Data processing method and system based on wireless context awareness and super object model, terminal and storage medium

Abstract

The invention discloses a data processing method and system based on wireless context awareness and a super object model, a terminal and a storage medium, and the method comprises the steps: periodically obtaining wireless communication signal data between devices, carrying out the analysis and processing of the wireless communication signal data, and generating a structured context label representing a physical context between the devices; fusing the structured scene label serving as a dynamic attribute into a logic equipment model of a distributed operating system, and constructing and maintaining an enhanced distributed super object model; when a data synchronization request is received, querying the distributed super object model to obtain a scene label of the target equipment, and matching a corresponding data synchronization strategy in a preset strategy library based on the scene label; and processing the to-be-synchronized data according to the data synchronization strategy, and differentially distributing the processed data to the corresponding target equipment through the distributed soft bus. According to the method, the physical context awareness capability of the hyperterminal model is enhanced, and the data management efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of distributed computing and wireless communication technologies, and in particular to a data processing method, system, terminal, and computer-readable storage medium based on wireless context awareness and super object model. Background Technology

[0002] While existing hyperterminal models in distributed operating systems can achieve logical connections and data synchronization between devices, their core problem lies in the lack of awareness of the physical context between devices. Specifically: The lack of physical context: The system only focuses on the logical connections of devices (such as whether they are online or belong to the same user), but cannot perceive the relative position, distance, or movement trend of devices in physical space. For example, the system cannot distinguish between "a phone right next to the smart screen" and "a phone in another room." This "physical blindness" prevents the super terminal from understanding the true physical environment in which the device is located.

[0003] The "one-size-fits-all" approach to data management and resource waste: Due to a lack of physical context awareness, distributed data management strategies often adopt a "one-size-fits-all" approach. For example, a 10MB high-resolution photo might be indiscriminately synchronized to all devices, including smartwatches with screens as small as 1.5 inches. This not only consumes valuable network bandwidth but also drains the battery and storage space of small-screen devices, which users on those devices don't need such high-resolution images, resulting in significant resource waste.

[0004] Fragmented Interaction Experience: Existing cross-device interactions often rely on explicit user actions. The system cannot intelligently recommend interaction methods or preload data based on scenarios such as the physical proximity or distance of the device, resulting in an unnatural and unintelligent user experience.

[0005] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0006] The main objective of this invention is to provide a data processing method, system, terminal, and computer-readable storage medium based on wireless context awareness and super terminal models, aiming to solve the problems of low data management efficiency, resource waste, and insufficient intelligent interactive experience caused by the lack of physical context awareness in existing super terminal models.

[0007] To achieve the above objectives, the present invention provides a data processing method based on wireless scene awareness and a super object model, the data processing method based on wireless scene awareness and a super object model comprising the following steps: The wireless communication signal data between devices is acquired periodically, and the wireless communication signal data is analyzed and processed to generate structured context labels that characterize the physical context between the devices. The structured scenario tags are integrated as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model. Upon receiving a data synchronization request, the distributed super-object model is queried to obtain the scenario tag of the target device. Based on the scenario tag, the corresponding data synchronization strategy is matched in the preset strategy library. According to the data synchronization strategy, the data to be synchronized is processed, and the processed data is distributed to the corresponding target devices in a differentiated manner through a distributed soft bus.

[0008] Optionally, the data processing method based on wireless context awareness and super-object model, wherein the periodic acquisition of wireless communication signal data between devices and the analysis and processing of the wireless communication signal data to generate structured context labels characterizing the physical context between devices specifically includes: The raw wireless data is periodically obtained from the underlying HDF framework, and the raw wireless data is preprocessed to obtain wireless communication signal data. The Wi-Fi signal strength or Bluetooth signal strength in the wireless communication signal data is analyzed, and the distance relationship between devices is quantified into a proximity level based on the analysis results. The relative orientation and motion perception between devices are identified by analyzing the Wi-Fi channel state information in the wireless communication signal data and by analyzing the phase and amplitude changes of the Wi-Fi channel state information. Structured context labels representing the physical context between devices are constructed based on the proximity level, the relative orientation, and the motion perception.

[0009] Optionally, the data processing method based on wireless context awareness and super object model, wherein identifying the relative orientation and motion perception between devices through the phase and amplitude changes of the Wi-Fi channel state information specifically includes: The phase information of multiple subcarriers in the Wi-Fi channel state information is extracted, and the signal angle of arrival difference is calculated by analyzing the changes of the phase information between different antenna pairs. Based on the signal arrival angle difference, determine the relative orientation of the source device with respect to the target device; By tracking the change pattern of the phase information or signal angle difference over time, the motion trend of the source device relative to the target device is identified, including relative or moving away.

[0010] Optionally, the data processing method based on wireless scene awareness and super object model, wherein constructing structured scene labels representing the physical scene between devices based on the proximity level, the relative orientation, and the motion perception specifically includes: The unique identifier of the target device and the proximity level are associated and encapsulated to form a basic tag unit containing the target and proximity fields; When a valid relative orientation or motion perception is identified, the orientation information or motion trend information is added as a new field, and the new field is merged with the basic label unit; The output includes key-value pair structured data containing at least the target device identifier and proximity information, which serves as the structured context label.

[0011] Optionally, the data processing method based on wireless context awareness and the super-object model, wherein the step of fusing the structured context labels as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model, further includes: Receive and maintain the structured context labels generated within a certain time window, analyze the structured context labels, and calculate the intent stability score; When the intent stability score reaches a third preset threshold, an enhanced context label is generated, which includes intent stability attributes. The enhanced context labels are used to construct the distributed super-object model, and the intent stability attribute is used as a trigger condition when matching the corresponding data synchronization strategy based on the context labels.

[0012] Optionally, the data processing method based on wireless context awareness and the super-object model, wherein the step of fusing the structured context labels as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model specifically includes: Subscribe to and listen for update events from the structured scenario tags. When an update event is detected, obtain the logical device model of all devices in the current super terminal from the model service of the distributed operating system. The logical device model includes device identifier, device type and hardware capability attributes. The structured scenario label is used as a dynamic attribute group, and the dynamic attribute group is added to the device node in the logical device model that corresponds to the target device identifier in the scenario label. Output and update an enhanced distributed super-object model that includes all the original attributes of the logical device model and the dynamic attribute group.

[0013] Optionally, in the data processing method based on wireless context awareness and super object model, the preset strategy library contains at least one differentiated strategy based on screen size. The logic of the differentiation strategy is as follows: if the screen size of the target device is greater than the first preset threshold, then the strategy of synchronizing the original data or the high-quality version data is matched. If the target device's screen size is smaller than the second preset threshold, then a strategy of synchronizing compressed or cropped low-quality version data will be used. Wherein, the first preset threshold is greater than the second preset threshold.

[0014] Optionally, the data processing method based on wireless context awareness and super object model, wherein the step of matching a corresponding data synchronization strategy in a preset strategy library based on the context tag, further includes: If no policy is matched in the policy library based on the scenario tag, a default synchronization policy is executed to synchronize the original data or the default processed version of the data to be synchronized to the target device.

[0015] Optionally, the data processing method based on wireless context awareness and super object model, wherein processing the data to be synchronized according to the data synchronization strategy specifically includes: Based on the proximity information in the scene tags and the screen size information of the target device, the data to be synchronized is compressed or cropped to generate data versions of different quality or specifications. When the scenario tag indicates that the target device and the source device are moving closer to each other, the high-definition version of the data to be synchronized is pre-cached to the target device.

[0016] Optionally, the data processing method based on wireless context awareness and super object model further includes: When there are wireless communication signal data and / or sensor data from multiple sources, the wireless communication signal data and / or sensor data are fused through a preset weight allocation and conflict resolution algorithm to generate structured scene labels.

[0017] Optionally, in the data processing method based on wireless scene awareness and super object model, the wireless communication signal data further includes ranging and positioning signals from an ultra-wideband (UWB) chip. The ranging and positioning signals are used to generate distance and / or orientation values ​​with centimeter-level accuracy, and the distance and / or orientation values ​​are used as the quantitative basis for proximity and / or orientation in the structured scene label.

[0018] Optionally, the data processing method based on wireless context awareness and super object model further includes: Construct a graph model in which devices are treated as nodes, and use the structured context labels between devices to define and weight the edges connecting the nodes; Based on the scenario tags, a corresponding data synchronization strategy is matched in the preset strategy library, and the data flow path and resource allocation are optimized based on graph theory algorithms.

[0019] Optionally, in the data processing method based on wireless context awareness and super object model, the generation process of the data synchronization strategy in the preset strategy library is as follows: Analyze users' historical data synchronization operation records and corresponding contextual tags using machine learning models; Based on the historical data synchronization operation records and the corresponding scenario tags, the triggering conditions of the existing strategy are automatically adjusted or new personalized data synchronization strategies are dynamically generated.

[0020] Furthermore, to achieve the above objectives, the present invention also provides a data processing system based on wireless scene awareness and a super object model, wherein the data processing system based on wireless scene awareness and a super object model includes: The wireless context awareness module is used to periodically acquire wireless communication signal data between devices, analyze and process the wireless communication signal data, and generate structured context labels that characterize the physical context between devices. The super-object model fusion module is used to fuse the structured scenario tags as dynamic attributes into the logical device model of the distributed operating system, thereby constructing and maintaining an enhanced distributed super-object model. The synchronization strategy matching module is used to query the distributed super-object model when a data synchronization request is received, obtain the scenario label of the target device, and match the corresponding data synchronization strategy in the preset strategy library based on the scenario label. The distributed data management module is used to process the data to be synchronized according to the data synchronization strategy, and to distribute the processed data to the corresponding target devices in a differentiated manner through a distributed soft bus.

[0021] Optionally, in the data processing system based on wireless context awareness and super object model, the wireless context awareness module includes a raw data processing unit, a signal strength analysis unit, a channel state analysis unit, and a tag construction unit. The raw data processing unit is used to periodically obtain raw wireless data from the underlying HDF framework, preprocess the raw wireless data, and obtain wireless communication signal data. The signal strength analysis unit is used to analyze the Wi-Fi signal strength or Bluetooth signal strength in the wireless communication signal data, and quantify the distance relationship between devices into proximity level based on the analysis results; The channel state analysis unit is used to analyze the Wi-Fi channel state information in the wireless communication signal data, and to identify the relative position and motion perception between devices by the phase and amplitude changes of the Wi-Fi channel state information. The tag construction unit is used to construct structured context tags that characterize the physical context between devices based on the proximity level, the relative orientation, and the motion perception.

[0022] Optionally, in the data processing system based on wireless context awareness and super object model, the super object model fusion module includes a logic device model acquisition unit, a dynamic attribute addition unit, and an enhanced model output unit. The logical device model acquisition unit is used to subscribe to and listen to update events from the structured scenario tags. When the update event is heard, it obtains the logical device models of all devices in the current super terminal from the model service of the distributed operating system. The logical device model includes device identifier, device type and hardware capability attributes. The dynamic attribute adding unit is used to take the structured scenario label as a dynamic attribute group and add the dynamic attribute group to the device node in the logical device model that corresponds to the target device identifier in the scenario label. The enhanced model output unit is used to output and update an enhanced distributed super-object model that includes all the original attributes of the logical device model and the dynamic attribute group.

[0023] Optionally, in the data processing system based on wireless context awareness and super object model, the synchronization strategy matching module includes a context label matching unit and a default synchronization strategy execution unit. The scenario tag matching unit is used to query the distributed super-object model when a data synchronization request is received to obtain the scenario tag of the target device, and match the corresponding data synchronization strategy in the preset strategy library based on the scenario tag. The default synchronization strategy execution unit is used to execute a default synchronization strategy if no strategy is matched in the strategy library based on the scenario tag, and synchronize the original data or the default processed version of the data to be synchronized to the target device.

[0024] Optionally, in the data processing system based on wireless context awareness and super object model, the distributed data management module includes a synchronous data processing unit, a data caching unit, and a data distribution unit. The synchronization data processing unit is used to compress or crop the data to be synchronized based on the proximity information in the scene tag and the screen size information of the target device, and generate data versions of different quality or specifications. The data caching unit is used to pre-cache the high-definition version of the data to be synchronized to the target device when the scene tag indicates that the target device and the source device are in a moving trend of approaching each other; The data distribution unit is used to distribute the processed data to the corresponding target devices in a differentiated manner through a distributed soft bus.

[0025] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a data processing program based on wireless context awareness and a super object model stored in the memory and executable on the processor, wherein when the data processing program based on wireless context awareness and a super object model is executed by the processor, it implements the steps of the data processing method based on wireless context awareness and a super object model as described above.

[0026] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a data processing program based on wireless context awareness and a super object model, and the data processing program based on wireless context awareness and a super object model, when executed by a processor, implements the steps of the data processing method based on wireless context awareness and a super object model as described above.

[0027] In this invention, wireless communication signal data between devices is acquired periodically, and the wireless communication signal data is analyzed and processed to generate structured scenario tags that characterize the physical scenario between devices. The structured scenario tags are then integrated as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model. Upon receiving a data synchronization request, the distributed super-device model is queried to obtain the context label of the target device. Based on the context label, a corresponding data synchronization strategy is matched from a preset strategy library. According to the data synchronization strategy, the data to be synchronized is processed, and the processed data is distributed differentially to the corresponding target devices via a distributed soft bus. This invention transforms the wireless communication link between devices from a simple data pipeline into an environmental sensor, enhancing the physical context awareness capability of the super-terminal model and improving data management efficiency. Attached Figure Description

[0028] Figure 1 This is a flowchart of a preferred embodiment of the data processing method based on wireless scene awareness and super object model of the present invention; Figure 2This is a schematic diagram of the data processing method based on wireless scene awareness and super object model of the present invention. Figure 3 This is a flowchart of the data processing method based on wireless scene awareness and super object model of the present invention for generating structured scene tags that represent the physical scene between devices; Figure 4 This is a flowchart illustrating the process of identifying the relative orientation and motion perception between devices in the data processing method based on wireless scene awareness and super object model of this invention. Figure 5 This is a flowchart of the data processing method based on wireless scene awareness and super object model of the present invention, which constructs structured scene labels representing the physical scene between devices according to proximity level, relative orientation and motion perception; Figure 6 This is a flowchart of the data processing method based on wireless context awareness and super object model of the present invention for calculating the intent stability score; Figure 7 This is a flowchart illustrating the construction and maintenance of an enhanced distributed super-object model in the data processing method based on wireless context awareness and super-object model of this invention. Figure 8 This is a flowchart of the context-aware distributed data management process in the data processing method based on wireless context awareness and super object model of the present invention. Figure 9 This is a flowchart of the data synchronization strategy generation method in the data processing method based on wireless scene awareness and super object model of the present invention; Figure 10 This is a flowchart illustrating the process of processing data to be synchronized according to the data synchronization strategy in the data processing method based on wireless scene awareness and super object model of the present invention. Figure 11 This is a flowchart of data synchronization based on a graph model in the data processing method based on wireless scene awareness and super object model of the present invention; Figure 12 This is a structural diagram of a preferred embodiment of the data processing system based on wireless scene awareness and super object model of the present invention; Figure 13 This is a structural diagram of the wireless sensing module of the data processing system based on wireless scene awareness and super object model of the present invention; Figure 14 This is a structural diagram of the super object model fusion module of the data processing system based on wireless scene awareness and super object model of the present invention; Figure 15 This is a structural diagram of the synchronization strategy matching module of the data processing system based on wireless scene awareness and super object model of the present invention; Figure 16This is a structural diagram of the distributed data management module of the data processing system based on wireless scene awareness and super object model of the present invention; Figure 17 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0029] This application provides a data processing method, system, terminal, and storage medium based on wireless scene awareness and super object model. To make the objectives, technical solutions, and effects of this application clearer and more explicit, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining this application and are not intended to limit this application.

[0030] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0031] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0032] The preferred embodiment of the data processing method based on wireless scene awareness and super object model of the present invention, such as Figure 1 and Figure 2 As shown, the data processing method based on wireless context awareness and super object model includes the following steps: Step S10: Periodically acquire wireless communication signal data between devices, and analyze and process the wireless communication signal data to generate structured scenario tags that characterize the physical scenario between devices.

[0033] like Figure 3 As shown, the periodic acquisition of wireless communication signal data between devices, and the analysis and processing of the wireless communication signal data to generate structured context labels characterizing the physical context between devices, specifically includes: Step S21: Periodically obtain raw wireless data from the underlying HDF framework, preprocess the raw wireless data to obtain wireless communication signal data; Step S22: Analyze the Wi-Fi signal strength or Bluetooth signal strength in the wireless communication signal data, and quantify the distance relationship between devices into proximity level based on the analysis results; Step S23: Analyze the Wi-Fi channel state information in the wireless communication signal data, and identify the relative orientation and motion perception between devices by the phase and amplitude changes of the Wi-Fi channel state information; Step S24: Construct structured scene labels representing the physical scene between devices based on the proximity level, the relative orientation, and the motion perception.

[0034] Specifically, raw wireless data is periodically obtained from the underlying HDF framework, and the raw wireless data is preprocessed to obtain wireless communication signal data.

[0035] Raw wireless data is periodically retrieved from the underlying Hardware Driver Foundation (HDF) framework. This raw data consists of low-level signal information from the device's wireless network card or Bluetooth chip. Subsequently, the raw wireless data undergoes preprocessing (such as filtering, noise reduction, and calibration) to eliminate environmental interference and device differences, resulting in clean and usable wireless communication signal data.

[0036] Furthermore, the Wi-Fi signal strength or Bluetooth signal strength in the wireless communication signal data is analyzed, and the distance relationship between devices is quantified into a proximity level based on the analysis results.

[0037] Understandably, this involves analyzing the Wi-Fi signal strength (RSSI, Received Signal Strength Indicator) or Bluetooth signal strength in the pre-processed wireless communication signal data. Signal strength is correlated with propagation distance; by analyzing this strength value, the distance relationship between devices can be quantified into several discrete proximity levels such as "very close," "relatively close," "relatively far," and "far away."

[0038] Furthermore, the Wi-Fi channel state information in the wireless communication signal data is analyzed, and the relative orientation and motion perception between devices are identified by the phase and amplitude changes of the Wi-Fi channel state information.

[0039] For hardware platforms that support Wi-Fi Channel State Information (CSI), this invention further analyzes the CSI data. CSI reveals fine-grained characteristics of the wireless channel. By analyzing the phase and amplitude variations of its subcarriers, richer physical scenarios can be identified, such as the relative orientation of devices (e.g., on the left or right) and whether the devices are "approaching" or "moving away."

[0040] like Figure 4 As shown, the method of identifying the relative position and motion sensing between devices through the phase and amplitude changes of the Wi-Fi channel state information specifically includes: The phase information of multiple subcarriers in the Wi-Fi channel state information is extracted, and the signal angle of arrival difference is calculated by analyzing the changes of the phase information between different antenna pairs. Based on the signal arrival angle difference, determine the relative orientation of the source device with respect to the target device; By tracking the change pattern of the phase information or signal angle difference over time, the motion trend of the source device relative to the target device is identified, including relative or moving away.

[0041] In this embodiment, by performing in-depth analysis of the Wi-Fi channel state information generated by inter-device communication, a refined physical scene perception that goes beyond simple signal strength is achieved, specifically including relative orientation judgment and motion trend recognition.

[0042] When Wi-Fi signals propagate through space, they are reflected, refracted, and scattered by environmental objects, forming multiple propagation paths. Wi-Fi network cards that support CSI can measure and report the channel frequency response on each subcarrier, which includes phase and amplitude information crucial for sensing. Phase information is extremely sensitive to changes in the length of the signal propagation path, and multipath effects cause interference between signals from different directions at the receiving antenna; this interference pattern is directly related to the signal's angle of arrival.

[0043] Specifically, raw CSI data is periodically acquired from the hardware driver layer. First, phase information on multiple orthogonal frequency division multiplexing (OFDM) subcarriers is extracted. Since carrier frequency offsets and sampling frequency offsets between transceivers contaminate phase readings, they must first be calibrated and cleaned using algorithms to obtain usable phase information. On a receiving device equipped with at least two antennas, a small path difference exists along the path of the same signal to different antennas, resulting in a phase difference related to the angle of arrival (AHA) of the same subcarrier received by the two antennas. By analyzing this phase difference (or more generally, analyzing the CSI matrix relationship between multiple antennas), the AHA of the main signal components can be calculated. By comparing the AHA of the signal received by device A from device B with the AHA of the signal received by device B from device A (or, given the orientation of the devices themselves), the system can infer their relative azimuth in the horizontal plane. The calculated azimuth estimate is quantized into discrete direction labels or continuous angle values ​​as part of a structured scene label.

[0044] This invention achieves low-cost, highly covert, and continuous proactive sensing of spatial relationships between devices without requiring additional dedicated sensors (such as cameras or LiDAR). This provides valuable contextual information on orientation and dynamic dimensions for enhanced super-object models, enabling subsequent data management strategies to make more accurate and forward-looking decisions.

[0045] Furthermore, structured context labels representing the physical context between devices are constructed based on the proximity level, the relative orientation, and the motion perception.

[0046] Understandably, by combining the above analysis results, the proximity level, the relative orientation, and the motion perception information are structurally encapsulated to construct machine-readable structured context labels that characterize the physical context between devices. For example, a label might have the following format: {target_device:"LivingRoom_TV",proximity:"high",motion:"approaching"}.

[0047] like Figure 5 As shown, the construction of structured context labels representing the physical context between devices based on the proximity level, the relative orientation, and the motion perception specifically includes: Step S41: Associate and encapsulate the unique identifier of the target device and the proximity level to form a basic tag unit containing the target and proximity fields; Step S42: When a valid relative orientation or motion perception is identified, the orientation information or motion trend information is added as a new field, and the new field is merged with the basic label unit; Step S43: Output key-value pair structured data containing at least the target device identifier and proximity information as the structured context label.

[0048] After generating the perception results of the physical scenario, this invention encapsulates this raw, multi-dimensional information into standardized, machine-readable structured scenario labels. This step ensures a clear, efficient, and scalable data interface between the perception module and the model fusion and policy decision-making modules.

[0049] The core objective of building structured contextual labels is to transform dynamic, multi-source physical sensing data into a unified, self-descriptive, and easily queryable data structure. Its design follows these principles: Structured: Data has predefined fields and formats, avoiding the parsing complexity of free text or non-standard data. Semantic: Each field has a clear name and meaning (e.g., target represents the target device, proximity represents proximity). Scalability: It can flexibly accommodate different dimensions of sensing information, from basic to advanced. Lightweight: The data structure should be concise to meet the requirements of high-frequency updates and low-bandwidth transmission.

[0050] The construction process described is a typical layered, conditional encapsulation process: Step 1: Construct the Basic Tag Unit. This step generates the essential core component of the context tag. The system associates the unique identifier of the detected target device (such as device ID, MAC address, or logical name) with a quantified proximity level (such as "high" or "low"). During encapsulation, key-value pairs are formed using explicit field names (target, proximity) and corresponding values. For example, the basic unit might be represented in memory or a message as: {target: "TV_A", proximity: "high"}. This basic unit contains the most fundamental context information necessary for cross-device data management.

[0051] Step Two: Conditional Merging of Extended Information. This step processes optional, more refined sensing information. The system determines whether valid "relative orientation" or "motion sensing" has been successfully identified within the current sensing cycle. Judgment Logic: If the confidence level output by the orientation judgment algorithm is higher than a threshold, it is considered valid; if the motion trend analysis shows a clear pattern (such as continuous approach), it is considered valid. Merging Operation: For each valid extended information, the system dynamically adds (merges) it as a new key-value pair field (such as orientation: "left" or motion: "approaching") to the existing basic label unit. This process maintains the stability of the core data while allowing for progressive enhancement of sensing capabilities.

[0052] Step 3: Output Structured Data. After the above steps, the system finally outputs a complete structured scenario label. It is a standard collection of key-value pairs (e.g., a JSON object) that contains at least target and proximity fields. This label, as a standardized data package carrying physical scenario semantics, is published to the system's message bus or directly passed to subscribers (such as the Super Object Model Fusion module).

[0053] The method of the present invention will be described in detail below with reference to a preferred embodiment.

[0054] Scenario setting: A user has a smart screen (node ​​A) equipped with OpenHarmony, a HarmonyOS phone (node ​​B), and a HarmonyOS watch (node ​​C) in their home, which together form a "super terminal".

[0055] The wireless context awareness module of the mobile phone (Node B) starts working. It periodically reads the raw wireless data from the local Wi-Fi chip through the underlying HDF framework.

[0056] The module first preprocesses the raw wireless data to filter out random fluctuations and obtain stable wireless communication signal data.

[0057] Proximity analysis: The module analyzes the signal data and finds that the Wi-Fi RSSI value for communicating with the smart screen (node ​​A) is -50dBm. Based on the preset threshold, its proximity level is determined to be "relatively close". At the same time, it finds that the Wi-Fi RSSI value for communicating with the watch (node ​​C) is -85dBm, and its proximity level is determined to be "far away".

[0058] Motion perception analysis: The module further analyzes the Wi-Fi CSI data obtained from the smart screen (node ​​A). By tracking the continuous changes in phase and amplitude, it determines that the motion perception of the mobile phone relative to the smart screen is "stable" (i.e., there is no significant movement trend).

[0059] Tag generation: Based on the above analysis results, the module constructs and outputs structured context labels: {target: A, proximity: "relatively close", motion: "stable"} and {target: C, proximity: "far away", motion: "unknown"}.

[0060] Through the above implementation methods, the present invention successfully endows the distributed operating system with physical context awareness capabilities, transforming the wireless communication link from a "data pipeline" into an "environmental sensor," fundamentally solving the resource waste problem caused by the "one-size-fits-all" approach to data management, and laying a key technological foundation for achieving a truly intelligent and seamless cross-device experience.

[0061] It should be noted that, in another implementation, when periodically acquiring wireless communication signal data between devices and analyzing and processing the wireless communication signal data to generate structured scenario tags characterizing the physical scenario between devices, the wireless communication signal data can also come from standard wireless communication protocols, and the analysis and processing of the wireless communication signal data can be implemented in pure software without relying on additional dedicated sensor hardware.

[0062] Furthermore, the wireless communication signal data may also include signals from an ultra-wideband (UWB) chip; high-precision proximity and / or orientation information is generated based on the UWB signals and used as part of the structured scene label. This is geared towards high-end devices requiring high precision and explicitly relies on additional dedicated hardware.

[0063] Furthermore, the data processing method based on wireless context awareness and super object model also includes: When there are wireless communication signal data and / or sensor data from multiple sources, the wireless communication signal data and / or sensor data are fused through a preset weight allocation and conflict resolution algorithm to generate structured scene labels.

[0064] Understandably, in real-world deployment environments, single wireless context-aware methods have inherent limitations: Wi-Fi / Bluetooth RSSI: susceptible to environmental obstruction and multipath interference, resulting in large fluctuations in measurement results and limited accuracy. Wi-Fi CSI: while highly accurate, it relies on specific hardware support, and resolution in complex environments remains challenging. UWB: extremely high accuracy but requires dedicated hardware, limiting its widespread adoption. NFC: only suitable for very short-range detection. Audio / Gyroscope: limited sensing range and susceptible to environmental noise.

[0065] When a system utilizes multiple sensors simultaneously, inconsistent or even conflicting perception results may occur. For example, due to environmental obstruction, Wi-Fi RSSI might indicate that a device is "far away," while UWB might accurately measure it as "very close." Without an effective fusion and conflict resolution mechanism, the system will be unable to determine which data source to rely on, leading to confusion in context perception and consequently, inaccurate upper-level intelligent decision-making.

[0066] Furthermore, the wireless communication signal data also includes ranging and positioning signals from the ultra-wideband (UWB) chip; The ranging and positioning signals are used to generate distance and / or orientation values ​​with centimeter-level accuracy, and the distance and / or orientation values ​​are used as the quantitative basis for proximity and / or orientation in the structured scene label.

[0067] Understandably, this invention possesses excellent scalability, and its sensing capabilities can be significantly enhanced by integrating higher-precision hardware. Specifically, when the device is equipped with an ultra-wideband (UWB) chip, the system can utilize the ranging and positioning signals it generates to produce physical scene data with centimeter-level accuracy, thereby injecting unprecedentedly refined spatial dimensional information into the enhanced super object model. By fusing UWB high-precision sensing, sub-meter to centimeter-level scene perception is achieved, greatly improving the accuracy and reliability of sensing.

[0068] Therefore, the purpose of this solution is to comprehensively utilize the advantages of each sensor, make up for the shortcomings of a single sensor, handle data inconsistencies, and ultimately generate more accurate, reliable, and comprehensive unified scene labels through a standardized multi-sensor data fusion and conflict resolution algorithm.

[0069] Step S20: Integrate the structured scenario tags as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model.

[0070] Understandably, this step aims to address the core flaw of the existing "super terminal" model, which is "logically connected but physically blind." By injecting a physical context dimension, it upgrades a static logical model that only describes the inherent capabilities of the device into a dynamic model that can reflect the real-time status and relationships of the device in the physical world.

[0071] Specifically, whenever a new "structured context label" is generated or updated (for example, the distance between the mobile phone and the smart screen changes from "far" to "near"), the logical device model of all devices within the current "super terminal" is retrieved from the standard services of the distributed operating system. This standard model typically describes the static attributes of the devices in JSON or a similar structured data format.

[0072] Furthermore, the received "context labels" are treated as new dynamic attributes and attached to the corresponding device nodes in the logical device model. This is the core operation for building the enhanced model. "Dynamic" emphasizes that these attribute values ​​are updated in real time as the physical location of the device changes, in stark contrast to static attributes such as the device's "screen size." After attribute attachment is completed, the final enhanced distributed super-thing model is generated.

[0073] It should be noted that the enhanced distributed super-object model in this embodiment is not built all at once, but exists as a dynamic and continuously updated system state view. This invention continuously monitors the context label stream and updates the dynamic attributes in the enhanced model accordingly, ensuring that the system's perception of the physical world is always real-time and accurate.

[0074] like Figure 6As shown, further, the step of fusing the structured scenario tags as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model also includes: Step S51: Receive and maintain the structured context labels generated within a certain time window, analyze the structured context labels, and calculate the intent stability score; Step S52: When the intent stability score reaches a third preset threshold, an enhanced context label is generated, wherein the enhanced context label contains intent stability attributes; Step S53: Use the enhanced context label to construct the distributed super object model, and use the intent stability attribute as a trigger condition when matching the corresponding data synchronization strategy based on the context label.

[0075] Understandably, before constructing the enhanced super-object model, this embodiment also introduces a step of intent inference and stability assessment of the structured context labels. This step aims to address the problem of instantaneous intent misjudgment that may occur in the core solution during dynamic user movement scenarios, thereby further improving the system's intelligence level and the smoothness of the user experience.

[0076] Although the core solution of this invention can perceive the physical context between devices in real time, when a user moves through space with a device (such as a mobile phone), the system may trigger data synchronization or pre-caching operations based solely on a momentary physical proximity (e.g., the user briefly passing in front of the smart screen). However, this momentary physical proximity does not equate to a stable and continuous intention from the user to engage in cross-device interaction. Such misjudgment can lead to unnecessary network bandwidth consumption, wasted device power, and may interrupt the user's current task, causing confusion in the interaction.

[0077] This greatly improves the accuracy of interaction, fundamentally avoiding false triggers caused by instantaneous changes in the physical environment, and making the system behavior more consistent with the user's true intentions. It eliminates unnecessary data transmission and processing due to misjudgment, saving network bandwidth, device power, and computing resources.

[0078] like Figure 7 As shown, further, the step of fusing the structured scenario tags as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model specifically includes: Step S61: Subscribe to and listen for update events from the structured scenario tags. When the update event is heard, obtain the logical device model of all devices in the current super terminal from the model service of the distributed operating system. The logical device model includes device identifier, device type and hardware capability attributes. Step S62: Treat the structured scenario label as a dynamic attribute group, and add the dynamic attribute group to the device node in the logical device model that corresponds to the target device identifier in the scenario label; Step S63: Output and update an enhanced distributed super-object model that includes all the original attributes of the logical device model and the dynamic attribute group.

[0079] This implementation integrates structured scenario tags as dynamic attributes into the logical device model of the distributed operating system, forming an enhanced distributed super-object model that can reflect the scenario context in real time, thus achieving deep binding between the device model and scenario information.

[0080] Understandably, in step S61, the system first registers its subscription to structured scenario label update events with the event bus of the distributed operating system, establishing a continuous listening mechanism. The update events for structured scenario labels include key metadata such as label content, update timestamp, and a list of target device identifiers (e.g., the label "Commercial and Industrial Rooftop Photovoltaic Power Station - Shading Scenario" needs to be associated with the identifiers of photovoltaic panels affected by shading). When an update event is detected, the model enhancement process is triggered, and the system immediately calls the model service interface of the distributed operating system to retrieve a snapshot of the logical device model for all devices in the current super terminal. The logical device model here is the basic static model of the device. This basic static model includes three types of attributes: device identifier (a globally unique device ID used for precise matching of device nodes), device type (device classification information used to distinguish attribute adaptation rules), and hardware capability attributes (the inherent hardware parameters of the device used to distinguish attribute adaptation rules).

[0081] In step S62, the system encapsulates the monitored structured scenario tags into dynamic attribute groups. The structure of the dynamic attribute groups is consistent with the tag structure, including dynamic attributes such as scenario type, scenario parameters, effective time, and impact range (for example, the dynamic attribute group for "shadow occlusion scenario" includes parameters such as occlusion source, occlusion area, and occlusion duration). Subsequently, the system uses the target device identifier within the dynamic attribute group as the matching keyword, traverses the list of logical device models obtained from the model service, locates the corresponding device node, and adds the dynamic attribute group as a sub-attribute set to the attribute structure of that device node. If a dynamic attribute group is associated with multiple target device identifiers, the above binding process is repeated to achieve attribute enhancement for multiple devices using the same scenario tag. For dynamic attribute groups without matching device identifiers, the system temporarily stores them in the matching queue of the model service and records the logs, automatically completing the binding once the device comes online.

[0082] In step S63, after binding the dynamic attribute groups, the system integrates the original attributes (device identifier, device type, hardware capability attributes) of the logical device model with the newly added dynamic attribute groups to generate an enhanced distributed super-object model. This model is pushed to all subscribed nodes (such as edge computing nodes, cloud management platforms, and application service modules) within the super terminal via the model synchronization interface of the distributed operating system, achieving consistent updates of the model across the entire system. Simultaneously, the model service persistently stores the enhanced distributed super-object model, overwriting the original logical device model snapshot, ensuring that subsequent model query requests can obtain complete device information including the scene context. Furthermore, the system records the model update version number and timestamp, supporting model version rollback and incremental updates.

[0083] Through the above implementation methods, the system is endowed with a sense of physical space: enabling the distributed operating system to move from the abstract logical world into the concrete physical world, and to understand the real spatial relationships between devices. It provides a data foundation for intelligent decision-making: this enhanced model becomes the "intelligent hub" connecting the underlying physical perception with the upper-layer application strategies, making "context-aware distributed data management" possible. It achieves model-driven automation: the data flow strategy of the entire system is automatically driven by this model, without manual user intervention, realizing a truly intelligent experience.

[0084] Step S30: Upon receiving a data synchronization request, query the distributed super-object model to obtain the scenario tag of the target device, and match the corresponding data synchronization strategy in the preset strategy library based on the scenario tag.

[0085] Specifically, this process is triggered by a data synchronization request event in the distributed operating system. This request may originate from a direct user action (such as clicking share in the photo album) or be automatically generated by a system event (such as the camera application generating a new photo). Upon receiving the request, data synchronization does not begin immediately. Instead, a query is first sent to the "Super Object Model Fusion Module" to access the enhanced distributed super object model. From this, context labels rich in physical context (such as proximity, motion, etc.) for all potential target devices are extracted. The obtained context labels are then matched against rules in a pre-defined policy library. The policy library is a collection of multiple "condition-action" rules. Finally, a clear and executable instruction is output, specifying what processing should be performed on the data to be synchronized (such as "synchronize original image," "synchronize thumbnail," "pre-cachive high-definition data").

[0086] like Figure 7 As shown, further, the preset strategy library contains at least one differentiated strategy based on screen size; The logic of the differentiation strategy is as follows: if the screen size of the target device is greater than the first preset threshold, then the strategy of synchronizing the original data or the high-quality version data is matched. If the target device's screen size is smaller than the second preset threshold, then a strategy of synchronizing compressed or cropped low-quality version data will be used. Wherein, the first preset threshold is greater than the second preset threshold.

[0087] It is understandable that the design of this embodiment is based on a clear user need and technological reality: the quality of data consumption experience is strongly correlated with the physical screen size of the display device. Large-screen devices (such as smart screens and tablets) have higher display resolutions and larger viewable areas, capable of fully showcasing the details of high-resolution images and high-definition videos. Providing them with low-quality data will result in blurry images and a degraded experience. Small-screen devices (such as smartwatches and small speaker screens) have extremely limited display area and resolution; most pixel information in high-resolution data cannot be effectively presented, instead wasting the device's processing power, storage space, and network bandwidth. Therefore, the core logic of the differentiation strategy is to establish a mapping relationship between a "screen size threshold" and a "data processing strategy": The first preset threshold (e.g., 10 inches) serves as the threshold for determining a "large screen". When the target device's screen size exceeds this threshold, the system determines that the device has the capability and need to consume high-quality data, and thus matches a strategy of synchronizing raw data or high-quality version data (such as original images or high-definition video streams). This ensures a superior visual experience on large screens.

[0088] The second preset threshold (e.g., 4 inches) serves as a threshold for determining a "small screen". When the target device's screen size is smaller than this threshold, the system determines that the device does not need to receive the full amount of data and is not beneficial to it, thus matching the strategy of synchronizing low-quality versions of data (such as thumbnails, low-bitrate audio) that have been compressed, cropped, or transcoded. This achieves resource protection for small-screen devices.

[0089] The first preset threshold is greater than the second preset threshold, and a buffer zone with medium screen size can be formed between the two, which can correspond to a medium quality data strategy, making the strategy more flexible.

[0090] As can be seen, this invention eliminates the waste of resources caused by blindly synchronizing large amounts of data to small-screen devices, significantly saving network bandwidth, terminal power consumption, and storage space. It ensures that any device receives data quality commensurate with its hardware capabilities, providing users with a predictable and reliable basic experience.

[0091] Furthermore, the step of matching a corresponding data synchronization strategy in a preset strategy library based on the scenario tag further includes: If no policy is matched in the policy library based on the scenario tag, a default synchronization policy is executed to synchronize the original data or the default processed version of the data to be synchronized to the target device.

[0092] It is understood that the policy library is the brain of the intelligent decision-making process of this invention, and its rule set defines the mapping relationship from physical scenarios to data operations. The policy library typically contains, but is not limited to, the following types of rules: Basic resource optimization strategies: IF (proximity == "very close" OR "closer") AND (target.screen_size>10 inch)THEN sync_quality = "original image"; IF (proximity == "far" OR "far away") OR (target.screen_size < 4 inch)THEN sync_quality = "thumbnail"; Predictive experience optimization strategies: IF (motion == "approaching") AND (intent_stable == true) THEN action = "Pre-cached high-definition data"; Default protection policy: IF (no_rule_matched == true) THEN action = "Synchronize default quality data".

[0093] The default protection strategy is the strategy that is executed by default when the scenario label fails to match any of the first two optimization strategies (basic resource optimization strategy and predictive experience optimization strategy) in the strategy library. This mechanism serves as an important security guarantee for the entire scenario-aware data management system, ensuring the robustness of the system and the reliability of its basic functions when facing unforeseen scenarios or an incomplete strategy library.

[0094] Although this invention pre-defines a rich set of context-aware strategies, situations may still arise in actual deployment where strategies cannot be matched. For example, a new type of device may be added to the system, and the strategy library may not yet have defined specific rules for it; or the device may be in a unique combination of physical scenarios that has not been pre-defined. If the system simply reports an error or stops synchronization in such cases, it will disrupt the core user experience of the distributed system, rendering the functionality unusable, which is unacceptable. Therefore, the purpose of this mechanism is to ensure that basic data synchronization functions are executed under any circumstances, guaranteeing the ultimate availability of the system.

[0095] like Figure 8 As shown, specifically, after the module completes its query and matching attempts in the policy library, it enters a clear decision branch: determining whether a specific data synchronization policy has been matched. If yes, the matched specific policy is executed. If not, the default synchronization policy is executed. The default policy is a predefined degradation scheme that does not rely on complex scenario logic. Its typical action is to synchronize the original data or the default processed version of the data to be synchronized to the target device.

[0096] like Figure 9 As shown, further, the generation process of the data synchronization strategy in the preset strategy library is as follows: Step S71: Analyze the user's historical data synchronization operation records and corresponding contextual tags using a machine learning model; Step S72: Based on the historical data synchronization operation records and the corresponding scenario tags, automatically adjust the triggering conditions of the existing strategy or dynamically generate a new personalized data synchronization strategy.

[0097] In this embodiment, the intelligent evolution of the preset strategy library is the core of achieving personalized data management. Traditional strategy libraries based on preset rules, while capable of solving differentiated synchronization problems in basic scenarios, struggle to cover the diverse habits of users and dynamically changing environments. Therefore, the system introduces a machine learning mechanism, transforming the strategy library from a static set of rules into an intelligent decision engine capable of continuous learning and dynamic optimization. This engine's operation begins with in-depth mining of users' historical behavioral data. The system anonymously collects and analyzes massive amounts of "data synchronization operation records" generated by users in the past (e.g., on which device, when, and where, and what type of data was synchronized to which target device), and precisely associates each operation with the complete "contextual label" captured by the system at the time of its occurrence. These contextual labels not only include real-time physical relationships between devices (such as proximity and movement trends) but also cover static device attributes (such as screen size) and possible contextual information (such as time and application type). Through training on these "operation-context" paired datasets, the machine learning model can automatically discover hidden complex patterns and strongly correlated rules, thereby understanding the user's true intentions and preferences in specific multidimensional contexts.

[0098] Based on this understanding, the system can perform two main intelligent behaviors: adjustment, which involves dynamically fine-tuning the trigger thresholds or confidence levels of existing strategies to better align with users' regular habits; and generation, which involves directly creating entirely new, highly personalized synchronization strategies to cover specific user scenarios that frequently occur but are not covered by preset rules. For example, the system might learn that if a user's phone is close to their office tablet during weekday working hours and they are using a document-related application, there is a very high probability that they will trigger the synchronization of the original document to the tablet, thus automatically generating a dedicated and efficient strategy for this complex scenario. In this way, the overall intelligence level of the system and user satisfaction are significantly improved.

[0099] Step S40: According to the data synchronization strategy, process the data to be synchronized, and distribute the processed data to the corresponding target devices in a differentiated manner through a distributed soft bus.

[0100] like Figure 10 As shown, the process of processing the data to be synchronized according to the data synchronization strategy specifically includes: Step S81: Based on the proximity information in the scene tag and the screen size information of the target device, compress or crop the data to be synchronized to generate data of different quality or specifications. Step S82: When the scenario tag indicates that the target device and the source device are moving closer to each other, the high-definition version of the data to be synchronized is pre-cached to the target device.

[0101] Understandably, existing distributed data management suffers from two shortcomings: at the data processing content level, it employs a "one-size-fits-all" synchronization approach, ignoring the physical location and hardware capabilities of the target device, leading to resource waste. At the synchronization timing level, data transmission can only begin after the user issues an explicit command, making it impossible to predict user intent and resulting in interaction delays. Therefore, this invention deeply optimizes data management from both the data processing content and synchronization timing dimensions to achieve a unification of static resource optimization and dynamic experience optimization.

[0102] Specifically, regarding data processing content, this embodiment transforms the traditional push of raw data into pushing the most suitable data version. Its decision-making logic is based on two key scenario dimensions: physical proximity, reflecting the cost of data transmission (bandwidth, latency), and target device screen size, reflecting data consumption needs (display precision). Regarding synchronization timing, this embodiment shifts from passive response to proactive prediction. It utilizes the correlation signal of "proximity movement trends" between devices to predict upcoming user interactions.

[0103] As can be seen, this invention enables on-demand adaptation of data content, providing the most suitable data version based on the physical context and hardware capabilities of the target device. Simultaneously, it achieves intelligent pre-synchronization, predicting user intent based on physical context and completing the transmission of large amounts of data in advance, eliminating waiting time.

[0104] Furthermore, the processed data is distributed to the corresponding target devices in a differentiated manner through a distributed soft bus.

[0105] Specifically, the distributed soft bus of the distributed operating system accurately and efficiently distributes intelligently processed data to different target devices. After policy matching and data processing are completed, one or more processed data versions (such as the original image and thumbnail) and their corresponding list of target devices are submitted to the distributed soft bus. The distributed soft bus acts as an intelligent logistics system, abstracting and unifying the various heterogeneous network connections at the underlying level, eliminating the need for applications to concern themselves with the specific transmission details.

[0106] like Figure 11 As shown, the data processing method based on wireless context awareness and super object model further includes: Step S91: Construct a graph model, in which devices are used as nodes, and the structured context labels between devices are used to define and weight the edges connecting the nodes. Step S92: Based on the scenario tags, match the corresponding data synchronization strategy in the preset strategy library, and optimize the data flow path and resource allocation based on graph theory algorithm.

[0107] Understandably, besides attaching the context as an attribute to the device, a more complex "graph model" can be constructed. In this graph, devices are "nodes," and the "edges" between devices are defined and weighted by dynamic contextual information (such as distance and relative speed). Data management then becomes a graph-based path and resource optimization problem. This approach is an alternative and advanced extension to the aforementioned "attribute attachment" fusion method. By introducing a graph theory model, it elevates the system's understanding of the physical context from the "device attribute" level to the "device relationship network" level, thereby achieving more global and intelligent data management.

[0108] Specifically, a graph model is constructed, abstracting each physical device (such as a mobile phone, smart screen, watch, or speaker) in the "super terminal" network as a node in the graph. Each node retains its inherent logical attributes, such as device type, screen size, and computing power.

[0109] The structured context labels between devices are concretized into edges connecting these nodes. The weights of the edges are quantified from the specific values ​​or levels in the context labels. For example: proximity: closer can be quantified as weight 2; proximity: farther can be quantified as weight 8; relative speed: 0.5 m / s can be directly used as weight; network bandwidth: 50 Mbps can be converted into weight 1 / 50 = 0.02. The weight value should reflect the cost of data transmission on that edge; the farther the distance and the worse the network, the higher the cost, and therefore the greater the weight.

[0110] Based on this graph model, the matching and execution of data synchronization strategies transforms from simple rule matching into a complex graph-based path and resource optimization problem. The system can run mature graph theory algorithms to achieve globally optimal decisions.

[0111] The following is a detailed description of the method of the present invention, following the preferred embodiments mentioned above.

[0112] After phone B generates contextual tags for smart screen A and watch C: Triggering and Acquisition: The "Super Object Model Fusion Module" receives a new scenario label {target: A, proximity: "relatively close", motion: "stable"}. It then retrieves the current logical device model from the system.

[0113] Integration and Construction: The module locates the node with device ID "TV_A" in the logical device model and creates or updates a dynamic attribute group named "context" under it, writing "proximityTo_Phone_B: "nearer" and "motionTrend_Phone_B: "stable" into it. Similarly, the node for watch C is updated.

[0114] At this point, the system no longer simply "knows there is a mobile phone, a smart screen, and a watch." Instead, it explicitly knows: "Mobile phone B is currently very close to smart screen A in physical space and is in a stable state, while watch C is far away from mobile phone B." This "enhanced super-object model," rich in physical context information, provides crucial judgment criteria for subsequent intelligent data-driven decision-making.

[0115] At this point, the soft bus does not broadcast or perform full synchronization, but instead executes differentiated distribution routing based on the received instructions. For example, the soft bus sends a 12MB original image data stream to the smart screen in the living room via a high-bandwidth Wi-Fi link; simultaneously, it sends a mere 200KB thumbnail data packet to the smartwatch in the bedroom via a low-power Bluetooth connection or an optimized narrowband Wi-Fi link. This process is fully automated and completed almost synchronously. In this way, the present invention achieves "delivering the right data to the right device at the right time via the optimal path," ultimately realizing fine-grained scheduling of resources such as network bandwidth, device power consumption, and storage space at the system level, and providing a seamless, smooth, and highly intelligent cross-device experience at the user level.

[0116] It is evident that the present invention has the following significant beneficial effects: (1) Ultimate intelligence and user experience: This enables the "super terminal" to truly have environmental awareness capabilities. Data and services can flow automatically and intelligently according to the physical scene of the user and the device, providing users with a seamless experience that "understands you".

[0117] (2) Significant resource savings: By synchronizing data on demand and in a differentiated manner, unnecessary network bandwidth, device power consumption and storage space consumption are greatly reduced, which is especially beneficial for resource-constrained devices such as wearable devices and IoT sensors.

[0118] (3) Fostering new application scenarios: Based on physical context awareness, numerous innovative applications can be derived. For example, when a user approaches a drone with their mobile phone, the drone automatically and seamlessly switches the high-definition video stream to the mobile phone; when multiple users are having a meeting in a conference room, the document will automatically be synchronized to the few devices that are physically closest to the drone.

[0119] (4) Low cost and easy implementation: This invention mainly relies on software analysis of existing standard wireless signals, without the need to add additional sensor hardware (such as UWB chips), and has extremely high feasibility and cost-effectiveness on existing platforms such as RK3568 / RK3588.

[0120] Furthermore, such as Figure 12 As shown, based on the above-described data processing method based on wireless context awareness and super-object model, the present invention also provides a data processing system based on wireless context awareness and super-object model, wherein the data processing system based on wireless context awareness and super-object model includes: The wireless context awareness module 50 is used to periodically acquire wireless communication signal data between devices, and to analyze and process the wireless communication signal data to generate structured context labels that characterize the physical context between devices. The super-object model fusion module 60 is used to fuse the structured scenario tags as dynamic attributes into the logical device model of the distributed operating system, thereby constructing and maintaining an enhanced distributed super-object model. The synchronization strategy matching module 70 is used to query the distributed super-object model when a data synchronization request is received, obtain the scenario label of the target device, and match the corresponding data synchronization strategy in the preset strategy library based on the scenario label. The distributed data management module 80 is used to process the data to be synchronized according to the data synchronization strategy, and to distribute the processed data to the corresponding target devices in a differentiated manner through a distributed soft bus.

[0121] like Figure 13 As shown, the wireless context awareness module 50 includes a raw data processing unit 501, a signal strength analysis unit 502, a channel state analysis unit 503, and a tag construction unit 504. The raw data processing unit 501 is used to periodically obtain raw wireless data from the underlying HDF framework, preprocess the raw wireless data, and obtain wireless communication signal data. The signal strength analysis unit 502 is used to analyze the Wi-Fi signal strength or Bluetooth signal strength in the wireless communication signal data, and quantify the distance relationship between devices into proximity level based on the analysis results; The channel state analysis unit 503 is used to analyze the Wi-Fi channel state information in the wireless communication signal data, and to identify the relative orientation and motion perception between devices by the phase and amplitude changes of the Wi-Fi channel state information. The tag construction unit 504 is used to construct structured context tags representing the physical context between devices based on the proximity level, the relative orientation, and the motion perception.

[0122] like Figure 14 As shown, the super object model fusion module 60 includes a logic device model acquisition unit 601, a dynamic attribute addition unit 602, and an enhanced model output unit 603; The logical device model acquisition unit 601 is used to subscribe to and listen to update events from the structured scenario tags. When the update event is heard, it obtains the logical device models of all devices in the current super terminal from the model service of the distributed operating system. The logical device model includes device identifier, device type and hardware capability attributes. The dynamic attribute adding unit 602 is used to add the structured scenario label as a dynamic attribute group to the device node in the logical device model that corresponds to the target device identifier in the scenario label. The enhanced model output unit 603 is used to output and update an enhanced distributed super-object model that includes all the original attributes of the logical device model and the dynamic attribute group.

[0123] like Figure 15 As shown, the super object model fusion module 70 includes a logic device model acquisition unit 701, a dynamic attribute addition unit 702, and an enhanced model output unit 703; The logical device model acquisition unit 701 is used to subscribe to and listen to update events from the structured scenario tags. When the update event is heard, it obtains the logical device models of all devices in the current super terminal from the model service of the distributed operating system. The logical device model includes device identifier, device type and hardware capability attributes. The dynamic attribute adding unit 702 is used to add the structured scenario label as a dynamic attribute group to the device node in the logical device model that corresponds to the target device identifier in the scenario label. The enhanced model output unit 703 is used to output and update an enhanced distributed super-object model that includes all the original attributes of the logic device model and the dynamic attribute group.

[0124] like Figure 16 As shown, the synchronization strategy matching module 80 includes a scenario tag matching unit 801 and a default synchronization strategy execution unit 802; The scenario tag matching unit 801 is used to query the distributed super-object model when a data synchronization request is received to obtain the scenario tag of the target device, and match the corresponding data synchronization strategy in the preset strategy library based on the scenario tag. The default synchronization strategy execution unit 802 is used to execute a default synchronization strategy if no strategy is matched in the strategy library based on the scenario tag, and synchronize the original data or the default processed version of the data to be synchronized to the target device.

[0125] Furthermore, such as Figure 17 As shown, based on the above-mentioned data processing method and system based on wireless context awareness and super object model, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 17 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0126] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a data processing program 40 based on wireless context awareness and a super object model, which can be executed by the processor 10 to implement the data processing method based on wireless context awareness and a super object model in this application.

[0127] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the data processing method based on wireless scene awareness and super object model.

[0128] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information about the vehicle and to display a visual user interface. The components of the vehicle communicate with each other via a system bus.

[0129] In one embodiment, when the processor 10 executes the data processing program 40 based on wireless context awareness and super object model in the memory 20, the following steps are performed: The wireless communication signal data between devices is acquired periodically, and the wireless communication signal data is analyzed and processed to generate structured context labels that characterize the physical context between the devices. The structured scenario tags are integrated as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model. Upon receiving a data synchronization request, the distributed super-object model is queried to obtain the scenario tag of the target device. Based on the scenario tag, the corresponding data synchronization strategy is matched in the preset strategy library. According to the data synchronization strategy, the data to be synchronized is processed, and the processed data is distributed to the corresponding target devices in a differentiated manner through a distributed soft bus.

[0130] The process of periodically acquiring wireless communication signal data between devices and analyzing and processing the wireless communication signal data to generate structured context labels characterizing the physical context between devices specifically includes: The raw wireless data is periodically obtained from the underlying HDF framework, and the raw wireless data is preprocessed to obtain wireless communication signal data. The Wi-Fi signal strength or Bluetooth signal strength in the wireless communication signal data is analyzed, and the distance relationship between devices is quantified into a proximity level based on the analysis results. The relative orientation and motion perception between devices are identified by analyzing the Wi-Fi channel state information in the wireless communication signal data and by analyzing the phase and amplitude changes of the Wi-Fi channel state information. Structured context labels representing the physical context between devices are constructed based on the proximity level, the relative orientation, and the motion perception.

[0131] Specifically, identifying the relative position and motion sensing between devices through the phase and amplitude changes of the Wi-Fi channel state information includes: The phase information of multiple subcarriers in the Wi-Fi channel state information is extracted, and the signal angle of arrival difference is calculated by analyzing the changes of the phase information between different antenna pairs. Based on the signal arrival angle difference, determine the relative orientation of the source device with respect to the target device; By tracking the change pattern of the phase information or signal angle difference over time, the motion trend of the source device relative to the target device is identified, including relative or moving away.

[0132] The step of constructing structured context labels representing the physical context between devices based on the proximity level, the relative orientation, and the motion perception specifically includes: The unique identifier of the target device and the proximity level are associated and encapsulated to form a basic tag unit containing the target and proximity fields; When a valid relative orientation or motion perception is identified, the orientation information or motion trend information is added as a new field, and the new field is merged with the basic label unit; The output includes key-value pair structured data containing at least the target device identifier and proximity information, which serves as the structured context label.

[0133] The step of fusing the structured scenario tags as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model includes, prior to: Receive and maintain the structured context labels generated within a certain time window, analyze the structured context labels, and calculate the intent stability score; When the intent stability score reaches a third preset threshold, an enhanced context label is generated, which includes intent stability attributes. The enhanced context labels are used to construct the distributed super-object model, and the intent stability attribute is used as a trigger condition when matching the corresponding data synchronization strategy based on the context labels.

[0134] Specifically, the step of fusing the structured scenario tags as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model includes: Subscribe to and listen for update events from the structured scenario tags. When an update event is detected, obtain the logical device model of all devices in the current super terminal from the model service of the distributed operating system. The logical device model includes device identifier, device type and hardware capability attributes. The structured scenario label is used as a dynamic attribute group, and the dynamic attribute group is added to the device node in the logical device model that corresponds to the target device identifier in the scenario label. Output and update an enhanced distributed super-object model that includes all the original attributes of the logical device model and the dynamic attribute group.

[0135] The preset strategy library contains at least one differentiated strategy based on screen size. The logic of the differentiation strategy is as follows: if the screen size of the target device is greater than the first preset threshold, then the strategy of synchronizing the original data or the high-quality version data is matched. If the target device's screen size is smaller than the second preset threshold, then a strategy of synchronizing compressed or cropped low-quality version data will be used. Wherein, the first preset threshold is greater than the second preset threshold.

[0136] The step of matching a corresponding data synchronization strategy from a preset strategy library based on the scenario tag further includes: If no policy is matched in the policy library based on the scenario tag, a default synchronization policy is executed to synchronize the original data or the default processed version of the data to be synchronized to the target device.

[0137] The step of processing the data to be synchronized according to the data synchronization strategy specifically includes: Based on the proximity information in the scene tags and the screen size information of the target device, the data to be synchronized is compressed or cropped to generate data versions of different quality or specifications. When the scenario tag indicates that the target device and the source device are moving closer to each other, the high-definition version of the data to be synchronized is pre-cached to the target device.

[0138] The data processing method based on wireless scene awareness and super object model further includes: When there are wireless communication signal data and / or sensor data from multiple sources, the wireless communication signal data and / or sensor data are fused through a preset weight allocation and conflict resolution algorithm to generate structured scene labels.

[0139] The wireless communication signal data also includes ranging and positioning signals from the ultra-wideband (UWB) chip; The ranging and positioning signals are used to generate distance and / or orientation values ​​with centimeter-level accuracy, and the distance and / or orientation values ​​are used as the quantitative basis for proximity and / or orientation in the structured scene label.

[0140] The data processing method based on wireless scene awareness and super object model further includes: Construct a graph model in which devices are treated as nodes, and use the structured context labels between devices to define and weight the edges connecting the nodes; Based on the scenario tags, a corresponding data synchronization strategy is matched in the preset strategy library, and the data flow path and resource allocation are optimized based on graph theory algorithms.

[0141] The generation process of the data synchronization strategy in the preset strategy library is as follows: Analyze users' historical data synchronization operation records and corresponding contextual tags using machine learning models; Based on the historical data synchronization operation records and the corresponding scenario tags, the triggering conditions of the existing strategy are automatically adjusted or new personalized data synchronization strategies are dynamically generated.

[0142] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a data processing program based on wireless scene awareness and super object model, and the data processing program based on wireless scene awareness and super object model, when executed by a processor, implements the steps of the data processing method based on wireless scene awareness and super object model as described above.

[0143] In summary, this invention provides a data processing method, system, terminal, and storage medium based on wireless context awareness and a super-device model. The method includes: periodically acquiring wireless communication signal data between devices and analyzing and processing the wireless communication signal data to generate structured context tags representing the physical context between devices; integrating the structured context tags as dynamic attributes into the logical device model of a distributed operating system to construct and maintain an enhanced distributed super-device model; upon receiving a data synchronization request, querying the distributed super-device model to obtain the context tag of the target device, and matching the corresponding data synchronization strategy in a preset strategy library based on the context tag; processing the data to be synchronized according to the data synchronization strategy, and distributing the processed data differentially to the corresponding target devices via a distributed soft bus. This invention enhances the physical context awareness capability of the super terminal model and improves data management efficiency.

[0144] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0145] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0146] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A data processing method based on wireless context awareness and super object model, characterized in that, The data processing method based on wireless context awareness and super object model includes: The wireless communication signal data between devices is acquired periodically, and the wireless communication signal data is analyzed and processed to generate structured context labels that characterize the physical context between the devices. The structured scenario tags are integrated as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model. Upon receiving a data synchronization request, the distributed super-object model is queried to obtain the scenario tag of the target device. Based on the scenario tag, the corresponding data synchronization strategy is matched in the preset strategy library. According to the data synchronization strategy, the data to be synchronized is processed, and the processed data is distributed to the corresponding target devices in a differentiated manner through a distributed soft bus.

2. The data processing method based on wireless context awareness and super object model according to claim 1, characterized in that, The process of periodically acquiring wireless communication signal data between devices and analyzing and processing the wireless communication signal data to generate structured context labels characterizing the physical context between devices specifically includes: The raw wireless data is periodically obtained from the underlying HDF framework, and the raw wireless data is preprocessed to obtain wireless communication signal data. The Wi-Fi signal strength or Bluetooth signal strength in the wireless communication signal data is analyzed, and the distance relationship between devices is quantified into a proximity level based on the analysis results. The relative orientation and motion perception between devices are identified by analyzing the Wi-Fi channel state information in the wireless communication signal data and by analyzing the phase and amplitude changes of the Wi-Fi channel state information. Structured context labels representing the physical context between devices are constructed based on the proximity level, the relative orientation, and the motion perception.

3. The data processing method based on wireless scene awareness and super object model according to claim 2, characterized in that, The method of identifying the relative position and motion sensing between devices through the phase and amplitude changes of the Wi-Fi channel state information specifically includes: The phase information of multiple subcarriers in the Wi-Fi channel state information is extracted, and the signal angle of arrival difference is calculated by analyzing the changes of the phase information between different antenna pairs. Based on the signal arrival angle difference, determine the relative orientation of the source device with respect to the target device; By tracking the change pattern of the phase information or signal angle difference over time, the motion trend of the source device relative to the target device is identified, including relative or moving away.

4. The data processing method based on wireless scene awareness and super object model according to claim 3, characterized in that, The construction of structured context labels representing the physical context between devices based on the proximity level, the relative orientation, and the motion perception specifically includes: The unique identifier of the target device and the proximity level are associated and encapsulated to form a basic tag unit containing the target and proximity fields; When a valid relative orientation or motion perception is identified, the orientation information or motion trend information is added as a new field, and the new field is merged with the basic label unit; The output includes key-value pair structured data containing at least the target device identifier and proximity information, which serves as the structured context label.

5. The data processing method based on wireless scene awareness and super object model according to claim 1, characterized in that, The step of fusing the structured scenario tags as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model also includes: Receive and maintain the structured context labels generated within a certain time window, analyze the structured context labels, and calculate the intent stability score; When the intent stability score reaches a third preset threshold, an enhanced context label is generated, which includes intent stability attributes. The enhanced context labels are used to construct the distributed super-object model, and the intent stability attribute is used as a trigger condition when matching the corresponding data synchronization strategy based on the context labels.

6. The data processing method based on wireless scene awareness and super object model according to claim 1, characterized in that, The process of fusing the structured scenario tags as dynamic attributes into the logical device model of the distributed operating system to construct and maintain an enhanced distributed super-object model specifically includes: Subscribe to and listen for update events from the structured scenario tags. When an update event is detected, obtain the logical device model of all devices in the current super terminal from the model service of the distributed operating system. The logical device model includes device identifier, device type and hardware capability attributes. The structured scenario label is used as a dynamic attribute group, and the dynamic attribute group is added to the device node in the logical device model that corresponds to the target device identifier in the scenario label. Output and update an enhanced distributed super-object model that includes all the original attributes of the logical device model and the dynamic attribute group.

7. The data processing method based on wireless context awareness and super object model according to claim 1, characterized in that, The preset strategy library contains at least one differentiated strategy based on screen size; The logic of the differentiation strategy is as follows: if the screen size of the target device is greater than the first preset threshold, then the strategy of synchronizing the original data or the high-quality version data is matched. If the target device's screen size is smaller than the second preset threshold, then a strategy of synchronizing compressed or cropped low-quality version data will be used. Wherein, the first preset threshold is greater than the second preset threshold.

8. The data processing method based on wireless scene awareness and super object model according to claim 1, characterized in that, The step of matching a corresponding data synchronization strategy in a preset strategy library based on the scenario tag further includes: If no policy is matched in the policy library based on the scenario tag, a default synchronization policy is executed to synchronize the original data or the default processed version of the data to be synchronized to the target device.

9. The data processing method based on wireless scene awareness and super object model according to claim 1, characterized in that, The process of processing the data to be synchronized according to the data synchronization strategy specifically includes: Based on the proximity information in the scene tags and the screen size information of the target device, the data to be synchronized is compressed or cropped to generate data versions of different quality or specifications. When the scenario tag indicates that the target device and the source device are moving closer to each other, the high-definition version of the data to be synchronized is pre-cached to the target device.

10. The data processing method based on wireless context awareness and super object model according to claim 1, characterized in that, The data processing method based on wireless context awareness and super object model also includes: When there are wireless communication signal data and / or sensor data from multiple sources, the wireless communication signal data and / or sensor data are fused through a preset weight allocation and conflict resolution algorithm to generate structured scene labels.

11. The data processing method based on wireless context awareness and super object model according to claim 1, characterized in that, The wireless communication signal data also includes ranging and positioning signals from the ultra-wideband (UWB) chip; The ranging and positioning signals are used to generate distance and / or orientation values ​​with centimeter-level accuracy, and the distance and / or orientation values ​​are used as the quantitative basis for proximity and / or orientation in the structured scene label.

12. The data processing method based on wireless scene awareness and super object model according to claim 1, characterized in that, The data processing method based on wireless context awareness and super object model also includes: Construct a graph model in which devices are treated as nodes, and use the structured context labels between devices to define and weight the edges connecting the nodes; Based on the scenario tags, a corresponding data synchronization strategy is matched in the preset strategy library, and the data flow path and resource allocation are optimized based on graph theory algorithms.

13. The data processing method based on wireless context awareness and super object model according to claim 1, characterized in that, The process of generating the data synchronization strategy in the preset strategy library is as follows: Analyze users' historical data synchronization operation records and corresponding contextual tags using machine learning models; Based on the historical data synchronization operation records and the corresponding scenario tags, the triggering conditions of the existing strategy are automatically adjusted or new personalized data synchronization strategies are dynamically generated.

14. A data processing system based on wireless context awareness and a super object model, characterized in that, The data processing system based on wireless context awareness and super object model includes: The wireless context awareness module is used to periodically acquire wireless communication signal data between devices, analyze and process the wireless communication signal data, and generate structured context labels that characterize the physical context between devices. The super-object model fusion module is used to fuse the structured scenario tags as dynamic attributes into the logical device model of the distributed operating system, thereby constructing and maintaining an enhanced distributed super-object model. The synchronization strategy matching module is used to query the distributed super-object model when a data synchronization request is received, obtain the scenario label of the target device, and match the corresponding data synchronization strategy in the preset strategy library based on the scenario label. The distributed data management module is used to process the data to be synchronized according to the data synchronization strategy, and to distribute the processed data to the corresponding target devices in a differentiated manner through a distributed soft bus.

15. The data processing system based on wireless scene awareness and super object model according to claim 14, characterized in that, The wireless context awareness module includes a raw data processing unit, a signal strength analysis unit, a channel state analysis unit, and a tag construction unit; The raw data processing unit is used to periodically obtain raw wireless data from the underlying HDF framework, preprocess the raw wireless data, and obtain wireless communication signal data. The signal strength analysis unit is used to analyze the Wi-Fi signal strength or Bluetooth signal strength in the wireless communication signal data, and quantify the distance relationship between devices into proximity level based on the analysis results; The channel state analysis unit is used to analyze the Wi-Fi channel state information in the wireless communication signal data, and to identify the relative position and motion perception between devices by the phase and amplitude changes of the Wi-Fi channel state information. The tag construction unit is used to construct structured context tags that characterize the physical context between devices based on the proximity level, the relative orientation, and the motion perception.

16. The data processing system based on wireless scene awareness and super object model according to claim 14, characterized in that, The super object model fusion module includes a logic device model acquisition unit, a dynamic attribute addition unit, and an enhanced model output unit; The logical device model acquisition unit is used to subscribe to and listen to update events from the structured scenario tags. When the update event is heard, it obtains the logical device models of all devices in the current super terminal from the model service of the distributed operating system. The logical device model includes device identifier, device type and hardware capability attributes. The dynamic attribute adding unit is used to take the structured scenario label as a dynamic attribute group and add the dynamic attribute group to the device node in the logical device model that corresponds to the target device identifier in the scenario label. The enhanced model output unit is used to output and update an enhanced distributed super-object model that includes all the original attributes of the logical device model and the dynamic attribute group.

17. The data processing system based on wireless scene awareness and super object model according to claim 14, characterized in that, The synchronization strategy matching module includes a scenario tag matching unit and a default synchronization strategy execution unit; The scenario tag matching unit is used to query the distributed super-object model when a data synchronization request is received to obtain the scenario tag of the target device, and match the corresponding data synchronization strategy in the preset strategy library based on the scenario tag. The default synchronization strategy execution unit is used to execute a default synchronization strategy if no strategy is matched in the strategy library based on the scenario tag, and synchronize the original data or the default processed version of the data to be synchronized to the target device.

18. The data processing system based on wireless scene awareness and super object model according to claim 14, characterized in that, The distributed data management module includes a synchronous data processing unit, a data caching unit, and a data distribution unit; The synchronization data processing unit is used to compress or crop the data to be synchronized based on the proximity information in the scene tag and the screen size information of the target device, and generate data versions of different quality or specifications. The data caching unit is used to pre-cache the high-definition version of the data to be synchronized to the target device when the scene tag indicates that the target device and the source device are in a moving trend of approaching each other; The data distribution unit is used to distribute the processed data to the corresponding target devices in a differentiated manner through a distributed soft bus.

19. A terminal, characterized in that, The terminal includes: a memory, a processor, and a data processing program based on wireless context awareness and a super object model stored in the memory and executable on the processor. When the data processing program based on wireless context awareness and a super object model is executed by the processor, it implements the steps of the data processing method based on wireless context awareness and a super object model as described in any one of claims 1-13.

20. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a data processing program based on wireless context awareness and a super object model, which, when executed by a processor, implements the steps of the data processing method based on wireless context awareness and a super object model as described in any one of claims 1-13.

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