Equipment Data Synchronization Strategy Formulation Method and Device

Abstract

This application discloses a method and apparatus for formulating a device data synchronization strategy. The method includes: determining the target user's usage behavior pattern in the current time period based on a dataset of historical user behavior of the target user using the mobile device over multiple historical time periods; determining an initial data synchronization strategy matching the usage behavior pattern from a preset database; acquiring device status data reported by the mobile device during various low-usage periods in the current time period according to the initial data synchronization strategy; if the device status data reported during a target low-usage period exceeds a preset device status threshold, postponing the data synchronization time within the initial data synchronization strategy by a preset duration from the end time of the target low-usage period to obtain a first target data synchronization strategy. This application solves the technical problem that the periodic synchronization of device status information by mobile devices to an MDM platform significantly increases the power consumption of mobile devices and affects their battery life.

Description

Technical Field

[0001] This application relates to the field of equipment management technology, and more specifically, to a method and apparatus for formulating equipment data synchronization strategies. Background Technology

[0002] With the widespread use of mobile devices in daily life and work, Mobile Device Management (MDM) solutions have become crucial tools for ensuring device security, data protection, and meeting operator requirements. However, MDM solutions often collect device status information periodically. While this data synchronization method ensures data consistency, it doesn't consider the actual usage and battery status of the mobile device. For example, even when the device battery is low or the user is not actively using the device, data synchronization will still be performed according to a pre-set cycle, significantly consuming battery resources and degrading the user experience.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method and apparatus for formulating a device data synchronization strategy, which at least solves the technical problem that the power consumption of mobile devices is greatly increased and the battery life of mobile devices is affected by the periodic synchronization of their own device status information with the MDM platform.

[0005] According to one aspect of the embodiments of this application, a method for formulating a device data synchronization strategy is provided, comprising: determining the target user's usage behavior pattern in the current time period based on a historical user behavior dataset of the target user using the mobile device in multiple historical time periods, wherein the 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; 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 includes at least: the data synchronization time for the mobile device to synchronize its own device status information is a low usage period, the device status information includes at least one of the following: user behavior data, device status data; acquiring 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 a preset device status threshold, postponing the data synchronization time in the initial data synchronization strategy from the end time of the target low usage period by a preset duration to obtain a first target data synchronization strategy, wherein the preset duration is the duration from the end time of the target low usage period to the time for the device status data of the mobile device to recover to the device status threshold.

[0006] Optionally, the usage behavior pattern of the target user in the current time period is determined based on the target user's historical user behavior data of using mobile devices in multiple historical time periods, including: obtaining a historical user behavior dataset of the target user using mobile devices in multiple historical time periods, wherein the historical user behavior dataset includes historical user behavior data of each historical sub-time period within the corresponding historical time period, and the historical user behavior data includes at least one of the following: application usage data, device location information; and using a pre-trained Markov chain model to determine the target user's usage behavior pattern in the current time period from the multiple historical user behavior datasets.

[0007] Optionally, the target user's historical user behavior datasets for using mobile devices in multiple historical time periods are obtained, including: in each historical time period, obtaining the initial historical user behavior dataset of the mobile device synchronized according to the historical target data synchronization strategy; and preprocessing each initial historical user behavior dataset to obtain the corresponding historical user behavior dataset.

[0008] Optionally, a pre-trained Markov chain model is used to determine the target user's usage behavior pattern in the current time period using multiple historical user behavior datasets. This includes: analyzing multiple historical user behavior datasets and constructing a state transition matrix; determining the target user's behavior state in the previous historical sub-time period of the current time period as the initial behavior state, wherein the initial behavior state includes at least 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 based on the state transition matrix, and continuing to execute the above simulated 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 have been traversed, thus obtaining the state transition sequence of the mobile device in the current time period; taking multiple sub-time periods in the state transition sequence that are continuously in a high usage state as a high usage period, and taking multiple sub-time periods in the state transition sequence that are continuously in a low usage state as a low usage period, and determining the target user's usage behavior pattern in the current time period based on each high usage period and each low usage period.

[0009] Optionally, multiple historical user behavior datasets are analyzed to construct a state transition matrix, including: determining the probability of each historical sub-time period transitioning from one behavior state to another based on the historical user behavior dataset corresponding to each historical time period; 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 as the matrix elements.

[0010] Optionally, the first target data synchronization strategy further includes: an encoding method for the mobile device to synchronize its own device status information. After obtaining the first target data synchronization strategy, the method further 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 is obtained by encoding all device status data within a preset time period by the mobile device 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: acquiring user behavior data of the mobile device within any sub-time period of the current time period according to the initial data synchronization strategy, wherein the user behavior data includes at least: the event occurrence time of a key configuration event triggered by the 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 embodiments of this application, a device data synchronization strategy formulation apparatus is also provided, comprising: a first determining module, configured to determine the target user's usage behavior pattern in the current time period based on a historical user behavior dataset of the target user using the mobile device in multiple historical time periods, wherein the 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; and a second determining module, configured to determine 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 includes at least: the mobile device synchronizing its own... The data synchronization time for device status information is during low usage periods. The device status information includes at least one of the following: user behavior data and device status data. The acquisition module is used to acquire 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. The first adjustment module is 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 duration when the device status data reported during the target low usage period exceeds a preset device status threshold, so as to obtain the first target data synchronization strategy. The preset duration 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.

[0013] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-described device data synchronization strategy formulation method by running the computer program.

[0014] According to another aspect of the embodiments of this application, a computer program product is also provided, the computer program product including a stored computer program, wherein the computer program, when executed by a processor, implements the above-described device data synchronization strategy formulation method.

[0015] In this embodiment, the MDM server analyzes multiple sets of historical user behavior datasets of mobile devices to predict usage behavior patterns within the current time period and determines an initial data synchronization strategy that matches the usage behavior patterns. Then, based on the device status data reported by the mobile device during various low-usage periods within the current time period according to the initial data synchronization strategy, the initial data synchronization strategy is adjusted. This results in a more optimized data synchronization plan that is dynamically generated based on the current device status and usage behavior patterns, thereby achieving the goals of optimizing power consumption management, extending battery life, and improving user experience. This solves the technical problem that the periodic synchronization of device status information by mobile devices to the MDM platform greatly increases the power consumption of mobile devices and affects their battery life. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 This is a flowchart illustrating an optional device data synchronization strategy formulation method according to an embodiment of this application;

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

[0019] Figure 3 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort 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, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] Furthermore, all information and data (including but not limited to user device information, user personal information, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties. For example, this system has an interface with the relevant user or organization. Before obtaining relevant information, it needs to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving consent from the aforementioned user or organization.

[0023] To better understand the embodiments of this application, the technical terms involved in the embodiments of this application are explained below:

[0024] Mobile Device Management (MDM) is a technology for centralized management of mobile devices. It provides complete lifecycle management of mobile devices, from registration, activation, use, and retirement, and 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 this application, an embodiment of a device data synchronization strategy formulation method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] Figure 1 This is a flowchart illustrating a method for formulating a device data synchronization strategy according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0028] Step S102: Determine the target user's usage behavior pattern in the current time period based on the target user's historical user behavior dataset of using mobile devices in multiple historical time periods.

[0029] In the technical solution provided in step S102 above, the aforementioned usage behavior pattern reflects at least one high-usage period and at least one low-usage period of the target user's use of the mobile device within the current time period. High-usage periods typically refer to times when the device is used frequently and consumes a lot of power, while low-usage periods represent times when the device is used less and consumes less power. Therefore, the Mobile Device Management (MDM) server analyzes historical user behavior datasets of mobile devices across multiple historical time periods to gain insights into user habits at different times, providing a basis for formulating data synchronization strategies.

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

[0031] In the technical solution provided in step S104 above, the database pre-stores various usage behavior patterns and corresponding personalized data synchronization strategies. These strategies aim to adjust the frequency and timing of data synchronization based on the user behavior patterns of mobile devices to optimize mobile device power consumption. Therefore, the data synchronization strategy at least uses data synchronization time reflecting the synchronization of the mobile device's own device status information during low-usage periods. That is, data synchronization operations are only scheduled during low-usage periods when the device usage frequency is low and power consumption is low, in order to reduce the burden on the mobile device, extend battery life, and maintain a good user experience. The device status information includes at least one of the following: user behavior data and device status data.

[0032] Step S106: Obtain 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.

[0033] In the technical solution provided in step S106 above, after determining the initial data synchronization strategy of the mobile device within the current time period, the MDM server can receive device status data reported by the mobile device according to the data synchronization time—each low-usage period within the initial data synchronization strategy. This 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 level, CPU (Central Processing Unit) and GPU (Graphics Processing Unit) usage, memory usage, storage space status, and temperature sensor readings.

[0034] Step S108: If 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 postponed by a preset time from the end time of the target low usage period to obtain the first target data synchronization strategy.

[0035] In the technical solution provided in step S108 above, the aforementioned device status thresholds are pre-set indicators used to determine whether the device status has reached a level requiring adjustment of the data synchronization strategy. These include, but are not limited to, battery power below a specific percentage, CPU or GPU load exceeding a preset limit, unstable network connection or excessive data traffic usage, storage space usage reaching a warning level, and device temperature exceeding the normal range. Therefore, when the MDM server detects that the device status data reported by the mobile device during a target low-usage period (at least one low-usage period within the current time period) reaches or exceeds these device status thresholds, it indicates that the mobile device may be in a high-power or unstable state during the target low-usage period. At this time, the MDM server will automatically adjust the aforementioned initial data synchronization strategy, that is, delay the data synchronization time within the initial data synchronization strategy by a preset duration. The extension duration is from the end time of the target low-usage period until 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 until the critical value of restoring the device status data to the device status threshold, in order to reduce the burden on the mobile device, extend battery life, and maintain a good user experience.

[0036] Based on the technical methods provided in steps S102 to S108 above, it can be understood that in this embodiment of the application, the MDM server analyzes multiple sets of historical user behavior datasets of the mobile device to predict the usage behavior pattern in the current time period 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 the mobile device in each low usage period in the current time period reported by the mobile device in accordance with the initial data synchronization strategy. This results in a more optimized data synchronization plan that is dynamically generated based on the current device status and usage behavior pattern, thereby achieving the purpose of optimizing power consumption management, extending battery life and improving user experience.

[0037] The following section explains the steps involved in formulating a device data synchronization strategy, using a specific implementation process as an example.

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

[0039] Step S1021: Obtain the historical user behavior dataset of the target user using mobile devices in multiple historical time periods.

[0040] The aforementioned historical user behavior dataset includes historical user behavior data for each historical sub-time period within the corresponding historical time period. This 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 on / off status, sensor data (such as acceleration and gyroscope readings), and user interaction behaviors (such as touchscreen operation and button usage).

[0041] Specifically, within each historical time period, the MDM server can first obtain the initial historical user behavior dataset of the mobile devices synchronized according to the historical target data synchronization strategy within the historical time period. Each initial historical user behavior dataset is then preprocessed to obtain the corresponding historical user behavior dataset.

[0042] The preprocessing operations mentioned above include, but are not limited to, data cleaning, data standardization, and data dimensionality reduction. Data cleaning is necessary because the initial historical user behavior datasets reported by mobile devices over various historical time periods may contain erroneous, incomplete, or irrelevant data, such as outliers, missing values, or information unrelated to the current analysis objective. The data cleaning step in preprocessing can identify and address these data quality issues, ensuring the accuracy of subsequent analysis. Data standardization is necessary because different types of historical user behavior data may have inconsistent formats and structures; therefore, converting the data into a consistent format facilitates subsequent analysis and model training. Data dimensionality reduction is necessary because the original data may have high dimensionality and contain many potentially irrelevant features. Therefore, data dimensionality reduction techniques, such as principal component analysis, are used to reduce data dimensionality, lower computational complexity, while retaining the main information in the dataset, thus improving analytical efficiency.

[0043] Step S1022: Use a pre-trained Markov chain model to determine the target user's usage behavior pattern in the current time period from multiple historical user behavior datasets.

[0044] Specifically, since Markov chain models can capture the sequential and state transition characteristics of user behavior, this application embodiment uses a pre-trained Markov chain model to analyze these historical user behavior datasets in order to predict the high and low usage periods of user habits.

[0045] Optionally, step S1022 above can be implemented through the following steps:

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

[0047] The method for constructing the state transition matrix includes: determining the probability of each historical sub-period transitioning from one behavior state to another based on the historical user behavior dataset corresponding to each historical time period; constructing the state transition matrix by using each behavior state as the rows and columns of the matrix and the probability of each historical sub-period transitioning from one behavior state to another as the matrix elements.

[0048] In other words, each row in the state transition matrix represents a behavioral state (such as a high-use state or a low-use state), and each column corresponds to other possible states that may be transitioned to in the next time step. Therefore, the elements in the matrix represent the probability of transitioning from the current state to the next state.

[0049] Step 2: Determine the initial behavioral 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's behavior state in the previous historical sub-period of the current time period as the initial behavior state at the first moment of the current time period. This initial behavior state includes at least a high usage state or a low usage state. If the user frequently used the application, consumed a large amount of data, or frequently interacted with the device in the previous sub-period, the initial behavior state may be marked as a high usage state; otherwise, it may be marked as a low usage state.

[0051] Step 3: Using the Markov chain model, starting from the initial behavioral state, randomly select the next behavioral state as the behavioral state of the first sub-time period within the current time period according to the state transition matrix, and continue to execute the above simulated state transition process starting from the behavioral state of the first sub-time period within the current time period until all sub-time periods within the current time period have been traversed, thus obtaining the state transition sequence of the mobile device within the current time period.

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

[0053] Step 4: Take multiple sub-time periods that are continuously in a high-use state within the state transition sequence as a high-use period, and take multiple sub-time periods that are continuously in a low-use state within the state transition sequence as a low-use period. Determine the target user's usage behavior pattern within the current time period based on each high-use period and each low-use period.

[0054] High-usage periods are typically characterized by frequent user activity and large data transfer volumes, while low-usage periods are typically characterized by less user activity and smaller data transfer volumes. By summarizing these high- and low-usage periods, the target user's usage behavior pattern within the current time frame can be determined. This usage behavior pattern allows us to understand when users will use their devices extensively and when they will use them relatively infrequently within the current time frame.

[0055] Furthermore, the MDM server can determine whether to adjust the initial data synchronization strategy by comparing the device status data reported by the mobile device during various low-usage periods within 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 remains unchanged, and the initial data synchronization strategy is sent to the mobile terminal so that the mobile terminal can perform data synchronization operation according to the initial data synchronization strategy.

[0057] If the device status data reported during the target low usage period exceeds the preset device status threshold, the data synchronization time within the initial data synchronization strategy is delayed by a preset duration to obtain the first target data synchronization strategy. This initial data synchronization strategy is then sent to the mobile terminal so that the mobile terminal can perform data synchronization operations according to the initial data synchronization strategy.

[0058] Furthermore, the aforementioned first target data synchronization strategy also includes an encoding method for the mobile device to synchronize its own device status information. Therefore, the MDM server can obtain the encoded data transmitted by the mobile device according to the data synchronization time within the first target data synchronization strategy (i.e., the time after a preset delay from the target low-usage period). This encoded data is obtained by encoding all device status data within the preset time period using the encoding method. By transmitting encoded data, the mobile device can not only significantly reduce the amount of data transmitted, but also improve transmission efficiency and reduce unnecessary data redundancy.

[0059] As an alternative implementation, the MDM system continuously collects data synchronized by mobile devices within any sub-time period of the current time period according to the initial data synchronization strategy. This data may also include user behavior data. Since this user behavior data may contain event data related to critical configuration events (such as changes in device settings, application installation or uninstallation, system updates, etc.), and these critical configuration events impose specific requirements on the timeliness and frequency of data synchronization, the MDM server can also adjust the initial data synchronization strategy based on user-triggered events, enabling mobile devices to synchronize data when important events are detected.

[0060] Specifically, the MDM server can first obtain user behavior data of the mobile device in any sub-time period within the current time period according to the initial data synchronization strategy. The user behavior data includes at least the event occurrence time of the key configuration event triggered by the target user. The initial data synchronization strategy is then adjusted 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.

[0061] The MDM server can use machine learning algorithms or a pre-defined rule engine to identify and classify critical configuration events, and extract the event occurrence times related to these events. Based on these extracted event occurrence times, the MDM server can adjust the initial data synchronization strategy, setting the data synchronization time to the occurrence time of the critical configuration event, or synchronizing within a reasonable timeframe before or after the event's occurrence time, resulting in a revised second target data synchronization strategy. This adjustment ensures that when a mobile device encounters a critical configuration event, data synchronization is triggered immediately or promptly to reflect the latest device status and configuration information. Simultaneously, the strategy is more closely aligned with key user behaviors, improving the targeting and efficiency of data synchronization while also considering 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 user behavior patterns based on historical user behavior data to determine the corresponding initial data synchronization strategy, avoid synchronization during active user periods, reduce the impact on normal user use, and ensure data synchronization when users are inactive or the device is in a low power consumption state, thereby improving device availability and user satisfaction.

[0064] (2) By dynamically adjusting the book data synchronization strategy based on the target user’s usage behavior pattern and the actual device status data of the mobile device, the actual power consumption of the device and the user’s usage pattern can be understood more accurately, thereby optimizing the data synchronization strategy and reducing unnecessary energy consumption. This fine management is especially important when the power is low.

[0065] (3) By adjusting the initial data synchronization strategy based on the occurrence time of important events triggered by the target user, timely updates and applications of configuration data are ensured. This is crucial for scenarios requiring immediate response (such as security updates, installation or uninstallation of important applications), improving the response speed and processing efficiency for critical events.

[0066] (4) The MDM system intelligently adjusts the data synchronization strategy according to the device status and user behavior, enabling it to dynamically adjust the data synchronization strategy based on the real-time device status and user behavior. This enhances the system's adaptability to changes, better addresses different scenarios and needs, and provides more personalized services.

[0067] Example 2

[0068] According to an embodiment of this application, a device for formulating a device data synchronization strategy to implement the device data synchronization strategy formulating method in Embodiment 1 is also provided, such as... Figure 2 As shown, the device for formulating a data synchronization strategy includes at least: a first determining module 21, a second determining module 22, an acquisition module 23, and an adjustment module 24, wherein:

[0069] The first determining module 21 is used to determine the target user's usage behavior pattern in the current time period based on the target user's historical user behavior dataset of using mobile devices in multiple historical time periods. The usage behavior pattern is used to reflect at least one high usage period and at least one low usage period of the target user's use of mobile devices in the current time period.

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

[0071] The acquisition module 23 is used to acquire 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;

[0072] The first adjustment module 24 is 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 duration when the reported device status data exceeds the preset device status threshold, so as to obtain the first target data synchronization strategy. The preset duration 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.

[0073] The following section explains the functions of each module of the device for formulating equipment data synchronization strategies, based on a specific implementation process.

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

[0075] Step S1: Obtain a dataset of historical user behavior data of the target user using mobile devices over multiple historical time periods.

[0076] The aforementioned historical user behavior dataset includes historical user behavior data for each historical sub-time period within the corresponding historical time period. This 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 on / off status, sensor data (such as acceleration and gyroscope readings), and user interaction behaviors (such as touchscreen operation and button usage).

[0077] Specifically, within each historical time period, the first determining module 21 can first obtain the initial historical user behavior dataset of the mobile device synchronized according to the historical target data synchronization strategy within the historical time period. Each initial historical user behavior dataset is then preprocessed to obtain the corresponding historical user behavior dataset.

[0078] The preprocessing operations mentioned above include, but are not limited to, data cleaning, data standardization, and data dimensionality reduction. Data cleaning is necessary because the initial historical user behavior datasets reported by mobile devices over various historical time periods may contain erroneous, incomplete, or irrelevant data, such as outliers, missing values, or information unrelated to the current analysis objective. The data cleaning step in preprocessing can identify and address these data quality issues, ensuring the accuracy of subsequent analysis. Data standardization is necessary because different types of historical user behavior data may have inconsistent formats and structures; therefore, converting the data into a consistent format facilitates subsequent analysis and model training. Data dimensionality reduction is necessary because the original data may have high dimensionality and contain many potentially irrelevant features. Therefore, data dimensionality reduction techniques, such as principal component analysis, are used to reduce data dimensionality, lower computational complexity, while retaining the main information in the dataset, thus improving analytical efficiency.

[0079] Step S2: Use a pre-trained Markov chain model to determine the target user's usage behavior pattern in the current time period from multiple historical user behavior datasets.

[0080] Specifically, since Markov chain models can capture the sequential and state transition characteristics of user behavior, this application embodiment uses a pre-trained Markov chain model to analyze these historical user behavior datasets in order to predict the high and low usage periods of user habits.

[0081] Alternatively, step S2 above can be implemented through the following steps:

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

[0083] The method for constructing the state transition matrix includes: determining the probability of each historical sub-period transitioning from one behavior state to another based on the historical user behavior dataset corresponding to each historical time period; constructing the state transition matrix by using each behavior state as the rows and columns of the matrix and the probability of each historical sub-period transitioning from one behavior state to another as the matrix elements.

[0084] In other words, each row in the state transition matrix represents a behavioral state (such as a high-use state or a low-use state), and each column corresponds to other possible states that may be transitioned to in the next time step. Therefore, the elements in the matrix represent the probability of transitioning from the current state to the next state.

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

[0086] Specifically, the first determining module 21 can directly use the user's behavior state in the previous historical sub-period of the current time period as the initial behavior state of the first moment in the current time period. This initial behavior state includes at least two conditions: a high usage state or a low usage state. If the user frequently used the application, consumed a large amount of data, or frequently interacted with the device in the previous sub-period, the initial behavior state may be marked as a high usage state; conversely, it may be marked as a low usage state.

[0087] Step 3: Using the Markov chain model, starting from the initial behavioral state, randomly select the next behavioral state as the behavioral state of the first sub-time period within the current time period according to the state transition matrix, and continue to execute the above simulated state transition process starting from the behavioral state of the first sub-time period within the current time period until all sub-time periods within the current time period have been traversed, thus obtaining the state transition sequence of the mobile device within the current time period.

[0088] Specifically, the first determining module 21 uses a Markov chain model to start from a determined initial behavioral state, randomly select the next behavioral state based on the state transition matrix, and repeat this process to simulate the user's state transitions within the current time period, generating a corresponding state transition sequence. Since the state transition process is probabilistic, the sequence may contain multiple sub-time periods of high or low usage states, reflecting the randomness and dynamic changes in user behavior.

[0089] Step 4: Take multiple sub-time periods that are continuously in a high-use state within the state transition sequence as a high-use period, and take multiple sub-time periods that are continuously in a low-use state within the state transition sequence as a low-use period. Determine the target user's usage behavior pattern within the current time period based on each high-use period and each low-use period.

[0090] High-usage periods are typically characterized by frequent user activity and large data transfer volumes, while low-usage periods are typically characterized by less user activity and smaller data transfer volumes. By summarizing these high- and low-usage periods, the target user's usage behavior pattern within the current time frame can be determined. This usage behavior pattern allows us to understand when users will use their devices extensively and when they will use them relatively infrequently within the current time frame.

[0091] Furthermore, the first determining module 21 can decide whether to adjust the initial data synchronization strategy by comparing the device status data reported by the mobile device during various low-usage periods within the current time period with a 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 remains unchanged, and the initial data synchronization strategy is sent to the mobile terminal so that the mobile terminal can perform data synchronization operation according to the initial data synchronization strategy.

[0093] If the device status data reported during the target low usage period exceeds the preset device status threshold, the data synchronization time within the initial data synchronization strategy is delayed by a preset duration to obtain the first target data synchronization strategy. This initial data synchronization strategy is then sent to the mobile terminal so that the mobile terminal can perform data synchronization operations according to the initial data synchronization strategy.

[0094] Furthermore, the aforementioned first target data synchronization strategy also includes an encoding method for the mobile device to synchronize its own device status information. Therefore, the first determining module 21 can obtain the encoded data transmitted by the mobile device according to the data synchronization time within the first target data synchronization strategy (i.e., the time after a preset delay from the target low-usage period). The encoded data is obtained by encoding all device status data within the preset time period using the encoding method. By transmitting encoded data, the mobile device can not only significantly reduce the amount of data transmitted, but also improve transmission efficiency and reduce unnecessary data redundancy.

[0095] As an alternative implementation, the device also includes a second adjustment module 25. This second adjustment module 25 can continuously collect data synchronized by the mobile device within any sub-time period of the current time period according to the initial data synchronization strategy. This data may also include user behavior data. Since this user behavior data may contain event data related to critical configuration events (such as changes in device settings, application installation or uninstallation, system updates, etc.), and these critical configuration events impose specific requirements on the timeliness and frequency of data synchronization, 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 important events are detected.

[0096] Specifically, the second adjustment module 25 can first obtain user behavior data of the mobile device in any sub-time period within the current time period according to the initial data synchronization strategy. The user behavior data includes at least the event occurrence time of the 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 the second target data synchronization strategy.

[0097] The second adjustment module 25 can use machine learning algorithms or a pre-defined rule engine to identify and classify critical configuration events, and extract the event occurrence times related to these critical configuration events. Based on the extracted event occurrence times related to the critical configuration events, the second adjustment module 25 can adjust the initial data synchronization strategy, setting the data synchronization time to the occurrence time of the critical configuration event, or synchronizing within a reasonable timeframe before or after the event occurrence time, resulting in an 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 promptly to reflect the latest device status and configuration information. Simultaneously, the strategy is more closely aligned with the user's key behaviors, thereby improving the targeting and efficiency of data synchronization, while also considering power consumption optimization and user experience.

[0098] It should be noted that each module in the device data synchronization strategy formulation device in this application embodiment corresponds one-to-one with each implementation step of the device data synchronization strategy formulation method in embodiment 1. Since embodiment 1 has been described in detail, some details not shown in this embodiment can be referred to embodiment 1, and will not be elaborated further here.

[0099] Example 3

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

[0101] According to an embodiment of this 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 Embodiment 1 by running the computer program.

[0102] According to an embodiment of this application, a processor is also provided for running a computer program, wherein the computer program executes the device data synchronization strategy formulation method in Embodiment 1 during runtime.

[0103] According to an embodiment of this application, an electronic device is also provided, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the device data synchronization strategy formulation method of Embodiment 1 through the computer program.

[0104] Specifically, the computer program executes the following steps during runtime: Based on a dataset of historical user behavior of the target user using the mobile device over multiple historical time periods, determine the target user's usage behavior pattern in the current time period. The usage behavior pattern reflects at least one high-usage period and one low-usage period of the target user's mobile device use in the current time period. Determine an initial data synchronization strategy matching the usage behavior pattern from a preset database. The database includes multiple usage behavior patterns and corresponding data synchronization strategies, and the data synchronization strategy includes at least one of the following: the data synchronization time for the mobile device to synchronize its own device status information is during a low-usage period. The device status information includes at least one of the following: user behavior data and device status data. Obtain the 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. If the device status data reported during the target low-usage period exceeds a preset device status threshold, postpone the data synchronization time in the initial data synchronization strategy from the end time of the target low-usage period by a preset duration to obtain a first target data synchronization strategy. The preset duration is the time between the end time of the target low-usage period and the time required for the mobile device's device status data to recover to the device status threshold.

[0105] As an alternative implementation, the above-mentioned electronic device may exist in the form of a mobile terminal, a computer terminal, or a similar computing device. Figure 3 A hardware block diagram of an electronic device for implementing a method for formulating device data synchronization strategies is shown. Figure 3 As shown, the electronic device 30 may include one or more processors 302 (shown as 302a, 302b, ..., 302n in the figure) (processor 302 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), 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 a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 3 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, electronic device 30 may also include... Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown.

[0106] It should be noted that the aforementioned one or more processors 302 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element of the electronic device 30. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination 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 this embodiment. The processor 302 executes various functional applications and data processing by running the software programs and modules stored in the memory 304, thereby implementing the above-mentioned application vulnerability detection method. The memory 304 may include high-speed random access memory, and may also include 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 memory remotely located relative to the processor 302, and these remote memories can be connected to the electronic device 30 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0108] The transmission device 306 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the electronic device 30. In one example, the transmission device 306 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 306 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0109] The display may be, for example, a touchscreen liquid crystal display (LCD), which allows the user to interact with the user interface of the electronic device 30.

[0110] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0111] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions 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. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0115] If the integrated unit is implemented as 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 this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0116] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for formulating a device data synchronization strategy, characterized in that, include: The usage behavior pattern of the target user in the current time period is determined based on the historical user behavior dataset of the target user using the mobile device in multiple historical time periods. The 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. An initial data synchronization strategy matching the usage behavior pattern is determined from a preset database. The database includes multiple usage behavior patterns and corresponding data synchronization strategies. The data synchronization strategy includes at least one of the following: the data synchronization time for the mobile device to synchronize its own device status information is during a low usage period. The device status information includes at least one of the following: user behavior data and device status data. Obtain 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; 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 delayed by a preset duration from the end time of the target low usage period to obtain a first target data synchronization strategy. The preset duration 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 target user's usage behavior pattern in the current time period based on historical user behavior data of the target user's use of mobile devices over multiple historical time periods includes: Obtain a historical user behavior dataset of the target user using the mobile device within multiple historical time periods. The historical user behavior dataset includes historical user behavior data for each historical sub-time period within the corresponding historical time period, and the historical user behavior data includes at least one of the following: application usage data and device location information. Using a pre-trained Markov chain model on multiple historical user behavior datasets, the usage behavior pattern of the target user in the current time period is determined.

3. The method according to claim 2, characterized in that, Obtain a dataset of historical user behavior of the target user using the mobile device over multiple historical time periods, including: Within each historical time period, the initial historical user behavior dataset of the mobile device within the historical time period is obtained, synchronized by the mobile device according to the historical target data synchronization strategy. Each of the initial historical user behavior datasets is preprocessed to obtain the corresponding historical user behavior dataset.

4. The method according to claim 2, characterized in that, Using a pre-trained Markov chain model on multiple historical user behavior datasets, determine the target user's usage behavior pattern within the current time period, including: Analyze multiple historical user behavior datasets to construct a state transition matrix; The initial behavioral state is determined by the behavioral state of the target user in the previous historical sub-time period of the current time period, wherein the initial behavioral state includes at least: high usage state or low usage state; Using the Markov chain model, starting from the initial behavioral state, the next behavioral state is randomly selected as the behavioral state of the first sub-time period within the current time period according to the state transition matrix. The simulated state transition process is then executed starting from the behavioral state of the first sub-time period within the current time period until all sub-time periods within the current time period have been traversed, thus obtaining the state transition sequence of the mobile device within the current time period. The state transition sequence is divided into multiple sub-time periods that are continuously in the high usage state as a high usage period, and the state transition sequence is divided into multiple sub-time periods that are continuously in the low usage state 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.

5. The method according to claim 4, characterized in that, Analyze multiple historical user behavior datasets to construct a state transition matrix, including: Based on the historical user behavior dataset corresponding to each historical time period, determine the probability of each historical sub-time period transitioning from one behavior state to another within each historical time period; The state transition matrix is ​​constructed by using each of the aforementioned behavioral states as the rows and columns of the matrix, and by using the probability of transitioning from one behavioral state to another in each historical sub-time period as the matrix elements.

6. The method according to claim 1, characterized in that, The first target data synchronization strategy further 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 further includes: The encoded data transmitted by the mobile device according to the data synchronization time within the first target data synchronization strategy is obtained. The encoded data is obtained by the mobile device encoding all device status data within the preset time period according to the encoding method.

7. The method according to claim 1, characterized in that, After determining an initial data synchronization strategy that matches the usage behavior pattern from a preset database, the method further includes: The user behavior data of the mobile device is obtained 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: the event occurrence time reflecting the key configuration event triggered by the target user; The initial data synchronization strategy is adjusted 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, thereby obtaining a second target data synchronization strategy.

8. A device for formulating a data synchronization strategy for a device, characterized in that, include: The first determining module is used to determine the target user's usage behavior pattern in the current time period based on the target user's historical user behavior dataset of using the mobile device in multiple historical time periods, wherein the usage behavior pattern is used to reflect at least one high usage period and at least one low usage period of the target user's use of the mobile device in the current time period. The second determining module is used to determine an initial data synchronization strategy that matches the usage behavior pattern from a preset database. The database includes multiple usage behavior patterns and corresponding data synchronization strategies, and the data synchronization strategy includes at least one of the following: the data synchronization time for the mobile device to synchronize its own device status information is during a low usage period. The device status information includes at least one of the following: user behavior data and device status data. The acquisition module is used to acquire 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; The first adjustment module is 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 duration when the reported device status data exceeds a preset device status threshold, so as to obtain a first target data synchronization strategy. The preset duration 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.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores a computer program, wherein the device containing the non-volatile storage medium executes the device data synchronization strategy formulation method according to 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 executed by a processor, the computer program implements the device data synchronization strategy formulation method according to any one of claims 1 to 7.

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