CN110399043B - Data processing method, device and system - Google Patents
Data processing method, device and system Download PDFInfo
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- CN110399043B CN110399043B CN201910693654.XA CN201910693654A CN110399043B CN 110399043 B CN110399043 B CN 110399043B CN 201910693654 A CN201910693654 A CN 201910693654A CN 110399043 B CN110399043 B CN 110399043B
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Abstract
The present disclosure provides a data processing method applied to an electronic device including a sensor for continuously acquiring distance data for characterizing a distance between an external object and the electronic device. The data processing method comprises the steps of firstly screening first effective distance data from the distance data according to a first rule in a first state of the electronic equipment, screening second effective distance data from the distance data according to a second rule in a second state of the electronic equipment, and then calculating an effective control distance range for controlling the electronic equipment by the external object based on the first effective distance data and the second effective distance data. The present disclosure also provides a data processing apparatus and a data processing system.
Description
Technical Field
The disclosure relates to a data processing method, device and system.
Background
The time of flight ranging (TOF) method can be applied to automatic wake-up and unlock of electronic devices. The electronic device generally learns the usage habits of the user by itself, so as to remotely control the state of the electronic device according to the distance between the user and the electronic device. In the current self-learning process, when a sensor detects data representing the distance between an external object and an electronic device, almost all of the data is used for electronic self-learning. However, some of these data may be invalid, which may negatively impact the outcome of self-learning. For example, if a person who is not a registered user of the electronic device approaches, the electronic device is not unlocked or logged in, but the sensor is still detecting data. If this part of data is used in self-learning, the finally obtained user habits and the actual situation may deviate.
Disclosure of Invention
One aspect of the present disclosure provides a data processing method applied to an electronic device including a sensor for continuously acquiring distance data representing a distance between an external object and the electronic device. The data processing method comprises the following steps: screening first effective distance data from the distance data according to a first rule in a first state of the electronic equipment; screening second effective distance data from the distance data according to a second rule in a second state of the electronic equipment; and calculating an effective control distance range in which the electronic device is controlled by the external object based on the first effective distance data and the second effective distance data.
According to an embodiment of the disclosure, in a first state of the electronic device, screening first effective distance data from the distance data according to a first rule includes, when a state switching event occurs in the electronic device, screening data acquired within a predetermined period associated with the switching event from the distance data as the first effective distance data, where the state switching event includes at least one of power on, wake up, unlock, sleep, or power off. And in the second state of the electronic equipment, screening second effective distance data from the distance data according to a second rule, wherein when the electronic equipment is in a normal operation state, the data acquired in a period lasting in the normal operation state is screened from the distance data and is used as the second effective distance data.
According to an embodiment of the present disclosure, the predetermined period associated with the switching event includes a time period formed with a time point that is advanced from the occurrence time of the switching event by a first time interval as a timing start point and a time point that is advanced from the occurrence time of the switching event by a second time interval as a timing end point.
According to an embodiment of the present disclosure, the predetermined period associated with the switching event includes a time period formed by taking a time point at which a distance between the external object and the electronic device reaches a predetermined distance before an occurrence time of the switching event as a timing start point and taking the occurrence time of the switching event as a timing end point.
According to an embodiment of the present disclosure, the calculating an effective control distance range in which the external object controls the electronic device based on the first effective distance data and the second effective distance data includes: processing the first effective distance data according to a first statistical rule to obtain first statistical data; processing the second effective distance data according to a second statistical rule to obtain second statistical data; obtaining the effective control distance range based on the first statistical data and the second statistical data; wherein the first statistical rule is different from the second statistical rule.
According to an embodiment of the present disclosure, the processing the first effective distance data according to a first statistical rule to obtain first statistical data includes: obtaining a first normal distribution of the first effective distance data through regression; and taking data, which is greater than or equal to a first probability under the first normal distribution, in the first effective distance data as the first statistical data. And processing the second effective distance data according to a second statistical rule to obtain second statistical data, wherein the step of calculating an arithmetic mean of the second effective distance data comprises taking the arithmetic mean as the second statistical data.
According to an embodiment of the present disclosure, the obtaining the effective control distance range based on the first statistical data and the second statistical data includes: acquiring a numerical range determined by the maximum value and the minimum value in the first statistical data; and determining whether the arithmetic mean is within the range of values; if so, taking the numerical range as the effective control distance range; if not, the numerical range is adjusted to include the second statistical data, and the adjusted numerical range is used as the effective control distance range.
According to an embodiment of the present disclosure, the obtaining the effective control distance range based on the first statistical data and the second statistical data includes: obtaining a second normal distribution of the first statistical data by regression, wherein the arithmetic mean is taken as a mean value of the second normal distribution; and a range of data having a distribution probability greater than or equal to a second probability in the second normal distribution is used as the effective control distance range.
Another aspect of the present disclosure provides a data processing apparatus applied to an electronic device including a sensor for continuously acquiring distance data representing a distance between an external object and the electronic device. The data processing device comprises an effective data screening module and an effective control distance calculating module. The effective data screening module is used for screening first effective distance data from the distance data according to a first rule in a first state of the electronic equipment, and screening second effective distance data from the distance data according to a second rule in a second state of the electronic equipment. And the effective control distance calculation module is used for calculating an effective control distance range of the external object for controlling the electronic equipment based on the first effective distance data and the second effective distance data.
According to an embodiment of the present disclosure, the valid data screening module includes a first screening submodule and a second screening submodule. The first screening submodule is used for screening out data acquired in a preset period associated with a switching event from the distance data as the first effective distance data when the electronic equipment has the state switching event, wherein the state switching event comprises at least one of starting up, waking up, unlocking, sleeping or shutting down. And the second screening submodule is used for screening data acquired in a period lasting in a normal operation state from the distance data as second effective distance data when the electronic equipment is in the normal operation state.
According to the embodiment of the disclosure, the effective control distance calculation module comprises a first statistic submodule, a second statistic submodule and an effective control distance obtaining submodule. The first statistic submodule is used for processing the first effective distance data according to a first statistic rule to obtain first statistic data. The second statistical submodule is used for processing the second effective distance data according to a second statistical rule to obtain second statistical data, wherein the first statistical rule is different from the second statistical rule. The effective control distance obtaining submodule is used for obtaining the effective control distance range based on the first statistical data and the second statistical data.
According to the embodiment of the disclosure, the first statistical submodule is specifically configured to obtain a first normal distribution of first effective distance data through regression, and use data, in the first effective distance data, of which the distribution probability under the first normal distribution is greater than or equal to a first probability as the first statistical data. The second statistical submodule is specifically configured to calculate an arithmetic mean of the second effective distance data, where the arithmetic mean is used as the second statistical data.
According to an embodiment of the present disclosure, the effective control distance obtaining sub-module is specifically configured to obtain a numerical range determined by a maximum value and a minimum value in the first statistical data, and determine whether the arithmetic mean is within the numerical range: if so, taking the numerical range as the effective control distance range; if not, the numerical range is adjusted to include the second statistical data, and the adjusted numerical range is used as the effective control distance range.
According to an embodiment of the present disclosure, the effective control distance obtaining submodule is specifically configured to obtain a second normal distribution of the first statistical data by regression, where the arithmetic mean is used as a mean of the second normal distribution, and a range of data having a distribution probability greater than or equal to a second probability in the second normal distribution is used as the effective control distance range.
Another aspect of the present disclosure provides a data processing system. The data processing system includes a sensor, a processor, and a memory. The sensor is configured to continuously acquire distance data characterizing a distance between an external object and the electronic device. The memory has stored thereon executable instructions. Wherein the instructions, when executed by the processor, cause the processor to perform the method as described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
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For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
fig. 1 schematically illustrates an application scenario of a data processing method, apparatus and system according to an embodiment of the present disclosure;
FIG. 2 schematically shows a flow diagram of a data processing method according to an embodiment of the present disclosure;
FIG. 3 schematically shows a flow chart of a data processing method according to another embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow chart of a method of calculating an effective control distance range in a data processing method according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow chart of a method of calculating an effective control distance range in a data processing method according to another embodiment of the present disclosure;
FIG. 6 schematically illustrates a schematic diagram of calculating an effective control distance range according to an embodiment of the present disclosure;
FIG. 7 schematically illustrates a flow chart of a method of calculating an effective control distance range in a data processing method according to yet another embodiment of the present disclosure;
FIG. 8 schematically shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure; and
FIG. 9 schematically shows a block diagram of a data processing system according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.
The embodiment of the disclosure provides a data processing method, a data processing device and a data processing system, which are applied to electronic equipment. The electronic device includes a sensor for continuously acquiring distance data characterizing a distance between an external object and the electronic device. The data processing method comprises the steps of firstly screening first effective distance data from the distance data according to a first rule in a first state of the electronic equipment, screening second effective distance data from the distance data according to a second rule in a second state of the electronic equipment, and then calculating an effective control distance range of an external object for controlling the electronic equipment based on the first effective distance data and the second effective distance data.
According to the embodiment of the disclosure, effective data can be selected from data collected by the sensor, and the effective data is used for self-learning of the electronic equipment to obtain the effective control distance range for automatically controlling the state of the electronic equipment, so that the automatic control of the electronic equipment is more fit with the use habit of a user, and the user experience is improved.
Fig. 1 schematically shows an application scenario of a data processing method, apparatus and system according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the application scenario includes an electronic device 11 and an external object (i.e., a user 12), where the electronic device 11 includes a sensor 111. Sensor 111 is used to continuously acquire distance data that characterizes the distance between user 12 and electronic device 11.
The distance data acquired by the sensor 111 may provide data for time of flight ranging (TOF). The electronic device 11 may control the state of the electronic device 11 according to the distance data, for example, to control the electronic device 11 to wake up, unlock, sleep, power on, power off, or operate normally.
The state in which the user 12 is at three different distance positions from the electronic device 11 is illustrated in fig. 1. For example, when the distance of the user 12 from the electronic apparatus 11 is within the range of [ L1, L2], the state of the electronic apparatus 11 can be remotely controlled according to the distance of the user 12 from the electronic apparatus 11. For example, electronic device 11 may be controlled to automatically wake up when user 12 is detected to be at a distance L2 from electronic device 11 and is continuously approaching electronic device 11. And then controls the electronic device 11 to operate normally when the distance from the user 12 to the electronic device 11 is between L1 and L2. Then, if it is detected that the distance between the user 12 and the electronic device 11 is greater than L2 and is kept for a certain time (e.g., 1 minute), the electronic device may be controlled to automatically sleep. Here, [ L1, L2] is an effective control distance range for controlling the electronic device 11.
According to the embodiment of the present disclosure, the electronic device 11 may screen the effective distance data from the distance data acquired by the sensor 111 according to different rules according to different operating states of the electronic device 11, and perform self-learning according to the effective distance data to continuously learn and correct the effective control distance range [ L1, L2 ]. For example, when the electronic device 11 wakes up, the data acquired by the sensor 111 within a certain time period before and after the wake-up is selected as valid data. For another example, during normal use of electronic device 11 by user 12, the data acquired by normal use process sensor 111 may be all used as valid data.
In addition, according to some embodiments of the present disclosure, it may be considered that the values of the distance data collected by the sensor 111 exhibit a certain distribution law (e.g., normal distribution) while the user 12 is approaching or departing from the electronic device 11, so that the control range during the movement of the user 12 relative to the electronic device 11 may be estimated by the normal distribution. According to other embodiments of the present disclosure, it may be assumed that the distance data collected by sensor 111 during normal use of electronic device 11 by user 12 (e.g., editing a document, or viewing a video, etc.) is maintained at a substantially constant value, such that the distance from electronic device during normal use of electronic device 11 by user 12 may be estimated as an arithmetic average. Then, according to the embodiment of the present disclosure, an effective control distance range [ L1, L2] may be determined according to the control range during the movement of the user 12 relative to the electronic device and the distance from the electronic device 11 during the normal use of the electronic device 11 by the user 12.
It is understood that the electronic device 11 in fig. 1 is illustrated as a notebook computer, and the position of the sensor 111 in the electronic device 11 is also exemplary and does not limit the technical solution of the present disclosure.
Fig. 2 schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure.
As shown in fig. 2, the data processing method is applied to the electronic device 11. The data processing method includes operations S210 to S230.
In operation S210, in a first state of the electronic device 11, first effective distance data is screened from the distance data according to a first rule.
In operation S220, in the second state of the electronic device 11, second effective distance data is screened from the distance data according to a second rule.
In operation S230, an effective control distance range [ L1, L2] in which the electronic device 11 is controlled by the external object is calculated based on the first effective distance data and the second effective distance data. Specific embodiments of operation S230 may refer to the description below with respect to fig. 4-6, according to an embodiment of the present disclosure.
According to the embodiment of the present disclosure, the distance data continuously acquired by the sensor 111 may be distinguished, and effective data may be screened out according to the operation state of the electronic device 11, and then used for self-learning of the electronic device 11. In this way, the influence of invalid data, such as distance data between a non-registered user and the electronic device 11 collected by using the sensor 111 or distance data collected during a process that the user 12 approaches the electronic device 11 for a short moment and leaves the electronic device 11 soon, on the self-learning of the electronic device 11 can be avoided, so that the finally obtained valid control distance range [ L1, L2] for controlling the state of the electronic device 11 is more suitable for the use habit of the user 12, and the user experience and the control efficiency of the electronic device 11 are improved.
Fig. 3 schematically shows a flow chart of a data processing method according to another embodiment of the present disclosure.
As shown in fig. 3, the data processing method applied to the electronic device 11 may include operation S211, operation S221, and operation S230. Wherein operation S230 may refer to the related description of fig. 2. Operation S211 is one specific embodiment of operation S210, and operation S221 is one specific embodiment of operation S220.
In operation S211, when a state switching event occurs in the electronic device 11, data acquired within a predetermined period associated with the switching event is screened from the distance data as first effective distance data, where the state switching event includes at least one of power on, wake up, unlock, sleep, or power off.
According to an embodiment of the present disclosure, the predetermined period associated with the switching event may be a time period formed with a time point, which is advanced from the occurrence time of the switching event by a first time interval (e.g., 30s), as a timing start point, and a time point, which is advanced from the occurrence time of the switching event by a second time interval (e.g., 10s), as a timing end point. It is to be understood that the first time interval and the second time interval may be equal or unequal, and may be specifically set according to actual needs.
According to another embodiment of the present disclosure, the predetermined period associated with the switching event may be a time period formed with a time point at which the distance between the user 12 and the electronic device 11 before the occurrence time of the switching event reaches a predetermined distance (e.g., 1m) as a timing start point and with the occurrence time of the switching event as a timing end point. For example, the user is considered to have come when the user 12 is approaching and reaches 1m from the electronic device 11. And upon occurrence of a switching event (e.g., an automatic wake-up) of the electronic device, it is indicated that the user 12 is close to the electronic device 11 and wants to use the electronic device, so that the distance data collected from the time point when the user is expected to come to the time point when the switching event is completed can be taken as the effective distance data.
Screening out data acquired within a predetermined period associated with the switching event as first effective distance data, so that data collected by the sensor 111, for example, when the user 12 is only accidentally approaching the electronic device 11 momentarily, can be rejected when calculating the effective control distance range [ L1, L2 ]; alternatively, data collected by sensor 111, for example, when proximate to electronic device 11 but did not cause a corresponding state switch event (e.g., a non-registered user is proximate to electronic device 12) may be culled.
In operation S221, when the electronic device 11 is in the normal operation state, data acquired during a period in which the normal operation state continues is screened out from the distance data as second effective distance data. The normal operation state may be, for example, a state in which the display screen of the electronic device 11 is normally displayed, a state in which at least one program is operated in the foreground, or a state in which a user operation can be continuously detected.
According to an embodiment of the present disclosure, operations S211 and S221 may be filtering from a large amount of distance data acquired by the sensor 111 during daily frequent use of the electronic device 11 by the user 12.
Fig. 4 schematically shows a flowchart of a method of calculating an effective control distance range in operation S230 in a data processing method according to an embodiment of the present disclosure.
As shown in fig. 4, example operation S230 according to the present disclosure may include operations S401 to S403.
In operation S401, the first effective distance data is processed according to a first statistical rule to obtain first statistical data.
Operation S402 is performed to process the second effective distance data according to a second statistical rule, so as to obtain second statistical data. Wherein the first statistical rule is different from the second statistical rule.
In operation S403, based on the first statistical data and the second statistical data, an effective control distance range [ L1, L2] is obtained.
Since the first effective distance data and the second effective distance data are distance data screened for different states of the electronic device 11, processing may be performed using different statistical rules based on distribution characteristics of the distance data of the user 12 and the electronic device 11 in the different states of the electronic device 11.
For example, when the first state of the electronic device 11 is a state in which a state switching event occurs in the electronic device 11, the user is in a moving process close to or away from the electronic device 11, and therefore the first effective distance data screened for the state in which the state switching event occurs in the electronic device 11 will exhibit a certain probability distribution characteristic. For example, the probability density function of the first significant data may be derived from a statistical regression of the first significant distance data. Alternatively, the first effective distance data may be approximately considered to be normally distributed, and the normal distribution of the first effective distance data may be calculated by regression, so as to obtain the first statistical data.
For another example, when the second state of the electronic device 11 is the normal operation state, the position of the user 12 is substantially kept stable. The statistical processing of the second significant distance data for this process may be, for example, calculating an arithmetic mean of all data in the second significant distance data; alternatively, for example, the second statistical data may be obtained by, after obtaining an arithmetic average of all data in the second effective distance data, eliminating data in the second effective distance data whose difference from the arithmetic average is larger than a certain threshold (for example, to eliminate distance data obtained due to the user 12 getting away or close occasionally during the operation of the electronic device 11), and then averaging the remaining data or taking a numerical range of the remaining data.
Fig. 5 schematically shows a flowchart of a method of calculating an effective control distance range in operation S230 in a data processing method according to another embodiment of the present disclosure. The method flow shown in fig. 5 is a specific embodiment of the method shown in fig. 4.
Fig. 6 schematically illustrates a schematic diagram of calculating an effective control distance range according to an embodiment of the present disclosure.
As shown in fig. 5, in conjunction with fig. 6, operation S230 may include operations S501 to S505, and operation S506 or operation S507.
In operation S501, regression obtains a first normal distribution of the first effective distance data. This first normal distribution (μ,) may be exemplified as the normal distribution curve 61 shown in fig. 6, for example.
Then, in operation S502, data having a distribution probability under a first normal distribution (μ,) in the first effective distance data that is greater than or equal to a first probability is taken as first statistical data. For example, assuming that the first probability is 95%, the first statistical data in conjunction with fig. 6 is data in the numerical range of [ μ -1.96, μ +1.96] in the first valid data.
In some embodiments, the process of obtaining the first normal distribution (μ,) may be to perform multiple regression on the first valid distance data. For example, a first effective data obtained by screening in the process that the user 12 uses the electronic device 11 on the first day is subjected to primary normal regression to obtain a first normal regression curve; then, performing primary normal regression on first effective data obtained by screening in the process that the user 12 uses the electronic equipment 11 the next day to obtain a second normal regression curve; and by analogy, obtaining a plurality of normal regression curves. And then screening out data with the distribution probability being larger than or equal to the first probability from each normal regression curve in the plurality of normal regression curves. Then, normal regression is performed again on the selected portion of data to obtain the first normal distribution (. mu.). This multiple regression approach may reduce the amount of data calculated per regression.
With continued reference to fig. 5, operation S503 is performed after operation S502. Wherein, in operation S503, an arithmetic mean of the second effective distance data is calculated, wherein the arithmetic mean is taken as the second statistical data. This arithmetic mean example can be illustrated in fig. 6 as a vertical line 62, for example, wherein the distance value of the vertical line 62 on the horizontal axis is the arithmetic mean.
Next, in operation S504, a numerical range determined by the maximum value and the minimum value in the first statistical data is acquired.
Then, in operation S505, it is determined whether the arithmetic mean is within a numerical range. If yes, operation S506 is performed. If not, operation S507 is performed.
In operation S506, when the arithmetic mean is within the numerical range, the numerical range is used as the effective control distance range. For example, the first statistical data is data in the first significant data within a numerical range of [ μ -1.96, μ +1.96], within which the vertical line 62 representing the arithmetic mean in fig. 6 is located. The arithmetic mean is within a numerical range indicating that the distance from the user 12 to the electronic apparatus 11 in normal use is substantially within a range of the distance in controlling the switching state of the electronic apparatus 11 such as on/off. Accordingly, the numerical range determined in operation S504 may be the effective control distance range [ L1, L2 ].
In operation S507, when the arithmetic mean is outside the value range, the value range is adjusted to include the second statistical data, and the adjusted value range is used as the effective control distance range [ L1, L2 ]. The arithmetic mean is outside the range of values indicating that the distance between the user 12 and the electronic device 11 during normal use is substantially outside the distance controlling the switching state of the electronic device 11, such as on/off, and the like, and the boundary of the range of values may be expanded such that the adjusted range of values includes the arithmetic mean.
Fig. 7 schematically shows a flowchart of a method of calculating an effective control distance range in operation S230 in a data processing method according to still another embodiment of the present disclosure.
As shown in fig. 7, operation S230 may include operations S501 to S503, operation S704, and operation S705 according to an embodiment of the present disclosure. Here, operations S501 to S503 may refer to the description of fig. 5.
In operation S704, the regression obtains a second normal distribution of the first statistical data, wherein the arithmetic mean is taken as a mean of the second normal distribution. In the embodiment of the present disclosure, the normal distribution of the first statistical data is obtained by taking an arithmetic mean of distances between the user 12 and the electronic device 11 in a normal operating state of the electronic device 11 as a mean regression.
Then, in operation S705, a range of data having a probability greater than or equal to the second probability is distributed in the second normal distribution as an effective control distance range [ L1, L2 ].
Fig. 8 schematically shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure.
As shown in fig. 8, the data processing apparatus 800 may be disposed in the electronic device 11 according to an embodiment of the disclosure. The apparatus 800 may include an effective data filtering module 810 and an effective control distance calculation module 820. The apparatus 800 may be used to perform the methods described with reference to fig. 2-7.
The valid data filtering module 810 is configured to filter first valid distance data from the distance data according to a first rule in a first state of the electronic device 11, and filter second valid distance data from the distance data according to a second rule in a second state of the electronic device 11.
The effective control distance calculation module 820 is configured to calculate an effective control distance range [ L1, L2] for the external object to control the electronic device 11 based on the first effective distance data and the second effective distance data.
The valid data screening module 810 includes a first screening submodule 811 and a second screening submodule 812 according to an embodiment of the present disclosure.
The first filtering submodule 811 is configured to, when a state switching event occurs in the electronic device 11, filter, from the distance data, data acquired within a predetermined period associated with the switching event as first effective distance data, where the state switching event includes at least one of power on, wake up, unlock, hibernation, or power off.
The second filtering sub-module 812 is configured to, when the electronic device 11 is in the normal operation state, filter, from the distance data, data acquired during a period in which the normal operation state continues, as second effective distance data.
According to an embodiment of the present disclosure, the effective control distance calculation module 820 includes a first statistics submodule 821, a second statistics submodule 822, and an effective control distance obtaining submodule 823.
The first statistics submodule 821 is configured to process the first effective distance data according to a first statistics rule to obtain first statistics data.
The second statistical submodule 822 is configured to process the second effective distance data according to a second statistical rule to obtain second statistical data, where the first statistical rule is different from the second statistical rule.
The effective control distance obtaining sub-module 823 is configured to obtain an effective control distance range based on the first statistical data and the second statistical data.
According to an embodiment of the present disclosure, the first statistical submodule 821 is specifically configured to obtain a first normal distribution of first effective distance data through regression, and use data, in the first effective distance data, of which a distribution probability under the first normal distribution is greater than or equal to a first probability as the first statistical data. The second statistic submodule 822 is specifically configured to calculate an arithmetic mean of the second effective distance data, wherein the arithmetic mean is used as the second statistic data.
According to the embodiment of the present disclosure, the effective control distance obtaining sub-module 823 is specifically configured to obtain a numerical range determined by the maximum value and the minimum value in the first statistical data, and determine whether the arithmetic mean is within the numerical range: if yes, taking the numerical range as an effective control distance range; if not, the numerical range is adjusted to include the second statistical data, and the adjusted numerical range is used as the effective control distance range.
According to the embodiment of the present disclosure, the effective control distance obtaining sub-module 823 is specifically configured to obtain a second normal distribution of the first statistical data by regression, where an arithmetic mean is used as a mean of the second normal distribution, and a range of data having a distribution probability greater than or equal to a second probability in the second normal distribution is used as the effective control distance range.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any plurality of the effective data filtering module 810, the effective control distance calculating module 820, the first filtering submodule 811, the second filtering submodule 812, the first statistical submodule 821, the second statistical submodule 822, and the effective control distance obtaining submodule 823 may be combined and implemented in one module, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the effective data filtering module 810, the effective control distance calculating module 820, the first filtering submodule 811, the second filtering submodule 812, the first statistical submodule 821, the second statistical submodule 822, and the effective control distance obtaining submodule 823 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or by a suitable combination of any of them. Alternatively, at least one of the effective data filtering module 810, the effective control distance calculating module 820, the first filtering submodule 811, the second filtering submodule 812, the first statistics submodule 821, the second statistics submodule 822, and the effective control distance obtaining submodule 823 may be at least partially implemented as a computer program module, which may perform corresponding functions when being executed.
FIG. 9 schematically shows a block diagram of a data processing system 900 according to an embodiment of the present disclosure. The data processing system 900 shown in FIG. 9 is only an example and should not impose any limitations on the scope of use or functionality of embodiments of the present disclosure.
As shown in fig. 9, system 900 includes a processor 910, a computer-readable storage medium 920, and a sensor 930. The system 900 may perform a method according to an embodiment of the disclosure.
In particular, processor 910 may include, for example, a general purpose microprocessor, an instruction set processor and/or related chip set and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), and/or the like. The processor 910 may also include onboard memory for caching purposes. The processor 910 may be a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
Computer-readable storage media 920, for example, may be non-volatile computer-readable storage media, specific examples including, but not limited to: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and so on.
The computer-readable storage medium 920 may include a computer program 921, which computer program 921 may include code/computer-executable instructions that, when executed by the processor 910, cause the processor 910 to perform a method according to an embodiment of the present disclosure, or any variation thereof.
The computer program 921 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 921 may include one or more program modules, including 921A, modules 921B, … …, for example. It should be noted that the division and number of the modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, so that the processor 910 may execute the method according to the embodiment of the present disclosure or any variation thereof when the program modules are executed by the processor 910.
According to an embodiment of the present disclosure, the sensor 930 is configured to continuously acquire distance data characterizing a distance between an external object and the electronic device 11. The processor 910 may interact with the sensor 930 to perform a method according to an embodiment of the disclosure, or any variation thereof.
According to an embodiment of the present invention, at least one of the effective data filtering module 810, the effective control distance calculating module 820, the first filtering submodule 811, the second filtering submodule 812, the first statistics submodule 821, the second statistics submodule 822, and the effective control distance obtaining submodule 823 may be implemented as a computer program module described with reference to fig. 9, which may implement the corresponding operations described above when being executed by the processor 910.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
While the disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (9)
1. A data processing method applied to an electronic device including a sensor for continuously acquiring distance data representing a distance between an external object and the electronic device, wherein the data processing method includes:
in a first state of the electronic device, screening first effective distance data from the distance data according to a first rule, including:
when the electronic equipment generates a state switching event, screening data acquired in a preset period associated with the switching event from the distance data as the first effective distance data, wherein the switching event comprises at least one of starting, waking, unlocking, sleeping or shutting down;
in a second state of the electronic device, screening second effective distance data from the distance data according to a second rule, including:
when the electronic equipment is in a normal operation state, screening data acquired in a period lasting in the normal operation state from the distance data as second effective distance data;
and
and calculating an effective control distance range of the external object for controlling the electronic equipment based on the first effective distance data and the second effective distance data.
2. The method of claim 1, wherein the predetermined period associated with the handover event comprises:
a time period formed with a time point that is advanced from the occurrence time of the switching event by a first time interval as a timing start point, and a time point that is advanced from the occurrence time of the switching event by a second time interval as a timing end point.
3. The method of claim 1, wherein the predetermined period associated with the handover event comprises:
and a time period formed by taking a time point before the occurrence time of the switching event when the distance between the external object and the electronic equipment reaches a preset distance as a timing starting point and taking the occurrence time of the switching event as a timing end point.
4. The method of claim 1, wherein the calculating an effective control distance range over which the external object controls the electronic device based on the first effective distance data and the second effective distance data comprises:
processing the first effective distance data according to a first statistical rule to obtain first statistical data;
processing the second effective distance data according to a second statistical rule to obtain second statistical data; and
obtaining the effective control distance range based on the first statistical data and the second statistical data;
wherein the first statistical rule is different from the second statistical rule.
5. The method of claim 4, wherein:
the processing the first effective distance data according to the first statistical rule to obtain first statistical data includes:
obtaining a first normal distribution of the first effective distance data through regression;
taking data with a distribution probability under the first normal distribution in the first effective distance data being greater than or equal to a first probability as the first statistical data;
the processing the second effective distance data according to a second statistical rule to obtain second statistical data includes:
and calculating an arithmetic mean of the second effective distance data, wherein the arithmetic mean is taken as the second statistical data.
6. The method of claim 5, wherein the deriving the effective control distance range based on the first and second statistics comprises:
acquiring a numerical range determined by the maximum value and the minimum value in the first statistical data;
determining whether the arithmetic mean is within the range of values; if so, taking the numerical range as the effective control distance range; if not, the numerical range is adjusted to include the second statistical data, and the adjusted numerical range is used as the effective control distance range.
7. The method of claim 5, wherein the deriving the effective control distance range based on the first and second statistics comprises:
obtaining a second normal distribution of the first statistical data by regression, wherein the arithmetic mean is taken as a mean value of the second normal distribution; and
and taking the range of the data with the distribution probability greater than or equal to the second probability in the second normal distribution as the effective control distance range.
8. A data processing apparatus applied to an electronic device including a sensor for continuously acquiring distance data representing a distance between an external object and the electronic device, wherein the data processing apparatus comprises:
an effective data screening module for:
screening first effective distance data from the distance data according to a first rule in a first state of the electronic equipment, wherein when a state switching event occurs in the electronic equipment, data acquired within a preset period associated with the switching event are screened from the distance data to serve as the first effective distance data, and the switching event comprises at least one of starting, waking up, unlocking, sleeping or shutting down; and
screening second effective distance data from the distance data according to a second rule in a second state of the electronic equipment, wherein when the electronic equipment is in a normal operation state, the data acquired in a period lasting in the normal operation state is screened from the distance data and is used as the second effective distance data;
and
and the effective control distance calculation module is used for calculating an effective control distance range of the external object for controlling the electronic equipment based on the first effective distance data and the second effective distance data.
9. A data processing system comprising:
a sensor for continuously acquiring distance data characterizing a distance between an external object and the electronic device;
a processor; and
a memory having stored thereon executable instructions that, when executed by the processor, cause the processor to perform the method of any of claims 1-7.
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