CN106649069A - User behavior statistical method and system - Google Patents

Abstract

The present invention is applicable to the field of user behavior recording, and provides a method and system for user behavior statistics. The method includes: obtaining the current first user behavior data and recording it in the user behavior file; if the first user behavior data is detected within a preset time When the user behavior data changes, record the second user behavior data obtained after the change to the user behavior file; if no change is detected in the first user behavior data within the preset time, calculate and record the second user behavior data according to the first user behavior data. second user behavior data to the user behavior file; using the second user behavior data as the first user behavior data, returning to the step of monitoring whether the first user behavior data changes. Each user's behavior data can be recorded in real time, and at the same time, when the interval between two user behavior operations exceeds the preset time, the behavior data between the two behavior operations can also be predicted, making the recorded behavior data more accurate.

Description

Method and system for user behavior statistics

technical field

The invention belongs to the field of user behavior records, and in particular relates to a user behavior statistics method and system.

Background technique

With the widespread popularization of smart terminals, people's lifestyles have undergone great changes, and people's dependence on smart terminals has become stronger and stronger. Therefore, the user behavior data based on the user's specific operation behavior has also shown explosive growth in quantity. "Big data" has become a key technology in today's era, which is mainly used to analyze and mine the potential laws and application value of user behavior data. User specific behavioral information and behavioral characteristics are crucial for service providers to provide better recommendation services.

In the prior art, a method of writing the current user behavior data into a corresponding record file is generally adopted at regular intervals to record the user behavior data. Because the application sometimes shuts down abnormally, such as when the smart terminal runs out of memory resources, the application is forcibly closed. The user behavior data between the closing of the application will result in the loss of user behavior data.

Contents of the invention

In view of this, the present invention provides a user behavior statistics method and system to solve the problem that in the prior art, when the application program is closed abnormally, it is impossible to record the user behavior data between the last recording and the application program closing. The problem that leads to the loss of user behavior data.

In the first aspect, a user behavior statistics method is provided, including:

Obtain the current first user behavior data and record it to the user behavior file;

monitoring whether the first user behavior data changes;

If it is detected that the first user behavior data changes within the preset time, record the second user behavior data obtained after the change to the user behavior file;

If no change is detected in the first user behavior data within the preset time, calculate and record the second user behavior data to the user behavior file according to the first user behavior data;

Using the second user behavior data as the first user behavior data, return to the step of monitoring whether the first user behavior data changes.

In the second aspect, a user behavior statistics system is provided, including:

an acquisition unit, configured to acquire the current first user behavior data, and record it to the user behavior file;

a monitoring unit, configured to monitor whether the first user behavior data changes;

The first recording unit is configured to record the second user behavior data obtained after the change to the user behavior file if it is detected that the first user behavior data changes within a preset time;

The first computing unit is configured to calculate and record the second user behavior data to the user behavior files;

A returning unit, configured to use the second user behavior data as the first user behavior data, and return to execute the step of monitoring whether the first user behavior data changes.

In the present invention, the change of the first user behavior data is monitored in real time and recorded in the user behavior file. When no change of the first user behavior data is detected within the preset time, the second user behavior data is predicted and calculated, and the The predicted second user behavior data is recorded in the user behavior file. The user behavior data generated by each behavior operation of the user can be recorded in real time. Even when the application program is closed abnormally, the user behavior data between the last recording and the closing of the application program can be recorded in time. When the interval between two connected behavioral operations of the user exceeds the preset time, the behavioral data between the two connected behavioral operations can also be predicted, making the recorded behavioral data more accurate.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings that need to be used in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only the present invention. For some embodiments of the invention, those skilled in the art can also obtain other drawings according to these drawings without paying creative efforts.

Fig. 1 is a flowchart of the user behavior statistics method in the embodiment 1 of the present invention;

Fig. 2 is a structural block diagram of the user behavior statistics system in Embodiment 2 of the present invention.

detailed description

In the following description, for purposes of illustration rather than limitation, specific details, such as specific system architectures and techniques, are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

The present invention provides a method for user behavior statistics, the method comprising: acquiring the current first user behavior data and recording it in a user behavior file; monitoring whether the first user behavior data changes; When the first user behavior data changes, record the second user behavior data obtained after the change to the user behavior file; if no change is detected in the first user behavior data within the preset time, according to For the first user behavior data, calculate and record the second user behavior data to the user behavior file.

User behavior data includes switch machine data, source data, application operation data, system information data, key operation data and peripheral information data, etc.

Among them, the power-on and power-off data refer to the start-up time point data and the power-off time point data; the source data refers to the related records viewed by the source; the application operation data refers to the data of the user operating the application, including the application name data, the entry time point data and the application operation data. Data at the time point of leaving the application; system information data is a collective term for a series of data related to the system when the user acts and operates, such as memory data occupied by the application, etc.; key operation data mainly refers to relevant data when the key is triggered, such as the start time of the key; The setting information data refers to the relevant data of the external device, such as the time point data of the USB flash drive insertion/unplugging and the USB flash drive capacity data.

In actual situations, since data such as source data, system information data, and peripheral information data can be directly or indirectly calculated from key operation data and application operation data, in this specification, user behavior in all embodiments The data are all described by taking button operation data, application name data, start-up time point data, and power-off time point data as examples, and user behavior data includes first user behavior data and second user behavior data.

In order to illustrate the technical solutions of the present invention, specific examples are used below to illustrate.

Example 1

Figure 1 shows the implementation process of the behavior data recording method provided by Embodiment 1 of the present invention, which is described in detail as follows:

In step S101, the current first user behavior data is acquired and recorded in a user behavior file.

The obtained first user behavior data is not only recorded as a part of the user behavior data, but also provides a basis for judging whether the subsequent user behavior data changes. In this embodiment, the obtained user behavior data is not saved in the running memory, but is directly recorded in the user behavior file, so as to prevent the user behavior data from being lost during an unexpected shutdown.

Step S102, monitoring whether the first user behavior data changes.

In this embodiment, user behavior data recording is described by taking the user operation of the smart terminal as an example of only one power-on and power-off operation, and user behavior data records for multiple power-on and power-off operations only need to repeat the operations of this embodiment.

Since the user behavior data in this embodiment mainly refers to key operation data, application name data, power-on time point data and power-off time point data, and when the smart terminal only performs power-on and power-off operations once, the power-on time point data and power-off time point There is only one piece of data, and no real-time monitoring is required. Therefore, when monitoring changes in user behavior data, it is only necessary to monitor key operation data and application name data. Can.

Step S103, if it is detected that the first user behavior data changes within a preset time, record the second user behavior data obtained after the change to the user behavior file.

When the user presses the button to operate the application, the button operation data and application name data will change accordingly. For example, when the application clicks to open the calendar application from the main interface of the smart terminal, the start time of the button in the button operation data will be replaced by the time of the click operation point, the application name data will be replaced with the application name of the calendar application to obtain the second user behavior data, and record the second user behavior data to the user behavior file.

Step S104, if no change of the first user behavior data is detected within the preset time, calculate and record the second user behavior data to the user behavior file according to the first user behavior data.

In actual situations, if the interval between two consecutive behavior operations of the user is too long, or the user behavior data corresponding to part of the behavior operations in multiple consecutive behavior operations is lost, the smart terminal will not be able to obtain new user behavior data for a long period of time. , that is, the first user behavior data will not change in a relatively long period of time. In order to supplement the lost user behavior data, in this embodiment, a technician sets a preset time according to the actual user's habit of continuous behavior operation. During this preset time, it is considered as the user's normal continuous behavior operation, but when When the change time of the first user behavior data exceeds the preset time, it is considered that a part of the user behavior data is lost. At this time, the prediction algorithm is used to calculate the first user behavior data, and the calculated second user behavior data is used as Complementary to user behavior data.

The prediction algorithm refers to the general term for algorithms that can predict and calculate ungenerated data based on historical data, including but not limited to the common Markov algorithm.

Step S105, using the second user behavior data as the first user behavior data, returning to the step of monitoring whether the first user behavior data changes.

Before shutting down, the smart terminal will continue to record user behavior data, so after the second user behavior data is recorded in steps S103 and S104, the second user behavior data will be used as the first user behavior data, and the subsequent user behavior data Whether there is a change provides a judgment basis, and returns to step S102 to continue monitoring and recording subsequent user behavior data.

Before step S101, it also includes:

Acquire data at the time of booting, and record it into the user behavior file.

After the smart terminal is powered on, it will read and record the data of the starting time point of the smart terminal into the user behavior file.

As a specific embodiment of step S104, including:

The first user behavior data is processed by using a Markov algorithm to obtain the second user behavior data through calculation. The Markov algorithm is a common forecasting algorithm, and data forecasting using the Markov algorithm is also a well-known technology, which will not be described in detail in this specification.

Recording the second user behavior data to the user behavior file.

As an extended embodiment of the present invention, after step S104, it also includes:

Matching the second user behavior data with a standard behavior database. Since the second user behavior data generated in step S104 is calculated by a prediction algorithm and is not real user behavior data, in order to verify the accuracy of the second user behavior data, in this embodiment, the second user Behavior data is matched with a standard behavior database to determine whether the second user behavior data can be used as real user behavior data.

Technicians use the Markov algorithm to predict and calculate standard user behavior data for installed applications and user behavior operations in smart terminals, and collect the standard user behavior data to obtain a standard behavior database.

If the matching result is failure, it is determined that the second user behavior data is false user behavior data. If there is no standard user behavior data matching the second user behavior data in the standard behavior database, the matching result is a failure. At this time, the accuracy of the second user behavior data is low and cannot be used as real user behavior data. User behavior data is false user behavior data.

If the matching result is successful, it is determined that the second user behavior data is real user behavior data. If there is standard user behavior data matching the second user behavior data in the standard behavior database, the matching result is a failure. At this time, the accuracy of the second user behavior data is high, and it can be used as real user behavior data, that is, to determine the second user behavior data. 2. User behavior data is real user behavior data.

As an extended embodiment of the present invention, it also includes:

calculating the percentage of the false user behavior data recorded in the user behavior file and the recorded user behavior data, where the user behavior data includes the first user behavior data and the second user behavior data. Because the generated user behavior file contains part of the user behavior data generated by the prediction algorithm. It can be seen from the above that the user behavior data generated by the prediction algorithm may contain false user behavior data, and the false user behavior data will affect the accuracy of user behavior file records. The percentage of all user behavior data is used to judge the accuracy of user behavior file records.

If the percentage is higher than the preset threshold, it is determined that the user behavior file record is inaccurate. The technicians preset a threshold according to the actual needs. If the percentage of false user behavior data in all user behavior data exceeds the preset threshold, it is determined that there are too many false user behavior data and the user behavior file records are inaccurate.

In this embodiment, the data at the boot time point is recorded, and the change of the first user behavior data is monitored in real time and recorded in the user behavior file. When no change in the first user behavior data is detected within the preset time, the second user behavior The data is predicted and calculated using the Markov algorithm, and the predicted second user behavior data is recorded in the user behavior file. While the user behavior data generated by each user behavior operation is recorded in real time, the lost user behavior data can also be predicted, calculated and supplemented for verification. Even when the application is closed abnormally, it can record the user behavior data between the last record and the application closing in time, and at the same time supplement and verify the lost user behavior data, so that the recorded user behavior data more complete and accurate.

Example 2

Corresponding to the behavior data recording method described in the above embodiments, FIG. 2 shows a structural block diagram of a behavior data recording system provided by Embodiment 2 of the present invention.

Referring to Figure 2, the system includes:

An acquisition unit 21, configured to acquire the current first user behavior data and record it in the user behavior file;

A monitoring unit 22, configured to monitor whether the first user behavior data changes;

The first recording unit 23 is configured to record the second user behavior data obtained after the change to the user behavior file if it is detected that the first user behavior data changes within a preset time;

The first computing unit 24 is configured to calculate and record the second user behavior data to the Describe user behavior files;

The returning unit 25 is configured to use the second user behavior data as the first user behavior data, and return to execute the step of monitoring whether the first user behavior data changes.

Further, before the acquisition unit 21, it also includes:

The second recording unit is configured to record the system startup time point to the user behavior file.

Further, the first computing unit 24 includes:

A calculation subunit, configured to process the first user behavior data by using a Markov algorithm, and calculate and obtain the second user behavior data;

A recording subunit, configured to record the second user behavior data to the user behavior file.

Further, after the first calculation unit 24, it also includes:

a matching unit, configured to match the second user behavior data with a standard behavior database;

A first judging unit, configured to judge that the second user behavior data is false user behavior data if the matching result is a failure;

The second determining unit is configured to determine that the second user behavior data is real user behavior data if the matching result is successful.

Further, the system also includes:

The second calculation unit is used to calculate the percentage of the false user behavior data recorded in the user behavior file and the recorded user behavior data, and the user behavior data includes the first user behavior data and the second user behavioral data;

A third judging unit, configured to judge that the user behavior file record is inaccurate if the percentage is higher than a preset threshold.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention. It should be understood that the sequence numbers of the steps in the above embodiments do not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present invention.

Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described system, system and unit can refer to the corresponding process in the foregoing method embodiments, and details are not repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed system, system and method can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of systems or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.

If the functions described above are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes. .

The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still carry out the foregoing embodiments Modifications to the technical solutions recorded in the examples, or equivalent replacement of some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should be included in within the protection scope of the present invention.

Claims (10)

1. A method for user behavior statistics, characterized in that, comprising: Obtain the current first user behavior data and record it to the user behavior file; monitoring whether the first user behavior data changes; If it is detected that the first user behavior data changes within the preset time, record the second user behavior data obtained after the change to the user behavior file; If no change is detected in the first user behavior data within the preset time, calculate and record the second user behavior data to the user behavior file according to the first user behavior data; Using the second user behavior data as the first user behavior data, return to the step of monitoring whether the first user behavior data changes. 2. The method according to claim 1, characterized in that, before acquiring the current first user behavior data and recording, further comprising: Acquire data at the time of booting, and record it into the user behavior file. 3. The method according to claim 1, wherein the calculating and recording the second user behavior data to the user behavior file according to the first user behavior data comprises: Processing the first user behavior data by using a Markov algorithm to calculate and obtain the second user behavior data; Recording the second user behavior data to the user behavior file. 4. The method according to claim 1, wherein, after calculating and recording the second user behavior data to the user behavior file according to the first user behavior data, further comprising: matching the second user behavior data with a standard behavior database; If the matching result is a failure, determining that the second user behavior data is false user behavior data; If the matching result is successful, it is determined that the second user behavior data is real user behavior data. 5. method as claimed in claim 4, is characterized in that, described method, also comprises: calculating the percentage of the false user behavior data recorded in the user behavior file and the recorded user behavior data, the user behavior data including the first user behavior data and the second user behavior data; If the percentage is higher than the preset threshold, it is determined that the user behavior file record is inaccurate. 6. A user behavior statistics system, comprising: an acquisition unit, configured to acquire the current first user behavior data, and record it to the user behavior file; a monitoring unit, configured to monitor whether the first user behavior data changes; The first recording unit is configured to record the second user behavior data obtained after the change to the user behavior file if it is detected that the first user behavior data changes within a preset time; The first computing unit is configured to calculate and record the second user behavior data to the user behavior files; A returning unit, configured to use the second user behavior data as the first user behavior data, and return to execute the step of monitoring whether the first user behavior data changes. 7. system as claimed in claim 6, is characterized in that, before described acquiring unit, also comprises: The second recording unit is used to acquire the data of the boot time point and record it into the user behavior file. 8. The system according to claim 6, wherein the first computing unit comprises: A calculation subunit, configured to process the first user behavior data by using a Markov algorithm, and calculate and obtain the second user behavior data; A recording subunit, configured to record the second user behavior data to the user behavior file. 9. The system according to claim 6, further comprising: after the first computing unit: a matching unit, configured to match the second user behavior data with a standard behavior database; A first judging unit, configured to judge that the second user behavior data is false user behavior data if the matching result is a failure; The second determining unit is configured to determine that the second user behavior data is real user behavior data if the matching result is successful. 10. The system according to claim 9, further comprising: The second calculation unit is used to calculate the percentage of the false user behavior data recorded in the user behavior file and the recorded user behavior data, and the user behavior data includes the first user behavior data and the second user behavioral data; A third judging unit, configured to judge that the user behavior file record is inaccurate if the percentage is higher than a preset threshold.