A method and device for calculating the human body health entropy value based on a wearable device

Abstract

The present invention is applicable to the computer field and provides a method and device for calculating the human health entropy value based on a wearable device. Among them, the method includes: obtaining human health data based on the wearable device to obtain a human health data set; using the K-means clustering algorithm to cluster the human health data set to obtain a clustering data set; based on the clustering data set, obtaining the clustering evaluation data corresponding to each clustering center to obtain a standard data set; based on the standard data set and the clustering evaluation data, using the KL divergence formula to calculate the human health entropy to obtain a human health entropy value data set. The present invention obtains human health data based on a wearable device, processes the human health data using the K-means clustering algorithm, and calculates the human health entropy value using the KL divergence formula, thereby realizing a reasonable evaluation and timely monitoring of the human health status.

Description

Technical Field

[0001] The present invention belongs to the field of computers, and particularly relates to a method and device for calculating the human health entropy value based on a wearable device. Background Art

[0002] Health refers to a state in which a person is in good condition in terms of body, mind, and society. The human health entropy value (health degree) is a parameter that comprehensively measures the degree of a person's physical health and the degree of life regularity. The larger this entropy value, the more chaotic a person's life is, and the more likely the person is to be unhealthy. Some current medical tests only detect one organ of the body and cannot comprehensively evaluate the degree of human health. Moreover, many physical examinations need to be carried out in a hospital. However, most people go to the hospital for treatment only when their bodies have shown serious abnormalities. At this time, the disease may have caused irreversible damage to the human body. Therefore, it is very important to monitor the human health status in a timely manner. In addition, most medical data use the same health index to evaluate the human health status. However, due to differences in personal age and physical constitution, the health data corresponding to different individuals in a healthy state will also vary. Therefore, a one-size-fits-all approach cannot reasonably evaluate the health status of each person. Summary of the Invention

[0003] In view of this, the present invention provides a method and device for calculating the human health entropy value based on a wearable device. By calculating the human health entropy value based on the wearable device, a comprehensive and reasonable evaluation of the human health degree and timely monitoring of the human health status are realized. At the same time, the problems existing in the human body can be analyzed, which can help people discover their problems in time and thus provide health guidance to people.

[0004] In the first aspect of the embodiments of the present invention, a method for calculating the human health entropy value based on a wearable device is provided, including:

[0005] Based on the wearable device, obtain human health data to obtain a human health data set, where the human health data includes health evaluation data and health impact data;

[0006] Use the K-means clustering algorithm to cluster the human health data set to obtain a clustering data set, where the clustering data set includes clustering evaluation data and clustering impact data;

[0007] Based on the clustering data set, obtain the clustering evaluation data corresponding to each clustering center to obtain a standard data set;

[0008] Based on the standard data set and the clustering evaluation data, use the KL divergence formula to calculate the human health entropy to obtain a human health entropy value data set.

[0009] In some embodiments, based on the standard data set and the clustering evaluation data, the KL divergence formula is used to calculate the human health entropy, and a human health entropy value data set is obtained, specifically including:

[0010] Based on the clustering data set, all clustering evaluation data belonging to the same cluster are obtained;

[0011] Based on the standard data set, the standard data corresponding to different clustering evaluation data in the same cluster are obtained;

[0012] The KL divergence formula is used to calculate the human health entropy values of all users in the same cluster;

[0013] The human health entropy values of each cluster are integrated to obtain a human health entropy value data set.

[0014] In some embodiments, the KL divergence formula is:

[0015]

[0016] Where P[i] is the clustering evaluation data, Q[i] is the standard data corresponding to the clustering evaluation data, and i is the type corresponding to the clustering evaluation data and the standard data.

[0017] In some embodiments, the K-means clustering algorithm is used to cluster the human health data set to obtain a clustering data set, specifically including:

[0018] Set the number of clustering clusters K;

[0019] Based on the health impact data, the K-means clustering algorithm is used to cluster all health evaluation data;

[0020] Mark each clustering center to obtain a clustering data set.

[0021] In some embodiments, after marking each clustering center to obtain a clustering data set, it further includes:

[0022] Judge whether the clustering data set meets the preset conditions;

[0023] If not, return to the step of setting the number of clustering clusters K.

[0024] In some embodiments, after using the KL divergence formula to calculate the human health entropy based on the standard data set and the clustering evaluation data to obtain a human health entropy value data set, it further includes:

[0025] Judge whether the human health entropy value of the wearable device user meets the preset conditions;

[0026] If not, send a warning message to prompt the user.

[0027] In some embodiments, determining whether the human health entropy value of the wearable device user meets a preset condition specifically includes:

[0028] Based on the clustering data set, obtain the cluster corresponding to the health evaluation data of the wearable device user;

[0029] Based on the cluster, obtain the human health entropy value;

[0030] Determine whether the human health entropy value of the wearable device user meets the preset condition.

[0031] In a second aspect of the embodiments of the present invention, there is provided a human health entropy value calculation device based on a wearable device, including:

[0032] A data acquisition module configured to obtain human health data based on the wearable device to obtain a human health data set, where the human health data includes health evaluation data and health impact data;

[0033] A data clustering module configured to cluster the human health data set by using a K-means clustering algorithm to obtain a clustering data set, where the clustering data set includes clustering evaluation data and clustering impact data;

[0034] A standard data set acquisition module configured to obtain the clustering evaluation data corresponding to each cluster center based on the clustering data set to obtain a standard data set;

[0035] A health entropy value acquisition module configured to calculate the human health entropy by using the KL divergence formula based on the standard data set and the clustering evaluation data to obtain a human health entropy value data set.

[0036] In a third aspect of the embodiments of the present invention, there is provided a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where when the processor executes the computer program, the steps of the human health entropy value calculation method based on the wearable device are implemented.

[0037] In a fourth aspect of the embodiments of the present invention, there is provided a computer-readable storage medium storing a computer program, where when the computer program is executed by a processor, the steps of the human health entropy value calculation method based on the wearable device are implemented.

[0038] The beneficial effects of a method for calculating the human health entropy value based on a wearable device provided by an embodiment of the present invention are at least as follows: First, based on the wearable device, human health data is obtained to obtain a human health data set, and the human health data includes health evaluation data and health impact data; Second, the K-means clustering algorithm is used to cluster the human health data set to obtain a clustering data set, and the clustering data set includes clustering evaluation data and clustering impact data; Third, based on the clustering data set, the clustering evaluation data corresponding to each clustering center is obtained to obtain a standard data set; Finally, based on the standard data set and the clustering evaluation data, the KL divergence formula is used to calculate the human health entropy to obtain a human health entropy value data set; thus, a comprehensive and reasonable evaluation of the human health level is realized, the human health status is monitored in a timely manner, and at the same time, the problems existing in the human body can be analyzed, which can help people discover their own problems in a timely manner, thereby providing health guidance to people. Based on the sensors of the wearable device, the present invention can obtain human health data, which can realize the timely monitoring of human health data when people travel simply; The K-means clustering algorithm is used to classify the human health data to obtain a clustering data set, and a standard data set is obtained based on the clustering data set, avoiding the problem of unreasonable evaluation results caused by using the same health index to evaluate the human health status, and fully considering individual differences; Based on the standard data set, the KL divergence formula is used to calculate the human health entropy value, thereby realizing a comprehensive evaluation of the human health level when people travel simply, and achieving the purpose of timely monitoring of the human health status. The present invention also identifies abnormal data of the human health entropy value of the wearable device user and issues a warning message to prompt the user, which plays a role in reminding the user, can help people discover their own problems in a timely manner, and thereby provides health guidance to people. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0040] Figure 1 is a flowchart of a method for calculating the human health entropy value based on a wearable device provided by an embodiment of the present invention;

[0041] Figure 2 is a flow implementation diagram of using the K-means clustering algorithm to cluster the human health data set to obtain a clustering data set provided by an embodiment of the present invention;

[0042] Figure 3The flowchart of the process for calculating the human health entropy using the KL divergence formula based on the standard data set and the clustering evaluation data provided by the embodiments of the present invention to obtain the human health entropy value data set;

[0043] Figure 4 is the flowchart of the warning information prompting method provided by the embodiments of the present invention;

[0044] Figure 5 is the flowchart of the human health entropy value calculation device based on a wearable device provided by the embodiments of the present invention;

[0045] Figure 6 is the schematic diagram of the terminal device provided by the embodiments of the present invention. Detailed Embodiments

[0046] In the following description, specific details such as specific system architectures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention. Based on the described embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present invention. Unless otherwise specified, the technical means used in the embodiments are conventional means well-known to those skilled in the art.

[0047] To illustrate the technical solutions described in the present invention, the following will be described through specific embodiments.

[0048] First Embodiment

[0049] Figure 1 is the flowchart of the human health entropy value calculation method based on a wearable device provided by the present invention in an embodiment.

[0050] As Figure 1 shown, the human health entropy value calculation method based on a wearable device includes steps S110 - S140:

[0051] S110, based on the wearable device, obtain human health data to obtain a human health data set;

[0052] S120, use the K - means clustering algorithm to cluster the human health data set to obtain a clustering data set;

[0053] S130, based on the clustering data set, obtain the clustering evaluation data corresponding to each clustering center to obtain a standard data set;

[0054] S140. Calculate the human health entropy using the KL divergence formula based on the standard data set and the clustering evaluation data to obtain a human health entropy value data set.

[0055] An embodiment of the present invention provides a method for calculating the human health entropy value based on a wearable device. The method first obtains human health data based on the wearable device to obtain a human health data set. The human health data includes health evaluation data and health impact data. Secondly, the K-means clustering algorithm is used to cluster the human health data set to obtain a clustering data set. The clustering data set includes clustering evaluation data and clustering impact data. Thirdly, based on the clustering data set, the clustering evaluation data corresponding to each clustering center is obtained to obtain a standard data set. Finally, based on the standard data set and the clustering evaluation data, the human health entropy is calculated using the KL divergence formula to obtain a human health entropy value data set, thereby realizing a comprehensive and reasonable evaluation of the human health level and timely monitoring of the human health state. At the same time, it can also analyze the problems existing in the human body, help people discover their problems in time, and thus provide health guidance for people. The method obtains human health data based on the sensors of the wearable device, and can realize the timely monitoring of human health data when people travel simply. The K-means clustering algorithm is used to classify the human health data to obtain a clustering data set, and a standard data set is obtained based on the clustering data set, avoiding the problem of unreasonable evaluation results caused by using the same health index to evaluate the human health state, and fully considering individual differences. Based on the standard data set, the human health entropy value is calculated using the KL divergence formula, thereby realizing a comprehensive evaluation of the human health level when people travel simply and achieving the purpose of timely monitoring of the human health state.

[0056] Specifically, based on the sensors of the wearable device, human health data can be obtained, and the timely monitoring of human health data can be realized when people travel simply. The human health data includes health evaluation data and health impact data. The health evaluation data is data for directly judging whether human health is abnormal, such as blood sugar content. For example, if the fasting plasma glucose exceeds 7.0 mmol / L, it is diabetes. The health impact data is data that affects the health evaluation data, such as height, weight, age, and deep sleep duration, etc. Due to the differences in their health impact data (age, physical fitness, current status, etc.) among different individuals, the health evaluation data corresponding to different individuals in a healthy state will also be different. Therefore, for different individuals, the health evaluation data corresponding to the healthy state can be determined based on the health impact data.

[0057] Specifically, step S110, obtaining human health data based on the wearable device to obtain a human health data set specifically includes: obtaining human health evaluation data and health impact data based on the wearable device to obtain a human health data set.

[0058] Specifically, the K-means clustering algorithm is used to cluster the human health data set. For the specific implementation method of obtaining the clustered data set, please refer to Figure 2 , Figure 2 which is the flowchart implementation diagram of using the K-means clustering algorithm to cluster the human health data set provided by the present invention in an embodiment to obtain the clustered data set.

[0059] As Figure 2 shown, using the K-means clustering algorithm to cluster the human health data set to obtain the clustered data set may include the following steps S210-S230:

[0060] S210, set the number of clustering clusters K;

[0061] S220, based on the health impact data, use the K-means clustering algorithm to cluster all the health evaluation data;

[0062] S230, mark each clustering center to obtain the clustered data set.

[0063] Specifically, the K-means clustering algorithm is an iterative clustering analysis algorithm. Its steps are as follows: initially divide the data into K groups, then randomly select K objects as the initial clustering centers, and then calculate the distance between each object and each seed clustering center, and assign each object to the clustering center closest to it. The clustering centers and the objects assigned to them represent a cluster. By setting the number of clustering clusters K, based on the health impact data, using the K-means clustering algorithm to cluster all the health evaluation data, marking each clustering center, and obtaining the clustered data set. The clustered data set includes clustered evaluation data and clustered impact data. The clustered evaluation data is the health evaluation data before clustering, and the clustered impact data is the health impact data before clustering. Due to the differences in their health impact data (age, physical fitness, current status, etc.), different individuals will have differences in the health evaluation data corresponding to their health status. Therefore, based on the health impact data, the K-means clustering algorithm is used to classify all the health evaluation data; thus, the differences in the health impact data in the same cluster are relatively small, that is, the individual differences in the same cluster are relatively small. Then, based on the clustered data set, the clustered evaluation data corresponding to each clustering center is obtained to obtain the standard data set; the standard data obtained by this method fully considers individual differences and avoids the problem of unreasonable evaluation results caused by using the same health index to evaluate the human health status, thereby achieving the purpose of reasonably evaluating the individual health status.

[0064] Specifically, when using the K-means clustering algorithm for clustering, the initial number of clustering clusters K needs to be set. The initial number of clustering clusters K can be determined according to practical experience. For example, K = 6, etc. If the obtained clustering data set does not meet the preset conditions, the value of K needs to be adjusted. The preset condition is that based on the current health impact data, after using the K-means clustering algorithm to cluster all health evaluation data, the obtained health evaluation data can relatively accurately evaluate the health status of an individual; if not, the value of K needs to be increased to achieve that the health evaluation data can relatively accurately evaluate the health status of an individual.

[0065] Specifically, based on the standard data set and the clustering evaluation data, the method for calculating the human health entropy using the KL divergence formula to obtain the human health entropy value data set can be referred to Figure 3 , Figure 3 FIG. is a flowchart showing the implementation of calculating the human health entropy using the KL divergence formula based on the standard data set and the clustering evaluation data to obtain the human health entropy value data set provided by the present invention in an embodiment.

[0066] As Figure 3 shown, based on the standard data set and the clustering evaluation data, the method for calculating the human health entropy using the KL divergence formula to obtain the human health entropy value data set may include the following steps S310 - S340:

[0067] S310, based on the clustering data set, obtain all clustering evaluation data belonging to the same cluster;

[0068] S320, based on the standard data set, obtain the standard data corresponding to different clustering evaluation data in the same cluster;

[0069] S330, use the KL divergence formula to calculate the human health entropy values of all users in the same cluster;

[0070] S340, integrate the human health entropy values of each cluster to obtain the human health entropy value data set.

[0071] Specifically, the KL divergence is an asymmetric measure of the difference between two probability distributions, that is, the asymmetric measure between the user clustering evaluation data and the standard data. The human health entropy value is a parameter that comprehensively measures the degree of a person's physical health and the degree of regularity of life. The larger this entropy value, the more the health evaluation data of the human body deviates from the standard data, the more chaotic a person's life is, and the more likely the person is to be unhealthy. First, based on the clustering data set, all clustering evaluation data belonging to the same cluster are obtained; secondly, based on the standard data set, the standard data corresponding to different clustering evaluation data in the same cluster are obtained; finally, the KL divergence formula is used to calculate the human health entropy value of all users in the same cluster; the health impact data in the same cluster have small differences, that is, the individual differences in the same cluster are small. Therefore, the human health entropy value calculated by this method fully takes into account the individual differences and avoids the problem of unreasonable evaluation results caused by using the same health index to evaluate the human health status. By calculating the human health entropy value through the KL divergence, a comprehensive evaluation of the human health degree is realized when people travel simply, and the purpose of timely monitoring of the human health status is achieved.

[0072] The KL divergence formula is as follows:

[0073]

[0074] Among them, P[i] is the clustering evaluation data, Q[i] is the standard data corresponding to the clustering evaluation data, and i is the type corresponding to the clustering evaluation data and the standard data.

[0075] Specifically, to obtain the human health entropy value data set, it is also necessary to determine whether the human health entropy value of the user is abnormal; if it is abnormal, the user needs to be reminded. For the specific implementation method of reminding the user, please refer to Figure 4 , Figure 4 which is the flow implementation diagram of the warning information prompt method provided in an embodiment of the present invention.

[0076] As Figure 4 shown, the warning information prompt method may include the following steps S410 - S420:

[0077] S410, determine whether the human health entropy value of the wearable device user meets the preset conditions;

[0078] S420, if so, send a warning message to prompt the user.

[0079] Specifically, in step 410, it is determined whether the human health entropy value of the wearable device user meets the preset conditions, which specifically includes: based on the clustering data set, obtaining the cluster corresponding to the health evaluation data of the wearable device user; based on the cluster, obtaining the human health entropy value; and determining whether the human health entropy value of the wearable device user meets the preset conditions. The preset conditions can be determined by experience. For example, it can be set that the top 5% of the human health entropy values are abnormal human health entropy values, etc. By identifying the abnormal data of the human health entropy value of the wearable device user and sending out a warning message to prompt the user, it plays a role in reminding the user, which can help people discover their own problems in time, so as to provide health guidance to people.

[0080] In this embodiment, first, based on the wearable device, human health data is obtained to obtain a human health data set, where the human health data includes health evaluation data and health impact data; secondly, the K-means clustering algorithm is used to cluster the human health data set to obtain a clustering data set, where the clustering data set includes clustering evaluation data and clustering impact data; thirdly, based on the clustering data set, the clustering evaluation data corresponding to each cluster center is obtained to obtain a standard data set; finally, based on the standard data set and the clustering evaluation data, the KL divergence formula is used to calculate the human health entropy to obtain a human health entropy value data set; thus, a comprehensive and reasonable evaluation of the human health level and timely monitoring of the human health status are realized. At the same time, the problems existing in the human body can also be analyzed, which can help people discover their own problems in time, so as to provide health guidance to people. This method is based on the sensors of the wearable device to obtain human health data, and can realize the timely monitoring of human health data when people travel simply; the K-means clustering algorithm is used to classify the human health data to obtain a clustering data set, and a standard data set is obtained based on the clustering data set, avoiding the problem of unreasonable evaluation results caused by using the same health index to evaluate the human health status, and fully considering individual differences; based on the standard data set, the KL divergence formula is used to calculate the human health entropy value, thus realizing a comprehensive evaluation of the human health level when people travel simply, and achieving the purpose of timely monitoring of the human health status. This method also identifies the abnormal data of the human health entropy value of the wearable device user and sends out a warning message to prompt the user, which plays a role in reminding the user, can help people discover their own problems in time, so as to provide health guidance to people.

[0081] Second Embodiment

[0082] Based on the same inventive concept as the method in the first embodiment, correspondingly, this embodiment also provides a human health entropy value calculation device based on a wearable device.

[0083] Figure 5Flow chart of the human health entropy value calculation device based on a wearable device provided by the present invention.

[0084] As Figure 5 shown, the device 5 includes: a data acquisition module 51, a data clustering module 52, a standard data set acquisition module 53, and a health entropy value acquisition module 54.

[0085] Among them, the data acquisition module is configured to acquire human health data based on a wearable device to obtain a human health data set, and the human health data includes health evaluation data and health impact data;

[0086] The data clustering module is configured to cluster the human health data set using the K-means clustering algorithm to obtain a clustering data set, and the clustering data set includes clustering evaluation data and clustering impact data;

[0087] The standard data set acquisition module is configured to acquire the clustering evaluation data corresponding to each clustering center based on the clustering data set to obtain a standard data set;

[0088] The health entropy value acquisition module is configured to calculate the human health entropy using the KL divergence formula based on the standard data set and the clustering evaluation data to obtain a human health entropy value data set.

[0089] In some exemplary embodiments, the data clustering module specifically includes:

[0090] A cluster number setting unit configured to set the clustering number K;

[0091] A clustering unit configured to cluster all health evaluation data using the K-means clustering algorithm based on health impact data;

[0092] A clustering data set acquisition unit configured to mark each clustering center to obtain a clustering data set;

[0093] A first determination unit configured to determine whether the clustering data set meets a preset condition;

[0094] A return unit configured to, if not, return to the step of setting the clustering number K.

[0095] In some exemplary embodiments, the health entropy value acquisition module specifically includes:

[0096] A clustering evaluation data acquisition unit configured to acquire all clustering evaluation data belonging to the same cluster based on the clustering data set;

[0097] A standard data acquisition unit configured to acquire the standard data corresponding to different clustering evaluation data in the same cluster based on the standard data set;

[0098] The first health entropy value acquisition unit is configured to calculate the human health entropy value of all users in the same cluster by using the KL divergence formula;

[0099] The health entropy value data set acquisition unit is configured to integrate the human health entropy values of each cluster to obtain a human health entropy value data set.

[0100] In some exemplary embodiments, the device further includes:

[0101] The judgment module is configured to judge whether the human health entropy value of the wearable device user meets a preset condition;

[0102] The reminder module is configured to, if so, send a warning message to prompt the user.

[0103] In some exemplary embodiments, the judgment module specifically includes:

[0104] The cluster acquisition unit is configured to obtain the cluster corresponding to the health evaluation data of the wearable device user based on the cluster data set;

[0105] The second health entropy value acquisition unit is configured to obtain the human health entropy value based on the cluster;

[0106] The second judgment unit is configured to judge whether the human health entropy value of the wearable device user meets a preset condition.

[0107] The third embodiment

[0108] The above methods and devices can be applied to terminal devices such as desktop computers, notebooks, handheld computers, and cloud servers.

[0109] Figure 6 The following is a schematic diagram of a terminal device that can apply the above methods and devices provided in an embodiment of the present invention. As shown in the figure, the device 6 includes a memory 61, a processor 60, and a computer program 62 stored in the memory 61 and executable on the processor 60. When the processor 60 executes the computer program 62, the steps of the method for calculating the human health entropy value based on the wearable device are implemented. For example Figure 5 the functions of the modules 51 to 54 shown.

[0110] The device 6 may be a computing device such as a cloud server. The terminal device may include, but is not limited to, the processor 60 and the memory 61. Those skilled in the art can understand, Figure 6It is only an example of the device 6 and does not constitute a limitation on the terminal device 6. It may include more or fewer components than those shown in the figure, or combine some components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0111] The processor 60 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0112] The memory 61 may be an internal storage unit of the device 6, such as the hard disk or memory of the device 6. The memory 61 may also be an external storage device of the device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the device 6. Further, the memory 61 may also include both the internal storage unit and the external storage device of the device 6. The memory 61 is used to store the computer program and other programs and data required by the terminal device. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0113] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0114] Specifically, as follows, the embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium can be the computer-readable storage medium included in the memory in the above-described embodiment; it can also exist independently and be a computer-readable storage medium not assembled into a terminal device. The computer-readable storage medium stores one or more computer programs:

[0115] A computer-readable storage medium, including that the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for calculating the human body health entropy value based on a wearable device are implemented.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0117] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0118] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0119] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0120] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] In addition, in each embodiment of the present invention, each functional unit may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0122] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it may also be completed by instructing relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0123] The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for calculating the human body health entropy value based on a wearable device, characterized in that, Including: Based on a wearable device, obtaining human health data to obtain a human health data set, where the human health data includes health evaluation data and health impact data; Using the K-means clustering algorithm to cluster the human health data set to obtain a clustering data set, where the clustering data set includes clustering evaluation data and clustering impact data; Based on the clustering data set, obtaining the clustering evaluation data corresponding to each clustering center to obtain a standard data set; Based on the standard data set and the clustering evaluation data, using the KL divergence formula to calculate the human health entropy to obtain a human health entropy value data set, where the KL divergence is used to characterize the asymmetry measure between the clustering evaluation data and the standard data set; After using the KL divergence formula to calculate the human health entropy based on the standard data set and the clustering evaluation data to obtain a human health entropy value data set, it further includes: Based on the clustering data set, obtaining the cluster corresponding to the health evaluation data of the wearable device user; Based on the cluster, obtaining the human health entropy value; Judging whether the human health entropy value of the wearable device user meets a preset condition; If not, sending a warning message to prompt the user.

2. The method according to claim 1, wherein Using the KL divergence formula to calculate the human health entropy based on the standard data set and the clustering evaluation data to obtain a human health entropy value data set, specifically including: Based on the clustering data set, obtaining all the clustering evaluation data belonging to the same cluster; Based on the standard data set, obtaining the standard data corresponding to different clustering evaluation data in the same cluster; Using the KL divergence formula to calculate the human health entropy values of all users in the same cluster; Integrating the human health entropy values of each cluster to obtain a human health entropy value data set.

3. The method according to claim 2, characterized in that, The KL divergence formula is: ； Where P[i] is the clustering evaluation data, Q[i] is the standard data corresponding to the clustering evaluation data, and i is the type corresponding to the clustering evaluation data and the standard data.

4. The method according to claim 1, wherein Using the K-means clustering algorithm to cluster the human health data set to obtain a clustering data set, specifically including: Setting the number of clustering clusters K; Based on the health impact data, using the K-means clustering algorithm to cluster all the health evaluation data; Marking each clustering center to obtain a clustering data set.

5. The method according to claim 4, wherein After marking each clustering center to obtain a clustering data set, it further includes: Judging whether the clustering data set meets a preset condition; If not, returning to the step of setting the number of clustering clusters K.

6. A human body health entropy value calculation device based on a wearable device, characterized in that The device includes: A data acquisition module configured to obtain human health data based on a wearable device to obtain a human health data set, where the human health data includes health evaluation data and health impact data; A data clustering module configured to use the K-means clustering algorithm to cluster the human health data set to obtain a clustering data set, where the clustering data set includes clustering evaluation data and clustering impact data; A standard data set acquisition module configured to obtain the clustering evaluation data corresponding to each clustering center based on the clustering data set to obtain a standard data set; A health entropy value acquisition module, configured to calculate the human health entropy by using the KL divergence formula based on the standard data set and the clustering evaluation data, so as to obtain a human health entropy value data set, where the KL divergence is used to characterize the asymmetry measure between the clustering evaluation data and the standard data set; After calculating the human health entropy by using the KL divergence formula based on the standard data set and the clustering evaluation data to obtain a human health entropy value data set, the method further includes: Based on the clustering data set, obtaining the cluster corresponding to the health evaluation data of the wearable device user; based on the cluster, obtaining the human health entropy value; determining whether the human health entropy value of the wearable device user meets a preset condition; if not, sending a warning message to prompt the user.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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