Mobile terminal data dynamic caching method based on user habits and equipment resources

Abstract

The invention relates to the technical field of smart home application, in particular to a mobile terminal data dynamic caching method based on user habits and equipment resources, which comprises the following steps: establishing an association model of user operation behaviors and terminal resources by identifying a user account and binding a common mobile terminal; generating a high-frequency usage scene template based on user historical operation data, and dynamically updating the template in combination with time distribution, position environment, network environment information and a terminal resource occupancy rate; the resource occupation state of the terminal is monitored, and data preloading is triggered in an idle period or a low resource occupation state; and dynamic updating and synchronization of the data are completed through a cooperation mechanism of the cloud and the client. According to the method, user behaviors, equipment states and time factors are comprehensively considered, efficient data management and caching are realized, and the performance and user experience of the smart home system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of smart home application technology, and in particular to a method for dynamically caching mobile terminal data based on user habits and device resources. Background Art

[0002] In the smart home sector, mobile apps have become the core hub for users to manage smart devices. Through apps, users can remotely control, monitor the status, and interact with scenarios for smart lights, air conditioners, door locks, and appliances. With the proliferation of smart home devices, users are increasingly demanding data loading speeds and user experience. However, traditional static caching methods cannot meet user needs in diverse scenarios. For example, in a smart home system, users may want to dynamically preload and update data based on their lifestyles and device resource status to avoid re-requesting when data is unavailable, which would result in long wait times.

[0003] Based on the above, the following problems still exist in current technologies: (1) Lack of comprehensive consideration of user operation habits and device resource status, resulting in inflexible data caching strategies. (2) In the field of smart homes, there are many types of devices and complex network environments, making traditional methods difficult to adapt to diverse usage scenarios. (3) The data synchronization mechanism is not perfect, especially in the case of multi-terminal collaboration, which affects the user experience.

[0004] Therefore, there is an urgent need for a dynamic mobile data caching method based on user habits and device resources to solve the above problems and improve the performance and user experience of smart home systems. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention specifically adopts the following technical solutions.

[0006] Design a dynamic caching method for mobile data based on user habits and device resources, including the following steps: S1: Identify user accounts and bind them to frequently used mobile terminals, and establish a correlation model between user operation behaviors and terminal resources; S2: Generate a high-frequency usage scenario template based on the user's historical operation data. The template includes the user's usage frequency, operation type, time distribution, and network connection type for different functional modules; S3: Collect user operation behavior data in real time, combine time distribution, location environment, network environment information and terminal resource occupancy rate, and dynamically update the high-frequency scenario template; S4: monitoring the terminal resource occupancy status, and triggering data preloading in idle time or low resource occupancy state according to the high-frequency scenario template and resource occupancy status; S5: Dynamic data update and synchronization are completed through the collaborative mechanism between the cloud and the client.

[0007] Preferably, the time distribution in step S2 is divided into seasons, holidays, weekends and daily time periods according to date types, and the network connection types include WIFI and mobile data networks.

[0008] Preferably, the terminal resource occupancy status in step S4 includes CPU usage. If it is detected that the CPU usage exceeds a threshold, the data update operation is delayed and the update is completed in a low-load period in the background through cloud coordination.

[0009] Preferably, in step S5, the coordination mechanism between the cloud and the client includes: a) When the client is active, it will proactively update. The proactive update rule is: the cloud pushes the data to be sent in the form of data tags. The tags include the type of data to be sent, the size, and the priority. The client proactively obtains data based on the tags and the current client resource situation. b) When the client is inactive, the cloud pushes update instructions based on the user's usual time to wake up the client's background process to complete data update synchronization.

[0010] Preferably, the data tag further includes data usage classification, which includes user operation data, advertising data and log data, wherein the download priority of advertising data is dynamically adjusted based on user operation habits.

[0011] Preferably, the priority judgment logic of the high-frequency scenario template in step S2 is: the weight of user operation time is higher than the weight of terminal resource occupancy, and the trigger condition for the cloud push instruction to wake up the client preferentially matches the user's historical active time period.

[0012] Preferably, it also includes: when the client detects a system vulnerability or abnormal log, automatically triggering data upload to the cloud and suspending low-priority data preloading.

[0013] Preferably, the user operation data stored in the cloud adopts an encryption mechanism and is shared among multiple terminals based on user authorization.

[0014] Preferably, the triggering conditions for data preloading further include: (a) Predicting future high-probability usage scenarios based on user history; (b) Dynamically adjust the preloading strategy based on the power level of the mobile terminal and the network status.

[0015] Preferably, the updating of the high-frequency usage scenario template also includes data synchronization among multiple terminals of the user, wherein the data is synchronized to other associated terminals based on the user's frequently used mobile terminal.

[0016] 1. By identifying user accounts and binding frequently used mobile terminals, a correlation model between user operation behaviors and terminal resources is established, enabling personalized data management and preloading based on user habits.

[0017] 2. By analyzing historical user operation data, we generate high-frequency usage scenario templates, and combine them with time distribution (seasons, holidays, weekends, and daily time periods) and network connection types (Wi-Fi, mobile data networks) to make data caching more accurate and efficient.

[0018] 3. Monitor terminal resource usage (such as CPU usage) and trigger data preloading during idle periods or when resource usage is low to avoid impacting device performance. When CPU usage exceeds the threshold, data updates are delayed and completed in the background through cloud coordination.

[0019] 4. Cloud-client collaboration: When the client is active, it proactively updates. The proactive update rule is: the cloud pushes the data to be distributed in the form of data tags. The tags include the data type, size, and priority to be pushed. The client proactively retrieves the data based on these tags and current client resources. When the client is inactive, the cloud pushes update instructions based on the user's preferred schedule, waking up the client's background process to complete data update synchronization and ensure data timeliness.

[0020] 5. Data tagging further includes data usage classification (user operation data, advertising data and log data). The download priority of advertising data is dynamically adjusted based on user operation habits, improving resource utilization.

[0021] 6. The priority judgment logic for high-frequency scenario templates is that the user operation time weight is higher than the terminal resource utilization weight, and the trigger condition for waking up the client with cloud push instructions prioritizes matching the user's historical active time period, ensuring a smooth user experience.

[0022] 7. When the client detects a system vulnerability or abnormal log, it automatically triggers data upload to the cloud and suspends low-priority data preloading. At the same time, user operation data stored in the cloud adopts an encryption mechanism and is shared across multiple terminals based on user authorization to ensure data security.

[0023] 8. Data preloading is triggered by not only predicting high-probability future usage scenarios based on historical user actions, but also dynamically adjusting the preloading strategy based on the mobile terminal's battery level and network status. Furthermore, updates to high-frequency usage scenario templates also include data synchronization across multiple user terminals, improving the efficiency of multi-device collaboration.

[0024] In summary, the present invention achieves more efficient and intelligent data caching and management in the field of smart home, significantly improving the user experience and the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of the present invention; DETAILED DESCRIPTION

[0026] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention. Example 1

[0027] A method for dynamically caching mobile data based on user habits and device resources, comprising the following steps: S1: Identify user accounts and bind them to frequently used mobile terminals, and establish a correlation model between user operation behaviors and terminal resources; In the smart home system, users log in to their accounts through the smart home mobile application. After the system recognizes the user account, it binds it to the user's frequently used mobile terminal, such as a mobile phone or tablet. When a user logs in to the smart home app using a mobile phone, the system binds the user's account to the mobile phone.

[0028] Next, the system begins to build a correlation model between user operations and terminal resources. For example, in daily use, users often use mobile apps to turn on smart lights, adjust the temperature of smart air conditioners, and start smart sweeping robots after returning home from get off work in the evening. The system records these user operations and collects resource information from the mobile terminal, such as CPU usage and memory usage. After a period of data accumulation, the system analyzes the CPU utilization rate, memory usage, and other resource status of the mobile terminal when the user performs these operations. This establishes a correlation model between user operations and terminal resources, providing basic data support for subsequent data caching strategies.

[0029] S2: Generates high-frequency usage scenario templates based on historical user operation data. The templates include the user's usage frequency, operation type, time distribution, and network connection type for different functional modules. The time distribution is divided into seasons, holidays, weekends, and daily time periods according to date type, and the network connection types include Wi-Fi and mobile data networks.

[0030] The system generates high-frequency usage scenario templates based on the user's historical operation data. Suppose the user's historical operation data shows that on weekdays, they usually arrive home around 7 pm. After arriving home, they will first turn on the smart lights in the living room (high frequency of use). The operation type is to turn on the lights. The time is distributed between 7:00 PM and 7:10 PM on weekdays. At this time, the network connection type is generally the home WIFI; then the air conditioner temperature will be adjusted. The operation type is to set the temperature. The time is distributed between 7:10 PM and 7:15 PM. The network connection type is also WIFI. On weekends, users sometimes start the smart sweeping robot around 10 am. The operation type is to start the cleaning task. The time is distributed between 10:00 AM and 10:10 AM on weekends. The network connection type is also WIFI.

[0031] Based on this data, the system generates high-frequency usage scenario templates for different smart home modules (such as smart lighting, smart air conditioning, and smart robot vacuums), including usage frequency, operation type, time distribution (by day type, weekdays, weekends), and network connection type (Wi-Fi). This template clearly reflects users' usage habits for each smart home module at different times and in different network environments.

[0032] The priority judgment logic for high-frequency scenario templates is: the weight of user operation time is higher than the weight of terminal resource occupancy, and the trigger condition for waking up the client by cloud push instructions is prioritized to match the user's historical active time period.

[0033] For example, users frequently use smart home devices around 7 p.m. on weekdays. Even if the mobile terminal resource usage rate is high at this time, the system still gives priority to user operation time and preloads data and performs related operations in this time period according to the high-frequency scenario template; when the cloud pushes update instructions to wake up the client, it gives priority to the user's historically active time period (such as 7 a.m. and 7 p.m.) to improve the timeliness and effectiveness of data updates and better meet user usage needs.

[0034] Furthermore, the update of frequently used scenario templates also includes data synchronization across multiple users' devices, with data being synchronized from the user's frequently used mobile device (such as their phone) to other associated devices (such as a tablet). If a user's operating habits for smart home devices on their phone change, resulting in an update to a frequently used scenario template, the system will synchronize the updated template to the tablet, ensuring that users can use the same, up-to-date scenario templates on different devices, providing a consistent user experience and data caching strategy. S3: Real-time collection of user operation behavior data, combined with time distribution, location environment, network environment information and terminal resource utilization, dynamically updates high-frequency scenario templates; For example, in the summer, users notice high temperatures and begin using the air conditioner's cooling mode more frequently, extending their usage time. The system collects recent changes in the user's air conditioner operating behavior and, based on the current summer's time distribution, the user's home location (obtained through their phone's GPS), their home Wi-Fi network, and their mobile device's resource usage, dynamically updates the high-frequency scenario template for smart air conditioners. This increases the frequency of cooling mode use and expands the usage time range, making the template more tailored to the user's current usage habits.

[0035] For example, if a user goes out for a trip on a weekend and does not perform any smart home operations, the system will adjust the high-frequency scenario template for the weekend based on the user's location environment information (displayed as being away from home) and operation behavior data, reducing the predicted frequency of use of the smart home function module during this time period, so that the template can reflect changes in user behavior in a timely manner.

[0036] S4: Monitor the terminal resource occupancy status, and trigger data preloading during idle periods or low resource occupancy states based on high-frequency scenario templates and resource occupancy status; among which, the terminal resource occupancy status includes CPU usage. If it is detected that the CPU usage exceeds the threshold, the data update operation is delayed and the update is completed during the background low-load period through cloud coordination.

[0037] When the system detects that the user's phone is in use at night (typically during low-utility hours with low terminal resource utilization), combined with the prediction in the high-frequency scenario template that the user is likely to use smart lighting and air conditioning the next morning, it triggers the pre-loading of smart lighting and air conditioning related data during the nighttime idle period. If the phone's CPU usage is detected to exceed a threshold (such as 70%), the data update operation is delayed and the cloud is coordinated to complete the update during a low-load period in the background. For example, if the phone's CPU usage reaches 80% while the user is using the phone, the system will delay the update of the smart home data and report it to the cloud, waiting one minute and then retrying.

[0038] Furthermore, the triggering conditions for data preloading include: (a) Predicting future high-probability usage scenarios based on user history; (b) Dynamically adjust the preloading strategy based on the power level of the mobile terminal and the network status.

[0039] For example, based on the user's historical operation data, the system predicts that the user is likely to use the smart oven for baking on the upcoming weekend. Before the weekend, if the user's phone has sufficient battery and is connected to a Wi-Fi network, the system preloads the smart oven's relevant operation data and recipe data. If the phone's battery is low, the system reduces the amount of preloaded data based on the battery level or waits until the phone is connected to a power source to preload. If the user is on a mobile data network, the system decides whether to preload or reduces the resolution of the preloaded data based on network traffic and user settings to save data, enabling flexible adjustment of the data preloading strategy.

[0040] S5: Dynamic data update and synchronization are completed through the collaborative mechanism between the cloud and the client.

[0041] Furthermore, the collaboration mechanism between the cloud and the client includes: a) When the client is active, it will proactively update. The proactive update rule is: the cloud pushes the data to be sent in the form of data tags. The tags include the type of data to be sent, the size, and the priority. The client proactively obtains data based on the tags and the current client resource situation. b) When the client is inactive, the cloud pushes update instructions based on the user's usual time to wake up the client's background process to complete data update synchronization.

[0042] When a user's phone is active (for example, when using a smart home app to check device status or perform an operation), the mobile client proactively requests an update. The cloud pushes information based on data tags, including data type (such as smart light control data or air conditioner status data), size, and priority. For example, when a user checks the status of a smart light in the app, the client requests an update from the cloud. Based on the data tags, the cloud determines that the smart light status data represents user operation data and pushes its data type, size, and higher priority to the client. The client then updates the data based on this information. When a user's phone is inactive (such as with the screen locked or an app running in the background), the cloud pushes an update command based on the user's usual time (for example, based on a high-frequency scenario template, users typically access smart home devices after waking up at 7:00 AM) to wake up the client's background process and complete data synchronization. For example, at 6:00 AM, the cloud pushes an update command based on the user's usual time, waking up the mobile client's background process and updating smart home-related data, ensuring that the data is up to date when the user wakes up and uses the app.

[0043] Data tagging further includes data usage classification, which includes user operation data, advertising data, and log data. The download priority of advertising data is dynamically adjusted based on user operation habits. In the field of smart home, user operation data (such as lighting control, device switching, and other operation data) has a higher priority; the download priority of advertising data is dynamically adjusted based on user operation habits. For example, if the user has never clicked on an advertisement in a smart home app, the system will lower the download priority of the advertising data; if the user occasionally clicks on an advertisement, the system will appropriately increase the download priority of the advertising data based on the frequency of the click. Log data (such as device operation logs, operation logs, etc.) is used for system maintenance and troubleshooting, has a moderate priority, and is transmitted and stored on the premise of ensuring that user operation data and important system data are updated.

[0044] Furthermore, when the client detects a system vulnerability or abnormal log, it automatically triggers data upload to the cloud and suspends low-priority data preloading.

[0045] For example, when a smart home app detects an abnormal operation log of a smart door lock (such as multiple incorrect password attempts), the system automatically uploads the abnormal log and related data to the cloud for technical personnel to analyze and process; at the same time, it suspends the preloading of low-priority data such as advertising data, and concentrates resources to ensure the upload of important data and the safe operation of the system.

[0046] Furthermore, user operation data stored in the cloud is encrypted and shared across multiple terminals based on user authorization.

[0047] In addition to using their phones to control smart home devices, users also use tablets for operations. After users authorize the multi-terminal sharing function in the system, the cloud will synchronize the encrypted user operation data to the user's tablet, ensuring that users can obtain consistent smart home device status and operation records on different terminals, while also ensuring data security and preventing data leakage.

[0048] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for dynamic caching of mobile data based on user habits and device resources, characterized in that: The following steps are involved: S1: Identify user accounts and bind them to frequently used mobile terminals, and establish a correlation model between user operation behaviors and terminal resources; S2: Generate a high-frequency usage scenario template based on the user's historical operation data. The template includes the user's usage frequency, operation type, time distribution, and network connection type for different functional modules; S3: Collect user operation behavior data in real time, combine time distribution, location environment, network environment information and terminal resource occupancy rate, and dynamically update the high-frequency scenario template; S4: monitoring the terminal resource occupancy status, and triggering data preloading in idle time or low resource occupancy state according to the high-frequency scenario template and resource occupancy status; S5: Dynamic data update and synchronization are completed through the collaborative mechanism between the cloud and the client.

2. The method for dynamic caching of mobile terminal data based on user habits and device resources according to claim 1, characterized in that: The time distribution in step S2 is divided into seasons, holidays, weekends and daily time periods according to date types, and the network connection types include WIFI and mobile data network.

3. The method for dynamic caching of mobile terminal data based on user habits and device resources according to claim 1, characterized in that: The terminal resource occupancy status in step S4 includes the CPU usage rate. If it is detected that the CPU usage rate exceeds the threshold, the data update operation is delayed and the update is completed during the background low-load period through cloud coordination.

4. The method for dynamic caching of mobile terminal data based on user habits and device resources according to claim 1, characterized in that: In step S5, the coordination mechanism between the cloud and the client includes: a) When the client is active, it will proactively update. The proactive update rule is: the cloud pushes the data to be sent in the form of data tags. The tags include the type of data to be sent, the size, and the priority. The client proactively obtains data based on the tags and the current client resource situation. b) When the client is inactive, the cloud pushes update instructions based on the user's usual time to wake up the client's background process to complete data update synchronization.

5. The method for dynamic caching of mobile terminal data based on user habits and device resources according to claim 4, characterized in that: The data tag further includes data usage classification, which includes user operation data, advertising data and log data, wherein the download priority of advertising data is dynamically adjusted based on user operation habits.

6. The method for dynamic caching of mobile terminal data based on user habits and device resources according to claim 1, characterized in that: The priority judgment logic of the high-frequency scenario template in step S2 is: the weight of user operation time is higher than the weight of terminal resource occupancy rate, and the trigger condition of the cloud push instruction to wake up the client is preferentially matched with the user's historical active time period.

7. The method for dynamic caching of mobile terminal data based on user habits and device resources according to claim 1, characterized in that: Also includes: When the client detects a system vulnerability or abnormal log, it automatically triggers data upload to the cloud and suspends low-priority data preloading.

8. The method for dynamic caching of mobile terminal data based on user habits and device resources according to claim 1, characterized in that: The user operation data stored in the cloud adopts an encryption mechanism and is shared across multiple terminals based on user authorization.

9. The method for dynamic caching of mobile terminal data based on user habits and device resources according to claim 1, characterized in that: The triggering conditions for data preloading further include: (a) Predicting future high-probability usage scenarios based on user history; (b) Dynamically adjust the preloading strategy based on the power level of the mobile terminal and the network status.

10. The method for dynamic caching of mobile terminal data based on user habits and device resources according to claim 1, characterized in that: The updating of the high-frequency usage scenario template also includes data synchronization between multiple terminals of the user, wherein the user's frequently used mobile terminal is used as a benchmark and synchronized to other associated terminals.

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