Quantum magnetometer-based breathing state monitoring method

Abstract

The invention relates to the technical field of respiration state monitoring, and discloses a respiration state monitoring method based on quantum magnetometers, which is applied to a respiration monitoring system comprising a plurality of quantum magnetometers and a respiration state processing server. The method comprises the steps that a quantum magnetometer collects respiration-related magnetic field data to generate a data set; the server receives the real-time data to construct a breathing state matrix; through matrix processing, feature extraction and modeling analysis, a breathing mode is predicted in combination with magnetic field intensity gradient and breathing path information, space-time distribution features are output, and a data set is updated; and monitoring the respiration state in real time according to the distribution characteristics, wherein the respiration state comprises mapping region identifiers, controlling equipment actions and dynamically allocating monitoring resources. According to the method, non-contact monitoring is achieved through high sensitivity of the quantum magnetometer, the spatial-temporal characteristics of the breathing mode are obtained through multi-node cooperation and multi-dimensional analysis, the monitoring precision and real-time performance are improved, the method is suitable for the fields of medical treatment, sports, safety and the like, and an intelligent solution is provided for breathing state monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of respiratory status monitoring, and in particular to a respiratory status monitoring method based on a quantum magnetometer. Background Art

[0002] Respiratory status monitoring is of great significance in healthcare, sports science, safety, and other fields. Traditional respiratory monitoring technologies, such as chest-strap respiratory sensors and impedance-based respiratory measurement, suffer from issues such as inconvenience in wearing, susceptibility to motion interference, and limited monitoring range. For example, chest-strap sensors require direct contact with the human body, which can cause discomfort if worn for extended periods. Furthermore, poor contact can occur when the patient is agitated or sweating profusely, affecting data accuracy. Impedance-based measurement uses electrodes to detect changes in chest electrical impedance, which is susceptible to interference from electromyographic signals generated by human movement, limiting its application in monitoring special populations such as infants, young children, and critically ill patients.

[0003] With the development of non-contact monitoring technology, optical and acoustic respiratory monitoring methods have gradually emerged. Optical methods, such as video respiratory monitoring, use a camera to capture the rise and fall of the chest, but are significantly affected by lighting conditions. Monitoring effectiveness is significantly reduced in dimly lit environments or when obscured, and there is a risk of privacy leakage. Acoustic methods, such as respiratory sound monitoring, rely on microphones to capture breathing sounds. These methods are easily affected by ambient noise, making it difficult to accurately extract respiratory features in noisy environments and unable to monitor the spatial distribution of breathing.

[0004] In the existing technology, there are still technical bottlenecks in the spatial distribution monitoring and multi-source data fusion analysis of respiratory status. Traditional single-point monitoring cannot obtain the distribution characteristics of respiratory patterns in different areas, and it is difficult to meet the needs of group respiratory monitoring or respiratory status analysis in complex scenarios. For example, in sleep monitoring, traditional methods cannot distinguish the differences in respiratory patterns of different individuals in the same monitoring area; in public safety monitoring, it is impossible to grasp the spatial distribution changes of the respiratory status of the crowd in real time, and it is difficult to warn of abnormal situations in advance. In addition, the existing monitoring system does not make sufficient use of the magnetic field environment and lacks respiratory feature extraction and modeling methods based on magnetic field characteristics, resulting in the need to improve the accuracy and real-time performance of respiratory status analysis.

[0005] As a highly sensitive magnetic field measurement device, the quantum magnetometer offers advantages such as high measurement accuracy, fast response speed, and non-contact measurement, providing a new technical approach for respiratory monitoring. However, there is no mature technical solution for combining the magnetic field measurement advantages of the quantum magnetometer with respiratory state analysis to build a multi-node collaborative respiratory monitoring system that can extract and monitor the spatiotemporal distribution of respiratory patterns in real time. Existing technologies lack a comprehensive methodology for feature fusion, modeling analysis, and real-time monitoring of magnetic field data collected by quantum magnetometers, hindering the full realization of the technical potential of quantum magnetometers in respiratory monitoring. Summary of the Invention

[0006] The purpose of the present invention is to provide a respiratory status monitoring method based on a quantum magnetometer to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a quantum magnetometer-based respiratory state monitoring method, applied to a respiratory monitoring system, wherein the system includes multiple quantum magnetometers deployed in a monitoring area and a respiratory state processing server connected to the quantum magnetometers, with adjacent quantum magnetometers separated by a set distance. The method includes:

[0008] The quantum magnetometer is used to collect respiration-related magnetic field data in the region to generate a respiratory magnetic field dataset;

[0009] The respiratory state processing server receives real-time magnetic field data from multiple quantum magnetometers and constructs a respiratory state matrix;

[0010] Determining distribution characteristics of a respiratory pattern based on the respiratory magnetic field dataset and real-time data from each quantum magnetometer, wherein determining the distribution characteristics of the respiratory pattern comprises: processing a respiratory state matrix, performing feature extraction in combination with the respiratory magnetic field dataset, predicting the respiratory pattern based on a magnetic field intensity gradient and respiratory path information, outputting spatiotemporal distribution characteristics of the respiratory pattern through a respiratory pattern analysis model, and updating the respiratory magnetic field dataset based on the spatiotemporal distribution characteristics;

[0011] The respiratory state is monitored in real time according to the distribution characteristics.

[0012] Preferably, determining the distribution characteristics of the breathing pattern includes:

[0013] processing the respiratory state matrix to extract magnetic field intensity distribution, frequency response characteristics, and respiratory pattern trends;

[0014] Performing magnetic field intensity modeling on the respiratory state matrix according to the magnetic field intensity distribution and frequency response characteristics, dividing the monitoring area into multiple sub-areas and marking the area identifiers, correlating and matching the magnetic field intensity of the sub-areas with the respiratory magnetic field dataset, and marking the area identifiers in the respiratory magnetic field dataset;

[0015] Calculating a magnetic field intensity gradient according to the position of the quantum magnetometer, predicting a distribution of a breathing pattern according to the magnetic field intensity gradient and the breathing pattern trend, and calculating pattern prediction information for each sub-region;

[0016] Constructing a breathing pattern analysis model, using the pattern prediction information as an input parameter of the breathing pattern analysis model, performing spatial correlation modeling on the pattern prediction information through the breathing pattern analysis model, and outputting spatiotemporal distribution characteristics of the breathing pattern;

[0017] The respiratory magnetic field dataset is updated according to the spatiotemporal distribution characteristics to obtain the distribution characteristics of the respiratory pattern.

[0018] Preferably, the processing of the respiratory state matrix includes:

[0019] Normalizing the respiratory state matrix, intercepting the respiratory hotspot area in the matrix through a sliding window, filtering the hotspot area for noise, and calculating the respiratory pattern trend through an eigendecomposition algorithm;

[0020] Calculating the spatial correlation characteristics of the respiratory state matrix, calculating the interference intensity, signal stability coefficient and magnetic field blind zone index between regions based on the spatial correlation characteristics, constructing a feature fusion network, and calculating the frequency response characteristics through the feature fusion network;

[0021] The time domain features and frequency domain features collected by each quantum magnetometer are extracted, and the breathing feature vector of the magnetometer is calculated based on the phase difference between the time domain features and the frequency domain features. The quantum magnetometers at different positions are feature matched based on the breathing feature vector, and the breathing pattern trend is calculated.

[0022] Preferably, the performing magnetic field intensity modeling on the respiratory state matrix includes:

[0023] According to the magnetic field intensity distribution, magnetic field intensity sampling points are extracted from each frame of data, and the sampling points are correlated and mapped with the frequency response characteristics to generate a magnetic field intensity spectrum. The magnetic field intensity spectrum collected by multiple intensity meters is spatially aligned to calculate the magnetic field intensity distribution of the region;

[0024] Set the interference threshold value, locate the interference source according to the magnetic field strength value of the multi-frame breathing state matrix, calculate the interference strength difference, if the interference strength difference is greater than or equal to the interference threshold value, it means that there is a magnetic field blind spot in the area, perform propagation model constraint compensation on the current area, iteratively correct the magnetic field strength distribution of the current area according to the path loss model corresponding to the current area, and calculate the intensity compensation value of the blind spot magnetic field based on the correction result;

[0025] The magnetic field intensity modeling is performed on the respiratory state matrix according to the magnetic field intensity distribution, and the response annotation of the magnetic field model of the region is performed through the frequency response characteristics.

[0026] Preferably, the calculating of the magnetic field intensity gradient according to the position of the quantum magnetometer comprises:

[0027] Based on multiple sets of magnetic field monitoring data, the magnetic field intensity change points are extracted and mapped to a unified geographic coordinate system according to the deployment location of the intensity meter. The change points are fitted using a spatial interpolation algorithm to generate a regional magnetic field model.

[0028] Performing equal-interval sampling along the respiratory path of the magnetic field model, calculating the magnetic field attenuation rate, interference fluctuation index, and magnetic field change slope of the path based on the sampling results, and calculating the magnetic field change parameter based on the magnetic field attenuation rate, interference fluctuation index, and magnetic field change slope;

[0029] Based on the deployment parameters and acquisition accuracy of the quantum magnetometer, the distribution characteristics of the breathing pattern in each frame of data are projected onto the magnetic field model. The magnetic field model is partitioned along the breathing direction according to the number of magnetometers. The variation pattern of the breathing pattern in the partition is analyzed, and the pattern distribution characteristics are calculated based on the variation pattern.

[0030] The magnetic field intensity gradient is calculated based on the magnetic field change parameter and the pattern distribution characteristics. The calculation process of the magnetic field intensity gradient includes: based on the geographical location range from the first quantum magnetometer to the last quantum magnetometer, selecting spatial coordinate points in the magnetometer deployment direction, cumulatively calculating the product of the magnetic field intensity characteristic weight value and the breathing pattern distribution characteristic weight value within the spatial resolution range, and superimposing the influence value of the magnetometer acquisition frequency on the magnetic field intensity change rate.

[0031] Preferably, the calculating of the pattern prediction information of each sub-region includes:

[0032] Using the main breathing diameter of the magnetic field model as a baseline and the peak position of the breathing pattern in each frame of data as a reference point, the pattern offset is calculated and a pattern distribution curve is drawn according to geographic coordinates;

[0033] Correcting the rate and direction of change in the breathing pattern trend based on the magnetic field intensity gradient;

[0034] Starting from the nearest pattern distribution point, the distribution curve is continuously drawn according to the correction results of the change rate and direction to generate the pattern distribution points for the next period until the distribution points cover the entire target area and the pattern prediction information is generated.

[0035] Preferably, the constructing of the breathing pattern analysis model includes:

[0036] The input layer is used to organize the pattern prediction information into spatial distribution data and perform normalization processing;

[0037] The feature fusion layer is used to extract regional correlation features of respiratory patterns by processing spatial distribution data and construct dependency relationships between physiological units;

[0038] The state monitoring layer is used to integrate the association between breathing patterns in spatial units and generate a breathing state monitoring strategy.

[0039] Preferably, the obtaining of the distribution characteristics of the breathing pattern includes:

[0040] According to the spatiotemporal distribution characteristics of the breathing pattern output by the breathing pattern analysis model, the identifiers of the sub-regions are matched with the spatiotemporal distribution characteristics;

[0041] The regional data in the respiratory magnetic field dataset are reorganized according to the spatiotemporal characteristics to generate a regional distribution map sorted by the intensity of the respiratory pattern;

[0042] According to the reorganized regional distribution map, the optimized breathing pattern distribution features are output.

[0043] Preferably, the real-time monitoring of the respiratory state includes:

[0044] Mapping the region identifiers to the regions of the distribution characteristics of the breathing pattern one by one;

[0045] According to the spatiotemporal distribution characteristics of the breathing pattern, the monitoring equipment is controlled to perform actions, including sensor sensitivity adjustment, filter parameter setting and sampling frequency operation;

[0046] Based on the spatial distribution of breathing patterns and preset monitoring strategies, monitoring resources are dynamically allocated to corresponding physiological areas.

[0047] Preferably, the execution action of the control monitoring device includes:

[0048] When the breathing pattern reaches a preset intensity threshold in the target area, it triggers a sensitivity increase instruction for the adjacent sensors;

[0049] Dynamically combine available filtering resources according to filtering strategies to generate sampling frequency vectors;

[0050] The acquisition parameters of the target area monitoring nodes are adjusted based on the sampling frequency vector.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The use of quantum magnetometers for contactless respiratory monitoring avoids the problems of traditional contact sensors, such as being inconvenient to wear and susceptible to motion interference, and improves the comfort and adaptability of monitoring. Quantum magnetometers do not require direct contact with the human body and are suitable for special groups such as infants and critically ill patients, as well as respiratory monitoring during exercise, broadening the application scenarios. By collaboratively collecting magnetic field data through multiple quantum magnetometers, the spatial distribution characteristics of respiratory patterns can be obtained, solving the problem that traditional single-point monitoring cannot reflect spatial differences in respiratory status. For example, in sleep monitoring, the breathing patterns of people in different beds in the same room can be distinguished, providing data support for respiratory health analysis of multiple groups; in public places, the spatial distribution of people's respiratory status can be monitored in real time, abnormal areas can be detected in a timely manner, and technical support can be provided for public safety management.

[0053] By standardizing the respiratory state matrix, filtering noise, and performing feature decomposition, key information such as magnetic field intensity distribution, frequency response characteristics, and respiratory pattern trends is effectively extracted. Combined with magnetic field intensity modeling and interference source location, the system achieves sub-regional division of the monitoring area and magnetic field intensity correction, improving data accuracy and reliability. For example, by setting interference thresholds and path loss models, it compensates for magnetic field blind spots, ensuring the validity of respiratory data in complex magnetic field environments and enhancing the system's anti-interference capabilities.

[0054] By calculating the magnetic field intensity gradient based on the position of the quantum magnetometer and combining it with respiratory pattern trends for prediction, a respiratory pattern analysis model was constructed, enabling dynamic prediction and updating of the spatiotemporal distribution characteristics of respiratory patterns. Through spatial correlation modeling, this model enables in-depth analysis of the correlations between different subregions of respiratory patterns, providing a scientific basis for real-time monitoring of respiratory status. For example, by generating a pattern distribution curve based on pattern prediction information, changing trends in respiratory patterns can be predicted in advance, providing a time window for medical intervention or safety warnings.

[0055] Based on the spatiotemporal distribution of breathing patterns, the system dynamically adjusts the monitoring device's actions, such as sensor sensitivity, filtering parameters, and sampling frequency, to optimize the allocation of monitoring resources. When the breathing pattern reaches a preset intensity threshold in the target area, the sensitivity of adjacent sensors is automatically increased to ensure accurate monitoring of critical areas. By dynamically combining filtering resources and adjusting the sampling frequency, the system improves the efficiency and quality of data collection and reduces system energy consumption. Furthermore, based on the spatial distribution of breathing patterns, monitoring resources are dynamically allocated to corresponding physiological areas, enabling intelligent and refined monitoring and enhancing the system's monitoring efficiency and response speed.

[0056] The quantum magnetometer-based respiratory status monitoring method developed in this paper forms a complete technical chain from data acquisition, feature extraction, modeling and analysis to real-time monitoring, providing a high-precision, highly reliable, and intelligent solution for respiratory monitoring. This solution not only improves the accuracy and real-time performance of respiratory status analysis, but also provides new technical means for medical diagnosis, sports health management, public safety monitoring and other fields, with significant social and economic benefits and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a diagram showing the working principle of the respiratory status monitoring method based on quantum magnetometer according to the present invention;

[0058] Figure 2 Schematic diagram of the working principle for determining the distribution characteristics of breathing patterns;

[0059] Figure 3 Schematic diagram of the working principle for modeling magnetic field strength for the respiratory state matrix;

[0060] Figure 4 A diagram showing the working principle of calculating the magnetic field intensity gradient based on the position of the quantum magnetometer. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] See also Figures 1-4 The present invention provides a method for monitoring respiratory status based on quantum magnetometers, which is applied to a respiratory monitoring system. The system includes multiple quantum magnetometers deployed in a monitoring area and a respiratory status processing server connected to the quantum magnetometers. Adjacent quantum magnetometers are separated by a set distance. The specific implementation steps are as follows:

[0063] A quantum magnetometer collects respiration-related magnetic field data within the area to generate a respiratory magnetic field dataset. In practical applications, the quantum magnetometer samples the magnetic field within the monitoring area in real time at a predetermined sampling frequency. The collected raw magnetic field data undergoes preliminary preprocessing, such as denoising and filtering, and is then stored as a respiratory magnetic field dataset.

[0064] The breathing state processing server receives real-time magnetic field data from multiple quantum magnetometers and constructs a breathing state matrix. The server establishes a communication connection with each quantum magnetometer, acquiring magnetic field data from each in real time. Based on the quantum magnetometer's location information and the time sequence of acquisition, the server organizes this data into a multidimensional breathing state matrix. This matrix reflects the magnetic field state at different locations within the monitoring area at different times.

[0065] The distribution characteristics of the breathing pattern are determined based on the respiratory magnetic field dataset and the real-time data from each quantum magnetometer. This process includes processing the respiratory state matrix, extracting features from the respiratory magnetic field dataset, predicting the breathing pattern based on the magnetic field intensity gradient and respiratory path information, and outputting the spatiotemporal distribution characteristics of the breathing pattern through the respiratory pattern analysis model. The respiratory magnetic field dataset is then updated based on these spatiotemporal distribution characteristics.

[0066] Based on the distribution characteristics, the respiratory status is monitored in real time.

[0067] Example 1:

[0068] To determine the distribution characteristics of breathing patterns, the breathing state matrix must first be processed to extract the magnetic field intensity distribution, frequency response characteristics, and breathing pattern trends. In practice, the breathing state matrix is constructed by a breathing state processing server receiving real-time magnetic field data from multiple quantum magnetometers. This matrix contains magnetic field information at different locations and time points within the monitoring area. Because the original breathing state matrix may have large data volumes and non-uniform formats, it must first be standardized. This standardization process makes the data collected by different quantum magnetometers comparable, laying the foundation for subsequent analysis.

[0069] After standardization, a sliding window technique is used to capture the respiratory hotspots in the matrix. The size and step size of the sliding window can be adjusted based on actual conditions. The goal is to accurately capture areas where significant respiration-related magnetic field changes are observed. After capturing the respiratory hotspots, noise filtering is necessary. This is because data acquisition is inevitably subject to interference from various external factors, generating noise that can affect subsequent analysis results. Noise filtering can employ a variety of filtering algorithms, such as Gaussian filtering and median filtering. These algorithms can remove high-frequency and random noise from the data, preserving the true respiratory magnetic field signal.

[0070] After noise filtering, the eigendecomposition algorithm is used to calculate the breathing pattern trend. This algorithm decomposes the complex breathing state matrix into several eigenvectors and eigenvalues. By analyzing these eigenvectors and eigenvalues, the trend of breathing pattern changes over time can be extracted. For example, the main components of the breathing pattern can be determined by analyzing the magnitude of the eigenvalues, and the direction of the breathing pattern change can be determined by the direction of the eigenvectors.

[0071] At the same time, the spatial correlation characteristics of the respiratory state matrix also need to be calculated. The spatial correlation characteristics reflect the correlation between magnetic field changes between different locations within the monitoring area. By calculating the spatial correlation characteristics, the spatial distribution pattern and propagation characteristics of the magnetic field can be understood. Based on the spatial correlation characteristics, the interference intensity, signal stability coefficient, and magnetic field blind zone index between regions are further calculated. The interference intensity is used to measure the degree of magnetic field interference between different regions, the signal stability coefficient is used to evaluate the stability of the magnetic field signal, and the magnetic field blind zone index is used to determine whether there are areas where the magnetic field cannot be effectively monitored.

[0072] After obtaining these parameters, a feature fusion network is constructed to fuse these parameters and calculate the frequency response characteristics. The feature fusion network can use algorithms such as neural networks to comprehensively analyze multiple features and extract frequency response characteristics that better reflect the essence of the breathing pattern.

[0073] Furthermore, it is necessary to extract the time-domain and frequency-domain features collected by each quantum magnetometer. Time-domain features, such as waveform and amplitude, reflect how the magnetic field changes over time; frequency-domain features, such as peak frequency and bandwidth, reflect the distribution of the magnetic field across different frequency components. Based on the phase difference between the time-domain and frequency-domain features, the quantum magnetometer's breathing feature vector is calculated. This vector, a combination of time-domain and frequency-domain information, provides a more comprehensive description of the breathing magnetic field characteristics collected by the quantum magnetometer.

[0074] By matching the characteristics of quantum magnetometers at different locations based on the breathing eigenvectors, the correlation and consistency between different quantum magnetometers can be determined, thereby calculating the trend of the breathing pattern. For example, by calculating the similarity between the breathing eigenvectors, the consistency of breathing patterns at different locations can be determined, and the spatial propagation trend of the breathing pattern can be inferred.

[0075] After processing the respiratory state matrix and extracting the magnetic field intensity distribution, frequency response characteristics, and respiratory pattern trends, magnetic field intensity modeling is performed based on these characteristics. The monitoring area is divided into multiple sub-areas, each with a unique region identifier. The region division can be rationally planned based on factors such as the quantum magnetometer deployment location and the size and shape of the monitoring area.

[0076] The magnetic field strength of each subregion is correlated and matched with the respiratory magnetic field dataset. The respiratory magnetic field dataset is generated by using a quantum magnetometer to collect respiratory-related magnetic field data within the region, which contains a large amount of historical respiratory magnetic field information. Through correlation matching, the current magnetic field strength data for each subregion can be compared and analyzed with historical data, thereby marking the corresponding region in the respiratory magnetic field dataset for subsequent query and analysis.

[0077] The magnetic field intensity gradient is calculated based on the quantum magnetometer position. This gradient reflects the rate of change of magnetic field intensity over space. Based on this gradient and the previously extracted breathing pattern trends, the distribution of the breathing pattern is predicted. This prediction process takes into account factors such as the propagation characteristics of the magnetic field and the changing patterns of the breathing pattern to calculate pattern prediction information for each subregion.

[0078] After obtaining the pattern prediction information, a breathing pattern analysis model is constructed. The breathing pattern analysis model uses the pattern prediction information as input. This model performs spatial correlation modeling on the pattern prediction information, analyzing the correlation and spatial distribution patterns of breathing patterns across different subregions. This outputs the spatiotemporal distribution characteristics of the breathing patterns. These spatiotemporal distribution characteristics reflect not only the spatial distribution of the breathing patterns but also their temporal trends.

[0079] Example 2:

[0080] When modeling the magnetic field intensity of the respiratory state matrix, a series of operations need to be performed based on the magnetic field intensity distribution and frequency response characteristics. In each frame of the respiratory state matrix data, valid sampling points are extracted based on the preset magnetic field intensity threshold. These sampling points can reflect the key change positions of the magnetic field intensity in the current frame. These sampling points are associated with the frequency response features previously calculated by the feature fusion network. The frequency response features contain the response characteristics of the magnetic field signal in different frequency bands. Through mapping, a correspondence between spatial position and frequency characteristics can be established, and then a magnetic field intensity map can be generated. This map presents the spatial distribution and frequency characteristics of the magnetic field intensity in the monitoring area in a visual way. The magnetic field intensity maps collected by multiple quantum magnetometers are then spatially aligned. Through coordinate transformation and interpolation methods, the maps of different meters are placed in the same geographic coordinate system, so that the magnetic field intensity distribution of the region can be accurately calculated.

[0081] Set an interference threshold value, which can be set according to the historical interference data of the monitoring environment and the actual monitoring needs. Based on the magnetic field strength value of the multi-frame respiratory state matrix, the interference source is located by analyzing the abnormal fluctuations in the magnetic field strength. Specifically, the magnetic field intensity changes at the same position in each frame of data are compared. If the magnetic field intensity in a certain area shows continuous abnormal fluctuations in multiple frames of data, it is judged that there may be an interference source in the area. After locating the interference source, calculate the interference intensity difference, that is, the difference between the magnetic field intensity in the interference area and the magnetic field intensity in the normal area. If the interference intensity difference is greater than or equal to the preset interference threshold value, it indicates that there is a magnetic field blind spot in the area, that is, the magnetic field signal cannot be effectively transmitted or accurately collected in this area due to interference.

[0082] When it is determined that there is a magnetic field blind spot, the propagation model constraint compensation needs to be performed on the current area. First, based on the geographical location, surrounding environment and other factors of the current area, the corresponding path loss model is determined. The path loss model describes the attenuation law of the magnetic field signal when it propagates in the area. Then, based on the path loss model, the magnetic field intensity distribution of the current area is iteratively corrected. The specific process is: according to the path loss model, the theoretical magnetic field intensity distribution is calculated, and it is compared with the actual collected magnetic field intensity distribution to find the difference points. After adjusting the parameters of the difference points, the calculation is repeated, and the iterative process is repeated until the error between the theoretical distribution and the actual distribution is within an acceptable range. Based on the final correction result, the intensity compensation value of the blind spot magnetic field is calculated, and this compensation value is used for the subsequent correction of the blind spot magnetic field data.

[0083] After completing the aforementioned interference processing and correction, magnetic field intensity modeling is performed on the respiratory state matrix based on the final determined magnetic field intensity distribution. During the modeling process, the magnetic field intensity data, frequency response characteristics, and corrected compensation values for each subregion are integrated into the model, enabling it to accurately reflect the magnetic field distribution within the monitoring area. At the same time, the regional magnetic field model is annotated using its frequency response characteristics. This means that the response characteristics of different regions to magnetic field signals of different frequencies are marked in the model, such as high-frequency response regions and low-frequency response regions, to facilitate subsequent analysis and prediction of respiratory patterns.

[0084] Throughout the magnetic field intensity modeling process, data needs to be continuously processed and optimized. For example, when extracting sampling points, the threshold needs to be adjusted according to actual conditions to ensure the validity of the sampling points; when performing spatial alignment, the deployment position error and measurement error of the quantum magnetometer need to be considered, and an appropriate algorithm needs to be used to improve alignment accuracy; when calculating the interference intensity difference, fluctuations caused by accidental factors need to be eliminated to ensure the accuracy of the judgment of the magnetic field blind area. Through this series of meticulous operations, an accurate and reliable magnetic field intensity model can be established, providing a solid data foundation for subsequent respiratory pattern analysis and respiratory state monitoring. This modeling process fully considers the various interference factors and magnetic field propagation characteristics in the actual monitoring environment. Through scientific processing methods and processes, the accuracy and adaptability of the model are improved, so that the respiratory state matrix can more realistically reflect the magnetic field changes related to breathing, thus laying the foundation for accurate monitoring of respiratory status.

[0085] Example 3:

[0086] Calculating the magnetic field intensity gradient based on the quantum magnetometer's position requires processing multiple sets of magnetic field monitoring data. From this data, magnetic field intensity change points—those locations where significant changes in magnetic field intensity occur—are extracted. Extracting these change points can be achieved by setting a threshold for magnetic field intensity changes. When the magnetic field intensity change at a certain location exceeds this threshold, it is marked as a change point. Based on the quantum magnetometer's deployment location, these change points are mapped to a unified geographic coordinate system. This unified geographic coordinate system provides a benchmark for subsequent spatial analysis, ensuring that change points at different locations can be processed within the same spatial framework. The change points are fitted using a spatial interpolation algorithm. Based on the known locations and magnetic field intensity values of the change points, the spatial interpolation algorithm can infer the magnetic field intensity values at unknown locations, thereby generating a regional magnetic field model. This model can relatively fully describe the distribution of the magnetic field within the monitoring area.

[0087] After the magnetic field model is generated, equally spaced sampling is performed along the respiratory path of the magnetic field model. The respiratory path refers to the path where the magnetic field changes related to breathing are more obvious. Equally spaced sampling can ensure that the acquired data is regular and representative. Based on the sampling results, the magnetic field attenuation rate, interference fluctuation index and magnetic field change slope of the path are calculated. The magnetic field attenuation rate is used to describe the attenuation rate of the magnetic field intensity along the respiratory path, the interference fluctuation index reflects the degree of interference to the magnetic field during the sampling process, and the magnetic field change slope reflects the speed of change of the magnetic field intensity along the path. These three parameters reflect the changing characteristics of the magnetic field along the respiratory path from different angles. Based on them, the magnetic field change parameter can be calculated. This parameter integrates the information of the above three parameters and more comprehensively describes the changes in the magnetic field.

[0088] Based on the quantum magnetometer deployment parameters and acquisition accuracy, the distribution characteristics of the breathing pattern in each frame of data are projected onto the magnetic field model. Deployment parameters include information such as the location and spacing of the quantum magnetometers, while acquisition accuracy determines the accuracy of the data. The projection process combines the distribution characteristics of the breathing pattern with the magnetic field model, allowing the distribution of the breathing pattern to be reflected in the magnetic field model. Based on the number of quantum magnetometers, the magnetic field model is partitioned along the breathing direction. The number and size of the partitions can be determined based on the number of magnetometers and the length of the breathing direction, ensuring that each partition has an appropriate number of quantum magnetometers for data collection. The variation patterns of the breathing pattern within the partitions are analyzed. By analyzing the time series data of the breathing pattern in each partition, the variation characteristics and trends of the breathing pattern in different partitions are understood. Based on the variation patterns, the pattern distribution characteristics are calculated, which describe the spatial distribution and variation patterns of the breathing pattern.

[0089] The magnetic field intensity gradient is calculated based on the magnetic field change parameters and pattern distribution characteristics. The calculation process of the magnetic field intensity gradient is as follows: Based on the geographical location range from the first quantum magnetometer to the last quantum magnetometer, a spatial coordinate point is selected in the deployment direction of the quantum magnetometer. For each selected spatial coordinate point, the product of the magnetic field intensity characteristic weight value and the breathing pattern distribution characteristic weight value within the spatial resolution range is cumulatively calculated, where the magnetic field intensity characteristic weight value reflects the importance of the magnetic field intensity near the spatial coordinate point, and the breathing pattern distribution characteristic weight value reflects the influence of the breathing pattern distribution on the point. Then, the influence value of the meter acquisition frequency on the magnetic field intensity change rate is superimposed. The higher the meter acquisition frequency, the more accurate the measurement of the magnetic field intensity change rate, and the greater its influence value. It can be expressed as:

[0090]

[0091] Where G represents the magnetic field intensity gradient; n is the number of selected spatial coordinate points; w E,iis the weight value of the magnetic field intensity characteristic of the i-th spatial coordinate point. Its value is related to the change of the magnetic field intensity near the point. The more significant the change of the magnetic field intensity, the greater the weight value. P,i is the weight value of the breathing pattern distribution characteristic of the i-th spatial coordinate point, and its value depends on the typicality and representativeness of the breathing pattern in the area where the point is located; f is the acquisition frequency of the quantum magnetometer, in Hertz (Hz), which represents the number of acquisitions per unit time; k is the influence coefficient, a dimensionless constant used to adjust the influence of the acquisition frequency on the rate of change of the magnetic field intensity. Its value can be determined according to the actual monitoring environment and the performance of the quantum magnetometer.

[0092] Throughout the calculation process, attention must be paid to the accuracy and rationality of each step. For example, when extracting change points, the threshold should be set appropriately to avoid missing important change points or introducing too many noise points. When performing spatial interpolation, an appropriate interpolation algorithm should be selected to ensure that the generated magnetic field model accurately reflects the actual magnetic field distribution. When calculating various parameters and weights, the actual situation should be fully considered to ensure the rationality of the parameters and the accuracy of the weights.

[0093] Example 4:

[0094] When calculating pattern prediction information for each subregion, the main respiratory path of the magnetic field model is used as a baseline. The magnetic field model is generated by processing and fitting magnetic field data collected by multiple quantum magnetometers. It describes the distribution of the magnetic field within the monitoring area. The main respiratory path is the main path in the magnetic field model associated with respiratory activity, typically the path where magnetic field intensity changes most significantly. Using this main path as a baseline provides a unified reference framework for subsequent pattern predictions.

[0095] The peak position of the breathing pattern in each frame of data is used as a reference point. The peak position of the breathing pattern refers to the location where the magnetic field intensity reaches its maximum value in each frame of data. It reflects the primary area of respiratory activity in the current frame. By determining these peak positions, the spatial distribution of the breathing pattern at different time points can be understood. Then, based on these reference points, the pattern offset is calculated. The pattern offset refers to the degree of deviation of the peak position of the current frame from the baseline or the peak position of the previous frame. It can be expressed as the difference in spatial coordinates. By calculating the pattern offset, the spatial variation trend of the breathing pattern can be analyzed.

[0096] After obtaining the pattern offset, a pattern distribution curve is drawn using geographic coordinates. Geographic coordinates are a unified spatial coordinate system that accurately represents the spatial position of every location within the monitoring area. When drawing the pattern distribution curve, the peak position and pattern offset at each time point are plotted against the coordinate system, with time as the horizontal axis and spatial coordinates as the vertical axis. This creates a curve that reflects the temporal evolution of the breathing pattern. This curve intuitively demonstrates the spatial movement and changes of the breathing pattern.

[0097] Based on the previously calculated magnetic field intensity gradient, the rate of change and direction of the breathing pattern trend are corrected. The magnetic field intensity gradient reflects the rate of change of the magnetic field intensity in space, which is closely related to the propagation and change of the breathing pattern. The magnetic field intensity gradient can be used to understand the impact of spatial changes in the magnetic field on the breathing pattern, thereby adjusting the rate of change and direction of the breathing pattern to make it more consistent with actual physical laws. For example, if the magnetic field intensity gradient is large, it means that the magnetic field is changing rapidly in space. The breathing pattern may be affected by the magnetic field change, accelerating the propagation speed or changing the propagation direction. In this case, the rate of change and direction need to be corrected accordingly.

[0098] After correcting the rate and direction of change, the distribution curve is drawn based on the corrected rate and direction, starting from the most recent pattern distribution point. The most recent pattern distribution point is the last pattern distribution point before the current time point and serves as the starting point for subsequent predictions. Following the corrected rate and direction of change, the pattern distribution point for the next time point is calculated and plotted on the distribution curve.

[0099] This process is repeated until the distribution points cover the entire target area, generating pattern prediction information. The target area refers to the entire area where respiratory status is to be monitored. Covering the entire target area means that the pattern prediction information reflects the future distribution of respiratory patterns within the entire monitoring area. During this process, new distribution points are continuously calculated based on the corrected rate and direction of change to ensure the accuracy and reliability of the prediction.

[0100] For example, suppose the monitoring area is a rectangular room with multiple quantum magnetometers deployed within it. The main breathing path of the magnetic field model is the central axis of the room. At a certain moment, the peak position of the breathing pattern in the first frame of data is located in front of the central axis of the room, with a pattern offset of 0. Over time, in the second frame of data, the peak position may have shifted a certain distance backward along the central axis, resulting in a positive pattern offset. Calculations of the magnetic field intensity gradient reveal a larger gradient at the rear end of the room, indicating that the breathing pattern may be accelerating as it propagates backward due to magnetic field variations. Therefore, when correcting the rate and direction of change of the breathing pattern trend, the rate of change needs to be increased so that the predicted peak position moves backward more quickly. Then, starting from the peak position of the second frame, the peak positions of the third and fourth frames, and so on, are predicted according to the corrected rate and direction of change, until these peak positions cover the entire room, generating complete pattern prediction information.

[0101] During the entire calculation process, the following points need to be noted: First, the baseline must be determined accurately, which directly affects the calculation of the pattern offset and the drawing of the distribution curve; second, the peak position must be determined accurately, which requires careful analysis and processing of each frame of data; third, the calculation of the magnetic field intensity gradient must be accurate, which is the key to correcting the breathing pattern trend; finally, when drawing the distribution curve and generating pattern prediction information, various possible interference factors and uncertainties must be taken into account to ensure the reliability and practicality of the prediction results.

[0102] Example 5:

[0103] When monitoring respiratory status in real time, it is first necessary to map the regional identifiers to the regions of the distribution characteristics of the respiratory pattern. For example, within a rectangular monitoring area, it is divided into four sub-regions based on the deployment location of the quantum magnetometer, labeled as Region A, Region B, Region C, and Region D. Each sub-region corresponds to a specific geographical range, and the spatiotemporal distribution characteristics output by the respiratory pattern analysis model will show the intensity and distribution of different respiratory patterns in these regions. At this time, the identifiers of regions A, B, C, and D are associated with the respiratory pattern distribution characteristics in their respective regions to establish a clear correspondence, so that the respiratory status information of the region can be quickly obtained based on the regional identifier.

[0104] After the mapping is completed, the execution actions of the monitoring equipment need to be controlled according to the spatiotemporal distribution characteristics of the breathing pattern, which includes sensor sensitivity adjustment, filter parameter setting and sampling frequency operation. Taking sensor sensitivity adjustment as an example, when the breathing pattern reaches the preset intensity threshold in the target area (such as area A mentioned above), the system will trigger the sensitivity improvement instruction of the adjacent sensors. Assuming that the preset intensity threshold is 80 magnetic field strength units, when the calculated value of the breathing pattern intensity in area A reaches 85, the system immediately sends instructions to the quantum magnetometers deployed in and around area A to increase its sensitivity from the default 10% to 30%, thereby more accurately capturing subtle magnetic field changes in the area and obtaining more detailed breathing-related data.

[0105] In terms of filter parameter setting, the system dynamically combines available filter resources based on the filtering strategy to generate a sampling frequency vector. For example, the system has multiple filtering methods such as low-pass filtering and band-pass filtering, and each filtering method corresponds to different filtering parameters. When high-frequency interference is detected in the breathing pattern in area B, the system automatically selects the band-pass filtering method and combines the corresponding filtering parameters, such as setting the passband frequency to 5-15Hz, and generating a corresponding sampling frequency vector, such as [100Hz, 150Hz, 200Hz], to adapt to different filtering requirements. Based on this sampling frequency vector, the acquisition parameters of the monitoring nodes in the target area (area B) are adjusted so that the monitoring nodes collect data according to the new sampling frequency, improving the quality and effectiveness of the data.

[0106] In addition, monitoring resources are dynamically allocated to corresponding physiological regions based on the spatial distribution of breathing patterns and pre-set monitoring strategies. For example, if the breathing pattern analysis model finds that the breathing pattern is more concentrated and has higher intensity in the physiological region corresponding to the lungs in region C, the system will allocate more monitoring resources, such as the sampling frequency of the quantum magnetometer and data processing priority, to region C based on the pre-set monitoring strategy. Specific operations may include increasing the sampling frequency of the quantum magnetometer in region C from 100Hz to 200Hz, while setting the data transmission priority to the highest, ensuring that the respiratory data in this region is processed and analyzed first.

[0107] For example, if the respiratory pattern detected in region D is weak and dispersed, the system will determine that this region may be experiencing weak respiratory activity. To optimize monitoring resources, the system will reduce the sampling frequency of the quantum magnetometer in region D, for example, from 100Hz to 50Hz. It will also reduce the processing resources allocated to this region's data and concentrate more computing resources on areas with stronger respiratory activity, such as regions A or C, to optimize the allocation of monitoring resources.

[0108] Throughout the real-time monitoring process, the monitoring strategy also needs to be continuously adjusted based on the spatiotemporal distribution characteristics of the breathing pattern. For example, when the breathing pattern moves from area A to area B, the system will track this change in real time and promptly shift the focus of monitoring resources from area A to area B. Specifically, when it is detected that the breathing pattern intensity in area A begins to decrease, while the breathing pattern intensity in area B gradually increases and approaches the preset threshold, the system will gradually reduce the sensitivity of the sensors in area A, while increasing the sensitivity of the sensors in area B, and adjust the sampling frequency and filtering parameters to ensure that the monitoring equipment can always accurately detect changes in the breathing pattern.

[0109] Furthermore, the dynamic allocation of monitoring resources requires consideration of the quantum magnetometers' deployment locations and acquisition capabilities. For example, three quantum magnetometers might be deployed in Area C, and two in Area D. Resource allocation requires appropriate adjustment of sampling parameters based on the number and location of magnetometers in each area to ensure comprehensive and focused data collection across the entire monitoring area. Furthermore, bandwidth limitations for data transmission and processing must be considered to avoid data congestion or processing delays caused by inappropriate resource allocation.

[0110] Through the above series of operations, real-time monitoring of respiratory status is achieved. This monitoring method can dynamically adjust the operating parameters of the monitoring equipment and the allocation of monitoring resources according to the actual distribution of breathing patterns, improving the accuracy and efficiency of monitoring, while rationally utilizing resources and reducing the operating costs of the system. For example, in actual application scenarios, when the patient is in different breathing states, such as calm breathing, deep breathing, or breathing abnormalities, the system can monitor the changes in breathing patterns in real time and adjust the monitoring strategy in a timely manner, providing accurate and real-time data support for medical diagnosis and monitoring.

[0111] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0112] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A respiratory status monitoring method based on quantum magnetometers, applied to a respiratory monitoring system, wherein the system comprises a plurality of quantum magnetometers deployed in a monitoring area and a respiratory status processing server connected to the quantum magnetometers, wherein two adjacent quantum magnetometers are separated by a set distance, and wherein: The method comprises: The quantum magnetometer is used to collect respiration-related magnetic field data in the region to generate a respiratory magnetic field dataset; The respiratory state processing server receives real-time magnetic field data from multiple quantum magnetometers to construct a respiratory state matrix; Determining distribution characteristics of a respiratory pattern based on the respiratory magnetic field dataset and real-time data from each quantum magnetometer, wherein determining the distribution characteristics of the respiratory pattern comprises: processing a respiratory state matrix, performing feature extraction in combination with the respiratory magnetic field dataset, predicting the respiratory pattern based on a magnetic field intensity gradient and respiratory path information, outputting spatiotemporal distribution characteristics of the respiratory pattern through a respiratory pattern analysis model, and updating the respiratory magnetic field dataset based on the spatiotemporal distribution characteristics; The respiratory state is monitored in real time according to the distribution characteristics.

2. A respiratory state monitoring method based on quantum magnetometer according to claim 1, characterized in that: Determining the distribution characteristics of the breathing pattern includes: processing the respiratory state matrix to extract magnetic field intensity distribution, frequency response characteristics, and respiratory pattern trends; Performing magnetic field intensity modeling on the respiratory state matrix according to the magnetic field intensity distribution and frequency response characteristics, dividing the monitoring area into multiple sub-areas and marking the area identifiers, correlating and matching the magnetic field intensity of the sub-areas with the respiratory magnetic field dataset, and marking the area identifiers in the respiratory magnetic field dataset; Calculating a magnetic field intensity gradient according to the position of the quantum magnetometer, predicting a distribution of a breathing pattern according to the magnetic field intensity gradient and the breathing pattern trend, and calculating pattern prediction information for each sub-region; Constructing a breathing pattern analysis model, using the pattern prediction information as an input parameter of the breathing pattern analysis model, performing spatial correlation modeling on the pattern prediction information through the breathing pattern analysis model, and outputting spatiotemporal distribution characteristics of the breathing pattern; The respiratory magnetic field dataset is updated according to the spatiotemporal distribution characteristics to obtain the distribution characteristics of the respiratory pattern.

3. A respiratory state monitoring method based on quantum magnetometer according to claim 2, characterized in that: The processing of the respiratory state matrix includes: Normalizing the respiratory state matrix, intercepting the respiratory hotspot area in the matrix through a sliding window, filtering the hotspot area for noise, and calculating the respiratory pattern trend through an eigendecomposition algorithm; Calculating the spatial correlation characteristics of the respiratory state matrix, calculating the interference intensity, signal stability coefficient and magnetic field blind zone index between regions based on the spatial correlation characteristics, constructing a feature fusion network, and calculating the frequency response characteristics through the feature fusion network; The time domain features and frequency domain features collected by each quantum magnetometer are extracted, and the breathing feature vector of the magnetometer is calculated based on the phase difference between the time domain features and the frequency domain features. The quantum magnetometers at different positions are feature matched based on the breathing feature vector, and the breathing pattern trend is calculated.

4. A respiratory state monitoring method based on quantum magnetometer according to claim 3, characterized in that: The magnetic field intensity modeling of the respiratory state matrix includes: According to the magnetic field intensity distribution, magnetic field intensity sampling points are extracted from each frame of data, and the sampling points are correlated and mapped with the frequency response characteristics to generate a magnetic field intensity spectrum. The magnetic field intensity spectrum collected by multiple intensity meters is spatially aligned to calculate the magnetic field intensity distribution of the region; Set the interference threshold value, locate the interference source according to the magnetic field strength value of the multi-frame breathing state matrix, calculate the interference strength difference, if the interference strength difference is greater than or equal to the interference threshold value, it means that there is a magnetic field blind spot in the area, perform propagation model constraint compensation on the current area, iteratively correct the magnetic field strength distribution of the current area according to the path loss model corresponding to the current area, and calculate the intensity compensation value of the blind spot magnetic field based on the correction result; The magnetic field intensity modeling is performed on the respiratory state matrix according to the magnetic field intensity distribution, and the response annotation of the magnetic field model of the region is performed through the frequency response characteristics.

5. The respiratory state monitoring method based on quantum magnetometer according to claim 4, characterized in that: The step of calculating the magnetic field intensity gradient according to the position of the quantum magnetometer comprises: Based on multiple sets of magnetic field monitoring data, the magnetic field intensity change points are extracted and mapped to a unified geographic coordinate system according to the deployment location of the intensity meter. The change points are fitted using a spatial interpolation algorithm to generate a regional magnetic field model. Performing equal-interval sampling along the respiratory path of the magnetic field model, calculating the magnetic field attenuation rate, interference fluctuation index, and magnetic field change slope of the path based on the sampling results, and calculating the magnetic field change parameter based on the magnetic field attenuation rate, interference fluctuation index, and magnetic field change slope; Based on the deployment parameters and acquisition accuracy of the quantum magnetometer, the distribution characteristics of the breathing pattern in each frame of data are projected onto the magnetic field model. The magnetic field model is partitioned along the breathing direction according to the number of magnetometers. The variation pattern of the breathing pattern in the partition is analyzed, and the pattern distribution characteristics are calculated based on the variation pattern. The magnetic field intensity gradient is calculated based on the magnetic field change parameter and the pattern distribution characteristics. The calculation process of the magnetic field intensity gradient includes: based on the geographical location range from the first quantum magnetometer to the last quantum magnetometer, selecting spatial coordinate points in the magnetometer deployment direction, cumulatively calculating the product of the magnetic field intensity characteristic weight value and the breathing pattern distribution characteristic weight value within the spatial resolution range, and superimposing the influence value of the magnetometer acquisition frequency on the magnetic field intensity change rate.

6. The method for monitoring respiratory status based on a quantum magnetometer according to claim 5, wherein: The calculating of the pattern prediction information of each sub-region includes: Using the main breathing diameter of the magnetic field model as a baseline and the peak position of the breathing pattern in each frame of data as a reference point, the pattern offset is calculated and a pattern distribution curve is drawn according to geographic coordinates; Correcting the rate and direction of change in the breathing pattern trend based on the magnetic field intensity gradient; Starting from the nearest pattern distribution point, the distribution curve is continuously drawn according to the correction results of the change rate and direction to generate the pattern distribution points for the next period until the distribution points cover the entire target area and the pattern prediction information is generated.

7. The method for monitoring respiratory status based on a quantum magnetometer according to claim 2, wherein: The constructing of the breathing pattern analysis model comprises: The input layer is used to organize the pattern prediction information into spatial distribution data and perform normalization processing; The feature fusion layer is used to extract regional correlation features of respiratory patterns by processing spatial distribution data and construct dependency relationships between physiological units; The state monitoring layer is used to integrate the association between breathing patterns in spatial units and generate a breathing state monitoring strategy.

8. The method for monitoring respiratory status based on a quantum magnetometer according to claim 2, wherein: The obtaining of distribution characteristics of the breathing pattern includes: According to the spatiotemporal distribution characteristics of the breathing pattern output by the breathing pattern analysis model, the identifiers of the sub-regions are matched with the spatiotemporal distribution characteristics; The regional data in the respiratory magnetic field dataset are reorganized according to the spatiotemporal characteristics to generate a regional distribution map sorted by the intensity of the respiratory pattern; According to the reorganized regional distribution map, the optimized breathing pattern distribution features are output.

9. The respiratory status monitoring method based on quantum magnetometer according to claim 1, characterized in that: The real-time monitoring of the respiratory state includes: Mapping the region identifiers to the regions of the distribution characteristics of the breathing pattern one by one; According to the spatiotemporal distribution characteristics of the breathing pattern, the monitoring equipment is controlled to perform actions, including sensor sensitivity adjustment, filter parameter setting and sampling frequency operation; Based on the spatial distribution of breathing patterns and preset monitoring strategies, monitoring resources are dynamically allocated to corresponding physiological areas.

10. A respiratory status monitoring method based on quantum magnetometer according to claim 9, characterized in that: The execution action of the control monitoring device includes: When the breathing pattern reaches a preset intensity threshold in the target area, it triggers a sensitivity increase instruction for the adjacent sensors; Dynamically combine available filtering resources according to filtering strategies to generate sampling frequency vectors; The acquisition parameters of the target area monitoring nodes are adjusted based on the sampling frequency vector.

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