A respiratory abnormality analysis system for respiratory data monitoring and analysis

Abstract

The present invention discloses a respiratory anomaly analysis system for respiratory data monitoring and analysis, relating to the field of data processing technology. The system comprises: a monitoring data sequence acquisition module for obtaining a respiratory monitoring data sequence and an environmental monitoring data sequence; a respiratory monitoring data subsequence acquisition module for obtaining K respiratory monitoring data subsequences; an environmental monitoring data subsequence acquisition module for obtaining K environmental monitoring data subsequences; a feature vector acquisition module for obtaining an associated fusion multidimensional monitoring feature vector; an abnormal record set acquisition module for retrieving and obtaining a matching monitoring abnormality record set; an asynchronous monitoring step set acquisition module; a monitoring data set acquisition module; and a respiratory anomaly analysis result acquisition module for obtaining a respiratory anomaly analysis result. The present invention solves the technical problems of low respiratory anomaly analysis accuracy and long data analysis cycle in the prior art, thereby achieving the technical effect of improving the reliability of anomaly analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a respiratory abnormality analysis system for monitoring and analyzing respiratory data. Background Art

[0002] Respiratory abnormality analysis has widespread applications in healthcare, environmental monitoring, and personalized health management, such as diagnosing sleep apnea, monitoring chronic respiratory diseases, and assessing the health of specific occupational groups. However, respiratory data collection is often affected by external factors such as ambient temperature, humidity, and air pressure. Existing respiratory monitoring devices often operate independently, resulting in low accuracy in extracting respiratory abnormality features and poorly reflecting actual conditions. Furthermore, these systems often rely on fixed acquisition frequencies and linear processing flows, making it difficult to quickly respond to complex scenarios during data collection and analysis, resulting in significant lags in analysis results.

[0003] The existing technology has technical problems of low accuracy in abnormal breathing analysis and long data analysis cycle. Summary of the Invention

[0004] The present application provides a respiratory abnormality analysis system for respiratory data monitoring and analysis, which is used to solve the technical problems of low accuracy of respiratory abnormality analysis and long data analysis cycle in the prior art.

[0005] In view of the above problems, the present application provides a respiratory abnormality analysis system for monitoring and analyzing respiratory data, the system comprising:

[0006] A monitoring data sequence acquisition module is used to interact with the target monitoring device and the environmental monitoring module to extract monitoring data of a preset monitoring window, and obtain a respiratory monitoring data sequence and an environmental monitoring data sequence, wherein the respiratory monitoring data corresponds to the environmental monitoring data one to one;

[0007] a respiratory monitoring data subsequence obtaining module, configured to conditionally select K-1 respiratory monitoring data from the respiratory monitoring data sequence as K-1 starting division points, divide the respiratory monitoring data sequence by the K-1 starting division points, and obtain K respiratory monitoring data subsequences, where K is an integer greater than or equal to 3;

[0008] an environmental monitoring data subsequence obtaining module, configured to divide the environmental monitoring data sequence into subsequences based on the K respiratory monitoring data subsequences to obtain K environmental monitoring data subsequences;

[0009] A feature vector acquisition module is used to sequentially perform association fusion analysis on the K respiratory monitoring data subsequences as foreground data and the K environmental monitoring data subsequences as background data to obtain an association fusion multidimensional monitoring feature vector;

[0010] An abnormal record set acquisition module is used to obtain the basic user characteristics of the target user, combine the associated fusion multi-dimensional monitoring feature vector to perform data mining in big data, and retrieve and obtain a matching monitoring abnormal record set;

[0011] an asynchronous monitoring step length set obtaining module, configured to perform cluster analysis on the matching monitoring anomaly record set using the anomaly type as an index, and determine an asynchronous monitoring step length set, wherein the asynchronous monitoring step length set includes a first monitoring step length and a second monitoring step length, and the first monitoring step length is less than or equal to the second monitoring step length;

[0012] a monitoring data set obtaining module, configured to control the target monitoring device to perform asynchronous respiration monitoring on the target user within a feedback monitoring window according to the first monitoring step size and the second monitoring step size, respectively, to obtain a first respiration monitoring data set and a second respiration monitoring data set;

[0013] The respiratory abnormality analysis result obtaining module is used to use a respiratory abnormality analyzer to perform abnormality analysis on the first respiratory monitoring data set and the second respiratory monitoring data set to obtain a respiratory abnormality analysis result.

[0014] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0015] The present application extracts monitoring data of a preset monitoring window through an interactive target monitoring device and an environmental monitoring module to obtain a respiratory monitoring data sequence and an environmental monitoring data sequence, wherein the respiratory monitoring data corresponds one to one with the environmental monitoring data, and then conditionally selects K-1 respiratory monitoring data from the respiratory monitoring data sequence as K-1 starting division points, divides the respiratory monitoring data sequence with the K-1 starting division points, and obtains K respiratory monitoring data subsequences, wherein K is an integer greater than or equal to 3, and then subsequences the environmental monitoring data sequence based on the K respiratory monitoring data subsequences to obtain K environmental monitoring data subsequences, takes the K respiratory monitoring data subsequences as foreground data, and takes the K environmental monitoring data subsequences as background data, and sequentially performs correlation fusion analysis to obtain a correlation fusion multidimensional monitoring data sequence. The method measures the characteristic vector, obtains the basic characteristics of the target user, combines the associated fusion multi-dimensional monitoring characteristic vector to conduct data mining in the big data, retrieves the matching monitoring anomaly record set, and then uses the anomaly type as the index to perform cluster analysis on the matching monitoring anomaly record set to determine the asynchronous monitoring step set, wherein the asynchronous monitoring step set includes the first monitoring step and the second monitoring step, the first monitoring step is less than or equal to the second monitoring step, and the target monitoring device is controlled according to the first monitoring step and the second monitoring step to perform asynchronous breathing monitoring on the target user in the feedback monitoring window, and obtain the first breathing monitoring data set and the second breathing monitoring data set, and then uses the breathing anomaly analyzer to perform anomaly analysis on the first breathing monitoring data set and the second breathing monitoring data set to obtain the breathing anomaly analysis result. The method achieves the technical effect of analyzing the user's breathing anomaly in accordance with the actual situation and improving the efficiency of monitoring and analysis on the basis of ensuring the monitoring accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a structural diagram of a respiratory abnormality analysis system for monitoring and analyzing respiratory data provided by an embodiment of the present invention.

[0017] Figure 2 The present invention provides a flow chart of the execution steps of an environmental monitoring data subsequence acquisition module in a respiratory anomaly analysis system for respiratory data monitoring and analysis provided by an embodiment of the present invention.

[0018] Explanation of the accompanying drawings: monitoring data sequence acquisition module 11, respiratory monitoring data subsequence acquisition module 12, environmental monitoring data subsequence acquisition module 13, feature vector acquisition module 14, abnormal record set acquisition module 15, asynchronous monitoring step set acquisition module 16, monitoring data set acquisition module 17, respiratory abnormality analysis result acquisition module 18. DETAILED DESCRIPTION

[0019] The present application provides a respiratory abnormality analysis system for respiratory data monitoring and analysis, which is used to solve the technical problems of low accuracy of respiratory abnormality analysis and long data analysis cycle in the prior art.

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, systems, products or devices.

[0022] Examples, such as Figure 1 As shown, the present application provides a respiratory abnormality analysis system for respiratory data monitoring and analysis, wherein the system includes:

[0023] The monitoring data sequence acquisition module 11 is used to interact with the target monitoring device and the environmental monitoring module to extract the monitoring data of the preset monitoring window, and obtain the respiratory monitoring data sequence and the environmental monitoring data sequence, wherein the respiratory monitoring data corresponds to the environmental monitoring data one by one;

[0024] In one possible embodiment, the preset monitoring window is a monitoring period pre-set by a person skilled in the art, such as a 15-minute window period, to obtain representative data in a shorter period of time. This window should be optimized based on the device's usage scenario and the user's actual situation to ensure real-time data.

[0025] Preferably, within each monitoring window, the target monitoring device (e.g., respiratory waveform monitor and oximeter) and the environmental monitoring module (e.g., temperature sensor, humidity sensor, light sensor) start collecting data simultaneously. To ensure a one-to-one correspondence between respiratory monitoring data and environmental monitoring data, the target monitoring device and the environmental monitoring module must ensure that physiological data and environmental data are collected at the same time or frequency.

[0026] Preferably, the respiratory monitoring data (such as respiratory rate, respiratory waveform, minute ventilation, etc.) collected by the target monitoring device are integrated to form a "respiratory monitoring data sequence," and the environmental data (such as temperature, humidity, light, etc.) monitored by the environmental monitoring module are integrated to form an "environmental monitoring data sequence." In these two sequences, each data point corresponds one-to-one to a time point, so that subsequent analysis can simultaneously reference respiratory status and environmental changes.

[0027] By generating synchronized and corresponding respiratory monitoring data sequences and environmental data sequences, the technical effect of providing accurate data support for subsequent respiratory abnormality analysis and monitoring strategy adjustment is achieved.

[0028] a respiratory monitoring data subsequence obtaining module 12, configured to conditionally select K-1 respiratory monitoring data from the respiratory monitoring data sequence as K-1 starting partitioning points, and divide the respiratory monitoring data sequence using the K-1 starting partitioning points to obtain K respiratory monitoring data subsequences, where K is an integer greater than or equal to 3;

[0029] Furthermore, the respiratory monitoring data subsequence obtaining module 12 is configured to perform the following steps:

[0030] Randomly selecting K-1 respiratory monitoring data from the respiratory monitoring data sequence as K-1 first division points;

[0031] Performing left and right neighbor similarity analysis on the K-1 first division points to determine the K-1 neighbor similarities of the K-1 first division points;

[0032] Determine whether the K-1 nearest neighbor similarities are all less than or equal to a preset similarity difference threshold; if not, reselect K-1 first division points;

[0033] If so, the K-1 first division points are used as the K-1 starting division points.

[0034] In one possible embodiment, K-1 data points are selected from the entire respiratory monitoring data sequence according to a preset condition and used as partitioning points. The selection of each partitioning point should meet specific conditions so as to accurately partition the respiratory monitoring data sequence and obtain K respiratory monitoring data subsequences. Optionally, the preset condition is that the data similarity on the left and right sides of the K-1 starting partitioning points should be less than or equal to a preset similarity threshold, indicating that it can be used as a partitioning point. These partitioning points divide the sequence into K subsequences, where K is an integer greater than or equal to 3.

[0035] Optionally, K-1 respiratory monitoring data are randomly selected from the respiratory monitoring data sequence as K-1 first dividing points. The respiratory monitoring data sequence between the two first dividing points is respectively used as the left data sequence of one first dividing point and the right data sequence of another first dividing point. Then, the cosine similarity formula is used to perform similarity identification on the left data sequence and the right data sequence of each first dividing point to obtain K-1 near-neighbor similarities. Wherein, each near-neighbor similarity reflects the similarity between the data sequences on the left and right sides of each first dividing point. The larger the near-neighbor similarity, the higher the similarity between the data sequences on the left and right sides of the first dividing point, and the first dividing point cannot accurately divide different data in the respiratory monitoring data sequence.

[0036] After obtaining the K-1 nearest neighbor similarities, determine whether the K-1 nearest neighbor similarities are all less than or equal to a preset similarity difference threshold. If not, reselect the K-1 first partition points. If so, use the K-1 first partition points as the K-1 starting partition points.

[0037] The respiratory monitoring data sequence is divided using these final K-1 starting points to form K respiratory monitoring data subsequences. By performing reasonable subsequence division on the respiratory monitoring data sequence, a technical effect of providing more accurate respiratory monitoring data for subsequent analysis is achieved.

[0038] An environment monitoring data subsequence obtaining module 13 is configured to divide the environment monitoring data sequence into subsequences based on the K respiratory monitoring data subsequences to obtain K environment monitoring data subsequences;

[0039] In one embodiment, since there is a one-to-one correspondence between respiratory monitoring data and environmental monitoring data, the environmental monitoring data sequence is divided according to the start and end times of each subsequence in the K respiratory monitoring data subsequences to obtain the corresponding K environmental monitoring data subsequences. The K environmental monitoring data subsequences represent the environmental changes corresponding to the K respiratory monitoring data subsequences. This achieves the technical effect of providing data support for subsequent multidimensional data association and fusion analysis.

[0040] a feature vector obtaining module 14, configured to divide the environmental monitoring data sequence into subsequences based on the K respiratory monitoring data subsequences to obtain K environmental monitoring data subsequences;

[0041] Furthermore, the feature vector obtaining module 14 is configured to perform the following steps:

[0042] Traversing the K respiratory monitoring data subsequences and the K environmental monitoring data subsequences to perform data concentration trend analysis respectively, and determining K centralized respiratory monitoring data and K centralized environmental monitoring data;

[0043] Extracting first centralized respiratory monitoring data and first centralized environmental monitoring data from the K centralized respiratory monitoring data and the K centralized environmental monitoring data respectively, and using the first centralized respiratory monitoring data and the first centralized environmental monitoring data as a first memory space;

[0044] Extracting again the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data located second in the K concentrated respiratory monitoring data and the K concentrated environmental monitoring data, capturing associated keywords for the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data using the first memory space as an index, and adding the capture result and the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data into the second memory space;

[0045] Extracting the Kth set of respiratory monitoring data and the Kth set of environmental monitoring data, respectively using the first memory space, the second memory space, and up to the K-1th memory space as indexes, capturing associated keywords for the Kth set of respiratory monitoring data and the Kth set of environmental monitoring data, and adding the capture results and the Kth set of respiratory monitoring data and the Kth set of environmental monitoring data into the Kth memory space;

[0046] The semantic recognition network layer is used to perform fusion feature vector recognition on the data in the K-th memory space to obtain the associated fusion multi-dimensional monitoring feature vector.

[0047] In one possible embodiment, the K subsequences of respiratory monitoring data are set as "foreground data" to describe the user's respiratory state. The K subsequences of environmental monitoring data are set as "background data" to analyze environmental conditions that affect the respiratory state. This allows for simultaneous analysis of the user's breathing and the environment.

[0048] Optionally, by performing a central tendency analysis on the data in each of the K respiratory monitoring data subsequences and the K environmental monitoring data subsequences, the centralized value of the data in each subsequence is determined, thereby reducing the amount of data analysis for subsequent association fusion analysis. Moreover, since the most representative centralized data with a larger distribution in each subsequence is selected, the accuracy of the analysis will not be reduced.

[0049] Then, each set of centralized respiratory monitoring data and each set of centralized environmental monitoring data is analyzed in turn to determine the user's respiratory condition and environmental status reflected in the next set of centralized respiratory monitoring data and the next set of centralized environmental monitoring data. The first set of centralized respiratory monitoring data and the first set of centralized environmental monitoring data, which are ranked first among the K sets of centralized respiratory monitoring data and the K sets of centralized environmental monitoring data, are extracted and used as a first memory space. The first memory space is used to store the foreground data and background data that are ranked first.

[0050] Optionally, the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data, which are located in the second position among the K concentrated respiratory monitoring data and the K concentrated environmental monitoring data, are extracted again. With the first memory space as an index, the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data are captured with associated keywords, that is, the data associated with the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data and the data stored in the first memory space are retrieved, and then the associated keywords are identified according to the retrieval results to obtain a capture result. The capture result reflects the association between the first memory space and the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data.

[0051] Exemplarily, when the respiratory rate of the first concentrated respiratory monitoring data stored in the first memory space is 25 times / minute, the temperature of the first concentrated environmental monitoring data is 22.8°. At this time, the heart rate of the second concentrated respiratory monitoring data is 20 times / minute, and the temperature of the second concentrated environmental monitoring data is 23°. When the first memory space is used as an index to capture associated keywords, keywords such as "respiratory rate change" and "temperature rise" can be obtained, thereby obtaining the current respiratory state and the trend of environmental changes. Then, the capture results, the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data are added to the second memory space. Therefore, the second memory space stores the data in the first memory space, the capture results of the associated fusion analysis of the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data, and the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data.

[0052] Based on the same acquisition principle as that of the second memory space, the first memory space, the second memory space and the K-1th memory space are used as indexes respectively to capture the associated keywords of the K-th concentrated respiratory monitoring data and the K-th concentrated environmental monitoring data, and then the capture results and the K-th concentrated respiratory monitoring data and the K-th concentrated environmental monitoring data are added to the K-th memory space. The K-th memory space reflects the user's respiratory state and environmental state at the last time point of the preset monitoring window, as well as the user's respiratory state change associated data and environmental state change associated data at different time points within the preset monitoring window. By obtaining the K-th memory space, the changes in the user's respiratory state and environmental state within the preset monitoring window can be precisely grasped.

[0053] In one possible embodiment, multiple sample data sets and multiple sample associated fusion multidimensional monitoring feature vectors of multiple sample memory spaces are obtained as training data, and supervised training is performed on the framework constructed based on the feedforward neural network. During the training, the mapping relationship between the data in the memory space and the associated fusion multidimensional monitoring feature vector is learned until the training converges, and the trained semantic recognition network layer is obtained. Furthermore, the data in the K-th memory space is input into the semantic recognition network layer for fusion feature vector identification to obtain the associated fusion multidimensional monitoring feature vector. Exemplarily, the associated fusion multidimensional monitoring feature vector can be "stable breathing stage, moderate temperature". The associated fusion multidimensional monitoring feature vector reflects the user's respiratory state and environmental state within the preset monitoring window.

[0054] Through hierarchical analysis and keyword capture of the user's breathing status and environmental data, the gradually accumulated and integrated monitoring data are fused into a feature vector reflecting the user's actual breathing condition, providing accurate multi-dimensional feature support for subsequent intelligent monitoring strategy adjustments.

[0055] Furthermore, the feature vector obtaining module 14 is configured to perform the following steps:

[0056] Extracting a first respiratory monitoring data subsequence from the K respiratory monitoring data subsequences, wherein the first respiratory monitoring data subsequence includes a plurality of first respiratory monitoring data;

[0057] Performing mean processing on the plurality of first respiratory monitoring data to obtain a mean of the first respiratory monitoring data;

[0058] Taking the mean of the first respiratory monitoring data as a starting point, iterating through the plurality of first respiratory monitoring data according to a preset iteration step size to determine first concentrated respiratory monitoring data;

[0059] Performing data concentration trend analysis on the K-1 respiratory monitoring data subsequences to obtain K-1 concentrated respiratory monitoring data;

[0060] Performing data concentration trend analysis on the K environmental monitoring data subsequences to obtain the K centralized environmental monitoring data.

[0061] Further, such as Figure 2 As shown, the feature vector obtaining module 14 is used to perform the following steps:

[0062] Taking the mean of the first respiratory monitoring data as a starting point, iterating through the plurality of first respiratory monitoring data according to the preset iteration step size to obtain iterated first respiratory monitoring data;

[0063] Determine whether the neighborhood concentration density of the iterative first respiratory monitoring data is greater than or equal to the neighborhood concentration density of the mean of the first respiratory monitoring data, and if so, update the iterative first respiratory monitoring data as the starting point;

[0064] If not, calling the rand function to randomly generate a first value and a second value, and when the first value is greater than or equal to the second value, updating the iterative first respiratory monitoring data to the starting point;

[0065] After multiple iterative updates until the preset number of iterations is met, the starting point corresponding to the maximum value of the neighborhood concentration density during the iteration process is used as the target starting point, and the first respiratory monitoring data corresponding to the target starting point is used as the first concentrated respiratory monitoring data.

[0066] Furthermore, the feature vector obtaining module 14 is configured to perform the following steps:

[0067] Constructing a mean neighborhood of the mean of the first respiratory monitoring data based on the preset iteration step size, wherein the mean neighborhood includes first respiratory monitoring data whose difference from the mean of the first respiratory monitoring data is within the preset iteration step size among the plurality of first respiratory monitoring data;

[0068] The total number of the first respiratory monitoring data in the mean neighborhood is counted, and the statistical result is compared with the difference between the maximum value and the minimum value of the first respiratory monitoring data in the mean neighborhood to obtain the neighborhood concentration density of the first respiratory monitoring data mean.

[0069] In one embodiment, a first subsequence, i.e., a first respiratory monitoring data subsequence, is selected from the K respiratory monitoring data subsequences. Mean processing is performed on multiple data points in this subsequence, and the mean of the first respiratory monitoring data of this subsequence is calculated as the starting point for analysis. For example, assuming that the first respiratory monitoring data subsequence is [18, 18, 17, 15, 19], the calculated mean is 17.4.

[0070] Taking the mean of the first respiratory monitoring data as a starting point, iterating through the plurality of first respiratory monitoring data according to the preset iteration step size to obtain iterated first respiratory monitoring data. The preset iteration step size is a difference value of the first respiratory monitoring data in a single iteration pre-set by those skilled in the art.

[0071] Preferably, a mean neighborhood of the mean of the first respiratory monitoring data is constructed based on the preset iteration step, wherein the mean neighborhood includes the first respiratory monitoring data whose difference with the mean of the first respiratory monitoring data among the multiple first respiratory monitoring data is within the preset iteration step. Further, the total number of the first respiratory monitoring data within the mean neighborhood is counted, and the statistical result is compared with the difference between the maximum value of the first respiratory monitoring data and the minimum value of the first respiratory monitoring data in the mean neighborhood to obtain the neighborhood concentration density of the mean of the first respiratory monitoring data. The neighborhood concentration density of the mean of the first respiratory monitoring data reflects the distribution density of the mean of the first respiratory monitoring data gathered around the mean of the first respiratory monitoring data. The larger the neighborhood concentration density, the more representative the data is. Based on the same principle, the neighborhood concentration density of the iterative first respiratory monitoring data is obtained.

[0072] Furthermore, determine whether the neighborhood concentration density of the iterative first respiratory monitoring data is greater than or equal to the neighborhood concentration density of the mean of the first respiratory monitoring data. If so, it indicates that the iterative first respiratory monitoring data obtained is more representative. At this time, the iterative first respiratory monitoring data is updated to the starting point.

[0073] If not, it indicates that the mean of the first respiratory monitoring data is more representative. However, in order to avoid falling into a local optimal solution, two random numbers are generated by the rand function, and the sizes of the two random numbers are compared to determine whether it is necessary to accept the inferior solution during the iteration process.

[0074] Optionally, call the rand function to randomly generate a first numerical value and a second numerical value. When the first numerical value is greater than or equal to the second numerical value, the inferior solution is accepted and the iterative first respiratory monitoring data is updated to the starting point. After multiple iterative updates, until the preset number of iterations (the maximum number of iterations pre-set by those skilled in the art) is met, the starting point corresponding to the maximum value of the neighborhood concentrated density during the iteration is used as the target starting point, and the first respiratory monitoring data corresponding to the target starting point is used as the first concentrated respiratory monitoring data. When the first numerical value is less than the second numerical value, the inferior solution is not accepted, and the iteration is stopped at this time, and the mean of the first respiratory monitoring data is used as the first concentrated respiratory monitoring data.

[0075] Based on the same principle as for obtaining the first centralized respiratory monitoring data, data concentration trend analysis is performed on the K-1 respiratory monitoring data subsequences and the K environmental monitoring data subsequences to obtain the K-1 centralized respiratory monitoring data and the K centralized environmental monitoring data. This achieves the technical effect of performing data concentration trend analysis and reducing the amount of data analysis.

[0076] The abnormal record set acquisition module 15 is used to obtain the basic user characteristics of the target user, perform data mining in the big data based on the associated fusion multi-dimensional monitoring feature vector, and retrieve and obtain the matching monitoring abnormal record set;

[0077] In one possible embodiment, the target user is any user who needs to undergo respiratory abnormality analysis. Collect and record the basic information of the target user, including but not limited to age, gender, health status (such as whether there are chronic diseases), daily living habits, etc. Exemplary: the target user is a 65-year-old male with mild hypertension. This information is recorded as the basic features for subsequent analysis and is used to screen and match abnormal records. The obtained associated fusion multi-dimensional monitoring feature vector is retrieved, which contains multi-dimensional information of the current user's respiratory status and environmental influences. Example: The feature vector may include information such as respiratory frequency, respiratory rate, temperature influence, etc. to form a comprehensive respiratory feature of the current user.

[0078] Preferably, a big data analysis platform is used to perform data mining based on the user's basic characteristics and the monitored feature vectors, and retrieve historical abnormal records with similar characteristics from the large database. For example, if the feature vector shows frequent respiratory rate fluctuations, the system can filter out historical records with similar respiratory rate fluctuations in the database.

[0079] Based on the user's basic characteristics and monitored feature vectors, matching conditions are set, such as similar respiratory rate fluctuations, minute ventilation volume, ambient temperature range, etc. Matching conditions should also take into account the personalized characteristics of the target user.

[0080] Based on the set matching conditions, the abnormal monitoring records that meet the conditions are retrieved from the database. Each abnormal record should have characteristics similar to the current user's monitoring data. In the set of abnormal records initially screened, incomplete matching records are further removed, and the most relevant abnormal data is retained to ensure the accuracy of the matching set, and the matching monitoring abnormal record set is obtained. For example, similar abnormal records of users are sorted by frequency of occurrence or importance, and more common or more influential abnormal records are retained first, so that subsequent abnormal analysis is more accurate.

[0081] By effectively identifying abnormal situations similar to the user's current monitoring data, it provides a basis for subsequent monitoring warnings and dynamic regulation, and provides reliable reference data for personalized respiratory data monitoring.

[0082] an asynchronous monitoring step length set obtaining module 16, configured to perform cluster analysis on the matching monitoring anomaly record set using the anomaly type as an index, and determine an asynchronous monitoring step length set, wherein the asynchronous monitoring step length set includes a first monitoring step length and a second monitoring step length, and the first monitoring step length is less than or equal to the second monitoring step length;

[0083] The monitoring data set acquisition module 17 is used to control the target monitoring device to perform asynchronous breathing monitoring on the target user within the feedback monitoring window according to the first monitoring step size and the second monitoring step size, and obtain a first breathing monitoring data set and a second breathing monitoring data set.

[0084] Furthermore, the asynchronous monitoring step set acquisition module 16 is configured to perform the following steps:

[0085] Performing cluster analysis on the matching monitoring anomaly record set using the anomaly type as an index to determine multiple cluster matching monitoring anomaly record sets;

[0086] Traversing the plurality of cluster matching monitoring anomaly record sets to extract the mean of the anomaly interval duration, and obtaining a plurality of cluster monitoring anomaly interval duration mean sets;

[0087] Extracting the maximum and minimum values ​​of the plurality of cluster monitoring abnormality interval mean value sets respectively, and determining the first monitoring step length and the second monitoring step length according to the extraction results;

[0088] The first monitoring step length and the second monitoring step length are used as the asynchronous monitoring step length set.

[0089] Furthermore, the asynchronous monitoring step set acquisition module 16 is configured to perform the following steps:

[0090] Extracting the minimum values ​​of the plurality of cluster monitoring abnormality interval duration mean value sets respectively to obtain the plurality of cluster monitoring abnormality interval duration mean minimum values, and calculating the average of the plurality of cluster monitoring abnormality interval duration mean minimum values ​​to obtain the first monitoring step length;

[0091] The maximum values ​​of the plurality of cluster monitoring abnormality interval duration mean value sets are extracted respectively to obtain the plurality of cluster monitoring abnormality interval duration mean maximum values, and the mean of the plurality of cluster monitoring abnormality interval duration mean maximum values ​​is calculated to obtain the second monitoring step.

[0092] In one possible embodiment, abnormal data in a set of matched monitoring abnormality records is indexed by abnormality type (e.g., abnormal respiratory rate, abnormal respiratory waveform, frequent body movements, etc.), so that abnormal records of the same type can be more accurately classified during cluster analysis. After comprehensive analysis, an asynchronous monitoring step set is determined, wherein the asynchronous monitoring step set includes a first monitoring step and a second monitoring step. Furthermore, the first monitoring step is less than or equal to the second monitoring step, ensuring simultaneous monitoring with a shorter monitoring interval and a longer monitoring interval, thereby ensuring monitoring accuracy and timely response.

[0093] Cluster analysis is performed on the abnormal record sets under each abnormality type, grouping similar abnormal events together to determine the distribution of abnormal data in different categories. For example, under the "abnormal respiratory rate fluctuation" type, cluster analysis can separate high-frequency respiratory rate fluctuations from low-frequency respiratory rate fluctuations, forming multiple cluster sets. The abnormal records in each cluster are traversed, and the mean of the abnormality occurrence intervals of each cluster is calculated to obtain a set of cluster monitoring abnormality interval mean durations.

[0094] For example, for the “abnormal respiratory rate fluctuation” cluster set, if the intervals between multiple abnormal respiratory rate fluctuation events are 10 minutes, 12 minutes, and 15 minutes, respectively, the average interval of the cluster is 12.3 minutes.

[0095] The maximum and minimum values ​​of the mean values ​​of each cluster monitoring anomaly interval duration are extracted to determine the step range of asynchronous monitoring. The minimum value of the plurality of cluster monitoring anomaly interval duration mean values ​​is extracted to obtain the minimum values ​​of the plurality of cluster monitoring anomaly interval duration mean values. The mean of the plurality of cluster monitoring anomaly interval duration mean minimum values ​​is calculated and used as the first monitoring step.

[0096] Preferably, the maximum values ​​of the plurality of cluster monitoring abnormality interval time mean value sets are extracted respectively to obtain the plurality of cluster monitoring abnormality interval time mean maximum values, and the average value of the plurality of cluster monitoring abnormality interval time mean maximum values ​​is calculated and used as the second monitoring step.

[0097] In one embodiment, the asynchronous respiration monitoring frequency of a target monitoring device is set based on the first and second monitoring step sizes, and the target monitoring device is controlled to perform asynchronous respiration monitoring on the target user. This achieves the technical effect of adjusting the frequency of monitoring the user's respiration while balancing monitoring accuracy and device energy consumption, thereby improving the intelligence and targeted nature of monitoring.

[0098] The respiratory abnormality analysis result obtaining module 18 is configured to perform abnormality analysis on the first respiratory monitoring data set and the second respiratory monitoring data set using a respiratory abnormality analyzer to obtain a respiratory abnormality analysis result.

[0099] In one possible embodiment, the respiratory abnormality analyzer is used to perform an overall intelligent analysis of the respiratory monitoring data of different monitoring frequencies to obtain the respiratory abnormality analysis results. Preferably, a plurality of sample first respiratory monitoring data sets and a plurality of sample second respiratory monitoring data sets, as well as the corresponding plurality of sample respiratory abnormality analysis results are obtained as training sample data. The training sample data is used to perform supervised training on a framework constructed based on a feedforward neural network, and a two-to-one mapping relationship between the first respiratory monitoring data set and the second respiratory monitoring data set and the respiratory abnormality analysis results is learned until the training converges, thereby obtaining the trained respiratory abnormality analyzer.

[0100] The first respiratory monitoring data set and the second respiratory monitoring data set are input into the respiratory abnormality analyzer for abnormality analysis to obtain the respiratory abnormality analysis result, wherein the respiratory abnormality analysis result reflects the user's respiratory abnormality within the feedback monitoring window.

[0101] In summary, the embodiments of the present application have at least the following technical effects:

[0102] This application realizes the correlation analysis between environmental changes and respiratory status through the synchronous collection of respiratory monitoring data and environmental monitoring data, so that the device can more accurately reflect the user's actual respiratory status. Then, K respiratory monitoring data subsequences are used as foreground data and K environmental monitoring subsequences are used as background data for correlation fusion analysis. The generated multi-dimensional monitoring feature vector can accurately characterize the respiratory status characteristics, which is helpful for identifying anomalies. It also uses two monitoring steps for simultaneous monitoring to ensure that the abnormal conditions reflected by important respiratory monitoring data are captured in time, achieving the technical effect of improving the reliability of user respiratory abnormality analysis and effectively avoiding false alarms and omissions caused by standardized monitoring.

[0103] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0105] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A respiratory abnormality analysis system for respiratory data monitoring and analysis, characterized in that: The system comprises: A monitoring data sequence acquisition module is used to interact with the target monitoring device and the environmental monitoring module to extract monitoring data of a preset monitoring window, and obtain a respiratory monitoring data sequence and an environmental monitoring data sequence, wherein the respiratory monitoring data corresponds to the environmental monitoring data one to one; a respiratory monitoring data subsequence obtaining module, configured to conditionally select K-1 respiratory monitoring data from the respiratory monitoring data sequence as K-1 starting division points, divide the respiratory monitoring data sequence by the K-1 starting division points, and obtain K respiratory monitoring data subsequences, where K is an integer greater than or equal to 3; an environmental monitoring data subsequence obtaining module, configured to divide the environmental monitoring data sequence into subsequences based on the K respiratory monitoring data subsequences to obtain K environmental monitoring data subsequences; A feature vector acquisition module is used to sequentially perform association fusion analysis on the K respiratory monitoring data subsequences as foreground data and the K environmental monitoring data subsequences as background data to obtain an association fusion multidimensional monitoring feature vector; An abnormal record set acquisition module is used to obtain the basic user characteristics of the target user, combine the associated fusion multi-dimensional monitoring feature vector to perform data mining in big data, and retrieve and obtain a matching monitoring abnormal record set; an asynchronous monitoring step length set obtaining module, configured to perform cluster analysis on the matching monitoring anomaly record set using the anomaly type as an index, and determine an asynchronous monitoring step length set, wherein the asynchronous monitoring step length set includes a first monitoring step length and a second monitoring step length, and the first monitoring step length is less than or equal to the second monitoring step length; a monitoring data set obtaining module, configured to control the target monitoring device to perform asynchronous respiration monitoring on the target user within a feedback monitoring window according to the first monitoring step size and the second monitoring step size, respectively, to obtain a first respiration monitoring data set and a second respiration monitoring data set; The respiratory abnormality analysis result obtaining module is used to use a respiratory abnormality analyzer to perform abnormality analysis on the first respiratory monitoring data set and the second respiratory monitoring data set to obtain a respiratory abnormality analysis result.

2. A respiratory abnormality analysis system for respiratory data monitoring and analysis according to claim 1, characterized in that: The feature vector acquisition module is used to perform the following steps: Traversing the K respiratory monitoring data subsequences and the K environmental monitoring data subsequences to perform data concentration trend analysis respectively, and determining K centralized respiratory monitoring data and K centralized environmental monitoring data; Extracting first centralized respiratory monitoring data and first centralized environmental monitoring data from the K centralized respiratory monitoring data and the K centralized environmental monitoring data respectively, and using the first centralized respiratory monitoring data and the first centralized environmental monitoring data as a first memory space; Extracting again the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data located second in the K concentrated respiratory monitoring data and the K concentrated environmental monitoring data, capturing associated keywords for the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data using the first memory space as an index, and adding the capture result and the second concentrated respiratory monitoring data and the second concentrated environmental monitoring data into the second memory space; Extracting the Kth set of respiratory monitoring data and the Kth set of environmental monitoring data, respectively using the first memory space, the second memory space, and up to the K-1th memory space as indexes, capturing associated keywords for the Kth set of respiratory monitoring data and the Kth set of environmental monitoring data, and adding the capture results and the Kth set of respiratory monitoring data and the Kth set of environmental monitoring data into the Kth memory space; The semantic recognition network layer is used to perform fusion feature vector recognition on the data in the K-th memory space to obtain the associated fusion multi-dimensional monitoring feature vector.

3. A respiratory abnormality analysis system for respiratory data monitoring and analysis according to claim 2, characterized in that: The feature vector acquisition module is used to perform the following steps: Extracting a first respiratory monitoring data subsequence from the K respiratory monitoring data subsequences, wherein the first respiratory monitoring data subsequence includes a plurality of first respiratory monitoring data; Performing mean processing on the plurality of first respiratory monitoring data to obtain a mean of the first respiratory monitoring data; Taking the mean of the first respiratory monitoring data as a starting point, iterating through the plurality of first respiratory monitoring data according to a preset iteration step length to determine first concentrated respiratory monitoring data; Performing data concentration trend analysis on the K-1 respiratory monitoring data subsequences to obtain K-1 concentrated respiratory monitoring data; Performing data concentration trend analysis on the K environmental monitoring data subsequences to obtain the K centralized environmental monitoring data.

4. A respiratory abnormality analysis system for monitoring and analyzing respiratory data according to claim 3, characterized in that: The feature vector acquisition module is used to perform the following steps: Taking the mean of the first respiratory monitoring data as a starting point, iterating through the plurality of first respiratory monitoring data according to the preset iteration step size to obtain iterated first respiratory monitoring data; Determine whether the neighborhood concentration density of the iterative first respiratory monitoring data is greater than or equal to the neighborhood concentration density of the mean of the first respiratory monitoring data, and if so, update the iterative first respiratory monitoring data as the starting point; If not, calling the rand function to randomly generate a first value and a second value, and when the first value is greater than or equal to the second value, updating the iterative first respiratory monitoring data to the starting point; After multiple iterative updates until the preset number of iterations is met, the starting point corresponding to the maximum value of the neighborhood concentration density during the iteration process is used as the target starting point, and the first respiratory monitoring data corresponding to the target starting point is used as the first concentrated respiratory monitoring data.

5. A respiratory abnormality analysis system for respiratory data monitoring and analysis according to claim 4, characterized in that: The feature vector acquisition module is used to perform the following steps: Constructing a mean neighborhood of the mean of the first respiratory monitoring data based on the preset iteration step size, wherein the mean neighborhood includes first respiratory monitoring data whose difference from the mean of the first respiratory monitoring data is within the preset iteration step size among the plurality of first respiratory monitoring data; The total number of the first respiratory monitoring data in the mean neighborhood is counted, and the statistical result is compared with the difference between the maximum value and the minimum value of the first respiratory monitoring data in the mean neighborhood to obtain the neighborhood concentration density of the first respiratory monitoring data mean.

6. A respiratory abnormality analysis system for respiratory data monitoring and analysis according to claim 1, characterized in that: The asynchronous monitoring step set acquisition module is used to perform the following steps: Performing cluster analysis on the matching monitoring anomaly record set using the anomaly type as an index to determine multiple cluster matching monitoring anomaly record sets; Traversing the plurality of cluster matching monitoring anomaly record sets to extract the mean of the anomaly interval duration, and obtaining a plurality of cluster monitoring anomaly interval duration mean sets; Extracting the maximum and minimum values ​​of the plurality of cluster monitoring abnormality interval mean value sets respectively, and determining the first monitoring step length and the second monitoring step length according to the extraction results; The first monitoring step length and the second monitoring step length are used as the asynchronous monitoring step length set.

7. A respiratory abnormality analysis system for monitoring and analyzing respiratory data according to claim 6, characterized in that: The asynchronous monitoring step set acquisition module is used to perform the following steps: Extracting the minimum values ​​of the plurality of cluster monitoring abnormality interval duration mean value sets respectively to obtain the plurality of cluster monitoring abnormality interval duration mean minimum values, and calculating the average of the plurality of cluster monitoring abnormality interval duration mean minimum values ​​to obtain the first monitoring step length; The maximum values ​​of the plurality of cluster monitoring abnormality interval duration mean value sets are extracted respectively to obtain the plurality of cluster monitoring abnormality interval duration mean maximum values, and the mean of the plurality of cluster monitoring abnormality interval duration mean maximum values ​​is calculated to obtain the second monitoring step.

8. A respiratory abnormality analysis system for monitoring and analyzing respiratory data according to claim 1, characterized in that: The respiratory monitoring data subsequence acquisition module is used to perform the following steps: Randomly selecting K-1 respiratory monitoring data from the respiratory monitoring data sequence as K-1 first division points; Performing left and right neighbor similarity analysis on the K-1 first division points to determine the K-1 neighbor similarities of the K-1 first division points; Determine whether the K-1 nearest neighbor similarities are all less than or equal to a preset similarity difference threshold; if not, reselect K-1 first division points; If so, the K-1 first division points are used as the K-1 starting division points.

Images