real time according to the above physical feedback parameters, ensuring the adaptive following of the spatial beam projection angle, and ensuring the continuity and stability of the data demodulation and gas concentration inversion process when the node is in motion. Attached Figure Description

[0018] Figure 1This is a flowchart of a centralized management method and system for anesthesia equipment based on the Internet of Things proposed in this invention. Figure 2 This is a system module diagram of a centralized management method and system for anesthesia equipment based on the Internet of Things proposed in this invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0020] In a first embodiment of the present invention, the present invention provides a centralized management method for anesthesia equipment based on the Internet of Things, such as... Figure 1 As shown, it includes the following steps: Step S1: Obtain device instructions, and use a frequency division multiplexing algorithm to fuse the high-frequency communication envelope carrying the device instructions with the low-frequency acoustic excitation envelope with locked resonance characteristics, and control the light-emitting diode array to emit composite optical signals; In step S1, the step of fusing the high-frequency communication envelope carrying device commands with the low-frequency acoustic excitation envelope of locked resonance characteristics using a frequency division multiplexing algorithm to control the light-emitting diode array to emit a composite optical signal includes: The frequency value of the low-frequency acoustic excitation envelope is strictly matched to the inherent acoustic resonant frequency of the underlying photoacoustic resonant cavity. The DC lighting bias data, high-frequency communication envelope, and low-frequency acoustic excitation envelope are superimposed in the frequency domain to calculate the driving data, which is then output to the LED array for broadcasting.

[0021] Specifically, in step S1, the control terminal of the centralized management system receives and parses the device instructions issued for the anesthesia equipment within the region. The device instructions consist of a binary data sequence containing the device identification code, status query request, and parameter configuration information. The system extracts the device instructions, converts the digital baseband signal into a waveform signal suitable for transmission through the spatial optical channel, and generates a high-frequency communication envelope.

[0022] Simultaneously, the system generates a low-frequency acoustic excitation envelope to stimulate the target anesthetic gas molecules to produce a photoacoustic effect. The low-frequency acoustic excitation envelope consists of periodic waveforms at specific frequencies, which are used to induce periodic thermal expansion of the gas molecules at the subsequent receiving end.

[0023] The system executes a frequency division multiplexing (FDM) algorithm, allocating the spectral range of the high-frequency communication envelope and the spectral range of the low-frequency acoustic excitation envelope to mutually isolated independent frequency bands in the frequency domain. The high-frequency communication envelope is configured in the megahertz band for carrier modulation, while the low-frequency acoustic excitation envelope is configured in the kilohertz band. By setting the center frequency and allocating guard band intervals, the system ensures that the downlink data communication signal and the sensing excitation signal are protected from cross-frequency overlap interference during spatial transmission and separation at the receiving end.

[0024] In step S1, the step of controlling the light-emitting diode array to emit a composite optical signal by fusing the high-frequency communication envelope carrying the device command with the low-frequency acoustic excitation envelope of the locked resonance characteristic through the frequency division multiplexing algorithm includes the following execution process.

[0025] First, the frequency value of the low-frequency acoustic excitation envelope is strictly matched to the inherent acoustic resonant frequency of the underlying photoacoustic resonant cavity.

[0026] The physical geometry of the underlying photoacoustic resonator defines its standing wave amplification capability for sound waves of a specific frequency. The system pre-reads the structural parameters of the photoacoustic resonator mounted on the IoT device and calculates its inherent acoustic resonant frequency based on a one-dimensional resonant acoustic physical model. The inherent acoustic resonant frequency is determined by the following formula: ; in, The first-order longitudinal natural acoustic resonant frequency represents the underlying photoacoustic resonant cavity. This represents the speed of sound propagation of the target anesthetic gas under standard atmospheric pressure conditions. It represents the effective physical length of the internal channel of the underlying photoacoustic resonant cavity.

[0027] The system control unit precisely locks the carrier frequency of the low-frequency acoustic excitation envelope to the calculated inherent acoustic resonant frequency. This frequency-locking mechanism causes the light power emitted by the LED array to be generated in a frequency-locked manner. The oscillation is periodic. When the beam enters the photoacoustic resonant cavity and is absorbed by the gas, the gas expands due to heat, generating an initial sound wave of the same frequency. The frequency of this initial sound wave is the same as the frequency of the cavity's intrinsic standing wave mode, and the cascade amplification of the physical sound pressure signal amplitude is achieved by utilizing the cavity's own resonant quality factor.

[0028] Secondly, the DC lighting bias data, high-frequency communication envelope, and low-frequency acoustic excitation envelope are superimposed in the frequency domain and the driving data is output to the LED array for broadcasting.

[0029] To maintain the LED array light-emitting devices in the forward conduction linear response range, and to take into account the static lighting brightness index of the operating room environment, the system sets a constant amplitude DC current as the DC lighting bias data.

[0030] The system calculation module performs a linear addition operation on the DC lighting bias data, high-frequency communication envelope signal, and low-frequency acoustic excitation envelope signal in the time domain to construct a composite driving current signal for driving the LED to emit light. The superposition calculation of the composite driving current signal is based on the following formula: ; in, This represents the composite drive current signal output to the LED array at the corresponding moment, and represents the magnitude of the constant DC bias current corresponding to the DC lighting bias data. The amplitude control coefficient representing the envelope of high-frequency communication. The normalized digital baseband modulated signal represents the instruction carried by the equipment, and the high-frequency carrier frequency represents the high-frequency communication envelope. The amplitude control coefficient representing the envelope of low-frequency acoustic excitation. This represents the normalized low-frequency acoustic excitation baseband signal. This represents the inherent acoustic resonant frequency of the aforementioned match.

[0031] The system control module will calculate the composite drive current signal. The signal is converted into corresponding analog voltage and current outputs to the driver chip of the LED array. Responding to fluctuations in the composite drive current signal, the LED array, through the electro-optic conversion characteristics of semiconductor devices, emits a composite optical signal whose instantaneous optical power is linearly related to the drive current into the space environment. The instantaneous power output equation of the composite optical signal is as follows: ; in, The instantaneous output optical power represents the composite optical signal broadcast by the LED array into free space. This represents the effective electro-optical conversion efficiency constant of the LED array.

[0032] The composite optical signal is broadcast to the surrounding environment in the indoor physical space, covering the physical coordinate area where the anesthesia equipment is located, and completing the transmission of downlink data transmission commands and the transmission of sensor-driven energy for the Internet of Things node.

[0033] Step S2: Collect mechanical vibration rhythm data, identify the end-expiratory phase in the mechanical vibration rhythm data through feature extraction algorithm, and generate a timing gating opening signal in the interval corresponding to the end-expiratory phase; Step S2, which involves collecting mechanical vibration rhythm data and identifying the end-expiratory phase in the mechanical vibration rhythm data using a feature extraction algorithm, includes the following steps: The mechanical vibration rhythm data of the bellows is collected by calling microelectromechanical sensors, and the time peak of the mechanical vibration rhythm data is extracted by zero-crossing detection algorithm; The maximum pressure range corresponding to the time peak is defined as the end-expiratory phase.

[0034] Specifically, the system uses microelectromechanical sensors to collect mechanical vibration rhythm data of the bellows and extracts the time peak of the mechanical vibration rhythm data using a zero-crossing detection algorithm.

[0035] A microelectromechanical accelerometer (MEMS) sensor is attached to the outer surface of the bellows in the breathing circuit of the anesthesia device. During the mechanical ventilation cycle of the anesthesia device, the reciprocating motion of the bellows and the periodic changes in internal airway pressure are converted into physical vibrations of the shell. The MEMS sensor detects these physical vibrations and converts them into continuous analog voltage signals. The analog-to-digital converter within the microcontroller unit performs discretization sampling of this analog voltage signal at a set sampling rate, outputting a set of mechanical vibration rhythm data consisting of a time series, denoted as […]. ,in Represents the discrete time sampling point number.

[0036] To eliminate high-frequency mechanical vibration waves that are not part of the respiratory rhythm in the environment, the microcontroller unit processes the mechanical vibration rhythm data. Perform a low-pass filter operation to output a smoothed rhythm signal. Subsequently, the processor runs a zero-detection algorithm to calculate... The first-order backward difference is used to locate the time point where the peak occurs. The formula for calculating the first-order backward difference is as follows: ; in, Represents discrete time points The amplitude difference calculation results at the point, This represents the amplitude of the smooth rhythmic signal at the current sampling point. This represents the amplitude of the smooth rhythmic signal from the immediately preceding sampling point.

[0037] The system iterates through the data in chronological order. Numerical sequence. When a point is detected in the sequence... ,satisfy When the condition is met, the determination is made at the time point. When the smooth rhythm signal changes from an increasing trend to a decreasing trend, a zero-crossing derivative phenomenon occurs. The microcontroller will record the time point at which this zero-crossing derivative phenomenon occurs. Extract and record the time peaks as mechanical vibration rhythm data .

[0038] Secondly, the maximum pressure range corresponding to the time peak is defined as the end-expiratory phase.

[0039] In the physical process of mechanical ventilation in anesthesia equipment, the bellows vibration amplitude reaches its time peak. The operating state corresponds to the physical condition where the airflow within the breathing circuit is compressed to its physical limit and the internal air pressure reaches its maximum. This physical condition occurs at the boundary between the end of the patient's expiratory phase and the initiation of the inspiratory phase. The system uses the extracted time peaks... Using the central reference time point, fixed time offsets are extended in the leading and lagging directions of the time axis, respectively. Extracting the closed time window The system directly labels the region corresponding to this time window as the end-expiratory phase interval. The aforementioned time offset... The values ​​are calculated and assigned in reverse based on the mechanical ventilation frequency set by the anesthesia equipment to ensure that the time window of the closed interval covers the highest steady-state maintenance period of the circuit air pressure.

[0040] Finally, the system determines whether the current hardware clock is within the aforementioned end-expiratory phase interval. When the hardware clock enters... Within the specified time range, the microcontroller output pin of the IoT device's end node generates and outputs a high-level timing gating open signal; when the hardware clock exceeds this closed time range, the output pin switches to a low level. The duration of the high-level state is equal to the physical time length of the calibrated end-expiratory phase interval. Through the above steps, the system establishes a dynamically adjusted physical time window based on the device's operating rhythm, restricting subsequent photoacoustic sensor acquisition to the time period when the internal pressure of the anesthesia device is at its maximum and physical leakage characteristics are most pronounced.

[0041] Step S3: In response to the timing gate opening signal, open the air intake valve of the photoacoustic resonant cavity and the sound acquisition circuit to receive the composite optical signal; In step S3, the steps of responding to the timing gate opening signal and opening the air intake valve of the photoacoustic resonant cavity and the sound acquisition circuit to receive the composite optical signal include: When a high-level timing gate opening signal is detected, a drive command is sent to open the air intake valve of the photoacoustic resonant cavity, allowing surrounding gas to flow in. Synchronous power supply activates the photodetector to receive composite optical signals and wakes up the microphone to listen to the sound generated inside the photoacoustic resonant cavity.

[0042] Specifically, when a high-level timing gate opening signal is detected, a drive command is sent to open the air intake valve of the photoacoustic resonant cavity, allowing surrounding gas to flow in.

[0043] The microcontroller within the IoT device's end node continuously monitors the voltage levels of its internal general-purpose input / output pins. When it detects a pin level transition from low to high and maintains that transition, the microcontroller determines that the current hardware cycle has entered the timing-gated open range.

[0044] The microcontroller then sends a high-level drive command to the intake valve drive circuit. Upon receiving this command, the intake valve drive circuit outputs a preset voltage to the miniature solenoid valve coil at the entrance of the photoacoustic resonant cavity. The energized miniature solenoid valve coil generates a magnetic field, which overcomes the mechanical resistance of the return spring, driving the valve core to mechanically displace, thereby opening the intake valve.

[0045] After the inlet valve is opened, the internal space of the photoacoustic resonant cavity is connected to the external physical environment of the anesthesia device. During this stage, the mixed gas in the surrounding environment flows into the photoacoustic resonant cavity through the inlet valve according to the pressure gradient and the law of gas molecule diffusion, reaching a state of pressure equilibrium. The flowing-in mixed gas contains ambient background gas as well as anesthetic gas molecules leaking from the anesthesia device's piping.

[0046] Synchronous power supply activates the photodetector to receive composite optical signals.

[0047] Simultaneously with sending the drive command to open the intake valve, the microcontroller's control pin sends an enable trigger signal to the external power management module. The power management module then closes the corresponding solid-state electronic switch, providing a working bias voltage to the photodetector installed behind the light-transmitting window of the photoacoustic resonator.

[0048] The photodetector receives the composite light signal incident through the window and, based on its internal semiconductor physical mechanism, generates the photoelectric effect, converting the incident photon energy into a continuous photocurrent. The physical dimensions of this photocurrent are directly proportional to the instantaneous photopower received, and the conversion calculation formula is as follows: ; in, This represents the instantaneous photocurrent output by the photodetector at the corresponding time point. This represents the responsivity constant of the photodetector for the center wavelength of the emission spectrum of the light-emitting diode. The instantaneous power of the composite optical signal received by the photosensitive effective physical receiving surface of the photodetector. This represents the background dark current parameter of the photodetector under the current bias voltage.

[0049] Through this hardware response process, the system physically receives the optical signal containing downlink communication data and acoustically excited electrical energy in the free space link and converts it into an analog electrical signal that can be read by subsequent processing circuits.

[0050] The microphone is activated to listen to the sound generated inside the photoacoustic resonator.

[0051] During the same clock cycle that the microcontroller activates the photodetector, it connects the power supply circuit of the microphone sensor and the sampling channel of the analog-to-digital converter chip, switching the microphone from low-power standby mode to full-power monitoring mode.

[0052] Leaking anesthetic gas molecules entering the photoacoustic resonant cavity physically absorb the low-frequency acoustic excitation envelope of a specific wavelength in the composite optical signal. After absorbing light energy, the gas molecules undergo non-radiative transitions in their internal energy levels, causing the local gas temperature to change periodically with the frequency of the low-frequency acoustic excitation envelope.

[0053] This temperature change causes the gas volume to undergo periodic physical expansion and contraction, which in turn radiates sound pressure outward. This sound pressure is reflected within the physical boundaries of the photoacoustic resonant cavity, forming an interference standing wave, which then achieves cascaded amplitude amplification, generating sound internally. The formula for calculating the characteristic amplitude of this initial photoacoustic pressure is as follows: ; in, This represents the amplitude of sound pressure generated by the air pressure pulsations inside the photoacoustic resonator. The cavity constant represents the underlying photoacoustic resonant cavity, and this constant is determined by the internal geometry of the cavity and the resonant acoustic quality factor. The light absorption coefficient representing the target anesthetic gas molecules at a specific emission light source wavelength. This represents the effective average optical power that enters the cavity through the low-frequency acoustic excitation envelope coupling in the composite optical signal.

[0054] The microphone's physical diaphragm senses the fluctuations in sound pressure in real time and deforms using capacitor plates or piezoelectric materials, converting the changes into corresponding AC voltage signals. This enables real-time monitoring of the photoacoustic physical phenomena inside the cavity, providing a complete sensing baseband level input for the subsequent demodulation module.

[0055] Step S4: Separate the composite optical signal into communication demodulation data and photoacoustic pressure signal using a filtering algorithm, extract the base station coordinates from the communication demodulation data, and run the concentration inversion algorithm to calculate the leakage concentration based on the photoacoustic pressure signal; In step S4, the steps of separating the composite optical signal into communication demodulation data and photoacoustic pressure signal using a filtering algorithm, and extracting the base station coordinates from the communication demodulation data, include: A photodetector is used to convert a composite optical signal into an electrical signal, and a high-pass filtering algorithm is used to filter out low-frequency components in the electrical signal to obtain communication demodulated data. The frame structure of the demodulated communication data is parsed to extract the base station coordinates; Step S4, which involves running the concentration inversion algorithm to calculate the leakage concentration based on the photoacoustic pressure signal, includes the following steps: The change in sound pressure generated by the expansion of gas flowing into the photoacoustic resonant cavity after absorbing low-frequency acoustic excitation envelope light energy is obtained, and the change in sound pressure is converted into a photoacoustic pressure signal. The amplitude characteristics of the photoacoustic pressure signal are extracted, and the leakage concentration is derived by combining the cavity structure constant, gas light absorption coefficient and effective excitation light power parameters through a concentration inversion algorithm.

[0056] Specifically, a photodetector is used to convert the composite optical signal into an electrical signal, and a high-pass filtering algorithm is used to filter out the low-frequency components in the electrical signal to obtain communication demodulation data.

[0057] The system transmits the initial electrical signal output from the transimpedance amplifier before the photodetector to the internal filtering circuit or digital signal processor. This initial electrical signal, in the frequency domain, is a mixture of the DC bias level, the low-frequency current corresponding to photoacoustic excitation, and the high-frequency current corresponding to downlink data transmission. The signal processor invokes a high-pass filter algorithm, setting its cutoff frequency parameter to be greater than the low-frequency acoustic excitation carrier frequency and less than the high-frequency communication carrier frequency. The transfer function of this high-pass filter acts on the initial electrical signal, attenuating and filtering out components with frequencies below the cutoff frequency, outputting the separated high-frequency time-domain signal. The calculation formula for this filtering process is as follows: ; Wherein, represents the time-domain signal of the communication demodulated data output after high-pass filtering. This represents the initial electrical signal output by the photodetector. This represents the unit impulse response function of the defined high-pass filter. This represents the integration variable in the convolution integration process.

[0058] The frame structure of the communication demodulated data is parsed to extract the base station coordinates.

[0059] The baseband processing module of the microcontroller performs discretization sampling and threshold decision algorithms on the time-domain signal of the communication demodulated data output after high-pass filtering, restoring the analog waveform to a binary digital bitstream. The baseband processing module then performs frame synchronization operations, comparing the binary digital bitstream with a preset frame preamble sequence using sliding correlation detection. After locating the matching frame start boundary, the module sequentially reads the payload segment in the data link layer frame structure according to the system's communication protocol field definitions. The system parses the coordinate field within this payload segment, extracting the base station coordinates composed of floating-point values. These base station coordinates record the position data of the LED array transmitting the command data in three-dimensional physical space, denoted as . .

[0060] In step S4, the step of running the concentration inversion algorithm to calculate the leakage concentration based on the photoacoustic pressure signal specifically includes the following execution process.

[0061] The change in sound pressure generated by the expansion of gas flowing into the photoacoustic resonant cavity after absorbing low-frequency acoustic excitation envelope light energy is obtained, and this change in sound pressure is converted into a photoacoustic pressure signal.

[0062] A microphone sensor placed inside the photoacoustic resonant cavity senses the mechanical sound pressure pulsations generated by the absorption of alternating light within the cavity's gas. The microphone diaphragm undergoes physical deformation driven by this sound pressure change, and its internal circuitry linearly converts this mechanical deformation into a corresponding analog AC voltage signal. To eliminate ambient broadband noise, the system inputs this analog AC voltage signal to a bandpass filter, whose center frequency parameter is configured to match the inherent acoustic resonant frequency of the underlying photoacoustic resonant cavity. The bandpass filter outputs a separated photoacoustic pressure signal, the physical conversion formula of which is as follows: ; in, The instantaneous voltage value representing the output photoacoustic pressure signal. This represents the inherent acoustic-to-electrical conversion sensitivity constant of the microphone sensor. It represents the instantaneous change in sound pressure generated by the expansion of gas inside the photoacoustic resonant cavity after absorbing the envelope light energy of low-frequency acoustic excitation.

[0063] The amplitude characteristics of the photoacoustic pressure signal are extracted, and the leakage concentration is derived by combining the cavity structure constant, gas light absorption coefficient and effective excitation light power parameters through a concentration inversion algorithm.

[0064] The microcontroller performs quadrature lock-in amplification calculations on the photoacoustic pressure signal output after bandpass filtering. The processor generates a reference sine wave signal with the same frequency as the low-frequency acoustic excitation envelope, performs time-domain multiplication and mixing calculations on the photoacoustic pressure signal and the reference sine wave signal, and performs a low-pass integration operation on the product result to extract the DC voltage amplitude characteristics of the photoacoustic pressure signal at its inherent acoustic resonant frequency.

[0065] After obtaining this amplitude characteristic, the system calls the preset physical model constants in the internal memory and performs algebraic calculations of the concentration inversion algorithm to obtain the target gas concentration. The specific calculation formula of the concentration inversion algorithm is as follows: ; in, This represents the concentration of anesthetic gas leaked, derived from the inversion process. This represents the amplitude characteristics of the photoacoustic pressure signal extracted through quadrature lock-in amplification. This represents the inherent acoustic-to-electrical conversion sensitivity constant of the microphone sensor. The cavity structure constants represent the underlying photoacoustic resonant cavity. The absorption coefficient of the target anesthetic gas molecules at the set center wavelength of the LED. This represents the effective excitation light power parameter that is actually irradiated into the photoacoustic resonator cavity.

[0066] The microcontroller will calculate the leakage concentration value. The corresponding base station coordinate data is formatted, packaged, and stored in a cache register as the input physical quantity for the next step of location calculation and ledger generation.

[0067] Step S5: Input the amplitude of the photoacoustic pressure signal and the bit error rate of the communication demodulation data into the beamforming optimization algorithm, adjust the emission weight and phase of the light-emitting diode array, and calculate the spatial location by combining the base station coordinates and leakage concentration to generate a ledger; In step S5, the steps of inputting the amplitude of the photoacoustic pressure signal and the bit error rate of the communication demodulation data into the beamforming optimization algorithm to adjust the emission weight and phase of the light-emitting diode array include: Maximizing the amplitude of the photoacoustic pressure signal and minimizing the bit error rate of the communication demodulated data are used as the joint optimization function of the beamforming optimization algorithm; The spatial projection angle of the emitted beam is controlled by iteratively calculating and redistributing the emission weight and phase of each light-emitting unit in the LED array. Step S5, which involves calculating the spatial location and generating a ledger by combining the base station coordinates and the leakage concentration, includes: Based on the base station coordinates extracted from multiple LED arrays, the three-dimensional spatial coordinates of the anesthesia equipment are calculated using a time difference of arrival (TDOA) positioning algorithm. The three-dimensional spatial location coordinates are bound to the corresponding leakage concentration and stored in the database to generate a ledger; The steps for generating the ledger also include: The leakage concentration at different time points is summarized to form a time series set, and the concentration change gradient of the time series set with the service life of the anesthesia equipment is calculated. When the concentration change gradient exceeds the safety benchmark threshold, the corresponding equipment is marked in the ledger as having a risk of pipeline aging, and an early warning command is issued.

[0068] Specifically, maximizing the amplitude of the photoacoustic pressure signal and minimizing the bit error rate of the communication demodulated data are used as the joint optimization function of the beamforming optimization algorithm.

[0069] The system extracts the amplitude of the photoacoustic pressure signal fed back from the IoT device and the bit error rate of the communication demodulated data. To compensate for the attenuation of received optical power caused by the movement of the anesthesia equipment in physical space, the system establishes a beamforming control closed loop with the transmission matrix parameters as independent variables. The system control module constructs a joint optimization function, the mathematical expression of which is as follows: ; in, This represents the fitness function value output by the beamforming optimization algorithm. This represents the emission weight vector of each light-emitting unit in the LED array. This represents the phase vector of each light-emitting unit in the LED array. This represents the positive weighting coefficient that assigns the amplitude of the photoacoustic pressure signal. This represents the penalty weighting coefficient assigned to the bit error rate. This represents the amplitude of the photoacoustic pressure signal extracted at the receiver under the current transmit weight and phase. This represents the system's preset amplitude normalization reference constant. This represents the bit error rate of the demodulated data at the receiver under the current transmit weight and phase. This represents the system's preset bit error rate normalization reference constant. The system will adjust the fitness function value... The goal of mathematical solutions is to achieve the maximum vector combination.

[0070] The spatial projection angle of the emitted beam is controlled by iteratively calculating and redistributing the emission weights and phases of each light-emitting unit in the LED array.

[0071] The system's computational unit employs the gradient descent algorithm to solve for the optimal solution of the aforementioned joint optimization function. Within a set time control period, the system updates the emission weight vector and phase vector according to the following iterative equation: ; ; in, and Representing the first The emission weight vector and phase vector calculated after the next iteration and Representing the first The emission weight vector and phase vector at the next iteration and These represent the iteration step size parameters for the weights and phase, respectively. This represents the gradient operator for multivariate functions.

[0072] The system converts the updated emission weight vector and phase vector into multiple analog control voltage signals, which are then input to the driver chip of the LED array. Each emitting unit in the array radiates light waves into space according to the redistributed emission power ratio and emission time delay. The wavefront propagation direction is changed by the physical interference and superposition of the light waves, thus achieving physical control of the spatial projection angle of the emitted beam. This process ensures that the energy focusing center of the beam is always aligned with the receiving target surface of the moving IoT device, maintaining the communication bandwidth of the physical link and the energy supply for sensing excitation.

[0073] Step S5, which combines the base station coordinates and leakage concentration to calculate the spatial location and generate a ledger, specifically includes the following execution process.

[0074] Based on the base station coordinates extracted from multiple LED arrays, the three-dimensional spatial coordinates of the anesthesia equipment are obtained by using a time difference of arrival (TDOA) positioning algorithm.

[0075] Within the same acquisition cycle, IoT devices receive composite optical signals from at least four non-overlapping LED arrays located indoors. The system extracts the corresponding coordinates of multiple base stations, denoted as... ,in The number representing the LED array and The system records the system timestamp of each signal arriving at the receiver and calculates the [number]th [signal]. Time difference of arrival of the road signal relative to the signal from the No. 1 master station Based on the speed constant of light in air. The system establishes a set of nonlinear spatial distance difference equations: ; in, This represents the unknown three-dimensional spatial coordinates of the anesthesia equipment to be solved. The system's computation module uses the Taylor series expansion method to solve this nonlinear equation system and outputs the absolute three-dimensional spatial coordinates of the anesthesia equipment's current location.

[0076] The three-dimensional spatial coordinates are bound to the corresponding leakage concentration and stored in the database to generate a ledger.

[0077] The system server extracts the calculated three-dimensional spatial coordinates. The system data management module uses the unique hardware identifier of the anesthesia device as the primary key to encapsulate the location coordinates, leakage concentration, and current system absolute timestamp into a data structure. The encapsulated data packet is then transmitted via the internal network interface to the backend relational database for write operations, establishing a physical status ledger for the device during the current monitoring period.

[0078] Step S5, the step of generating the ledger, also includes the following execution process.

[0079] The leakage concentrations at different time points are aggregated to form a time series set, and the concentration change gradient of the time series set with the service life of the anesthesia equipment is calculated.

[0080] The system data management module queries the database for historical status records of the same anesthesia device based on the set backtracking cycle instructions. The system extracts data on the device's status during continuous... Time nodes The following are the recorded leakage concentration measurements. A time series dataset consisting of a two-dimensional array is constructed in memory. The system's computational unit performs linear fitting calculations on this time series dataset using the least squares method to solve for the concentration change gradient parameters reflecting the pipeline aging rate. ; in, This represents the gradient of concentration change in the calculated output. This represents the total number of historical data samples extracted. Representing the The time node value corresponding to each sampling point Representing the The leakage concentration values ​​corresponding to each time point. This gradient parameter characterizes the increasing slope of the anesthetic gas leakage as the physical service time increases.

[0081] When the concentration change gradient exceeds the safety benchmark threshold, the corresponding equipment is marked in the ledger as having a risk of pipeline aging, and an early warning command is issued.

[0082] The system will calculate the concentration change gradient. Compared with the security benchmark threshold pre-stored in non-volatile memory Perform a numerical comparison. The comparison logic outputs true, meaning the condition is satisfied. When the conditions are met, the system determines that the physical gas path sealing components of the anesthesia device are in an abnormal deformation or material degradation period. The system control module sends an update command to the database, writing a pipeline aging risk marker into the equipment risk status field of the ledger. Simultaneously, the system outputs a warning command containing the device identification code, three-dimensional spatial coordinates, and fault type code to the terminal monitoring interface via the downlink, triggering the pop-up window and audible alarm drive circuit on the terminal display interface. Example

[0083] In modern operating room settings, using traditional radio frequency IoT for centralized management of anesthesia equipment can easily cause electromagnetic interference to surrounding life support instruments. Furthermore, the use of external, independently excitation light sources for photoacoustic leak monitoring modules leads to redundant node hardware, excessive size, and high power consumption. In addition, conventional continuous sampling methods are not synchronized with the physical pressure rhythm of the anesthesia machine, making them highly susceptible to environmental noise interference, resulting in low detection rates for minute aging leaks. Equipment movement also frequently causes communication and sensor link attenuation and interruptions. To address these issues, this invention provides an IoT-based centralized management system for anesthesia equipment, the structure of which is as follows: Figure 2 As shown. The specific implementation process of this system is as follows: The signal transmission module acquires device commands and uses a frequency division multiplexing algorithm to fuse the high-frequency communication envelope carrying the device commands with the low-frequency acoustic excitation envelope with locked resonance characteristics, thereby controlling the light-emitting diode array to emit composite optical signals. The rhythm sensing module collects mechanical vibration rhythm data, identifies the end-expiratory phase in the mechanical vibration rhythm data through a feature extraction algorithm, and generates a timing gating opening signal in the interval corresponding to the end-expiratory phase. The gating receiver module responds to the timing gating open signal to open the air intake valve of the photoacoustic resonant cavity and the sound acquisition circuit to receive the composite optical signal. The data demodulation module separates the composite optical signal into communication demodulation data and photoacoustic pressure signal through a filtering algorithm, extracts the base station coordinates from the communication demodulation data, and runs a concentration inversion algorithm to calculate the leakage concentration based on the photoacoustic pressure signal. The optimization management module inputs the amplitude of the photoacoustic pressure signal and the bit error rate of the communication demodulation data into the beamforming optimization algorithm, adjusts the emission weight and phase of the light-emitting diode array, and calculates the spatial location by combining the base station coordinates and leakage concentration to generate a ledger.

[0084] Specifically, the signal transmission module is configured at the control end of the centralized management system to obtain equipment commands. It then uses a frequency division multiplexing algorithm to fuse the high-frequency communication envelope carrying the equipment commands with the low-frequency acoustic excitation envelope with locked resonance characteristics, thereby controlling the LED array to emit composite optical signals.

[0085] The baseband processing unit within the signal transmission module receives a binary data sequence containing the device identification code and configuration information, and generates a high-frequency communication envelope configured in the megahertz band. Simultaneously, the baseband processing unit generates a low-frequency acoustic excitation envelope configured in the kilohertz band based on the inherent acoustic resonant frequency of the photoacoustic cavity at the IoT device. The frequency division multiplexer performs a linear superposition operation in the time domain on the DC lighting bias data, the high-frequency communication envelope, and the low-frequency acoustic excitation envelope, outputting a composite drive current signal to the LED array. The superposition calculation formula for the composite drive current signal is as follows: ; in, This represents the composite drive current signal output to the LED array. This represents the value of a constant DC bias current. The amplitude control coefficient representing the envelope of high-frequency communication. Represents the normalized digital baseband modulation signal. Represents the high-frequency carrier frequency. The amplitude control coefficient representing the envelope of low-frequency acoustic excitation. This represents the normalized low-frequency acoustic excitation baseband signal. This represents the inherent acoustic resonant frequency matched to the underlying photoacoustic resonant cavity. The LED array responds to the composite drive current signal and emits a composite optical signal into space.

[0086] The rhythm sensing module is configured on the IoT device to collect mechanical vibration rhythm data, identify the end-expiratory phase in the mechanical vibration rhythm data through feature extraction algorithm, and generate a timing gating opening signal in the interval corresponding to the end-expiratory phase.

[0087] The rhythm sensing module uses microelectromechanical sensors to collect the mechanical vibration rhythm data of the bellows in the breathing circuit of the anesthesia equipment, and performs low-pass filtering on the data to obtain a smoothed signal. The processor runs a zero-crossing detection algorithm to calculate the first-order backward difference of the smoothed signal. The difference calculation formula is as follows: ; in, Representing discrete time points The amplitude difference calculation results at the point, This represents the amplitude of the smooth rhythmic signal at the current sampling point. This represents the amplitude of the smoothed rhythmic signal at the previous sampling point.

[0088] When satisfied At this point, the processor extracts the time peak. Using this time peak as the center reference point, time windows are extracted by extending a time offset to both ends of the time axis, and this closed time window is marked as the end-expiratory phase. When the hardware clock is within this window, the rhythm sensing module outputs a high-level timing gating open signal.

[0089] The gated receiver module responds to the timing gate opening signal to open the air intake valve of the photoacoustic resonant cavity and the sound acquisition circuit to receive the composite optical signal.

[0090] Upon detecting a high-level timing gate open signal, the gate receiving module outputs a drive voltage to the miniature solenoid valve coil, overcoming mechanical resistance to open the air inlet valve of the photoacoustic resonator, allowing the external mixed gas to flow in. The power management chip synchronously outputs a bias voltage to activate the photodetector behind the light-transmitting window of the photoacoustic resonator. The photodetector receives the composite optical signal and outputs a photocurrent, calculated using the following formula: ; in, This represents the instantaneous photocurrent output by the photodetector. Represents the responsiveness constant. This represents the instantaneous power of the composite optical signal received by the effective receiving surface. This represents the background dark current parameter.

[0091] During the same period, the power management chip connects the power supply circuit for the microphone sensor. Gas molecules entering the cavity absorb low-frequency light energy, generating periodic temperature changes and acoustic pressure. The formula for calculating the initial photoacoustic pressure characteristic amplitude is as follows: ; in, Represents the amplitude of sound pressure. The cavity constant represents the photoacoustic resonator. Represents the light absorption coefficient of the target gas. This represents the effective average optical power. The microphone sensor detects this pressure pulsation and outputs an analog AC voltage signal.

[0092] The data demodulation module separates the composite optical signal into communication demodulation data and photoacoustic pressure signal through a filtering algorithm, extracts the base station coordinates from the communication demodulation data, and runs a concentration inversion algorithm to calculate the leakage concentration based on the photoacoustic pressure signal.

[0093] The first filtering branch within the data demodulation module uses a high-pass filtering algorithm to process the electrical signal output from the photodetector, filtering out low-frequency components to output the communication demodulated data. The filtering formula is as follows: ; in, Represents the time-domain signal of the demodulated communication data. Represents the initial electrical signal. The unit impulse response function representing a high-pass filter. This represents the integral variable. The baseband parsing unit performs frame structure parsing on the communication demodulated data to extract the three-dimensional coordinate data of the base station.

[0094] The second filtering branch uses a bandpass filter to process the analog signal output from the microphone to remove broadband noise and output a photoacoustic pressure signal. The conversion formula is as follows: ; in, This represents the voltage value of the photoacoustic pressure signal. Represents the sensitivity constant for acoustic-to-electrical conversion. This represents the instantaneous change in sound pressure. The baseband analysis unit performs quadrature lock-in amplification calculations on the photoacoustic pressure signal, extracts amplitude features, and runs a concentration inversion algorithm to derive the leakage concentration. ; in, This represents the leakage concentration value. The amplitude characteristics represent the photoacoustic pressure signal. Represents the sensitivity constant for acoustic-to-electrical conversion. Represents the cavity constant. Represents the light absorption coefficient. This represents the effective excitation light power parameter.

[0095] The optimization management module inputs the amplitude of the photoacoustic pressure signal and the bit error rate of the communication demodulation data into the beamforming optimization algorithm, adjusts the emission weight and phase of the light-emitting diode array, and calculates the spatial location by combining the base station coordinates and leakage concentration to generate a ledger.

[0096] The optimization management module constructs a joint optimization function with the objectives of maximizing the amplitude of the photoacoustic pressure signal and minimizing the bit error rate: ; in, Represents the fitness function value. Represents the emission weight vector. Represents the phase vector. and Represents the weighting coefficient. Represents amplitude characteristics, Represents the amplitude reference constant. Represents the bit error rate. This represents the bit error rate reference constant.

[0097] The computational unit uses a gradient descent algorithm to iteratively update the vector to control the spatial projection angle of the emitted beam. ; ; Among them, subscript and Represents the iteration order. and Represents the iteration step size parameter. This represents the gradient operator for multivariate functions.

[0098] Within the same period, the optimization management module uses a time difference of arrival (TDOA) positioning algorithm based on the base station coordinates of at least four LED arrays. To avoid computational ambiguity in the distance equation, the distance from the anesthesia device to the [missing information] is first calculated. The absolute spatial distance of each array : ; Next, calculate the absolute spatial distance from the device to the first master station array: ; Based on the speed constant of light in air Establish a system of nonlinear spatial distance difference equations: ; in, The three-dimensional spatial coordinates representing the anesthesia equipment. Representing the Array coordinates, Represents the coordinates of the main station. Representing the The time difference in arrival of the road signal relative to the master station signal.

[0099] The optimization management module solves the system of equations to obtain the absolute location coordinates and binds them to the corresponding leakage concentration to generate a ledger. The system aggregates leakage concentration data from consecutive time points to form a time series dataset, and uses the least squares method to calculate the concentration change gradient. ; in, Represents the concentration gradient. Represents the total number of historical samples. Representing the The time node value corresponding to each sampling point This represents the corresponding leakage concentration value. When the concentration change gradient exceeds the safety baseline threshold, the system writes a pipeline aging risk marker in the ledger and outputs an early warning command containing spatial location coordinates and a fault code.

[0100] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A centralized management method for anesthesia equipment based on the Internet of Things, characterized in that, Includes the following steps: Step S1: Obtain device instructions, and use a frequency division multiplexing algorithm to fuse the high-frequency communication envelope carrying the device instructions with the low-frequency acoustic excitation envelope with locked resonance characteristics, and control the light-emitting diode array to emit composite optical signals; Step S2: Collect mechanical vibration rhythm data, identify the end-expiratory phase in the mechanical vibration rhythm data through a feature extraction algorithm, and generate a timing gating opening signal in the interval corresponding to the end-expiratory phase; Step S3: In response to the timing gate opening signal, open the air intake valve of the photoacoustic resonant cavity and the sound acquisition circuit to receive the composite optical signal; Step S4: Separate the composite optical signal into communication demodulation data and photoacoustic pressure signal using a filtering algorithm, extract the base station coordinates from the communication demodulation data, and run a concentration inversion algorithm to calculate the leakage concentration based on the photoacoustic pressure signal; Step S5: Input the amplitude of the photoacoustic pressure signal and the bit error rate of the communication demodulation data into the beamforming optimization algorithm, adjust the emission weight and phase of the light-emitting diode array, and calculate the spatial location by combining the base station coordinates and the leakage concentration to generate a ledger.

2. The method for centralized management of anesthesia equipment based on the Internet of Things according to claim 1, characterized in that, In step S1, the step of fusing the high-frequency communication envelope carrying the device instructions with the low-frequency acoustic excitation envelope with locked resonance characteristics using a frequency division multiplexing algorithm to control the light-emitting diode array to emit a composite optical signal includes: The frequency value of the low-frequency acoustic excitation envelope is strictly matched to the inherent acoustic resonant frequency of the underlying photoacoustic resonant cavity. The DC lighting bias data, the high-frequency communication envelope, and the low-frequency acoustic excitation envelope are superimposed in the frequency domain to calculate the driving data, which is then broadcast to the LED array.

3. The method for centralized management of anesthesia equipment based on the Internet of Things according to claim 1, characterized in that, In step S2, the step of collecting mechanical vibration rhythm data and identifying the end-expiratory phase in the mechanical vibration rhythm data through a feature extraction algorithm includes: The mechanical vibration rhythm data of the bellows is collected by calling the microelectromechanical sensor, and the time peak of the mechanical vibration rhythm data is extracted by the zero-crossing detection algorithm; The maximum pressure range corresponding to the time peak is defined as the end-expiratory phase.

4. The method for centralized management of anesthesia equipment based on the Internet of Things according to claim 1, characterized in that, In step S3, the step of opening the air intake valve of the photoacoustic resonant cavity and the sound acquisition circuit to receive the composite optical signal in response to the timing gate opening signal includes: When the timing gate opening signal in a high-level state is detected, a drive command is sent to open the air inlet valve of the photoacoustic resonant cavity to allow surrounding gas to flow in; Synchronous power supply activates the photodetector to receive the composite optical signal and wakes up the microphone to listen to the sound generated inside the photoacoustic resonant cavity.

5. The method for centralized management of anesthesia equipment based on the Internet of Things according to claim 1, characterized in that, In step S4, the step of separating the composite optical signal into communication demodulation data and photoacoustic pressure signal using a filtering algorithm, and extracting the base station coordinates from the communication demodulation data, includes: The composite optical signal is converted into an electrical signal using a photodetector, and the low-frequency components in the electrical signal are filtered out using a high-pass filtering algorithm to obtain the communication demodulation data. The frame structure of the communication demodulated data is parsed to extract the coordinates of the base station.

6. The centralized management method for anesthesia equipment based on the Internet of Things according to claim 1, characterized in that, In step S4, the step of running the concentration inversion algorithm to calculate the leakage concentration based on the photoacoustic pressure signal includes: The change in sound pressure generated by the expansion of gas flowing into the photoacoustic resonant cavity after absorbing low-frequency acoustic excitation envelope light energy is obtained, and the change in sound pressure is converted into the photoacoustic pressure signal. The amplitude characteristics of the photoacoustic pressure signal are extracted, and the leakage concentration is derived by combining the cavity structure constant, gas light absorption coefficient and effective excitation light power parameters through the concentration inversion algorithm.

7. The method for centralized management of anesthesia equipment based on the Internet of Things according to claim 1, characterized in that, In step S5, the step of inputting the amplitude of the photoacoustic pressure signal and the bit error rate of the communication demodulation data into the beamforming optimization algorithm to adjust the emission weight and phase of the light-emitting diode array includes: The beamforming optimization algorithm uses the maximum amplitude of the photoacoustic pressure signal and the minimum bit error rate of the communication demodulated data as the joint optimization function. The spatial projection angle of the emitted beam is controlled by iteratively calculating and redistributing the emission weight and phase of each light-emitting unit in the LED array.

8. The method for centralized management of anesthesia equipment based on the Internet of Things according to claim 1, characterized in that, Step S5, which combines the base station coordinates with the leakage concentration to calculate the spatial location and generate a ledger, includes: Based on the base station coordinates extracted from multiple light-emitting diode arrays, the three-dimensional spatial position coordinates of the anesthesia device are calculated using a time difference of arrival (TDOA) positioning algorithm. The three-dimensional spatial coordinates are bound to the corresponding leakage concentration and stored in the database to generate the ledger.

9. A centralized management method for anesthesia equipment based on the Internet of Things according to claim 8, characterized in that, The steps for generating the ledger also include: The leakage concentrations at different time points are aggregated to form a time series set, and the concentration change gradient of the time series set with the service life of the anesthesia equipment is calculated. When the concentration change gradient exceeds the safety benchmark threshold, the corresponding equipment is marked as having a risk of pipeline aging in the ledger, and an early warning command is output.

10. A centralized management system for anesthesia equipment based on the Internet of Things (IoT), employing the centralized management method for anesthesia equipment based on the IoT as described in any one of claims 1 to 9, characterized in that, The system includes: The signal transmission module acquires device commands and uses a frequency division multiplexing algorithm to fuse the high-frequency communication envelope carrying the device commands with the low-frequency acoustic excitation envelope with locked resonance characteristics, thereby controlling the light-emitting diode array to emit a composite optical signal. The rhythm sensing module collects mechanical vibration rhythm data, identifies the end-expiratory phase in the mechanical vibration rhythm data through a feature extraction algorithm, and generates a timing gating opening signal in the interval corresponding to the end-expiratory phase. The gating receiving module responds to the timing gating opening signal by opening the air intake valve of the photoacoustic resonant cavity and the sound acquisition circuit to receive the composite optical signal. The data demodulation module separates the composite optical signal into communication demodulation data and photoacoustic pressure signal through a filtering algorithm, extracts the base station coordinates from the communication demodulation data, and runs a concentration inversion algorithm to calculate the leakage concentration based on the photoacoustic pressure signal. The optimization management module inputs the amplitude of the photoacoustic pressure signal and the bit error rate of the communication demodulation data into the beamforming optimization algorithm, adjusts the emission weight and phase of the light-emitting diode array, and calculates the spatial location by combining the base station coordinates and the leakage concentration to generate a ledger.

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