A decentralized near-field physical interaction system and method based on continuous hierarchical feature depth calculation

Abstract

The application discloses a decentralized near-field physical interaction system and method based on continuous hierarchical feature depth calculation, and belongs to the technical field of short-distance wireless communication and offline feature matching. The system comprises at least two independently portable user node devices, each device being integrated with a control unit, a feature data storage module, a wireless near-field communication module, a near-field existence confirmation module, a physical resonance prompt module and a threshold adjustment human-computer interaction component. The device broadcasts and scans feature codes in a low duty cycle mode, and the near-field existence confirmation module determines the effective near-field range; the control unit locally compares the feature codes of both parties, and calculates the maximum hierarchical number K of successful continuous matching from the first hierarchical level. When K is not less than the user-adjustable resonance threshold T, the device outputs an audible and tactile physical feedback; when the number of devices meeting the condition reaches three or more, a distributed resonance network is automatically constructed to synchronously emit resonance sound. The application is operated offline throughout the whole process, can adaptively adjust the matching sensitivity, and is suitable for various human flow scenes.

Description

Technical Field

[0001] This invention belongs to the fields of short-range wireless communication, offline feature matching and physical interaction technology, specifically involving a decentralized near-field device matching system and method that does not rely on cloud networks and supports scene-adaptive signal-to-noise ratio adjustment. Background Technology

[0002] Currently, interest matching and social connections between people mainly rely on smart terminals and LBS location service applications. This matching architecture, which relies on a central server and digital screen interaction, has several inherent technical defects in practical applications: the interaction carrier is singular, completely dependent on screen display, lacking independent physical interaction hardware, resulting in high visual footprint and low recognition; traditional LBS solutions require users to continuously gaze at the screen to observe coordinates, lacking a passive screen perception mechanism, leading to a low probability of effective matching at close range; existing near-field matching devices use fixed matching logic, which cannot adapt to changes in crowd density, resulting in low matching probability at low density and frequent false triggers at high density; at the same time, the system is highly dependent on the network, GPS, and centralized cloud, failing in shielded scenarios, and the upload of user preference data to the cloud poses a privacy risk. Existing commercial wireless beacon tracking devices are only used for object tracking and lack multi-level feature matching capabilities, while BLE Beacon matching logic relies on backend servers. Neither of these has the ability to perform continuous hierarchical feature depth calculations and adaptive threshold adjustment, and they cannot complete local autonomous matching in environments without cellular communication signals. In summary, existing near-field social matching technologies suffer from drawbacks such as high screen dependence, poor offline adaptability, signal-to-noise ratio imbalance, high false triggering rate, and insufficient privacy. Summary of the Invention

[0003] This invention provides a decentralized near-field physical interaction system and method based on continuous hierarchical feature depth calculation, aiming to solve the problems of existing technologies such as missing multi-level feature matching, inability to adaptively adjust scene matching accuracy, reliance on cloud and screen, and easy privacy leakage. The system of this invention includes multiple portable user node devices, each integrating a control unit, a feature data storage module, a wireless near-field communication module, a user configuration interface, and a physical resonance prompting module. The device internally stores N-level tree-structured feature encoding vectors, with the encoded data decoupled from user identity for anonymous storage, eliminating the need to upload any user identity information to an external server throughout the process. The wireless near-field communication module alternately performs broadcasting and scanning in a low duty cycle mode. The control unit has a built-in near-field existence confirmation module to determine the effective near-field range, and a built-in continuous matching depth calculation module to locally compare the features of both parties layer by layer to calculate the continuous matching depth K. The device is equipped with... This invention relates to a threshold-adjustable human-computer interaction component. This component adapts to different physical forms based on the overall design of the product casing, including physical press buttons, rotary adjustment knobs, and toggle switches. Users can manually adjust the resonance threshold T through purely physical operation, flexibly adapting to various crowd scenarios such as low-density social gatherings and high-density crowd gatherings. Users can freely raise or lower the matching judgment standard to control the strictness of the physical feedback triggered by the device. When three or more devices meet the matching conditions, the devices can automatically form a distributed resonance network, achieving group synchronous resonance through distributed clock synchronization. This can be integrated into portable accessories such as keychains and bracelets. This invention can distinguish different matching degrees, adaptively optimize the matching signal-to-noise ratio, operate offline to protect privacy, requires no screen interaction, and simultaneously support multi-device group collaborative resonance interaction. Attached Figure Description

[0004] Figure 1 is a schematic diagram of the overall topology and matching interaction principle of the system of the present invention. The matching depth logic K = max {k | L1~Lk continuous matching} is marked in a simplified form in the figure. The complete standard calculation formula of the present invention is K=max {k∈[1,N]|For all levels that satisfy i≤k, Lia=Lib}.

[0005] Figure 2 is a schematic diagram of the functional modules and connections of the user node device, showing the electrical connection relationship between the various hardware modules and control units inside the device; among them, the threshold adjustment human-machine interaction component can match different physical structures with the appearance of the device shell, and is used to manually modify the matching resonance threshold T.

[0006] Figure 3 is a schematic diagram of multi-level tree-structured feature encoding and continuous matching depth K calculation, showing the parent-child dependency matching logic of N-level hierarchical features, and providing a matching calculation example with K=2.

[0007] Figure 4 is a flowchart of near-field existence confirmation and continuous matching depth calculation. The simplified matching depth calculation formula in the figure is K=max {k | For all levels that satisfy i≤k, Lia=Lib}. The complete standard calculation formula of this invention is K=max {k∈[1,N]| For all levels that satisfy i≤k, Lia=Lib}.

[0008] Figure 5 shows the constant drive signal waveform of the physical resonance prompt module, illustrating the acoustic, optical, and tactile feedback waveforms of constant intensity when K≥T.

[0009] Figure 6 is a schematic diagram of the formation and propagation principle of the group resonance network. When three or more devices meet the condition K≥T, they automatically form a network and achieve group collaborative resonance by relying on distributed clock synchronization.

[0010] Figure 7 shows the differentiated physical feedback waveforms corresponding to different continuous matching depths K. The larger the K value, the higher the intensity of the audio-visual tactile feedback and the more types of activated elements. Detailed Implementation

[0011] Example 1: Portable Keychain-Type User Node Device. In this invention, RSSI refers to Radio Frequency Signal Strength, and TDOA and AoA correspond to ranging and angle of arrival detection methods, respectively, and are the standardized technical terms corresponding to radio frequency strength, ranging, and angle of arrival in the attached drawings. The device carrier in this example is a locomotive keychain, which integrates a control unit, a feature data storage module, a wireless near-field communication module, a USB user configuration interface, a physical resonance prompt module, a threshold adjustment human-machine interaction component, and a power management unit. The external form of the threshold adjustment human-machine interaction component can be flexibly adapted to the overall design style of the device shell, and can be selected as a passive pure physical operating component such as a physical press button, a rotary adjustment knob, or a toggle switch, to achieve a unified and coordinated appearance. The user writes a custom 4-level tree-structured feature encoding vector through the USB interface via an offline configuration terminal. The encoding vector is stored in a non-volatile storage area as a binary byte stream. The entire device only stores hierarchical feature values ​​and does not bind user identity information such as name or mobile phone number. There is no personal data upload during the offline matching process, realizing physical isolation and logical decoupling of identity and features. The wireless near-field communication module adopts a low duty cycle asynchronous working mode, periodically alternating between feature broadcasting and signal scanning. The near-field presence confirmation module can use any one of RSSI smoothing filtering, TDOA ranging, or AoA angle of arrival detection to determine whether the peer device has entered a valid matching range of several meters. The control unit locally compares the feature codes of each level of both devices layer by layer, and calculates the maximum continuous matching depth K according to K=max {k∈[1,N]| For all levels that satisfy i≤k, Lia=Lib}. Users can manually adjust the resonance threshold T by using the human-computer interaction component. The value range of T is 1≤T≤N. In densely populated scenarios, T can be increased to reduce false triggers, and T can be decreased when it is necessary to relax the matching conditions. All adjustment operations do not require a screen or network connection. When K≥T, the physical resonance prompt module outputs a constant audio-visual tactile feedback signal with a fixed frequency and duty cycle. When K<T, the device remains silent. The entire device operates locally offline throughout the process, without cloud or GPS involvement.

[0012] Example 2: Wearable Device for Interest-Based Hanging Ornaments. In this example, the carrier is a fabric hanging ornament. Near-field detection employs an UWB (Ultra-Wideband) ranging scheme, with a pre-set distance threshold for effective near-field screening. Feature encoding follows a coarse-to-fine hierarchical logic; lower-level feature comparisons are only meaningful when the upper-level encoding is a complete match. The control unit outputs differentiated feedback based on the current K value: K=1: only a low-frequency buzzer and dim light, tactile elements are not activated; K=2: a mid-frequency buzzer, medium-brightness LED, and low-frequency vibration are simultaneously activated; K=3: a high-frequency buzzer, bright light, and strong vibration are all activated. Users can intuitively distinguish the matching depth level based on the feedback waveform.

[0013] Example 3: Multi-device Distributed Swarm Resonance Network. When at least three user devices in the local area simultaneously satisfy K≥T, the devices automatically form a distributed resonance network without a central gateway. The networking process consists of three stages: Stage 1, any two devices complete near-field confirmation and feature matching verification; Stage 2, the total number of devices meeting the matching conditions in the space reaches three or more, and the devices broadcast synchronization beacons to establish a unified distributed clock reference; Stage 3, all devices in the network lock onto the same clock phase and synchronously output physical feedback waveforms of the same frequency and amplitude to form swarm resonance. The network supports dynamic expansion; newly entering near-field devices that satisfy K≥T can automatically connect to the existing network and participate in collaborative resonance. The more devices participating in the network, the higher the swarm resonance output intensity level.

Claims

1. A decentralized near-field physical interaction system based on continuous hierarchical feature depth computation, characterized in that, The system includes at least two independently portable user node devices. Each user node device includes: a control unit, and a feature data storage module, a wireless near-field communication module, a near-field presence confirmation module, and a physical resonance prompting module, all electrically connected to the control unit. The feature data storage module stores user-defined N-level tree-structured feature encoding vectors. The wireless near-field communication module alternately performs feature broadcasting and near-field scanning in a low-duty-cycle asynchronous mode to exchange feature encoding vectors with neighboring peer devices without relying on a central server. The near-field presence confirmation module collects wireless signal feature streams and determines whether the peer device is within a preset effective near-field range based on the signal features. When the control unit determines that the peer device is within the effective near-field range, it performs a layer-by-layer comparison of its own feature encoding vector with the received feature encoding vector of the peer device offline, calculating the maximum continuous matching depth value K, where K = max {k∈[1,N]|For all levels satisfying i≤k, Lia = Lib}; where Lia and Lib represent the feature encoding values ​​of the device and the peer device at the i-th level, respectively. When the maximum continuous matching depth value K is not less than the locally stored user-adjustable resonance threshold T, the physical resonance prompt module is driven to output a physical feedback signal.

2. The decentralized near-field physical interaction system based on continuous hierarchical feature depth calculation according to claim 1, characterized in that, Each of the user node devices further includes: a threshold adjustment human-computer interaction component, electrically connected to the control unit, for receiving manual operations from the user and sending a threshold adjustment signal to the control unit; the control unit dynamically adjusts the value of the user adjustable resonance threshold T according to the threshold adjustment signal, where 1≤T≤N.

3. The decentralized near-field physical interaction system based on continuous hierarchical feature depth calculation according to claim 1, characterized in that, The wireless signal feature stream collected by the near-field presence confirmation module includes at least one of Received Signal Strength Indication (RSSI), Time Difference of Arrival (TDOA), and Phase Difference of Arrival (AoA). The near-field presence confirmation module performs sliding window smoothing filtering or Kalman filtering on the wireless signal feature stream within a time window to eliminate environmental multipath interference and calculates the real-time physical distance of the peer device. When the real-time physical distance is less than a preset distance threshold, it is determined that the peer device is within the effective near-field range.

4. The decentralized near-field physical interaction system based on continuous hierarchical feature depth calculation according to claim 1, characterized in that, The N-level tree-structured feature encoding vector stored in the feature data storage module has a topological structure that maps to the pre-defined tree-structured branching structure of the social interest graph. The encoding vector is divided into N consecutive levels in a logical order from coarse to fine, and there is a strong parent-child dependency nesting relationship between adjacent levels. That is, the comparison of lower-level features only has statistical significance when the upper-level features are completely consistent.

5. The decentralized near-field physical interaction system based on continuous hierarchical feature depth calculation according to claim 1, characterized in that, The control unit calculates the maximum continuous matching depth value K in the following way: Starting from the first level feature, perform bit alignment comparisons sequentially level by level. If the current level comparison is consistent, continue to compare the next level. If the current level comparison is inconsistent, terminate the comparison immediately and record the maximum number of consecutively matched levels before termination as K, K=max {k∈[1,N]|For all levels that satisfy i≤k, Lia=Lib}.

6. The decentralized near-field physical interaction system based on continuous hierarchical feature depth calculation according to claim 1, characterized in that, The physical resonance cues module includes at least one of an acoustic transducer, a linear motor haptic feedback component, and a multi-color LED optical component; the expression characteristics of the physical feedback signal output by the physical resonance cues module driven by the control unit are positively correlated with the magnitude of the maximum continuous matching depth value K.

7. The decentralized near-field physical interaction system based on continuous hierarchical feature depth calculation according to claim 6, characterized in that, The expressive features include at least one of the following: driving voltage amplitude, duty cycle, frequency, emission color sequence, and multimodal combination beat.

8. The decentralized near-field physical interaction system based on continuous hierarchical feature depth calculation according to claim 1, characterized in that, The system also supports distributed group self-organizing network resonance. When the number of user node devices within the effective near field range and meeting the condition K≥T reaches three or more, the adjacent user node devices automatically construct a decentralized, centerless topology network through the wireless near field communication module, and achieve full network clock alignment through a distributed clock synchronization algorithm.

9. The decentralized near-field physical interaction system based on continuous hierarchical feature depth calculation according to claim 8, characterized in that, Each user node device in the decentralized topology network drives its respective physical resonance prompt module to output physical feedback signals synchronously at the same absolute clock trigger point with exactly the same frequency and phase.

10. A decentralized near-field physical interaction method based on continuous hierarchical feature depth calculation, characterized in that, The method, applied to a user node device in any of the systems described in claims 1-9, is executed by a control unit built into the user node device and includes the following steps: Reading the user's own 1-N level tree-structured associated feature encoding vector FA = {LA1, LA2, ..., LAN} stored in the local feature data storage module; Controlling the wireless near-field communication module to alternately perform feature broadcasting and near-field scanning in a low duty cycle asynchronous mode, and receiving feature encoding vector FB = {LB1, LB2, ..., LBN} sent by the peer device within the effective near-field range; Starting from the first level, sequentially performing bit-alignment comparisons on the feature encoding vectors FA and FB; If the current i-th level LAi = LBi, then the current level is determined to be a successful match and the comparison continues to the (i+1)-th level; If LAi is not equal to LBi, then the comparison is immediately terminated; Recording the maximum number of consecutively successful matches from the first level as the maximum consecutive matching depth value K, where K is equal to the maximum number of consecutively successful matches; If the first level does not match, then K = 0. The maximum continuous matching depth value K is compared with the locally stored user-adjustable resonance threshold T. When K ≥ T, it is determined to be a valid match and the physical resonance prompt module is driven to output a physical feedback signal; when K < T, it is determined to be a mismatch and the device is controlled to remain silent.

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