CN111314177A

CN111314177A - Work and rest time period identification method based on wireless signals and related device

Info

Publication number: CN111314177A
Application number: CN202010108543.0A
Authority: CN
Inventors: 吴加海; 张长旺; 张纪红; 黄新营
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2020-02-21
Filing date: 2020-02-21
Publication date: 2020-06-19
Anticipated expiration: 2040-02-21
Also published as: CN111314177B

Abstract

The application discloses a work and rest period identification method based on wireless signals and a related device, which are used for identifying a user portrait, and the connection information between terminal equipment associated with a target user and at least one wireless signal source in a preset period is acquired, wherein the preset period comprises a plurality of acquisition periods; then dividing each acquisition time period into a plurality of time slices; respectively counting switching information of the fragments at each moment according to the connection information; and then the work and rest time interval of the target user is identified according to the switching information. The method for counting the switching information in the preset time period reflects the activity of the user, so that the interference of accidental conditions on the work and rest period identification can be reduced, and the accuracy of the work and rest period identification process is improved.

Description

Work and rest time period identification method based on wireless signals and related device

Technical Field

The present application relates to the field of computer technologies, and in particular, to a work and rest period identification method and a related device based on wireless signals.

Background

With the development of cloud technology, more and more data applications appear in people's lives, wherein the identification of user figures corresponding to intelligent devices is particularly prominent on the basis of big data, and how to apply massive data to the identification of user figures becomes a difficult problem.

Generally, the user image is recognized by a fixed feature correspondence method, for example: and the big data comprises a work and rest schedule recorded by the user, and a work and rest rule corresponding to the user is identified according to the work and rest schedule.

However, in an actual application scenario, since there may not be strongly correlated data in the big data, the process corresponding to the fixed feature may not be run at this time, and some accidental situations may also affect the accuracy of the work and rest period identification process.

Disclosure of Invention

In view of this, the present application provides a method for recognizing a work and rest period based on a wireless signal, which can effectively avoid inaccurate work and rest period recognition caused by accidental situations, and improve the accuracy of the work and rest period recognition process.

A first aspect of the present application provides a method for recognizing work and rest periods, which can be applied to a system or a program including a function of recognizing work and rest periods, and specifically includes: acquiring connection information of terminal equipment associated with a target user and at least one wireless signal source within a preset time period, wherein the preset time period comprises a plurality of acquisition time periods;

dividing each acquisition time period into N time slices, wherein N is more than or equal to 2 and is an integer;

respectively counting switching information of each time slice according to the connection information, wherein the switching information is determined based on the difference of wireless signal source access conditions of adjacent time slices;

and identifying the work and rest time period of the target user according to the switching information.

Optionally, in some possible implementation manners of the present application, the identifying the work and rest time period of the target user according to the switching information includes:

calculating extreme value information in the switching information according to a maximum likelihood estimation algorithm;

and determining the time slice corresponding to the extreme value information to determine the work and rest time period.

Optionally, in some possible implementation manners of the present application, the calculating extreme value information in the handover information according to a maximum likelihood estimation algorithm includes:

acquiring reference information to determine a mask matrix, wherein the reference information is used for indicating an average value of work and rest periods of users associated with the target user;

and calculating a maximum likelihood estimation algorithm for the switching information based on the mask matrix to determine extreme value information.

Optionally, in some possible implementation manners of the present application, the obtaining reference information to determine a mask matrix includes:

acquiring the work information of the target user;

determining the average value of the work and rest time periods of the associated users according to the work information to acquire the reference information;

and determining a mask matrix according to the reference information.

Optionally, in some possible implementation manners of the present application, the determining the time slice corresponding to the extremum information to determine the work and rest time period includes:

determining a time slice corresponding to the extreme value information;

acquiring adjacent time slices of the time slices corresponding to the extreme value information;

determining adjacent time slices meeting preset conditions in the adjacent time slices;

and determining the work and rest time interval according to the time slice corresponding to the extreme value information and the adjacent time slice meeting the preset condition.

Optionally, in some possible implementation manners of the present application, after the identifying the work and rest period of the target user according to the switching information, the method further includes:

determining rest duration corresponding to the rest time interval;

and if the rest duration meets the reference duration condition, updating the work and rest time interval.

Optionally, in some possible implementation manners of the present application, the respectively counting the switching information of each time slice according to the connection information includes:

acquiring time sequence information in the connection information;

sequencing the time slices according to the time sequence information;

and respectively counting the sorted switching information of the time slices.

Optionally, in some possible implementation manners of the present application, after the respectively counting the sorted switching information of the time slices, the method further includes:

projecting in a preset frequency spectrum according to the switching information to obtain a shear spectrum diagram;

determining fluctuation information according to the shear spectrum diagram, wherein a peak or a trough corresponding to the fluctuation information is used for indicating a work and rest time period of the target user;

and updating the user portrait of the target user according to the fluctuation information.

Optionally, in some possible implementation manners of the present application, the obtaining connection information between the terminal device associated with the target user and at least one wireless signal source within a preset time period includes:

determining a terminal device associated with the target user;

acquiring operation information of a target program in the terminal equipment;

and determining the connection information of the terminal equipment and at least one wireless signal source in a preset time period according to the operation information.

Optionally, in some possible implementations of the present application, the method further includes: determining corresponding prompt information according to the work and rest time interval;

and carrying out prompt setting on the terminal equipment associated with the target user according to the prompt information.

Optionally, in some possible implementations of the present application, the method further includes:

determining activity periods within the work and rest periods;

acquiring a corresponding active wireless signal source in the active time period;

determining position information corresponding to the active wireless signal source to determine an active range;

and if the terminal equipment moves to the range of activity, performing target operation on the terminal equipment associated with the target user.

Optionally, in some possible implementations of the present application, the wireless signal source is a wireless local area network router, the user representation includes a work and rest period of the target user, and the work and rest period is used to indicate a rest period of the target user.

A second aspect of the present application provides a device for recognizing a work and rest period, comprising: the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the connection information between terminal equipment associated with a target user and at least one wireless signal source in a preset time period, and the preset time period comprises a plurality of acquisition time periods;

the processing unit is used for dividing each acquisition time period into N time slices, wherein N is more than or equal to 2 and is an integer;

a counting unit, configured to count switching information of each time slice according to the connection information, where the switching information is determined based on differences in access conditions of wireless signal sources of adjacent time slices;

and the identification unit is used for identifying the work and rest time interval of the target user according to the switching information.

Optionally, in some possible implementation manners of the present application, the identifying unit is specifically configured to calculate extreme value information in the switching information according to a maximum likelihood estimation algorithm;

the identification unit is specifically configured to determine the time slice corresponding to the extremum information to determine the work and rest time period.

Optionally, in some possible implementation manners of the present application, the identifying unit is specifically configured to obtain reference information to determine a mask matrix, where the reference information is used to indicate an average value of rest periods of users associated with the target user;

the identification unit is specifically configured to perform calculation of a maximum likelihood estimation algorithm on the switching information based on the mask matrix to determine extreme value information.

Optionally, in some possible implementation manners of the present application, the identification unit is specifically configured to obtain work information of the target user;

the identification unit is specifically configured to determine an average value of work and rest periods of the associated user according to the work information to obtain the reference information;

the identification unit is specifically configured to determine a mask matrix according to the reference information.

Optionally, in some possible implementation manners of the present application, the identification unit is specifically configured to determine a time slice corresponding to the extremum information;

the identification unit is specifically configured to acquire adjacent time slices of the time slice corresponding to the extremum information;

the identification unit is specifically configured to determine adjacent time slices that meet a preset condition in the adjacent time slices;

the identification unit is specifically configured to determine a work and rest time period according to the time slice corresponding to the extremum information and the adjacent time slice satisfying the preset condition.

Optionally, in some possible implementation manners of the present application, the identification unit is further configured to determine a rest duration corresponding to the rest period;

the identification unit is further configured to update the work and rest time period if the rest time period meets a reference time period condition.

Optionally, in some possible implementation manners of the present application, the statistical unit is specifically configured to obtain timing information in the connection information;

the statistical unit is specifically configured to sort the time slices according to the timing information;

the counting unit is specifically configured to count the sorted switching information of the time slices respectively.

Optionally, in some possible implementation manners of the present application, the statistical unit is specifically configured to perform projection in a preset frequency spectrum according to the switching information to obtain a shear spectrum map;

the statistical unit is specifically configured to determine fluctuation information according to the shear spectrum map, where a peak or a trough corresponding to the fluctuation information is used to indicate a work and rest period of the target user;

the identification unit is specifically configured to update the user representation of the target user according to the fluctuation information.

Optionally, in some possible implementation manners of the present application, the obtaining unit is specifically configured to determine a terminal device associated with the target user;

the acquiring unit is specifically configured to acquire operation information of a target program in the terminal device;

the obtaining unit is specifically configured to determine, according to the operation information, connection information between the terminal device and at least one wireless signal source within a preset time period.

Optionally, in some possible implementation manners of the present application, the identification unit is further configured to determine corresponding prompt information according to the work and rest time period;

and the identification unit is also used for carrying out prompt setting on the terminal equipment associated with the target user according to the prompt information.

Optionally, in some possible implementation manners of the present application, the identification unit is further configured to determine an activity period in the work and rest period;

the identification unit is further configured to acquire an active wireless signal source corresponding to the active time interval;

the identification unit is further configured to determine location information corresponding to the active wireless signal source to determine an active range;

the identification unit is further configured to perform a target operation on the terminal device associated with the target user if the terminal device moves into the activity range.

A third aspect of the present application provides a computer device comprising: a memory, a processor, and a bus system; the memory is used for storing program codes; the processor is configured to execute the method for recognizing a work and rest period according to any one of the first aspect or the first aspect according to an instruction in the program code.

A fourth aspect of the present application provides a computer-readable storage medium, having stored therein instructions, which, when run on a computer, cause the computer to perform the method for rest period identification of the first aspect or any one of the first aspects.

According to the technical scheme, the embodiment of the application has the following advantages:

the method comprises the steps that connection information of terminal equipment associated with a target user and at least one wireless signal source in a preset time period is obtained, wherein the preset time period comprises a plurality of acquisition time periods; then dividing each acquisition time period into a plurality of time slices; respectively counting switching information of each time slice according to the connection information, namely the difference of the access conditions of the wireless signal sources; and then the work and rest time interval of the target user is identified according to the switching information. The method has the advantages that automatic generation of the work and rest periods is achieved, the user portrait of the target user is identified, due to the fact that connection information of the wireless signal source is easy to obtain, specific data do not need to be collected, the method for counting switching information in the preset time period is adopted to reflect the activity of the user, interference of accidental conditions on work and rest period identification can be reduced, and accuracy of the work and rest period identification process is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

FIG. 1 is a network architecture diagram of the operation of the work and rest period identification system;

fig. 2 is a flowchart of work and rest period identification according to an embodiment of the present disclosure;

fig. 3 is a flowchart of a method for work and rest period identification according to an embodiment of the present disclosure;

fig. 4 is a frequency spectrum diagram of a work and rest period identification according to an embodiment of the present disclosure;

fig. 5 is a flowchart of another method for work and rest period identification according to an embodiment of the present disclosure;

fig. 6 is a flowchart of another method for work and rest period identification according to an embodiment of the present disclosure;

fig. 7 is a flowchart of another method for work and rest period identification according to an embodiment of the present disclosure;

fig. 8 is a scene schematic diagram of work and rest period identification according to an embodiment of the present disclosure;

fig. 9 is a flowchart of another method for work and rest period identification according to an embodiment of the present disclosure;

fig. 10 is a schematic structural diagram of an identification apparatus for work and rest periods according to an embodiment of the present disclosure;

fig. 11 is a schematic structural diagram of a terminal device according to an embodiment of the present application.

Detailed Description

The embodiment of the application provides a work and rest period identification method and a related device, which can be applied to a system or a program with a work and rest period identification function, and can be used for acquiring connection information between terminal equipment associated with a target user and at least one wireless signal source within a preset time period, wherein the preset time period comprises a plurality of acquisition time periods; then dividing each acquisition time period into a plurality of time slices; respectively counting switching information of each time slice according to the connection information, namely the difference of the access conditions of the wireless signal sources; and then the work and rest time interval of the target user is identified according to the switching information. The method has the advantages that automatic generation of the work and rest periods is achieved, the user portrait of the target user is identified, due to the fact that connection information of the wireless signal source is easy to obtain, specific data do not need to be collected, the method for counting switching information in the preset time period is adopted to reflect the activity of the user, interference of accidental conditions on work and rest period identification can be reduced, and accuracy of the work and rest period identification process is improved.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "corresponding" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

First, some nouns that may appear in the embodiments of the present application are explained.

A wireless signal source: a wireless access point, also called a hotspot, as a mobile device terminal connected to a wired network; the device is mainly used in broadband families, buildings, parks, warehouses, factories and other places, and the distance between the devices is dozens of meters to hundreds of meters.

In the work and rest period: the time periods corresponding to different actions of the user, for example: rest periods, activity periods or work periods.

Switching information: and comparing the information determined by the connection condition of the terminal and the surrounding wireless signal sources at the adjacent time.

The maximum likelihood estimation algorithm: also called argmax, is a function of parameters (sets) to the function. When there is another function y ═ f (x), if there is a result x0 ═ argmax (f (x)), then it means that when the function f (x) takes x ═ x0, the maximum value of the value range of f (x) is obtained; if there are multiple points such that f (x) takes the same maximum value, the result of argmax (f (x)) is a set of points. That is, argmax (f (x)) is a set of variable points x or x corresponding to the maximum value of f (x).

softargmax: an improved calculation method for argmax is to increase the relative maximum value and decrease the influence of other values by adding a coefficient, so that a more accurate position estimation can be finally obtained.

User portrait: the user information tagging is to abstract a tag set of a user by collecting and analyzing data of main information such as social attributes, living habits, consumption behaviors and the like of the user.

It should be understood that the work and rest period identification method provided by the present application may be applied to a system or a program including a work and rest period identification function, such as an intelligent alarm clock, specifically, the work and rest period identification system may operate in a network architecture as shown in fig. 1, which is a network architecture diagram in which the work and rest period identification system operates, as can be seen from the diagram, the work and rest period identification system may provide work and rest period identification with a plurality of information sources, the terminal establishes a connection with the server through the network, receives interaction information with a plurality of wireless information sources sent by the server, and performs work and rest period identification according to the terminal itself, or the server performs work and rest period identification; it can be understood that, fig. 1 shows a plurality of terminal devices, in an actual scenario, there may be more or fewer types of terminal devices participating in the process of recognizing the work and rest period, and the specific number and type are determined by the actual scenario, and this is not limited herein, and in addition, fig. 1 shows one server, but in an actual scenario, there may also be participation of a plurality of servers, especially in a scenario of large-volume data application interaction, the specific number of servers is determined by the actual scenario.

The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, big data and artificial intelligence platform. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein.

It should be noted that the work and rest period identification method provided in this embodiment may also be performed offline, that is, without the participation of a server, at this time, the terminal is locally connected with other terminals, and then the process of identifying the work and rest periods between the terminals is performed.

It is understood that the work and rest period recognition system may be operated in a personal mobile terminal, for example: the intelligent alarm clock can be used as an application of an intelligent alarm clock, can run in a server, and can also be used as an application of a third-party device for providing work and rest period identification so as to obtain a work and rest period identification processing result of an information source; the specific work and rest period identification system may be operated in the above-mentioned device in the form of a program, may also be operated as a system component in the above-mentioned device, and may also be used as one of cloud service programs, and a specific operation mode is determined by an actual scene, which is not limited herein.

Cloud technology (Cloud technology) is based on a general term of network technology, information technology, integration technology, management platform technology, application technology and the like applied in a Cloud computing business model, can form a resource pool, is used as required, and is flexible and convenient. Cloud computing technology will become an important support. Background services of the technical network system require a large amount of computing and storage resources, such as video websites, picture-like websites and more web portals. With the high development and application of the internet industry, each article may have its own identification mark and needs to be transmitted to a background system for logic processing, data in different levels are processed separately, and various industrial data need strong system background support and can only be realized through cloud computing.

In the cloud technology, interaction with big data is often used; big data (Big data) refers to a data set which cannot be captured, managed and processed by a conventional software tool within a certain time range, and is a massive, high-growth-rate and diversified information asset which can have stronger decision-making power, insight discovery power and flow optimization capability only by a new processing mode. With the advent of the cloud era, big data has attracted more and more attention, and the big data needs special technology to effectively process a large amount of data within a tolerance elapsed time. The method is suitable for the technology of big data, and comprises a large-scale parallel processing database, data mining, a distributed file system, a distributed database, a cloud computing platform, the Internet and an extensible storage system.

With the development of the related technologies of mobile terminals, more and more intelligent devices appear in the life of people, wherein identification of user figures corresponding to the intelligent devices is particularly prominent on the basis of big data, and how to apply massive data to identification of the user figures becomes a difficult problem.

In order to solve the above problem, the present application provides a method for recognizing a work and rest period, which is applied to a flow framework of work and rest period recognition shown in fig. 2, and as shown in fig. 2, for a flow framework of work and rest period recognition provided in an embodiment of the present application, connection information of a peripheral wireless signal source is collected, then statistics of switching information is performed according to a plurality of time slices, an active state of each time slice user is obtained, and thus, the work and rest period of the user is analyzed and recognized.

It can be understood that the method provided by the present application may be a program written as a processing logic in a hardware system, or may be a work and rest period recognition device, and the processing logic is implemented in an integrated or external manner. As an implementation manner, the work and rest period identification device acquires connection information between the terminal device associated with the target user and at least one wireless signal source within a preset time period, wherein the preset time period comprises a plurality of acquisition time periods; then dividing each acquisition time period into a plurality of time slices; respectively counting switching information of each time slice according to the connection information, namely the difference of the access conditions of the wireless signal sources; and then the work and rest time interval of the target user is identified according to the switching information. The method has the advantages that automatic generation of the work and rest periods is achieved, the user portrait of the target user is identified, due to the fact that connection information of the wireless signal source is easy to obtain, specific data do not need to be collected, the method for counting switching information in the preset time period is adopted to reflect the activity of the user, interference of accidental conditions on work and rest period identification can be reduced, and accuracy of the work and rest period identification process is improved.

With reference to the above flow architecture, the following describes a method for recognizing work and rest periods in the present application, please refer to fig. 3, where fig. 3 is a flow chart of a method for recognizing work and rest periods according to an embodiment of the present application, where the embodiment of the present application at least includes the following steps:

301. and acquiring the connection information of the terminal equipment associated with the target user and at least one wireless signal source in a preset time period.

In this embodiment, the preset time period includes a plurality of acquisition time periods, for example: the collection time period is in units of days, and the preset time period is 30 days, so that 30 collection time periods are included.

In addition, the terminal device associated with the target user may be any terminal device such as a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), a point of sale (POS), and a vehicle-mounted computer.

The wireless signal source may be a router corresponding to wireless fidelity (wifi), a communication device corresponding to bluetooth low energy communication (iBeacon), or a communication device corresponding to other access modes for wireless communication of the terminal, and the specific form is determined by an actual scene and is not limited herein.

It is understood that, in order to ensure the connection information is representative of the target user's work and rest periods, a combination of multiple wireless signal sources may be used to determine the wireless signal source of the connection information. Specifically, the connection information may be the target user associated terminal even if the wireless signal device probing is initiated, for example: the wifi of cell-phone sniffs the function, then acquires the access record of the wifi router that sniffs to obtain connection information.

Optionally, the connection information may also be derived from a record of the target program in the associated terminal of the target user, for example: the wifi housekeeping application program of the mobile phone records the historical wifi access record of the mobile phone, so that the connection information is determined according to the access record.

Optionally, the connection information may also be derived from a set of interaction records of multiple terminals or multiple applications of the target user, that is, a multi-source configuration manner, for example: determining 3 terminal devices commonly used by a target user, selecting wifi access new conditions of 3 application programs with the highest utilization rate in the 3 terminal devices, and further counting to obtain connection information.

It should be noted that, the above-mentioned obtaining process of the connection information is obtained based on the terminal province, and in a possible scenario, the connection information may also be issued by the server, that is, the wireless connection condition of the terminal is recorded in the server associated with the terminal, and when the terminal needs to identify the work and rest period, the issuing process of the wireless information is performed.

It is understood that the connection information may further include identification information of the wireless signal source and time information, for example: the connection information comprises the unique identification and the acquisition timestamp of the wifi router. Therefore, the integrity of the connection information is ensured, and the subsequent data processing process is facilitated.

302. Each acquisition time period is divided into N time slices.

In the embodiment, N is more than or equal to 2 and is an integer; in order to obtain the activity of the user corresponding to the work and rest period, the activity judgment can be performed through the switching information in the connection information corresponding to a plurality of moments.

It can be understood that, for the dividing process of each acquisition time period, the average division may be performed, that is, the time intervals of the slices at adjacent time are the same; uneven partitioning may also be employed, for example: the time slices are sparsely distributed at the corresponding time in the morning, and the time slices are densely distributed at the corresponding time in the daytime, and the specific form is determined by the actual scene, which is not limited herein.

In one possible scenario, to ensure the balance of the distribution of multiple time instants, each acquisition time period may be divided equally, for example: each day is divided into 48 time slices. Further, in order to ensure readability of data, the divided time slices can be expressed by the following formula:

wherein, A is a time shard amount set contained in a preset time period; n is the number of time slices divided by each acquisition time period; n is the number of the acquisition time periods contained in the preset time period.

303. And respectively counting the switching information of the fragments at each moment according to the connection information.

In the embodiment, the switching information is determined based on the difference of the access conditions of the wireless signal sources of adjacent time slices; that is, the access condition of the wireless signal source corresponding to each time slice is taken as a set, and if the access condition at the next time changes, for example: if the connection of one wifi route is reduced, determining that the wifi route changes, and marking; correspondingly, if the access condition is not changed, the access condition of the wireless signal sources corresponding to the adjacent time is the same.

Specifically, the above process may be described by using the formula in step 302, and if the access condition is not changed, it is marked as a_i，j0; if the access situation is not changed, it is marked as a_i，j1. Wherein i is the identification of the number of days, and j is the identification of the time slice. Through the above data processing procedure, switching can be performedThe information is represented by a matrix a.

Further, in order to visually represent the activity of each time interval corresponding to the switching information, a may be projected onto a parameter. That is, in order to represent the cumulative activity of the time corresponding to each time slice, the activity is counted based on all data in the connection information, and the activity is described by the switching information. Specifically, setting the one-dimensional projection matrix p to be multiplied by the matrix a can be written as:

and Ap is a cumulative activity statistical matrix, p is a parameter matrix, N is the number of acquisition time periods contained in a preset time period, j is an identification of a time slice, and N is the number of time slices divided by each acquisition time period.

Then, performing matrix projection, and selecting p ≡ 1, so as to obtain a spectrogram shown in fig. 4, where fig. 4 is a spectrogram for work and rest period identification provided in an embodiment of the present application, and the spectrogram shows an activity accumulation situation at a time corresponding to each time slice, where a higher frequency indicates that a switching process of a wireless signal source corresponding to the time is more frequent, and further indicates that a user is more active at this time, that is, the user is in an active period; correspondingly, the lower frequency indicates that the switching process of the wireless signal source corresponding to the time is less, and further indicates that the user is inactive at the time, that is, the user is in the rest period.

304. And identifying the work and rest time period of the target user according to the switching information.

In this embodiment, the time with the highest activity indicated by the switching information in step 303 may be determined as the work and rest period, and the work and rest period may be determined according to the fluctuation condition of the spectrogram in step 303.

Specifically, the information of each time slice in the spectrogram is sorted based on time sequence, namely, sorted according to the time sequence, and then projected in a preset frequency spectrum according to the switching information to obtain a shear spectrogram; determining fluctuation information according to the shear spectrogram, wherein the fluctuation information can be a connecting line with corresponding frequency of each time slice; then determining a wave crest or a wave trough corresponding to the fluctuation information so as to determine the work and rest time period of the target user; and further, the user portrait of the target user is identified according to the work and rest period of the target user. For example: and recording the time slice corresponding to the wave crest as an active period, and recording the time slice corresponding to the wave trough as a rest period.

Optionally, to avoid inaccurate identification of the work and rest period due to data errors, a rest period for reference may be defined, for example: the reference rest time is 4-16 hours, if the time length indicating the rest time period in the work and rest time period is not within the range, the data at the position can be considered to be wrong, the error information is deleted and reported, and the data is updated on the corresponding relevant part in the work and rest time period, so that the accuracy of the data is ensured.

With the above embodiments, connection information between the terminal device associated with the target user and at least one wireless signal source in a preset time period is obtained, where the preset time period includes multiple acquisition time periods; then dividing each acquisition time period into a plurality of time slices; respectively counting switching information of each time slice according to the connection information, namely the difference of the access conditions of the wireless signal sources; and then the work and rest time interval of the target user is identified according to the switching information. The method has the advantages that automatic generation of the work and rest periods is achieved, the user portrait of the target user is identified, due to the fact that connection information of the wireless signal source is easy to obtain, specific data do not need to be collected, the method for counting switching information in the preset time period is adopted to reflect the activity of the user, interference of accidental conditions on work and rest period identification can be reduced, and accuracy of the work and rest period identification process is improved.

The above embodiment describes the process of work and rest period identification, which can reflect different activity rates of the user at different times, but the process is not easy to be directly used for interpretation of the work and rest period. In a large-scale data set, for better anti-noise processing and application expression, a process of a highly simulated estimation calculation may be performed, and please refer to fig. 5, where fig. 5 is a flowchart of another method for recognizing a rest period provided in an embodiment of the present application, and the embodiment of the present application at least includes the following steps:

501. and acquiring the connection information of the terminal equipment associated with the target user and at least one wireless signal source in a preset time period.

502. Each acquisition time period is divided into N time slices.

503. And respectively counting the switching information of the fragments at each moment according to the connection information.

In this embodiment, steps 501-503 are similar to steps 301-303 in the embodiment described in fig. 3, and the description of the related features can be referred to, which is not repeated herein.

504. A large tentative estimation calculation is performed based on the handover information.

In this embodiment, it is greatly assumed that the estimation calculation is performed to detect the extremum information of the liveness corresponding to the sub-slices at each time. Specifically, reference information may be obtained to determine a mask matrix, the reference information indicating an average value of rest periods of users associated with the target user; and then, calculating a maximum likelihood estimation algorithm for the switching information based on the mask matrix to determine extreme value information. Namely, in the process of maximum tentative estimation calculation, a mask matrix can be set according to the reference information a, and as an extreme value screening condition, the mask matrix can be expressed as follows:

wherein for each m_i，jThe value of (A) is represented by the formula:

wherein M is a mask matrix; n is the number of time slices divided by each acquisition time period; i is an identification of the number of days; j is the mark of the time slice; and S is the number of time slices in a time period corresponding to the reference information. For example: if the average sleep time of the adult is 8 hours and the reference information a is divided into 48 time slices in 24 hours each day, S corresponding to the reference information is 16.

It is to be understood that the reference information may be determined based on a group to which the target user belongs, for example: the target user is a worker group, and the average sleeping time corresponding to the worker group is 7 hours, then the reference information a is 7; if the target user has no special group affiliation, the average adult sleep time length can be set to be 8 hours, wherein the specific time length is determined by an actual scene.

505. And determining a time period corresponding to the extreme value information to generate a work and rest time period.

In this embodiment, the extremum information includes a maximum value or a minimum value, and correspondingly reflects a situation where the activity of the user is maximum or minimum, that is, the working time period or the rest time period of the target user can be indicated, so as to form the work and rest time period of the target user.

In the following, the prediction of the rest period, that is, the section with the lowest predicted activity frequency is used as the preferred living rest period, can be performed by using the following formula:

E[x]＝softargmax{(1-Ap)^TM}

wherein E [ x ] is a set of extracted rest time periods, T is a matrix transpose, M is a mask matrix, and Ap is a cumulative activity statistical matrix.

From the above calculations it can be found that:

E[x]＝[m_1,x,m_2,x,...,m_N,x]^T

wherein m is_N,xNamely the extracted rest period.

It can be understood that, because the adjacent time slices may have similar features to the extracted time slices, the extracted adjacent time slices of the time slices corresponding to the rest period can be further judged, and specifically, the time slices corresponding to the extreme value information can be determined; then, adjacent time slices of the time slices corresponding to the extreme value information are obtained; determining adjacent time slices meeting preset conditions in the adjacent time slices; thereby slicing the time corresponding to the extreme value information and meeting the adjacent time of the preset conditionThe tick slices determine the work and rest periods. For example: the frequency ratio of the adjacent time slices to the extraction time slices is between 0.9 and 1.1, and the time period corresponding to the adjacent time slices is determined to be the extracted rest time period, so as to carry out E [ x ]]Is updated to obtain

In addition, the above calculation process can also be used for extracting the working period, and at this time, the corresponding frequency is high, and the following formula can be adopted:

E¹[x]＝softargmax{(Ap)^TM¹}

E²[x]＝softargmax{(Ap)^TM²}

wherein E is¹[x]To extract the set of working periods, E²[x]To extract a set of active time periods, T is a matrix transpose, M¹For a mask matrix corresponding to the average operating time, M²And Ap is a mask matrix corresponding to the average activity duration, and Ap is a cumulative activity statistical matrix.

It can be understood that the working duration and the activity duration appear in the above formula, and the essential calculation process is similar, but considering that the working duration of the user is different from the activity duration of the user on holiday, the working day and the rest day can be divided according to the average duration difference of the working duration and the activity duration; specifically, the work information of the target user can be obtained; then, determining the average value of the work and rest time periods of the associated users according to the work information so as to refer to the information, such as the average work duration; the mask matrix is then determined based on the reference information.

With the combination of the embodiments, the work and rest periods of the target user can be obtained through the above maximum simulated estimation calculation of different activity degrees of the user in each time period and the extracted rest time period, work time period or activity time period, so that the work and rest rules of the target user can be analyzed; the process can automatically identify the work and rest rule of the target user, allows uncertain sampling intervals, only needs reliable key sampling and relatively uniform time domain coverage, has the characteristics of low acquisition difficulty and high robustness, and improves the accuracy of work and rest period identification.

The foregoing embodiment describes a process of work and rest period identification, and reference is made to fig. 6 in the following, where a wireless signal source is a wifi router, and fig. 6 is a flowchart of another method for work and rest period identification provided in the embodiment of the present application, where the embodiment of the present application at least includes the following steps:

601. and acquiring the connection information of the terminal equipment associated with the target user and at least one wifi router within n days.

In this embodiment, the terminal device associated with the target user may be a mobile phone currently used by the user, or may be a device recorded in an internet of things platform on which a user account logs. The value range of n can be as large as 30 days, namely one month, so that the connection information is representative. The connection information of the wifi router can be obtained through program information in the terminal device, can also be obtained through wifi sniffing, and can also be obtained through historical access information issued by the receiving server. The specific acquisition mode depends on the actual scene, and is not limited here.

602. Each day is divided into N time slices.

In this embodiment, too dense time slices require a large amount of system computing resources, while sparse time slices may cause insufficient representativeness of the time slices and inaccuracy in work and rest period recognition, and as an example, a day may be divided into 48 time slices, that is, one time slice every half an hour, so that the uniform distribution is ensured, and the system resources are saved. Specifically, the connection information after fragmentation can be represented by the following matrix:

wherein n is an identifier of a preset time period.

603. Detecting an active state of the target user based on a timing sequence.

In this embodiment, the active state of the target user is further counted by using the matrix of the connection information in step 602, specifically, the following formula may be used:

and Ap is a cumulative activity statistical matrix, p is a parameter matrix, n is the number of acquisition time periods contained in a preset time period, and j is the identifier of the time slice.

604. A maximum likelihood estimation calculation is performed based on the average rest duration.

In this embodiment, after obtaining the matrix for counting the activity, further performing a maximum simulated estimation calculation, selecting a collection time period as one day, and dividing the one day into 48 time slices, where the reference information adopts an average sleep time of 8 hours, and may adopt the following formula:

wherein for each m_i，jThe value of (A) is represented by the formula:

wherein M is a mask matrix; i is an identification of the number of days; j is the identifier of the time slice.

605. And determining the work and rest period of the target user.

Through the process of the maximum simulated estimation calculation, the rest time period, the activity time period and the working time period of the target user can be respectively obtained, and the work and rest time period of the target user is recorded.

In one possible scenario, table 1 may be obtained, where table 1 is an identification distribution table of work and rest time.

TABLE 1 recognition distribution table of work and rest time

	Rest period	Period of operation	Period of activity
				Working day	23：00～07：00	09：00～18：00	--
Holiday period	23：00～08：00	--	09：00～21：00

Through the process of the embodiment, the average extracted rest time of residence is 10 hours and the activity time of the residence is 12 hours when the operation is carried out on the large-scale data set. Through manual sampling evaluation, under the observation condition of not less than 31 days, the effective recognition user rate is more than 89%, wherein the error of 90% in the rest period of residence is within 2 hours, and the error of 80% in the work period and the activity period is within 3 hours, so that the accuracy of the work and rest period recognition is ensured.

It can be understood that after the work and rest period, the related prompt information may be automatically generated for the terminal, and please refer to fig. 7 in the following description, where fig. 7 is a flowchart of another work and rest period identification method provided in the embodiment of the present application, and the embodiment of the present application at least includes the following steps:

701. and starting automatic identification of the work and rest time intervals.

In this embodiment, the process of automatically identifying the work and rest time periods may refer to the process of identifying the work and rest time periods in the embodiments described in fig. 3 to fig. 6, which is not repeated herein.

702. And updating the user portrait of the target user according to the identified work and rest time interval.

In this embodiment, the server may be operated to update the user portrait of the target user after the identified work and rest period, where the user portrait may specifically include sleep time, work time, or activity time of the target user.

703. Setting of an object program in the terminal device is performed based on the user profile.

In this embodiment, when the time period indicated in the user portrait is reached, a prompt instruction for the terminal device to respond may be issued, for example: if the rest time interval indicated in the user picture is 12:30-13:30, the server issues an alarm instruction at 13:30, and the terminal equipment performs automatic alarm reminding.

In addition, based on the recognition of the target user portrait, the user portrait can be applied to applications such as crowd recognition and data filtering, for example: the method comprises the steps of presuming possible occupation of a target user according to the work and rest time interval of the target user, or carrying out advertisement pushing in an activity time interval according to the work and rest time interval of the target user, or carrying out pushing of a related online or offline user data packet.

By the embodiment, the intelligent program or the intelligent home can be operated, and the related prompt information is automatically generated under the condition that the user does not set the related information, so that the intelligent degree of the terminal equipment is improved, the relevance among a large amount of data is improved, and the user experience is improved.

In another possible scenario, the wireless signal source may be an iBeacon access device, and the device may be applied to an indoor positioning scenario, as shown in fig. 8, which is another scenario diagram for identifying a work and rest period provided in this embodiment of the present application, and the diagram shows an operation scenario of the iBeacon access device, that is, while accessing the user device, location information of the user is also recorded, since the iBeacon access device itself also has coordinates, after determining the work and rest period of the user through the above embodiment, the iBeacon access device with a relatively high frequency may be obtained, and when the user performs a coverage a1 of the iBeacon access device, a corresponding prompt may be sent.

Referring to fig. 9, fig. 9 is a flowchart of another work and rest period identification method provided in the embodiment of the present application, where the embodiment of the present application at least includes the following steps:

901. and starting automatic identification of the work and rest time intervals.

902. And determining a wireless signal source corresponding to the activity time interval in the work and rest time interval.

In this embodiment, in an indoor scenario, the activity period often corresponds to the operation of the relevant smart device, for example: after the user carries out bedroom, connecting iBeacon access equipment to carry out intelligent playing of the Bluetooth sound box; thereby determining the identity of these iBeacon access devices.

903. The activity range is determined from the location information of the wireless signal source.

In this embodiment, the iBeacon access device has respective location information and access range, and when the user enters the access range, the user is just in the time period identified by the work and rest period, that is, a corresponding prompt is performed. For example: when a user enters the access range of iBeacon access equipment in a bedroom at 19:00 and the user indicates that 19:00-20:00 is an activity time period in the work and rest period, an activity prompt can be sent to a terminal associated with the user, and the specific activity prompt can be music starting, television starting, light change and the like.

By the embodiment, the automatic interaction process of the intelligent terminal based on the work and rest time period of the user can be realized, the matching degree of the interaction process of the intelligent terminal and the portrait of the user is improved, and the user experience is improved.

In order to better implement the above-mentioned aspects of the embodiments of the present application, the following also provides related apparatuses for implementing the above-mentioned aspects. Referring to fig. 10, fig. 10 is a schematic structural diagram of a work and rest time interval recognition apparatus according to an embodiment of the present application, in which the work and rest time interval recognition apparatus 1000 includes:

an obtaining unit 1001, configured to obtain connection information between a terminal device associated with a target user and at least one wireless signal source within a preset time period, where the preset time period includes multiple acquisition time periods;

the processing unit 1002 is configured to divide each acquisition time period into N time slices, where N is greater than or equal to 2 and is an integer;

a counting unit 1003, configured to count switching information of each time slice according to the connection information, where the switching information is determined based on differences in access conditions of wireless signal sources of adjacent time slices;

an identifying unit 1004, configured to identify the work and rest period of the target user according to the switching information.

Optionally, in some possible implementation manners of the present application, the identifying unit 1004 is specifically configured to calculate extreme value information in the switching information according to a maximum likelihood estimation algorithm;

the identifying unit 1004 is specifically configured to determine the time slice corresponding to the extremum information to determine the work and rest time period.

Optionally, in some possible implementations of the present application, the identifying unit 1004 is specifically configured to obtain reference information to determine a mask matrix, where the reference information is used to indicate an average value of rest periods of users associated with the target user;

the identifying unit 1004 is specifically configured to perform calculation of a maximum likelihood estimation algorithm on the switching information based on the mask matrix to determine extreme value information.

Optionally, in some possible implementations of the present application, the identifying unit 1004 is specifically configured to obtain work information of the target user;

the identifying unit 1004 is specifically configured to determine an average value of work and rest periods of the associated user according to the work information to obtain the reference information;

the identifying unit 1004 is specifically configured to determine a mask matrix according to the reference information.

Optionally, in some possible implementation manners of the present application, the identifying unit 1004 is specifically configured to determine a time slice corresponding to the extremum information;

the identifying unit 1004 is specifically configured to obtain an adjacent time slice of the time slice corresponding to the extremum information;

the identifying unit 1004 is specifically configured to determine adjacent time slices that meet a preset condition in the adjacent time slices;

the identifying unit 1004 is specifically configured to determine a work and rest time period according to the time slice corresponding to the extremum information and the adjacent time slice meeting the preset condition.

Optionally, in some possible implementations of the present application, the identifying unit 1004 is further configured to determine a rest duration corresponding to the rest period;

the identifying unit 1004 is further configured to update the work and rest time period if the rest time period meets a reference time period condition.

Optionally, in some possible implementation manners of the present application, the statistics unit 1003 is specifically configured to obtain timing information in the connection information;

the counting unit 1003 is specifically configured to sort the time slices according to the timing information;

the counting unit 1003 is specifically configured to count the sorted switching information of the time slices respectively.

Optionally, in some possible implementation manners of the present application, the statistics unit 1003 is specifically configured to perform projection in a preset spectrum according to the switching information to obtain a shear spectrum map;

the counting unit 1003 is specifically configured to determine fluctuation information according to the shear spectrum map, where a peak or a trough corresponding to the fluctuation information is used to indicate a work and rest period of the target user;

the identifying unit 1004 is specifically configured to update the user representation of the target user according to the fluctuation information.

Optionally, in some possible implementation manners of the present application, the obtaining unit 1001 is specifically configured to determine a terminal device associated with the target user;

the acquiring unit 1001 is specifically configured to acquire operation information of a target program in the terminal device;

the obtaining unit 1001 is specifically configured to determine, according to the operation information, connection information between the terminal device and at least one wireless signal source within a preset time period.

Optionally, in some possible implementation manners of the present application, the identifying unit 1004 is further configured to determine corresponding prompt information according to the work and rest time period;

the identifying unit 1004 is further configured to perform prompt setting on the terminal device associated with the target user according to the prompt information.

Optionally, in some possible implementations of the present application, the identifying unit 1004 is further configured to determine an activity period in the work and rest period;

the identifying unit 1004 is further configured to obtain a corresponding active wireless signal source in the active time period;

the identifying unit 1004 is further configured to determine location information corresponding to the active wireless signal source to determine an activity range;

the identifying unit 1004 is further configured to perform a target operation on the terminal device associated with the target user if the terminal device moves into the activity range.

An embodiment of the present application further provides a terminal device, as shown in fig. 11, which is a schematic structural diagram of the terminal device provided in the embodiment of the present application, and for convenience of description, only a part related to the embodiment of the present application is shown, and details of the specific technology are not disclosed, please refer to a method part in the embodiment of the present application. The terminal may be any terminal device including a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), a point of sale (POS), a vehicle-mounted computer, and the like, taking the terminal as the mobile phone as an example:

fig. 11 is a block diagram illustrating a partial structure of a mobile phone related to a terminal provided in an embodiment of the present application. Referring to fig. 11, the cellular phone includes: radio Frequency (RF) circuitry 1110, memory 1120, input unit 1130, display unit 1140, sensors 1150, audio circuitry 1160, wireless fidelity (Wifi) module 1170, processor 1180, and power supply 1190. Those skilled in the art will appreciate that the handset configuration shown in fig. 11 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.

The following describes each component of the mobile phone in detail with reference to fig. 11:

RF circuit 1110 may be used for receiving and transmitting signals during a message transmission or call, and in particular, for receiving downlink messages from a base station and then processing the received downlink messages to processor 1180; in addition, the data for designing uplink is transmitted to the base station. In general, the RF circuitry 1110 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the RF circuitry 1110 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Message Service (SMS), etc.

The memory 1120 may be used to store software programs and modules, and the processor 1180 may execute various functional applications and data processing of the mobile phone by operating the software programs and modules stored in the memory 1120. The memory 1120 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 1120 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.

The input unit 1130 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the cellular phone. Specifically, the input unit 1130 may include a touch panel 1131 and other input devices 1132. The touch panel 1131, also referred to as a touch screen, can collect touch operations of a user on or near the touch panel 1131 (for example, operations of the user on or near the touch panel 1131 using any suitable object or accessory such as a finger, a stylus pen, etc., and a range of touch operations on the touch panel 1131 in an interval), and drive the corresponding connection device according to a preset program. Alternatively, the touch panel 1131 may include two parts, namely, a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 1180, and can receive and execute commands sent by the processor 1180. In addition, the touch panel 1131 can be implemented by using various types, such as resistive, capacitive, infrared, and surface acoustic wave. The input unit 1130 may include other input devices 1132 in addition to the touch panel 1131. In particular, other input devices 1132 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.

The display unit 1140 may be used to display information input by the user or information provided to the user and various menus of the cellular phone. The display unit 1140 may include a display panel 1141, and optionally, the display panel 1141 may be configured in the form of a Liquid Crystal Display (LCD), an organic light-emitting diode (OLED), or the like. Further, the touch panel 1131 can cover the display panel 1141, and when the touch panel 1131 detects a touch operation on or near the touch panel, the touch panel is transmitted to the processor 1180 to determine the type of the touch event, and then the processor 1180 provides a corresponding visual output on the display panel 1141 according to the type of the touch event. Although in fig. 11, the touch panel 1131 and the display panel 1141 are two independent components to implement the input and output functions of the mobile phone, in some embodiments, the touch panel 1131 and the display panel 1141 may be integrated to implement the input and output functions of the mobile phone.

The handset may also include at least one sensor 1150, such as a light sensor, motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 1141 according to the brightness of ambient light, and the proximity sensor may turn off the display panel 1141 and/or the backlight when the mobile phone moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone, further description is omitted here.

Audio circuitry 1160, speakers 1161, and microphone 1162 may provide an audio interface between a user and a cell phone. The audio circuit 1160 may transmit the electrical signal converted from the received audio data to the speaker 1161, and convert the electrical signal into a sound signal for output by the speaker 1161; on the other hand, the microphone 1162 converts the collected sound signals into electrical signals, which are received by the audio circuit 1160 and converted into audio data, which are then processed by the audio data output processor 1180, and then transmitted to, for example, another cellular phone via the RF circuit 1110, or output to the memory 1120 for further processing.

Wifi belongs to short distance wireless transmission technology, and the cell-phone can help the user to receive and dispatch the email, browse the webpage and visit the streaming media etc. through Wifi module 1170, and it provides wireless broadband internet access for the user. Although fig. 11 shows the Wifi module 1170, it is understood that it does not belong to the essential constitution of the handset, and can be omitted entirely as needed within the scope not changing the essence of the invention.

The processor 1180 is a control center of the mobile phone, and is connected to various parts of the whole mobile phone through various interfaces and lines, and executes various functions of the mobile phone and processes data by operating or executing software programs and/or modules stored in the memory 1120 and calling data stored in the memory 1120, thereby performing overall monitoring of the mobile phone. Optionally, processor 1180 may include one or more processing units; optionally, the processor 1180 may integrate an application processor and a modem processor, wherein the application processor mainly handles operating systems, user interfaces, application programs, and the like, and the modem processor mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated within processor 1180.

The mobile phone further includes a power supply 1190 (e.g., a battery) for supplying power to each component, and optionally, the power supply may be logically connected to the processor 1180 through a power management system, so that functions of managing charging, discharging, power consumption management, and the like are implemented through the power management system.

Although not shown, the mobile phone may further include a camera, a bluetooth module, etc., which are not described herein.

In the embodiment of the present application, the processor 1180 included in the terminal further has a function of executing the steps of the page processing method.

An embodiment of the present application further provides a computer-readable storage medium, in which a work and rest period identification instruction is stored, and when the computer-readable storage medium is run on a computer, the computer is enabled to execute the steps executed by the work and rest period identification apparatus in the methods described in the embodiments shown in fig. 3 to 9.

The embodiment of the present application further provides a computer program product including a work and rest period identification instruction, which when run on a computer, causes the computer to execute the steps performed by the work and rest period identification apparatus in the method described in the embodiments shown in fig. 3 to 9.

The embodiment of the present application further provides a work and rest period identification system, where the work and rest period identification system may include the work and rest period identification device in the embodiment described in fig. 10, or the terminal device described in fig. 11.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a work and rest period recognition apparatus, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims

1. A method for identifying a work and rest period based on a wireless signal is characterized by comprising the following steps:

acquiring connection information of terminal equipment associated with a target user and at least one wireless signal source within a preset time period, wherein the preset time period comprises a plurality of acquisition time periods;

2. The method of claim 1, wherein the identifying the target user's work and rest periods based on the handover information comprises:

3. The method of claim 2, wherein the calculating extreme information in the handover information according to a maximum likelihood estimation algorithm comprises:

4. The method of claim 3, wherein obtaining the reference information to determine the mask matrix comprises:

acquiring the work information of the target user;

and determining a mask matrix according to the reference information.

5. The method of claim 2, wherein the determining the time slice corresponding to the extremum information to determine the work and rest period comprises:

determining a time slice corresponding to the extreme value information;

6. The method of claim 1, wherein after identifying the target user's work and rest period based on the handover information, the method further comprises:

determining rest duration corresponding to the rest time interval;

7. The method according to claim 1, wherein the separately counting the handover information of each time slice according to the connection information includes:

acquiring time sequence information in the connection information;

sequencing the time slices according to the time sequence information;

and respectively counting the sorted switching information of the time slices.

8. The method according to claim 7, wherein after the separately counting the switching information of the ordered time slices, the method further comprises:

9. The method of claim 1, wherein the obtaining connection information of the terminal device associated with the target user with at least one wireless signal source within a preset time period comprises:

determining a terminal device associated with the target user;

acquiring operation information of a target program in the terminal equipment;

10. The method according to any one of claims 1-10, further comprising:

determining corresponding prompt information according to the work and rest time interval;

11. The method of claim 10, further comprising:

determining activity periods within the work and rest periods;

12. The method of claim 1, wherein the wireless signal source is a wireless local area network router, and wherein the user representation comprises a rest period of the target user, the rest period indicating a rest period of the target user.

13. An apparatus for identifying a work and rest period based on a wireless signal, comprising:

the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the connection information between terminal equipment associated with a target user and at least one wireless signal source in a preset time period, and the preset time period comprises a plurality of acquisition time periods;

14. A computer device, the computer device comprising a processor and a memory:

the memory is used for storing program codes; the processor is configured to execute the method for rest period identification according to any one of claims 1 to 12 according to instructions in the program code.

15. A computer readable storage medium having stored therein instructions which, when run on a computer, cause the computer to perform the method of work and rest period identification of any of the preceding claims 1 to 12.