WO2019056659A1 - 状态检测方法、装置和存储介质 - Google Patents

状态检测方法、装置和存储介质 Download PDF

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Publication number
WO2019056659A1
WO2019056659A1 PCT/CN2018/071626 CN2018071626W WO2019056659A1 WO 2019056659 A1 WO2019056659 A1 WO 2019056659A1 CN 2018071626 W CN2018071626 W CN 2018071626W WO 2019056659 A1 WO2019056659 A1 WO 2019056659A1
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Prior art keywords
sample
state
mobile terminal
classifier
accommodating
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PCT/CN2018/071626
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English (en)
French (fr)
Inventor
李国盛
刘颖红
Original Assignee
北京小米移动软件有限公司
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Application filed by 北京小米移动软件有限公司 filed Critical 北京小米移动软件有限公司
Priority to JP2018507708A priority Critical patent/JP2020526941A/ja
Priority to KR1020197006442A priority patent/KR102154457B1/ko
Publication of WO2019056659A1 publication Critical patent/WO2019056659A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0251—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity
    • H04W52/0254—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity detecting a user operation or a tactile contact or a motion of the device
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
    • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
    • H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
    • H04M1/72454—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to context-related or environment-related conditions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
    • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
    • H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
    • H04M1/72463—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions to restrict the functionality of the device
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
    • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
    • H04M1/72484—User interfaces specially adapted for cordless or mobile telephones wherein functions are triggered by incoming communication events
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0225—Power saving arrangements in terminal devices using monitoring of external events, e.g. the presence of a signal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0251—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity
    • H04W52/0258—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity controlling an operation mode according to history or models of usage information, e.g. activity schedule or time of day
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M2201/00—Electronic components, circuits, software, systems or apparatus used in telephone systems
    • H04M2201/34—Microprocessors
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M2250/00—Details of telephonic subscriber devices
    • H04M2250/12—Details of telephonic subscriber devices including a sensor for measuring a physical value, e.g. temperature or motion
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the present disclosure relates to the field of mobile technologies, and in particular, to a state detection method, apparatus, and storage medium.
  • the mobile terminal usually determines whether it is located in a pocket by various means, and then performs corresponding operations according to the judgment result. For example, when the mobile terminal determines that it is located in the pocket, the mobile terminal automatically enters the sleep mode.
  • a state detecting method for detecting whether a mobile terminal provided with a distance sensor (English: psensor) and an acceleration sensor (English: gsensor) is located in a pocket in which the mobile terminal determines the distance by a distance sensor. Whether there is an obstacle in a certain direction, and the acceleration of the mobile terminal is acquired by the acceleration sensor. When there is an obstacle in a certain direction of the mobile terminal and the mobile terminal has an acceleration, it can be determined that the mobile terminal is located in the pocket.
  • the inventors have found that the related art has at least the following problems: in some special scenarios, the accuracy of the above method is low, for example, when the mobile terminal is on a traveling vehicle (the mobile terminal is always due to the shaking of the vehicle) When there is acceleration) and its distance sensor is occluded, the mobile terminal will always determine that it is in the pocket.
  • the embodiments of the present disclosure provide a state detection method, apparatus, and storage medium, which can solve the problem of low accuracy of the state detection method in the related art.
  • the technical solution is as follows:
  • a state detection comprising:
  • Obtaining a state sample set of the plurality of sample mobile terminals where the state sample set includes n accommodating state samples collected by the plurality of sample mobile terminals in a process of presetting the accommodating space, and moving in the plurality of samples The m non-receiving state samples collected by the terminal in the process of the preset accommodating space, where n and m are integers greater than 0;
  • the classifier Sending, to the mobile terminal to be detected, the classifier, the to-be-detected mobile terminal is configured to determine, according to the state sample of the mobile terminal to be detected, whether the mobile terminal to be detected is located in the preset capacity Set the space.
  • the acquiring the state sample set of the sample mobile terminal includes:
  • the sending the classifier to the mobile terminal to be detected includes:
  • the classification algorithm is any one of a decision tree algorithm, a logistic regression algorithm, and a support vector machine algorithm.
  • any one of the n accommodating state samples includes a p feature parameter and a label for indicating a category of the any accommodating state sample, and the any accommodating state sample The category is located in the accommodating space, and the p is an integer greater than zero.
  • any one of the m non-receiving state samples includes p feature parameters and a label for indicating a category of the any non-receiving state sample, where the non- The category of the accommodating state sample is not located in the accommodating space.
  • the p feature parameters include: a parameter acquired by the distance sensor, a parameter acquired by the acceleration sensor, a system time of the sample mobile terminal, a parameter acquired by the light sensor, a program of the sample mobile terminal, and the At least three of the screen display interfaces of the sample mobile terminal.
  • the preset accommodating space is a pocket, a backpack or a bag carried by the user.
  • a state detecting method comprising:
  • the state sample set includes n accommodating state samples collected during the process in which the sample mobile terminal is located in the accommodating space, and m unacceptable states collected during the process in which the sample mobile terminal is not located in the accommodating space. a sample, wherein n and m are integers greater than 0;
  • the label is used to indicate a category of the status sample, and the category of the status sample is located in the accommodating space or not in the accommodating space.
  • the method further includes:
  • the preset function of the mobile terminal to be detected is turned off
  • the mobile terminal to be detected is controlled to enter a sleep state.
  • the preset function includes at least one of a bright screen notification, a gesture bright screen, a fingerprint unlocking, and a double-click button to activate the camera.
  • a state detecting device comprising:
  • a sample set obtaining module configured to acquire a state sample set of the plurality of sample mobile terminals, where the state sample set includes n accommodating state samples collected by the plurality of sample mobile terminals in a preset accommodating space, and And m non-receiving state samples collected during the process that the plurality of sample mobile terminals are not located in the preset accommodating space, wherein n and m are integers greater than 0;
  • a classifier training module configured to use the state sample set as training data, and obtain a classifier according to the classification algorithm
  • a sending module configured to send the classifier to the mobile terminal to be detected, where the to-be-detected mobile terminal is configured to determine, according to the state sample of the mobile terminal to be detected, whether the mobile terminal to be detected is located based on the classifier The preset accommodation space.
  • the sample set obtaining module is configured to:
  • the sending module is configured to:
  • the classification algorithm is any one of a decision tree algorithm, a logistic regression algorithm, and a support vector machine algorithm.
  • any one of the n accommodating state samples includes a p feature parameter and a label for indicating a category of the any accommodating state sample, and the any accommodating state sample The category is located in the accommodating space, and the p is an integer greater than zero.
  • any one of the m non-receiving state samples includes p feature parameters and a label for indicating a category of the any non-receiving state sample, where the non- The category of the accommodating state sample is not located in the accommodating space.
  • the p feature parameters include: a parameter acquired by the distance sensor, a parameter acquired by the acceleration sensor, a system time of the sample mobile terminal, a parameter acquired by the light sensor, a program of the sample mobile terminal, and the At least three of the screen display interfaces of the sample mobile terminal.
  • the preset accommodating space is a pocket, a backpack or a bag carried by the user.
  • a state detecting device comprising:
  • a receiving module configured to receive a classifier sent by the classifier generating device, wherein the classifier is configured to use the state sample set as the training data, and the training method according to the classification algorithm is used to determine whether the mobile terminal to be detected is located in the capacity a space classifier, the state sample set includes n accommodating state samples collected during a process in which the sample mobile terminal is located in the accommodating space, and m collected during the process in which the sample mobile terminal is not located in the accommodating space a non-receiving state sample, wherein n and m are integers greater than 0;
  • a sample obtaining module configured to acquire a state sample of the current moment of the mobile terminal to be detected
  • An input module configured to input the status sample into the classifier
  • a label obtaining module configured to acquire a label output by the classifier according to a status sample of the current time, the label is used to indicate a category of the status sample, and the category of the status sample is located in an accommodating space or not located Accommodate space.
  • the state detecting device further includes:
  • a function closing module configured to close a preset function of the mobile terminal to be detected when the output label indicates that the category of the status sample is located in the accommodating space
  • a hibernation module configured to control the to-be-detected mobile terminal to enter a dormant state when the outputted tag indicates that the category of the status sample is located in the accommodating space.
  • the preset function includes at least one of a bright screen notification, a gesture bright screen, a fingerprint unlocking, and a double-click button to activate the camera.
  • a state detecting device comprising:
  • a memory for storing processor executable instructions
  • the processor is configured to perform the state detection method of the first aspect.
  • a state detecting device comprising:
  • a memory for storing processor executable instructions
  • the processor is configured to perform the state detection method of the second aspect.
  • a computer readable storage medium having stored thereon instructions, wherein the instructions are executed by a processor to implement the first aspect State detection method.
  • a computer readable storage medium having stored thereon instructions, wherein the instructions are executed by a processor to implement the second aspect State detection method.
  • the method for judging whether the mobile terminal is located in the pocket according to individual features in the related art is solved, and the problem of low accuracy in some special scenarios is solved.
  • the accuracy of the state detection method is achieved.
  • FIG. 1 is a schematic diagram of an implementation environment involved in an embodiment of the present disclosure
  • 3-1 is a flowchart of another state detection method according to an embodiment of the present disclosure.
  • FIG. 3-2 is a flowchart of obtaining a sample of a accommodating state in the embodiment shown in FIG. 2-1;
  • 3-3 is a flowchart of obtaining a non-receiving state sample in the embodiment shown in FIG. 2-1;
  • 3-4 is a flowchart of a transmission classifier in the embodiment shown in FIG. 2-1;
  • FIG. 4 is a block diagram of a state detecting apparatus according to an embodiment of the present disclosure.
  • 5-1 is a block diagram of a state detecting apparatus according to an embodiment of the present disclosure.
  • 5-2 is a block diagram of another state detecting apparatus according to an embodiment of the present disclosure.
  • FIG. 6 is a block diagram of an apparatus for state detection, according to an exemplary embodiment
  • FIG. 7 is a block diagram of an apparatus for state detection, according to an exemplary embodiment.
  • FIG. 1 is a schematic diagram of an implementation environment involved in various embodiments of the present disclosure, which may include a mobile terminal 11 to be detected, a classifier generating device 12, and a plurality of sample mobile terminals 13.
  • the mobile terminal 11 to be detected may be a mobile phone, a tablet computer, a handheld game console, and various smart wearable devices.
  • the classifier generating device 12 may be a desktop computer, a notebook computer, a server or a server cluster, or the like.
  • the classifier generating means 12 is capable of establishing a connection with the mobile terminal 11 to be detected and the plurality of sample mobile terminals 13 by wire or wirelessly.
  • the plurality of sample mobile terminals 13 may be a plurality of mobile terminals having the same category or model as the mobile terminal 11 to be detected.
  • FIG. 2-1 is a flowchart of a state detecting method according to an embodiment of the present disclosure. This embodiment is exemplified by the state detecting method applied to the classifier generating device in the implementation environment shown in FIG. 1.
  • the state detection method can include the following steps:
  • Step 201 Acquire a state sample set of a plurality of sample mobile terminals, where the state sample set includes n accommodating state samples collected by the plurality of sample mobile terminals in a preset accommodating space, and is not located in the plurality of sample mobile terminals.
  • the m non-receiving state samples collected during the preset accommodating space, n and m are integers greater than 0.
  • Step 202 Determine a classification algorithm.
  • Step 203 Using the state sample set as the training data, and training the classifier according to the classification algorithm.
  • Step 204 Send a classifier to the mobile terminal to be detected.
  • the to-be-detected mobile terminal is configured to determine, according to the classifier, whether the mobile terminal to be detected is located in the preset accommodating space according to the state sample of the mobile terminal to be detected.
  • the state detection method acquires a state sample set of a plurality of sample mobile terminals, and uses the state sample set as training data to train a classifier according to a classification algorithm, and classifies the classifier.
  • the device is sent to the mobile terminal to be detected, so that the mobile terminal to be detected can determine whether it is located in the preset accommodating space according to the classifier that comprehensively considers various factors.
  • the method for judging whether the mobile terminal is located in the pocket according to individual features in the related art is solved, and the problem of low accuracy in some special scenarios is solved.
  • the accuracy of the state detection method is achieved.
  • the state detection method can include the following steps:
  • Step 205 Receive a classifier sent by the classifier generating device, where the classifier is a classifier that uses the state sample set as the training data, and the classifier that is trained according to the classification algorithm to determine whether the mobile terminal to be detected is located in the accommodating space,
  • the state sample set includes n accommodating state samples collected during the process in which the sample mobile terminal is located in the accommodating space, and m unacceptable state samples collected during the process in which the sample mobile terminal is not located in the accommodating space, n and m Both are integers greater than zero.
  • Step 206 Obtain a state sample of a current moment of the mobile terminal to be detected.
  • Step 207 Input a state sample into the classifier.
  • Step 208 Acquire a label output by the classifier according to a status sample of the current time, the label is used to indicate a category of the status sample, and the category of the status sample is located in the accommodating space or not in the accommodating space.
  • the state detection method inputs a state sample of a current moment of a mobile terminal to be tested into a classifier generated according to a classification algorithm and a state sample set, and classifies a plurality of factors according to the comprehensive consideration.
  • the device determines whether the current moment of the mobile terminal to be tested is in the pocket.
  • the method for judging whether the mobile terminal is located in the pocket according to individual features in the related art is solved, and the problem of low accuracy in some special scenarios is solved.
  • the accuracy of the state detection method is achieved.
  • FIG. 3-1 A flowchart of another state detection method according to an embodiment of the present disclosure may be as shown in FIG. 3-1.
  • This embodiment uses the state detection method to be applied to a mobile terminal to be detected.
  • the state detection method may include the following. Several steps:
  • Step 301 The classifier generating apparatus acquires n pieces of accommodating state samples of the plurality of sample mobile terminals.
  • the accommodating state sample is a state sample collected during the process in which the sample mobile terminal is located in the accommodating space.
  • the accommodating space may be a pocket, a backpack or a bag carried by the user, and the classifier may acquire n accommodating state samples when the plurality of sample mobile terminals are located in the accommodating spaces.
  • step 301 can include the following three sub-steps:
  • the classifier generating device acquires an operation log in a process in which a plurality of sample mobile terminals are located in the accommodating space.
  • the running log usually includes various information and records when the sample mobile terminal is running.
  • an operational log of the sample mobile terminal during a period of time in the pocket may be obtained.
  • Sub-step 3012 the classifier generating device acquires a plurality of accommodating state samples from the running log.
  • Any one of the plurality of accommodating state samples may include p feature parameters and a label for indicating a category of any of the accommodating state samples (English: label), a category of any accommodating state sample To be in the accommodating space, p is an integer greater than zero.
  • the p characteristic parameters may include: a parameter acquired by the distance sensor, a parameter acquired by the acceleration sensor, a system time of the sample mobile terminal, a parameter acquired by the light sensor, a program running by the sample mobile terminal, and a screen display interface of the sample mobile terminal. At least three. The larger p, the higher the accuracy of the generated classifier, but the slower the generation of the classifier.
  • the parameters acquired by the distance sensor are used to reflect whether there is occlusion of the object around the mobile terminal of the sample.
  • the parameters acquired by the acceleration sensor are used to reflect the acceleration of the mobile terminal of the sample.
  • the acceleration of the sample mobile terminal continues to be 0, the possibility that the sample mobile terminal is located in the accommodating space is large; the parameters acquired by the light sensor are used for the reaction sample mobile terminal.
  • the sample mobile terminal when the light intensity around the sample mobile terminal is low, the sample mobile terminal is more likely to be located in the accommodating space; the system time of the sample mobile terminal can be used to assist and calibrate the parameters acquired by the distance sensor And the occasional abnormality of the parameters acquired by the light sensor (such as the object around the sample mobile terminal is occluded and the intensity of the surrounding light is low, but the sample mobile terminal is not located in the accommodating space); the program of the sample mobile terminal runs and the screen of the sample mobile terminal
  • the display interface can be used to reflect the user's current behavior.
  • the sample mobile terminal runs a game program and the screen display interface is a game interface of the game program, and the sample mobile terminal is less likely to be located in the accommodating space.
  • the p feature parameters may also include other feature parameters of the sample mobile terminal, which are not limited in the embodiment of the present disclosure.
  • the classifier generating means may acquire a plurality of accommodating state samples from the running log according to the predetermined type of the characteristic parameter to be acquired.
  • the plurality of accommodating state samples may be obtained from running logs at different times. The larger the number of accommodating state samples, the higher the accuracy of the generated classifier, but the slower the generation of the classifier.
  • the plurality of sample mobile terminals may be a plurality of sample mobile terminals of the same type (such as mobile phones, tablet computers, smart watches, etc.) or the same model, and each sample mobile terminal is used for collecting the accommodating state.
  • sample and non-receiving state samples the more the number of sample mobile terminals, the faster the generation of the state sample set, and the faster the classifier will be generated.
  • Sub-step 3013 The classifier generating device performs format check processing and missing value complement processing on the plurality of accommodating state samples to obtain n accommodating state samples.
  • the step may include: 1. performing format verification processing on multiple accommodating state samples;
  • the format check process is a check of the format of each accommodating state sample to verify that the format of each accommodating state sample is correct, and to remove the malformed accommodating state sample.
  • the format may refer to a preset format for transmitting and recording the accommodating state samples, and the reason for the format of the accommodating state samples may be incorrect, which may be due to the classifier generating device and the sample mobile terminal.
  • the number n of the accommodating state samples acquired may not be a fixed value, and may be changed according to the case of the format check processing.
  • Missing value completion is to replace some parameters or data missing in the accommodating state sample. These parameters and data can be some characteristic parameters in the accommodating state sample.
  • the missing value is complemented, the average value of the feature parameters in the same state as the missing feature parameter may be used as the complementary value, or the missing feature parameter may be complemented by the preset value. .
  • the method of missing value completion reference may also be made to related technologies, and details are not described herein again.
  • There may be multiple reasons for the occurrence of missing values in the sample of the accommodating state possibly due to some unknown bugs in the classifier generating device and the system of the sample mobile terminal, or possibly due to the sample mobile terminal transmitting to the classifier generating device.
  • the vacant state sample has a missing value due to a problem of the transmission path, or may be due to some accidental interruption of the sample mobile terminal when acquiring the accommodating state sample.
  • Step 302 The classifier generating apparatus acquires m non-receiving state samples of the plurality of sample mobile terminals.
  • step 302 can include the following three sub-steps:
  • the classifier generating device acquires an operation log in a process in which the sample mobile terminal is not located in the accommodating space.
  • the running log usually includes various information and records when the sample mobile terminal is running.
  • an operational log of the sample mobile terminal for a period of time not in the pocket may be obtained.
  • Sub-step 3022 the classifier generating device acquires a plurality of non-receiving state samples from the running log.
  • any non-receiving state sample of the plurality of non-receiving state samples includes p feature parameters and a label for indicating a category of any non-receiving state sample, and a category of any non-receiving state sample. Not in the accommodation space.
  • the p feature parameters are the same as the p feature parameters in the accommodating state samples acquired in step 301.
  • the classifier generating device performs format check processing and missing value complement processing on the plurality of non-receiving state samples to obtain m non-accommodating state samples.
  • step 301 For this step, refer to sub-step 3013 in step 301 above, and details are not described herein again.
  • Step 302 may also be performed before step 301, or step 302 may also be performed concurrently with step 301, and the embodiment of the present disclosure does not limit.
  • Step 303 The classifier generating device combines the n accommodating state samples and the m non-accommodating state samples into a state sample set.
  • the n accommodating state samples can be used as positive samples in the state sample set, and the m non-receiving state samples can be used as negative samples in the state sample set.
  • the positive sample refers to a sample belonging to a certain category
  • the negative sample refers to a sample that does not belong to the certain category.
  • the certain category is located in the accommodating space.
  • Step 304 The classifier generating device determines a classification algorithm.
  • the classification algorithm may be any one of a decision tree algorithm, a logistic regression algorithm, and a support vector machine algorithm.
  • the classifier generating means can determine the classification algorithm based on the set of state samples.
  • the classifier generating device may further be provided with a classification algorithm in advance, and the classifier generating device may directly determine the pre-set classification algorithm as a classification algorithm for generating the classifier.
  • Step 305 The classifier generating device uses the state sample set as the training data, and trains the classifier according to the classification algorithm.
  • the classifier generating means may use the state sample set as the training data, and train the classifier according to the classification algorithm.
  • the process of training a classifier with a labeled state sample in the disclosed embodiment is a process of supervised learning (English: Machine Learning).
  • the classifier generating device may perform multiple iterative calculations on the training data in the state sample set by using a classification algorithm to gradually adjust various parameters of the classifier, so that the performance of the classifier gradually reaches the preset requirement.
  • the parameters of the classifier can be adjusted step by step so that the accuracy of the classifier is greater than 80%.
  • the classifier generating device may also separately train the feature parameters of each class in the state sample set.
  • the training method may refer to related technologies, and details are not described herein again.
  • Step 306 The classifier generating device sends the classifier to the mobile terminal to be detected.
  • this step can include the following two sub-steps:
  • Sub-step 3061 the classifier generating device performs format conversion on the classifier, so that the classifier can be applied to the operating environment of the mobile terminal.
  • the classifier generating device is usually a running environment of a computer, and the framework of the running environment of the computer is usually a frame structure of a format of spark (a cluster computing environment running on a server), and the framework structure of the mobile terminal is usually a predictive model.
  • the framework structure of the markup language (English: Predictive Model Markup Language; referred to as: pmml) format.
  • Classifiers generated in the framework of the spark format may be difficult to apply to the framework of the pmml format, so the classifier generation device can be pmml conversion method or jpmml (Java-based pmml application interface)-sparkml (Spark-based machine learning)
  • the conversion method converts the binary file of the classifier of the spark format into the pmml file of the classifier of the pmml format, so that the classifier can be applied to the operating environment of the mobile terminal.
  • the classifier generating means sends the format-converted classifier to the mobile terminal to be detected.
  • the classifier generating means may transmit the format-converted classifier to the mobile terminal to be detected by wire or wirelessly.
  • Step 301 to step 306 are optional steps, that is, when the classifier exists in the mobile terminal to be detected, step 307 may be directly performed.
  • Step 307 The mobile terminal to be detected acquires a state sample of the current time.
  • the to-be-detected mobile terminal may acquire a state sample of the current time according to the classifier, where the state sample is an unlabeled state sample, that is, the state sample includes only a plurality of feature parameters, and the plurality of feature parameters are The type and the feature parameter of any of the accommodating state samples acquired in step 301 (or any non-accommodating state samples acquired in step 302) are the same.
  • any of the accommodating state samples acquired in step 301 includes a feature parameter “a parameter acquired by the sensor, a parameter acquired by the acceleration sensor, and a system time of the sample mobile terminal”, and the state of the current time acquired by the mobile terminal to be detected is detected.
  • the sample includes parameters acquired by the distance sensor of the mobile terminal to be detected at the current time, parameters acquired by the acceleration sensor, and system time.
  • Step 308 The mobile terminal to be detected inputs a status sample into the classifier.
  • the mobile terminal to be detected may input the status sample into the classifier.
  • Step 309 The mobile terminal to be detected acquires a label output by the classifier according to the status sample of the current time.
  • the tag obtained by the mobile terminal to be detected is used to indicate the category of the state sample of the current time of the mobile terminal to be detected, and the category is the state of the mobile terminal to be detected, that is, located in the accommodating space or not in the accommodating space.
  • Step 310 The mobile terminal to be detected closes the preset function of the mobile terminal to be detected when the outputted label indicates that the category of the status sample is located in the accommodating space.
  • the mobile terminal to be detected When the output label indicates that the category of the status sample is in the accommodating space, it indicates that the mobile terminal to be detected may be located in the accommodating space, and the preset function of the mobile terminal to be detected may be turned off to reduce power consumption and false touch, wherein The preset function may include at least one of a bright screen notification, a gesture bright screen, a fingerprint unlock, and a double tap button to activate the camera.
  • the mobile terminal to be detected may enter a sleep mode when the output label indicates that the category of the status sample is located in the accommodating space.
  • the operation mode of the mobile terminal to be detected may refer to the operation mode of the mobile terminal in the sleep mode in the related art. , will not repeat them here.
  • the to-be-detected mobile terminal starts the preset function of the mobile terminal to be detected when the outputted label indicates that the category of the status sample is not located in the accommodating space.
  • the mobile terminal to be detected may continue to determine whether the mobile terminal to be detected is located in the accommodating space through steps 307 to 310. After the user removes the mobile terminal from the accommodating space, the mobile terminal to be detected immediately determines that the mobile terminal is not located in the accommodating space. (and start the preset function), when the user wants to illuminate the screen of the mobile terminal to be detected by a button or gesture, the mobile terminal to be detected can quickly brighten the screen without the user having to illuminate the screen of the mobile terminal through a button or a gesture. Then, the parameters of the respective components are used to determine whether the mobile terminal to be detected is located in the accommodating space.
  • the embodiment of the present disclosure sends a classifier to the mobile terminal to be detected, and the mobile terminal to be detected determines whether it is located in the accommodating space by the classifier, and when the classifier is located in other external devices, the mobile terminal to be detected may be due to It is impossible to judge whether it is located in the accommodating space due to reasons such as the network.
  • the state detection method acquires a state sample set of a plurality of sample mobile terminals, and uses the state sample set as training data to train a classifier according to a classification algorithm, and classifies the classifier.
  • the device sends the signal to the mobile terminal to be detected, so that the mobile terminal to be detected determines whether it is located in the preset accommodating space according to the classifier that comprehensively considers various factors.
  • the method for judging whether the mobile terminal is located in the pocket according to individual features in the related art is solved, and the problem of low accuracy in some special scenarios is solved.
  • the accuracy of the state detection method is achieved.
  • the state detecting device 400 includes:
  • the sample set obtaining module 410 is configured to acquire a state sample set of the plurality of sample mobile terminals, where the state sample set includes n pieces of the accommodating state samples collected by the plurality of sample mobile terminals in the process of the preset accommodating space, and multiple The m mobile non-receiving state samples collected by the sample mobile terminal are not located in the preset accommodating space, and n and m are integers greater than 0.
  • the algorithm determination module 420 is configured to determine a classification algorithm.
  • the classifier training module 430 is configured to use the state sample set as the training data, and train the classifier according to the classification algorithm.
  • the sending module 440 is configured to send a classifier to the mobile terminal to be detected.
  • the to-be-detected mobile terminal is configured to determine, according to the classifier, whether the mobile terminal to be detected is located in the preset accommodating space according to the state sample of the mobile terminal to be detected.
  • the state detecting apparatus acquires a state sample set of a plurality of sample mobile terminals, and uses the state sample set as training data to train a classifier according to a classification algorithm, and classifies the classifier.
  • the device is sent to the mobile terminal to be detected, so that the mobile terminal to be detected can determine whether it is located in the preset accommodating space according to the classifier that comprehensively considers various factors.
  • the method for judging whether the mobile terminal is located in the pocket according to individual features in the related art is solved, and the problem of low accuracy in some special scenarios is solved.
  • the accuracy of the state detection method is achieved.
  • the sample set obtaining module 410 is configured to:
  • a state sample set is obtained by performing format check processing and missing value completion processing on a plurality of accommodating state samples and a plurality of non-receiving state samples.
  • the sending module 440 is configured to:
  • the format converted classifier is sent to the mobile terminal to be detected.
  • the classification algorithm is any one of a decision tree algorithm, a logistic regression algorithm, and a support vector machine algorithm.
  • any one of the n accommodating state samples includes p feature parameters and a label for indicating a category of any accommodating state sample, and the category of any accommodating state sample is located Space, p is an integer greater than zero.
  • any non-receiving state sample of the m non-receiving state samples includes p feature parameters and a label for indicating a category of any non-receiving state sample, and a category of any non-receiving state sample. Not in the accommodation space.
  • the p characteristic parameters include: a parameter acquired by the distance sensor, a parameter acquired by the acceleration sensor, a system time of the sample mobile terminal, a parameter acquired by the light sensor, a program of the sample mobile terminal, and a screen display interface of the sample mobile terminal. At least three of them.
  • the preset accommodation space is a pocket, a backpack or a satchel carried by the user.
  • the state detecting apparatus acquires a state sample set of a plurality of sample mobile terminals, and uses the state sample set as training data to train a classifier according to a classification algorithm, and classifies the classifier.
  • the device is sent to the mobile terminal to be detected, so that the mobile terminal to be detected can determine whether it is located in the preset accommodating space according to the classifier that comprehensively considers various factors.
  • the method for judging whether the mobile terminal is located in the pocket according to individual features in the related art is solved, and the problem of low accuracy in some special scenarios is solved.
  • the accuracy of the state detection method is achieved.
  • the state detecting apparatus 500 includes:
  • the receiving module 510 is configured to receive a classifier sent by the classifier generating device, where the classifier is configured to use the state sample set as the training data, and the training method according to the classification algorithm is used to determine whether the mobile terminal to be detected is located in the accommodating space.
  • the state sample set includes n accommodating state samples collected during a process in which the sample mobile terminal is located in the accommodating space, and m unacceptable state samples collected during a process in which the sample mobile terminal is not located in the accommodating space, Both n and m are integers greater than 0;
  • a sample obtaining module 520 configured to acquire a state sample of a current moment of the mobile terminal to be detected
  • An input module 530 configured to input a state sample into the classifier
  • the label obtaining module 540 is configured to obtain a label output by the classifier according to the status sample of the current time, and the label is used to indicate the category of the status sample, and the category of the status sample is located in the accommodating space or not in the accommodating space.
  • FIG. 5-2 it is a block diagram of another state detecting apparatus according to an embodiment of the disclosure.
  • the state detecting apparatus 500 further includes:
  • the function closing module 550 is configured to: when the output label indicates that the category of the status sample is in the accommodating space, close the preset function of the mobile terminal to be detected;
  • the hibernation module 560 is configured to control the to-be-detected mobile terminal to enter a dormant state when the output tag indicates that the class of the status sample is located in the accommodating space.
  • the preset function includes at least one of a bright screen notification, a gesture bright screen, a fingerprint unlocking, and a double-click button to activate the camera.
  • the state detecting apparatus inputs a state sample of a current moment of a mobile terminal to be tested into a classifier generated according to a classification algorithm and a state sample set, and classifies a plurality of factors according to the comprehensive consideration.
  • the device determines whether the current moment of the mobile terminal to be tested is in the pocket.
  • the method for judging whether the mobile terminal is located in the pocket according to individual features in the related art is solved, and the problem of low accuracy in some special scenarios is solved.
  • the accuracy of the state detection method is achieved.
  • FIG. 6 is a block diagram of an apparatus 600 for status detection, according to an exemplary embodiment.
  • device 600 can be provided as a computer.
  • apparatus 600 includes a processing component 622 that further includes one or more processors, and memory resources represented by memory 632 for storing instructions, such as applications, that are executable by processing component 622.
  • An application stored in memory 632 can include one or more modules each corresponding to a set of instructions.
  • processing component 622 is configured to execute instructions to perform the state detection methods described above.
  • Device 600 may also include a power supply component 626 configured to perform power management of device 600, a wired or wireless network interface 650 configured to connect device 600 to the network, and an input/output (I/O) interface 658.
  • Device 600 can operate based on an operating system stored in memory 632, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.
  • FIG. 7 is a block diagram of a state detection device 700, according to an exemplary embodiment.
  • the modified state detecting device 700 can be provided as a mobile terminal.
  • the apparatus 700 can include one or more of the following components: a processing component 702, a memory 704, a power component 706, a multimedia component 708, an audio component 710, an input/output (I/O) interface 712, and a sensor component 714. And communication component 716.
  • Processing component 702 typically controls the overall operation of device 700, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
  • Processing component 702 can include one or more processors 720 to execute instructions to perform all or part of the steps described above.
  • processing component 702 can include one or more modules to facilitate interaction between component 702 and other components.
  • processing component 702 can include a multimedia module to facilitate interaction between multimedia component 708 and processing component 702.
  • Memory 704 is configured to store various types of data to support operation at the device 700. Examples of such data include instructions for any application or method operating on the device 700, contact data, phone book data, messages, pictures, videos, and the like. Memory 704 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Disk or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read only memory
  • EPROM erasable Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Disk Disk or Optical Disk.
  • Power component 706 provides power to various components of device 700.
  • Power component 706 can include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the device 700.
  • the multimedia component 708 includes a screen between the device 700 and the user that provides an output interface.
  • the screen can include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor may sense not only the boundary of the touch or sliding action, but also the duration and pressure associated with the touch or slide operation.
  • the multimedia component 708 includes a front camera and/or a rear camera. When the device 700 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
  • the audio component 710 is configured to output and/or input audio signals.
  • audio component 710 includes a microphone (MIC) that is configured to receive an external audio signal when device 700 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode.
  • the received audio signal may be further stored in memory 704 or transmitted via communication component 716.
  • audio component 710 also includes a speaker for outputting an audio signal.
  • the I/O interface 712 provides an interface between the processing component 702 and the peripheral interface module, which may be a keyboard, a click wheel, a button, or the like. These buttons may include, but are not limited to, a home button, a volume button, a start button, and a lock button.
  • Sensor assembly 714 includes one or more sensors for providing state assessment of various aspects of device 700.
  • sensor component 714 can detect an open/closed state of device 700, relative positioning of components, such as the display and keypad of device 700, and sensor component 714 can also detect device 700 or a component of device 700. The position changes, the presence or absence of contact of the user with the device 700, the device 700 orientation or acceleration/deceleration and temperature changes of the device 700.
  • Sensor assembly 714 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact.
  • Sensor component 714 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor component 714 can also include an acceleration sensor, a gyro sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • Communication component 716 is configured to facilitate wired or wireless communication between the device 700 and other devices.
  • the device 700 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof.
  • communication component 716 receives broadcast signals or broadcast associated information from an external broadcast management system via a broadcast channel.
  • the communication component 716 also includes a near field communication (NFC) module to facilitate short range communication.
  • NFC near field communication
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the apparatus 700 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), A program gate array (FPGA), controller, microcontroller, microprocessor or other electronic component implementation is used to perform the state detection methods provided by the various embodiments described above.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGA program gate array
  • controller, microcontroller, microprocessor or other electronic component implementation is used to perform the state detection methods provided by the various embodiments described above.
  • non-transitory computer readable storage medium comprising instructions, such as a memory 704 comprising instructions executable by processor 720 of apparatus 700 to perform the above method.
  • the non-transitory computer readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device.
  • a non-transitory computer readable storage medium when the instructions in the storage medium are executed by a processor of the state detecting device, enabling the state detecting device to perform a state detecting method, the method comprising:
  • Obtaining a state sample set of the plurality of sample mobile terminals where the state sample set includes n accommodating state samples collected by the plurality of sample mobile terminals in the process of the preset accommodating space, and the plurality of sample mobile terminals are not located in the preset capacity m non-receiving state samples collected during the process of setting space, n and m are integers greater than 0;
  • the classifier is trained according to the classification algorithm
  • Sending a classifier to the mobile terminal to be detected is configured to determine, according to the classifier, whether the mobile terminal to be detected is located in the preset accommodating space according to the state sample of the mobile terminal to be detected.
  • obtaining a state sample set of the sample mobile terminal including:
  • a state sample set is obtained by performing format check processing and missing value completion processing on a plurality of accommodating state samples and a plurality of non-receiving state samples.
  • sending the classifier to the mobile terminal to be detected including:
  • the format converted classifier is sent to the mobile terminal to be detected.
  • the classification algorithm is any one of a decision tree algorithm, a logistic regression algorithm, and a support vector machine algorithm.
  • any one of the n accommodating state samples includes p feature parameters and a label for indicating a category of any accommodating state sample, and the category of any accommodating state sample is located Space, p is an integer greater than zero.
  • any non-receiving state sample of the m non-receiving state samples includes p feature parameters and a label for indicating a category of any non-receiving state sample, and a category of any non-receiving state sample. Not in the accommodation space.
  • the p characteristic parameters include: a parameter acquired by the distance sensor, a parameter acquired by the acceleration sensor, a system time of the sample mobile terminal, a parameter acquired by the light sensor, a program of the sample mobile terminal, and a screen display interface of the sample mobile terminal. At least three of them.
  • the preset accommodation space is a pocket, a backpack or a satchel carried by the user.
  • a non-transitory computer readable storage medium when the instructions in the storage medium are executed by a processor of the state detecting device, enabling the state detecting device to perform a state detecting method, the method comprising:
  • the classifier is a classifier that uses the state sample set as the training data, and the classifier that is trained according to the classification algorithm to determine whether the mobile terminal to be detected is located in the accommodating space, and the state sample set
  • n and m are both greater than An integer of 0;
  • the label is used to indicate the category of the status sample, and the category of the status sample is located in the accommodating space or not in the accommodating space.
  • the method further includes:
  • the mobile terminal to be detected is controlled to enter a sleep state.
  • the preset function includes at least one of a bright screen notification, a gesture bright screen, a fingerprint unlocking, and a double-click button to activate the camera.
  • At least one of A and B in the present disclosure is merely an association relationship describing an associated object, indicating that there may be three relationships, for example, at least one of A and B, which may indicate that A exists separately, while There are three cases of A and B, and B alone.
  • at least one of A, B, and C means that there are seven relationships, which can be expressed as: A exists separately, B exists separately, C exists separately, A and B exist simultaneously, and A and C exist simultaneously. C and B, there are seven cases of A, B and C.
  • "at least one of A, B, C, and D” means that there may be fifteen relationships, which may indicate that A exists separately, B exists separately, C exists separately, D exists separately, and A and B exist simultaneously.
  • the disclosed apparatus and method may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the modules is only a logical function division.
  • there may be another division manner for example, multiple modules 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 module, and may be electrical, mechanical or otherwise.
  • the modules described as separate components may or may not be physically separated.
  • the components displayed as modules may or may not be physical modules, that is, may be located in one place, or may be distributed to multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • a person skilled in the art may understand that all or part of the steps of implementing the above embodiments may be completed by hardware, or may be instructed by a program to execute related hardware, and the program may be stored in a computer readable storage medium.
  • the storage medium mentioned may be a read only memory, a magnetic disk or an optical disk or the like.

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Abstract

本公开公开了一种状态检测方法、装置和存储介质，属于移动技术领域。方法包括：获取多个样本移动终端的状态样本集；确定分类算法；以状态样本集作为训练数据，根据分类算法训练得到分类器；向待检测移动终端发送分类器，待检测移动终端用于根据待检测移动终端的状态样本，判断待检测移动终端是否位于预设容置空间中。本公开通过获取多个样本移动终端的的状态样本集，并以该状态样本集作为训练数据，根据分类算法训练得到分类器，并将该分类器发送给待检测移动终端，使待检测移动终端能够根据该综合考量多种因素的分类器来确定其是否位于预设容置空间。解决了相关技术中的状态检测方法准确性较低的问题。达到了状态检测方法的准确性较高的效果。

Description

状态检测方法、装置和存储介质 技术领域
本公开涉及移动技术领域，特别涉及一种状态检测方法、装置和存储介质。
背景技术
目前，移动终端通常会通过各种方式来判断其是否位于口袋中，然后根据判断结果进行相应的操作，例如，当移动终端确定自身位于口袋中后，移动终端自动进入休眠模式。
相关技术中有一种状态检测方法，用于检测设置有距离传感器(英文：psensor)和加速度传感器(英文：gsensor)的移动终端是否位于口袋中，在该方法中，移动终端通过距离传感器来确定其某个方向上是否有障碍物，并通过加速度传感器来获取移动终端的加速度，在移动终端的某个方向上有障碍物且该移动终端具有加速度时，可以确定移动终端位于口袋中。
在实现本公开的过程中，发明人发现相关技术至少存在以下问题：在一些特殊场景中，上述方法的准确性较低，例如当移动终端在行驶的车辆上(由于车辆的晃动，移动终端始终具有加速度)，且其距离传感器被遮挡时，移动终端会始终确定自身位于口袋。
发明内容
本公开实施例提供了一种状态检测方法、装置和存储介质，可以解决相关技术中的状态检测方法的准确性较低的问题。所述技术方案如下：
根据本公开实施例的第一方面，提供了一种状态检测，方法包括：
获取多个样本移动终端的状态样本集，所述状态样本集包括所述多个样本移动终端位于预设容置空间的过程中采集的n个容置状态样本，和在所述多个样本移动终端不位于所述预设容置空间的过程中采集的m个非容置状态样本，所述n和m均为大于0的整数；
确定分类算法；
以所述状态样本集作为训练数据，根据所述分类算法训练得到分类器；
向待检测移动终端发送所述分类器，所述待检测移动终端用于根据所述待检测移动终端的状态样本，基于所述分类器，判断所述待检测移动终端是否位于所述预设容置空间中。
可选的，所述获取样本移动终端的状态样本集，包括：
获取所述多个样本移动终端位于容置空间的过程中采集的多个容置状态样本；
获取所述多个样本移动终端不位于容置空间的过程中采集的多个非容置状态样 本；
对所述多个容置状态样本和所述多个非容置状态样本进行格式校验处理和缺失值补全处理后得到所述状态样本集。
可选的，所述向待检测移动终端发送所述分类器，包括：
对所述分类器进行格式转换，使所述分类器能够适用于移动终端的运行环境；
向所述待检测移动终端发送格式转换后的所述分类器。
可选的，所述分类算法为决策树算法、逻辑回归算法和支持向量机算法中的任意一种。
可选的，所述n个容置状态样本中的任一容置状态样本包括p个特征参数和用于指示所述任一容置状态样本的类别的标签，所述任一容置状态样本的类别为位于容置空间，所述p为大于0的整数。
可选的，所述m个非容置状态样本中的任一非容置状态样本包括p个特征参数和用于指示所述任一非容置状态样本的类别的标签，所述任一非容置状态样本的类别为不位于容置空间。
可选的，所述p个特征参数包括：距离传感器获取的参数、加速度传感器获取的参数、所述样本移动终端的系统时间、光线传感器获取的参数、所述样本移动终端运行的程序和所述样本移动终端的屏幕显示界面中的至少三种。
可选的，所述预设容置空间为用户携带的口袋、背包或挎包。
根据本公开实施例的第二方面，提供了一种状态检测方法，所述方法包括：
接收分类器生成装置发送的分类器，所述分类器是所述分类器生成装置以状态样本集作为训练数据，根据分类算法训练得到的用于确定待检测移动终端是否位于容置空间的分类器，所述状态样本集中包括在样本移动终端位于容置空间的过程中采集的n个容置状态样本，和在所述样本移动终端不位于容置空间的过程中采集的m个非容置状态样本，所述n和m均为大于0的整数；
获取所述待检测移动终端当前时刻的状态样本；
将所述状态样本输入所述分类器；
获取所述分类器根据所述当前时刻的状态样本输出的标签，所述标签用于指示所述状态样本的类别，所述状态样本的类别为位于容置空间或不位于容置空间。
可选的，所述方法还包括：
在所述输出的标签指示所述状态样本的类别为位于容置空间时，关闭所述待检测移动终端的预设功能；
或者，在所述输出的标签指示所述状态样本的类别为位于容置空间时，控制所述 待检测移动终端进入休眠状态。
可选的，所述预设功能包括亮屏通知、手势亮屏、指纹解锁和双击按键启动相机中的至少一种。
根据本公开实施例的第三方面，提供了一种状态检测装置，所述状态检测装置包括：
样本集获取模块，用于获取多个样本移动终端的状态样本集，所述状态样本集包括所述多个样本移动终端位于预设容置空间的过程中采集的n个容置状态样本，和在所述多个样本移动终端不位于所述预设容置空间的过程中采集的m个非容置状态样本，所述n和m均为大于0的整数；
算法确定模块，用于确定分类算法；
分类器训练模块，用于以所述状态样本集作为训练数据，根据所述分类算法训练得到分类器；
发送模块，用于向待检测移动终端发送所述分类器，所述待检测移动终端用于根据所述待检测移动终端的状态样本，基于所述分类器，判断所述待检测移动终端是否位于所述预设容置空间中。
可选的，所述样本集获取模块，用于：
获取所述多个样本移动终端位于容置空间的过程中采集的多个容置状态样本；
获取所述多个样本移动终端不位于容置空间的过程中采集的多个非容置状态样本；
对所述多个容置状态样本和所述多个非容置状态样本进行格式校验处理和缺失值补全处理后得到所述状态样本集。
可选的，所述发送模块，用于：
对所述分类器进行格式转换，使所述分类器能够适用于移动终端的运行环境；
向所述待检测移动终端发送格式转换后的所述分类器。
可选的，所述分类算法为决策树算法、逻辑回归算法和支持向量机算法中的任意一种。
可选的，所述n个容置状态样本中的任一容置状态样本包括p个特征参数和用于指示所述任一容置状态样本的类别的标签，所述任一容置状态样本的类别为位于容置空间，所述p为大于0的整数。
可选的，所述m个非容置状态样本中的任一非容置状态样本包括p个特征参数和用于指示所述任一非容置状态样本的类别的标签，所述任一非容置状态样本的类别为不位于容置空间。
可选的，所述p个特征参数包括：距离传感器获取的参数、加速度传感器获取的参数、所述样本移动终端的系统时间、光线传感器获取的参数、所述样本移动终端运行的程序和所述样本移动终端的屏幕显示界面中的至少三种。
可选的，所述预设容置空间为用户携带的口袋、背包或挎包。
根据本公开实施例的第四方面，提供了一种状态检测装置，所述状态检测装置包括：
接收模块，用于接收分类器生成装置发送的分类器，所述分类器是所述分类器生成装置以状态样本集作为训练数据，根据分类算法训练得到的用于确定待检测移动终端是否位于容置空间的分类器，所述状态样本集中包括在样本移动终端位于容置空间的过程中采集的n个容置状态样本，和在所述样本移动终端不位于容置空间的过程中采集的m个非容置状态样本，所述n和m均为大于0的整数；
样本获取模块，用于获取所述待检测移动终端当前时刻的状态样本；
输入模块，用于将所述状态样本输入所述分类器；
标签获取模块，用于获取所述分类器根据所述当前时刻的状态样本输出的标签，所述标签用于指示所述状态样本的类别，所述状态样本的类别为位于容置空间或不位于容置空间。
可选的，所述状态检测装置还包括：
功能关闭模块，用于在所述输出的标签指示所述状态样本的类别为位于容置空间时，关闭所述待检测移动终端的预设功能；
休眠模块，用于在所述输出的标签指示所述状态样本的类别为位于容置空间时，控制所述待检测移动终端进入休眠状态。
可选的，所述预设功能包括亮屏通知、手势亮屏、指纹解锁和双击按键启动相机中的至少一种。
根据本公开实施例的第五方面，提供了一种状态检测装置，所述状态检测装置包括：
处理器；
用于存储处理器可执行指令的存储器；
其中，所述处理器被配置为执行第一方面所述的状态检测方法。
根据本公开实施例的第六方面，提供了一种状态检测装置，所述状态检测装置包括：
处理器；
用于存储处理器可执行指令的存储器；
其中，所述处理器被配置为执行第二方面所述的状态检测方法。
根据本公开实施例的第七方面，提供了一种计算机可读存储介质，所述计算机可读存储介质上存储有指令，其特征在于，所述指令被处理器执行时实现第一方面所述的状态检测方法。
根据本公开实施例的第八方面，提供了一种计算机可读存储介质，所述计算机可读存储介质上存储有指令，其特征在于，所述指令被处理器执行时实现第二方面所述的状态检测方法。
本公开实施例提供的技术方案带来的有益效果是：
通过获取多个样本移动终端的状态样本集，并以该状态样本集作为训练数据，根据分类算法训练得到分类器，并将该分类器发送给待检测移动终端，使待检测移动终端能够根据该综合考量了多种因素的分类器来确定其是否位于预设容置空间。解决了相关技术中根据个别特征来判断移动终端是否位于口袋中的方法，在一些特殊场景中准确性较低的问题。达到了状态检测方法的准确性较高的效果。
附图说明
为了更清楚地说明本公开实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本公开实施例所涉及的一种实施环境的示意图；
图2-1是本公开实施例示出的一种状态检测方法的流程图；
图2-2是本公开实施例示出的另一种状态检测方法的流程图
图3-1是本公开实施例示出的另一种状态检测方法的流程图；
图3-2是图2-1所示实施例中一种获取容置状态样本的流程图；
图3-3是图2-1所示实施例中一种获取非容置状态样本的流程图；
图3-4是图2-1所示实施例中一种发送分类器的流程图；
图4是本公开实施例提供的一种状态检测装置的框图；
图5-1是本公开实施例提供的一种状态检测装置的框图；
图5-2是本公开实施例提供的另一种状态检测装置的框图；
图6是根据一示例性实施例示出的一种用于状态检测的装置的框图；
图7是根据一示例性实施例示出的一种用于状态检测的装置的框图。
通过上述附图，已示出本公开明确的实施例，后文中将有更详细的描述。这些附 图和文字描述并不是为了通过任何方式限制本公开构思的范围，而是通过参考特定实施例为本领域技术人员说明本公开的概念。
具体实施方式
为使本公开的目的、技术方案和优点更加清楚，下面将结合附图对本公开实施方式作进一步地详细描述。
图1是本公开各个实施例所涉及的实施环境的示意图，该实施环境可以包括待检测移动终端11、分类器生成装置12和多个样本移动终端13。
待检测移动终端11可以是手机、平板电脑、掌上游戏机和各种智能穿戴设备。
分类器生成装置12可以是台式计算机、笔记本计算机、服务器或服务器集群等。分类器生成装置12能够通过有线或无线的方式与待检测移动终端11以及多个样本移动终端13建立连接。多个样本移动终端13可以是多个和待检测移动终端11类别或型号相同的移动终端。
图2-1是本公开实施例示出的一种状态检测方法的流程图，本实施例以该状态检测方法应用于图1所示实施环境中的分类器生成装置中来举例说明。该状态检测方法可以包括如下几个步骤：
步骤201、获取多个样本移动终端的状态样本集，状态样本集包括多个样本移动终端位于预设容置空间的过程中采集的n个容置状态样本，和在多个样本移动终端不位于预设容置空间的过程中采集的m个非容置状态样本，n和m均为大于0的整数。
步骤202、确定分类算法。
步骤203、以状态样本集作为训练数据，根据分类算法训练得到分类器。
步骤204、向待检测移动终端发送分类器，待检测移动终端用于根据待检测移动终端的状态样本，基于分类器，判断待检测移动终端是否位于预设容置空间中。
综上所述，本公开实施例提供的状态检测方法，通过获取多个样本移动终端的的状态样本集，并以该状态样本集作为训练数据，根据分类算法训练得到分类器，并将该分类器发送给待检测移动终端，使待检测移动终端能够根据该综合考量了多种因素的分类器来确定其是否位于预设容置空间。解决了相关技术中根据个别特征来判断移动终端是否位于口袋中的方法，在一些特殊场景中准确性较低的问题。达到了状态检测方法的准确性较高的效果。
图2-2是本公开实施例示出的一种状态检测方法的流程图，本实施例以该状态检测方法应用于图1所示实施环境中的待检测移动终端中来举例说明。该状态检测方法可以包括如下几个步骤：
步骤205、接收分类器生成装置发送的分类器，分类器是分类器生成装置以状态样本集作为训练数据，根据分类算法训练得到的用于确定待检测移动终端是否位于容置空间的分类器，状态样本集中包括在样本移动终端位于容置空间的过程中采集的n个容置状态样本，和在样本移动终端不位于容置空间的过程中采集的m个非容置状态样本，n和m均为大于0的整数。
步骤206、获取待检测移动终端当前时刻的状态样本。
步骤207、将状态样本输入分类器。
步骤208、获取分类器根据当前时刻的状态样本输出的标签，标签用于指示状态样本的类别，状态样本的类别为位于容置空间或不位于容置空间。
综上所述，本公开实施例提供的状态检测方法，通过将待测试移动终端当前时刻的状态样本输入根据分类算法和状态样本集生成的分类器，并根据该综合考量了多种因素的分类器来确定待测试移动终端当前时刻是否位于口袋中。解决了相关技术中根据个别特征来判断移动终端是否位于口袋中的方法，在一些特殊场景中准确性较低的问题。达到了状态检测方法的准确性较高的效果。
本公开实施例示出的另一种状态检测方法的流程图可以如图3-1所示，本实施例以该状态检测方法应用于待检测移动终端中来举例说明，该状态检测方法可以包括如下几个步骤：
步骤301、分类器生成装置获取多个样本移动终端的n个容置状态样本。
容置状态样本为样本移动终端位于容置空间的过程中采集的状态样本。本公开实施例中，容置空间可以为用户携带的口袋、背包或挎包等，分类器可以在着多个样本移动终端位于这些容置空间中时，获取n个容置状态样本。
如图3-2所示，步骤301可以包括下面三个子步骤：
子步骤3011、分类器生成装置获取多个样本移动终端位于容置空间的过程中的运行日志。
运行日志通常包括样本移动终端运行时的各种信息与记录。示例性的，可以获取样本移动终端在口袋的过程中一个时间段内的运行日志。
子步骤3012、分类器生成装置从运行日志中获取多个容置状态样本。
其中，多个容置状态样本中的任一容置状态样本可以包括p个特征参数和用于指示任一容置状态样本的类别的标签(英文：label)，任一容置状态样本的类别为位于容置空间，p为大于0的整数。
其中，p个特征参数可以包括：距离传感器获取的参数、加速度传感器获取的参数、样本移动终端的系统时间、光线传感器获取的参数、样本移动终端运行的程序和样本 移动终端的屏幕显示界面中的至少三种。p越大，生成的分类器的准确性越高，但生成分类器的速度也会越慢。
p个特征参数中，距离传感器获取的参数用于反应样本移动终端的周围是否有物体的遮挡，当样本移动终端的周围有物体遮挡时，样本移动终端位于容置空间中的可能性较大；加速度传感器获取的参数用于反应样本移动终端的加速度，当样本移动终端的加速度持续为0时，样本移动终端位于容置空间中的可能性较大；光线传感器获取的参数用于反应样本移动终端周围的光线强度，当样本移动终端周围的光线强度较低时，样本移动终端位于容置空间中的可能性较大；所述样本移动终端的系统时间可以用于辅助和校准距离传感器获取的参数和光线传感器获取的参数偶尔的异常(如样本移动终端周围有物体遮挡且其周围光线的强度较低，但样本移动终端不位于容置空间)；样本移动终端运行的程序和样本移动终端的屏幕显示界面可以用于反应用户当前的行为，示例性的，样本移动终端运行有游戏程序且屏幕显示界面为该游戏程序的游戏界面，则样本移动终端位于容置空间中的可能性较小。此外，p个特征参数还可以包括样本移动终端的其他特征参数，本公开实施例不作出限制。
在子步骤3012中，分类器生成装置可以根据预先确定的所要获取的特征参数的类型来从运行日志中获取多个容置状态样本。这多个容置状态样本可以是从不同时刻的运行日志中获取的。容置状态样本的数量越大，生成的分类器的准确性越高，但生成分类器的速度也会越慢。
本公开实施例中，多个样本移动终端可以为类型(如手机类、平板电脑类和智能手表类等)或型号相同的多个样本移动终端，每个样本移动终端均用于采集容置状态样本和非容置状态样本，样本移动终端的数量越多，状态样本集的生成速度越快，进而分类器的生成速度也会越快。
子步骤3013、分类器生成装置对多个容置状态样本进行格式校验处理和缺失值补全处理，得到n个容置状态样本。
本步骤可以包括：1、对多个容置状态样本进行格式校验处理；
格式校验处理为对每个容置状态样本的格式进行的校验，以验证每个容置状态样本的格式是否正确，并去除格式不正确的容置状态样本。其中，格式可以是指预先设定的用于传输和记录容置状态样本的格式，而导致容置状态样本的格式不正确的原因可以有多种，可能是由于分类器生成装置以及样本移动终端的系统中一些未知的漏洞(英文：BUG)，或者可能是由于样本移动终端向分类器生成装置传输容置状态样本时由于传输路径的问题导致容置状态样本的格式出现错误等。
由于格式校验处理会去除格式不正确的容置状态样本，因而获取的容置状态样本 的数量n可以不是一个定值，可以根据格式校验处理的情况而变化。
2、对进行格式校验的容置状态样本进行缺失值补全；
缺失值补全为补全容置状态样本中缺失的一些参数或数据，这些参数和数据可以是容置状态样本中的一些特征参数。在补全缺失值时，可以以与缺失的特征参数同类的特征参数在多个容置状态样本中的平均值来作为补全的值，也可以以预设的值来补全缺失的特征参数。缺失值补全的方式还可以参考相关技术，在此不再赘述。而导致容置状态样本中出现缺失值的原因也可以有多种，可能是由于分类器生成装置以及样本移动终端的系统中一些未知的BUG，或者可能是由于样本移动终端向分类器生成装置传输容置状态样本时由于传输路径的问题导致容置状态样本出现缺失值，或者可能是由于样本移动终端在获取容置状态样本时由于一些意外被打断等。
步骤302、分类器生成装置获取多个样本移动终端的m个非容置状态样本。
如图3-3所示，步骤302可以包括下面三个子步骤：
子步骤3021、分类器生成装置获取样本移动终端不位于容置空间的过程中的运行日志。
运行日志通常包括样本移动终端运行时的各种信息与记录。示例性的，可以获取样本移动终端在不位于口袋的过程中一个时间段内的运行日志。
子步骤3022、分类器生成装置从运行日志中获取多个非容置状态样本。
可选的，多个非容置状态样本中的任一非容置状态样本包括p个特征参数和用于指示任一非容置状态样本的类别的标签，任一非容置状态样本的类别为不位于容置空间。该p个特征参数和步骤301中获取的容置状态样本中的p个特征参数相同。
子步骤3023、分类器生成装置对多个非容置状态样本进行格式校验处理和缺失值补全处理，得到m个非容置状态样本。
本步骤可以参考上述步骤301中的子步骤3013，在此不再赘述。
步骤302还可以在步骤301之前执行，或者步骤302还可以与步骤301同时执行，本公开实施例不作出限制。
步骤303、分类器生成装置将n个容置状态样本和m个非容置状态样本合并为状态样本集。
n个容置状态样本可以作为状态样本集中的正样本，m个非容置状态样本可以作为状态样本集中的负样本。其中，正样本是指属于某一类别的样本，负样本是指不属于该某一类别的样本，在本公开实施例中，该某一类别为位于容置空间。
步骤304、分类器生成装置确定分类算法。
该分类算法可以为决策树算法、逻辑回归算法和支持向量机算法中的任意一种。 分类器生成装置可以根据状态样本集来确定分类算法。
此外，分类器生成装置中还可以预先设置有分类算法，分类器生成装置可以直接将该预先设置的分类算法确定为用于生成分类器的分类算法。
步骤305、分类器生成装置以状态样本集作为训练数据，根据分类算法训练得到分类器。
在确定了分类算法和状态样本集之后，分类器生成装置可以以状态样本集作为训练数据，根据分类算法训练得到分类器。本公开实施例以带有标签的状态样本训练得到分类器的过程是机器学习(英文：Machine Learning)中的一种有监督学习(英文：Supervised learning)的过程。
分类器生成装置在训练分类器的过程中，可以以分类算法对状态样本集中的训练数据进行多次迭代计算，以逐步调整分类器的各个参数，使分类器的性能逐步达到预设的要求，示例性的，可以逐步调整分类器的各个参数，使分类器的准确性大于80％。
此外，分类器生成装置还可以对状态样本集中的每一类的特征参数分别进行训练，该训练方法可以参考相关技术，在此不再赘述。
步骤306、分类器生成装置将分类器发送至待检测移动终端。
如图3-4所示，本步骤可以包括下面两个子步骤：
子步骤3061、分类器生成装置对分类器进行格式转换，使分类器能够适用于移动终端的运行环境。
分类器生成装置通常为计算机的运行环境，计算机的运行环境的框架结构通常为spark(一种适用于在服务器上运行的集群计算环境)格式的框架结构，而移动终端的框架结构通常为预测模型标记语言(英文：Predictive Model Markup Language；简称：pmml)格式的框架结构。
在spark格式的框架结构中生成的分类器可能难以应用于pmml格式的框架结构中，因而分类器生成装置可以pmml转换方法或jpmml(基于Java的pmml应用程序接口)-sparkml(基于Spark的机器学习)转换方法，将spark格式的分类器的二进制文件转换为pmml格式的分类器的pmml文件，使该分类器能够适用于移动终端的运行环境。
子步骤3062、分类器生成装置向待检测移动终端发送格式转换后的分类器。
分类器生成装置可以通过有线或无线的方式将向待检测移动终端发送格式转换后的分类器。
步骤301至步骤306为可选的步骤，即在待检测移动终端中存在分类器时，可以直接执行步骤307。
步骤307、待检测移动终端获取当前时刻的状态样本。
在获取分类器后，待检测移动终端可以根据分类器来获取当前时刻的状态样本，该状态样本为无标签的状态样本，即该状态样本仅包括多个特征参数，且该多个特征参数的类型和步骤301中获取的任一容置状态样本(或步骤302中获取的任一非容置状态样本)的特征参数的类型相同。示例性的，步骤301中获取的任一容置状态样本包括特征参数“距离传感器获取的参数、加速度传感器获取的参数和样本移动终端的系统时间”，则待检测移动终端获取的当前时刻的状态样本中包括待检测移动终端在当前时刻的距离传感器获取的参数、加速度传感器获取的参数和系统时间。
步骤308、待检测移动终端将状态样本输入分类器。
在获取当前时刻的状态样本后，待检测移动终端可以将状态样本输入分类器。
步骤309、待检测移动终端获取分类器根据当前时刻的状态样本输出的标签。
待检测移动终端获取的该标签用于指示待检测移动终端当前时刻的状态样本的类别，该类别为待检测移动终端的状态，即位于容置空间或不位于容置空间。
步骤310、待检测移动终端在输出的标签指示状态样本的类别为位于容置空间时，关闭待检测移动终端的预设功能。
在输出的标签指示状态样本的类别为位于容置空间时，表明待检测移动终端可能位于容置空间中，此时可以关闭待检测移动终端的预设功能以减少功耗与误触，其中，预设功能可以包括亮屏通知、手势亮屏、指纹解锁和双击按键启动相机中的至少一种。或者待检测移动终端可以在输出的标签指示状态样本的类别为位于容置空间时，进入休眠模式，在该模式下，待检测移动终端的运行方式可以参考相关技术中移动终端在休眠模式的运行方式，在此不再赘述。
此外，待检测移动终端在输出的标签指示状态样本的类别为不位于容置空间时，启动待检测移动终端的预设功能。待检测移动终端可以持续通过步骤307至步骤310判断待检测移动终端是否位于容置空间中，这样在用户从容置空间中取出移动终端后，待检测移动终端立刻会确定其不位于容置空间中(并启动预设功能)，用户通过按键或手势要点亮待检测移动终端的屏幕时，待检测移动终端能够快速的亮屏，而无需在用户通过按键或手势要点亮移动终端的屏幕时再通过各个组件的参数来确定待检测移动终端是否位于容置空间中。
本公开实施例通过将分类器发送给待检测移动终端，由待检测移动终端通过分类器来判断其是否位于容置空间，避免了分类器位于其他外部装置中时，待检测移动终端可能会由于网络等原因无法判断其是否位于容置空间的问题。
综上所述，本公开实施例提供的状态检测方法，通过获取多个样本移动终端的的 状态样本集，并以该状态样本集作为训练数据，根据分类算法训练得到分类器，并将该分类器发送给待检测移动终端，使待检测移动终端根据该综合考量了多种因素的分类器来确定其是否位于预设容置空间。解决了相关技术中根据个别特征来判断移动终端是否位于口袋中的方法，在一些特殊场景中准确性较低的问题。达到了状态检测方法的准确性较高的效果。
下述为本公开装置实施例，可以用于执行本公开方法实施例。对于本公开装置实施例中未披露的细节，请参照本公开方法实施例。
图4是本公开实施例提供的一种状态检测装置的框图，该状态检测装置可以应用于图1所示实施环境中的分类器生成装置，该状态检测装置400包括：
样本集获取模块410，用于获取多个样本移动终端的状态样本集，状态样本集包括多个样本移动终端位于预设容置空间的过程中采集的n个容置状态样本，和在多个样本移动终端不位于预设容置空间的过程中采集的m个非容置状态样本，n和m均为大于0的整数。
算法确定模块420，用于确定分类算法。
分类器训练模块430，用于以状态样本集作为训练数据，根据分类算法训练得到分类器。
发送模块440，用于向待检测移动终端发送分类器，待检测移动终端用于根据待检测移动终端的状态样本，基于分类器，判断待检测移动终端是否位于预设容置空间中。
综上所述，本公开实施例提供的状态检测装置，通过获取多个样本移动终端的的状态样本集，并以该状态样本集作为训练数据，根据分类算法训练得到分类器，并将该分类器发送给待检测移动终端，使待检测移动终端能够根据该综合考量了多种因素的分类器来确定其是否位于预设容置空间。解决了相关技术中根据个别特征来判断移动终端是否位于口袋中的方法，在一些特殊场景中准确性较低的问题。达到了状态检测方法的准确性较高的效果。
可选的，样本集获取模块410，用于：
获取多个样本移动终端位于容置空间的过程中采集的多个容置状态样本；
获取多个样本移动终端不位于容置空间的过程中采集的多个非容置状态样本；
对多个容置状态样本和多个非容置状态样本进行格式校验处理和缺失值补全处理后得到状态样本集。
可选的，发送模块440，用于：
对分类器进行格式转换，使分类器能够适用于移动终端的运行环境；
向待检测移动终端发送格式转换后的分类器。
可选的，分类算法为决策树算法、逻辑回归算法和支持向量机算法中的任意一种。
可选的，n个容置状态样本中的任一容置状态样本包括p个特征参数和用于指示任一容置状态样本的类别的标签，任一容置状态样本的类别为位于容置空间，p为大于0的整数。
可选的，m个非容置状态样本中的任一非容置状态样本包括p个特征参数和用于指示任一非容置状态样本的类别的标签，任一非容置状态样本的类别为不位于容置空间。
可选的，p个特征参数包括：距离传感器获取的参数、加速度传感器获取的参数、样本移动终端的系统时间、光线传感器获取的参数、样本移动终端运行的程序和样本移动终端的屏幕显示界面中的至少三种。
可选的，预设容置空间为用户携带的口袋、背包或挎包。
综上所述，本公开实施例提供的状态检测装置，通过获取多个样本移动终端的的状态样本集，并以该状态样本集作为训练数据，根据分类算法训练得到分类器，并将该分类器发送给待检测移动终端，使待检测移动终端能够根据该综合考量了多种因素的分类器来确定其是否位于预设容置空间。解决了相关技术中根据个别特征来判断移动终端是否位于口袋中的方法，在一些特殊场景中准确性较低的问题。达到了状态检测方法的准确性较高的效果。
图5-1是本公开实施例提供的一种状态检测装置的框图，该状态检测装置可以应用于图1所示实施环境中的待检测移动终端，该状态检测装置500包括：
接收模块510，用于接收分类器生成装置发送的分类器，分类器是分类器生成装置以状态样本集作为训练数据，根据分类算法训练得到的用于确定待检测移动终端是否位于容置空间的分类器，状态样本集中包括在样本移动终端位于容置空间的过程中采集的n个容置状态样本，和在样本移动终端不位于容置空间的过程中采集的m个非容置状态样本，n和m均为大于0的整数；
样本获取模块520，用于获取待检测移动终端当前时刻的状态样本；
输入模块530，用于将状态样本输入分类器；
标签获取模块540，用于获取分类器根据当前时刻的状态样本输出的标签，标签用于指示状态样本的类别，状态样本的类别为位于容置空间或不位于容置空间。
可选的，如图5-2所示，其为本公开实施例示出的另一种状态检测装置的框图，该状态检测装置500还包括：
功能关闭模块550，用于在输出的标签指示状态样本的类别为位于容置空间时，关闭待检测移动终端的预设功能；
休眠模块560，用于在输出的标签指示状态样本的类别为位于容置空间时，控制待检测移动终端进入休眠状态。
可选的，预设功能包括亮屏通知、手势亮屏、指纹解锁和双击按键启动相机中的至少一种。
综上所述，本公开实施例提供的状态检测装置，通过将待测试移动终端当前时刻的状态样本输入根据分类算法和状态样本集生成的分类器，并根据该综合考量了多种因素的分类器来确定待测试移动终端当前时刻是否位于口袋中。解决了相关技术中根据个别特征来判断移动终端是否位于口袋中的方法，在一些特殊场景中准确性较低的问题。达到了状态检测方法的准确性较高的效果。
图6是根据一示例性实施例示出的一种用于状态检测的装置600的框图。例如，装置600可以被提供为一计算机。参照图6，装置600包括处理组件622，其进一步包括一个或多个处理器，以及由存储器632所代表的存储器资源，用于存储可由处理部件622执行的指令，例如应用程序。存储器632中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件622被配置为执行指令，以执行上述状态检测方法。
装置600还可以包括一个电源组件626被配置为执行装置600的电源管理，一个有线或无线网络接口650被配置为将装置600连接到网络，和一个输入输出(I/O)接口658。装置600可以操作基于存储在存储器632的操作系统，例如Windows ServerTM，Mac OS XTM，UnixTM,LinuxTM，FreeBSDTM或类似。
图7是根据一示例性实施例示出的一种状态检测装置700的框图。改状态检测装置700可以被提供为一移动终端。参照图7，该装置700可以包括以下一个或多个组件：处理组件702，存储器704，电源组件706，多媒体组件708，音频组件710，输入/输出(I/O)的接口712，传感器组件714，以及通信组件716。
处理组件702通常控制该装置700的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理组件702可以包括一个或多个处理器720来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件702可以包括一个或多个模块，便于处理组件702和其他组件之间的交互。例如，处理组件702可以包括多媒体模块，以方便多媒体组件708和处理组件702之间的交互。
存储器704被配置为存储各种类型的数据以支持在该装置700的操作。这些数据的示例包括用于在该装置700上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器704可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读 存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电源组件706为该装置700的各种组件提供电源。电源组件706可以包括电源管理系统，一个或多个电源，及其他与为该装置700生成、管理和分配电源相关联的组件。
多媒体组件708包括在所述该装置700和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件708包括一个前置摄像头和/或后置摄像头。当该装置700处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜系统或具有焦距和光学变焦能力。
音频组件710被配置为输出和/或输入音频信号。例如，音频组件710包括一个麦克风(MIC)，当装置700处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器704或经由通信组件716发送。在一些实施例中，音频组件710还包括一个扬声器，用于输出音频信号。
I/O接口712为处理组件702和外围接口模块之间提供接口，上述外围接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件714包括一个或多个传感器，用于为该装置700提供各个方面的状态评估。例如，传感器组件714可以检测到该装置700的打开/关闭状态，组件的相对定位，例如所述组件为该装置700的显示器和小键盘，传感器组件714还可以检测装置700或该装置700一个组件的位置改变，用户与该装置700接触的存在或不存在，该装置700方位或加速/减速和该装置700的温度变化。传感器组件714可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件714还可以包括光传感器，如CMOS或CCD图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件714还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件716被配置为便于该装置700和其他设备之间有线或无线方式的通信。 该装置700可以接入基于通信标准的无线网络，如WiFi，2G或3G，或它们的组合。在一个示例性实施例中，通信组件716经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，所述通信组件716还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，该装置700可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述各个实施例提供的状态检测方法。
在示例性实施例中，还提供了一种包括指令的非临时性计算机可读存储介质，例如包括指令的存储器704，上述指令可由该装置700的处理器720执行以完成上述方法。例如，所述非临时性计算机可读存储介质可以是ROM、随机存取存储器(RAM)、CD-ROM、磁带、软盘和光数据存储设备等。
一种非临时性计算机可读存储介质，当所述存储介质中的指令由状态检测装置的处理器执行时，使得状态检测装置能够执行一种状态检测方法，所述方法包括：
获取多个样本移动终端的状态样本集，状态样本集包括多个样本移动终端位于预设容置空间的过程中采集的n个容置状态样本，和在多个样本移动终端不位于预设容置空间的过程中采集的m个非容置状态样本，n和m均为大于0的整数；
确定分类算法；
以状态样本集作为训练数据，根据分类算法训练得到分类器；
向待检测移动终端发送分类器，待检测移动终端用于根据待检测移动终端的状态样本，基于分类器，判断待检测移动终端是否位于预设容置空间中。
可选的，获取样本移动终端的状态样本集，包括：
获取多个样本移动终端位于容置空间的过程中采集的多个容置状态样本；
获取多个样本移动终端不位于容置空间的过程中采集的多个非容置状态样本；
对多个容置状态样本和多个非容置状态样本进行格式校验处理和缺失值补全处理后得到状态样本集。
可选的，向待检测移动终端发送分类器，包括：
对分类器进行格式转换，使分类器能够适用于移动终端的运行环境；
向待检测移动终端发送格式转换后的分类器。
可选的，分类算法为决策树算法、逻辑回归算法和支持向量机算法中的任意一种。
可选的，n个容置状态样本中的任一容置状态样本包括p个特征参数和用于指示任一容置状态样本的类别的标签，任一容置状态样本的类别为位于容置空间，p为大于0的整数。
可选的，m个非容置状态样本中的任一非容置状态样本包括p个特征参数和用于指示任一非容置状态样本的类别的标签，任一非容置状态样本的类别为不位于容置空间。
可选的，p个特征参数包括：距离传感器获取的参数、加速度传感器获取的参数、样本移动终端的系统时间、光线传感器获取的参数、样本移动终端运行的程序和样本移动终端的屏幕显示界面中的至少三种。
可选的，预设容置空间为用户携带的口袋、背包或挎包。
一种非临时性计算机可读存储介质，当所述存储介质中的指令由状态检测装置的处理器执行时，使得状态检测装置能够执行一种状态检测方法，所述方法包括：
接收分类器生成装置发送的分类器，分类器是分类器生成装置以状态样本集作为训练数据，根据分类算法训练得到的用于确定待检测移动终端是否位于容置空间的分类器，状态样本集中包括在样本移动终端位于容置空间的过程中采集的n个容置状态样本，和在样本移动终端不位于容置空间的过程中采集的m个非容置状态样本，n和m均为大于0的整数；
获取待检测移动终端当前时刻的状态样本；
将状态样本输入分类器；
获取分类器根据当前时刻的状态样本输出的标签，标签用于指示状态样本的类别，状态样本的类别为位于容置空间或不位于容置空间。
可选的，方法还包括：
在输出的标签指示状态样本的类别为位于容置空间时，关闭待检测移动终端的预设功能；
或者，在输出的标签指示状态样本的类别为位于容置空间时，控制待检测移动终端进入休眠状态。
可选的，预设功能包括亮屏通知、手势亮屏、指纹解锁和双击按键启动相机中的至少一种。
本公开中术语“A和B的至少一种”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和B的至少一种，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。同理，“A、B和C的至少一种”表示可以存在七种关系，可以表示：单独存在A，单独存在B，单独存在C，同时存在A和B，同时存 在A和C，同时存在C和B，同时存在A、B和C这七种情况。同理，“A、B、C和D的至少一种”表示可以存在十五种关系，可以表示：单独存在A，单独存在B，单独存在C，单独存在D，同时存在A和B，同时存在A和C，同时存在A和D，同时存在C和B，同时存在D和B，同时存在C和D，同时存在A、B和C，同时存在A、B和D，同时存在A、C和D，同时存在B、C和D，同时存在A、B、C和D，这十五种情况。
在本申请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，所述模块的划分，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，例如多个模块或组件可以结合或者可以集成到另一个系统，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，装置或模块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模块可以是或者也可以不是物理上分开的，作为模块显示的部件可以是或者也可以不是物理模块，即可以位于一个地方，或者也可以分布到多个网络模块上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
本领域普通技术人员可以理解实现上述实施例的全部或部分步骤可以通过硬件来完成，也可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，上述提到的存储介质可以是只读存储器，磁盘或光盘等。
以上所述仅为本公开实施例的较佳实施例，并不用以限制本公开实施例，凡在本公开实施例的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本公开实施例的保护范围之内。

Claims (26)

  1. 一种状态检测方法，其特征在于，所述方法包括：
    获取多个样本移动终端的状态样本集，所述状态样本集包括所述多个样本移动终端位于预设容置空间的过程中采集的n个容置状态样本，和在所述多个样本移动终端不位于所述预设容置空间的过程中采集的m个非容置状态样本，所述n和m均为大于0的整数；
    确定分类算法；
    以所述状态样本集作为训练数据，根据所述分类算法训练得到分类器；
    向待检测移动终端发送所述分类器，所述待检测移动终端用于根据所述待检测移动终端的状态样本，基于所述分类器，判断所述待检测移动终端是否位于所述预设容置空间中。
  2. 根据权利要求1所述的方法，其特征在于，所述获取样本移动终端的状态样本集，包括：
    获取所述多个样本移动终端位于容置空间的过程中采集的多个容置状态样本；
    获取所述多个样本移动终端不位于容置空间的过程中采集的多个非容置状态样本；
    对所述多个容置状态样本和所述多个非容置状态样本进行格式校验处理和缺失值补全处理后得到所述状态样本集。
  3. 根据权利要求1所述的方法，其特征在于，所述向待检测移动终端发送所述分类器，包括：
    对所述分类器进行格式转换，使所述分类器能够适用于移动终端的运行环境；
    向所述待检测移动终端发送格式转换后的所述分类器。
  4. 根据权利要求1所述的方法，其特征在于，所述分类算法为决策树算法、逻辑回归算法和支持向量机算法中的任意一种。
  5. 根据权利要求1所述的方法，其特征在于，所述n个容置状态样本中的任一容置状态样本包括p个特征参数和用于指示所述任一容置状态样本的类别的标签，所述任一容置状态样本的类别为位于容置空间，所述p为大于0的整数。
  6. 根据权利要求1所述的方法，其特征在于，所述m个非容置状态样本中的任一非容置状态样本包括p个特征参数和用于指示所述任一非容置状态样本的类别的标签，所述任一非容置状态样本的类别为不位于容置空间。
  7. 根据权利要求5或6所述的方法，其特征在于，所述p个特征参数包括：距离传感器获取的参数、加速度传感器获取的参数、所述样本移动终端的系统时间、光线传感器获取的参数、所述样本移动终端运行的程序和所述样本移动终端的屏幕显示界面中的至少三 种。
  8. 根据权利要求1至6任一所述的方法，其特征在于，所述预设容置空间为用户携带的口袋、背包或挎包。
  9. 一种状态检测方法，其特征在于，所述方法包括：
    接收分类器生成装置发送的分类器，所述分类器是所述分类器生成装置以状态样本集作为训练数据，根据分类算法训练得到的用于确定待检测移动终端是否位于容置空间的分类器，所述状态样本集中包括在样本移动终端位于容置空间的过程中采集的n个容置状态样本，和在所述样本移动终端不位于容置空间的过程中采集的m个非容置状态样本，所述n和m均为大于0的整数；
    获取所述待检测移动终端当前时刻的状态样本；
    将所述状态样本输入所述分类器；
    获取所述分类器根据所述当前时刻的状态样本输出的标签，所述标签用于指示所述状态样本的类别，所述状态样本的类别为位于容置空间或不位于容置空间。
  10. 根据权利要求9所述的方法，其特征在于，所述方法还包括：
    在所述输出的标签指示所述状态样本的类别为位于容置空间时，关闭所述待检测移动终端的预设功能；
    或者，在所述输出的标签指示所述状态样本的类别为位于容置空间时，控制所述待检测移动终端进入休眠状态。
  11. 根据权利要求10所述的方法，其特征在于，所述预设功能包括亮屏通知、手势亮屏、指纹解锁和双击按键启动相机中的至少一种。
  12. 一种状态检测装置，其特征在于，所述状态检测装置包括：
    样本集获取模块，用于获取多个样本移动终端的状态样本集，所述状态样本集包括所述多个样本移动终端位于预设容置空间的过程中采集的n个容置状态样本，和在所述多个样本移动终端不位于所述预设容置空间的过程中采集的m个非容置状态样本，所述n和m均为大于0的整数；
    算法确定模块，用于确定分类算法；
    分类器训练模块，用于以所述状态样本集作为训练数据，根据所述分类算法训练得到分类器；
    发送模块，用于向待检测移动终端发送所述分类器，所述待检测移动终端用于根据所述待检测移动终端的状态样本，基于所述分类器，判断所述待检测移动终端是否位于所述预设容置空间中。
  13. 根据权利要求12所述的状态检测装置，其特征在于，所述样本集获取模块，用于：
    获取所述多个样本移动终端位于容置空间的过程中采集的多个容置状态样本；
    获取所述多个样本移动终端不位于容置空间的过程中采集的多个非容置状态样本；
    对所述多个容置状态样本和所述多个非容置状态样本进行格式校验处理和缺失值补全处理后得到所述状态样本集。
  14. 根据权利要求12所述的状态检测装置，其特征在于，所述发送模块，用于：
    对所述分类器进行格式转换，使所述分类器能够适用于移动终端的运行环境；
    向所述待检测移动终端发送格式转换后的所述分类器。
  15. 根据权利要求12所述的状态检测装置，其特征在于，所述分类算法为决策树算法、逻辑回归算法和支持向量机算法中的任意一种。
  16. 根据权利要求12所述的状态检测装置，其特征在于，所述n个容置状态样本中的任一容置状态样本包括p个特征参数和用于指示所述任一容置状态样本的类别的标签，所述任一容置状态样本的类别为位于容置空间，所述p为大于0的整数。
  17. 根据权利要求12所述的状态检测装置，其特征在于，所述m个非容置状态样本中的任一非容置状态样本包括p个特征参数和用于指示所述任一非容置状态样本的类别的标签，所述任一非容置状态样本的类别为不位于容置空间。
  18. 根据权利要求16或17所述的状态检测装置，其特征在于，所述p个特征参数包括：距离传感器获取的参数、加速度传感器获取的参数、所述样本移动终端的系统时间、光线传感器获取的参数、所述样本移动终端运行的程序和所述样本移动终端的屏幕显示界面中的至少三种。
  19. 根据权利要求12至18任一所述的状态检测装置，其特征在于，所述预设容置空间为用户携带的口袋、背包或挎包。
  20. 一种状态检测装置，其特征在于，所述状态检测装置包括：
    接收模块，用于接收分类器生成装置发送的分类器，所述分类器是所述分类器生成装置以状态样本集作为训练数据，根据分类算法训练得到的用于确定待检测移动终端是否位于容置空间的分类器，所述状态样本集中包括在样本移动终端位于容置空间的过程中采集的n个容置状态样本，和在所述样本移动终端不位于容置空间的过程中采集的m个非容置状态样本，所述n和m均为大于0的整数；
    样本获取模块，用于获取所述待检测移动终端当前时刻的状态样本；
    输入模块，用于将所述状态样本输入所述分类器；
    标签获取模块，用于获取所述分类器根据所述当前时刻的状态样本输出的标签，所述 标签用于指示所述状态样本的类别，所述状态样本的类别为位于容置空间或不位于容置空间。
  21. 根据权利要求20所述的状态检测装置，其特征在于，所述状态检测装置还包括：
    功能关闭模块，用于在所述输出的标签指示所述状态样本的类别为位于容置空间时，关闭所述待检测移动终端的预设功能；
    休眠模块，用于在所述输出的标签指示所述状态样本的类别为位于容置空间时，控制所述待检测移动终端进入休眠状态。
  22. 根据权利要求21所述的状态检测装置，其特征在于，所述预设功能包括亮屏通知、手势亮屏、指纹解锁和双击按键启动相机中的至少一种。
  23. 一种状态检测装置，其特征在于，所述状态检测装置包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为执行权利要求1-8任一所述的状态检测方法。
  24. 一种状态检测装置，其特征在于，所述状态检测装置包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为执行权利要求9-12任一所述的状态检测方法。
  25. 一种计算机可读存储介质，所述计算机可读存储介质上存储有指令，其特征在于，所述指令被处理器执行时实现权利要求1-8任一所述的状态检测方法。
  26. 一种计算机可读存储介质，所述计算机可读存储介质上存储有指令，其特征在于，所述指令被处理器执行时实现权利要求9-11任一所述的状态检测方法。
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