A sleep management method for analyzing sleep recording data

Abstract

The present invention discloses a sleep management method for analyzing sleep record data, which relates to the field of sleep management technology. The method comprises: retrieving monitoring data from a sleep monitor that monitors the sleep of a target user within a preset historical window, obtaining L historical sleep monitoring data logs of L historical monitoring nodes; obtaining L historical sleep monitoring indicator groups; performing cluster analysis to obtain Q clustered historical sleep monitoring indicator group sets; performing centralized screening of the sleep monitoring indicator groups to obtain Q clustered historical sleep monitoring indicator concentrated groups; and analyzing the Q clustered historical sleep monitoring indicator concentrated groups using a sleep management solution identification network layer to obtain a target sleep management solution. The present invention solves the technical problems in the prior art of low fit between sleep management and the user's actual sleep situation and low reliability of sleep management. It achieves the technical effect of improving data analysis efficiency and enhancing sleep management quality.

Description

Technical Field

[0001] The present invention relates to the technical field of sleep management, and in particular to a sleep management method for analyzing sleep record data. Background Art

[0002] With the development of intelligent health management, devices such as sleep monitors have become widely used to record and analyze sleep data. These devices can collect multi-dimensional data such as sleep duration, deep sleep percentage, awakening frequency, and sleep onset latency, helping users understand their sleep status. However, most current sleep monitoring methods rely primarily on single-point or short-term data analysis, ignoring the underlying patterns of historical data over a continuous timeframe, resulting in low reliability for sleep management. Summary of the Invention

[0003] The present application provides a sleep management method for analyzing sleep record data, which is used to solve the technical problems in the prior art of low fit between sleep management and the user's actual sleep conditions and low reliability of sleep management.

[0004] In view of the above problems, the present application provides a sleep management method for analyzing sleep record data, the method comprising:

[0005] Retrieving monitoring data from a sleep monitor that monitors the target user's sleep within a preset historical window to obtain L historical sleep monitoring data logs of L historical monitoring nodes, where L is a positive integer;

[0006] Extract L historical sleep monitoring data logs based on the preset sleep monitoring indicator groups to obtain L historical sleep monitoring indicator groups;

[0007] Performing cluster analysis on the L historical sleep monitoring indicator groups of the L historical monitoring nodes based on two dimensions: the degree of similarity between historical monitoring nodes and the degree of similarity between historical sleep monitoring indicators, to obtain a set of Q clustered historical sleep monitoring indicator groups, where Q is a positive integer less than or equal to L;

[0008] Traversing the Q clustered historical sleep monitoring indicator group sets to perform centralized screening of the sleep monitoring indicator groups to obtain Q clustered historical sleep monitoring indicator centralized groups;

[0009] The Q clustered historical sleep monitoring indicator groups are analyzed using a sleep management solution identification network layer to obtain a target sleep management solution.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] This application retrieves monitoring data from a sleep monitor that monitors the sleep of a target user within a preset historical window to obtain L historical sleep monitoring data logs for L historical monitoring nodes, where L is a positive integer. The application then extracts the L historical sleep monitoring data logs based on preset sleep monitoring indicator groups to obtain L historical sleep monitoring indicator groups. Furthermore, the application performs cluster analysis on the L historical sleep monitoring indicator groups for the L historical monitoring nodes based on two dimensions: the degree of proximity of the historical monitoring nodes and the degree of similarity of the historical sleep monitoring indicators. This results in Q clustered historical sleep monitoring indicator group sets, where Q is a positive integer less than or equal to L. The application then traverses the Q clustered historical sleep monitoring indicator group sets to perform centralized screening of the sleep monitoring indicator groups, obtaining Q clustered historical sleep monitoring indicator concentrated groups. The application then uses a sleep management solution identification network layer to analyze the Q clustered historical sleep monitoring indicator concentrated groups to obtain a target sleep management solution. This achieves the technical effect of in-depth analysis of sleep record data and improved sleep management reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Attachment Figure 1 1 is a flowchart of a sleep management method for analyzing sleep record data provided by an embodiment of the present invention.

[0013] Attachment Figure 2 This is a flowchart of obtaining Q clustered historical sleep monitoring indicator groups in a sleep management method for analyzing sleep record data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall equally within the scope limited by the appended claims of the application.

[0015] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, 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, methods, products or devices.

[0016] Examples, such as the attached Figure 1 As shown, the present application provides a sleep management method for analyzing sleep record data, wherein the method includes:

[0017] S1: Retrieving monitoring data of a sleep monitor that monitors the sleep of a target user within a preset historical window, and obtaining L historical sleep monitoring data logs of L historical monitoring nodes, where L is a positive integer;

[0018] In one possible embodiment, the target user is any user who needs sleep management. The sleep monitor is a device that records the sleep data of the target user. The preset historical window is a time period for analyzing sleep record data pre-set by a person skilled in the art. The L historical monitoring nodes are discrete time points for monitoring the target user within the preset historical window. The L historical sleep monitoring data logs are detailed sleep records stored on each monitoring node, including multiple data (such as heart rate, respiratory rate, etc.). Among them, the historical monitoring nodes and the historical sleep monitoring data logs correspond one to one.

[0019] Optionally, the target user's monitoring data within a preset historical window is retrieved from the sleep monitor and organized chronologically into L independent historical monitoring nodes. The monitoring data for each node is stored as a log, containing raw data from multiple dimensions. By defining the historical monitoring window and monitoring nodes, the extracted data is ensured to have a sufficient time span and retain detailed temporal distribution information, laying the data foundation for subsequent cluster analysis.

[0020] S2: extracting L historical sleep monitoring data logs based on the preset sleep monitoring indicator groups to obtain L historical sleep monitoring indicator groups;

[0021] Furthermore, the preset sleep monitoring indicator group includes deep sleep duration, number of awakenings, sleep onset latency and total sleep duration.

[0022] In one embodiment, the preset sleep monitoring indicator group is a set of indicators for sleep quality assessment predefined by those skilled in the art, including deep sleep duration, number of awakenings, sleep latency and total sleep duration. Among them, deep sleep duration is the cumulative duration of the deep sleep stage (usually related to physical recovery). The number of awakenings is the number of times the user wakes up at night, reflecting sleep continuity. Sleep latency is the time from going to bed to entering stable sleep, which measures the efficiency of falling asleep. Total sleep duration is the total sleep time for the whole night, which is a basic indicator for evaluating sleep health.

[0023] Optionally, key information is extracted from L historical sleep monitoring data logs in turn according to preset sleep monitoring indicator groups (such as deep sleep duration, number of awakenings, etc.), and each extracted group of indicators is organized into a standardized historical sleep monitoring indicator group. For example, the historical sleep monitoring indicator group 1 corresponding to log 1 is: {deep sleep duration = 90 minutes, number of awakenings = 3, sleep onset latency = 20 minutes, total sleep duration = 7 hours}; the historical sleep monitoring indicator group 2 corresponding to log 2 is: {deep sleep duration = 100 minutes, number of awakenings = 2, sleep onset latency = 15 minutes, total sleep duration = 7.5 hours}.

[0024] By extracting key information from complex raw data, reducing data dimensions and focusing on core indicators of sleep quality, we achieve the technical effect of facilitating subsequent cluster analysis and generation of management plans.

[0025] S3: performing cluster analysis on the L historical sleep monitoring indicator groups of the L historical monitoring nodes based on two dimensions: the degree of similarity between historical monitoring nodes and the degree of similarity of historical sleep monitoring indicators, to obtain Q clustered historical sleep monitoring indicator group sets, where Q is a positive integer less than or equal to L;

[0026] Furthermore, cluster analysis is performed on the L historical sleep monitoring indicator groups of the L historical monitoring nodes based on the two dimensions of the similarity of the historical monitoring nodes and the similarity of the historical sleep monitoring indicators to obtain Q clustered historical sleep monitoring indicator group sets. In this embodiment of the application, step S3 further includes:

[0027] Perform Q extractions from the L historical sleep monitoring indicator groups using a random method without replacement to obtain Q initial cluster centers;

[0028] Clustering the L historical sleep monitoring indicator groups based on the Q initial cluster centers to obtain Q initial clustered historical sleep monitoring indicator group sets;

[0029] Using the clustering cost loss function, the overall clustering cost loss of the Q initial clustered historical sleep monitoring indicator groups is calculated from two dimensions: the similarity of historical monitoring nodes and the similarity of historical sleep monitoring indicators, to obtain the initial clustering cost loss;

[0030] Calculating the means of the Q initial clustered historical sleep monitoring indicator group sets respectively to obtain the means of the Q initial clustered historical sleep monitoring indicator groups, and updating the Q initial cluster centers according to the means of the Q initial clustered historical sleep monitoring indicator groups to obtain Q updated cluster centers;

[0031] Combining the L historical sleep monitoring indicator groups, constructing Q updated cluster historical sleep monitoring indicator group sets of the Q updated cluster centers, and performing overall cluster cost loss analysis using a cluster cost loss function to obtain an updated cluster cost loss amount;

[0032] Determine whether the updated cluster cost loss is less than or equal to the initial cluster cost loss. If so, calculate the means of the Q updated cluster historical sleep monitoring indicator group sets respectively, and update the Q updated cluster centers according to the calculation results until the difference between the two updated cluster cost losses of two adjacent updates is less than the preset difference. Cluster the L historical sleep monitoring indicator groups according to the Q updated cluster centers obtained from the last update to obtain Q cluster historical sleep monitoring indicator group sets.

[0033] Furthermore, step S3 of the embodiment of the present application further includes:

[0034] Construct a clustering cost loss function, wherein the clustering cost loss function is:

[0035]

[0036] Among them, LOSS is the initial clustering cost loss, n i is the set of initial cluster historical sleep monitoring indicator groups of the i-th initial cluster center among the Q initial cluster centers, is the initial cluster historical sleep monitoring indicator group of the i-th initial cluster center, μ ij is the jth historical sleep monitoring indicator group in the initial cluster historical sleep monitoring indicator group set of the i-th initial cluster center, is the historical monitoring node corresponding to the initial cluster historical sleep monitoring indicator group of the i-th initial cluster center, t ij is the historical monitoring node corresponding to the jth historical sleep monitoring indicator group in the set of historical sleep monitoring indicator groups of the initial cluster center of the i-th initial cluster, and λ is the weight for balancing the similarity between the historical monitoring nodes and the similarity between the historical sleep monitoring indicators.

[0037] In one possible embodiment, the degree of similarity of historical monitoring nodes refers to the time interval between the sleep data of different historical monitoring nodes. Nodes with a high degree of similarity indicate that they are adjacent in time and are suitable for joint analysis. The degree of similarity of historical sleep monitoring indicators is based on the feature similarity of sleep indicators (such as deep sleep duration, number of awake times, etc.), which indicates the degree of similarity of data content. Cluster analysis is performed on the L historical sleep monitoring indicator groups of L historical monitoring nodes from two dimensions to obtain a set of Q clustered historical sleep monitoring indicator groups. Among them, the set of Q clustered historical sleep monitoring indicator groups is the result of cluster analysis, and each group contains one or more historical sleep monitoring indicator groups with similar characteristics. By performing cluster analysis from two dimensions, the technical effect of improving the reliability of clustering the L historical sleep monitoring indicator groups and the quality of the cluster analysis results is achieved.

[0038] In one embodiment, random extraction without replacement is a random sampling method, which extracts Q different samples from L historical sleep monitoring indicator groups as initial cluster centers to ensure that there is no duplication between samples. Randomly select Q samples from the L historical indicator groups as initial cluster centers without replacement to obtain Q initial cluster centers, and each center corresponds to a cluster. For example, the distances between the L historical sleep monitoring indicator groups and the Q initial cluster centers are calculated (such as based on Euclidean distance or cosine similarity), and the L historical sleep monitoring indicator groups are assigned to the set to which the initial cluster center closest to them belongs, to obtain Q initial cluster historical sleep monitoring indicator group sets. Each initial cluster historical sleep monitoring indicator group set contains one or more historical sleep monitoring indicator groups.

[0039] The Q initial clustered historical sleep monitoring indicator sets and the Q initial cluster centers are input into the cluster cost loss function for overall cost loss analysis, obtaining the initial cluster cost loss. By using the two dimensions of historical monitoring node proximity and historical sleep monitoring indicator similarity, λ is used in the cluster cost loss function to balance the weights of these two factors during the loss analysis. This improves the quality and reliability of the overall cost loss analysis.

[0040] Optionally, the means of the Q initial clustered historical sleep monitoring indicator group sets are calculated to obtain the Q initial clustered historical sleep monitoring indicator group means, and the Q initial cluster centers are updated based on the Q initial clustered historical sleep monitoring indicator group means to obtain Q updated cluster centers. This is then analyzed to determine whether the Q updated cluster centers better represent the concentration of the Q initial clustered historical sleep monitoring indicator group sets.

[0041] Preferably, the L historical sleep monitoring indicator groups are clustered again based on the Q updated cluster centers to obtain Q updated cluster historical sleep monitoring indicator group sets. This further improves clustering accuracy. A clustering cost loss function is then used to perform an overall clustering cost loss analysis to obtain an updated clustering cost loss. The updated clustering cost loss reflects the overall cost loss of clustering based on the Q updated cluster centers.

[0042] Furthermore, a determination is made as to whether the updated cluster cost loss is less than or equal to the initial cluster cost loss. If so, the means of the Q updated cluster historical sleep monitoring indicator group sets are calculated, and the Q updated cluster centers are updated based on the calculated results. This is done until the difference between the cost losses of two consecutive updated clusters is less than a preset difference, indicating that a relatively reliable clustering result has been achieved. The L historical sleep monitoring indicator groups are then clustered based on the Q updated cluster centers obtained from the last update, resulting in Q cluster historical sleep monitoring indicator group sets. This achieves preliminary data grouping, providing an important basis for subsequent optimization and analysis.

[0043] S4: traversing the Q clustered historical sleep monitoring indicator group sets to perform centralized screening of the sleep monitoring indicator groups to obtain Q clustered historical sleep monitoring indicator centralized groups;

[0044] Further, as attached Figure 2 As shown, the Q clustered historical sleep monitoring indicator group sets are traversed to perform centralized screening of the sleep monitoring indicator groups to obtain Q clustered historical sleep monitoring indicator centralized groups. In this embodiment of the application, step S4 further includes:

[0045] Calculating the means of the Q clustered historical sleep monitoring indicator groups respectively to obtain Q clustered historical sleep monitoring indicator mean groups;

[0046] Taking the Q clustered historical sleep monitoring indicator mean groups as Q starting centralized screening points, constructing Q starting centralized screening point neighborhoods in the Q clustered historical sleep monitoring indicator group sets according to a preset centralized screening similarity threshold;

[0047] The Q clustered historical sleep monitoring indicator group sets are subjected to sleep monitoring indicator group centralized screening based on the Q starting centralized screening points and the Q starting centralized screening point neighborhoods to obtain Q clustered historical sleep monitoring indicator centralized groups.

[0048] Furthermore, based on the Q starting centralized screening points and the Q starting centralized screening point neighborhoods, the Q clustered historical sleep monitoring indicator group sets are subjected to centralized screening of sleep monitoring indicator groups to obtain Q clustered historical sleep monitoring indicator centralized groups. In this embodiment of the application, step S4 further includes:

[0049] Randomly extracting Q clustered historical sleep monitoring indicator groups from the edges of the neighborhoods of the Q starting set screening points as Q iterative clustered historical sleep monitoring groups;

[0050] Constructing Q iterative clustering historical sleep monitoring group neighborhoods of the Q iterative clustering historical sleep monitoring groups according to a preset centralized screening similarity threshold;

[0051] Determine whether the neighborhood density of the Q iterative clustered historical sleep monitoring groups is greater than or equal to the neighborhood density of the Q starting concentrated screening point neighborhoods. If so, use the Q iterative clustered historical sleep monitoring groups as Q iterative concentrated screening points, and use the Q iterative clustered historical sleep monitoring groups as Q iterative concentrated screening point neighborhoods. Based on the Q iterative concentrated screening points and the Q iterative concentrated screening point neighborhoods, continue to perform sleep monitoring indicator group centralized screening on the Q clustered historical sleep monitoring indicator group sets until the preset maximum screening times are met, and use the Q iterative concentrated screening points obtained in the last screening as the Q clustered historical sleep monitoring indicator centralized groups.

[0052] Further, if not, Q clustered historical sleep monitoring indicator groups are randomly extracted again from the edges of the neighborhoods of the Q starting set screening points, and the Q iterative clustered historical sleep monitoring groups are updated.

[0053] In one embodiment, after obtaining the Q clustered historical sleep monitoring indicator group sets, in order to improve the reliability of the subsequent sleep management plan, it is necessary to analyze the general situation of each cluster to provide a reliable basis for the generation of the sleep management plan. Preferably, the Q clustered historical sleep monitoring indicator group sets are obtained by performing sleep monitoring indicator group centralized screening on the Q clustered historical sleep monitoring indicator group sets. The Q clustered historical sleep monitoring indicator group sets are data that best represent the general situation of the Q clustered historical sleep monitoring indicator group sets.

[0054] Calculate the means of the Q clustered historical sleep monitoring indicator groups respectively to obtain Q clustered historical sleep monitoring indicator mean groups, wherein the Q clustered historical sleep monitoring indicator mean groups reflect the average level of the Q clustered historical sleep monitoring indicator groups.

[0055] Then, using the Q clustered historical sleep monitoring indicator mean groups as Q starting centralized screening points, Q starting centralized screening point neighborhoods are constructed within the set of Q clustered historical sleep monitoring indicator groups according to a preset centralized screening similarity threshold. In other words, similarity is calculated using the cosine similarity formula between the Q clustered historical sleep monitoring indicator groups and the corresponding Q clustered historical sleep monitoring indicator mean groups, and clustered historical sleep monitoring indicator groups whose calculated results are greater than a preset centralized screening similarity threshold (the minimum similarity pre-set by those skilled in the art for inclusion in the neighborhood) are added to the Q starting centralized screening point neighborhoods.

[0056] Furthermore, Q clustered historical sleep monitoring indicator groups are randomly extracted from the edges of the neighborhoods of the Q starting centralized screening points as Q iterative clustered historical sleep monitoring groups. Based on the same construction principle as the neighborhoods of the Q starting centralized screening points, Q iterative clustered historical sleep monitoring group neighborhoods of the Q iterative clustered historical sleep monitoring groups are constructed according to a preset centralized screening similarity threshold. The Q iterative clustered historical sleep monitoring group neighborhoods contain data whose similarity to the Q iterative clustered historical sleep monitoring groups is within the preset centralized screening similarity threshold.

[0057] Furthermore, the number of clustered historical sleep monitoring indicator groups in the neighborhoods of the Q starting centralized screening points is counted, and the calculated result is compared with 2 times the preset centralized screening similarity threshold to obtain the neighborhood density of the Q starting centralized screening points. Based on the same calculation principle, the neighborhood density of the Q iteratively clustered historical sleep monitoring groups is obtained.

[0058] Furthermore, it is determined whether the neighborhood density of the neighborhoods of the Q iterative clustered historical sleep monitoring groups is greater than or equal to the neighborhood density of the neighborhoods of the Q starting concentrated screening points. If so, it indicates that the Q iterative clustered historical sleep monitoring groups are more representative, and the Q iterative clustered historical sleep monitoring groups are used as Q iterative concentrated screening points, and the neighborhoods of the Q iterative clustered historical sleep monitoring groups are used as the neighborhoods of the Q iterative concentrated screening points. Based on the Q iterative concentrated screening points and the Q iterative concentrated screening point neighborhoods, the Q clustered historical sleep monitoring indicator group sets are continued to be concentratedly screened for sleep monitoring indicator groups until the preset maximum screening times (the maximum screening times pre-set by those skilled in the art) are met, and the Q iterative concentrated screening points obtained in the last screening are used as the Q clustered historical sleep monitoring indicator concentrated groups.

[0059] If not, Q clustered historical sleep monitoring indicator groups are randomly extracted again from the edges of the neighborhoods of the Q starting concentrated screening points, and the Q iterative clustered historical sleep monitoring indicator groups are updated. Then, based on the above steps, the analysis is re-performed. Preferably, the number of re-analysis attempts is counted, and if the number exceeds a preset allowable number, the Q starting concentrated screening points are used as the Q clustered historical sleep monitoring indicator concentrated groups.

[0060] S5: Analyze the Q clustered historical sleep monitoring indicator groups using a sleep management solution identification network layer to obtain a target sleep management solution.

[0061] Furthermore, the sleep management solution identification network layer is used to analyze the Q clustered historical sleep monitoring indicator groups to obtain a target sleep management solution. In this embodiment, step S5 further includes:

[0062] Obtaining a plurality of sample clustered historical sleep monitoring indicator concentrated group sets and corresponding plurality of sample target sleep management plans as a training data set;

[0063] The training data set is used to perform supervised training on a framework built based on a feedforward neural network to learn a one-to-one mapping relationship between a set of clustered historical sleep monitoring indicators and a target sleep management plan until the training converges, thereby obtaining the trained sleep management plan identification network layer.

[0064] In one possible embodiment, the sleep management plan identification network layer is a model built on a feedforward neural network (FNN), which inputs a clustered set of historical sleep monitoring indicators and outputs a target sleep management plan. Through supervised training of the sleep management plan identification network layer, the system can efficiently and accurately convert the clustered set of historical sleep monitoring indicators into a specific target sleep management plan. This step achieves intelligent integration from data analysis to plan output, greatly improving the practicality and applicability of the method.

[0065] Preferably, a clustered set of historical sleep monitoring indicators and corresponding sample target sleep management plans are obtained. The sample target sleep management plans are target outputs corresponding to the training samples, including personalized sleep adjustment suggestions, such as improving deep sleep duration or reducing awakenings.

[0066] The neural network model is trained with known inputs (sample clusters of historical sleep monitoring indicators) and outputs (target sleep management plans), learning a clear correspondence between indicator sets and management plans, ensuring that each cluster feature group corresponds to a specific management plan, until the model can accurately predict the output of unseen samples.

[0067] Optionally, a feedforward neural network is used to define the network layer structure (e.g., input layer, hidden layer, and output layer). Supervised learning is performed using the training data set. The model parameters are optimized using a backpropagation algorithm to gradually reduce the prediction error. The model is trained continuously until the training error converges and the model's performance on the validation dataset is stable. Q clustered historical sleep monitoring indicators are grouped and fed into the trained network model, which then outputs a target sleep management plan corresponding to each data set.

[0068] The supervised training of the sleep management solution identification network layer can efficiently and accurately transform clustered historical sleep monitoring indicators into specific target sleep management solutions. This achieves intelligent integration from data analysis to solution output, greatly improving the reliability of sleep management.

[0069] In summary, the embodiments of the present application have at least the following technical effects:

[0070] 1. This application achieves a comprehensive assessment of the user's sleep quality by retrieving the target user's historical sleep monitoring data and extracting key indicators (such as deep sleep duration, number of awakenings, etc.), and performs cluster analysis from the two dimensions of time proximity and indicator similarity, so that the potential patterns of historical data can be explored and the accuracy of the analysis results can be improved.

[0071] 2. This application uses clustering methods to achieve effective data dimensionality reduction, reducing redundant information while retaining the core characteristics of the data. It further optimizes the clustering results through centralized screening to ensure that each indicator group is more representative and consistent, providing high-quality input for subsequent analysis.

[0072] 3. This application utilizes a sleep management solution identification network layer and, through supervised training, achieves a one-to-one mapping between clustered indicator groups and sleep management solutions. This allows for the automatic generation of personalized sleep management recommendations based on user data. By learning from a large amount of training data, the reliability and adaptability of solution generation are improved, making the solutions more tailored to users' actual needs.

[0073] 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.

[0074] 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.

[0075] 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 sleep management method for analyzing sleep record data, characterized in that: The method comprises: Retrieving monitoring data from a sleep monitor that monitors the target user's sleep within a preset historical window to obtain L historical sleep monitoring data logs of L historical monitoring nodes, where L is a positive integer; Extract L historical sleep monitoring data logs based on the preset sleep monitoring indicator groups to obtain L historical sleep monitoring indicator groups; Performing cluster analysis on the L historical sleep monitoring indicator groups of the L historical monitoring nodes based on two dimensions: the proximity of historical monitoring nodes and the similarity of historical sleep monitoring indicators, to obtain Q clustered historical sleep monitoring indicator group sets, where Q is a positive integer less than or equal to L, the proximity of historical monitoring nodes refers to the time interval between sleep data of different historical monitoring nodes, and the similarity of historical sleep monitoring indicators refers to the feature similarity of the historical sleep monitoring indicators; Traversing the Q clustered historical sleep monitoring indicator group sets to perform centralized screening of the sleep monitoring indicator groups to obtain Q clustered historical sleep monitoring indicator centralized groups; Analyzing the Q clustered historical sleep monitoring indicator groups using a sleep management solution identification network layer to obtain a target sleep management solution; The Q clustered historical sleep monitoring indicator group sets are traversed to perform centralized screening of the sleep monitoring indicator groups to obtain Q clustered historical sleep monitoring indicator centralized groups, including: Calculating the means of the Q clustered historical sleep monitoring indicator groups respectively to obtain Q clustered historical sleep monitoring indicator mean groups; Taking the Q clustered historical sleep monitoring indicator mean groups as Q starting centralized screening points, constructing Q starting centralized screening point neighborhoods in the Q clustered historical sleep monitoring indicator group sets according to a preset centralized screening similarity threshold; Performing sleep monitoring indicator group centralized screening on the Q clustered historical sleep monitoring indicator group sets based on the Q starting centralized screening points and the Q starting centralized screening point neighborhoods to obtain Q clustered historical sleep monitoring indicator centralized groups; The Q clustered historical sleep monitoring indicator group sets are subjected to sleep monitoring indicator group centralized screening based on the Q starting centralized screening points and the Q starting centralized screening point neighborhoods to obtain Q clustered historical sleep monitoring indicator centralized groups, including: Randomly extracting Q clustered historical sleep monitoring indicator groups from the edges of the neighborhoods of the Q starting set screening points as Q iterative clustered historical sleep monitoring groups; Constructing Q iterative clustering historical sleep monitoring group neighborhoods of the Q iterative clustering historical sleep monitoring groups according to a preset centralized screening similarity threshold; Determine whether the neighborhood density of the Q iterative clustered historical sleep monitoring groups is greater than or equal to the neighborhood density of the Q starting concentrated screening point neighborhoods. If so, use the Q iterative clustered historical sleep monitoring groups as Q iterative concentrated screening points, and use the Q iterative clustered historical sleep monitoring groups as Q iterative concentrated screening point neighborhoods. Based on the Q iterative concentrated screening points and the Q iterative concentrated screening point neighborhoods, continue to perform sleep monitoring indicator group centralized screening on the Q clustered historical sleep monitoring indicator group sets until the preset maximum screening times are met, and use the Q iterative concentrated screening points obtained in the last screening as the Q clustered historical sleep monitoring indicator centralized groups.

2. The sleep management method for analyzing sleep record data according to claim 1, characterized in that: The preset sleep monitoring indicator group includes deep sleep duration, number of awakenings, sleep onset latency and total sleep duration.

3. The sleep management method for analyzing sleep record data according to claim 1, characterized in that: Cluster analysis is performed on the L historical sleep monitoring indicator groups of the L historical monitoring nodes based on two dimensions: the similarity of the historical monitoring nodes and the similarity of the historical sleep monitoring indicators, to obtain Q clustered historical sleep monitoring indicator group sets, including: Perform Q extractions from the L historical sleep monitoring indicator groups using a random method without replacement to obtain Q initial cluster centers; Clustering the L historical sleep monitoring indicator groups based on the Q initial cluster centers to obtain Q initial clustered historical sleep monitoring indicator group sets; Using the clustering cost loss function, the overall clustering cost loss of the Q initial clustered historical sleep monitoring indicator groups is calculated from two dimensions: the similarity of historical monitoring nodes and the similarity of historical sleep monitoring indicators, to obtain the initial clustering cost loss; Calculating the means of the Q initial clustered historical sleep monitoring indicator group sets respectively to obtain the means of the Q initial clustered historical sleep monitoring indicator groups, and updating the Q initial cluster centers according to the means of the Q initial clustered historical sleep monitoring indicator groups to obtain Q updated cluster centers; Combining the L historical sleep monitoring indicator groups, constructing Q updated cluster historical sleep monitoring indicator group sets of the Q updated cluster centers, and performing overall cluster cost loss analysis using a cluster cost loss function to obtain an updated cluster cost loss amount; Determine whether the updated cluster cost loss is less than or equal to the initial cluster cost loss. If so, calculate the means of the Q updated cluster historical sleep monitoring indicator group sets respectively, and update the Q updated cluster centers according to the calculation results until the difference between the two updated cluster cost losses of two adjacent updates is less than the preset difference. Cluster the L historical sleep monitoring indicator groups according to the Q updated cluster centers obtained from the last update to obtain Q cluster historical sleep monitoring indicator group sets.

4. The sleep management method for analyzing sleep record data according to claim 1, characterized in that: If not, Q clustered historical sleep monitoring indicator groups are randomly extracted again from the edges of the neighborhoods of the Q starting set screening points, and the Q iterative clustered historical sleep monitoring groups are updated.

5. The sleep management method for analyzing sleep record data according to claim 1, characterized in that: The sleep management solution identification network layer is used to analyze the Q clustered historical sleep monitoring indicator groups to obtain a target sleep management solution, including: Obtaining a plurality of sample clustered historical sleep monitoring indicator concentrated group sets and corresponding plurality of sample target sleep management plans as a training data set; The training data set is used to perform supervised training on a framework built based on a feedforward neural network to learn a one-to-one mapping relationship between a set of clustered historical sleep monitoring indicators and a target sleep management plan until the training converges, thereby obtaining the trained sleep management plan identification network layer.

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