WO2018090533A1 - 一种基于用户状态的分析推荐方法和装置 - Google Patents

一种基于用户状态的分析推荐方法和装置 Download PDF

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Publication number
WO2018090533A1
WO2018090533A1 PCT/CN2017/078765 CN2017078765W WO2018090533A1 WO 2018090533 A1 WO2018090533 A1 WO 2018090533A1 CN 2017078765 W CN2017078765 W CN 2017078765W WO 2018090533 A1 WO2018090533 A1 WO 2018090533A1
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data
user
policy
abnormal
data set
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PCT/CN2017/078765
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English (en)
French (fr)
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包磊
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深圳市行远科技发展有限公司
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Publication of WO2018090533A1 publication Critical patent/WO2018090533A1/zh

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • the invention belongs to the field of data monitoring, and in particular relates to an analysis recommendation method and device based on user status.
  • An object of the present invention is to provide an analysis and recommendation method based on a user state, so as to solve the problem that the prior art user is cumbersome to formulate a policy according to his or her own state, and the policy cannot effectively adapt to the state of the user.
  • an embodiment of the present invention provides an analysis and recommendation method based on a user state, where the method includes:
  • the abnormal data set including state data whose the change value is greater than a predetermined threshold state data, and the change trend exceeds a predetermined trend range;
  • the status data includes one of a user's weight, height, blood pressure, body temperature, heart rate, diet data, exercise data, sleep data, and emotion data. Or a variety.
  • the step of searching for the policy data corresponding to the abnormal data set in a preset database includes:
  • abnormal data set and the corresponding policy data do not exist in the historical data of the user, searching for the user feature corresponding to the abnormal data set;
  • the step of collecting the current state data of the user Thereafter, the method further includes:
  • the method further includes:
  • the state data of the user and the corresponding time record are stored in the database, and the policy data is generated according to the change result.
  • an embodiment of the present invention provides an analysis and recommendation device based on a user state, where The device includes:
  • a data collection unit configured to collect current state data of the user
  • a data calculation unit configured to calculate a change value of the current state data and a change trend according to the historical state data of the user recorded in advance;
  • An abnormal data set obtaining unit configured to acquire an abnormal data set, where the abnormal data set includes the state data that the change value is greater than a predetermined threshold state data, and the change trend exceeds a predetermined trend range;
  • the policy data searching unit is configured to search for policy data corresponding to the abnormal data set in a preset database, and alert the user according to the policy data.
  • the status data includes one of a user's weight, height, blood pressure, body temperature, heart rate, diet data, exercise data, sleep data, and emotion data. Or a variety.
  • the policy data searching unit includes:
  • a first data search subunit searching, in the historical data of the user, whether the abnormal data set and its corresponding policy data exist;
  • a user search subunit configured to search for a user feature corresponding to the abnormal data set if the abnormal data set and the corresponding policy data are not present in the historical data of the user;
  • a second data search subunit configured to find, among other users having the user feature, whether the abnormal data set and its corresponding policy data exist.
  • the device further includes:
  • a target data receiving unit configured to receive target data input by the user, and search for other users in the database that are similar to the state data of the user and successfully reach the target;
  • the matching unit is configured to acquire corresponding policy data according to other users that are searched, and remind the user according to the policy data.
  • the device further includes:
  • a policy generating unit configured to store the state data of the user and the corresponding time record in the database, and generate policy data according to the change result.
  • the invention can obtain the change value and the change trend of the current state data of the user by collecting the state data of the user and the historical state data of the user, compare the change value with the corresponding threshold value, and compare the change trend with the corresponding Comparing the trend range, if the predetermined requirement is exceeded, determining the status data as data in the abnormal data set, further searching for corresponding policy data according to the abnormal data set, and reminding the user according to the searched policy data, thereby enabling the user to obtain Policy data that more closely matches user status data, and is automatically detected and generated for user convenience.
  • FIG. 1 is a flowchart of an implementation of an analysis and recommendation method based on a user state according to a first embodiment of the present invention
  • FIG. 2 is a flowchart of implementing an analysis and recommendation method based on a user state according to a second embodiment of the present invention
  • FIG. 3 is a flowchart of implementing an analysis and recommendation method based on a user state according to a third embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of an apparatus for analyzing and recommending a user state according to a fourth embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of an analysis recommendation terminal according to a fifth embodiment of the present invention.
  • the embodiment of the present invention provides an analysis and recommendation method based on the user state, so as to solve the problem in the prior art, when the policy is formulated for the state of the user, the user needs to find the professional data, and after learning the professional data, the user can further determine the state according to the user. Formulating strategies is not only troublesome, but the accuracy of the production strategy is not high. In addition, during the execution of the policy, the user may encounter a special situation, which causes the policy to not effectively match the actual state of the user.
  • Embodiment 1 is a diagrammatic representation of Embodiment 1:
  • FIG. 1 is a flowchart showing an implementation process of an analysis and recommendation method based on a user state according to a first embodiment of the present invention, which is described in detail as follows:
  • step S101 current state data of the user is collected.
  • the user may be an adult, a teenager, a child, an infant, or the like. Since the state data of the infant, the child or the adolescent is more likely to change, the user state-based analysis and recommendation method according to the present invention is particularly suitable for adolescents, children, infants and the like.
  • the state data according to the embodiment of the present invention may include one or more of a user's weight, height, blood pressure, body temperature, heart rate, diet data, exercise data, sleep data, and emotion data.
  • the collection of the height of the user can be obtained through regular measurement.
  • the height measurement reminder can be sent, so that more comprehensive growth data can be obtained.
  • the measurement period of the height adopts different measurement periods according to different stages of growth.
  • the measurement period can be increased correspondingly as the user's age increases. For example, for infants, the measurement period can be one week, and for children or adolescents, the measurement period can be one month.
  • the user's weight may be monitored with other related data to monitor changes in the user's physical state, such as state data such as the user's heart rate, exercise data, diet data, age, etc., to form one or more related state data sets.
  • state data such as the user's heart rate, exercise data, diet data, age, etc.
  • the correspondence between these state data sets and policy data can be established in advance. For example, when the user's weight increases, the user's heart rate, exercise data, and diet data can be combined to determine whether the increase in weight is a muscle gain or a fat gain. For example, the user is in a certain time interval, according to the nutrient composition included in the user's diet data, the time of the diet, the time of exercise, and the strong exercise.
  • Changes in body weight and body weight, or the body fat content of the user can also be collected, and it is calculated whether the user gains weight because of muscle hyperplasia or fat gain, or can further calculate the weight of the muscle to increase or decrease, and the weight of the fat to increase or decrease.
  • the change in the weight of the fat is calculated by the body fat content and the body weight.
  • the user's weight can be collected by user input according to a predetermined period.
  • the blood pressure of the user may be associated with the user's emotional data, heart rate, diet data, sleep data, and exercise data to form one or more status data sets.
  • the correspondence between the state data set and the policy data may be preset in the database, or the user's policy data may be updated into the database.
  • the blood pressure data can be acquired in real time through portable devices such as wristbands, smart watches and the like.
  • the body temperature of the user may constitute a state data set with the user's motion data.
  • different strategy data is needed.
  • the motion data indicates that the user is exercising, and when the body temperature is also rising, the strategy related to the recovery after the exercise can be searched for as the corresponding policy data. If the user's body temperature rises or falls without motion data associated therewith, policy data related to the body temperature abnormality is generated.
  • the body temperature of the user can be obtained by means of an infrared temperature sensor provided on the portable device, or can be set as another temperature sensor.
  • the user's heart rate can be obtained by a wearable device such as a smart watch or a wristband.
  • a wearable device such as a smart watch or a wristband.
  • the heart rate per minute indicated by the heart rate is gradually decreased from the original 80 times/minute to 60 times per minute. It is possible that the user's cardiopulmonary function is getting better and better, and the exercise can be combined with exercise. Data, dietary data to make more accurate judgments, and push strategy data related to the judgment results.
  • the dietary data may include the portion of the user's diet, the type of diet, and the like. That is the weight of the diet.
  • the type of the user's diet may be a pasta.
  • the serving size may be about 200 g.
  • the type of the user's diet may include the pasta and the green vegetables, and the pasta is 120 g.
  • the serving size of green vegetables is 5g.
  • the motion data may include the type of user motion and/or the duration of the motion type, such as If the user walks for 1 km, the user's exercise type can be recorded as a walk, and the exercise lasts for 20 minutes.
  • the type of exercise may include walking, running, kicking, swimming, playing basketball, and the like. Depending on the type of exercise, the amount of heat consumed per unit time will vary. Similarly, for the same type of exercise, the amount of heat consumed is different for different durations.
  • the sleep data may include the depth of sleep and the duration of sleep at a certain depth of sleep. By combining the sleep concentration and the sleep duration, it can be converted into the score of the user's sleep. For example, under the first deep sleep condition, the user can get a sleep score of 90 points when the sleep time is 6 hours, and under the second sleep depth condition, The sleep score of 8 hours into sleep is 95 points. Under normal circumstances, the deeper the depth of sleep under the same sleep duration, the higher the score of the obtained sleep score. Correspondingly, under the same sleep depth condition, the longer the sleep duration, the higher the sleep score. However, depending on the user, the sleep duration also corresponds to the sleep duration limit. For example, adults should not sleep more than 10 hours a day.
  • step S102 calculating a change value of the current state data and a change trend according to the historical state data of the user recorded in advance;
  • the recording period is also different, for example, the recording period for the height can be longer, and the heart rate, blood pressure and body temperature, and the sleep data can be recorded in real time, and the diet data can be used in each diet. Recorded at the time.
  • the change value of the state data may be the current recorded state data, compared with the previous state data, or compared with the average value of the state data of a previous period of time, and the difference between the two is calculated.
  • the trend of the state data can be drawn by drawing the historical state data and the time coordinate system.
  • the time can be selected as the abscissa
  • the state data is plotted as the ordinate
  • the absolute value of the slope of the curve can be judged, or the slope is Judgment in size and direction.
  • step S103 an abnormal data set is acquired, where the abnormal data set includes state data whose the change value is greater than a predetermined threshold state data, and the change trend exceeds a predetermined trend range;
  • the predetermined trend range may be a preset trend change interval, and when the predetermined interval is exceeded, the current state data is considered to be abnormal data. For example, for the change of the state data of the height, a larger value and a smaller value of the change trend may be set, and when the change trend of the height is greater than the larger value, or the change trend of the height is less than the smaller value, then The current state data is judged to be state data in the abnormal data set.
  • step S104 the policy data corresponding to the abnormal data set is searched in a preset database, and the user is reminded according to the policy data.
  • the step of searching for the policy data corresponding to the abnormal data set in the preset database includes:
  • abnormal data set and the corresponding policy data do not exist in the historical data of the user, searching for the user feature corresponding to the abnormal data set;
  • the related data is preferentially searched according to the historical data of the user, and the abnormality that has occurred in the past is analyzed, and the historical behavior data made for the abnormality is combined with the change of the user state data.
  • the screening obtains valid behavior data, and uses the behavior data as the policy data corresponding to the abnormal data set.
  • the abnormal data may be divided according to the associated state data set, according to the divided two or more The exception data set is searched.
  • the user features corresponding to the abnormal data set may preset different user features according to different abnormal data. For example, when it is detected that the abnormal data set of the user includes abnormal weight, the weight corresponds to the weight
  • User characteristics may include: height, age, diet data, sleep data, and the like.
  • the change value and the change trend of the current state data of the user can be obtained, the change value is compared with the corresponding threshold, and the change trend is compared with Corresponding trend range comparison, if the predetermined requirement is exceeded, the state data is determined as data in the abnormal data set, and the corresponding policy data is further searched according to the abnormal data set, and the user is prompted according to the searched policy data, thereby making the user It can get the policy data that matches the user status data more automatically, and it is automatically detected and generated for the convenience of users.
  • Embodiment 2 is a diagrammatic representation of Embodiment 1:
  • FIG. 2 is a flowchart showing an implementation process of an analysis and recommendation method based on a user state according to a second embodiment of the present invention, which is described in detail as follows:
  • step S201 the current state data of the user is collected.
  • Step S201 is substantially the same as step S101 in the first embodiment, and details are not described herein again.
  • step S202 the target data input by the user is received, and other users who have been similar to the state data of the user and successfully reach the target are searched in the database.
  • the target data may be data that is different from the current state data of the user.
  • the user sets a goal for weight loss
  • the user's current body fat content is 40%
  • the target body fat content is 15.
  • look for a strategy with a high degree of matching For example, the user needs to find other users who have been similar in height and weight, and the user who is looking for successfully achieves the goal.
  • step S203 corresponding user data is obtained according to other users searched, and the user is reminded according to the policy data.
  • the phase in which the state data of other users changes refers to the phase in which the first state data is changed to the target data, and is acquired.
  • the strategy may include diet data, sleep data, exercise data, and the like.
  • the policy data may be sorted according to the popularity of the user, and may be sorted according to the influence of the user, so that the user can obtain better policy data.
  • the embodiment of the present invention further generates a policy with high reliability according to the set target data, combined with the user's own state data, and adjusts the policy data according to the user's state data in real time, thereby It is more conducive to users to achieve their goals.
  • Embodiment 3 is a diagrammatic representation of Embodiment 3
  • FIG. 3 is a flowchart showing an implementation process of an analysis and recommendation method based on a user state according to a third embodiment of the present invention, which is described in detail as follows:
  • step S301 the current state data of the user is collected.
  • step S302 the change value of the current state data and the change trend are calculated based on the history state data of the user recorded in advance.
  • step S303 an abnormal data set is acquired, the abnormal data set including state data whose the change value is greater than a predetermined threshold state data, and the change trend exceeds a predetermined trend range.
  • step S304 the policy data corresponding to the abnormal data set is searched in a preset database, and the user is reminded according to the policy data.
  • Steps S301-S304 are substantially the same as steps S101-S104 in the first embodiment, and are not described herein again.
  • step S305 the state data of the user and the corresponding time record are stored in the database, and the policy data is generated according to the change result.
  • step S305 the user state data is recorded in real time, and the correspondence between the state data directly related to the user behavior is in accordance with the state data indirectly related to the behavior of the user. Relationship, when one or more of the behaviors When the related state data changes, the change of the state data directly related to the user's behavior may be searched according to the correspondence relationship, and the state data directly related to the user's behavior is recorded as the policy data.
  • the status data indirectly related to the behavior of the user may include height, weight, body fat, blood pressure, and the like.
  • the status data directly related to the behavior of the user may include sleep data, diet data, exercise data, and the like.
  • the status data corresponds to the time, so that the policy data can be combined with specific time information, and the generated policy data information is more specific, prepared, and more practical.
  • Embodiment 4 is a diagrammatic representation of Embodiment 4:
  • FIG. 4 is a flowchart showing an implementation process of an analysis and recommendation device based on a user state according to a fourth embodiment of the present invention, which is described in detail as follows:
  • the data collection unit 401 is configured to collect current state data of the user.
  • the data calculation unit 402 is configured to calculate a change value of the current state data and a change trend according to the historical state data of the user recorded in advance;
  • the abnormal data set obtaining unit 403 is configured to acquire an abnormal data set, where the abnormal data set includes the state data that the change value is greater than a predetermined threshold state data, and the change trend exceeds a predetermined trend range;
  • the policy data searching unit 404 is configured to search for policy data corresponding to the abnormal data set in a preset database, and remind the user according to the policy data.
  • the status data includes one or more of a user's weight, height, blood pressure, body temperature, heart rate, diet data, exercise data, sleep data, and emotion data.
  • the policy data searching unit includes:
  • a first data search subunit searching, in the historical data of the user, whether the abnormal data set and its corresponding policy data exist;
  • a user search subunit configured to search for a user feature corresponding to the abnormal data set if the abnormal data set and the corresponding policy data are not present in the historical data of the user;
  • a second data search subunit configured to find, among other users having the user feature, whether the abnormal data set and its corresponding policy data exist.
  • the device further comprises:
  • a target data receiving unit configured to receive target data input by the user, and search for other users in the database that are similar to the state data of the user and successfully reach the target;
  • the matching unit is configured to acquire corresponding policy data according to other users that are searched, and remind the user according to the policy data.
  • the device further comprises:
  • a policy generating unit configured to store the state data of the user and the corresponding time record in the database, and generate policy data according to the change result.
  • the user state-based analysis and recommendation device corresponds to the user state-based analysis and recommendation method according to the first to third embodiments, and details are not described herein.
  • FIG. 5 is a structural block diagram of an analysis recommendation terminal according to a fifth embodiment of the present invention.
  • the monitoring terminal of the embodiment includes: an RF circuit 510, a memory 520, an input unit 530, a display unit 540, an audio circuit 560, and a network module 570. , processor 580, and power supply 590 and other components.
  • the terminal structure shown in FIG. 5 does not constitute a limitation to the terminal, and may include more or less components than those illustrated, or a combination of certain components, or different component arrangements.
  • the memory 520 can be used to store software programs and modules, and the processor 580 executes various functional applications and data processing of the terminals by running software programs and modules stored in the memory 520.
  • the memory 520 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may be stored according to Data created by the use of the terminal (such as audio data, phone book) and many more.
  • memory 520 can include high speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
  • the input unit 530 can be configured to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the terminal.
  • the input unit 530 may include a touch panel 531 and other input devices 532.
  • the touch panel 531 also referred to as a touch screen, can collect touch operations on or near the user (such as the user using a finger, a stylus, or the like on the touch panel 531 or near the touch panel 531. Operation), and drive the corresponding connecting device according to a preset program.
  • the touch panel 531 can include two parts: a touch detection device and a touch controller.
  • the touch detection device detects the touch orientation of the user, and detects a signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts the touch information into contact coordinates, and sends the touch information.
  • the processor 580 is provided and can receive commands from the processor 580 and execute them.
  • the touch panel 531 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves.
  • the input unit 530 may also include other input devices 532. Specifically, other input devices 532 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, joysticks, and the like.
  • the display unit 540 can be used to display information input by the user or information provided to the user as well as various menus of the terminal.
  • the display unit 540 can include a display panel 541.
  • the display panel 541 can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.
  • the touch panel 531 can cover the display panel 541. When the touch panel 531 detects a touch operation on or near it, the touch panel 531 transmits to the processor 580 to determine the type of the touch event, and then the processor 580 according to the touch event. The type provides a corresponding visual output on display panel 541.
  • the touch panel 531 and the display panel 541 are used as two independent components to implement the input and input functions of the terminal in FIG. 5, in some embodiments, the touch panel 531 may be integrated with the display panel 541. Realize the input and output functions of the terminal.
  • Audio circuit 560, speaker 561, and microphone 562 can provide an audio interface between the user and the terminal.
  • the audio circuit 560 can transmit the converted electrical data of the received audio data to the speaker 561, and convert it into a sound signal output by the speaker 561.
  • the microphone 562 converts the collected sound signal into an electrical signal, and the audio circuit 560 is used by the audio circuit 560. After receiving, it is converted into audio data, and then processed by the audio data output processor 580, sent to the other terminal via the network module 510, or output the audio data to the memory 520 for further processing.
  • the network module 570 can include a wireless fidelity (WiFi) module, a wired network module or a radio frequency module, wherein the wireless fidelity module belongs to a short-range wireless transmission technology, and the terminal can help the user to send and receive e-mails, browse web pages, and Access to streaming media, etc., it provides users with wireless broadband Internet access.
  • WiFi wireless fidelity
  • FIG. 5 shows the network module 570, it can be understood that it does not belong to the necessary configuration of the terminal, and can be omitted as needed within the scope of not changing the essence of the invention.
  • the processor 580 is the control center of the terminal, and connects various parts of the entire terminal using various interfaces and lines, by executing or executing software programs and/or modules stored in the memory 520, and calling data stored in the memory 520, executing The terminal's various functions and processing data, so as to monitor the terminal as a whole.
  • the processor 580 may include one or more processing units; preferably, the processor 580 may integrate an application processor and a modem processor, where the application processor mainly processes an operating system, a user interface, an application, and the like.
  • the modem processor primarily handles wireless communications. It will be appreciated that the above described modem processor may also not be integrated into the processor 580.
  • the terminal also includes a power source 590 (such as a battery) that supplies power to the various components.
  • a power source 590 such as a battery
  • the power source can be logically coupled to the processor 580 through a power management system to manage functions such as charging, discharging, and power management through the power management system.
  • the terminal may further include a camera, a Bluetooth module, and the like, and details are not described herein again.
  • the processor 580 included in the terminal further has the following functions: performing current data collection of the user; and calculating a change value and a change trend of the current state data according to the historical state data of the user recorded in advance; Obtaining an abnormal data set, the abnormal data set including a state in which the change value is greater than a predetermined threshold state data, and a state in which the change trend exceeds a predetermined trend range Data; searching for the policy data corresponding to the abnormal data set in a preset database, and reminding the user of the method corresponding to the method according to the policy data.
  • the disclosed apparatus and method may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
  • the technical solution of the present invention which is essential or contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product stored in a storage medium.
  • a number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the methods described in various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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Abstract

一种基于用户状态的分析推荐方法包括：采集用户当前的状态数据；根据预先记录的用户的历史状态数据，计算当前的状态数据的变化值以及变化趋势；获取异常数据集，所述异常数据集包括所述变化值大于预定的阈值状态数据，以及所述变化趋势超过预定的趋势范围的状态数据；在预设的数据库中查找所述异常数据集对应的策略数据，并根据所述策略数据提醒用户。使得用户能够得到与用户状态数据更为匹配的策略数据，并且自动进行检测和生成，方便用户使用。

Description

一种基于用户状态的分析推荐方法和装置 技术领域
本发明属于数据监控领域，尤其涉及一种基于用户状态的分析推荐方法和装置。
背景技术
随着社会物质生活水平的提高，人们对于自身的身体状况也变得越来越关注。比如健身的人们会关注自己的体脂含量，为了得到用户所期望的身形，会给自己制订一个饮食、运动的计划。以使得用户可以根据计划内容，控制自己的饮食、运动或者其它生活习惯等。
用户为了达到自己的目标状态，一般需要查询专业文献，或者向专业人士请教如何进行计划的制定。在计划制定后，用户根据计划中包括的事项对应的要求去执行。然而，普通用户制订计划的过程较为麻烦，而且在实施计划时，也可能会出现一些不可避免的意外情况，使得用户设定的计划或安排不能按期执行，因而会使得计划或者安排不能够很好的适应用户自身的变化，不能够有效的了解和控制用户自身的状态。
发明内容
本发明的目的在于提供一种基于用户状态的分析推荐方法，以解决现有技术用户根据自身的状态制定策略较为麻烦，而且策略不能够有效的适应用户自身的状态的问题。
第一方面，本发明实施例提供了一种基于用户状态的分析推荐方法，所述方法包括：
采集用户当前的状态数据；
根据预先记录的用户的历史状态数据，计算当前的状态数据的变化值以及 变化趋势；
获取异常数据集，所述异常数据集包括所述变化值大于预定的阈值状态数据，以及所述变化趋势超过预定的趋势范围的状态数据；
在预设的数据库中查找所述异常数据集对应的策略数据，并根据所述策略数据提醒用户。
结合第一方面，在第一方面的第一种可能实现方式中，所述状态数据包括用户的体重、身高、血压、体温、心率、饮食数据、运动数据、睡眠数据、情绪数据中的一种或者多种。
结合第一方面，在第一方面的第二种可能实现方式中，所述在预设的数据库中查找所述异常数据集对应的策略数据步骤包括：
在用户的历史数据中查找是否存在所述异常数据集及其对应的策略数据；
如果用户的历史数据中没有存在所述异常数据集及其对应的策略数据，则查找所述异常数据集所对应的用户特征；
在具有所述用户特征的其它用户中查找是否存在所述异常数据集及其对应的策略数据。
结合第一方面、第一方面的第一种可能实现方式、第一方面的第二种可能实现方式，在第一方面的第三种可能实现方式中，在所述采集用户当前的状态数据步骤之后，所述方法还包括：
接收用户输入的目标数据，在数据库中查找曾经与所述用户的状态数据相似，且成功达到目标的其它用户；
根据查找的其它用户获取相应的策略数据，并根据所述策略数据提醒用户。
结合第一方面、第一方面的第一种可能实现方式、第一方面的第二种可能实现方式，在第一方面的第四种可能实现方式中，所述方法还包括：
将所述用户的状态数据以及对应的时间记录存储在所述数据库，并根据变化结果生成策略数据。
第二方面，本发明实施例提供了一种基于用户状态的分析推荐装置，所述 装置包括：
数据采集单元，用于采集用户当前的状态数据；
数据计算单元，用于根据预先记录的用户的历史状态数据，计算当前的状态数据的变化值以及变化趋势；
异常数据集获取单元，用于获取异常数据集，所述异常数据集包括所述变化值大于预定的阈值状态数据，以及所述变化趋势超过预定的趋势范围的状态数据；
策略数据查找单元，用于在预设的数据库中查找所述异常数据集对应的策略数据，并根据所述策略数据提醒用户。
结合第二方面，在第二方面的第一种可能实现方式中，所述状态数据包括用户的体重、身高、血压、体温、心率、饮食数据、运动数据、睡眠数据、情绪数据中的一种或者多种。
结合第二方面，在第二方面的第二种可能实现方式中，所述策略数据查找单元包括：
第一数据查找子单元，在用户的历史数据中查找是否存在所述异常数据集及其对应的策略数据；
用户查找子单元，用于如果用户的历史数据中没有存在所述异常数据集及其对应的策略数据，则查找所述异常数据集所对应的用户特征；
第二数据查找子单元，用于在具有所述用户特征的其它用户中查找是否存在所述异常数据集及其对应的策略数据。
结合第二方面、第二方面的第一种可能实现方式、第二方面的第二种可能实现方式，在第二方面的第三种可能实现方式中，所述装置还包括：
目标数据接收单元，用于接收用户输入的目标数据，在数据库中查找曾经与所述用户的状态数据相似，且成功达到目标的其它用户；
匹配单元，用于根据查找的其它用户获取相应的策略数据，并根据所述策略数据提醒用户。
结合第二方面、第二方面的第一种可能实现方式、第二方面的第二种可能实现方式，在第二方面的第四种可能实现方式中，所述装置还包括：
策略生成单元，用于将所述用户的状态数据以及对应的时间记录存储在所述数据库，并根据变化结果生成策略数据。
本发明通过采集用户的状态数据，结合用户的历史状态数据，可以得到用户当前的状态数据的变化值以及变化趋势，将所述变化值与对应的阈值比较，以及将所述变化趋势与对应的趋势范围比较，如果超出预定的要求，则将所述状态数据确定为异常数据集中的数据，进一步根据所述异常数据集查找对应的策略数据，根据查找的策略数据提醒用户，从而使得用户能够得到与用户状态数据更为匹配的策略数据，并且自动进行检测和生成，方便用户使用。
附图说明
图1是本发明第一实施例提供的基于用户状态的分析推荐方法的实现流程图；
图2是本发明第二实施例提供的基于用户状态的分析推荐方法的实现流程图；
图3是本发明第三实施例提供的基于用户状态的分析推荐方法的实现流程图；
图4为本发明第四实施例提供的基于用户状态的分析推荐装置的结构示意图；
图5为本发明第五实施例提供的分析推荐终端的结构示意图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
本发明实施例提出一种基于用户状态的分析推荐方法，以解决现有技术中对于用户的状态制定策略时，需要用户查找专业的资料，在了解了专业资料后，才能进一步根据用户自身的状态制定策略，这样不仅操作麻烦，而且制作的策略的准确度不高。另外，在策略执行过程中，用户可能会碰到特殊情况时，导致策略不能有效的与用户实际状态相匹配的问题。下面结合附图，对本发明作进一步的说明。
实施例一：
图1示出了本发明第一实施例提供的基于用户状态的分析推荐方法的实现流程，详述如下：
在步骤S101中，采集用户当前的状态数据。
具体的，所述用户可以成人、青少年、儿童、婴儿等。由于婴儿、儿童或者青少年的状态数据更容易发生变化，因此，本发明所述基于用户状态的分析推荐方法，尤其适用于青少年、儿童、婴儿等用户。
本发明实施例所述状态数据，可以包括用户的体重、身高、血压、体温、心率、饮食数据、运动数据、睡眠数据、情绪数据中的一种或者多种。
其中，所述用户的身高的采集，可以通过定期的测量获取。当预设的测量时间到达时，可发送身高测量的提醒，从而能够得到较为全面的成长数据。所述身高的测量周期，根据成长的阶段不同，采用不同的测量周期。可以随着用户的年龄增大，相应的增大测量的周期。比如对于婴儿，测量周期可以为一个星期，对于儿童或者青少年，测量周期可以为一个月。
所述用户的体重，可与其它的相关数据监测用户的身体状态的变化，比如与用户的心率、运动数据、饮食数据、年龄等状态数据构成一个或者多个相关的状态数据集合。可以预先建立这些状态数据集与策略数据的对应关系。比如，当用户的体重增加时，可以结合用户的心率、运动数据、饮食数据，判断体重的增加是属于肌肉增重，还是属于脂肪增重。比如用户在一定的时间区间内，根据用户的饮食数据中包括的营养成份、饮食的时间、运动的时间、运动的强 度、体重的变化，或者还可以采集用户的体脂含量，计算得到用户体重增加是因为肌肉增生还是脂肪增重，或者还可以进一步计算肌肉增加或减少的重量，以及脂肪增加或减少的重量。比如通过体脂含量与体重计算脂肪的重量的变化等。所述用户的体重，可以按照预定的周期，通过用户输入的方式进行采集。
所述用户的血压，可以与用户的情绪数据、心率、饮食数据、睡眠数据、运动数据相关联，构成一个或者多个状态数据集。可以在数据库中预先设置状态数据集与策略数据的对应关系，或者将用户的策略数据更新至数据库中。所述血压的数据，可以通过便携式设备，比如手环、智能手表等设备，实时的获取。
所述用户的体温，可以与用户的运动数据构成状态数据集。当用户的体温发生变化，而运动数据没有发生变化时，或者用户的体温与运动数据一起发生变化，则需要采用不同的策略数据。比如运动数据表明用户正在运动，而体温也在上升时，则可以查找与运动后的恢复相关的策略作为与其对应的策略数据。而如果用户的体温上升或者下降，而没有与其相关的运动数据时，则生成与体温异常相关的策略数据。所述用户的体温，可以通过便携设备上设置的红外测温传感器的方式获取，或者也可以设置为其它温度传感器。
所述用户的心率，可以通过穿戴式设备，比如智能手表、手环等设备获取。所述心率出现变化时，比如心率所表示的每分钟的心跳次数，由原来的80次/分钟，逐渐的下降至60次每分钟，有可能是用户的心肺功能越来越好，可以结合运动数据、饮食数据做出更为准确的判断，以及推送与判断结果相关的策略数据。
所述饮食数据，可以包括用户饮食的份量，饮食的类型等。即饮食的重量。比如，用户吃一个馒头，则可以包括用户饮食的类型为面食，饮食的份量可以约为200g，用户吃一个青菜包子时，则可以包括用户饮食的类型包括面食和青菜，面食的份量为120g，青菜的份量是为5g。
所述运动数据，可以包括用户运动的类型和/或运动类型的持续时长，比如 用户散步了1公里，则可以记录用户的运动类型为散步，运动持续的时长为20分钟等。所述运动类型可以包括散步、跑步、踢球、游泳、打篮球等。根据运动类型的不同，在单位时间下消耗的热量也会不同。同样，对于同样的运动类型，在不同的持续时长所消耗的热量也不同。
所述睡眠数据，可以包括睡眠的深度和睡眠的在某种睡眠深度下的持续时长。通过对睡眠浓度和睡眠时长相结合，可以换算为用户睡眠的评分，比如，用户在第一深度睡眠条件下，睡眠时长6小时即可得到睡眠评分90分，而在第二睡眠深度条件下，睡眠时长8小进可得到的睡眠评分为95分。一般情况下，在同等睡眠时长条件下，睡眠深度越深，得到的睡眠评分的分值越高。相应的，在同等睡眠深度条件下，睡眠时长越长，睡眠评分也越高。但是，根据用户的不同，睡眠时长还对应的睡眠时长限值。比如成年人每天的睡眠时长不应该超过10小时。
在步骤S102中，根据预先记录的用户的历史状态数据，计算当前的状态数据的变化值以及变化趋势；
根据所述历史状态数据的具体类型，记录的周期也会不同，比如对于身高的记录周期可以较长，而对于心率、血压和体温、睡眠数据则可以实时记录，对于饮食数据可以在每次饮食时记录。
所述状态数据的变化值，可以为当前记录的状态数据，与之前的状态数据进行比较，或者与之前一段时间的状态数据的平均值进行比较，计算得到两者的差值。
所述状态数据的变化趋势，可以通过绘制历史状态数据与时间的坐标系，比如可以选用时间作为横坐标，状态数据作为纵坐标绘制曲线，对曲线的斜率的绝对值进行判断，或者对斜率的大小与方向进行判断。
在步骤S103中，获取异常数据集，所述异常数据集包括所述变化值大于预定的阈值状态数据，以及所述变化趋势超过预定的趋势范围的状态数据；
通过对用户的状态数据进行监测和判断，可以得到用户的状态数据出现明 显变化的数据，并根据出现变化的状态数据构成异常数据集。所述预定的趋势范围，可以为预先设定的趋势变化的区间，当超出预定的区间时则认为当前的状态数据为异常数据。比如，对于身高的状态数据的变化，可以设定变化趋势的较大值和较小值，当身高的变化趋势大于所述较大值，或者身高的变化趋势小于所述较小值时，则判断当前的状态数据为异常数据集中的状态数据。
在步骤S104中，在预设的数据库中查找所述异常数据集对应的策略数据，并根据所述策略数据提醒用户。
具体的，所述在预设的数据库中查找所述异常数据集对应的策略数据步骤包括：
在用户的历史数据中查找是否存在所述异常数据集及其对应的策略数据；
如果用户的历史数据中没有存在所述异常数据集及其对应的策略数据，则查找所述异常数据集所对应的用户特征；
在具有所述用户特征的其它用户中查找是否存在所述异常数据集及其对应的策略数据。
当生成用户当前时间的异常数据集后，优先根据用户的历史数据进行相关数据的查找，对于以往出现过的异常进行分析，针对所述异常所做的历史行为数据，结合用户状态数据的变化，筛选得到有效的行为数据，并将所述行为数据作为所述异常数据集对应的策略数据。
当所述异常数据集中的状态数据，不属于预先设定的同一个关联状态数据集时，则可以将异常数据根据所述关联状态数据集进行分割，根据分割后的两个或者两个以上的异常数据集进行查找。
当在用户的历史数据中没有查找到与所述异常数据集对应的数据时，则可以根据所述异常数据集对应的用户特征，查找与所述用户特征相同或相似的其它用户，并在其它用户中查找与所述异常数据集对应的策略数据。
所述异常数据集对应的用户特征，可以根据异常数据的不同，预设不同的用户特征。比如在检测到用户的异常数据集包括体重异常时，与所述体重对应 的用户特征可以包括：身高、年龄、饮食数据、睡眠数据等。
通过设定异常数据集查找的顺序，可以得到更为精确的策略数据，从而能够更为有效的处理用户出现的异常的状态数据。
本发明实施例通过采集用户的状态数据，结合用户的历史状态数据，可以得到用户当前的状态数据的变化值以及变化趋势，将所述变化值与对应的阈值比较，以及将所述变化趋势与对应的趋势范围比较，如果超出预定的要求，则将所述状态数据确定为异常数据集中的数据，进一步根据所述异常数据集查找对应的策略数据，根据查找的策略数据提醒用户，从而使得用户能够得到与用户状态数据更为匹配的策略数据，并且自动进行检测和生成，方便用户使用。
实施例二：
图2示出了本发明第二实施例提供的基于用户状态的分析推荐方法的实现流程，详述如下：
在步骤S201中，采集用户当前的状态数据。
步骤S201与实施例一中的步骤S101基本相同，在此不作重复赘述。
在步骤S202中，接收用户输入的目标数据，在数据库中查找曾经与所述用户的状态数据相似，且成功达到目标的其它用户。
具体的，所述目标数据，可以是与用户当前的状态数据存在区别的数据。比如，用户为了减肥而制定一个目标，用户当前的体脂含量为40％，目标体脂含量为15。在本发明实施例中，对于用户给出了目标数据后，需要根据所述目标数据，查找曾经与用户的状态数据相似，并且成功实现目标的用户，并根据具有相关经验的用户所提供的策略数据中，寻找匹配度高的策略。比如用户需要寻找曾经与自身的身高、体重相似的其它用户，而且所查找的用户成功的达到目标
在步骤S203中，根据查找的其它用户获取相应的策略数据，并根据所述策略数据提醒用户。
在查找的其它用户中获取策略数据时，可以对查找的其它用户的状态数据 发生变化的阶段，假设在步骤S201中采集的状态数据为第一状态数据，那么，其它用户的所述状态数据发生变化的阶段，是指由第一状态数据变化为目标数据的阶段，并获取在该阶段时由其它用户所记录的与所述状态数据相应的策略数据。所述策略可以包括饮食数据、睡眠数据、运动数据等。
优选的实施方式中，所述策略数据可以根据用户的好评度进行排序，还可以根据用户的影响力进行排序，使得用户能够得到更佳的策略数据。
本发明实施例在实施例一的基础上，进一步根据设定的目标数据，结合用户自身的状态数据，生成可靠度高的策略，并且实时的根据用户的状态数据，调整所述策略数据，从而更有利于用户实现目标。
实施例三：
图3示出了本发明第三实施例提供的基于用户状态的分析推荐方法的实现流程，详述如下：
在步骤S301中，采集用户当前的状态数据。
在步骤S302中，根据预先记录的用户的历史状态数据，计算当前的状态数据的变化值以及变化趋势。
在步骤S303中，获取异常数据集，所述异常数据集包括所述变化值大于预定的阈值状态数据，以及所述变化趋势超过预定的趋势范围的状态数据。
在步骤S304中，在预设的数据库中查找所述异常数据集对应的策略数据，并根据所述策略数据提醒用户。
步骤S301-S304与实施例一中的步骤S101-S104基本相同，在此不作重复赘述。
在步骤S305中，将所述用户的状态数据以及对应的时间记录存储在所述数据库，并根据变化结果生成策略数据。
为了进一步提高本发明的策略精度，在步骤S305中，对用户状态数据进行实时的记录，并且根据预先设定的与用户的行为间接相关的状态数据与用户行为直接相关的状态数据之间的对应关系，当其中一项或者几项与用户的行为间 接相关的状态数据发生变化时，可根据对应关系查找与其对应的与用户的行为直接相关的状态数据的变化，并将与用户的行为直接相关的状态数据记录为策略数据。所述与用户的行为间接相关的状态数据可以包括身高、体重、体脂、血压等。所述与用户的行为直接相关的状态数据可以包括睡眠数据、饮食数据、运动数据等。
所述状态数据与时间对应，可以使得策略数据能够结合具体的时间信息，生成的策略数据信息更为具体、准备，实用性更高。
将所述用户的状态数据以及对应的时间记录存储在所述数据库，并根据变化结果生成策略数据时，其它用户可以查找用户的状态数据的变化以及对应的策略数据，从而有利于用户获取更为精确、可靠的策略数据。
实施例四：
图4示出了本发明第四实施例提供的基于用户状态的分析推荐装置的实现流程，详述如下：
数据采集单元401，用于采集用户当前的状态数据；
数据计算单元402，用于根据预先记录的用户的历史状态数据，计算当前的状态数据的变化值以及变化趋势；
异常数据集获取单元403，用于获取异常数据集，所述异常数据集包括所述变化值大于预定的阈值状态数据，以及所述变化趋势超过预定的趋势范围的状态数据；
策略数据查找单元404，用于在预设的数据库中查找所述异常数据集对应的策略数据，并根据所述策略数据提醒用户。
优选的，所述状态数据包括用户的体重、身高、血压、体温、心率、饮食数据、运动数据、睡眠数据、情绪数据中的一种或者多种。
优选的，所述策略数据查找单元包括：
第一数据查找子单元，在用户的历史数据中查找是否存在所述异常数据集及其对应的策略数据；
用户查找子单元，用于如果用户的历史数据中没有存在所述异常数据集及其对应的策略数据，则查找所述异常数据集所对应的用户特征；
第二数据查找子单元，用于在具有所述用户特征的其它用户中查找是否存在所述异常数据集及其对应的策略数据。
优选的，所述装置还包括：
目标数据接收单元，用于接收用户输入的目标数据，在数据库中查找曾经与所述用户的状态数据相似，且成功达到目标的其它用户；
匹配单元，用于根据查找的其它用户获取相应的策略数据，并根据所述策略数据提醒用户。
优选的，所述装置还包括：
策略生成单元，用于将所述用户的状态数据以及对应的时间记录存储在所述数据库，并根据变化结果生成策略数据。
本发明实施例所述基于用户状态的分析推荐装置，与实施例一至三所述基于用户状态的分析推荐方法对应，在此不作重复赘述。
实施例五
图5为本发明第五实施例提供的分析推荐终端的结构框图，本实施例所述监测终端，包括：RF电路510、存储器520、输入单元530、显示单元540、音频电路560、网络模块570、处理器580、以及电源590等部件。本领域技术人员可以理解，图5中示出的终端结构并不构成对终端的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
下面结合图5对终端的各个构成部件进行具体的介绍：
存储器520可用于存储软件程序以及模块，处理器580通过运行存储在存储器520的软件程序以及模块，从而执行终端的各种功能应用以及数据处理。存储器520可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作***、至少一个功能所需的应用程序(比如声音播放功能、图像播放功能等)等；存储数据区可存储根据终端的使用所创建的数据(比如音频数据、电话本 等)等。此外，存储器520可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。
输入单元530可用于接收输入的数字或字符信息，以及产生与终端的用户设置以及功能控制有关的键信号输入。具体地，输入单元530可包括触控面板531以及其他输入设备532。触控面板531，也称为触摸屏，可收集用户在其上或附近的触摸操作(比如用户使用手指、触笔等任何适合的物体或附件在触控面板531上或在触控面板531附近的操作)，并根据预先设定的程式驱动相应的连接装置。可选的，触控面板531可包括触摸检测装置和触摸控制器两个部分。其中，触摸检测装置检测用户的触摸方位，并检测触摸操作带来的信号，将信号传送给触摸控制器；触摸控制器从触摸检测装置上接收触摸信息，并将它转换成触点坐标，再送给处理器580，并能接收处理器580发来的命令并加以执行。此外，可以采用电阻式、电容式、红外线以及表面声波等多种类型实现触控面板531。除了触控面板531，输入单元530还可以包括其他输入设备532。具体地，其他输入设备532可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆等中的一种或多种。
显示单元540可用于显示由用户输入的信息或提供给用户的信息以及终端的各种菜单。显示单元540可包括显示面板541，可选的，可以采用液晶显示器(Liquid Crystal Display，LCD)、有机发光二极管(Organic Light-Emitting Diode，OLED)等形式来配置显示面板541。进一步的，触控面板531可覆盖显示面板541，当触控面板531检测到在其上或附近的触摸操作后，传送给处理器580以确定触摸事件的类型，随后处理器580根据触摸事件的类型在显示面板541上提供相应的视觉输出。虽然在图5中，触控面板531与显示面板541是作为两个独立的部件来实现终端的输入和输入功能，但是在某些实施例中，可以将触控面板531与显示面板541集成而实现终端的输入和输出功能。
音频电路560、扬声器561，传声器562可提供用户与终端之间的音频接口。 音频电路560可将接收到的音频数据转换后的电信号，传输到扬声器561，由扬声器561转换为声音信号输出；另一方面，传声器562将收集的声音信号转换为电信号，由音频电路560接收后转换为音频数据，再将音频数据输出处理器580处理后，经网络模块510以发送给比如另一终端，或者将音频数据输出至存储器520以便进一步处理。
网络模块570可以包括无线保真(wireless fidelity，WiFi)模块，有线网络模块或者射频模块，其中无线保真模块属于短距离无线传输技术，终端通过网络模块570可以帮助用户收发电子邮件、浏览网页和访问流式媒体等，它为用户提供了无线的宽带互联网访问。虽然图5示出了网络模块570，但是可以理解的是，其并不属于终端的必须构成，完全可以根据需要在不改变发明的本质的范围内而省略。
处理器580是终端的控制中心，利用各种接口和线路连接整个终端的各个部分，通过运行或执行存储在存储器520内的软件程序和/或模块，以及调用存储在存储器520内的数据，执行终端的各种功能和处理数据，从而对终端进行整体监控。可选的，处理器580可包括一个或多个处理单元；优选的，处理器580可集成应用处理器和调制解调处理器，其中，应用处理器主要处理操作***、用户界面和应用程序等，调制解调处理器主要处理无线通信。可以理解的是，上述调制解调处理器也可以不集成到处理器580中。
终端还包括给各个部件供电的电源590(比如电池)，优选的，电源可以通过电源管理***与处理器580逻辑相连，从而通过电源管理***实现管理充电、放电、以及功耗管理等功能。
尽管未示出，终端还可以包括摄像头、蓝牙模块等，在此不再赘述。
在本发明实施例中，该终端所包括的处理器580还具有以下功能：执行采集用户当前的状态数据；根据预先记录的用户的历史状态数据，计算当前的状态数据的变化值以及变化趋势；获取异常数据集，所述异常数据集包括所述变化值大于预定的阈值状态数据，以及所述变化趋势超过预定的趋势范围的状态 数据；在预设的数据库中查找所述异常数据集对应的策略数据，并根据所述策略数据提醒用户的方法所对应的程序。
在本发明所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个***，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种基于用户状态的分析推荐方法，其特征在于，所述方法包括：
    采集用户当前的状态数据；
    根据预先记录的用户的历史状态数据，计算当前的状态数据的变化值以及变化趋势；
    获取异常数据集，所述异常数据集包括所述变化值大于预定的阈值状态数据，以及所述变化趋势超过预定的趋势范围的状态数据；
    在预设的数据库中查找所述异常数据集对应的策略数据，并根据所述策略数据提醒用户。
  2. 根据权利要求1所述方法，其特征在于，所述状态数据包括用户的体重、身高、血压、体温、心率、饮食数据、运动数据、睡眠数据、情绪数据等中的一种或者多种。
  3. 根据权利1所述方法，其特征在于，所述在预设的数据库中查找所述异常数据集对应的策略数据步骤包括：
    在用户的历史数据中查找是否存在所述异常数据集及其对应的策略数据；
    如果用户的历史数据中没有存在所述异常数据集及其对应的策略数据，则查找所述异常数据集所对应的用户特征；
    在具有所述用户特征的其它用户中查找是否存在所述异常数据集及其对应的策略数据。
  4. 根据权利要求1-3任一项所述方法，其特征在于，在所述采集用户当前的状态数据步骤之后，所述方法还包括：
    接收用户输入的目标数据，在数据库中查找曾经与所述用户的状态数据相似，且成功达到目标的其它用户；
    根据查找的其它用户获取相应的策略数据，并根据所述策略数据提醒用户。
  5. 根据权利要求1-3任一项所述方法，其特征在于，所述方法还包括：
    将所述用户的状态数据以及对应的时间记录存储在所述数据库，并根据变化结果生成策略数据。
  6. 一种基于用户状态的分析推荐装置，其特征在于，所述装置包括：
    数据采集单元，用于采集用户当前的状态数据；
    数据计算单元，用于根据预先记录的用户的历史状态数据，计算当前的状态数据的变化值以及变化趋势；
    异常数据集获取单元，用于获取异常数据集，所述异常数据集包括所述变化值大于预定的阈值状态数据，以及所述变化趋势超过预定的趋势范围的状态数据；
    策略数据查找单元，用于在预设的数据库中查找所述异常数据集对应的策略数据，并根据所述策略数据提醒用户。
  7. 根据权利要求6所述装置，其特征在于，所述状态数据包括用户的体重、身高、血压、体温、心率、饮食数据、运动数据、睡眠数据、情绪数据中的一种或者多种。
  8. 根据权利6所述装置，其特征在于，所述策略数据查找单元包括：
    第一数据查找子单元，在用户的历史数据中查找是否存在所述异常数据集及其对应的策略数据；
    用户查找子单元，用于如果用户的历史数据中没有存在所述异常数据集及其对应的策略数据，则查找所述异常数据集所对应的用户特征；
    第二数据查找子单元，用于在具有所述用户特征的其它用户中查找是否存在所述异常数据集及其对应的策略数据。
  9. 根据权利要求6-8任一项所述装置，其特征在于，所述装置还包括：
    目标数据接收单元，用于接收用户输入的目标数据，在数据库中查找曾经与所述用户的状态数据相似，且成功达到目标的其它用户；
    匹配单元，用于根据查找的其它用户获取相应的策略数据，并根据所述策略数据提醒用户。
  10. 根据权利要求6-8任一项所述装置，其特征在于，所述装置还包括：
    策略生成单元，用于将所述用户的状态数据以及对应的时间记录存储在所述数据库，并根据变化结果生成策略数据。
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