Lung function data multi-cycle analysis method, device and equipment

Abstract

The invention provides a lung function data multi-cycle analysis method, device and equipment. The method comprises the following steps: acquiring a breathing waveform signal of a target object; acquiring lung function time sequence data of the target object at the target time point; labeling the lung function time sequence data according to time periods of different dimensions, and constructing a time period factor variable; inputting the time period factor into a pre-trained lung function data prediction model to obtain a dynamic baseline value of the target object at the target time point; the pre-trained lung function data prediction model is obtained by training on the basis of historical lung function time sequence data and historical lung function actual measurement values of the target object; and obtaining a lung function analysis result of the target object based on the actual lung function measurement value of the target object at the target time point and the dynamic baseline value.

Description

Technical Field

[0001] This invention relates to the field of medical data processing technology, specifically to a method, apparatus, and equipment for multi-cycle analysis of lung function data. Background Technology

[0002] Current lung function monitoring relies heavily on single or intermittent testing. However, lung function time series data is a non-stationary signal that may change due to factors such as season, temperature, and allergens. Therefore, it is difficult to capture complex changes through single or intermittent testing alone, and it is difficult to distinguish between daily fluctuations and actual changes in the condition. The resulting analysis results are prone to significant bias and have poor accuracy. Summary of the Invention

[0003] In view of this, the present invention provides a method, apparatus and device for multi-cycle analysis of lung function data.

[0004] In a first aspect, embodiments of the present invention propose a multi-period analysis method for pulmonary function data, comprising: acquiring pulmonary function time series data of a target subject at a target time point; labeling the pulmonary function time series data according to time periods of different dimensions to construct time period factor variables; inputting the time period factors into a pre-trained pulmonary function data prediction model to obtain the dynamic baseline value of the target subject at the target time point; the pre-trained pulmonary function data prediction model is trained based on the historical pulmonary function time series data and historical actual pulmonary function measurements of the target subject; and obtaining the pulmonary function analysis results of the target subject based on the actual pulmonary function measurements and the dynamic baseline value of the target subject at the target time point.

[0005] Secondly, embodiments of the present invention propose a multi-period analysis device for lung function data, comprising: a time series data acquisition module configured to acquire lung function time series data of a target object at a target time point; a time period factor variable construction module configured to label the lung function time series data according to different time periods to construct time period factor variables; a dynamic baseline value generation module configured to input the time period factors into a pre-trained lung function data prediction model to obtain the dynamic baseline value of the target object at the target time point; the pre-trained lung function data prediction model is trained based on the historical lung function time series data and historical actual lung function measurements of the target object; and a lung function analysis module configured to obtain the lung function analysis results of the target object based on the actual measurements of the target object at the target time point and the dynamic baseline value.

[0006] Thirdly, embodiments of the present invention provide a multi-cycle lung function data analysis device, comprising: a processor and a memory connected to the processor; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to cause the processor to perform the multi-cycle lung function data analysis method of any embodiment of the first aspect described above.

[0007] This invention provides a method, apparatus, and device for multi-period analysis of lung function data. It generates a "dynamic baseline value" for each target individual that matches their current physiological state, season, week of the week, and intraday rhythm, allowing the baseline to adaptively adjust with individual changes. Furthermore, by stripping away normal, predictable multi-period fluctuations, it can more accurately identify truly abnormal data and data that may indicate abnormalities, significantly reducing false alarms caused by physiological fluctuations, thereby achieving accurate and timely lung function analysis.

[0008] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0009] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0010] Figure 1 This is an exemplary system architecture in which the present invention can be applied; Figure 2 A flowchart of a method for multi-cycle analysis of lung function data provided in an embodiment of the present invention; Figure 3 A flowchart of another method for multi-cycle analysis of lung function data provided in an embodiment of the present invention; Figure 4 A flowchart illustrating another method for multi-cycle analysis of lung function data provided in an embodiment of the present invention; Figure 5 A schematic diagram of the function curve corresponding to the annual cycle provided in this embodiment of the invention; Figure 6 This is a structural block diagram of a multi-cycle lung function data analysis device provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of an electronic device suitable for performing a multi-cycle analysis method of lung function data, provided as an embodiment of the present invention. Detailed Implementation

[0011] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0013] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can also refer to the internal connection of two components; and they can refer to a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0014] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0015] Figure 1 An exemplary system architecture 100 is shown, in which embodiments of the lung function data multi-cycle analysis method, apparatus and device of the present invention can be applied.

[0016] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0017] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various applications for enabling information communication between the terminal devices 101, 102, and 103 and server 105 can be installed. These applications include instant messaging applications.

[0018] Terminal devices 101, 102, and 103 and server 105 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices, and can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here. When server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here.

[0019] Server 105 can provide various services through its built-in applications. It should be noted that the data or information required to provide these services can be obtained from terminal devices 101, 102, and 103 via network 104, or it can be pre-stored locally on server 105 through various means. Therefore, when server 105 detects that this data is already stored locally, it can choose to retrieve it directly from the local storage. In this case, the exemplary system architecture 100 may not include terminal devices 101, 102, and 103 and network 104.

[0020] Since providing various services may require significant computing resources and power, the multi-cycle lung function data analysis method provided in the subsequent embodiments of this invention is generally executed by a server 105 with strong computing power and abundant computing resources. Correspondingly, the multi-cycle lung function data analysis device is also generally located within the server 105. However, it should also be noted that when terminal devices 101, 102, and 103 also possess sufficient computing power and resources, they can also perform the aforementioned calculations performed by the server 105 through their installed applications, thereby outputting the same results as the server 105. Especially when multiple terminal devices with different computing capabilities exist simultaneously, but the relevant application determines that the terminal device has strong computing power and abundant remaining computing resources, the terminal device can perform the aforementioned calculations, thereby appropriately reducing the computing pressure on the server 105. Accordingly, the multi-cycle lung function data analysis device can also be located within terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may also exclude the server 105 and the network 104.

[0021] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0022] Please refer to Figure 2 , Figure 2 A flowchart of a multi-cycle analysis method for lung function data provided in an embodiment of the present invention is included, wherein process 200 includes the following steps: Step 201: Obtain the lung function time series data of the target object at the target time point.

[0023] This step aims to be performed by the entity carrying out the multi-cycle analysis method of lung function data (e.g., Figure 1 The server 105 shown obtains the lung function time series data of the target subject at the target time point. In this embodiment, the target subject mainly refers to the person as the subject. The target time point can be a fixed time, such as 10:00 AM every day, which can be set and adjusted according to actual needs. The lung function time series data refers to the time series data of core lung function indicators, which may include respiratory rate, expiratory time, inspiratory time, inspiratory-expiratory ratio, etc. The time series data can be formed by forming an independent time series based on these indicators. For example, a respiratory rate curve per minute throughout the day, or the changing trend of expiratory time and inspiratory time each night, can be plotted.

[0024] Step 202: Label the lung function time series data according to different time periods to construct time period factor variables.

[0025] This step aims to have the aforementioned implementing entity label the lung function time series data according to different time periods (e.g., daily, weekly, monthly, etc.) to construct time period factor variables. In this embodiment, each data point in the lung function time series data is labeled with different time period labels. Where h(t) represents a certain time period within a day, d(t) represents a day of the week within a week, and s(t) represents a certain day of the year, thus constructing the time period factor variable.

[0026] Step 203: Input the time period factor into the pre-trained lung function data prediction model to obtain the dynamic baseline value of the target object at the target time point.

[0027] This step involves the aforementioned executing entity inputting the time period factor into a pre-trained lung function data prediction model. The output of this pre-trained model is the predicted lung function value for the target subject at the target time point, which is essentially the target subject's dynamic baseline value at the target time point. This pre-trained lung function data prediction model is trained based on the target subject's historical lung function time-series data and historical actual lung function measurements. The specific training process of this model will be further described below.

[0028] Step 204: Obtain the lung function analysis results of the target subject based on the actual measurement values ​​and dynamic baseline values ​​at the target time point.

[0029] This step aims to have the aforementioned implementing entity obtain the actual lung function measurements of the target subject at the target time point, and to obtain the lung function analysis results of the target subject based on the analysis of these actual lung function measurements and the dynamic baseline value. The dynamic baseline value is the baseline value at the target time point obtained through a lung function data prediction model. Therefore, by comparing the actual lung function measurements of the target subject with this dynamic baseline value, the lung function analysis results of the target subject can be obtained.

[0030] The multi-period analysis method for lung function data provided in this invention generates a "dynamic baseline value" for each target individual that matches their current physiological state, season, week of the week, and intraday rhythm, allowing the baseline to adaptively adjust with individual changes. Furthermore, by stripping away normal, predictable multi-period fluctuations, it can more accurately identify truly abnormal data and data that may indicate abnormalities, significantly reducing false alarms caused by physiological fluctuations, thereby achieving accurate and timely lung function analysis.

[0031] Please refer to Figure 3 , Figure 3 A flowchart of a multi-cycle analysis method for lung function data provided in this disclosure embodiment, namely for... Figure 2 Step 202 in process 200 provided a specific implementation. Other steps in process 200 are not adjusted; a new complete embodiment is obtained by replacing step 202 with the specific implementation provided in this embodiment. Process 300 includes the following steps: Step 301: Label the lung function time series data according to daily, weekly, and annual cycles, and determine the correlation between the lung function time series data in different cycle dimensions.

[0032] In this embodiment, lung function time series data are labeled according to three dimensions: daily cycle, weekly cycle, and yearly cycle. Where h(t) represents a certain time period within a day, d(t) represents a day of the week within a week, and s(t) represents a certain day of the year, to characterize the data of each lung function characteristic indicator in a certain time period of a certain day of a certain year.

[0033] Step 302: Extract features from the labeled lung function time series data to obtain the time period factor variable.

[0034] In this embodiment, after labeling the lung function time series data, feature extraction is performed to obtain data that can be directly called by the model.

[0035] In some optional implementations of this embodiment, such as Figure 4 As shown, the training process of the pre-trained lung function data prediction model mainly includes: Step 401: Obtain historical lung function time series data and historical actual lung function measurements of the target object within a preset time period.

[0036] In this embodiment, the data required to train the model consists of historical lung function time-series data for the target object over a preset time period (e.g., the past week, month, or year), and the corresponding historical actual lung function measurements as sample data. For example, for the t-th sample: h(t) can represent an integer from 1 to 24, representing the hour; d(t) can represent an integer from 1 to 7, representing the day of the week; and s(t) can represent an integer from 1 to 365, representing the day of the year.

[0037] Step 402: Calculate the global average lung function of the target subject based on historical lung function time series data.

[0038] In this embodiment, the lung function data prediction model is trained based on a generalized additive model (GAM). For this GAM, it is necessary to determine the smoothing function, global mean, and residual terms. In this step, the global mean of lung function for the target subject is calculated based on historical lung function time series data.

[0039] Step 403: Label the historical lung function time series data according to different time periods to construct historical time period factor variables.

[0040] In this embodiment, historical lung function time series data are labeled according to three dimensions: daily cycle, weekly cycle, and yearly cycle. Where h(t) represents a certain time period within a day, d(t) represents a day of the week within a week, and s(t) represents a certain day of the year, to characterize the data of each lung function characteristic indicator in a certain time period of a certain day of a certain year.

[0041] Step 404: Input the historical time period factor and the actual historical lung function measurement values ​​into the initial generalized additive model to train and obtain the lung function data prediction model.

[0042] In this embodiment, historical time period factors and historical actual lung function measurements are input into an initial generalized additive model, and this initial generalized additive model is trained to obtain the lung function data prediction model. Exemplarily, this training process may mainly include: Step 1: Specify the smoothing basis function and penalty term.

[0043] First, basis functions are selected. For each smoothing term in the initial generalized additive model, a set of basis functions is chosen.

[0044] Next, a penalty term is defined. To prevent overfitting, a penalty is imposed on the "curvature" of the basis functions. Typically, the square integral of the second derivative of the function (which measures the convexity of the curve) can be used as the penalty term. The greater the curvature, the larger the penalty. In this way, the model seeks a balance between "fitness" and "smoothness".

[0045] Step 2: Fit / learn the model parameters.

[0046] In this embodiment, the process can be implemented using the "penalized iterative reweighted least squares method". The loss function can be the squared loss function, and the objective function is equal to the loss function plus the penalty term.

[0047] During the fitting process, initial values ​​are first set for all coefficients (including the aforementioned global average and the coefficients of each basis function). Then, predicted values ​​and residuals are calculated based on the current coefficients. A mathematical optimization algorithm is used to simultaneously solve for all coefficients and smoothing parameters to minimize the objective function. During this optimization process, the coefficients are updated. Finally, a set of optimal coefficients is obtained, thus determining the coefficients of each smoothing function, and ultimately yielding the lung function data prediction model.

[0048] In some optional embodiments of this example, during the analysis of the target subject's lung function using the aforementioned lung function data prediction model, the lung function data prediction model can be updated based on actual data accumulated over a period of time, so that the lung function data prediction model can adapt to the long-term changes in the target subject's lung function. Specifically, this update process mainly includes: Step 1: Obtain lung function time series data, corresponding dynamic baseline values, and measured lung function values ​​within a preset time period after the target time point.

[0049] In this embodiment, lung function time series data over a period of time (e.g., one week, one month, or one year) is acquired, along with the corresponding dynamic baseline value and measured lung function value.

[0050] Step 2: Based on the lung function analysis results, screen the first lung function measured value and its corresponding first dynamic baseline value and first lung function time series data that are within the normal range.

[0051] In this embodiment, the aim is to make the output (i.e., dynamic baseline value) obtained by the lung function data prediction model more consistent with the standard value of normal lung function of the target subject, and to be dynamically adjusted. Therefore, in this process, it is necessary to screen the first measured lung function value and its corresponding first dynamic baseline value and first lung function time series data that are within the normal range, in order to eliminate the measured lung function value and its corresponding dynamic baseline value and lung function time series data that correspond to abnormal analysis results.

[0052] Step 3: Train the lung function data prediction model based on the first measured lung function value and its corresponding first dynamic baseline value and the first lung function time series data to update the lung function data prediction model.

[0053] In this embodiment, after determining the data to be updated, these data (the first measured value of lung function and its corresponding first dynamic baseline value, and the first lung function time series data) can be input into the lung function data prediction model to update it.

[0054] Through the above process, the lung function prediction model can be dynamically updated in real time to adapt to the long-term changes in the lung function of the target subject, so as to better match the actual physical condition of the target subject and make the output prediction results more accurate and effective.

[0055] In some optional embodiments of this example, step 204 above, the process of obtaining the lung function analysis results of the target object based on the actual measurement value and dynamic baseline value of the target object at the target time point, mainly includes: Step 1: Calculate the deviation between the actual lung function measurement and the dynamic baseline value.

[0056] In this embodiment, the deviation value is obtained by subtracting the dynamic baseline value from the actual lung function measurement value. Specifically, the deviation is determined by the following formula: , in, This represents the actual measured value of the target subject's lung function. This indicates the dynamic baseline value.

[0057] Correspondingly, this lung function data prediction model is expressed by the following formula: , in, This represents the global average. This represents the t-th time period in the daily cycle; This represents day t in the cycle; This represents day t in the annual cycle; These represent the three periodic components respectively; This indicates the interaction between different cycles; Represents the residual.

[0058] Dynamic baseline value , in, This represents the dynamic baseline value of lung function at time t. It is the mathematical expectation operator.

[0059] Step 2: Determine the lung function analysis results based on the preset threshold factor, the standard deviation of the prediction error of the lung function data prediction model, and the deviation value.

[0060] In this embodiment, the lung function analysis results are determined using the following formula: , Where k represents the threshold factor, The standard deviation of the prediction error is expressed as follows: .

[0061] In practical applications, the number corresponding to the threshold multiplier factor represents the allowable deviation range of the deviation value. For example, k=2 means that the allowable deviation range is 2 standard deviations. If an actual measurement value is significantly lower than the dynamic baseline value, that is, the deviation is greater than 2 standard deviations, the corresponding lung function analysis result is judged to be abnormal.

[0062] To enhance understanding, this invention also provides a specific implementation scheme in conjunction with a particular application scenario.

[0063] Assuming the target subject's lung function is measured 24 times daily (h(t)), 7 days a week (d(t)), and 365 days a year (s(t)), the lung function data prediction model is derived from this. ,in: It is the intercept term in the generalized additive model (GAM), representing the average level of lung function, for example... = 0.6.

[0064] The impact of time period: , The above formula shows that the target subject has high lung function in the early morning and poor lung function in the evening (evening and night).

[0065] Influence of weekdays: , The above formula shows that the target's physical condition (lung function) is best on the weekend and worst on Monday.

[0066] For annual cycles (fitted by a smooth function): , in, It represents the basic annual cycle trend (high in summer and low in winter). This indicates a short-term decrease in spring allergies (mid-March). This indicates a short-term decrease in autumn allergies (mid-September). This represents the additional drop during winter (the first 60 days and the last 35 days). The function curve is as follows: Figure 5 As shown.

[0067] Interactive function for hour × weekday: , The above formula shows that for this target group, the impact on lung function is small in the early morning hours on weekends, large in the early morning hours on weekdays, and greatest in the evening rush hour on weekdays. The impact on lung function is small in the late evening hours on weekends, and there is no impact at other times.

[0068] A weekday × yearly periodic interaction function: , The above formula shows that for this target group, spring + Monday has a greater impact on lung function, spring + weekend has a smaller impact on lung function, autumn + Monday has a greater impact on lung function, autumn + weekend has a smaller impact on lung function, and other times have no impact.

[0069] Based on this, the effects of all functions are summed to obtain the expected, normal lung function value (dynamic baseline) for the target subject at the specific time point of "Wednesday at 9:00 AM, the 100th day of spring (April 10th)":

[0070] = 0.60 + 0.60 + (-0.30) + (-0.50) + (-0.20) + 0 = 0.20.

[0071] If the actual measured lung function value of the target subject At this point, the deviation value is calculated as follows: =0.15-0.20=-0.05.

[0072] Assume the historical prediction error standard deviation of the model is σ = 0.02, and set the threshold k = 2.

[0073] Anomaly threshold: -k * σ = -2 * 0.02 = -0.04; Comparison: Residual (-0.05) < Threshold (-0.04). Therefore, the actual measured value is significantly lower than the dynamic baseline predicted value (less than 2 standard deviations), thus triggering an "indicator decline" warning.

[0074] Further reference Figure 6 As an implementation of the methods shown in the above figures, the present invention provides an embodiment of a multi-cycle lung function data analysis device, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0075] like Figure 6As shown, the multi-period lung function data analysis device 600 of this embodiment may include: a time series data acquisition module 601, a time period factor variable construction module 602, a dynamic baseline value generation module 603, and a lung function analysis module 604. The time series data acquisition module 601 is configured to acquire lung function time series data of the target object at a target time point; the time period factor variable construction module 602 is configured to label the lung function time series data according to different time periods to construct time period factor variables; the dynamic baseline value generation module 603 is configured to input the time period factors into a pre-trained lung function data prediction model to obtain the dynamic baseline value of the target object at the target time point; the pre-trained lung function data prediction model is trained based on the target object's historical lung function time series data and historical actual lung function measurements; the lung function analysis module 604 is configured to obtain the lung function analysis results of the target object based on the target object's actual lung function measurements at the target time point and the dynamic baseline value.

[0076] In this embodiment, the specific processing and technical effects of the time series data acquisition module 601, the time period factor variable construction module 602, the dynamic baseline value generation module 603, and the lung function analysis module 604 in the multi-period lung function data analysis device 600 can be referred to respectively. Figure 2 The relevant descriptions of steps 201-204 in the corresponding embodiments will not be repeated here.

[0077] In some optional embodiments of this example, the time period factor variable construction module 602 is specifically used to: label lung function time series data according to daily, weekly, and annual cycles, and determine the correlation between lung function time series data in different cycle dimensions; and extract features from the labeled lung function time series data to obtain time period factor variables.

[0078] In some optional embodiments of this example, the lung function analysis module 604 is specifically used to: calculate the deviation between the actual measured lung function value and the dynamic baseline value; and determine the lung function analysis result based on the preset threshold multiplier factor, the standard deviation of the prediction error of the lung function data prediction model, and the deviation value.

[0079] In some optional embodiments of this example, the multi-period lung function data analysis device 600 further includes: a historical data acquisition module, configured to acquire historical lung function time series data and historical actual lung function measurements of the target object within a preset time period; a global average calculation module, configured to calculate the global average lung function of the target object based on the historical lung function time series data; a historical time period factor variable construction module, configured to label the historical lung function time series data according to different dimensions of time periods to construct historical time period factor variables; and a lung function data prediction model generation module, configured to input the historical time period factors and historical actual lung function measurements into an initial generalized additive model to train and obtain a lung function data prediction model.

[0080] In some optional embodiments of this example, the multi-period lung function data analysis device 600 further includes a model update module, specifically used to perform the following processes: acquiring lung function time series data, corresponding dynamic baseline values, and measured lung function values ​​within a preset time period after the target time point; filtering the first measured lung function value and its corresponding first dynamic baseline value and first lung function time series data that are within the normal range based on the lung function analysis results; training the lung function data prediction model based on the first measured lung function value and its corresponding first dynamic baseline value and first lung function time series data to update the lung function data prediction model.

[0081] This embodiment exists as a device embodiment corresponding to the method embodiment described above. The multi-cycle lung function data analysis device provided in this embodiment generates a "dynamic baseline value" for each target object that matches its current physiological state, season, week of the week, and intraday rhythm, allowing the baseline to adaptively adjust with individual changes. Furthermore, by stripping away normal and predictable multi-cycle fluctuations, it can more accurately identify truly abnormal data that may indicate abnormalities, significantly reducing false alarms caused by physiological fluctuations, thereby achieving accurate and timely lung function analysis.

[0082] According to embodiments of the present invention, the present invention also provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the multi-cycle analysis method for lung function data described in any of the above embodiments.

[0083] According to embodiments of the present invention, the present invention also provides a readable storage medium storing computer instructions that enable a computer to implement the multi-cycle analysis method for lung function data described in any of the above embodiments when executed.

[0084] According to embodiments of the present invention, the present invention also provides a computer program product, which, when executed by a processor, can implement the multi-cycle analysis method for lung function data described in any of the above embodiments.

[0085] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0086] like Figure 7 As shown, device 700 includes a computing unit 701, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 702 or a computer program loaded into random access memory (RAM) 703 from storage unit 708. The RAM 703 may also store various programs and data required for the operation of device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via bus 704. Input / output (I / O) interface 705 is also connected to bus 704.

[0087] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as keyboard, mouse, etc.; output unit 707, such as various types of monitors, speakers, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0088] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as the multi-cycle analysis method of lung function data. For example, in some embodiments, the multi-cycle analysis method of lung function data can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the multi-cycle analysis method of lung function data described above can be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform a multi-cycle analysis method of lung function data by any other suitable means (e.g., by means of firmware).

[0089] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0093] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for multi-period analysis of lung function data, characterized in that, include: Obtain time-series data of lung function of the target object at the target time point; The lung function time series data were labeled according to different time periods to construct time period factor variables; The time period factor is input into a pre-trained lung function data prediction model to obtain the dynamic baseline value of the target object at the target time point; the pre-trained lung function data prediction model is trained based on the historical lung function time series data and historical actual lung function measurements of the target object. The lung function analysis results of the target object are obtained based on the actual lung function measurement values ​​of the target object at the target time point and the dynamic baseline value.

2. The method of claim 1, wherein, The step of labeling the lung function time series data according to different time periods to construct time period factor variables includes: The lung function time series data were labeled according to daily, weekly, and annual cycles, and the correlation between the lung function time series data in different cycle dimensions was determined. Feature extraction was performed on the labeled lung function time series data to obtain the time period factor variable.

3. The method of claim 1, wherein, Also includes: Obtain the historical lung function time series data and the actual historical lung function measurement values ​​of the target object within a preset time period; Calculate the global average lung function of the target subject based on the historical lung function time series data; The historical lung function time series data are labeled according to different time periods to construct historical time period factor variables; The historical time period factor and the actual historical lung function measurements are input into the initial generalized additive model to train the lung function data prediction model.

4. The method of claim 3, wherein, Also includes: Acquire lung function time series data, corresponding dynamic baseline values, and measured lung function values ​​within a preset time period after the target time point; Based on the lung function analysis results, the first lung function measured value and its corresponding first dynamic baseline value and first lung function time series data within the normal range were selected. The lung function data prediction model is trained based on the first measured lung function value and its corresponding first dynamic baseline value and the first lung function time series data to update the lung function data prediction model.

5. The method of claim 1, wherein, The lung function analysis results of the target object are obtained based on the actual measurement value of the target object at the target time point and the dynamic baseline value, including: Calculate the deviation between the actual measured lung function value and the dynamic baseline value; The lung function analysis results are determined based on a preset threshold factor, the standard deviation of the prediction error of the lung function data prediction model, and the deviation value.

6. The method of claim 5, wherein, The lung function data prediction model is expressed by the following formula: ， wherein, denotes the global average; denotes the t-th time period in the day cycle; denotes the t-th day in the week cycle; denotes the t-th day in the year cycle; denotes the three periodic components, respectively; denotes the interaction between the periods; denotes the residual.

7. The method of claim 6, wherein, The predicted value output by the lung function data prediction model is expressed by the following formula: ， wherein denotes a dynamic baseline value of the lung function at time t, is the mathematical expectation operator.

8. The method of claim 6, wherein, The results of the lung function analysis are determined using the following formula: ， wherein k denotes the threshold factor, denotes the standard deviation of the prediction error, the standard deviation is represented by: .

9. A lung function data multi-cycle analysis apparatus, characterized by include: The time series data acquisition module is configured to acquire lung function time series data of the target object at the target time point; The time-period factor variable construction module is configured to label the lung function time series data according to different time periods to construct time-period factor variables; The dynamic baseline value generation module is configured to input the time period factor into a pre-trained lung function data prediction model to obtain the dynamic baseline value of the target object at the target time point; the pre-trained lung function data prediction model is trained based on the historical lung function time series data and historical actual lung function measurements of the target object. The lung function analysis module is configured to obtain the lung function analysis results of the target object based on the actual lung function measurement value of the target object at the target time point and the dynamic baseline value.

10. A multi-cycle lung function data analysis device, characterized in that, include: A processor and a memory connected to the processor; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to cause the processor to perform the multi-cycle analysis method for lung function data as described in any one of claims 1-8.

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