Health risk early warning method and system, electronic equipment, storage medium and product

Abstract

The invention discloses a health risk early warning method and system, electronic equipment, a storage medium and a product, and relates to the technical field of data processing, and the disclosed health risk early warning method comprises the steps: obtaining user physiological data collected by each sensor, and fusing the user physiological data to obtain comprehensive physiological data; the comprehensive physiological data is input into a preset target health risk early warning model, a comprehensive health risk early warning result is obtained, and the target health risk early warning model is obtained through training with the comprehensive physiological test data as model input data and a real health result corresponding to the comprehensive physiological test data as a model training label in advance. The technical scheme of the invention aims to solve the technical problem of how to comprehensively evaluate the health level of a wearer.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a health risk early warning method, system, electronic device, storage medium and product. Background Art

[0002] Smart bracelets, smart watches and other devices are usually equipped with multiple sensors to detect the health status of the wearer. However, nowadays, usually one sensor corresponds to one health risk warning result, and the accuracy of the health risk warning result is low, making it difficult to comprehensively evaluate the wearer's health level.

[0003] Therefore, how to comprehensively evaluate the wearer's health level is a technical problem that has yet to be solved by those skilled in the art. Summary of the invention

[0004] The main purpose of this application is to provide a health risk warning method, system, electronic device, storage medium and product, aiming to solve the technical problem of how to comprehensively evaluate the wearer's health level.

[0005] To achieve the above objectives, the present application proposes a health risk early warning method, which comprises:

[0006] Acquire the user's physiological data collected by each sensor, and fuse the user's physiological data to obtain comprehensive physiological data;

[0007] The comprehensive physiological data is input into a preset target health risk warning model to obtain a comprehensive health risk warning result, wherein the target health risk warning model is pre-trained using the comprehensive physiological test data as model input data and using the real health results corresponding to the comprehensive physiological test data as model training labels.

[0008] In one embodiment, before the step of fusing the physiological data of each user to obtain comprehensive physiological data, the method further includes:

[0009] Based on the start collection time corresponding to each of the physiological data of the users, aligning the physiological data of each of the users;

[0010] Performing filtering processing on the physiological data of each user after data alignment, and extracting physiological features from the physiological data of each user after filtering processing;

[0011] The step of fusing the physiological data of each user to obtain comprehensive physiological data includes:

[0012] The physiological characteristics are fused to obtain comprehensive physiological data.

[0013] In one embodiment, the step of filtering the physiological data of each user after data alignment includes:

[0014] Acquire a preset filter corresponding to each of the sensors, and determine a target corresponding relationship between each of the filters and each of the user's physiological data according to the corresponding relationship between each of the sensors and each of the filters and the corresponding relationship between each of the sensors and each of the user's physiological data;

[0015] Based on the target correspondence, the physiological data of each user after data alignment is filtered through each filter.

[0016] In one embodiment, before the step of inputting the comprehensive physiological data into a preset target health risk warning model to obtain a comprehensive health risk warning result, the method further includes:

[0017] Obtain the audience label information corresponding to each preset health risk warning model;

[0018] According to the basic information of the wearer, the target audience label information is determined in each of the audience label information, and the health risk warning model corresponding to the target audience label information is used as the target health risk warning model.

[0019] In one embodiment, after the step of inputting the comprehensive physiological data into a preset target health risk warning model to obtain a comprehensive health risk warning result, the method further includes:

[0020] Based on the comprehensive health risk warning result, determine the prompt information corresponding to the comprehensive health risk warning result, and output the prompt information on a preset interface;

[0021] The wearer's motion state is monitored based on the comprehensive health risk warning result, and when the motion state is monitored to be an abnormal motion state, risk warning information is output.

[0022] In one embodiment, the method further comprises:

[0023] Acquiring a preset cross-validation relationship between the sensors;

[0024] Based on the cross-validation relationship, determining a first target sensor and a second target sensor in each of the sensors;

[0025] Determine a first evaluation result corresponding to the user physiological data corresponding to the first target sensor, and determine a second evaluation result corresponding to the user physiological data corresponding to the second target sensor;

[0026] When it is detected that the first evaluation result and the second evaluation result are the same, the step of fusing the physiological data of each user to obtain comprehensive physiological data is performed.

[0027] In addition, to achieve the above purpose, the present application also proposes a health risk early warning system, the health risk early warning system comprising:

[0028] A data fusion module is used to obtain the user physiological data collected by each sensor, and fuse the user physiological data to obtain comprehensive physiological data;

[0029] The result warning module is used to input the comprehensive physiological data into a preset target health risk warning model to obtain a comprehensive health risk warning result, wherein the target health risk warning model is pre-trained using the comprehensive physiological test data as model input data and the real health results corresponding to the comprehensive physiological test data as model training labels.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the health risk warning method as described above.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the health risk warning method described above are implemented.

[0032] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the health risk warning method described above.

[0033] This application obtains the user's physiological data collected by each sensor and fuses the physiological data of each user to obtain comprehensive physiological data. The data collected by each sensor can be integrated together to obtain comprehensive physiological data for comprehensive evaluation of the user's health level; the comprehensive physiological data is then input into a preset target health risk warning model to obtain a comprehensive health risk warning result, wherein the target health risk warning model is pre-trained with comprehensive physiological test data as model input data and the real health results corresponding to the comprehensive physiological test data as model training labels. The pre-trained model can be used to analyze the comprehensive physiological data to obtain the user's comprehensive health risk warning result. Thus, this application achieves the effect of comprehensively evaluating the wearer's health level. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0036] Figure 1 A flowchart of the first embodiment of the health risk warning method of this application is provided;

[0037] Figure 2 A flow chart of the second embodiment of the health risk warning method of this application is provided;

[0038] Figure 3 A flowchart of the third embodiment of the health risk warning method of this application is provided;

[0039] Figure 4 This is a schematic diagram of the module structure of the health risk early warning system of the embodiment of the present application;

[0040] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the health risk warning method in the embodiment of the present application.

[0041] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0042] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0043] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0044] Smart bracelets, smart watches and other devices are usually equipped with multiple sensors to detect the health status of the wearer. However, nowadays, usually one sensor corresponds to one health risk warning result, and the accuracy of the health risk warning result is low, making it difficult to comprehensively evaluate the wearer's health level.

[0045] Therefore, how to comprehensively evaluate the wearer's health level is a technical problem that has yet to be solved by those skilled in the art.

[0046] In order to solve the above problems, the present application obtains the user's physiological data collected by each sensor and fuses the physiological data of each user to obtain comprehensive physiological data. The data collected by each sensor can be integrated together to obtain comprehensive physiological data for comprehensive evaluation of the user's health level; the comprehensive physiological data is then input into a preset target health risk warning model to obtain a comprehensive health risk warning result, wherein the target health risk warning model is pre-trained with comprehensive physiological test data as model input data and the real health results corresponding to the comprehensive physiological test data as model training labels. The pre-trained model can be used to analyze the comprehensive physiological data to obtain the user's comprehensive health risk warning result. Thus, the present application achieves the effect of comprehensively evaluating the wearer's health level.

[0047] It should be noted that the execution subject of the health risk warning method of the present application can be an electronic device with data processing, network communication and program running functions, such as a smart bracelet, a smart watch, etc. The following takes a smart bracelet as an example to illustrate various embodiments of the health risk warning method of the present application.

[0048] Based on this, the present application embodiment provides a health risk warning method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the health risk warning method of the present application.

[0049] In this embodiment, the health risk early warning method includes steps S10 to S20:

[0050] Step S10, acquiring the user physiological data collected by each sensor, and fusing the user physiological data to obtain comprehensive physiological data;

[0051] It should be noted that the user's physiological data refers to the data collected by the sensor. In this embodiment, multiple sensors can be integrated on the smart bracelet, so the user's health status can be comprehensively evaluated through the data collected by the multiple sensors. For example, the smart bracelet can integrate an electrocardiogram sensor, an accelerometer, a bioelectric sensor, a photoelectric sensor, an infrared sensor, etc.

[0052] In this embodiment, after obtaining the user physiological data collected by each sensor, the physiological data of each user can be fused through methods such as Kalman filtering, principal component analysis PCA, maximum correlation minimum redundancy algorithm, etc., and the fused data can be used as comprehensive physiological data.

[0053] Step S20, input the comprehensive physiological data into a preset target health risk warning model to obtain a comprehensive health risk warning result, wherein the target health risk warning model is pre-trained using the comprehensive physiological test data as model input data and using the real health results corresponding to the comprehensive physiological test data as model training labels.

[0054] It should be noted that the target health risk warning model can be a machine learning model, a deep learning model, or a model based on a knowledge graph. The comprehensive physiological test data is pre-set data for training the model. Specifically, the comprehensive physiological test data may include comprehensive data of physiological data of each user corresponding to a user in a normal state, and may also include comprehensive data of physiological data of each user corresponding to a user in an abnormal state. In addition, a known knowledge base that records health assessment rules can also be used as prior knowledge of the model to reduce the training time of the model. The real health result is the health assessment result corresponding to the comprehensive physiological test data. In a feasible implementation, the real health result is manually determined.

[0055] In this embodiment, after obtaining the comprehensive physiological data and the trained target health risk warning model, the comprehensive physiological data can be evaluated by the target health risk warning model to obtain a comprehensive health risk warning result.

[0056] This application obtains the user's physiological data collected by each sensor and fuses the physiological data of each user to obtain comprehensive physiological data. The data collected by each sensor can be integrated together to obtain comprehensive physiological data for comprehensive evaluation of the user's health level; the comprehensive physiological data is then input into a preset target health risk warning model to obtain a comprehensive health risk warning result, wherein the target health risk warning model is pre-trained with comprehensive physiological test data as model input data and the real health results corresponding to the comprehensive physiological test data as model training labels. The pre-trained model can be used to analyze the comprehensive physiological data to obtain the user's comprehensive health risk warning result. Thus, this application achieves the effect of comprehensively evaluating the wearer's health level.

[0057] Furthermore, based on the above-mentioned first embodiment of the health risk warning method of the present application, a second embodiment of the health risk warning method of the present application is proposed.

[0058] Please refer to Figure 2 In this embodiment, before the above step of fusing the physiological data of each user to obtain comprehensive physiological data, the method further includes:

[0059] Step S100, aligning the physiological data of each user based on the starting collection time corresponding to each of the physiological data of each user;

[0060] It should be noted that since different users' physiological data may be collected at different time points, it is meaningless to directly compare or merge these data. Therefore, it is necessary to align the data so that the available data are collected in the same time period. Specifically, the process of data alignment is to adjust all users' physiological data according to a common time reference system (such as the time difference calculated from a specific time point), so that although the collection start time of the original data is different, in the aligned data set, the data corresponding to all users at the same time point (relative to the alignment reference point) are comparable.

[0061] For example, the maximum start collection time among the start collection times can be used as the starting point, and the minimum end collection time among the end collection times corresponding to the physiological data of each user can be used as the midpoint to obtain the available collection time interval. Thus, health risk warning can be performed based on the physiological data of each user in the sampling time interval.

[0062] Step S200, filtering the physiological data of each user after data alignment, and extracting physiological features from the physiological data of each user after filtering;

[0063] After the data alignment is completed, the user's physiological data after data alignment can be filtered. Specifically, the filtering process is used to remove noise or interference from the original data to extract signal components that can be used for health risk warning. In this embodiment, the filtering process can remove the noise introduced by factors such as equipment errors, environmental changes, or human body activities, thereby obtaining a purer and more reliable physiological signal. In a feasible implementation, the filtering process can be a low-pass filter, a high-pass filter, or even a band-pass filter.

[0064] After filtering, physiological features can be extracted from the filtered data. Physiological features refer to specific indicators or parameters that can reflect the physiological state or function of the human body. In the analysis of physiological data, common physiological features include heart rate, blood pressure, respiratory rate, body temperature, etc. The process of extracting physiological features usually involves further mathematical processing or statistical analysis of the filtered data, such as calculating the mean, standard deviation, peak value, valley value, etc., and applying specific algorithms or models to identify and quantify physiological features. Through filtering and extraction of physiological features, the present application can extract useful information from the original physiological data for health risk warning.

[0065] Based on this, the above step S10 further includes:

[0066] Step S101, fusing the physiological characteristics to obtain comprehensive physiological data.

[0067] It is understandable that after the physiological characteristics are obtained, the various physiological characteristics can be fused through methods such as the Kalman filter method to obtain comprehensive physiological data.

[0068] In a feasible implementation manner, the above step S200 includes:

[0069] Step S2001, obtaining a preset filter corresponding to each of the sensors, and determining a target corresponding relationship between each of the filters and each of the user's physiological data according to a corresponding relationship between each of the sensors and each of the filters and a corresponding relationship between each of the sensors and each of the user's physiological data;

[0070] Step S2002: Based on the target correspondence, filtering is performed on the physiological data of each user after data alignment through each filter.

[0071] It is understandable that different sensors generate different noises during data collection. Therefore, different filters can be pre-set for different sensors, and the corresponding relationship between the filter and the sensor can be set, so that filtering can be performed according to the characteristics of the user's physiological data to improve the efficiency of filtering. In turn, the accuracy of the comprehensive health risk warning results assessed can be improved.

[0072] In this embodiment, the present application improves the accuracy of the assessed comprehensive health risk warning results by aligning and filtering the user's physiological data and setting multiple filters.

[0073] Furthermore, based on the above-mentioned first and second embodiments of the health risk warning method of the present application, a third embodiment of the health risk warning method of the present application is proposed.

[0074] Please refer to Figure 3 In this embodiment, before the above step S20, the method further includes:

[0075] Step S300, obtaining audience label information corresponding to each preset health risk warning model;

[0076] It should be noted that the user group labels corresponding to the training set data used in the training of the health risk warning model are the audience label information. It is understandable that when the collected data is the same, there will be differences in the comprehensive health risk warning results corresponding to different genders, different ages and even different weights. Therefore, the health risk warning model can be trained for different age groups, different genders and even different weights to improve the accuracy of the health risk warning results.

[0077] Step S400, according to the basic information of the wearer, determine the target audience label information in each of the audience label information, and use the health risk warning model corresponding to the target audience label information as the target health risk warning model.

[0078] It should be noted that basic information includes age, gender, weight, etc.

[0079] In this embodiment, health risk warning models corresponding to different age groups, different genders and different weights can be pre-trained, and health risk warning models that can provide health risk warnings under coupling factors such as age group and gender can also be trained to facilitate health risk warnings for current users, thereby improving the accuracy of health risk warnings.

[0080] Furthermore, based on the above-mentioned embodiments of the health risk warning method of the present application, a fourth embodiment of the health risk warning method of the present application is proposed.

[0081] In this embodiment, after the above step S20, the method further includes:

[0082] Step S30, based on the comprehensive health risk warning result, determining prompt information corresponding to the comprehensive health risk warning result, and outputting the prompt information on a preset interface;

[0083] It is understandable that different prompt information can be preset for different comprehensive health risk warning results. Therefore, after obtaining the comprehensive health risk warning result, the prompt information corresponding to the current comprehensive health risk warning result can be output according to the correspondence between the comprehensive health risk warning result and the prompt information to prompt the user. Specifically, the prompt information can be information such as work and rest prompts corresponding to the current comprehensive health risk warning result.

[0084] Step S40, monitoring the wearer's motion state based on the comprehensive health risk warning result, and outputting risk warning information when the motion state is detected to be an abnormal motion state.

[0085] It is understandable that the motion state is also measured based on the sensors in the smart bracelet. For example, based on the accelerometer and heart rate sensor in the smart bracelet, the user's motion state can be determined. Among them, the motion state includes resting state, walking state and running state. When the comprehensive health risk warning result indicates that the user is not suitable for exercise, if the user is in a running state, risk warning information can be output.

[0086] In this embodiment, the present application improves the intelligence of the present application by providing prompt information based on the comprehensive health risk warning results and monitoring the user's exercise status based on the comprehensive health risk warning results.

[0087] Furthermore, based on the above-mentioned embodiments of the health risk warning method of the present application, a fifth embodiment of the health risk warning method of the present application is proposed.

[0088] In this embodiment, the method further includes:

[0089] Step S50, obtaining a preset cross-validation relationship between the sensors;

[0090] It should be noted that the cross-validation relationship refers to a relationship in which data collected by multiple sensors validate each other.

[0091] It is understandable that in order to facilitate the evaluation of erroneous comprehensive health risk warning results due to sensor failure resulting from data collected by the faulty sensor, the present application also needs to verify the validity of the user's physiological data when fusing the user's physiological data collected by the sensor.

[0092] Step S60, determining a first target sensor and a second target sensor in each of the sensors based on the cross-validation relationship;

[0093] It is understandable that when the cross-validation relationship is that the user's physiological data collected by two sensors validate each other, the two mutually validated parties can be used as target sensors, that is, a first target sensor and a second target sensor are obtained.

[0094] Step S70, determining a first evaluation result corresponding to the user physiological data corresponding to the first target sensor, and determining a second evaluation result corresponding to the user physiological data corresponding to the second target sensor;

[0095] It is understandable that, for the first target sensor and the second target sensor, the user physiological data collected by the first target sensor and the user physiological data collected by the second target sensor can be evaluated respectively based on preset rules, so as to obtain a first evaluation result and a second evaluation result. It should be noted that the rules for obtaining the first evaluation result and the rules for obtaining the second evaluation result can be different.

[0096] Step S80, when it is detected that the first evaluation result and the second evaluation result are the same, executing the step of fusing the physiological data of each user to obtain comprehensive physiological data.

[0097] It can be understood that when it is detected that the first evaluation result and the second evaluation result are the same, it indicates that both the first target sensor and the second target sensor are not faulty, and thus the step of fusing the user's physiological data can be performed.

[0098] In addition, it should be noted that when there are multiple cross-validation relationships between sensors in the smart bracelet, the data fusion step can only be performed when all the multiple cross-validation relationships are verified.

[0099] In this embodiment, the present application can detect whether there is a faulty sensor through a pre-set cross-validation relationship, thereby further improving the probability of correctly evaluating the comprehensive health risk warning results.

[0100] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the health risk warning method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0101] This application also provides a health risk early warning system, please refer to Figure 4 , the health risk early warning system comprises:

[0102] The data fusion module 10 is used to obtain the user physiological data collected by each sensor, and fuse the user physiological data to obtain comprehensive physiological data;

[0103] The result warning module 20 is used to input the comprehensive physiological data into a preset target health risk warning model to obtain a comprehensive health risk warning result, wherein the target health risk warning model is pre-trained using the comprehensive physiological test data as model input data and the real health results corresponding to the comprehensive physiological test data as model training labels.

[0104] In one embodiment, the health risk early warning system further includes:

[0105] A data alignment module, used for aligning the physiological data of each user based on the starting collection time corresponding to each of the physiological data of each user;

[0106] A feature extraction module, used for filtering the physiological data of each user after data alignment, and extracting physiological features from the physiological data of each user after filtering;

[0107] Based on this, the data fusion module 10 is also used for:

[0108] The physiological characteristics are fused to obtain comprehensive physiological data.

[0109] In one embodiment, the feature extraction module is further used for:

[0110] Acquire a preset filter corresponding to each of the sensors, and determine a target corresponding relationship between each of the filters and each of the user's physiological data according to the corresponding relationship between each of the sensors and each of the filters and the corresponding relationship between each of the sensors and each of the user's physiological data;

[0111] Based on the target correspondence, the physiological data of each user after data alignment is filtered through each filter.

[0112] In one embodiment, the health risk early warning system further includes:

[0113] The label acquisition module is used to obtain the audience label information corresponding to each preset health risk warning model;

[0114] The model selection module is used to determine the target audience label information in each of the audience label information according to the basic information of the wearer, and use the health risk warning model corresponding to the target audience label information as the target health risk warning model.

[0115] In one embodiment, the health risk early warning system further includes:

[0116] A first prompt module is used to determine the prompt information corresponding to the comprehensive health risk warning result based on the comprehensive health risk warning result, and output the prompt information on a preset interface;

[0117] The second prompt module is used to monitor the wearer's movement state based on the comprehensive health risk warning result, and output risk warning information when the movement state is monitored to be an abnormal movement state.

[0118] In one embodiment, the health risk early warning system further includes:

[0119] A relationship acquisition module, used to acquire a preset cross-validation relationship between the sensors;

[0120] Based on the cross-validation relationship, determining a first target sensor and a second target sensor in each of the sensors;

[0121] a result evaluation module, configured to determine a first evaluation result corresponding to the user physiological data corresponding to the first target sensor, and to determine a second evaluation result corresponding to the user physiological data corresponding to the second target sensor;

[0122] The switching module is used to execute the step of fusing the physiological data of each user to obtain comprehensive physiological data when it is detected that the first evaluation result and the second evaluation result are the same.

[0123] The health risk warning system provided by the present application adopts the health risk warning method in the above embodiment, which can solve the technical problem of how to comprehensively evaluate the health level of the wearer. Compared with the prior art, the beneficial effects of the health risk warning system provided by the present application are the same as the beneficial effects of the health risk warning method provided by the above embodiment, and other technical features of the health risk warning system are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0124] The present application provides an electronic device, which includes: 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, and the instructions are executed by the at least one processor so that the at least one processor can execute the health risk warning method in the above-mentioned embodiment one.

[0125] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device suitable for implementing an embodiment of the present application. Figure 5 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0126] like Figure 5As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0127] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0128] The electronic device provided by the present application adopts the health risk warning method in the above embodiment to solve the technical problem of how to comprehensively evaluate the health level of the wearer. Compared with the prior art, the beneficial effects of the electronic device provided by the present application are the same as the beneficial effects of the health risk warning method provided by the above embodiment, and the other technical features in the electronic device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0129] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0130] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0131] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the health risk warning method in the above-mentioned embodiment.

[0132] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0133] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0134] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device: obtains the user physiological data collected by each sensor, and fuses the physiological data of each user to obtain comprehensive physiological data; inputs the comprehensive physiological data into a preset target health risk warning model to obtain a comprehensive health risk warning result, wherein the target health risk warning model is pre-trained using comprehensive physiological test data as model input data and using the real health results corresponding to the comprehensive physiological test data as model training labels.

[0135] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0136] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0137] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0138] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned health risk warning method, and can solve the technical problem of how to comprehensively evaluate the health level of the wearer. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the health risk warning method provided in the above-mentioned embodiment, and will not be repeated here.

[0139] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned health risk warning method when executed by a processor.

[0140] The computer program product provided by this application can solve the technical problem of how to comprehensively evaluate the health level of the wearer. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the health risk warning method provided by the above embodiment, which will not be repeated here.

[0141] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A health risk early warning method, characterized in that: The health risk early warning method comprises: Acquire the user's physiological data collected by each sensor, and fuse the user's physiological data to obtain comprehensive physiological data; The comprehensive physiological data is input into a preset target health risk warning model to obtain a comprehensive health risk warning result, wherein the target health risk warning model is pre-trained using the comprehensive physiological test data as model input data and using the real health results corresponding to the comprehensive physiological test data as model training labels.

2. The health risk early warning method according to claim 1, characterized in that: Before the step of fusing the physiological data of each user to obtain comprehensive physiological data, the method further includes: Based on the start collection time corresponding to each of the physiological data of the users, aligning the physiological data of the users; Performing filtering processing on the physiological data of each user after data alignment, and extracting physiological features from the physiological data of each user after filtering processing; The step of fusing the physiological data of each user to obtain comprehensive physiological data includes: The physiological characteristics are fused to obtain comprehensive physiological data.

3. The health risk early warning method according to claim 2, characterized in that: The step of filtering the physiological data of each user after data alignment includes: Acquire a preset filter corresponding to each of the sensors, and determine a target corresponding relationship between each of the filters and each of the user's physiological data according to the corresponding relationship between each of the sensors and each of the filters and the corresponding relationship between each of the sensors and each of the user's physiological data; Based on the target correspondence, the physiological data of each user after data alignment is filtered through each filter.

4. The health risk early warning method according to claim 1, characterized in that: Before the step of inputting the comprehensive physiological data into a preset target health risk warning model to obtain a comprehensive health risk warning result, the method further includes: Obtain the audience label information corresponding to each preset health risk warning model; According to the basic information of the wearer, the target audience label information is determined in each of the audience label information, and the health risk warning model corresponding to the target audience label information is used as the target health risk warning model.

5. The health risk early warning method according to claim 1, characterized in that: After the step of inputting the comprehensive physiological data into a preset target health risk warning model to obtain a comprehensive health risk warning result, the method further includes: Based on the comprehensive health risk warning result, determine the prompt information corresponding to the comprehensive health risk warning result, and output the prompt information on a preset interface; The wearer's motion state is monitored based on the comprehensive health risk warning result, and when the motion state is monitored to be an abnormal motion state, risk warning information is output.

6. The health risk early warning method according to claim 1, characterized in that: The method further comprises: Acquiring a preset cross-validation relationship between the sensors; Based on the cross-validation relationship, determining a first target sensor and a second target sensor in each of the sensors; Determine a first evaluation result corresponding to the user physiological data corresponding to the first target sensor, and determine a second evaluation result corresponding to the user physiological data corresponding to the second target sensor; When it is detected that the first evaluation result and the second evaluation result are the same, the step of fusing the physiological data of each user to obtain comprehensive physiological data is performed.

7. A health risk early warning system, characterized in that: The health risk early warning system includes: A data fusion module is used to obtain the user physiological data collected by each sensor, and fuse the user physiological data to obtain comprehensive physiological data; The result warning module is used to input the comprehensive physiological data into a preset target health risk warning model to obtain a comprehensive health risk warning result, wherein the target health risk warning model is pre-trained using the comprehensive physiological test data as model input data and the real health results corresponding to the comprehensive physiological test data as model training labels.

8. An electronic device, characterized in that: The electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the health risk warning method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the health risk warning method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the steps of the health risk early warning method according to any one of claims 1 to 6.

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