CN114258116A

CN114258116A - Power consumption optimization method and device

Info

Publication number: CN114258116A
Application number: CN202011026542.8A
Authority: CN
Inventors: 张舒博; 林于超; 林嵩晧; 郑理文
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-09-25
Filing date: 2020-09-25
Publication date: 2022-03-29
Also published as: CN115734323B; CN115734323A

Abstract

The embodiment of the application discloses a power consumption optimization method and device, which are used for optimizing the power consumption of equipment in near field distributed communication. The method can comprise the following steps: the method comprises the steps that a first terminal device obtains device connection rule information, wherein the device connection rule information is used for describing a connection rule between the first terminal device and other terminal devices; determining the probability of connection behavior between the first terminal equipment and the second terminal equipment in a future preset time period according to the equipment connection rule information, wherein other terminal equipment comprises the second terminal equipment; and executing a power consumption optimization strategy according to the probability.

Description

Power consumption optimization method and device

Technical Field

The present application relates to the field of communications technologies, and in particular, to a power consumption optimization method and apparatus.

Background

Distributed communication refers to a process of communication among a plurality of terminal devices in a near field environment. Distributed communication involves processes such as device discovery, topology networking, connection protocols, and channel maintenance among end devices.

In the existing device discovery process, the scanning gap of the terminal device is fixed, that is, the ratio between the scanning gap and the scanning period is fixed, that is, the scanning duty ratio is not changed. Wherein one scanning cycle includes a scanning gap and a scanning interval, the scanning interval generating no power consumption, the scanning gap generating power consumption.

Disclosure of Invention

The embodiment of the application provides a power consumption optimization method and device, which can optimize the power consumption of equipment in near field distributed communication.

In a first aspect, an embodiment of the present application provides a power consumption optimization method, which is applied to a first terminal device, and the method includes: acquiring equipment connection rule information, wherein the equipment connection rule information is used for describing a connection rule between the first terminal equipment and other terminal equipment; determining the probability of connection behavior between the first terminal equipment and the second terminal equipment in a future preset time period according to the equipment connection rule information, wherein other terminal equipment comprises the second terminal equipment; and executing a power consumption optimization strategy according to the probability.

According to the method and the device for optimizing the distributed communication power consumption, the probability that the first terminal device and other terminal devices send connection behaviors in a future period of time is predicted through the device connection rule information, and then the power optimization strategy is executed according to the probability, so that the distributed communication power consumption is optimized.

Illustratively, the power consumption optimization strategy includes adjusting the duty cycle, i.e., adjusting the duty cycle according to the probability magnitude. The power consumption optimization strategy comprises establishing connection with the second terminal equipment in advance and the like.

In some possible implementations of the first aspect, the process of executing the power consumption optimization policy according to the probability may include: when the probability is larger than a first threshold value, establishing connection with a second terminal device or increasing the duty ratio; and when the probability is smaller than a second threshold value, reducing the duty ratio, wherein the second threshold value is smaller than or equal to the first threshold value, and the duty ratio is the ratio of the scanning time slot to the scanning period.

In the implementation mode, according to the device connection rule, the probability of connection behavior of the first terminal device and the second terminal device within a period of time in the future is predicted, and then the duty ratio is increased or decreased based on the probability of the connection behavior, namely, the duty ratio is dynamically adjusted. Therefore, under the condition of low-probability connection, the power consumption generated by equipment scanning is reduced by reducing the duty ratio, and the invalid scanning power consumption under the low-probability connection scene is saved; and under the condition of high-probability connection, the equipment discovery efficiency is improved by increasing the duty ratio, so that the equipment connection response efficiency under the high-probability connection scene is improved, and the optimization of the equipment power consumption is realized.

In some possible implementation manners of the first aspect, the determining, according to the device connection rule information, a probability of a connection behavior occurring between the first terminal device and the second terminal device within a preset time period in the future may include: according to preset information, searching target connection rule information from the equipment connection rule information, wherein the preset information comprises a current location and at least one of the following items: the current time and the target application program opened by the first terminal device; when the target connection rule information is found, obtaining a first support degree corresponding to the target connection rule information; and obtaining the probability of the connection behavior between the first terminal equipment and the second terminal equipment in a future preset time period according to the first support degree.

In some possible implementation manners of the first aspect, the searching for the target connection rule information from the device connection rule information according to the preset information may include: searching a target high-frequency behavior rule from the high-frequency behavior rule set according to the current time and the current place, wherein the time period of the target high-frequency behavior rule comprises the current time, and the place of the target high-frequency behavior rule is consistent with the current place; when the target high-frequency behavior rule is found, taking the target high-frequency behavior rule as target connection rule information; when the target high-frequency behavior rule cannot be searched, determining that the target connection rule information cannot be searched; the preset information comprises current time and current place, and the equipment connection rule information comprises a high-frequency behavior rule set.

In some possible implementation manners of the first aspect, the searching for the target connection rule information from the device connection rule information according to the preset information may include: searching a target context behavior rule from the context behavior rule set according to the target application program and the current location, wherein the location of the target context behavior rule is consistent with the current location, and the target application program comprises an application program of the target context behavior rule; when the target context behavior rule is found, taking the target context behavior rule as target connection rule information; when the target context behavior rule cannot be searched, determining that the target connection rule information cannot be searched; the preset information comprises a target application program and a current place, and the equipment connection rule information comprises a context behavior rule set.

In some possible implementation manners of the first aspect, the searching for the target connection rule information from the device connection rule information according to the preset information may include: searching a target high-frequency behavior rule from the high-frequency behavior rule set according to the current time and the current place, wherein the time period of the target high-frequency behavior rule comprises the current time, and the place of the target high-frequency behavior rule is consistent with the current place; when the target high-frequency behavior rule is found, taking the target high-frequency behavior rule as target connection rule information; when the target high-frequency behavior rule cannot be searched, searching a target context behavior rule from the context behavior rule set according to the target application program and the current location, wherein the location of the target context behavior rule is consistent with the current location, and the target application program comprises an application program of the target context behavior rule; when the target context behavior rule is found, taking the target context behavior rule as target connection rule information; when the target context behavior rule cannot be searched, determining that the target connection rule information cannot be searched; the preset information comprises a target application program, a current place and a current time, and the equipment connection rule information comprises a context behavior rule set and a high-frequency behavior rule set.

In some possible implementations of the first aspect, the method may further include: acquiring first log data of first terminal equipment; and obtaining a high-frequency behavior rule set and/or a context behavior rule set according to the first log data, wherein the equipment connection rule information comprises the high-frequency behavior rule set and/or the context behavior rule set.

In some possible implementations of the first aspect, the obtaining, according to the first log data, a high-frequency behavior rule set may include: preprocessing the first log data to obtain preprocessed second log data, wherein the second log data comprises a first timestamp, a first place, a connection event type, a first main connecting device and a first connected device; processing the second log data to obtain a first rule set, wherein each behavior rule in the first rule set comprises a time point, a first place, first main connecting equipment, first connected equipment and a second support degree; clustering the first rule set to obtain a second rule set, wherein each behavior rule in the second rule set comprises a time period, a first place, first main connecting equipment, first connected equipment and a third support degree; and removing the behavior rule in the second rule set, wherein the third support degree is smaller than the first preset support degree threshold value, so as to obtain a high-frequency behavior rule set.

In some possible implementation manners of the first aspect, the processing the second log data to obtain the first rule set may include: removing second log data with the connection event type of connection ending, and mapping each first timestamp to each first time unit aiming at the second log data with the connection event type of connection starting to obtain third log data, wherein the third log data comprise the first time units, a first place, first main connecting equipment and first connected equipment, the first time units are time units obtained by dividing one day according to minutes, and one first time unit corresponds to one minute; merging a plurality of log data which belong to the same day and the same first time unit and are the same in the third log data and are obtained after the first main connecting device and the first connected device are combined into one log data; and carrying out classification statistics on the fourth log data according to a first dimension to generate a first rule set, wherein the first dimension comprises time, place and a first event pair, and the first event pair comprises a first main connecting device and a first connected device.

In some possible implementations of the first aspect, the obtaining of the set of contextual behavior rules from the first log data may include: preprocessing the first log data to obtain preprocessed fifth log data, wherein the fifth log data comprises a second timestamp, a second place, a connection event type, a main connection device application, a second main connection device and a second connected device; processing the fifth log data to obtain a third rule set, wherein each behavior rule in the third rule set comprises a second place, a main connecting device application, a second main connecting device, a second connected device and a fourth support degree; and removing the behavior rule in the third rule set, wherein the fourth support degree is smaller than a second preset support degree threshold value, so as to obtain a context behavior rule set.

In some possible implementation manners of the first aspect, the processing the fifth log data to obtain the third rule set may include: removing fifth log data with the connection event type of connection end, and mapping each second timestamp to each second time unit aiming at the fifth log data with the connection event type of connection start to obtain sixth log data, wherein the sixth log data comprise a second time unit, a second place, a main connecting device application, a second main connecting device and a second connected device, the second time unit is a time unit obtained by dividing one day according to minutes, and one second time unit corresponds to one minute; merging a plurality of log data which belong to the same day and the same second time unit and are the same as the log data of the main connecting device application, the second main connecting device and the second connected device into one log data to obtain seventh log data; and carrying out classification statistics on the seventh log data according to a second dimension to generate a third rule set, wherein the second dimension comprises a place, an application and a second event pair, and the second event pair comprises a second main connecting device and a second connected device.

In some possible implementations of the first aspect, the method may further include: detecting the current residual electric quantity; if the current residual electric quantity is larger than the third threshold value, reducing the first threshold value; and if the current residual capacity is smaller than the third threshold value, increasing the first threshold value.

In a second aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method according to any one of the first aspect is implemented.

In a third aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored, and when executed by a processor, the computer program implements the method according to any one of the above first aspects.

In a fourth aspect, embodiments of the present application provide a chip system, where the chip system includes a processor, and the processor is coupled with a memory, and executes a computer program stored in the memory to implement the method according to any one of the above first aspects. The chip system can be a single chip or a chip module consisting of a plurality of chips.

In a fifth aspect, embodiments of the present application provide a computer program product, which, when run on a terminal device, causes the terminal device to perform the method of any one of the first aspect.

It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.

Drawings

FIG. 1 is a schematic diagram of a scanning cycle according to an embodiment of the present disclosure;

FIG. 2 is a diagram illustrating distribution ratios of high-frequency behavior, contextual behavior, and random behavior provided by an embodiment of the present application;

fig. 3 is a schematic block diagram of a flow of a power consumption optimization method provided in an embodiment of the present application;

fig. 4 is a schematic view of a distributed gallery scene provided in an embodiment of the present application;

fig. 5 is a schematic diagram of a distributed communication scenario provided in an embodiment of the present application;

fig. 6 is a schematic block diagram of a power consumption optimization apparatus provided in an embodiment of the present application;

fig. 7 is another schematic block diagram of a power consumption optimization apparatus provided in an embodiment of the present application;

FIG. 8 is a schematic interaction flow diagram provided by an embodiment of the present application;

fig. 9 is a schematic hardware structure diagram of a terminal device 900 according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application.

Referring to the schematic diagram of the scanning cycle shown in fig. 1, one scanning cycle consists of one scanning slot and one scanning interval. In the scanning time slot, the terminal device generates power consumption, and in the scanning interval, the terminal device does not generate power consumption. The scanning period is generally fixed and unchanged, the scanning time slot is increased, the duty ratio is increased, the scanning time slot is decreased, and the duty ratio is decreased. On this basis, the larger the ratio between the scanning time slot and the scanning period, i.e. the larger the duty cycle, the higher the power consumption of the terminal device, and conversely, the smaller the duty cycle, the lower the power consumption of the terminal device.

In the near field distributed communication process, the size of the duty ratio is related to the speed of device discovery. The larger the duty cycle, the faster the device discovery and the better the user experience. Conversely, the smaller the duty cycle, the slower the device discovery and the worse the user experience.

As can be seen from the above, the larger the duty cycle, the faster the device discovery, the better the user experience, but the higher the power consumption of the terminal device. The smaller the duty cycle, the slower the device discovery and the worse the user experience, but the lower the power consumption of the terminal device. Device power consumption and user experience are contradictory. In specific application, the power consumption of the device and the user experience can be considered through setting a proper duty ratio.

In the prior art, the duty cycle is fixed, i.e. the ratio of the scanning gap to the scanning period is fixed and constant. When the terminal device scans according to the preset duty ratio, even if the devices do not have connection requirements, the terminal device still generates power consumption, and power consumption waste is caused. When the connection between the devices is required, the scanning interval is fixed, and the device finding speed is fixed, so that the connection establishment speed of the devices cannot be increased.

Aiming at the existing problem of distributed communication power consumption, the embodiment of the application provides a power consumption optimization scheme, which predicts the possibility of connection behaviors between terminal devices in a future period of time according to the device connection rule information, and executes a power consumption optimization strategy according to the possibility of the connection behaviors to realize power consumption optimization. The power consumption optimization strategy may be, for example, dynamically adjusting the duty cycle.

The dynamically adjusting the duty ratio specifically means: when the probability that the connection behavior between the states of the first terminal equipment and the second terminal equipment is more than a certain threshold value within a future period of time is predicted, the duty ratio is increased in a mode of increasing the scanning gap; and when the probability of the connection behavior between the first terminal equipment and the second terminal equipment is predicted to be smaller than a certain threshold value within a future period of time, reducing the duty ratio in a mode of reducing the scanning gap.

Compared with a non-dynamic adjustment duty ratio (namely, the duty ratio is fixed), the dynamic adjustment duty ratio can save the invalid scanning power consumption in a low-probability connection scene, and can improve the connection response efficiency in a high-probability connection scene, so that the connection establishment speed is higher.

Specifically, when the probability of the occurrence of the connection behavior is predicted to be less than a certain threshold, the first terminal device decreases the scanning time slot to decrease the duty ratio. Because the scanning time slot generates power consumption, the scanning interval does not generate power consumption, the scanning time slot is reduced, and further the power consumption of equipment is reduced. And when the probability of predicting the connection action is larger than a certain threshold value, the first terminal equipment increases the scanning gap so as to increase the duty ratio. The larger the scanning gap, the faster the device discovery. The faster the device discovery, the faster the connection can be established. I.e., increasing the duty cycle, the connection response efficiency can be improved.

In addition, in a low probability connection scenario, the duty cycle is reduced to reduce or save the ineffective scanning power consumption. And in a high-probability connection scene, the duty ratio is increased so as to utilize higher equipment power consumption and improve the equipment connection response speed. Therefore, the power consumption saved in the low-probability connection scene can be used in the high-probability connection scene to improve the connection response efficiency, so that the limited electric quantity of the terminal equipment is reflected by better communication quality, and the user experience is best under the same power consumption.

The power consumption optimization scheme provided by the embodiment of the application can comprise an equipment connection rule learning process and a process of dynamically adjusting the duty ratio based on the equipment connection rule. The device connection rule learning process may obtain device connection rule information according to the recorded user behavior data. These two processes will be described separately below.

1. Device connection law learning process

The user behavior rule is a special user portrait and is a describable rule set in all behaviors of the user. The user behavior rule mainly comprises three parts: high frequency behavior, contextual behavior, and random behavior. In general, all the behavior rules of the user can be composed of the above three behaviors. However, the distribution of these three behaviors is different for each person. For example, referring to the schematic diagram of the distribution ratio of the high-frequency behavior, the contextual behavior and the random behavior shown in fig. 2, as shown in fig. 2, the high-frequency behavior accounts for M%, the contextual behavior accounts for N%, the high-frequency behavior and the contextual behavior have an overlapping portion, and the overlapping portion accounts for P%. Random behavior accounts for K%. (M + N-P) + K equals 100%. For example, M is 40, N is 40, P is 10, and K is 30.

High frequency behavior refers to a repetitive pattern that is exhibited at a particular time, or place, or combination of time + place. For example, [ 12:30 ] [ company ] [ coffee shop ]: the user who buys coffee, namely, the user buys coffee at about 12 o 'clock and 30 o' clock every day, can buy coffee at the coffee shop of the company with high probability.

Contextual behavior refers to behavior that exhibits behavioral context, which may include multiple behaviors. For example, [ punch card at work ], [ buy breakfast ], that is, after the user opens the office application on the mobile phone to punch card at work, the payment application of the mobile phone is opened to buy breakfast.

Random behavior refers to random behavior that cannot be described. For example, [ 22:56 ] [ en route to work ] [ traffic policeman ]: and (4) traffic accidents.

In the embodiments of the present application, the high frequency behavior and the contextual behavior mentioned above are referred to. Specifically, the terminal device records user behavior data, analyzes and mines the user behavior data, and finally obtains device connection rule information. The device connection rule information may include a high frequency behavior rule set or a context behavior rule set, or both.

The high-frequency behavior law learning process and the context behavior law learning process are introduced below.

High frequency behavior law learning process

First, the terminal device acquires recorded log data. The log data includes user behavior data. For example, the terminal device is a mobile phone. Eight night a certain day, the user uses the cell-phone to connect the smart TV at home, throws the video picture of cell-phone to the smart TV. For this user behavior, the handset will record into the log data.

And then, the terminal equipment preprocesses the log data to obtain preprocessed log data. In the preprocessing, the time is divided into 1440 units for 1440 minutes with respect to the time of log data. Wherein 24 hours are divided into 1440 minutes, one time unit for each minute. In addition, the place is divided by a preset place, for example, into three units by home, company, and other places with respect to the position of the log data.

In the pre-processed log data, each log data may include a timestamp, a location, a connection event type, a main connection device, and a connected device. The specific form of the pre-processed log data can be as shown in table 1 below.

TABLE 1

Wherein, the main connection device refers to a device initiating connection. For example, the mobile phone actively establishes a connection with the smart television to project a video picture on the mobile phone to the smart television, and in the process, the mobile phone is a main connection device and the smart television is a connected device.

In table 1 above, terminal 8610XXXX0001 is the device itself. The host computer is connected to the terminal 8610XXXX0002 once, and the host computer is connected to the terminal 8610XXXX0003 once.

And then, the terminal equipment processes the preprocessed log data to obtain a rule set. The rule set comprises a plurality of behavior rules, and each behavior rule can comprise a time point, a place, a main connecting device, a connected device and a support degree. In a specific application, each behavior law may be expressed in the form of [ time point ] [ location ] [ main connecting device ] [ connected device ], for example, [ @8:15 ] [ home ] [ cell phone a ] [ tv B ].

In the specific application, in the process of obtaining the rule set according to the preprocessed log data, the connection event type is not required to be the log data corresponding to the connection end, so that the terminal equipment can remove the log data of which the connection event type is the connection end. For example, the log data corresponding to the sequence numbers 2 and 4 in the above table 1 are removed. The log data of the end of the connection is removed, and the obtained log data is shown in table 2 below.

TABLE 2

Of course, in some other embodiments, the terminal device may not remove the log data whose connection event type is connection end, but filter out the log data whose connection event type is connection start.

After removing the log data whose connection event type is connection end, each timestamp is mapped to 1440 minutes per day for the log data whose connection event type is connection start. For example, 0:00:01-0:01:00 is mapped to 1, and 23:59:01-0:00:00 is mapped to 1440. In the time stamp mapping process, it is not distinguished which day the log data is.

Illustratively, after mapping the time stamp to 1440 minutes per day, the resulting log data can be as shown in table 3 below.

TABLE 3

In table 3, time points 1230, 1235, 1236 are in minutes.

It is understood that at least two identical connection start events may occur in the same minute of the same day, and the identical connection event refers to a connection start event in which the location, the main connection device, and the connected device are identical. Multiple identical ligation events occurring in the same minute of the same day are combined into one.

For example, the time is 8 hours, 15 minutes and 3 seconds of 3 months and 21 days, the place is home, and the mobile phone is actively connected with the smart television once; the time is 8 hours, 15 minutes and 23 seconds of 3 months and 21 days, the place is home, and the mobile phone is actively connected with the smart television once; the time is 8 hours, 15 minutes and 53 seconds of 3 months and 21 days, the place is home, and the mobile phone is actively connected with the smart television once. I.e., 8 hours and 15 minutes, 3 identical ligation events occurred. The same connection events that occurred 3 times in 8 hours and 15 minutes are combined into one connection event. In other words, although 3 connection events have occurred, it is still considered to be one connection.

In addition, at least two identical connection initiation events may occur at the same minute on different days. For example, the time is 8 hours and 15 minutes of 3 months and 21 days, the place is home, and the mobile phone is actively connected with the smart television once; the time is 8 hours and 15 minutes of 3 months and 22 days, the place is home, and the mobile phone is actively connected with the smart television once. For this case, two identical connection start events cannot be merged into one.

Also, the same minutes on the same day, or the same minutes on different days, cannot be merged together if the locations are different. For example, 8 hours, 15 minutes and 23 seconds of 3 months and 21 days are provided, the place is home, and the mobile phone is actively connected with the smart television once; the time is 8 hours, 15 minutes and 23 seconds of 3 months and 21 days, the place is a company, and the mobile phone is actively connected with the smart television once; the two connection events cannot be merged into one.

Illustratively, after mapping each timestamp to 1440 minutes, the log data as shown in Table 4 below was obtained. Since the connection event types in table 4 are all connection start, table 4 omits the connection event types.

TABLE 4

Serial number	Point in time	Location of a site	Main connection device	Connected equipment
					1	1230 (21 days 3 month)	Home-use	8610XXXX0001	8610XXXX0002
2	1230 (21 days 3 month)	Home-use	8610XXXX0001	8610XXXX0002
					3	1230(3 month and 22 days)	Home-use	8610XXXX0001	8610XXXX0002
4	1236	Home-use	8610XXXX0001	8610XXXX0002

In table 4 above, since the time points of the serial numbers 1 and 2 are 1230 minutes every 3 months and 21 days, and the locations of the two data, the main connection device and the connected device are the same, the data of the serial numbers 1 and 2 are merged together, or one of the data is deleted. In contrast, since the time points of the numbers 3 and 1 are the same minute but not the same day, the data of the number 3 is not deleted. The consolidated log data is shown in table 5 below.

TABLE 5

Serial number	Point in time	Location of a site	Main connection device	Connected equipment
					1	1230 (21 days 3 month)	Home-use	8610XXXX0001	8610XXXX0002
3	1230 (month 3)22 days)	Home-use	8610XXXX0001	8610XXXX0002
					4	1236	Home-use	8610XXXX0001	8610XXXX0002

It will be appreciated that while the time stamp is mapped to within 1440 minutes, no distinction is made as to which day's data. The terminal device can still know which day the log data is.

After the log data corresponding to the same connection event for multiple times in the same minute on the same day are merged, the log data of the same place, the main connection equipment and the connected equipment only appear once. The log data may appear multiple times on different days, for the same minute, and for the same location, the primary connecting device, and the connected device.

Then, the three dimensions are counted according to the time point, the place and the event, and the frequency of the combination of the time point, the place and the event is obtained. Illustratively, after log data corresponding to the same connection event for multiple times in the same minute on the same day are merged, statistics is performed on three dimensions according to time points, places and events, and the obtained data is shown in table 6 below.

TABLE 6

In table 6 above, the support degree of the behavior rule corresponding to the serial number 1 is 4, which indicates that the behavior rule corresponding to the serial number 1 appears in 1230 minutes of 4 days in the log data of 30 days. Similarly, the support degree of the behavior rule corresponding to the sequence number 2 is 3, which indicates that the behavior rule corresponding to the sequence number 2 appears in 1235 minutes of 3 days in the log data of 30 days.

And finally, the terminal equipment processes the preprocessed log data to obtain a rule set, and then carries out clustering, support degree filtering and other steps on the rule set to obtain a final high-frequency behavior rule set.

Specifically, for each rule in the rule set, a time-dimension one-dimensional DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) may be called to perform Clustering with a threshold of 5 minutes to merge adjacent rules. After merging, the regular time in the regular set is no longer a single time unit, but a time period consisting of a plurality of time units, one time unit being one minute. For example, a rule in the rule set before merging is: [ @8:15 ] [ @ home ] [ cell phone a ] [ television B ]; some rule in the rule set after merging is as follows: [ @8:15-8:39 ] [ @ home ] [ cell phone A ] [ TV B ].

Illustratively, the rule set includes the behavior rules shown in table 6 above. After clustering and merging the behavior rules in table 6 above, the rules shown in table 7 below are obtained.

TABLE 7

It should be noted that, in the rule after the clustering and merging, the time period is arbitrary, and the time period can be determined by the connectable characteristic in the DBSCAN algorithm.

After the support degree is obtained, a probability value is calculated from the support degree and the total number of days of log data. For example, if the data of sequence number 1 in table 7 has a support degree of 15 and includes log data for 30 days in total, 15/30 is 0.5, that is, the probability is 50%.

Clustering the rule sets, combining adjacent rules, and filtering out some behavior rules with less frequency according to a preset support threshold. The support threshold can be set according to the actual application requirement.

Illustratively, taking the data shown in tables 6 and 7 as an example, since the log data contains 30 days, the support threshold may be set to 10%, i.e. the behavior rule with the removal support less than 10% or the frequency less than 3. In table 7 above, the support degree of the behavior rule corresponding to the serial number 2 is less than 10%, so the behavior rule corresponding to the serial number 2 is removed, and the support degree of the behavior rule corresponding to the serial number 1 is greater than 10%, so the behavior rule corresponding to the serial number 1 is retained. After deleting the behavior rule corresponding to sequence number 2, the data shown in table 8 below can be obtained.

TABLE 8

As can be seen from table 8 above, the format of one behavior rule in the high-frequency behavior rule set is: [ time period ] [ location ] [ main connecting device ] [ connected device ], and each behavior rule has corresponding support degree or probability.

And the terminal equipment obtains a high-frequency behavior rule set through a high-frequency behavior learning process based on the recorded log data. After the high frequency behavior law learning process is introduced, the context behavior law learning process will be described below.

Contextual behavioral law learning process

Firstly, the terminal device obtains the recorded log data, and after the log data is preprocessed, the preprocessed log data is obtained. In the preprocessing process, the location of the log data is divided into a plurality of units, for example, a home unit, a company unit and other three units; the time of the log data is also divided into a number of time units, each time unit being one minute, 1440 minutes a day, and 1440 time units.

In the pre-processed log data, each log data comprises a timestamp, a place, a connection event type, a main connection device, a connected device and a main connection device application. Illustratively, the pre-processed log data is shown in table 9 below.

TABLE 9

And then, processing the preprocessed log data to obtain a rule set. The rule set includes a plurality of context behavior rules. The format of each rule is: time points [ locations ] [ master joining device applications ] [ master joining device ] and [ connected devices ]. For example, a certain context behavior law is: [ @8:15 ] [ family ] [ Hua of mobile phone as video ] [ mobile phone ] and [ smart television ].

In the specific application, in the process of obtaining the rule set according to the preprocessed log data, the connection event type is not required to be the log data corresponding to the connection end. Therefore, the terminal device can remove log data whose connection event type is the connection end. For example, the log data corresponding to sequence numbers 2 and 4 in table 9 above is removed. The log data at the end of the connection is removed, and the obtained log data is shown in table 10 below.

Watch 10

Illustratively, after mapping the time stamp to 1440 minutes per day, the resulting log data can be as shown in table 11 below.

TABLE 11

The process of mapping the timestamp within 1440 minutes is the same as the mapping process in the above high-frequency behavior law learning process, and for related introduction, reference is made to the above description, and details are not repeated here.

After the timestamp is mapped to 1440 minutes, the log data corresponding to the same connection event for the same minute and multiple times on the same day are merged.

Illustratively, after mapping each timestamp to 1440 minutes, the log data as shown in Table 12 below was obtained. Since the connection event types in table 12 are all connection start, table 12 omits the connection event type.

TABLE 12

In table 12 above, since the time points of sequence numbers 1 and 2 are 1230 minutes every 3 months and 21 days, and the locations, applications of the main connection device, and the connected devices of the two data are the same, the data of sequence numbers 1 and 2 are merged together, or one of the data is deleted. In contrast, since the time points of the numbers 3 and 1 are the same minute but not the same day, the data of the number 3 is not deleted. The merged log data is shown in table 13 below.

Watch 13

In addition, two identical contextual behaviors are calculated as one time if the time interval between the two identical contextual behaviors is less than or equal to a preset time threshold, and are calculated as two times if the time interval between the two identical contextual behaviors is greater than the preset time threshold.

For example, there are two identical contextual behaviors, respectively: 1230 minutes, [ Hua is video ] [ cell phone ] [ smart television ]; 1234 minutes, [ Hua is video ] [ cell phone ] [ smart television ]. Since the time interval between the two contextual behaviors is 4 minutes and is less than 5 minutes, the contextual behavior is calculated once for [ Hua is video ] [ mobile phone ] [ smart television ].

As another example, there are two identical contextual behaviors, respectively: 1230 minutes, [ Hua is video ] [ cell phone ] [ smart television ]; 1238 minutes, [ Hua is video ] [ cell phone ] [ smart television ]. Since the time interval between the two contextual behaviors is 8 minutes and is greater than 5 minutes, the contextual behavior is calculated twice for [ Hua is video ] [ mobile phone ] [ smart television ].

That is, two identical context operations having a time interval less than or equal to the preset time threshold are merged into one.

Also, the time interval between two behaviors may also be considered when determining whether a behavior is a contextual operation. When the time interval is smaller than the preset time interval, the two behaviors are determined as one context behavior. For example, the mobile phone opens the Huawei video and connects the mobile phone and the smart television, and if the mobile phone is connected with the smart television within 5 minutes after the mobile phone opens the Huawei video, the two behaviors are considered to form a context behavior, namely [ Huawei video ] [ mobile phone ] [ smart television ]. And if the mobile phone is connected with the smart television only after 10 minutes after the mobile phone is opened to obtain the video, the two behaviors are not considered to form a context behavior. At this time, since the time interval is 5 minutes, this means that the maximum support is 1440/5 ═ 288.0, i.e., every 5 minutes each day, a context operation is performed.

Then, regardless of the time point, the three dimensions are counted according to the location, the application and the event, and the frequency of the combination of the application + the location + the event is obtained. The event pair includes a primary connecting device and a connected device. Illustratively, after log data corresponding to the same connection event for multiple times in the same minute on the same day are merged, statistics is performed on three dimensions according to the application, the location, and the event, and the obtained data is shown in table 14 below.

TABLE 14

In table 14 above, the support degree of the behavior rule corresponding to the sequence number 1 is 5, which indicates that the behavior rule corresponding to the sequence number 1 appears in log data of 30 days for 5 days. Similarly, the support degree of the behavior rule corresponding to the sequence number 2 is 1, which indicates that the behavior rule corresponding to the sequence number 2 appears in log data for 30 days for 3 days.

That is, the support is equal to the total number of occurrences/total number of days with data, e.g., 15 occurrences within 30 days, the support is 0.5, and if 60 occurrences within 30 days, the support is 2.0.

And finally, the terminal equipment processes the preprocessed log data to obtain a rule set, and then supports filtering is carried out on the rule set to obtain a final context behavior rule set.

Specifically, some behavior rules with less frequency are filtered out according to a preset support threshold. The support threshold can be set according to the actual application requirement.

Exemplarily, as the data shown in table 14 above is taken as an example, since the log data contains 30 days, the support threshold may be set to be 3, that is, the behavior rule with the support (frequency) less than 3 is removed. In table 14 above, the support degree of the behavior law corresponding to the serial numbers 3 and 4 is less than 3, so the behavior law corresponding to the serial numbers 3 and 4 is removed, and the support degree of the behavior law corresponding to the serial numbers 1 and 2 is greater than or equal to 3, so the behavior law corresponding to the serial numbers 1 and 2 is retained. After deleting the behavior rules corresponding to the sequence numbers 3 and 4, the data shown in table 15 below can be obtained.

Watch 15

As can be seen from table 8 above, the format of one behavior rule in the context behavior rule set is: [ location ] main connecting device application [ main connecting device ] connected device, and each behavior rule has corresponding support degree or probability.

After the support degree of the context behavior rule is calculated, the support degree is converted into the probability.

In a specific application, when the maximum support degree is greater than or equal to 1, the probability of the maximum support degree is calculated as 100%. Other support degrees than the maximum support degree are converted in an equal ratio. For example, three contextual behavior rules are obtained from 30 days of log data, and the three contextual behavior rules occur 60 times, 30 times and 20 times respectively, and the support degrees are 60/30 ═ 2, 30/30 ═ 1 and 20/30 ═ 0.5 respectively. The maximum support is 2, greater than 1, then the probability of maximum support is calculated as 100%, 60/60. Other degrees of support are converted in equal proportion, namely: the probability of the support degree being 1 is 30/60 ═ 0.5, namely, it is calculated as 50%; the probability of the degree of support being 0.5 is 20/60 ═ 0.33, i.e., 33% was calculated.

When the maximum support degree is less than 1, the support degree is directly divided by the total days to be used as a probability value. For example, three contextual behavior rules are obtained from log data of 30 days, the three contextual behavior rules occur 20 times, 15 times and 10 times respectively, and the support degrees of the three contextual behavior rules are respectively: 20/30-0.67, 15/30-0.5 and 10/30-0.33. At this time, since the maximum support degree is 0.67 and less than 1, the total number of occurrences/total number of days are directly taken as probability values, that is, the probability values corresponding to the support degrees 0.67, 0.5 and 0.33 are 67%, 50% and 33% in this order.

And the terminal equipment obtains a context behavior rule set through a context behavior learning process based on the recorded log data.

In the context behavior law learning process and the high-frequency behavior law learning process, some same places can be referred to each other, and are not described herein again.

2. Dynamic adjustment of duty cycle based on device connection rules

After the terminal equipment obtains the equipment connection rule information through the high-frequency behavior rule learning process and/or the context behavior rule learning process based on the log data, predicting the probability of the connection behavior between the terminal equipment and other terminal equipment in a future period of time based on the equipment connection rule information; and finally, increasing or decreasing the duty ratio according to the size of the probability value so as to dynamically adjust the duty ratio.

When the device connection rule information includes a high-frequency behavior rule set and a context behavior rule set, the terminal device acquires a current time point, a current location, and a currently opened application program. The current location refers to a current position of the terminal device. And then, the terminal equipment matches in the high-frequency behavior rule set according to the current time point and the current place, and when the time period of a certain high-frequency behavior rule in the high-frequency behavior rule set contains the current time point and the place of the high-frequency behavior rule is consistent with the current place, the high-frequency behavior rule is taken as a target high-frequency behavior rule. After the target high-frequency behavior rule is found out, the connected equipment and the support degree in the target high-frequency behavior rule are obtained, and the support degree is used as the probability of the connection behavior between the terminal equipment and the connected equipment in a future period of time.

For example, the current time point is 6 am, and the current location is home. Since the time period of each rule in the high frequency behavior rule set is in minutes, the 6 am point is mapped to 360 minutes. At this time, one high-frequency behavior rule exists in the high-frequency behavior rule set: 355-375, home, cell phone, sound box, the probability of correspondence of the high frequency behavior law is 50%. Because the current time point 360 minutes falls into the time period 355-375 and the current location is home, the current location and the current time are considered to be matched with the high-frequency behavior rule of (355-375), (mobile phone), (sound box), and the high-frequency behavior rule is taken as the target high-frequency behavior rule. At this time, the mobile phone determines that the mobile phone may be connected to the sound box within 15 minutes in the future, and the probability is 50%.

And if the target high-frequency behavior rule can not be searched in the high-frequency behavior rule set according to the current time point and the current place, continuing to search in the context behavior rule set according to the current place and the currently opened application program. And when the place of a certain context behavior rule in the context behavior rule set is consistent with the current place, and the currently opened application program contains the application of the main connection equipment or is consistent with the application of the main connection equipment, taking the context behavior rule as a target context behavior rule. After the target context behavior rule is found out, the connected device and the support degree in the target context behavior rule are obtained, and the probability of the connection behavior between the terminal device and the connected device in a future period is obtained according to the support degree.

For example, the current location is home, and the currently opened application of the mobile phone includes an alarm clock. A certain contextual behavior rule in the set of contextual behavior rules is: [ DOMESTIC ] [ MOBILE WARNING CLOCK ] [ MOBILE PHONE ] [ BANK ], the corresponding probability is 50%. Because the location of the context behavior law is consistent with the current location, and the currently opened application program of the mobile phone is consistent with the application in the context behavior law, the mobile phone judges that (family) ([ mobile phone alarm clock) ] [ mobile phone ] ([ sound box ]) is the target context behavior law. Based on the context behavior law of 'family', 'mobile phone alarm clock', 'mobile phone' and 'sound box', the mobile phone judges that the mobile phone is likely to be connected with the sound box in 15 minutes in the future, and the probability is 50%.

If the target context behavior rule cannot be searched in the context behavior rule set according to the current location and the currently opened application program, the terminal device cannot judge the probability of the occurrence of the connection behavior, and at the moment, the terminal device may not execute any power optimization strategy.

It should be noted that, when the device connection rule information includes a high-frequency behavior rule set and a context behavior rule set, a target high-frequency behavior rule may be searched in the high-frequency behavior rule set according to the current location and the current time point, and when the target high-frequency behavior rule is not searched, the target context behavior rule may be searched in the context behavior rule set according to the current location and the currently opened application; or searching a target context behavior rule in the context behavior rule set according to the current location and the currently opened application, and searching a target high-frequency behavior rule in the high-frequency behavior rule set according to the current location and the current time point when the target context behavior rule cannot be searched.

Of course, the two processes of searching the target high-frequency behavior rule in the high-frequency behavior rule set according to the current location and the current time point and searching the target context behavior rule in the context behavior rule set according to the current location and the currently opened application program can be performed simultaneously. At this time, when the target high-frequency behavior rule and the target context behavior rule are found, the terminal device predicts according to the target high-frequency behavior rule and the target context behavior respectively. And when only one of the target high-frequency behavior rule and the target context behavior rule is found, predicting according to the found behavior rule. When the target high-frequency behavior rule and the context behavior rule cannot be found, the terminal device can give a result which cannot be judged, namely the probability that the terminal device cannot judge the connection behavior transmitted with other devices within a period of time in the future. At this time, the terminal device may not perform any operation.

For example, according to the current location and the current time point, the mobile phone finds out a rule of a target high-frequency behavior as follows: 355-375, home, cell phone, sound box, the corresponding probability is 50%. According to the current location and the currently opened application program, the searched target context behavior rule is as follows: [ Home ] video [ cell phone ], and [ smart television ], the corresponding probability is 50%. According to the high-frequency behavior rule of ' 355-375 ' family ' mobile phone ' sound box ', the mobile phone predicts the behavior that the mobile phone may be connected with the sound box within 15 minutes in the future, and the probability is 50%. In addition, the mobile phone predicts that the mobile phone may be connected with the smart television within 15 minutes in the future according to the contextual behavior rule of home video mobile phone smart television, and the probability is 50%.

And when the equipment continuous rule information only comprises the high-frequency behavior rule set, the terminal equipment acquires the current time point and the current place. And then, the terminal equipment matches in the high-frequency behavior rule set according to the current time point and the current place, and when the time period of a certain high-frequency behavior rule in the high-frequency behavior rule set contains the current time point and the place of the high-frequency behavior rule is consistent with the current place, the high-frequency behavior rule is taken as a target high-frequency behavior rule. After the target high-frequency behavior rule is found out, the connected equipment and the support degree in the target high-frequency behavior rule are obtained, and the support degree is used as the probability of the connection behavior between the terminal equipment and the connected equipment in a future period of time. If the target high-frequency behavior rule cannot be found in the high-frequency behavior rule set according to the current time point and the current place, the terminal device can give a result which cannot be judged, namely the probability that the terminal device cannot judge the connection behavior transmitted with other devices within a period of time in the future.

When the device connection rule information only includes the context behavior rule set, the terminal device acquires the current location and the currently opened application program. And then searching in the context behavior rule set according to the current location and the currently opened application program. And when the place of a certain context behavior rule in the context behavior rule set is consistent with the current place, and the currently opened application program contains the application of the main connection equipment or is consistent with the application of the main connection equipment, taking the context behavior rule as a target context behavior rule. After the target context behavior rule is found out, the connected device and the support degree in the target context behavior rule are obtained, and the support degree is used as the probability of the connection behavior between the terminal device and the connected device in a future period of time. If the target context behavior rule cannot be found in the context behavior rule set according to the current location and the currently opened application program, the terminal device may give a result that cannot be determined, that is, the terminal device cannot determine the probability that the terminal device transmits a connection behavior with other devices within a future period of time. And the terminal equipment predicts the probability of the connection behavior between the terminal equipment and other equipment in a future period of time according to the equipment connection rule information, and then compares the probability with a preset threshold value. When the probability is larger than a first threshold value, the terminal equipment increases the scanning gap so as to increase the duty ratio; when the probability is smaller than the second threshold, the terminal equipment reduces the scanning gap so as to reduce the duty ratio.

When the probability is greater than the first threshold, the terminal device may establish a connection with other devices in advance, in addition to increasing the duty ratio. For example, the probability that the mobile phone is predicted to be connected with the smart television within 15 minutes in the future is 50%, and the mobile phone is connected with the smart television in advance.

As can be seen from the above, in the embodiment of the present application, device connection rule information is obtained through a high-frequency behavior rule learning process and/or a context behavior rule learning process according to log data. And predicting the probability of the connection behavior of the terminal equipment and other equipment in a future period of time according to the equipment connection rule information, and increasing or decreasing the duty ratio according to the probability.

It should be noted that the above-mentioned high-frequency behavior rule set and context behavior rule set may be applied to other scenarios besides the distributed communication power consumption optimization scenario. For example, after the high-frequency behavior rule set and the context behavior rule set, the terminal device can know what needs to be done in a future period of time according to the rule sets, and then prepare relevant system resources in advance and start relevant application programs in advance.

In addition, the power consumption optimization strategy may be embodied in other ways besides the above-mentioned dynamic adjustment of the duty cycle. For example, the power consumption optimization strategy includes closing or opening the switch, i.e., dynamically opening or closing the switch based on the predicted magnitude of the probability of the occurrence of the connection behavior. Illustratively, when the probability of the predicted connection behavior is greater than a certain probability threshold, the Wi-Fi switch is turned on, and otherwise, when the probability of the predicted connection behavior is less than a certain probability threshold, the Wi-Fi switch is turned off. And the wireless connection switch is dynamically closed according to the probability, so that the optimization of power consumption is realized.

For another example, the power consumption optimization policy may include establishing a connection with a peer device in advance, and the like. Illustratively, the probability of establishing connection with the smart television within 15 minutes in the future is predicted by the mobile phone to be 80%, and the mobile phone establishes connection with the smart television in advance.

For another example, the power consumption optimization strategy may further include changing from real connection to virtual connection, that is, when the probability of the predicted connection behavior is smaller than a certain probability threshold, the original real connection is changed to virtual connection. Of course, the virtual connection may also be changed to a real connection, that is, when the probability of the occurrence of the connection is higher than a certain probability threshold, the virtual connection between the devices is changed to a long connection. Or from a long connection to a short connection, i.e. when the probability of the predicted occurrence of a connection behavior is less than a certain probability threshold, then the long connection between the devices is changed to a short connection. Of course, it is also possible to change a short connection to a long connection, i.e. when the probability is larger than a certain probability threshold, then a short connection between devices is changed to a long connection.

In order to better describe the power consumption optimization scheme provided by the embodiment of the present application, the following description is provided with reference to the accompanying drawings.

Referring to fig. 3, which is a schematic flow diagram of a power consumption optimization method provided in an embodiment of the present application, the method may include the following steps:

step S301, the first terminal device obtains device connection rule information, where the device connection rule information is used to describe a connection rule between the first terminal device and another terminal device.

In a specific application, the first terminal device may read the device connection rule information stored locally to obtain the device connection rule information. Of course, the first terminal device may also obtain the device connection rule information in other manners.

The device connection law information may include a high frequency set of behavior laws and/or a set of context behavior laws. The first terminal device may obtain the device connection rule information through the above-mentioned high-frequency behavior rule learning process and/or the context behavior rule learning process based on the log data.

Of course, in some other embodiments, the manner of obtaining the device connection rule information according to the log data is not limited to the above-mentioned high-frequency behavior rule learning process and the context behavior rule learning process, and the device connection rule information may also be obtained through a simple statistical model according to the log data. However, compared to the above-mentioned high-frequency behavior law learning and context behavior law learning processes, the computation complexity is lower, and the accuracy of the subsequent connection probability prediction is higher.

The device connection rule information can be used for representing user portraits, namely obtaining user portraits according to log data, besides the user behavior characteristics such as high-frequency behavior rule sets and/or context behavior rule sets, and the user portraits comprise device connection rule information. Compared with the prior art, the power consumption optimization is carried out according to the user behavior characteristics, and the prediction accuracy of the connection prediction probability is higher.

Step S302, the first terminal device determines the probability of connection behavior between the first terminal device and the second terminal device in a future preset time period according to the device connection rule information, and the other terminal devices include the second terminal device.

The future preset time period may be set according to actual needs, for example, the future preset time period is 15 minutes, that is, the probability that the first terminal device predicts the connection behavior within 15 minutes in the future. Of course, the future preset time period may also be 0, and at this time, the first terminal device predicts the probability of discovering the connection behavior at the current time.

Specifically, the first terminal device first obtains preset information, where the preset information includes a current location and at least one of the following: the current time and the target application program opened by the first terminal device. That is, the preset information may include the current location and the current time, may include the current location and the target application program, and may also include the current location, the current time point, and the target application program.

Then, the first terminal device searches the target connection rule information from the device connection rule information according to the preset information. When the target connection rule information is found, a first support degree corresponding to the target connection rule information is obtained, and then the probability of connection between the first terminal equipment and the second terminal equipment in a future preset time period is obtained according to the first support degree. Of course, the corresponding probability value can also be directly searched from the device connection rule information.

In some embodiments, when the device connection rule information includes only the high-frequency behavior rule set, the preset information includes a current location and a current time. The first terminal device searches the target high-frequency behavior rule (i.e. target connection rule information) in the high-frequency behavior rule set according to the current location and the current time. And when the time period of a certain high-frequency behavior rule in the high-frequency behavior rule set comprises the current time and the place is consistent with the current place, considering the high-frequency behavior rule as a target high-frequency behavior rule, and acquiring the support degree corresponding to the target high-frequency behavior rule, the main connecting equipment and the connected equipment. And obtaining the probability of the connection behavior between the first terminal equipment and the second terminal equipment in a future period of time according to the support degree.

For example, the current time point is 20 pm and 45 pm, and the current location is home. Since the time period of each rule in the high frequency behavior rule set is in minutes, 20 am 45 minutes is mapped to 1245 minutes. At this time, one high-frequency behavior rule exists in the high-frequency behavior rule set: 1230-1260, home, mobile phone, smart tv, the probability of correspondence of the high-frequency behavior law is 50%. Because the current time 1245 minutes falls into the time period 1230-. At this time, the mobile phone determines that the probability of the connection between the mobile phone and the smart television is 50% within 15 minutes in the future.

In other embodiments, when the device connection rule information includes only the context behavior rule set, the preset information includes the current location and the currently opened target application. And the first terminal equipment searches the target context behavior rule from the context behavior rule set according to the target application program and the current location. And when the place of a certain context behavior rule in the context behavior rules is consistent with the current place and the target application program comprises the main connection equipment application of the context behavior rules, considering the context behavior rules as the target context behavior rules. At this time, the probability of occurrence of the connection behavior is obtained according to the support degree corresponding to the target context behavior rule. If the target context behavior rule cannot be found, the probability of occurrence of the connection behavior cannot be judged.

For example, the current location of the mobile phone is at home, and the currently opened application of the mobile phone includes hua is a video. One of the contextual behavior rules existing in the contextual behavior rule set is: [ Home ] video [ cell phone ], smart television, the probability is 50%. Since the current location and the target application program are both matched with the location and the master link device application of the context behavior law, it is determined that [ home ] [ Hua is video ] [ cell phone ] [ smart television ] is the target context behavior law. At the moment, the mobile phone judges that the probability of the connection between the mobile phone and the smart television is 50% within 15 minutes in the future.

In still other embodiments, when the device connection rule information includes only the high-frequency behavior rule set and the context behavior rule set, the preset information includes a current location, a current time, and the target application. The first terminal equipment searches a target high-frequency behavior rule from the high-frequency behavior rule set according to the current time and the current place; and when the target high-frequency behavior rule is found, taking the target high-frequency behavior rule as target connection rule information. When the target high-frequency behavior rule cannot be searched, continuously searching the target context behavior rule from the context behavior rule set according to the target application program and the current location; when the target context behavior rule is found, taking the target context behavior rule as target connection rule information; and when the target context behavior rule cannot be searched, determining that the target connection rule information cannot be searched.

Of course, the context behavior rule set may be searched first, and then the high frequency behavior rule set may be searched, or both may be searched.

And when the target high-frequency behavior rule is searched from the high-frequency behavior rule set, the target context behavior rule is also searched from the context behavior rule set, and then prediction is carried out according to the target high-frequency behavior rule and the target context behavior rule respectively.

And step S303, executing a power consumption optimization strategy according to the probability.

It should be noted that the power consumption optimization strategy may be arbitrary. For example, the power consumption optimization strategy is to dynamically adjust the duty cycle, establish a connection in advance, or dynamically turn on/off a switch, etc.

And when the power consumption optimization strategy is to dynamically adjust the duty ratio, when the probability is greater than a first threshold value, establishing connection with the second terminal equipment or increasing the duty ratio. And when the probability is smaller than a second threshold value, reducing the duty ratio, wherein the second threshold value is smaller than or equal to the first threshold value.

It should be noted that how much the duty ratio is increased or how much the duty ratio is decreased can be set according to actual needs. For example, if the probability is zero, the duty cycle is reduced to zero, and the device is put into a sleep state. If the probability is greater than zero and less than a second threshold, the duty cycle is reduced to a predetermined duty cycle.

The first threshold and the second threshold may be set according to actual needs. For example, the first threshold value and the second threshold value are set according to the power consumption that each terminal device can withstand. In addition, in the same terminal device, the first threshold value and the second threshold value can be dynamically adjusted according to the residual electric quantity. When the remaining power is greater than the third threshold, the current remaining power of the terminal device is considered to be higher, and the first threshold may be decreased. And when the residual capacity is smaller than the third threshold, the current residual capacity of the terminal equipment is considered to be lower, and the first threshold is increased. In other words, the power consumption management of the terminal device is relaxed at high power, and is stricter at low power.

In the embodiment of the application, under the condition of low-probability connection, the power consumption generated by equipment scanning is reduced by reducing the duty ratio, so that the invalid scanning power consumption under the low-probability connection scene is saved; and under the condition of high-probability connection, the equipment discovery efficiency is improved by increasing the duty ratio, so that the equipment connection response efficiency under the high-probability connection scene is improved, and the optimization of the equipment power consumption is realized.

In addition, the power consumption optimization scheme of the embodiment of the application can also be used for waking up from a dormant state when the probability of connection with other equipment is high in a period of time in the future, and the duty ratio is improved, so that the problem that the terminal equipment cannot be connected due to unconditional dormancy is avoided as much as possible, and the user experience is improved. Specifically, the terminal device is generally installed with an application program such as a power saving sprite, and the application program may cause the terminal device to be in a sleep state for a certain period of time (e.g., at night) in order to save power for the terminal device. The terminal device is in an unconditional sleep state within a certain period of time, which may cause some functions of the terminal device to be unavailable, and influence user experience.

See, for example, the distributed gallery scene diagram shown in fig. 4. As shown in fig. 4, the scene includes a mobile phone 41 and a tablet 42. Pictures are stored in both the mobile phone 41 and the tablet computer 42, and through the distributed gallery function, the mobile phone 41 can inquire and browse the pictures stored in the tablet computer 42, and similarly, the tablet computer 42 can also inquire and browse the pictures stored in the mobile phone 41. At some point in the evening, the power saving sprite on the tablet 42 puts the tablet into a sleep state, so that the mobile phone 41 cannot query the picture 421 stored on the tablet 42 through the wireless connection.

After the power consumption optimization scheme provided by the embodiment of the application is used, the tablet computer 42 obtains the device connection rule information according to the log data, and predicts that the mobile phone 41 will be connected with the tablet computer 42 in a future period of time according to the device connection rule information, and the probability of occurrence of the connection behavior is greater than the first threshold, the tablet computer 42 wakes up from the sleep state, the duty ratio is increased, and the mobile phone 41 waits for establishing the wireless connection with the tablet computer 42, so that the mobile phone 41 queries and browses the picture 421 stored in the tablet computer 42 through the established wireless connection.

To better describe the power consumption optimization scheme provided in the embodiment of the present application, the following description is made with reference to a schematic diagram of a distributed communication scenario shown in fig. 5.

As shown in fig. 5, the scene includes a mobile phone 51, a tablet computer 52 and a smart television 53.

In the scene a, the mobile phone 51, the tablet computer 52, and the smart television 53 all use the existing device scanning mode, that is, the duty ratio is not dynamically adjusted. The mobile phone 51, the tablet computer 52 and the smart television 53 are connected to each other to perform corresponding distributed services, such as mutual screen projection. And measures the total power consumption and the average connection response speed at that time.

In the scene B, a high-frequency behavior rule set and a context behavior rule set are constructed, and the duty ratio is dynamically adjusted according to the high-frequency behavior rule set and the context behavior rule set by the mobile phone 51, the tablet personal computer 52 and the smart television 53. The other conditions remain the same as for scenario a. The total power consumption at this time and the average connection response speed were measured.

As a result of comparison, the total power consumption of the scene B is lower than that of the scene a, or the average connection response speed of the scene B is higher than that of the scene B. Alternatively, scenario B has a lower total power usage than scenario a and a higher average connection response speed than scenario a.

Based on the above embodiment, the method may further include: the first terminal equipment acquires the recorded first log data; and obtaining a high-frequency behavior rule set and/or a context behavior rule set according to the first log data, wherein the equipment connection rule information comprises the high-frequency behavior rule set and/or the context behavior rule set.

In some embodiments, deriving the high-frequency behavior law set from the first log data may include: the first terminal device preprocesses the first log data to obtain preprocessed second log data, wherein the second log data comprises a first timestamp, a first place, a connection event type, a first main connecting device and a first connected device. The second log data may include data as shown in table 1 above. And then, processing the second log data to obtain a first rule set, wherein each behavior rule in the first rule set comprises a time point, a first place, first main connecting equipment, first connected equipment and a second support degree. The first set of rules may include data as shown in table 6 above. And then clustering the first rule set to obtain a second rule set, wherein each behavior rule in the second rule set comprises a time period, a first place, first main connecting equipment, first connected equipment and a third support degree. This second set of rules may include data as shown in table 7 above. Finally, the behavior law in the second law set, in which the third support degree is smaller than the first preset support degree threshold value, is removed to obtain a high-frequency behavior law set, where the high-frequency behavior law may include data as shown in table 8 above.

Further, the processing, by the first terminal device, the second log data to obtain the first rule set may include: first, second log data with a connection event type of connection end is removed, and each first timestamp is mapped to each first time unit aiming at the second log data with the connection event type of connection start to obtain third log data, wherein the third log data comprises the first time units, a first place, first main connecting equipment and first connected equipment, the first time units are time units obtained by dividing a day according to minutes, and one first time unit corresponds to one minute. Specifically, there are 1440 minutes a day, so there are 1440 time units. The third log data may include data as shown in table 3 above.

And then, merging a plurality of log data which belong to the same day and the same first time unit and are the same in the third log data and are obtained after the first main connecting device and the first connected device are combined into one log data. The fourth log data may include data as shown in table 5 above. And finally, carrying out classification statistics on the fourth log data according to a first dimension to generate a first rule set, wherein the first dimension comprises time, place and a first event pair, and the first event pair comprises a first main connecting device and a first connected device.

In some embodiments, deriving the set of contextual behavior rules from the first log data may include: the first terminal device preprocesses the first log data to obtain preprocessed fifth log data, wherein the fifth log data comprises a second timestamp, a second place, a connection event type, a main connection device application, a second main connection device and a second connected device. The fifth log data may include the data shown in table 9 above. And then, processing the fifth log data to obtain a third rule set, wherein each behavior rule in the third rule set comprises a second place, a main connecting device application, a second main connecting device, a second connected device and a fourth support degree. This third set of rules may include data as shown in table 14 above. And finally, removing the behavior rule of which the fourth support degree is smaller than a second preset support degree threshold value in the third rule set to obtain a context behavior rule set. The set of contextual behavior rules may include data as shown in table 15 above.

Further, the processing the fifth log data to obtain the third rule set may include: first, removing fifth log data with a connection event type of connection end, and mapping each second timestamp to each second time unit for the fifth log data with the connection event type of connection start to obtain sixth log data, where the sixth log data includes a second time unit, a second location, a main connection device application, a second main connection device, and a second connected device, the second time unit is a time unit obtained by dividing a day by minutes, and one second time unit corresponds to one minute. The sixth log data may include data as shown in table 11 above.

And then, combining a plurality of log data which belong to the same second time unit on the same day and are the same in the sixth log data and are applied by the main connecting device, the second main connecting device and the second connected device into one log data to obtain seventh log data. The seventh log data may include data as shown in table 13 above. And finally, carrying out classification statistics on the seventh log data according to a second dimension to generate a third rule set, wherein the second dimension comprises a place, an application and a second event pair, and the second event pair comprises a second main connecting device and a second connected device.

For the high-frequency behavior rule set and the context behavior rule set, reference may be made to the high-frequency behavior rule learning process and the context behavior rule learning process shown above, which are not described herein again.

Corresponding to the above-mentioned power consumption optimization method, an embodiment of the present application provides a power consumption optimization apparatus, which is applied to a first terminal device. Referring to fig. 6, a schematic block diagram of a power consumption optimization apparatus provided in an embodiment of the present application may include:

the obtaining module 61 is configured to obtain device connection rule information, where the device connection rule information is used to describe a connection rule between the first terminal device and another terminal device.

And the prediction module 62 is configured to determine, according to the device connection rule information, a probability of a connection behavior occurring between the first terminal device and the second terminal device in a future preset time period, where the other terminal devices include the second terminal device.

And a power consumption optimization module 63, configured to execute a power consumption optimization strategy according to the probability.

In some possible implementations, the power consumption optimization module 63 is specifically configured to: and when the probability is greater than a first threshold value, establishing connection with the second terminal equipment or increasing the duty ratio, and when the probability is less than a second threshold value, reducing the duty ratio, wherein the second threshold value is less than or equal to the first threshold value, and the duty ratio is the ratio between the scanning time slot and the scanning period.

In some possible implementations, the prediction module 62 is specifically configured to: according to preset information, searching target connection rule information from the equipment connection rule information, wherein the preset information comprises a current location and at least one of the following items: the current time and the target application program opened by the first terminal device; when the target connection rule information is found, obtaining a first support degree corresponding to the target connection rule information; and obtaining the probability of the connection behavior between the first terminal equipment and the second terminal equipment in a future preset time period according to the first support degree.

In some possible implementations, the prediction module 62 is specifically configured to: searching a target high-frequency behavior rule from the high-frequency behavior rule set according to the current time and the current place, wherein the time period of the target high-frequency behavior rule comprises the current time, and the place of the target high-frequency behavior rule is consistent with the current place; when the target high-frequency behavior rule is found, taking the target high-frequency behavior rule as target connection rule information; when the target high-frequency behavior rule cannot be searched, determining that the target connection rule information cannot be searched; the preset information comprises current time and current place, and the equipment connection rule information comprises a high-frequency behavior rule set.

In some possible implementations, the prediction module 62 is specifically configured to: searching a target context behavior rule from the context behavior rule set according to the target application program and the current location, wherein the location of the target context behavior rule is consistent with the current location, and the target application program comprises an application program of the target context behavior rule; when the target context behavior rule is found, taking the target context behavior rule as target connection rule information; when the target context behavior rule cannot be searched, determining that the target connection rule information cannot be searched; the preset information comprises a target application program and a current place, and the equipment connection rule information comprises a context behavior rule set.

In some possible implementations, the prediction module 62 is specifically configured to: searching a target high-frequency behavior rule from the high-frequency behavior rule set according to the current time and the current place, wherein the time period of the target high-frequency behavior rule comprises the current time, and the place of the target high-frequency behavior rule is consistent with the current place; when the target high-frequency behavior rule is found, taking the target high-frequency behavior rule as target connection rule information; when the target high-frequency behavior rule cannot be searched, searching a target context behavior rule from the context behavior rule set according to the target application program and the current location, wherein the location of the target context behavior rule is consistent with the current location, and the target application program comprises an application program of the target context behavior rule; when the target context behavior rule is found, taking the target context behavior rule as target connection rule information; when the target context behavior rule cannot be searched, determining that the target connection rule information cannot be searched; the preset information comprises a target application program, a current place and a current time, and the equipment connection rule information comprises a context behavior rule set and a high-frequency behavior rule set.

In some possible implementations, the apparatus may further include: and the log data acquisition module is used for acquiring the first log data of the first terminal equipment. And the equipment connection rule learning module is used for obtaining a high-frequency behavior rule set and/or a context behavior rule set according to the first log data, and the equipment connection rule information comprises the high-frequency behavior rule set and/or the context behavior rule set.

In some possible implementations, the device connection rule learning module is specifically configured to: preprocessing the first log data to obtain preprocessed second log data, wherein the second log data comprises a first timestamp, a first place, a connection event type, a first main connecting device and a first connected device; processing the second log data to obtain a first rule set, wherein each behavior rule in the first rule set comprises a time point, a first place, first main connecting equipment, first connected equipment and a second support degree; clustering the first rule set to obtain a second rule set, wherein each behavior rule in the second rule set comprises a time period, a first place, first main connecting equipment, first connected equipment and a third support degree; and removing the behavior rule in the second rule set, wherein the third support degree is smaller than the first preset support degree threshold value, so as to obtain a high-frequency behavior rule set.

In some possible implementations, the device connection rule learning module is specifically configured to: removing second log data with the connection event type of connection ending, and mapping each first timestamp to each first time unit aiming at the second log data with the connection event type of connection starting to obtain third log data, wherein the third log data comprise the first time units, a first place, first main connecting equipment and first connected equipment, the first time units are time units obtained by dividing one day according to minutes, and one first time unit corresponds to one minute; merging a plurality of log data which belong to the same day and the same first time unit and are the same in the third log data and are obtained after the first main connecting device and the first connected device are combined into one log data; and carrying out classification statistics on the fourth log data according to a first dimension to generate a first rule set, wherein the first dimension comprises time, place and a first event pair, and the first event pair comprises a first main connecting device and a first connected device.

In some possible implementations, the device connection rule learning module is specifically configured to: preprocessing the first log data to obtain preprocessed fifth log data, wherein the fifth log data comprises a second timestamp, a second place, a connection event type, a main connection device application, a second main connection device and a second connected device; processing the fifth log data to obtain a third rule set, wherein each behavior rule in the third rule set comprises a second place, a main connecting device application, a second main connecting device, a second connected device and a fourth support degree; and removing the behavior rule in the third rule set, wherein the fourth support degree is smaller than a second preset support degree threshold value, so as to obtain a context behavior rule set.

In some possible implementations, the device connection rule learning module is specifically configured to: removing fifth log data with the connection event type of connection end, and mapping each second timestamp to each second time unit aiming at the fifth log data with the connection event type of connection start to obtain sixth log data, wherein the sixth log data comprise a second time unit, a second place, a main connecting device application, a second main connecting device and a second connected device, the second time unit is a time unit obtained by dividing one day according to minutes, and one second time unit corresponds to one minute; merging a plurality of log data which belong to the same day and the same second time unit and are the same as the log data of the main connecting device application, the second main connecting device and the second connected device into one log data to obtain seventh log data; and carrying out classification statistics on the seventh log data according to a second dimension to generate a third rule set, wherein the second dimension comprises a place, an application and a second event pair, and the second event pair comprises a second main connecting device and a second connected device.

In some possible implementations, the apparatus may further include: the threshold adjusting module is used for detecting the current residual electric quantity; if the current residual electric quantity is larger than the third threshold value, reducing the first threshold value; and if the current residual capacity is smaller than the third threshold value, increasing the first threshold value.

The power consumption optimization device has the function of implementing the power consumption optimization method, the function can be implemented by hardware, or can be implemented by hardware executing corresponding software, the hardware or the software includes one or more modules corresponding to the function, and the modules can be software and/or hardware.

In some distributed communication scenarios, the terminal devices may interact with each other through the distributed connection control module. At this time, referring to another schematic block diagram of the power consumption optimizing apparatus shown in fig. 7, the power consumption optimizing apparatus may include a user profile module 71, a decision module 72, and a distributed connection control module 73.

The user profile module 71 is configured to calculate user behavior characteristics according to the log data, where the user behavior characteristics refer to high-frequency behavior characteristics, contextual behavior characteristics, and the like. And the method is also used for calculating the connection behavior characteristics of the distributed connection control module according to the user behavior characteristics.

That is, the user profile module 71 is configured to obtain the high-frequency behavior rule set through a high-frequency behavior rule learning process according to the log data, and/or obtain the context behavior rule set through a context behavior rule learning process according to the log data.

The decision module 72 is configured to predict whether the distributed connection control module has a connection requirement according to the high-frequency behavior rule set and/or the context behavior rule set, and prompt the distributed connection control module 73. Specifically, the decision module 72 predicts the probability of the connection behavior between the device and other devices according to the high-frequency behavior rule set and/or the context behavior rule set, and prompts the distributed connection control module 73 to increase the duty ratio or decrease the duty ratio according to the magnitude of the probability.

The distributed connection control module 73 is configured to execute a corresponding power consumption optimization strategy according to the prompt information of the decision module 72. For example, the duty cycle is increased or decreased based on the prompt from the decision block 72. When the decision module 72 indicates that the probability of the occurrence of the connection behavior is small, the duty ratio is decreased, whereas when the decision module 72 indicates that the probability of the occurrence of the connection behavior is large, the duty ratio is increased.

To better illustrate the flow between the user profile module 71, the decision module 72 and the distributed connection control module 73, reference will now be made to FIG. 8.

Referring to fig. 8, which is a schematic diagram of an interaction flow provided by the embodiment of the present application, as shown in fig. 8, a terminal device includes a user profile module 71, a decision module 72, and a distributed connection control module 73. The interaction flow comprises the following steps:

step S801, the user profile module 71 calculates device connection rule information according to the log data, where the device connection rule information includes a high-frequency behavior rule set and/or a context behavior rule set.

In a specific application, the user profile module 71 may obtain daily log data, and update the rule set according to the daily log data.

Step S802, the decision module 72 predicts the probability of the connection behavior between the device and other devices in a future period according to the high-frequency behavior rule set and/or the context behavior rule set in the device connection rule information.

Specifically, the decision module 72 sends a query request to the user profiling module 71, and after receiving the query request, the user profiling module 71 searches the target high-frequency behavior rules from the high-frequency behavior rule set according to the current time point and the current location, and/or searches the target contextual behavior rules from several contextual behavior rules according to the current location and the currently opened application program. After the user profile module 71 finds the target high frequency behavior law and/or the target context behavior law, it returns the found behavior law to the decision module 72. When no matching behavior rules are found, the user profile module 71 may return a message to the decision module 72 to inform the decision module 72 that no matching behavior rules are found.

For example, the current location of the mobile phone is home, the current time point is 6 am, and the 6 am point is mapped to 360 minutes. And searching the high-frequency behavior rule set according to the current time and the current place. And (4) judging whether the current time and the current place fall into a certain high-frequency behavior rule or not. If so, the user profile module 71 of the handset returns the hit high frequency behavior rules to the decision module 72. At this time, if the high-frequency behavior rule of the hit is: 355-375, home, cell phone, sound box, the corresponding probability is 50%. Thus, the decision module 72 knows that the probability of the connection between the mobile phone and the sound box within 15 minutes in the future is 50% according to the high-frequency behavior rule returned by the user profile module 71.

The user profile module 71 may not search for a matching high frequency behavior pattern in the high frequency behavior pattern set according to the current time and the current location. The search for a set of contextual behavior rules continues according to the currently opened application and the current location. At this time, if the context behavior rule of the hit is: [ DOMESTIC ] [ MOBILE PHONE ALARM ] the [ MOBILE PHONE ] the corresponding probability is 50%, and the user portrayal module 71 returns the context behavior law to the decision module 72. The decision module 72 knows that the probability of the handset and box sending a connection behavior within 15 minutes in the future is 50% by the context behavior law returned by the user profile module 71.

Step S803, the decision module 72 prompts the distributed connection control module 73 whether there is a connection requirement according to the size of the probability.

Specifically, when the decision module 72 determines that the probability is greater than the first threshold, it considers that the probability of the occurrence of the connection behavior in a future period is a rough probability, and prompts the central controller 73 of the distributed connection control module that there is a connection requirement. When the probability is determined to be smaller than the second threshold, the probability that the connection behavior occurs within a future period of time is considered to be a small probability, and the distributed connection control module 73 is prompted to have no connection requirement.

In step S804, the distributed connection control module 73 responds to the prompt of the decision module 72 to increase the duty ratio, decrease the duty ratio, or establish connection with other devices in advance.

Specifically, when the probability of the occurrence of the connection behavior is high, the distributed connection control module 73 may establish a connection with the other device in advance, that is, the distributed connection control module 73 interacts with the distributed connection control modules of the other devices to establish a connection. After the connection is established, the device and other devices can perform service interaction through the established connection. And, after the service interaction is finished, the distributed connection control module 73 may automatically disconnect from the distributed connection control modules of other devices.

Of course, the distributed connection control module 73 may also increase the duty cycle when the probability of connection activity is high.

The distributed connection control module 73 may decrease the duty cycle when the probability of occurrence of the connection behavior is low. When the probability is zero, the distributed connection control module may enter a sleep state.

The present application further provides a terminal device, which may include, but is not limited to, a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the method according to any one of the above power consumption optimization method embodiments.

It can be understood that the type of the terminal device provided in the embodiment of the present application may be any, for example, the terminal device may be a portable terminal device such as a mobile phone and a tablet computer. The specific structure of the terminal device is also arbitrary. By way of example and not limitation, as shown in fig. 9, the terminal device 900 may include a processor 910, an external memory interface 920, an internal memory 921, a Universal Serial Bus (USB) interface 930, a charging management module 940, a power management module 941, a battery 942, an antenna 1, an antenna 2, a mobile communication module 950, a wireless communication module 960, an audio module 970, a speaker 970A, a receiver 970B, a microphone 970C, an earphone interface 970D, a sensor module 980, a button 990, a motor 991, a pointer 992, a camera 993, a display 994, and a Subscriber Identity Module (SIM) card interface 995, etc. Wherein sensor module 980 may include a pressure sensor 980A, a gyroscope sensor 980B, an air pressure sensor 980C, a magnetic sensor 980D, an acceleration sensor 980E, a distance sensor 980F, a proximity light sensor 980G, a fingerprint sensor 980H, a temperature sensor 980J, a touch sensor 980K, an ambient light sensor 980L, a bone conduction sensor 980M, and the like.

It is to be understood that the illustrated structure of the embodiment of the present application does not constitute a specific limitation to the terminal device 900. In other embodiments of the present application, terminal device 900 may include more or fewer components than shown, or some components may be combined, some components may be split, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

Processor 910 may include one or more processing units, such as: the processor 910 may include an Application Processor (AP), a modem processor, a Graphics Processing Unit (GPU), an Image Signal Processor (ISP), a controller, a memory, a video codec, a Digital Signal Processor (DSP), a baseband processor, and/or a neural-Network Processing Unit (NPU), etc. The different processing units may be separate devices or may be integrated into one or more processors.

The controller may be a neural center and a command center of the terminal apparatus 900, among others. The controller can generate an operation control signal according to the instruction operation code and the timing signal to complete the control of instruction fetching and instruction execution.

A memory may also be provided in processor 910 for storing instructions and data. In some embodiments, the memory in the processor 910 is a cache memory. The memory may hold instructions or data that have just been used or recycled by processor 910. If the processor 910 needs to reuse the instruction or data, it can be called directly from memory. Avoiding repeated accesses reduces the latency of the processor 910, thereby increasing the efficiency of the system.

In some embodiments, processor 910 may include one or more interfaces. The interface may include an integrated circuit (I2C) interface, an integrated circuit built-in audio (I2S) interface, a Pulse Code Modulation (PCM) interface, a universal asynchronous receiver/transmitter (UART) interface, a Mobile Industry Processor Interface (MIPI), a general-purpose input/output (GPIO) interface, a Subscriber Identity Module (SIM) interface, and/or a Universal Serial Bus (USB) interface, etc.

The I2C interface is a bi-directional synchronous serial bus that includes a serial data line (SDA) and a Serial Clock Line (SCL). In some embodiments, processor 910 may include multiple sets of I2C buses. The processor 910 may be coupled to the touch sensor 980K, the charger, the flash, the camera 993, etc. through different I2C bus interfaces. For example: the processor 910 may be coupled to the touch sensor 980K through an I2C interface, so that the processor 910 and the touch sensor 980K communicate through an I2C bus interface, thereby implementing the touch function of the terminal device 900.

The I2S interface may be used for audio communication. In some embodiments, processor 910 may include multiple sets of I2S buses. The processor 910 may be coupled to the audio module 970 via an I2S bus to enable communication between the processor 910 and the audio module 970.

The PCM interface may also be used for audio communication, sampling, quantizing and encoding analog signals. In some embodiments, audio module 970 and wireless communication module 960 may be coupled by a PCM bus interface. Both the I2S interface and the PCM interface may be used for audio communication.

The UART interface is a universal serial data bus used for asynchronous communications. The bus may be a bidirectional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, a UART interface is generally used to connect the processor 910 with the wireless communication module 960. For example: the processor 910 communicates with a bluetooth module in the wireless communication module 960 through a UART interface to implement a bluetooth function.

The MIPI interface may be used to connect the processor 910 with peripheral devices such as the display screen 994, the camera 993, and the like. The MIPI interface includes a Camera Serial Interface (CSI), a Display Serial Interface (DSI), and the like. In some embodiments, processor 910 and camera 993 communicate over a CSI interface to implement the capture functionality of terminal device 900. The processor 910 and the display screen 994 communicate through the DSI interface, and the display function of the terminal device 900 is realized.

The GPIO interface may be configured by software. The GPIO interface may be configured as a control signal and may also be configured as a data signal. In some embodiments, a GPIO interface may be used to connect the processor 910 with the camera 993, the display 994, the wireless communication module 960, the audio module 970, the sensor module 980, and the like. The GPIO interface may also be configured as an I2C interface, an I2S interface, a UART interface, a MIPI interface, and the like.

The USB interface 930 is an interface conforming to the USB standard specification, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, or the like. The USB interface 930 may be used to connect a charger to charge the terminal device 900, and may also be used to transmit data between the terminal device 900 and peripheral devices. And the earphone can also be used for connecting an earphone and playing audio through the earphone. The interface may also be used to connect other terminal devices, such as AR devices and the like.

It should be understood that the interface connection relationship between the modules illustrated in the embodiment of the present application is only an exemplary illustration, and does not constitute a limitation on the structure of the terminal device 900. In other embodiments of the present application, the terminal device 900 may also adopt different interface connection manners or a combination of multiple interface connection manners in the above embodiments.

The charging management module 940 is used to receive charging input from a charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 940 may receive charging input from a wired charger via the USB interface 930. In some wireless charging embodiments, the charging management module 140 may receive a wireless charging input through a wireless charging coil of the terminal device 900. The charging management module 940 can also supply power to the terminal device through the power management module 941 while charging the battery 942.

The power management module 941 is configured to connect the battery 942, the charging management module 940 and the processor 910. The power management module 941 receives input from the battery 942 and/or the charging management module 940 and provides power to the processor 910, the internal memory 921, the external memory, the display 994, the camera 993, the wireless communication module 960, and the like. The power management module 941 may also be used to monitor parameters such as battery capacity, battery cycle number, and battery health (leakage, impedance). In some other embodiments, a power management module 941 may also be disposed in the processor 910. In other embodiments, the power management module 941 and the charging management module 940 may be disposed in the same device.

The wireless communication function of the terminal device 900 may be implemented by the antenna 1, the antenna 2, the mobile communication module 950, the wireless communication module 960, a modem processor, a baseband processor, and the like.

The antennas 1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in terminal device 900 can be used to cover a single or multiple communication bands. Different antennas can also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 950 may provide a solution including 2G/3G/4G/5G wireless communication applied to the terminal device 900. The mobile communication module 950 may include at least one filter, a switch, a power amplifier, a Low Noise Amplifier (LNA), and the like. The mobile communication module 950 can receive electromagnetic waves from the antenna 1, filter, amplify and transmit the received electromagnetic waves to the modem processor for demodulation. The mobile communication module 950 can also amplify the signal modulated by the modem processor and convert the signal into electromagnetic wave through the antenna 1 to radiate the electromagnetic wave. In some embodiments, at least some of the functional modules of the mobile communication module 950 may be disposed in the processor 910. In some embodiments, at least some of the functional modules of the mobile communication module 950 may be disposed in the same device as at least some of the modules of the processor 910.

The modem processor may include a modulator and a demodulator. The modulator is used for modulating a low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used for demodulating the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then passes the demodulated low frequency baseband signal to a baseband processor for processing. The low frequency baseband signal is processed by the baseband processor and then transferred to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 970A, the receiver 970B, etc.) or displays an image or video through the display screen 994. In some embodiments, the modem processor may be a stand-alone device. In other embodiments, the modem processor may be separate from the processor 910 and may be located in the same device as the mobile communication module 950 or other functional modules.

The wireless communication module 960 may provide a solution for wireless communication applied to the terminal device 900, including Wireless Local Area Networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), Infrared (IR), and the like. The wireless communication module 960 may be one or more devices integrating at least one communication processing module. The wireless communication module 960 receives an electromagnetic wave via the antenna 2, performs frequency modulation and filtering on an electromagnetic wave signal, and transmits the processed signal to the processor 910. The wireless communication module 960 may also receive signals to be transmitted from the processor 910, frequency modulate, amplify, and convert the signals to electromagnetic waves via the antenna 2 for radiation.

In some embodiments, antenna 1 of terminal device 900 is coupled to mobile communications module 950 and antenna 2 is coupled to wireless communications module 960 so that terminal device 900 can communicate with networks and other devices via wireless communications techniques. The wireless communication technology may include global system for mobile communications (GSM), General Packet Radio Service (GPRS), code division multiple access (code division multiple access, CDMA), Wideband Code Division Multiple Access (WCDMA), time-division code division multiple access (time-division code division multiple access, TD-SCDMA), Long Term Evolution (LTE), LTE, BT, GNSS, WLAN, NFC, FM, and/or IR technologies, among others. GNSS may include Global Positioning System (GPS), global navigation satellite system (GLONASS), beidou satellite navigation system (BDS), quasi-zenith satellite system (QZSS), and/or Satellite Based Augmentation System (SBAS).

The terminal device 900 implements a display function by the GPU, the display screen 994, and the application processor. The GPU is a microprocessor for image processing, and is connected to the display screen 994 and an application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 910 may include one or more GPUs that execute program instructions to generate or alter display information.

The display screen 994 is used to display images, video, and the like. The display screen 994 includes a display panel. The display panel may adopt a Liquid Crystal Display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (active-matrix organic light-emitting diode, AMOLED), a flexible light-emitting diode (FLED), a miniature, a Micro-oeld, a quantum dot light-emitting diode (QLED), and the like. In some embodiments, terminal device 900 may include 1 or N display screens 994, N being a positive integer greater than 1.

The terminal device 900 may implement a shooting function through the ISP, the camera 993, the video codec, the GPU, the display screen 994, the application processor, and the like.

The ISP is used to process data fed back by the camera 993. For example, when a photo is taken, the shutter is opened, light is transmitted to the camera photosensitive element through the lens, the optical signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to the ISP for processing and converting into an image visible to naked eyes. The ISP can also carry out algorithm optimization on the noise, brightness and skin color of the image. The ISP can also optimize parameters such as exposure, color temperature and the like of a shooting scene. In some embodiments, the ISP may be provided in the camera 993.

The camera 993 is used to capture still images or video. The object generates an optical image through the lens and projects the optical image to the photosensitive element. The photosensitive element may be a Charge Coupled Device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The light sensing element converts the optical signal into an electrical signal, which is then passed to the ISP where it is converted into a digital image signal. And the ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into image signal in standard RGB, YUV and other formats. In some embodiments, terminal device 900 may include 1 or N cameras 993, N being a positive integer greater than 1.

The digital signal processor is used for processing digital signals, and can process digital image signals and other digital signals. For example, when the terminal device 900 selects a frequency bin, the digital signal processor is used to perform fourier transform or the like on the frequency bin energy.

Video codecs are used to compress or decompress digital video. Terminal device 900 may support one or more video codecs. In this way, terminal device 900 may play or record video in a variety of encoding formats, such as: moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, and the like.

The NPU is a neural-network (NN) computing processor that processes input information quickly by using a biological neural network structure, for example, by using a transfer mode between neurons of a human brain, and can also learn by itself continuously. The NPU can implement applications such as intelligent recognition of the terminal device 900, for example: image recognition, face recognition, speech recognition, text understanding, and the like.

The external memory interface 920 may be used to connect an external memory card, such as a Micro SD card, to extend the memory capability of the terminal device 900. The external memory card communicates with the processor 910 through the external memory interface 920 to implement a data storage function. For example, files such as music, video, etc. are saved in an external memory card.

The internal memory 921 may be used to store computer-executable program code, which includes instructions. The processor 910 executes various functional applications of the terminal apparatus 900 and data processing by executing instructions stored in the internal memory 921. The internal memory 921 may include a program storage area and a data storage area. The storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required by at least one function, and the like. The storage data area may store data (such as audio data, a phonebook, etc.) created during use of the terminal apparatus 900, and the like. In addition, the internal memory 921 may include a high-speed random access memory, and may further include a nonvolatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash memory (UFS), and the like.

The terminal device 900 may implement an audio function through the audio module 970, the speaker 970A, the receiver 970B, the microphone 970C, the earphone interface 970D, and the application processor, etc. Such as music playing, recording, etc.

The audio module 970 is used for converting digital audio information into an analog audio signal output and also for converting an analog audio input into a digital audio signal. The audio module 970 may also be used to encode and decode audio signals. In some embodiments, the audio module 970 may be disposed in the processor 910, or some functional modules of the audio module 970 may be disposed in the processor 910.

The speaker 970A, also called a "horn", is used to convert audio electrical signals into sound signals. The terminal apparatus 900 can listen to music through the speaker 970A or listen to a handsfree call.

Receiver 970B, also referred to as an "earpiece," is used to convert the electrical audio signal into an acoustic signal. When the terminal apparatus 900 answers a call or voice information, it can answer a voice by placing the receiver 970B close to the human ear.

Microphone 970C, also known as a "microphone," is used to convert acoustic signals into electrical signals. When making a call or sending voice information, the user can input a voice signal into the microphone 970C by making a sound by approaching the microphone 970C through the mouth of the user. Terminal device 900 can be provided with at least one microphone 970C. In other embodiments, terminal device 900 may be provided with two microphones 970C to implement a noise reduction function in addition to collecting sound signals. In other embodiments, three, four or more microphones 970C may be further disposed on the terminal device 900 to collect sound signals, reduce noise, identify sound sources, implement directional recording functions, and so on.

The earphone interface 970D is used to connect a wired earphone. The earphone interface 970D may be the USB interface 930, or may be an Open Mobile Terminal Platform (OMTP) standard interface of 3.5mm, or a cellular telecommunications industry association (cellular telecommunications industry association of the USA, CTIA) standard interface.

Pressure sensor 980A is configured to sense a pressure signal, which may be converted to an electrical signal. In some embodiments, the pressure sensor 980A may be disposed on the display screen 994. Pressure sensor 980A can be of a wide variety, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like. The capacitive pressure sensor may be a sensor comprising at least two parallel plates having an electrically conductive material. When a force acts on the pressure sensor 980A, the capacitance between the electrodes changes. The terminal apparatus 900 determines the intensity of the pressure from the change in the capacitance. When a touch operation is applied to the display screen 994, the terminal apparatus 900 detects the intensity of the touch operation based on the pressure sensor 980A. The terminal apparatus 900 can also calculate the position of the touch from the detection signal of the pressure sensor 980A. In some embodiments, the touch operations that are applied to the same touch position but different touch operation intensities may correspond to different operation instructions. For example: and when the touch operation with the touch operation intensity smaller than the first pressure threshold value acts on the short message application icon, executing an instruction for viewing the short message. And when the touch operation with the touch operation intensity larger than or equal to the first pressure threshold value acts on the short message application icon, executing an instruction of newly building the short message.

The gyro sensor 980B may be used to determine the motion attitude of the terminal device 900. In some embodiments, the angular velocity of terminal device 900 about three axes (i.e., x, y, and z axes) may be determined by gyroscope sensor 980B. The gyro sensor 980B may be used for photographing anti-shake. Illustratively, when the shutter is pressed, the gyroscope sensor 980B detects the shake angle of the terminal device 900, calculates the distance that the lens module needs to compensate according to the shake angle, and enables the lens to counteract the shake of the terminal device 900 through reverse movement, thereby achieving anti-shake. The gyro sensor 980B may also be used for navigation, somatosensory gaming scenarios.

Barometric pressure sensor 980C is used to measure barometric pressure. In some embodiments, terminal device 900 calculates altitude, aiding in positioning and navigation, from barometric pressure values measured by barometric pressure sensor 180C.

The magnetic sensor 980D includes a hall sensor. The terminal device 900 may detect the opening and closing of the flip holster using the magnetic sensor 980D. In some embodiments, when the terminal device 900 is a flip phone, the terminal device 900 can detect the opening and closing of the flip according to the magnetic sensor 980D. And then according to the opening and closing state of the leather sheath or the opening and closing state of the flip cover, the automatic unlocking of the flip cover is set.

The acceleration sensor 980E can detect the magnitude of acceleration of the terminal device 900 in various directions (typically three axes). The magnitude and direction of gravity may be detected when the terminal device 900 is stationary. The method can also be used for recognizing the posture of the terminal equipment, and is applied to horizontal and vertical screen switching, pedometers and other applications.

A distance sensor 980F for measuring distance. The terminal apparatus 900 may measure the distance by infrared or laser. In some embodiments, taking a picture of a scene, terminal device 90 may utilize range sensor 980F to measure distance to achieve fast focus.

The proximity light sensor 980G may include, for example, a Light Emitting Diode (LED) and a light detector, such as a photodiode. The light emitting diode may be an infrared light emitting diode. The terminal apparatus 900 emits infrared light to the outside through the light emitting diode. The terminal device 900 detects infrared reflected light from a nearby object using a photodiode. When sufficient reflected light is detected, it can be determined that there is an object near the terminal apparatus 900. When insufficient reflected light is detected, the terminal device 900 can determine that there is no object near the terminal device 900. The terminal device 900 can utilize the proximity light sensor 980G to detect that the user holds the terminal device 900 close to the ear for talking, so as to automatically extinguish the screen to achieve the purpose of saving power. The proximity light sensor 980G may also be used in a holster mode, a pocket mode automatically unlocks and locks the screen.

The ambient light sensor 980L is used to sense ambient light level. The terminal device 900 may adaptively adjust the brightness of the display 994 based on the perceived ambient light level. The ambient light sensor 980L can also be used to automatically adjust the white balance when taking a picture. Ambient light sensor 980L may also cooperate with proximity light sensor 980G to detect whether terminal device 900 is in a pocket to prevent inadvertent touches.

The fingerprint sensor 980H is used to capture a fingerprint. The terminal device 900 can utilize the collected fingerprint characteristics to realize fingerprint unlocking, access to an application lock, fingerprint photographing, fingerprint incoming call answering and the like.

The temperature sensor 980J is used to detect temperature. In some embodiments, terminal device 900 implements a temperature handling strategy using the temperature detected by temperature sensor 980J. For example, when the temperature reported by temperature sensor 980J exceeds a threshold, terminal device 900 performs a reduction in performance of a processor located near temperature sensor 980J in order to reduce power consumption and implement thermal protection. In other embodiments, terminal device 900 heats battery 942 when the temperature is below another threshold to avoid a low temperature causing terminal device 900 to shutdown abnormally. In other embodiments, terminal apparatus 900 performs boosting of the output voltage of battery 942 when the temperature is below a further threshold to avoid abnormal shutdown due to low temperature.

Touch sensor 980K, also referred to as a "touch panel". The touch sensor 980K may be disposed on the display screen 994, and the touch sensor 980K and the display screen 994 form a touch screen, which is also referred to as a "touch screen". The touch sensor 980K is used to detect a touch operation applied thereto or nearby. The touch sensor can communicate the detected touch operation to the application processor to determine the touch event type. Visual output associated with the touch operation may be provided through the display screen 994. In other embodiments, the touch sensor 980K can be disposed on a surface of the terminal device 900 at a location other than the display screen 994.

Bone conduction sensor 980M may acquire a vibration signal. In some embodiments, the bone conduction sensor 980M can acquire a vibration signal of a vibrating bone mass of a human voice. The bone conduction sensor 980M may also be in contact with the human pulse to receive blood pressure pulsation signals. In some embodiments, the bone conduction sensor 980M may also be provided in a headset, integrated into a bone conduction headset. The audio module 970 may analyze a voice signal based on the vibration signal of the sound part vibration bone block acquired by the bone conduction sensor 980M, so as to implement a voice function. The application processor can analyze heart rate information based on the blood pressure beating signal acquired by the bone conduction sensor 980M, and the heart rate detection function is realized.

The keys 990 include a power-on key, a volume key, and the like. Keys 990 may be mechanical keys. Or may be touch keys. The terminal apparatus 900 may receive a key input, and generate a key signal input related to user setting and function control of the terminal apparatus 900.

The motor 991 may generate a vibration cue. The motor 991 may be used for incoming call vibration prompts, as well as for touch vibration feedback. For example, touch operations applied to different applications (e.g., photographing, audio playing, etc.) may correspond to different vibration feedback effects. The motor 991 may also respond to different vibration feedback effects when it is operated by touching different areas of the display screen 994. Different application scenes (such as time reminding, receiving information, alarm clock, game and the like) can also correspond to different vibration feedback effects. The touch vibration feedback effect may also support customization.

The indicator 992 may be an indicator light, and may be used to indicate a charging status, a change in power, or a message, a missed call, a notification, or the like.

The SIM card interface 995 is used to connect SIM cards. The SIM card can be brought into and out of contact with the terminal device 900 by being inserted into the SIM card interface 995 or being pulled out of the SIM card interface 995. Terminal device 900 may support 1 or N SIM card interfaces, N being a positive integer greater than 1. The SIM card interface 995 may support a Nano SIM card, a Micro SIM card, a SIM card, etc. The same SIM card interface 995 can be used to insert multiple cards at the same time. The types of the plurality of cards may be the same or different. The SIM card interface 995 may also be compatible with different types of SIM cards. The SIM card interface 995 may also be compatible with external memory cards. The terminal device 900 interacts with the network through the SIM card to implement functions such as communication and data communication. In some embodiments, the terminal device 900 employs eSIM, namely: an embedded SIM card. The eSIM card can be embedded in the terminal apparatus 900 and cannot be separated from the terminal apparatus 900.

The embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps in the foregoing power consumption optimization method embodiments may be implemented.

The embodiments of the present application provide a computer program product, which, when running on a terminal device, enables the terminal device to implement the steps in the foregoing power consumption optimization method embodiments when executed.

An embodiment of the present application further provides a chip system, where the chip system includes a processor, the processor is coupled with a memory, and the processor executes a computer program stored in the memory to implement the methods according to the above-mentioned embodiments of the power consumption optimization method. The chip system can be a single chip or a chip module consisting of a plurality of chips.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment. It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance. Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise.

Finally, it should be noted that: the above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A power consumption optimization method is applied to a first terminal device, and is characterized by comprising the following steps:

acquiring equipment connection rule information, wherein the equipment connection rule information is used for describing a connection rule between the first terminal equipment and other terminal equipment;

determining the probability of connection behavior between the first terminal equipment and second terminal equipment in a future preset time period according to the equipment connection rule information, wherein the other terminal equipment comprises the second terminal equipment;

and executing a power consumption optimization strategy according to the probability.

2. The method of claim 1, wherein performing a power consumption optimization strategy based on the probability comprises:

when the probability is larger than a first threshold value, establishing connection with the second terminal equipment or increasing the duty ratio;

and when the probability is smaller than a second threshold value, reducing the duty ratio, wherein the second threshold value is smaller than or equal to the first threshold value, and the duty ratio is the ratio of the scanning time slot to the scanning period.

3. The method of claim 1, wherein determining, according to the device connection rule information, a probability of a connection behavior between the first terminal device and a second terminal device within a preset time period in the future comprises:

according to preset information, searching target connection rule information from the equipment connection rule information, wherein the preset information comprises a current location and at least one of the following items: the current time and the target application program opened by the first terminal device;

when the target connection rule information is found, obtaining a first support degree corresponding to the target connection rule information;

and obtaining the probability of the connection behavior between the first terminal equipment and the second terminal equipment in a future preset time period according to the first support degree.

4. The method of claim 3, wherein searching for target connection rule information from the device connection rule information according to preset information comprises:

searching a target high-frequency behavior rule from a high-frequency behavior rule set according to the current time and the current place, wherein the time period of the target high-frequency behavior rule comprises the current time, and the place of the target high-frequency behavior rule is consistent with the current place;

when the target high-frequency behavior rule is found, taking the target high-frequency behavior rule as the target connection rule information;

when the target high-frequency behavior rule cannot be searched, determining that the target connection rule information cannot be searched;

the preset information comprises the current time and the current place, and the equipment connection rule information comprises the high-frequency behavior rule set.

5. The method of claim 3, wherein searching for target connection rule information from the device connection rule information according to preset information comprises:

searching a target context behavior rule from a context behavior rule set according to the target application program and the current location, wherein the location of the target context behavior rule is consistent with the current location, and the target application program comprises an application program of the target context behavior rule;

when the target context behavior rule is found, taking the target context behavior rule as the target connection rule information;

when the target context behavior rule cannot be searched, determining that the target connection rule information cannot be searched;

the preset information comprises the target application program and the current location, and the device connection rule information comprises the context behavior rule set.

6. The method of claim 3, wherein searching for target connection rule information from the device connection rule information according to preset information comprises:

when the target high-frequency behavior rule cannot be searched, searching a target context behavior rule from a context behavior rule set according to the target application program and the current location, wherein the location of the target context behavior rule is consistent with the current location, and the target application program comprises an application program of the target context behavior rule;

the preset information includes the target application program, the current location and the current time, and the device connection rule information includes the context behavior rule set and the high-frequency behavior rule set.

7. The method according to any one of claims 1 to 6, further comprising:

acquiring first log data of the first terminal equipment;

and obtaining a high-frequency behavior rule set and/or a context behavior rule set according to the first log data, wherein the equipment connection rule information comprises the high-frequency behavior rule set and/or the context behavior rule set.

8. The method of claim 7, wherein deriving a set of high frequency behavior rules from the first log data comprises:

preprocessing the first log data to obtain preprocessed second log data, wherein the second log data comprises a first timestamp, a first place, a connection event type, a first main connecting device and a first connected device;

processing the second log data to obtain a first rule set, wherein each behavior rule in the first rule set comprises a time point, the first place, the first main connecting device, the first connected device and a second support degree;

clustering the first rule set to obtain a second rule set, wherein each behavior rule in the second rule set comprises a time period, the first place, the first main connecting device, the first connected device and a third support degree;

and removing the behavior rule in the second rule set, wherein the third support degree is smaller than a first preset support degree threshold value, so as to obtain the high-frequency behavior rule set.

9. The method of claim 8, wherein processing the second log data to obtain a first set of rules comprises:

removing second log data with a connection event type of connection end, and mapping each first timestamp to each first time unit aiming at the second log data with the connection event type of connection start to obtain third log data, wherein the third log data comprise the first time units, the first place, the first main connection device and the first connected device, the first time units are time units obtained by dividing one day according to minutes, and one first time unit corresponds to one minute;

merging a plurality of log data which belong to the same day and the same first time unit and are the same with the first main connecting device and the first connected device in the third log data into one log data to obtain fourth log data;

and performing classification statistics on the fourth log data according to a first dimension to generate the first rule set, where the first dimension includes time, a place, and a first event pair, and the first event pair includes the first main connection device and the first connected device.

10. The method of claim 7, wherein deriving a set of contextual behavior rules from the first log data comprises:

preprocessing the first log data to obtain preprocessed fifth log data, wherein the fifth log data comprises a second timestamp, a second place, a connection event type, a main connection device application, a second main connection device and a second connected device;

processing the fifth log data to obtain a third rule set, wherein each behavior rule in the third rule set comprises the second location, the application of the main connecting device, the second connected device and a fourth support degree;

and removing the behavior rule in the third rule set, wherein the fourth support degree is smaller than a second preset support degree threshold value, so as to obtain the context behavior rule set.

11. The method of claim 10, wherein processing the fifth log data to obtain a third rule set comprises:

removing fifth log data with a connection event type of connection end, and mapping each second timestamp to each second time unit aiming at the fifth log data with the connection event type of connection start to obtain sixth log data, wherein the sixth log data comprise the second time unit, the second place, the main connection device application, the second main connection device and the second connected device, the second time unit is a time unit obtained by dividing one day according to minutes, and one second time unit corresponds to one minute;

merging a plurality of log data which belong to the same day and the same second time unit and are the same in the sixth log data, wherein the log data are the same in the application of the main connecting device, the second main connecting device and the second connected device, and obtaining seventh log data;

and performing classification statistics on the seventh log data according to a second dimension to generate the third rule set, where the second dimension includes a place, an application, and a second event pair, and the second event pair includes the second main connection device and the second connected device.

12. The method of claim 2, further comprising:

detecting the current residual electric quantity;

when the current residual capacity is larger than a third threshold value, reducing the first threshold value;

and when the current residual capacity is smaller than the third threshold value, increasing the first threshold value.

13. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 12 when executing the computer program.

14. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 12.