Method and device for formulating equipment data synchronization strategy

Abstract

The invention discloses an equipment data synchronization strategy making method and device. The method comprises the following steps: determining a use behavior mode of a target user in a current time period according to a historical user behavior data set of using the mobile equipment by the target user in a plurality of historical time periods; determining an initial data synchronization strategy matched with the use behavior pattern from a preset database; acquiring equipment state data reported by the mobile equipment in each low-use time period in the current time period according to the initial data synchronization strategy; and when the equipment state data reported in the target low-usage time period exceeds a preset equipment state threshold value, delaying the data synchronization time in the initial data synchronization strategy for a preset duration from the end time of the target low-usage time period to obtain a first target data synchronization strategy. The technical problems that the power consumption of the mobile equipment is greatly increased and the service life of a battery of the mobile equipment is influenced due to the fact that the mobile equipment periodically synchronizes the equipment state information of the mobile equipment to the MDM platform are solved.

Description

Technical Field

[0001] The present application relates to the technical field of device management, and in particular to a method and apparatus for formulating a device data synchronization strategy. Background Art

[0002] With the popularity of mobile devices in life and work, mobile device management (MDM) solutions have become an important tool to ensure device security, data protection and meet operator needs. However, MDM solutions often collect device status information regularly. Although this data synchronization method can ensure the consistency of device data, it does not consider the actual usage and power status of mobile devices. For example, when the device battery is low or the user is not actively using the mobile device, the data synchronization operation will still be performed according to the pre-set cycle, which greatly consumes battery resources and reduces the user experience.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method and apparatus for formulating a device data synchronization strategy to at least solve the technical problem that the power consumption of the mobile device is greatly increased and the battery life of the mobile device is affected due to the mobile device periodically synchronizing its own device status information to the MDM platform.

[0005] According to one aspect of an embodiment of the present application, a method for formulating a device data synchronization strategy is provided, comprising: determining a usage behavior pattern of a target user in a current time period based on a historical user behavior data set of the target user using a mobile device in multiple historical time periods, wherein the usage behavior pattern is at least used to reflect at least one high-usage period and at least one low-usage period of the target user using the mobile device in the current time period; determining an initial data synchronization strategy matching the usage behavior pattern from a preset database, wherein the database includes multiple usage behavior patterns and corresponding data synchronization strategies, and the data synchronization strategy at least includes: a data synchronization time for the mobile device to synchronize its own device status information is a low-usage period, and the device status information includes at least one of the following: user behavior data, device status data; obtaining device status data reported by the mobile device in each low-usage period in the current time period according to the initial data synchronization strategy; if the device status data reported in the target low-usage period exceeds a preset device status threshold, the data synchronization time in the initial data synchronization strategy is postponed from the end time of the target low-usage period by a preset time length to obtain a first target data synchronization strategy, wherein the preset time length is the time length from the end time of the target low-usage period to the time when the device status data of the mobile device is restored to the device status threshold.

[0006] Optionally, determining the usage behavior pattern of the target user in the current time period based on historical user behavior data of the target user using the mobile device in multiple historical time periods includes: obtaining a historical user behavior data set of the target user using the mobile device in multiple historical time periods, wherein the historical user behavior data set includes historical user behavior data of each historical sub-time period in the corresponding historical time period, and the historical user behavior data includes at least one of the following: application usage data, device location information; using a pre-trained Markov chain model to analyze multiple historical user behavior data sets to determine the usage behavior pattern of the target user in the current time period.

[0007] Optionally, a historical user behavior dataset of the target user using a mobile device in multiple historical time periods is obtained, including: in each historical time period, obtaining an initial historical user behavior dataset of the mobile device in the historical time period synchronized by the mobile device according to a historical target data synchronization policy; and preprocessing each initial historical user behavior dataset to obtain a corresponding historical user behavior dataset.

[0008] Optionally, a pre-trained Markov chain model is used to determine the usage behavior pattern of the target user in the current time period for multiple historical user behavior data sets, including: analyzing multiple historical user behavior data sets to construct a state transfer matrix; determining the initial behavior state based on the behavior state of the target user in the previous historical sub-time period of the current time period, wherein the initial behavior state at least includes: a high-usage state or a low-usage state; using the Markov chain model, starting from the initial behavior state, randomly selecting the next behavior state as the behavior state of the first sub-time period in the current time period according to the state transfer matrix, and continuing to execute the above-mentioned simulation state transfer process starting from the behavior state of the first sub-time period in the current time period until all sub-time periods in the current time period are traversed, thereby obtaining a state transfer sequence of the mobile device in the current time period; taking multiple sub-time periods that are continuously in a high-usage state in the state transfer sequence as a high-usage period, and taking multiple sub-time periods that are continuously in a low-usage state in the state transfer sequence as a low-usage period, and determining the usage behavior pattern of the target user in the current time period based on each high-usage period and each low-usage period.

[0009] Optionally, multiple historical user behavior data sets are analyzed to construct a state transition matrix, including: determining the probability of each historical sub-time period in each historical time period transitioning from one behavior state to another behavior state based on the historical user behavior data set corresponding to each historical time period; and constructing a state transition matrix with each behavior state as the rows and columns of the matrix and the probability of each historical sub-time period transitioning from one behavior state to another behavior state as the matrix elements.

[0010] Optionally, the first target data synchronization strategy also includes: an encoding method for the mobile device to synchronize its own device status information, wherein, after obtaining the first target data synchronization strategy, the method also includes: obtaining encoded data transmitted by the mobile device according to the data synchronization time within the first target data synchronization strategy, the encoded data being obtained by the mobile device encoding all device status data within a preset time length according to the encoding method.

[0011] Optionally, after determining an initial data synchronization strategy that matches the usage behavior pattern from a preset database, the method further includes: obtaining user behavior data of the mobile device in any sub-time period within the current time period according to the initial data synchronization strategy, wherein the user behavior data includes at least: an event occurrence time for reflecting a key configuration event triggered by a target user; adjusting the initial data synchronization strategy based on the user behavior data to set the data synchronization time of the mobile device to the event occurrence time of the key configuration event triggered by the target user, so as to obtain a second target data synchronization strategy.

[0012] According to another aspect of the embodiment of the present application, a device data synchronization strategy formulation apparatus is also provided, including: a first determination module, used to determine the usage behavior pattern of the target user in the current time period based on a historical user behavior data set of the target user using the mobile device in multiple historical time periods, wherein the usage behavior pattern is at least used to reflect at least one high-usage period and at least one low-usage period of the target user using the mobile device in the current time period; a second determination module, used to determine an initial data synchronization strategy that matches the usage behavior pattern from a preset database, wherein the database includes multiple usage behavior patterns and corresponding data synchronization strategies, and the data synchronization strategy at least includes: the mobile device synchronizes its own The data synchronization time of the device status information is a low usage period, and the device status information includes at least one of the following: user behavior data, device status data; an acquisition module, used to obtain the device status data reported by the mobile device in each low usage period within the current time period according to the initial data synchronization strategy; a first adjustment module, used to delay the data synchronization time in the initial data synchronization strategy from the end time of the target low usage period by a preset time length when the device status data reported in the target low usage period exceeds a preset device status threshold, so as to obtain a first target data synchronization strategy, wherein the preset time length is the time length from the end time of the target low usage period to the time when the device status data of the mobile device is restored to the device status threshold.

[0013] According to another aspect of an embodiment of the present application, a non-volatile storage medium is also provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned device data synchronization strategy formulation method by running the computer program.

[0014] According to another aspect of an embodiment of the present application, a computer program product is also provided, which includes a stored computer program, wherein the computer program implements the above-mentioned device data synchronization strategy formulation method when executed by a processor.

[0015] In an embodiment of the present application, the MDM server predicts the usage behavior pattern in the current time period by analyzing multiple groups of historical user behavior data sets of the mobile device, and determines an initial data synchronization strategy that matches the usage behavior pattern; then, the initial data synchronization strategy is adjusted according to the device status data of each low-usage period in the current time period reported by the mobile device in accordance with the initial data synchronization strategy, thereby obtaining a more optimized data synchronization plan that is dynamically generated based on consideration of the current status and usage behavior pattern of the device, thereby achieving the purpose of optimizing power consumption management, extending battery life and improving user experience, and further solving the technical problem that the mobile device periodically synchronizes its own device status information to the MDM platform, which greatly increases the power consumption of the mobile device and affects the battery life of the mobile device. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 is a flow chart of an optional device data synchronization strategy formulation method according to an embodiment of the present application;

[0018] Figure 2 is a structural schematic diagram of an optional device data synchronization strategy formulation apparatus according to an embodiment of the present application;

[0019] Figure 3 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] In addition, the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set up between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0023] In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

[0024] Mobile Device Management (MDM) is a technology for centralized management of mobile devices. It provides complete management of the entire life cycle of mobile devices from device registration, activation, use, and elimination. It can realize functions such as user and device management, configuration management, security management, and asset management.

[0025] Example 1

[0026] According to an embodiment of the present application, an embodiment of a method for formulating a device data synchronization strategy is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] Figure 1 is a flow chart of a method for formulating a device data synchronization strategy according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0028] Step S102 : determining the usage behavior pattern of the target user in the current time period based on the historical user behavior data set of the target user using the mobile device in multiple historical time periods.

[0029] In the technical solution provided in the above step S102, the above usage behavior pattern is used to reflect at least one high usage period and at least one low usage period of the target user using the mobile device in the current time period, wherein the high usage period generally refers to a period of time when the device is used frequently and consumes a lot of power, while the low usage period refers to a period of time when the device is used less frequently and consumes less power. Therefore, the mobile device management MDM server analyzes the historical user behavior data set of the mobile device in multiple historical time periods to gain insight into the user's usage habits in different time periods, and provides a basis for the formulation of data synchronization strategies.

[0030] Step S104: determining an initial data synchronization strategy that matches the usage behavior pattern from a preset database.

[0031] In the technical solution provided in the above step S104, a plurality of usage behavior patterns and corresponding personalized data synchronization strategies are pre-stored in the above database. These strategies are intended to adjust the frequency and timing of data synchronization according to the user behavior patterns of the mobile device to optimize the power consumption of the mobile device. Therefore, the data synchronization strategy is at least used to reflect that the data synchronization time of the device status information of the mobile device itself is a low-use period, that is, the data synchronization operation is only arranged in the low-use period when the device is used less frequently and the power consumption is less, so as to reduce the burden on the mobile device, extend the battery life and maintain a good user experience. Among them, the device status information includes at least one of the following: user behavior data, device status data.

[0032] Step S106: acquiring device status data reported by the mobile device during each low-usage period in the current time period according to the initial data synchronization strategy.

[0033] In the technical solution provided in the above step S106, after the MDM server determines the initial data synchronization strategy of the mobile device in the current time period, it can receive the device status data reported by the mobile device according to the data synchronization time in the initial data synchronization strategy - each low-use period. The device status data includes information about the mobile device's own operating status and usage, including but not limited to: screen activity status, network connection type (such as Wi-Fi or cellular data), battery power, CPU (Central Processing Unit) and GPU (Graphics Processing Unit) usage, memory usage, storage space status, and temperature sensor readings.

[0034] Step S108, when the device status data reported during the target low usage period exceeds a preset device status threshold, the data synchronization time in the initial data synchronization strategy is postponed from the end time of the target low usage period by a preset time period to obtain a first target data synchronization strategy.

[0035] In the technical solution provided in the above step S108, the above device status threshold is a pre-set indicator used to determine whether the device status has reached a level that requires adjustment of the data synchronization strategy, including but not limited to battery power below a certain percentage, CPU or GPU load exceeding a preset limit, unstable network connection or excessive data traffic usage, storage space usage reaching a warning level, device temperature exceeding a normal range, etc. Therefore, when the MDM server detects that the device status data reported by the mobile device during the target low usage period (one of at least one low usage period in the current time period) reaches or exceeds these device status thresholds, it indicates that the mobile device may be in a high power consumption or unstable state during the target low usage period. At this time, the MDM server will automatically adjust the above initial data synchronization strategy, that is, delay the data synchronization time in the initial data synchronization strategy by a preset time, and the extension time is from the end time of the target low usage period to the time when the device status data of the mobile device can be restored to the device status threshold. In other words, the data synchronization operation is suspended during the period from the next moment of the target low usage period to the time when the device status data is restored to the critical value of the device status threshold, so as to reduce the burden on the mobile device, extend the battery life and maintain a good user experience.

[0036] Based on the technical methods provided by the above steps S102 to S108, it can be learned that in an embodiment of the present application, the MDM server predicts the usage behavior pattern in the current time period by analyzing multiple groups of historical user behavior data sets of the mobile device, and determines an initial data synchronization strategy that matches the usage behavior pattern; then, the initial data synchronization strategy is adjusted according to the device status data of each low-usage period in the current time period reported by the mobile device in accordance with the initial data synchronization strategy, thereby obtaining a more optimized data synchronization plan that is dynamically generated based on consideration of the current status and usage behavior pattern of the device, thereby achieving the purpose of optimizing power consumption management, extending battery life and improving user experience.

[0037] The following describes the various steps of the device data synchronization strategy formulation method in conjunction with a specific implementation process.

[0038] As an optional implementation, in the technical solution provided in the above step S102, the MDM server may determine the usage behavior pattern of the target user in the current time period according to the following steps, including:

[0039] Step S1021, obtaining a historical user behavior dataset of the target user using the mobile device in multiple historical time periods.

[0040] Among them, the above-mentioned historical user behavior data set includes historical user behavior data of each historical sub-time period within the corresponding historical time period, and the historical user behavior data includes but is not limited to: application usage data, device location information, device charging mode, screen brightness adjustment, volume control, camera usage frequency, microphone activation status, sensor data (such as acceleration, gyroscope readings) and user interaction behavior (such as touch screen operation, button usage), etc.

[0041] Specifically, in each historical time period, the MDM server can first obtain the initial historical user behavior data set of the mobile device synchronized by the mobile device according to the historical target data synchronization policy in the historical time period, and pre-process each initial historical user behavior data set to obtain a corresponding historical user behavior data set.

[0042] The above-mentioned preprocessing operations include but are not limited to: data cleaning, data standardization, data dimension reduction, etc. The reason for data cleaning is that the initial historical user behavior data set reported by the mobile device in each historical time period may contain erroneous, incomplete or irrelevant data, such as outliers, missing values ​​or information irrelevant to the current analysis target. The data cleaning step in the preprocessing can identify and deal with these data quality issues to ensure the accuracy of subsequent analysis. The reason for data standardization is that since the format and structure of different types of historical user behavior data may not be uniform, the data is converted into a consistent format to facilitate subsequent analysis and model training. The reason for data dimension reduction is that the original data may have high dimensions and contain a large number of features that may be irrelevant. Therefore, through data dimension reduction technology, such as principal component analysis, the data dimension is reduced, the computational complexity is reduced, and the main information in the data set is retained to improve the analysis efficiency.

[0043] Step S1022, using the pre-trained Markov chain model to analyze multiple historical user behavior data sets to determine the usage behavior pattern of the target user in the current time period.

[0044] Specifically, since the Markov chain model can capture the sequential and state transition characteristics of user behavior, the embodiments of the present application use a pre-trained Markov chain model to analyze these historical user behavior data sets in order to predict the high-usage and low-usage periods of user usage habits.

[0045] Optionally, the above step S1022 may be implemented by the following steps:

[0046] Step 1: Analyze multiple historical user behavior data sets and construct a state transition matrix.

[0047] The method for constructing the above-mentioned state transition matrix includes: determining the probability of each historical sub-time period in each historical time period transferring from one behavior state to another behavior state based on the historical user behavior data set corresponding to each historical time period; constructing the state transition matrix with each behavior state as the row and column of the matrix and the probability of each historical sub-time period transferring from one behavior state to another behavior state as the matrix element.

[0048] That is to say, each row in the above state transition matrix represents a behavior state (such as high usage state or low usage state), and each column corresponds to other states that may be transferred to at the next moment, so the elements in the matrix represent the probability of transferring from the current state to the next state.

[0049] Step 2: Determine the initial behavior state based on the behavior state of the target user in the previous historical sub-time period of the current time period.

[0050] Specifically, the MDM server can directly use the user behavior state of the target user in the previous historical sub-time period of the current time period as the initial behavior state at the first moment in the current time period, wherein the initial behavior state at least includes: a high-use state or a low-use state. If the user frequently uses applications, consumes a large amount of data, or frequently interacts with devices in the previous sub-time period, the initial behavior state may be marked as a high-use state; otherwise, it may be marked as a low-use state.

[0051] Step 3: Using the Markov chain model, starting from the initial behavior state, randomly select the next behavior state as the behavior state of the first sub-time period in the current time period according to the state transfer matrix, and continue to execute the above simulated state transfer process starting from the behavior state of the first sub-time period in the current time period until all sub-time periods in the current time period are traversed, and the state transfer sequence of the mobile device in the current time period is obtained.

[0052] Specifically, the MDM server uses the Markov chain model to start from the determined initial behavior state, randomly select the next behavior state according to the state transition matrix, and repeat this process to simulate the state transition of the user in the current time period and generate the corresponding state transition sequence. The state transition process is based on probability, so the sequence may contain high-usage states or low-usage states in multiple sub-time periods, reflecting the randomness and dynamic changes of user usage behavior.

[0053] Step 4: Multiple sub-time periods in the state transition sequence that are continuously in a high-usage state are regarded as a high-usage period, and multiple sub-time periods in the state transition sequence that are continuously in a low-usage state are regarded as a low-usage period. The usage behavior pattern of the target user in the current time period is determined based on each high-usage period and each low-usage period.

[0054] Among them, the high usage period is usually a period of frequent user activities and large data transmission volume, while the low usage period is usually a period of less user activities and small data transmission volume. These high usage periods and low usage periods are summarized to obtain the usage behavior pattern of the target user in the current time period. In this way, the usage behavior pattern can be used to know when the user will use the device a lot in the current time period and when the number of times the device is used is relatively small.

[0055] Furthermore, the MDM server may determine whether the initial data synchronization strategy needs to be adjusted by comparing the device status data reported by the mobile device during each low-usage period in the current time period with a preset device status threshold, wherein:

[0056] If the device status data reported during the target low usage period does not exceed the preset device status threshold, the initial data synchronization strategy is maintained unchanged and sent to the mobile terminal so that the mobile terminal can perform data synchronization operations according to the initial data synchronization strategy.

[0057] When the device status data reported during the target low-usage period exceeds the preset device status threshold, the data synchronization time in the initial data synchronization strategy is delayed by a preset period of time to obtain a first target data synchronization strategy, and the initial data synchronization strategy is sent to the mobile terminal so that the mobile terminal can perform data synchronization operations according to the initial data synchronization strategy.

[0058] In addition, the above-mentioned first target data synchronization strategy also includes: the encoding method of the device status information of the mobile device to synchronize itself. Therefore, the MDM server can obtain the encoded data transmitted by the mobile device according to the data synchronization time in the first target data synchronization strategy (i.e., the time after the target low-use period is delayed by a preset time), wherein the encoded data is obtained by the mobile device encoding all the device status data within the preset time according to the encoding method. By transmitting encoded data, the mobile device can not only greatly reduce the amount of data transmitted by the mobile device, but also improve the transmission efficiency and reduce unnecessary data redundancy.

[0059] As another optional implementation, the data that the MDM system continuously collects from the mobile device in any sub-time period within the current time period according to the initial data synchronization policy may also include user behavior data, and since these user behavior data may contain event data of key configuration events (such as changes involving device settings, installation or uninstallation of applications, system updates, etc.), and these key configuration events have specific requirements on the timeliness and frequency of data synchronization. To this end, the MDM server can also adjust the initial data synchronization policy based on user-triggered events, so that the mobile device synchronizes data when an important event is detected.

[0060] Specifically, the MDM server can first obtain the user behavior data of the mobile device in any sub-time period within the current time period according to the initial data synchronization policy, wherein the user behavior data at least includes: the event occurrence time reflecting the key configuration event triggered by the target user; adjust the initial data synchronization policy based on the user behavior data to set the data synchronization time of the mobile device to the event occurrence time of the key configuration event triggered by the target user, so as to obtain a second target data synchronization policy.

[0061] Among them, the MDM server can use machine learning algorithms or preset rule engines to identify and classify which are critical configuration events, and extract the event occurrence time related to the critical configuration events. The MDM server can adjust the initial data synchronization strategy based on the extracted event occurrence time related to the critical configuration events, set the data synchronization time to the event occurrence time of the critical configuration event, or synchronize within a reasonable time before and after the event occurrence time, and obtain the adjusted second target data synchronization strategy. This adjustment ensures that when a mobile device encounters a critical configuration event, data synchronization can be triggered immediately or in a timely manner to reflect the latest device status and configuration information. At the same time, the strategy is more closely matched to the user's key behavior, thereby improving the pertinence and efficiency of data synchronization, while taking into account power consumption optimization and user experience.

[0062] In summary, the device data synchronization strategy formulation method provided above has the following advantages:

[0063] (1) Analyze the user's usage behavior pattern based on the user's historical behavior data to determine the corresponding initial data synchronization strategy, avoid synchronization during the user's active period, reduce the impact on the user's normal use, and ensure data synchronization when the user is inactive or the device is in low power consumption state, thereby improving the device's availability and user satisfaction.

[0064] (2) Dynamically adjusting the data synchronization strategy based on the target user's usage behavior patterns and the actual device status data of the mobile device can more accurately understand the actual power consumption of the device and the user's usage patterns, thereby optimizing the data synchronization strategy and reducing unnecessary energy consumption. This kind of fine management is particularly important when the battery is low.

[0065] (3) By adjusting the initial data synchronization strategy based on the occurrence time of important events triggered by target users, the configuration data is ensured to be updated and applied in a timely manner. This is crucial for scenarios that require immediate response (such as security updates, installation or uninstallation of important applications, etc.), and improves the response speed and processing efficiency to key events.

[0066] (4) The MDM system intelligently adjusts data synchronization strategies based on device status and user behavior, enabling it to dynamically adjust data synchronization strategies based on real-time device status and user behavior, enhancing the system's ability to adapt to changes, enabling it to better respond to different scenarios and needs, and providing more personalized services.

[0067] Example 2

[0068] According to an embodiment of the present application, a device data synchronization strategy formulation device for implementing the device data synchronization strategy formulation method in embodiment 1 is also provided. Figure 2 As shown, the device data synchronization strategy formulation device at least includes: a first determination module 21, a second determination module 22, an acquisition module 23 and an adjustment module 24, wherein:

[0069] A first determination module 21 is used to determine a usage behavior pattern of a target user in a current time period based on a historical user behavior data set of the target user using a mobile device in multiple historical time periods, wherein the usage behavior pattern is used to at least reflect at least one high usage period and at least one low usage period of the target user using the mobile device in the current time period;

[0070] The second determination module 22 is used to determine an initial data synchronization strategy that matches the usage behavior pattern from a preset database, wherein the database includes a plurality of usage behavior patterns and corresponding data synchronization strategies, and the data synchronization strategy at least includes: the data synchronization time of the mobile device synchronizing its own device status information is a low usage period, and the device status information includes at least one of the following: user behavior data, device status data;

[0071] An acquisition module 23 is used to acquire device status data reported by the mobile device during each low-use period in the current time period according to the initial data synchronization strategy;

[0072] The first adjustment module 24 is used to delay the data synchronization time in the initial data synchronization strategy by a preset time period from the end time of the target low usage period when the device status data reported during the target low usage period exceeds a preset device status threshold, so as to obtain a first target data synchronization strategy, wherein the preset time period is the time period from the end time of the target low usage period to the time when the device status data of the mobile device is restored to the device status threshold.

[0073] The functions of each module of the device data synchronization strategy formulation device are explained below in conjunction with a specific implementation process.

[0074] As an optional implementation, the first determining module 21 may determine the usage behavior pattern of the target user in the current time period according to the following steps, including:

[0075] Step S1, obtaining a historical user behavior dataset of a target user using a mobile device in multiple historical time periods.

[0076] Among them, the above-mentioned historical user behavior data set includes historical user behavior data of each historical sub-time period within the corresponding historical time period, and the historical user behavior data includes but is not limited to: application usage data, device location information, device charging mode, screen brightness adjustment, volume control, camera usage frequency, microphone activation status, sensor data (such as acceleration, gyroscope readings) and user interaction behavior (such as touch screen operation, button usage), etc.

[0077] Specifically, in each historical time period, the first determination module 21 can first obtain the initial historical user behavior data set of the mobile device synchronized by the mobile device according to the historical target data synchronization strategy in the historical time period, and preprocess each initial historical user behavior data set to obtain a corresponding historical user behavior data set.

[0078] The above-mentioned preprocessing operations include but are not limited to: data cleaning, data standardization, data dimension reduction, etc. The reason for data cleaning is that the initial historical user behavior data set reported by the mobile device in each historical time period may contain erroneous, incomplete or irrelevant data, such as outliers, missing values ​​or information irrelevant to the current analysis target. The data cleaning step in the preprocessing can identify and deal with these data quality issues to ensure the accuracy of subsequent analysis. The reason for data standardization is that since the format and structure of different types of historical user behavior data may not be uniform, the data is converted into a consistent format to facilitate subsequent analysis and model training. The reason for data dimension reduction is that the original data may have high dimensions and contain a large number of features that may be irrelevant. Therefore, through data dimension reduction technology, such as principal component analysis, the data dimension is reduced, the computational complexity is reduced, and the main information in the data set is retained to improve the analysis efficiency.

[0079] Step S2, using a pre-trained Markov chain model to analyze multiple historical user behavior data sets to determine the usage behavior pattern of the target user in the current time period.

[0080] Specifically, since the Markov chain model can capture the sequential and state transition characteristics of user behavior, the embodiments of the present application use a pre-trained Markov chain model to analyze these historical user behavior data sets in order to predict the high-usage and low-usage periods of user usage habits.

[0081] Optionally, the above step S2 can be implemented by the following steps:

[0082] Step 1: Analyze multiple historical user behavior data sets and construct a state transition matrix.

[0083] The method for constructing the above-mentioned state transition matrix includes: determining the probability of each historical sub-time period in each historical time period transferring from one behavior state to another behavior state based on the historical user behavior data set corresponding to each historical time period; constructing the state transition matrix with each behavior state as the row and column of the matrix and the probability of each historical sub-time period transferring from one behavior state to another behavior state as the matrix element.

[0084] That is to say, each row in the above state transition matrix represents a behavior state (such as high usage state or low usage state), and each column corresponds to other states that may be transferred to at the next moment, so the elements in the matrix represent the probability of transferring from the current state to the next state.

[0085] Step 2: Determine the initial behavior state based on the behavior state of the target user in the previous historical sub-time period of the current time period.

[0086] Specifically, the first determination module 21 may directly use the user behavior state of the target user in the previous historical sub-time period of the current time period as the initial behavior state at the first moment in the current time period, wherein the initial behavior state at least includes: a high-use state or a low-use state. If the user frequently uses applications, consumes a large amount of data, or frequently interacts with devices in the previous sub-time period, the initial behavior state may be marked as a high-use state; otherwise, it may be marked as a low-use state.

[0087] Step 3: Using the Markov chain model, starting from the initial behavior state, randomly select the next behavior state as the behavior state of the first sub-time period in the current time period according to the state transfer matrix, and continue to execute the above simulated state transfer process starting from the behavior state of the first sub-time period in the current time period until all sub-time periods in the current time period are traversed, and the state transfer sequence of the mobile device in the current time period is obtained.

[0088] Specifically, the first determination module 21 uses the Markov chain model to start from the determined initial behavior state, randomly select the next behavior state according to the state transition matrix, and repeat this process to simulate the state transition of the user in the current time period and generate a corresponding state transition sequence. The state transition process is based on probability, so the sequence may contain high-usage states or low-usage states in multiple sub-time periods, reflecting the randomness and dynamic changes of user usage behavior.

[0089] Step 4: Multiple sub-time periods in the state transition sequence that are continuously in a high-usage state are regarded as a high-usage period, and multiple sub-time periods in the state transition sequence that are continuously in a low-usage state are regarded as a low-usage period. The usage behavior pattern of the target user in the current time period is determined based on each high-usage period and each low-usage period.

[0090] Among them, the high usage period is usually a period of frequent user activities and large data transmission volume, while the low usage period is usually a period of less user activities and small data transmission volume. These high usage periods and low usage periods are summarized to obtain the usage behavior pattern of the target user in the current time period. In this way, the usage behavior pattern can be used to know when the user will use the device a lot in the current time period and when the number of times the device is used is relatively small.

[0091] Furthermore, the first determination module 21 can determine whether the initial data synchronization strategy needs to be adjusted by comparing the size relationship between the device status data reported by the mobile device in each low-usage period in the current time period and the preset device status threshold, wherein:

[0092] If the device status data reported during the target low usage period does not exceed the preset device status threshold, the initial data synchronization strategy is maintained unchanged and sent to the mobile terminal so that the mobile terminal can perform data synchronization operations according to the initial data synchronization strategy.

[0093] When the device status data reported during the target low-usage period exceeds the preset device status threshold, the data synchronization time in the initial data synchronization strategy is delayed by a preset period of time to obtain a first target data synchronization strategy, and the initial data synchronization strategy is sent to the mobile terminal so that the mobile terminal can perform data synchronization operations according to the initial data synchronization strategy.

[0094] In addition, the first target data synchronization strategy also includes: an encoding method for the device status information of the mobile device to synchronize itself. Therefore, the first determination module 21 can obtain the encoded data transmitted by the mobile device according to the data synchronization time in the first target data synchronization strategy (i.e., the time after the target low-use period is delayed by a preset time), wherein the encoded data is obtained by encoding all device status data within the preset time by the mobile device according to the encoding method. By transmitting the encoded data, the mobile device can not only greatly reduce the amount of data transmitted by the mobile device, but also improve the transmission efficiency and reduce unnecessary data redundancy.

[0095] As another optional implementation, the device further includes a second adjustment module 25, wherein the second adjustment module 25 can continuously collect data synchronized by the mobile device in any sub-time period within the current time period according to the initial data synchronization strategy, and can also include user behavior data, and since these user behavior data may contain event data of key configuration events (such as changes involving device settings, installation or uninstallation of applications, system updates, etc.), and these key configuration events put forward specific requirements on the timeliness and frequency of data synchronization. To this end, the second adjustment module 25 can also adjust the initial data synchronization strategy based on user-triggered events, so that the mobile device performs data synchronization when an important event is detected.

[0096] Specifically, the second adjustment module 25 can first obtain the user behavior data of the mobile device in any sub-time period within the current time period according to the initial data synchronization strategy, wherein the user behavior data at least includes: the event occurrence time reflecting the key configuration event triggered by the target user; adjust the initial data synchronization strategy based on the user behavior data to set the data synchronization time of the mobile device to the event occurrence time of the key configuration event triggered by the target user, so as to obtain the second target data synchronization strategy.

[0097] Among them, the second adjustment module 25 can use a machine learning algorithm or a preset rule engine to identify and classify which are critical configuration events, and extract the event occurrence time related to the critical configuration events. The second adjustment module 25 can adjust the initial data synchronization strategy according to the extracted event occurrence time related to the critical configuration event, set the data synchronization time to the event occurrence time of the critical configuration event, or synchronize within a reasonable time before and after the event occurrence time, and obtain the adjusted second target data synchronization strategy. This adjustment ensures that when a mobile device encounters a critical configuration event, data synchronization can be triggered immediately or in a timely manner to reflect the latest device status and configuration information. At the same time, the strategy is more closely matched to the user's key behavior, thereby improving the pertinence and efficiency of data synchronization, while taking into account power consumption optimization and user experience.

[0098] It should be noted that each module in the device data synchronization strategy formulation apparatus in the embodiment of the present application corresponds one-to-one to each implementation step of the device data synchronization strategy formulation method in Example 1. Since a detailed description has been given in Example 1, some details not reflected in this embodiment can be referred to Example 1 and will not be repeated here.

[0099] Example 3

[0100] According to an embodiment of the present application, a computer program product is also provided, which includes a computer program, wherein when the computer program is executed by a processor, the device data synchronization strategy formulation method in Example 1 is implemented.

[0101] According to an embodiment of the present application, a non-volatile storage medium is also provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the device data synchronization strategy formulation method in Example 1 by running the computer program.

[0102] According to an embodiment of the present application, a processor is also provided, which is used to run a computer program, wherein the device data synchronization strategy formulation method in Example 1 is executed when the computer program is running.

[0103] According to an embodiment of the present application, an electronic device is also provided, which includes: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the device data synchronization strategy formulation method in Example 1 through the computer program.

[0104] Specifically, the computer program executes the following steps when it is running: determining the usage behavior pattern of the target user in the current time period based on the historical user behavior data set of the target user using the mobile device in multiple historical time periods, wherein the usage behavior pattern is at least used to reflect at least one high-usage period and at least one low-usage period of the target user using the mobile device in the current time period; determining an initial data synchronization strategy matching the usage behavior pattern from a preset database, wherein the database includes multiple usage behavior patterns and corresponding data synchronization strategies, and the data synchronization strategy at least includes: the data synchronization time of the mobile device synchronizing its own device status information is the low-usage period, and the device status information includes at least one of the following: user behavior data, device status data; obtaining the device status data reported by the mobile device in each low-usage period in the current time period according to the initial data synchronization strategy; if the device status data reported in the target low-usage period exceeds the preset device status threshold, the data synchronization time in the initial data synchronization strategy is postponed from the end time of the target low-usage period by a preset time length to obtain a first target data synchronization strategy, wherein the preset time length is the time length from the end time of the target low-usage period to the time when the device status data of the mobile device is restored to the device status threshold.

[0105] As an optional implementation, the electronic device may be in the form of a mobile terminal, a computer terminal or a similar computing device. Figure 3 The hardware structure block diagram of an electronic device for implementing a device data synchronization strategy formulation method is shown. Figure 3 As shown, the electronic device 30 may include one or more (302a, 302b, ..., 302n are used to illustrate) processors 302 (the processor 302 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 304 for storing data, and a transmission device 306 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 3 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 3 More or fewer components as shown, or with Figure 3 Different configurations are shown.

[0106] It should be noted that the one or more processors 302 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the electronic device 30. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0107] The memory 304 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the device data synchronization strategy formulation method in the embodiment of the present application. The processor 302 executes various functional applications and data processing by running the software programs and modules stored in the memory 304, that is, to implement the vulnerability detection method of the above-mentioned application. The memory 304 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 304 may further include a memory remotely arranged relative to the processor 302, and these remote memories may be connected to the electronic device 30 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0108] The transmission device 306 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the electronic device 30. In one example, the transmission device 306 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 306 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0109] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the electronic device 30 .

[0110] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0111] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0112] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0113] 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 distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0114] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0115] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0116] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for formulating a device data synchronization strategy, characterized in that: include: Determining a usage behavior pattern of the target user in a current time period based on a historical user behavior data set of the target user using the mobile device in multiple historical time periods, wherein the usage behavior pattern is at least used to reflect at least one high usage period and at least one low usage period of the target user using the mobile device in the current time period; Determining an initial data synchronization strategy that matches the usage behavior pattern from a preset database, wherein the database includes a plurality of usage behavior patterns and corresponding data synchronization strategies, and the data synchronization strategy at least includes: the data synchronization time of the mobile device synchronizing its own device status information is a low usage period, and the device status information includes at least one of the following: user behavior data, device status data; Acquire device status data reported by the mobile device at each low-usage period within the current time period according to the initial data synchronization strategy; If the device status data reported during the target low usage period exceeds a preset device status threshold, the data synchronization time within the initial data synchronization strategy is postponed by a preset time period from the end time of the target low usage period to obtain a first target data synchronization strategy, wherein the preset time period is the time from the end time of the target low usage period to the time when the device status data of the mobile device is restored to the device status threshold.

2. The method according to claim 1, characterized in that Determining the usage behavior pattern of the target user in the current time period based on the historical user behavior data of the target user using the mobile device in multiple historical time periods includes: Acquire a historical user behavior data set of the target user using the mobile device in multiple historical time periods, wherein the historical user behavior data set includes historical user behavior data of each historical sub-time period in the corresponding historical time period, and the historical user behavior data includes at least one of the following: application usage data, device location information; The pre-trained Markov chain model is used to determine the usage behavior pattern of the target user in the current time period for the plurality of historical user behavior data sets.

3. The method according to claim 2, characterized in that Acquiring a historical user behavior dataset of the target user using the mobile device in multiple historical time periods, including: In each historical time period, obtaining an initial historical user behavior data set of the mobile device in the historical time period synchronized by the mobile device according to the historical target data synchronization policy; Preprocess each of the initial historical user behavior data sets to obtain the corresponding historical user behavior data sets.

4. The method according to claim 2, characterized in that: Using the pre-trained Markov chain model to analyze the plurality of historical user behavior data sets, determining the usage behavior pattern of the target user in the current time period, including: Analyze the plurality of historical user behavior data sets to construct a state transition matrix; Determine an initial behavior state based on the behavior state of the target user in a previous historical sub-time period of the current time period, wherein the initial behavior state at least includes: a high usage state or a low usage state; Using the Markov chain model, starting from the initial behavior state, randomly selecting the next behavior state as the behavior state of the first sub-time period in the current time period according to the state transition matrix, and continuing to perform the above-mentioned simulation state transition process starting from the behavior state of the first sub-time period in the current time period until all sub-time periods in the current time period are traversed, thereby obtaining the state transition sequence of the mobile device in the current time period; A plurality of sub-time periods in the state transition sequence that are continuously in the high-usage state are regarded as a high-usage period, and a plurality of sub-time periods in the state transition sequence that are continuously in the low-usage state are regarded as a low-usage period, and a usage behavior pattern of the target user in the current time period is determined based on each of the high-usage period and each of the low-usage period.

5. The method according to claim 4, characterized in that Analyze multiple historical user behavior data sets to construct a state transition matrix, including: Determine, based on the historical user behavior data set corresponding to each of the historical time periods, the probability of each of the historical sub-time periods in each of the historical time periods transitioning from one behavior state to another behavior state; The state transition matrix is ​​constructed by taking the behavior states as the rows and columns of the matrix and taking the probability of transitioning from one behavior state to another behavior state in each historical sub-time period as the matrix element.

6. The method according to claim 1, characterized in that The first target data synchronization strategy also includes: an encoding method for synchronizing the device status information of the mobile device itself, wherein after obtaining the first target data synchronization strategy, the method further includes: The encoded data transmitted by the mobile device according to the data synchronization time in the first target data synchronization strategy is obtained, wherein the encoded data is obtained by encoding all device status data within the preset time period by the mobile device according to the encoding method.

7. The method according to claim 1, characterized in that After determining an initial data synchronization strategy matching the usage behavior pattern from a preset database, the method further includes: Acquire user behavior data of the mobile device in any sub-time period within the current time period according to the initial data synchronization strategy, wherein the user behavior data at least includes: an event occurrence time for reflecting a key configuration event triggered by the target user; The initial data synchronization strategy is adjusted according to the user behavior data to set the data synchronization time of the mobile device to the event occurrence time of the key configuration event triggered by the target user, so as to obtain a second target data synchronization strategy.

8. A device data synchronization strategy formulation device, characterized in that: include: A first determination module is used to determine a usage behavior pattern of the target user in a current time period based on a historical user behavior data set of the target user using the mobile device in multiple historical time periods, wherein the usage behavior pattern is used to at least reflect at least one high usage period and at least one low usage period of the target user using the mobile device in the current time period; A second determination module is used to determine an initial data synchronization strategy that matches the usage behavior pattern from a preset database, wherein the database includes a plurality of usage behavior patterns and corresponding data synchronization strategies, and the data synchronization strategy at least includes: the data synchronization time of the mobile device synchronizing its own device status information is a low usage period, and the device status information includes at least one of the following: user behavior data, device status data; An acquisition module, configured to acquire device status data reported by the mobile device during each low-usage period within the current time period according to the initial data synchronization strategy; The first adjustment module is used to delay the data synchronization time in the initial data synchronization strategy by a preset time period from the end time of the target low usage period when the device status data reported during the target low usage period exceeds a preset device status threshold, so as to obtain a first target data synchronization strategy, wherein the preset time period is the time period from the end time of the target low usage period to the time when the device status data of the mobile device is restored to the device status threshold.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a computer program, wherein the device where the non-volatile storage medium is located executes the device data synchronization strategy formulation method described in any one of claims 1 to 7 by running the computer program.

10. A computer program product, characterized in that include: A computer program, wherein when the computer program is executed by a processor, the device data synchronization strategy formulation method described in any one of claims 1 to 7 is implemented.

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