CN-121985285-A - Data processing method, system, terminal and storage medium based on wireless context awareness and super object model

CN121985285ACN 121985285 ACN121985285 ACN 121985285ACN-121985285-A

Abstract

The invention discloses a data processing method, a system, a terminal and a storage medium based on wireless context awareness and super object models, wherein the method comprises the steps of periodically acquiring wireless communication signal data among devices, analyzing and processing the wireless communication signal data to generate a structured context label representing physical context among the devices; the method comprises the steps of integrating a structured scene tag into a logic device model of a distributed operating system as a dynamic attribute, constructing and maintaining an enhanced distributed super object model, inquiring the distributed super object model when a data synchronization request is received to obtain a scene tag of target equipment, matching a corresponding data synchronization strategy in a preset strategy base based on the scene tag, processing data to be synchronized according to the data synchronization strategy, and differentially distributing the processed data to corresponding target equipment through a distributed soft bus. The invention enhances the physical context awareness capability of the super terminal model and improves the data management efficiency.

Inventors

XIA YAN

Assignees

深圳开鸿数字产业发展有限公司

Dates

Publication Date: 20260505
Application Date: 20251225

Claims (20)

1. The data processing method based on the wireless context awareness and the super object model is characterized by comprising the following steps of: periodically acquiring wireless communication signal data among devices, analyzing and processing the wireless communication signal data, and generating a structured scene tag for representing the physical scene among the devices; fusing the structured scene tag as a dynamic attribute into a logic device model of a distributed operating system, and constructing and maintaining an enhanced distributed super model; Inquiring the distributed super object model when a data synchronization request is received to obtain a scene tag of target equipment, and matching a corresponding data synchronization strategy in a preset strategy library based on the scene tag; And processing the data to be synchronized according to the data synchronization strategy, and differentially distributing the processed data to the corresponding target equipment through the distributed soft bus.
2. The data processing method based on wireless context awareness and super object model according to claim 1, wherein the periodically acquiring wireless communication signal data between devices and analyzing and processing the wireless communication signal data generate a structured context label characterizing a physical context between devices, specifically includes: Periodically acquiring original wireless data from an HDF frame at the bottom layer, and preprocessing the original wireless data to obtain wireless communication signal data; Analyzing Wi-Fi signal intensity or Bluetooth signal intensity in the wireless communication signal data, and quantifying the distance relation between devices into a proximity level according to an analysis result; Analyzing Wi-Fi channel state information in the wireless communication signal data, and identifying the relative azimuth and motion perception between devices through the phase and amplitude change of the Wi-Fi channel state information; And constructing a structured scene tag for representing the physical scene between devices according to the proximity level, the relative azimuth and the motion perception.
3. The data processing method based on wireless context awareness and super object model according to claim 2, wherein the identifying the relative azimuth and motion awareness between devices through the phase and amplitude changes of the Wi-Fi channel state information specifically comprises: extracting phase information of a plurality of subcarriers in the Wi-Fi channel state information, and calculating a signal arrival angle difference by analyzing the change of the phase information among different antenna pairs; Judging the relative orientation of the source equipment relative to the target equipment according to the signal arrival angle difference; and identifying the movement trend of the source equipment relative to the target equipment by tracking the change mode of the phase information or the signal arrival angle difference along with time, wherein the movement trend comprises relative or distant.
4. A data processing method based on wireless context awareness and superobject model according to claim 3, characterized in that said constructing a structured context label characterizing a physical context between devices from said proximity level, said relative orientation and said motion awareness, in particular comprises: Performing association packaging on the unique identifier of the target device and the proximity level to form a basic tag unit containing a target and a proximity field; When the effective relative azimuth or the motion perception is identified, azimuth information or motion trend information is used as a newly added field, and the newly added field is combined with the basic tag unit; and outputting key value structure data at least comprising the target equipment identification and the proximity information as the structured scene label.
5. The method for processing data based on wireless context awareness and super model according to claim 1, wherein said fusing the structured context label as dynamic attribute into a logical device model of a distributed operating system, constructing and maintaining an enhanced distributed super model, further comprises: Receiving and maintaining the structured scene label generated in a certain time window, analyzing the structured scene label, and calculating the intention stability score; When the intention stability score reaches a third preset threshold, generating an enhanced scene tag, wherein the enhanced scene tag comprises an intention stability attribute; and the enhanced scene tag is used for constructing the distributed super model, and the intention stability attribute is used as a trigger condition when the corresponding data synchronization strategy is matched based on the scene tag.
6. The data processing method based on wireless context awareness and super object model according to claim 1, wherein the fusing the structured context label as a dynamic attribute into a logic device model of a distributed operating system constructs and maintains an enhanced distributed super object model, specifically comprising: subscribing and monitoring an update event from the structured context label, and when the update event is monitored, acquiring a logic device model of all devices in the current super terminal from a model service of the distributed operating system, wherein the logic device model comprises a device identifier, a device type and a hardware capability attribute; Taking the structured scene tag as a dynamic attribute group, and adding the dynamic attribute group to the logic equipment model under an equipment node corresponding to a target equipment identifier in the scene tag; And outputting and updating an enhanced distributed super model containing all original attributes of the logic 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, wherein the preset policy library at least comprises a differentiation policy based on screen size; the logic of the differentiation strategy is that if the screen size of the target equipment is larger than a first preset threshold value, the strategy of synchronizing the original data or the high-quality version data is matched; if the screen size of the target device is smaller than a second preset threshold, matching a strategy for synchronizing the compressed or cut low-quality version data; Wherein the first preset threshold is greater than the second preset threshold.
8. The data processing method based on wireless context awareness and super object model according to claim 1, wherein the matching of corresponding data synchronization policies in a preset policy library based on the context label further comprises: And if the situation label is not matched with any strategy in the strategy library, executing a default synchronization strategy, and synchronizing the original data or the version of the data to be synchronized after default processing to the target equipment.
9. The data processing method based on wireless context awareness and super object model according to claim 1, wherein the processing the data to be synchronized according to the data synchronization policy specifically comprises: Compressing or cutting the data to be synchronized according to the proximity information in the scene tag and the screen size information of the target equipment to generate data with different quality or specification versions; And when the contextual model indicates that the target equipment and the source equipment are in a moving trend of approaching each other, the data to be synchronized in the high-definition version are cached to the target equipment in advance.
10. The data processing method based on wireless context awareness and superobject model according to claim 1, the data processing method based on the wireless context awareness and the super object model is characterized by further comprising the following steps: when the wireless communication signal data and/or the sensor data with multiple sources exist, fusion processing is carried out on the wireless communication signal data and/or the sensor data through a preset weight distribution and conflict resolution algorithm, and a structured scene label is generated.
11. The wireless context awareness and super-object model based data processing method of claim 1, wherein the wireless communication signal data further comprises ranging and positioning signals from an ultra wideband UWB chip; the distance measurement and positioning signals are used for generating distance values and/or azimuth values with centimeter-level precision, and the distance values and/or the azimuth values are used as quantification basis of the adjacency and/or the azimuth in the structured scene tag.
12. The data processing method based on wireless context awareness and superobject model according to claim 1, the data processing method based on the wireless context awareness and the super object model is characterized by further comprising the following steps: constructing a graph model, wherein devices are used as nodes in the graph model, and the structured scene labels among the devices are used for defining and weighting edges connecting the nodes; And matching corresponding data synchronization strategies in a preset strategy library based on the scene label, and optimizing data flow paths and resource allocation based on a graph theory algorithm.
13. The data processing method based on wireless context awareness and super object model according to claim 1, wherein the generating process of the data synchronization strategy in the preset strategy library is as follows: Analyzing the history data synchronous operation record of the user and the corresponding scene label through a machine learning model; And automatically adjusting the triggering condition of the existing strategy or dynamically generating a new personalized data synchronization strategy according to the historical data synchronization operation record and the corresponding scene label.
14. A data processing system based on wireless context awareness and superobject model, the data processing system based on wireless context awareness and superobject model comprising: the wireless context awareness module is used for periodically acquiring wireless communication signal data among devices, analyzing and processing the wireless communication signal data and generating a structured context label representing the physical context among the devices; The super object model fusion module is used for fusing the structured scene tag as a dynamic attribute into a logic device model of the distributed operating system, and constructing and maintaining an enhanced distributed super object model; the synchronization strategy matching module is used for inquiring the distributed super object model when receiving a data synchronization request to obtain a scene tag of the target equipment, and matching a corresponding data synchronization strategy in a preset strategy library based on the scene tag; And the distributed data management module is used for processing the data to be synchronized according to the data synchronization strategy and differentially distributing the processed data to the corresponding target equipment through the distributed soft bus.
15. The data processing system based on wireless context awareness and super-object model of claim 14, wherein the wireless context awareness module comprises a raw data processing unit, a signal strength analysis unit, a channel state analysis unit, and a tag construction unit; the original data processing unit is used for periodically acquiring original wireless data from the HDF frame at the bottom layer, and preprocessing the original wireless data to obtain wireless communication signal data; the signal strength analysis unit is used for analyzing Wi-Fi signal strength or Bluetooth signal strength in the wireless communication signal data and quantifying the distance relation between the devices into a proximity grade according to an analysis result; The channel state analysis unit is used for analyzing Wi-Fi channel state information in the wireless communication signal data and identifying the relative azimuth and motion perception among devices through the phase and amplitude change of the Wi-Fi channel state information; The tag construction unit is used for constructing a structured scene tag for representing the physical scene between devices according to the proximity level, the relative azimuth and the motion perception.
16. The data processing system based on wireless context awareness and super-object model of claim 14, wherein the super-object model fusion module comprises a logical device model acquisition unit, a dynamic attribute adding unit and an enhancement model output unit; The logic device model acquisition unit is used for subscribing and monitoring an update event from the structured scene tag, and acquiring logic device models of all devices in the current super terminal from a model service of the distributed operating system when the update event is monitored, wherein the logic device models comprise device identifiers, device types and hardware capability attributes; The dynamic attribute adding unit is used for taking the structured scene tag as a dynamic attribute group, and adding the dynamic attribute group to a device node corresponding to a target device identifier in the scene tag in the logic device model; the enhancement model output unit is used for outputting and updating an enhancement type distributed super model containing all original attributes of the logic device model and the dynamic attribute group.
17. The data processing system based on wireless context awareness and superobject model of claim 14, wherein the synchronization policy matching module comprises a context tag matching unit and a default synchronization policy enforcement unit; the scene tag matching unit is used for inquiring the distributed super object model when receiving a data synchronization request to obtain a scene tag of target equipment, and matching a corresponding data synchronization policy in a preset policy library based on the scene tag; And the default synchronization policy execution unit is used for executing a default synchronization policy if the situation label cannot be matched with any policy in the policy library, and synchronizing the original data or the version of the data to be synchronized after default processing to the target device.
18. The wireless context awareness and superobject model based data processing system of claim 14, wherein the distributed data management module comprises a synchronous data processing unit, a data caching unit, and a data distribution unit; The synchronous data processing unit is used for compressing or cutting the data to be synchronized according to the proximity information in the scene tag and the screen size information of the target equipment to generate data with different quality or specification versions; The data caching unit is used for caching the data to be synchronized in a high-definition version to the target equipment in advance when the situation label indicates that the target equipment and the source equipment are in a moving trend of approaching each other; The data distribution unit is used for differentially distributing the processed data to the corresponding target equipment through the distributed soft bus.
19. A terminal comprising a memory, a processor and a data processing program based on a wireless context awareness and super model stored on the memory and executable on the processor, which when executed by the processor implements the steps of the data processing method based on a wireless context awareness and super model according to any 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 a wireless context awareness and a super object model, which when executed by a processor implements the steps of the data processing method based on a wireless context awareness and a super object model according to any of claims 1-13.

Description

Data processing method, system, terminal and storage medium based on wireless context awareness and super object model Technical Field The invention relates to the technical field of distributed computing and wireless communication intersection, in particular to a data processing method, a system, a terminal and a computer readable storage medium based on wireless context awareness and a super object model. Background The super terminal model in the existing distributed operating system, while capable of realizing logic connection and data synchronous sharing among devices, has a core problem of lacking perception of physical scenes among devices. The concrete steps are as follows: The lack of the physical scene is that the system only focuses on the logical connection relation of the equipment (such as whether the equipment is online or not and belongs to the same user), but cannot sense the relative position, distance or movement trend of the equipment in the physical space. For example, the system cannot distinguish between "a cell phone next to a smart screen" and "a cell phone in another room". This "physical blindness" results in the super terminal not being able to understand the real physical environment in which the device is located. Data management is "one-cut" and resource waste-distributed data management policies typically employ "one-cut" approaches due to lack of physical context awareness. For example, a 10MB high resolution photograph would be synchronized indifferently to all devices, including a smart watch with a screen size of only 1.5 inches. This not only occupies valuable network bandwidth, but also consumes power and storage space of the small-screen device, and the user does not need such high-precision pictures on the small-screen device at all, resulting in serious resource waste. The existing cross-device interaction often depends on the explicit operation of a user, and a system cannot intelligently recommend interaction modes or preload data according to the situations such as physical approaching or separating of the device, so that the user experience is not natural and intelligent enough. Accordingly, the prior art is still in need of improvement and development. Disclosure of Invention The invention mainly aims to provide a data processing method, a system, a terminal and a computer readable storage medium based on wireless context awareness and a super object model, and aims to solve the problems of low data management efficiency, resource waste and insufficient intelligent interaction experience caused by lack of physical context awareness of the super terminal model in the prior art. In order to achieve the above object, the present invention provides a data processing method based on wireless context awareness and super object model, the data processing method based on wireless context awareness and super object model includes the following steps: periodically acquiring wireless communication signal data among devices, analyzing and processing the wireless communication signal data, and generating a structured scene tag for representing the physical scene among the devices; fusing the structured scene tag as a dynamic attribute into a logic device model of a distributed operating system, and constructing and maintaining an enhanced distributed super model; Inquiring the distributed super object model when a data synchronization request is received to obtain a scene tag of target equipment, and matching a corresponding data synchronization strategy in a preset strategy library based on the scene tag; And processing the data to be synchronized according to the data synchronization strategy, and differentially distributing the processed data to the corresponding target equipment through the distributed soft bus. Optionally, the method for processing data based on wireless context awareness and super object model, wherein the periodically acquiring wireless communication signal data between devices, and analyzing and processing the wireless communication signal data, generates a structured context label for characterizing a physical context between devices, specifically includes: Periodically acquiring original wireless data from an HDF frame at the bottom layer, and preprocessing the original wireless data to obtain wireless communication signal data; Analyzing Wi-Fi signal intensity or Bluetooth signal intensity in the wireless communication signal data, and quantifying the distance relation between devices into a proximity level according to an analysis result; Analyzing Wi-Fi channel state information in the wireless communication signal data, and identifying the relative azimuth and motion perception between devices through the phase and amplitude change of the Wi-Fi channel state information; And constructing a structured scene tag for representing the physical scene between devices according to the proximity level, the relative azimuth and the motion perception. Op