Information analysis method and intelligent wearable device

Abstract

The invention discloses an information analysis method and intelligent wearable equipment, and the method comprises the steps: obtaining a user instruction, and determining a target analysis mode specified by a user according to the user instruction; acquiring user information according to the target analysis mode; when the target analysis mode is a space-time analysis mode, calculating based on geographical location information and time information in the user information by adopting a first calculation mode to obtain a first result value, and mapping the first result value into first result information; a second result value is obtained through calculation based on geographical location information and time information in the user information in a second calculation mode, and the second result value is mapped into second result information; calculating change trend information based on the geographical location information, the time information and the physiological index information; and determining final result information based on the first result information, the second result information and the change trend information. Through the final result information, the physiological state of the user can be simply and clearly represented, and the information interpretation threshold is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent wearable devices, and in particular to an information analysis method and an intelligent wearable device. BACKGROUND

[0002] With the improvement of health awareness, intelligent wearable devices (such as smart watches, bracelets, etc.) have been widely used in daily health monitoring scenarios. The intelligent wearable devices can collect physiological index information such as heart rate, blood oxygen, and exercise steps of a user in real time, and provide multi-dimensional health reference data for the user.

[0003] In the prior art, the intelligent wearable devices usually directly present physiological index information, geographical position, time, and other information collected in the form of raw data or simple statistical charts to the user. For example, real-time heart rate values, current altitudes, specific time points, and step change curves in the past one hour are displayed simultaneously.

[0004] However, the information presentation method of the existing intelligent wearable devices has obvious deficiencies. Since the physiological state of the human body is correlated with geographical position, time of the year or day, and other information, and the correlation is complex and dynamically changes, a large amount of scattered raw data or independent indicators will cause the user to face an information overload problem. The user needs to interpret the correlation between different information by himself, which not only increases the difficulty of information understanding, but also may misjudge the physiological state of the user due to lack of professional knowledge, and the user cannot quickly and accurately understand the physiological state of the user. SUMMARY

[0005] In view of the deficiencies in the prior art, the present application provides an information analysis method and an intelligent wearable device. By mapping a first result value into first result information, mapping a second result value into second result information, and determining final result information based on the first result information, the second result information, and change trend information, the physiological state of the user can be simply and clearly represented, the user does not need to face a variety of complex and scattered raw data or independent indicators, does not need to analyze the correlation between a variety of information by himself, can quickly and accurately understand the physiological state of the user, reduces the information interpretation threshold, and improves the user experience and health management efficiency.

[0006] To solve the above problems, the present application provides the following technical solutions: In a first aspect, an information analysis method is provided. The method includes obtaining a user instruction, and determining a target analysis mode specified by the user according to the user instruction. According to the target analysis mode, user information is obtained, the user information including at least one of geographical position information, time information, and physiological index information of the user. When the target analysis mode is a spatio-temporal analysis mode, a first calculation manner is adopted to calculate a first result value based on geographical position information and time information in the user information, and the first result value is mapped as first result information; A second calculation manner is adopted to calculate a second result value based on the geographical position information and the time information in the user information, and the second result value is mapped as second result information; Change trend information is calculated based on the geographical position information, the time information, and the physiological index information; Final result information is determined based on the first result information, the second result information, and the change trend information, the final result information being used to represent a physiological state of a user.

[0007] In some embodiments, the first result value is calculated based on the geographical position information and the time information in the user information by using the first calculation manner, and the first result value is mapped as the first result information, including: The first result value is calculated based on a first calculation bit and a second calculation bit of a longitude value or a latitude value in the geographical position information and a third calculation bit of the time information by using the first calculation manner, and the first result value is mapped as the first result information according to a first mapping relationship.

[0008] In some embodiments, the second result value is calculated based on the geographical position information and the time information in the user information by using the second calculation manner, and the second result value is mapped as the second result information, including: The second result value is calculated based on a fourth calculation bit and a fifth calculation bit of the longitude value or the latitude value in the geographical position information and a sixth calculation bit of the time information by using the second calculation manner, and the second result value is mapped as the second result information according to a second mapping relationship.

[0009] In some embodiments, the change trend information is calculated based on the geographical position information, the time information, and the physiological index information, including: Initial change trend information is calculated based on a height value in the geographical position information and a seventh calculation bit in the time information; Final change trend information is calculated based on the initial change trend information and a current heart rate value in the physiological index information.

[0010] In some embodiments, the user information further includes motion information, and the method further includes: When the target analysis mode is a motion analysis mode, a motion trajectory of a user is identified according to the motion information; The third calculation manner is used to determine final result information based on the motion track.

[0011] In some embodiments, the user information further comprises image information, and the method further comprises: When the target analysis mode is an image analysis mode, identifying an object in the image; An artificial intelligence model is used to determine final result information according to the object.

[0012] In some embodiments, the artificial intelligence model is used to determine final result information according to the object, comprising: When the object is a human face, an artificial intelligence model is used to identify human face feature information of the human face; An artificial intelligence model is used to determine final result information based on the human face feature information.

[0013] In a second aspect, an embodiment of the present application provides an intelligent wearable device, comprising: An information acquisition module, which is used to acquire user information, and comprises at least one of a positioning module, a sensor module and a camera module; A processing module, comprising at least one processor and a memory in communication connection with 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 to enable the at least one processor to execute the information analysis method according to the user information.

[0014] In some embodiments, the sensor module comprises at least one of a heart rate sensor, an oxygen saturation sensor, a magnetoresistance sensor, an inclination sensor, an angular velocity sensor, an acceleration sensor, a light sensor, a temperature sensor and an air pressure sensor.

[0015] In some embodiments, the intelligent wearable device further comprises a communication module, which is used to realize at least one of Wi-Fi communication, cellular mobile communication, Bluetooth communication and near field communication.

[0016] The present application provides an information analysis method and an intelligent wearable device. The present application maps a first result value to first result information, maps a second result value to second result information, and determines final result information based on the first result information, the second result information and trend information, which can simply and clearly represent the physiological state of a user, so that the user does not need to face multiple complex and scattered raw data or independent indicators, does not need to analyze the correlation between multiple information by himself / herself, can quickly and accurately understand his / her own physiological state, reduces the information interpretation threshold, and improves the user's use experience and health management efficiency. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the information analysis method provided in the embodiments of this application.

[0018] Figure 2 This is a schematic diagram of the structure of the first embodiment of the smart wearable device provided in this application.

[0019] Figure 3 This is a schematic diagram of the structure of a second embodiment of the smart wearable device provided in this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0021] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "multiple" means two or more, unless otherwise explicitly specified.

[0022] This application provides an information analysis method and a smart wearable device. By mapping a first result value to first result information and a second result value to second result information, and determining the final result information based on the first result information, the second result information, and the trend information, the method can simply and clearly represent the user's physiological state. This allows users to quickly and accurately understand their own physiological state without having to deal with multiple complex and scattered raw data or independent indicators, or without having to analyze the correlation between multiple pieces of information themselves. This lowers the threshold for information interpretation and improves the user experience and health management efficiency.

[0023] The information analysis method provided in this application will be described in detail below with reference to the accompanying drawings.

[0024] Please see Figure 1 , Figure 1 This is a flowchart illustrating the information analysis method provided in an embodiment of this application. For example... Figure 1 As shown, the information analysis method includes steps S100 to S600.

[0025] Step S100: Obtain user instructions and determine the target analysis mode specified by the user based on the user instructions.

[0026] In some embodiments, the target analysis mode includes a space-time analysis mode, a motion analysis mode, and an image analysis mode.

[0027] Optionally, the space-time analysis mode is analyzed at least according to geographical location information, time information, and physiological index information of the user, the motion analysis mode is analyzed at least according to motion information of the user, and the image analysis mode is analyzed at least according to an image taken by the user.

[0028] Step S200: Obtain user information according to a target analysis mode.

[0029] The user information includes at least one of geographical location information, time information, and physiological index information of the user.

[0030] By obtaining user information according to a target analysis mode, a plurality of target analysis mode options can be provided for the user to meet different use requirements of the user.

[0031] In some embodiments, when the target analysis mode is the space-time analysis mode, the user information includes geographical location information, time information, and physiological index information of the user.

[0032] Optionally, the physiological index information includes at least one of a heart rate value, a blood oxygen value, a breathing frequency, and a blood pressure value of the user.

[0033] In some embodiments, when the target analysis mode is the motion analysis mode, the user information further includes motion information.

[0034] Optionally, the motion information can include motion parameter information generated by the motion of the smart wearable device caused by the motion of the user.

[0035] Optionally, the motion parameter information includes angular velocity and acceleration.

[0036] In some embodiments, the motion information can include information such as the number of steps and the step frequency of the user.

[0037] In some embodiments, when the target analysis mode is the image analysis mode, the user information further includes image information.

[0038] Optionally, the image information can be an image taken by the user using a camera module of the smart wearable device.

[0039] Optionally, the image information can be an image uploaded by the user to the smart wearable device or an image selected by the user from a network.

[0040] Step S300: When the target analysis mode is the space-time analysis mode, a first result value is calculated based on the geographic location information and the time information in the user information by using a first calculation manner, and the first result value is mapped as first result information.

[0041] In some embodiments, step S300 includes step S310.

[0042] Step S310: A first result value is calculated based on the first calculation bit and the second calculation bit of the longitude value or the latitude value in the geographic location information and the third calculation bit of the time information by using the first calculation manner, and the first result value is mapped as first result information according to a first mapping relationship.

[0043] Optionally, the first calculation manner, the first mapping relationship, the second calculation manner and the second mapping relationship are determined according to the result of big data analysis. For example, a large amount of user information and measured physiological state information of users can be used for big data analysis, and an artificial intelligence model can be used to identify the correlation between the feature information in the user information and the physiological state of the user to determine the first calculation manner, the first mapping relationship, the second calculation manner and the second mapping relationship. Wherein, the calculation bits can be used to represent different feature information, and the first mapping relationship and the second mapping relationship can be used to represent the correlation between the feature information and the physiological state of the user.

[0044] In some embodiments, a pre-trained artificial intelligence model is used to calculate a first result value based on the first calculation bit and the second calculation bit of the longitude value or the latitude value in the geographic location information and the third calculation bit of the time information, and the first result value is mapped as first result information according to a first mapping relationship.

[0045] Optionally, the artificial intelligence model includes a neural network model and a Transformer model.

[0046] In some embodiments, an artificial intelligence model is used to implement a target analysis mode.

[0047] In some embodiments, step S310 includes steps S311 to S312.

[0048] Step S311: The fraction bit and the second bit of the longitude value or the latitude value in the geographic location information and the second bit of the time information are added to obtain a first result value.

[0049] For example, the fraction bit and the second bit of the longitude value in the geographic location information and the second bit of the time information are added to obtain a first result value.

[0050] For example, the fraction bit and the second bit of the latitude value in the geographic location information and the second bit of the time information are added to obtain a first result value.

[0051] Step S312: mapping the first result value to first result information according to the first mapping relationship and a first remainder obtained by dividing the first result value by a preset first calculation coefficient.

[0052] Optionally, the first calculation coefficient is 3, 6, 7, 8, 9, or the like.

[0053] In some embodiments, the first mapping relationship is that one first remainder corresponds to one preset first result information.

[0054] In some embodiments, the first mapping relationship can be a fuzzy mapping relationship. For example, a neural network model can be used to map the first result value to the first result information based on the first remainder.

[0055] Illustratively, the first result information can include at least one of a number, a text, an image, and a chart.

[0056] In some embodiments, the first result information is information represented in binary.

[0057] Optionally, the first result information includes a 3-bit binary number.

[0058] Illustratively, the first result information is “001”, “101”, “011”, “111”, “110”, “000”, “010”, or “100”.

[0059] Optionally, the first result information is represented by a binary image symbol. For example, 1 long rectangle is used to represent “1”, and 2 short rectangles are used to represent “0”.

[0060] Step S400: calculating a second result value based on geographic location information and time information in the user information by using a second calculation method, and mapping the second result value to second result information.

[0061] In some embodiments, step S400 includes step S410.

[0062] Step S410: calculating the second result value based on the fourth calculation bit and the fifth calculation bit of the longitude value or the latitude value in the geographic location information and the sixth calculation bit of the time information by using the second calculation method, and mapping the second result value to the second result information according to a second mapping relationship.

[0063] In some embodiments, a pre-trained artificial intelligence model is used to calculate the second result value based on the fourth calculation bit and the fifth calculation bit of the longitude value or the latitude value in the geographic location information and the sixth calculation bit of the time information, and map the second result value to the second result information according to the second mapping relationship.

[0064] In some embodiments, the first mapping relationship can be a fuzzy mapping relationship. For example, a neural network model can be employed to map the first result value to the first result information based on the first remainder.

[0065] In some embodiments, the step S410 comprises steps S411-S412.

[0066] Step S411: adding the fraction bits and the second bits of the longitude value or the latitude value in the geographic location information and the second bits of the time information to obtain a second result value.

[0067] For example, the fraction bits and the second bits of the longitude value in the geographic location information and the second bits of the time information are added to obtain the second result value.

[0068] For example, the fraction bits and the second bits of the latitude value in the geographic location information and the second bits of the time information are added to obtain the second result value.

[0069] Step S412: mapping the second result value to a second result information according to a second mapping relationship and a second remainder obtained by dividing the second result value by a preset second calculation coefficient.

[0070] Optionally, the second calculation coefficient is 3, 6, 7, 8, or 9, etc.

[0071] In some embodiments, the second mapping relationship is that one second remainder corresponds to one second result information.

[0072] In some embodiments, the second mapping relationship can be a fuzzy mapping relationship. For example, a neural network model can be employed to map the second result value to the second result information based on the second remainder.

[0073] For example, the second result information can include at least one of a number, a text, an image, and a chart.

[0074] In some embodiments, the second result information is information represented in binary.

[0075] Optionally, the second result information includes a 3-bit binary number.

[0076] For example, the second result information is “001”, “101”, “011”, “111”, “110”, “000”, “010”, or “100”.

[0077] Optionally, the second result information is represented by a binary image symbol. For example, 1 long rectangle is used to represent “1”, and 2 short rectangles are used to represent “0”.

[0078] In some embodiments, the first result information and the second result information are used to represent different aspects of the physiological state of the user, respectively. For example, the first result information is used to represent the physiological state of the nervous system of the user, and the second result information is used to represent the physiological state of the digestive system of the user.

[0079] Step S500: calculating change trend information based on the geographic location information, the time information, and the physiological index information.

[0080] In some embodiments, the change trend information is used to reflect the change trend of the physiological state of the user in a future period of time.

[0081] In some embodiments, step S500 includes steps S510-S520.

[0082] Step S510: calculating initial change trend information based on the height value in the geographic location information and the seventh calculation bit in the time information.

[0083] In some embodiments, the third remainder obtained by dividing the result value obtained by adding the height value and the second bit of the time information by a preset third calculation coefficient is determined as the initial change trend information.

[0084] Optionally, the third calculation coefficient is 3, 6, 7, 8, or 9, etc.

[0085] Step S520: calculating final change trend information based on the initial change trend information and the current heart rate value of the user in the physiological index information.

[0086] In some embodiments, the fourth remainder obtained by dividing the heart rate value by a preset fourth calculation coefficient is added to the third remainder, and the result value is divided by a preset fifth calculation coefficient to obtain a fifth remainder, and the fifth remainder is determined as the final change trend information.

[0087] Optionally, the fourth calculation coefficient is 3, 6, 7, 8, or 9, etc.

[0088] Optionally, the fifth calculation coefficient is 3, 6, 7, 8, or 9, etc.

[0089] In some embodiments, the change trend information is used to represent a characteristic calculation bit reflecting the physiological state of the user.

[0090] In some embodiments, a pre-trained artificial intelligence model is used to calculate the final change trend information based on the initial change trend information and the current heart rate value of the user in the physiological index information.

[0091] Step S600: determining final result information based on the first result information, the second result information, and the change trend information.

[0092] The final result information is used to represent the physiological state of the user.

[0093] In some embodiments, the first result information and the second result information are spliced according to a preset splicing relationship to obtain spliced result information, and a feature calculation bit in the spliced result information is determined according to the change trend information, and then the final result information is determined based on the spliced result information and the feature calculation bit.

[0094] In some embodiments, a pre-trained artificial intelligence model is used to determine the final result information based on the spliced result information and the feature calculation bit.

[0095] Optionally, the final result information is used to represent the current physiological state of the user and the change trend of the physiological state in a preset time period in the future.

[0096] In some embodiments, the final result information can include at least one of a number, a text, an image, and a chart.

[0097] In some embodiments, the final result information is represented by a binary image symbol.

[0098] In some embodiments, the user information further includes motion information. At this time, the method further includes steps S700 to S800.

[0099] Step S700: When the target analysis mode is a motion analysis mode, a motion trajectory of the user is identified according to the motion information.

[0100] As described above, the motion information can include motion parameter information generated by the motion of the user to the smart wearable device. In some embodiments, the motion trajectory of the user can be identified according to the motion parameter information in the motion information. For example, the hand motion trajectory of the user can be identified according to the motion parameter information.

[0101] Step S800: The final result information is determined based on the motion trajectory by using a third calculation method.

[0102] In some embodiments, a pre-trained artificial intelligence model is used to determine the final result information based on the motion trajectory.

[0103] In some embodiments, the final result information can include at least one of a number, a text, an image, and a chart.

[0104] Exemplarily, the final result information can be used to represent the physiological state of the motion system of the user.

[0105] In some embodiments, the user information further includes image information, and at this time, the method further includes steps S910 to S920.

[0106] Step S910: When the target analysis mode is the image analysis mode, an object in the image is identified.

[0107] In some embodiments, the object in the image can include a human face. The feature information such as the complexion of the human face can reflect the physiological state of the person.

[0108] In some embodiments, the object in the image can include a human hand. The information such as the skin color, palm print and blood vessel color of the human hand can reflect the physiological state of the person.

[0109] Optionally, the image can be an image reflecting the environment where the user is located, and the object in the image can include trees, the sun, and the like.

[0110] Step S920: determining the final result information according to the object by using the artificial intelligence model.

[0111] In some embodiments, step S920 includes steps S921 to S922.

[0112] Step S921: when the object is a human face, identifying the human face feature information of the human face by using the artificial intelligence model.

[0113] In some embodiments, the human face feature information includes the skin color feature information, the eye feature information, the overall shape feature information of the human face, and the like.

[0114] Step S922: determining the final result information based on the human face feature information by using the artificial intelligence model.

[0115] In summary, the information analysis method provided by the embodiments of the present application has the following advantages: 1. By mapping the first result value to the first result information, mapping the second result value to the second result information, and determining the final result information based on the first result information, the second result information and the change trend information, the physiological state of the user can be simply and clearly represented, so that the user does not need to face a variety of complex and scattered raw data or independent indicators, and does not need to analyze the correlation between a variety of information by himself / herself, and can quickly and accurately understand the physiological state, thereby reducing the information interpretation threshold and improving the user experience and health management efficiency.

[0116] 2. By obtaining the user information according to the target analysis mode, the user can be provided with a variety of target analysis mode options to meet different use needs of the user.

[0117] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of a first embodiment of the smart wearable device provided by the embodiments of the present application. As Figure 2 shown, the present application further provides a smart wearable device 1, which includes an information acquisition module 10 and a processing module 20.

[0118] In some embodiments, the information acquisition module 10 is configured to acquire user information. The information acquisition module 10 comprises at least one of a positioning module, a sensor module and a camera module.

[0119] In some embodiments, the sensor module comprises at least one of a heart rate sensor, an oxygen saturation sensor, a magnetoresistance sensor, an inclination sensor, an angular velocity sensor, an acceleration sensor, a light sensor, a temperature sensor and a barometric pressure sensor.

[0120] In some embodiments, the magnetoresistance sensor and the inclination sensor are used to form an electronic compass that can identify the direction of a magnetic field. The geographical location information of the user can include the direction of the earth's magnetic field in which the user is facing.

[0121] In some embodiments, the positioning module comprises a U-blox M10 Global Navigation Satellite System (GNSS) chip developed by U-blox, a Swiss company. The chip can realize double-satellite positioning and Beidou single-satellite redundant reception, and is equipped with a high-gain ceramic patch antenna to ensure stable positioning in all weather conditions.

[0122] In some embodiments, the processing module 20 comprises at least one processor and a memory in communication connection with 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 to enable the at least one processor to execute the information analysis method according to the user information.

[0123] In some embodiments, the processor is a Snapdragon Wear 4100 chip or a Kirin chip, and a plurality of artificial intelligence models can be pre-deployed in the processor, each of which can realize a target analysis mode.

[0124] In some embodiments, the memory stores a health recommendation database.

[0125] Optionally, the health recommendation database comprises a traditional Chinese medicine regimen for health preservation.

[0126] Optionally, the information analysis method further comprises: generating and displaying health recommendations based on the final result information and the health recommendation database.

[0127] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of a second embodiment of the smart wearable device provided in the present application. As shown in Figure 3As shown, in some embodiments, the smart wearable device 1 further comprises a communication module 30, which is configured to implement at least one of Wi-Fi communication, cellular mobile communication, Bluetooth communication and near field communication. The near field communication is also referred to as NFC (Near Field Communication) communication.

[0128] Optionally, the smart wearable device 1 can be a watch, a bracelet or a pendant, etc.

[0129] In some embodiments, when the smart wearable device is a watch or a bracelet, the material of the watch case can include stainless steel, titanium, titanium alloy, ceramic and plastic, etc.

[0130] Optionally, the diameter of the watch face can be between 40 mm and 50 mm, for example, the diameter of the watch face can be 40 mm, 44 mm, 48 mm or 50 mm, etc.

[0131] Optionally, the thickness of the watch case can be between 11.5 mm and 12.5 mm.

[0132] Optionally, the material of the watchband can include silicone, fluororubber, leather and metal, etc.

[0133] In some embodiments, the memory is a non-volatile computer readable storage medium, which can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules of the information analysis method in the embodiments of the present application. The processor executes the non-volatile software programs, instructions and modules stored in the memory, thereby performing various functional applications and data processing of the smart wearable device 1, i.e. implementing the information analysis method of the above method embodiments.

[0134] In some embodiments, the memory can include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required by at least one function; the data storage area can store data created according to the use of the smart wearable device 1, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device or other non-volatile solid-state storage device. In some embodiments, the memory can optionally include a memory remotely arranged with respect to the processor, and these remote memories can be connected to the controller through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0135] In some embodiments, one or more modules are stored in the memory, and when executed by one or more processors, perform the information analysis method in any of the above method embodiments, for example, perform the information analysis method described above. Figure 1The method steps S100 to S600 in the method.

[0136] The application further provides a computer program product comprising a computer program which, when executed by a processor, implements the information analysis method described above.

[0137] To sum up, the application provides an information analysis method and an intelligent wearable device. The information analysis method comprises the following steps: obtaining a user instruction, determining a target analysis mode specified by the user according to the user instruction; obtaining user information according to the target analysis mode, wherein the user information comprises at least one of geographical location information, time information and physiological index information of the user; when the target analysis mode is a space-time analysis mode, using a first calculation method to calculate a first result value based on the geographical location information and the time information in the user information, mapping the first result value into first result information; using a second calculation method to calculate a second result value based on the geographical location information and the time information in the user information, and mapping the second result value into second result information; calculating change trend information based on the geographical location information, the time information and the physiological index information; and determining final result information based on the first result information, the second result information and the change trend information, wherein the final result information is used to represent a physiological state of the user. The application can simply and clearly represent the physiological state of the user by mapping the first result value into the first result information, mapping the second result value into the second result information, and determining the final result information based on the first result information, the second result information and the change trend information, so that the user can quickly and accurately understand the physiological state of the user without analyzing the correlation between multiple information and without facing multiple complex and scattered original data or independent indexes, thereby reducing the information interpretation threshold and improving the use experience and health management efficiency of the user.

[0138] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing examples, those skilled in the art will understand that they can still modify the technical solutions described in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not drive the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. An information analysis method characterized by, The method comprises the following steps: acquiring a user instruction, and determining a target analysis mode specified by the user according to the user instruction; acquiring user information according to the target analysis mode, the user information comprising at least one of geographical location information, time information and physiological index information of the user; when the target analysis mode is a space-time analysis mode, using a first calculation mode to calculate a first result value based on the geographical location information and the time information in the user information, and mapping the first result value into first result information; using a second calculation mode to calculate a second result value based on the geographical location information and the time information in the user information, and mapping the second result value into second result information; calculating trend information based on the geographical location information, the time information and the physiological index information; determining final result information based on the first result information, the second result information and the trend information, the final result information being used to represent a physiological state of the user.

2. The information analysis method according to claim 1, characterized by, The method comprises the following steps: using the first calculation mode to calculate the first result value based on a first calculation bit and a second calculation bit of a longitude value or a latitude value in the geographical location information and a third calculation bit of the time information, and mapping the first result value into the first result information according to a first mapping relationship.

3. The information analysis method according to claim 1, characterized by, The method comprises the following steps: using the second calculation mode to calculate the second result value based on a fourth calculation bit and a fifth calculation bit of the longitude value or the latitude value in the geographical location information and a sixth calculation bit of the time information, and mapping the second result value into the second result information according to a second mapping relationship.

4. The information analysis method according to claim 1, characterized by, The method comprises the following steps: calculating initial trend information based on an altitude value in the geographical location information and a seventh calculation bit in the time information; calculating final trend information based on the initial trend information and a current heart rate value in the physiological index information.

5. The information analysis method according to claim 1, characterized by, The user information further comprises motion information, and the method further comprises the following steps: when the target analysis mode is a motion analysis mode, identifying a motion track of the user according to the motion information; using a third calculation mode to determine the final result information based on the motion track.

6. The information analysis method according to claim 1, characterized by, The user information further comprises image information, and the method further comprises the following steps: when the target analysis mode is an image analysis mode, identifying an object in the image; using an artificial intelligence model to determine the final result information according to the object.

7. The information analysis method according to claim 6, characterized by, The method comprises the following steps: when the object is a face, using the artificial intelligence model to identify face feature information of the face; using the artificial intelligence model to determine the final result information based on the face feature information.

8. An intelligent wearable device, characterized by, The smart wearable device comprises: an information acquisition module, configured to acquire user information, the information acquisition module comprising at least one of a positioning module, a sensor module and a camera module; a processing module comprising at least one processor and a memory in communication connection with 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 to enable the at least one processor to execute the information analysis method according to any one of claims 1 to 7 according to the user information.

9. The smart wearable device according to claim 8, characterized in that, the sensor module comprises at least one of a heart rate sensor, an oxygen saturation sensor, a magnetoresistance sensor, an inclination sensor, an angular velocity sensor, an acceleration sensor, a light sensor, a temperature sensor and a barometric pressure sensor.

10. The smart wearable device according to claim 8, characterized in that, the smart wearable device further comprises a communication module, configured to implement at least one of Wi-Fi communication, cellular mobile communication, Bluetooth communication and near field communication.

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