An edge perception and instinct reflex control method and system based on bionic neural data flow

Abstract

The application discloses an edge perception and instinct reflex control method and system based on bionic nerve data flow, and belongs to the field of bionic robots and intelligent sensing control. The application collects photon cloud density matrix, nerve topology and particle oscillation frequency data flow through a global bionic nerve data bus, and analyzes puncture stress and temperature gradient characteristics through edge calculation. When puncture damage or extreme temperature change occurs, a bypass interruption link is built to bypass the top center and directly output risk avoidance and heat balance instructions, so that microsecond-level instinct reflex is realized. After disposal is completed, data is packaged according to a protocol and is reported to the top center asynchronously. The application decouples software and hardware, realizes perception by relying on algorithms, does not need special sensing hardware, reserves a standardized interface to extend multi-sensory modules such as hearing and smelling, solves the defects of high time delay and poor hardware adaptation of a traditional control link, and is suitable for humanoid robots, soft bodies, bionic prosthetic devices and the like.

Description

Technical Field

[0001] This invention belongs to the fields of artificial intelligence, bionic robots and intelligent sensing and control technology. Specifically, it is a pure software perception and control system that relies on a global bionic neural data bus to realize microsecond-level sharp puncture recognition, cold and heat perception mapping, and bodily instinctive reflexes. Background Technology

[0002] The existing tactile stimulation processing chain for bionic robots and humanoid devices is as follows: physical sensor data acquisition → centralized processing by the main control unit → issuance of action commands. This multi-stage serial transmission results in large response delays, making it impossible to simulate the instinctive reflex of biological skin to instantly retract upon being pricked. Sharp penetration or extreme temperature changes can easily cause permanent mechanical and electrical damage.

[0003] Traditional perception algorithms are deeply tied to dedicated sensing hardware, requiring program reconstruction for hardware replacement, resulting in poor system versatility. The industry lacks a pure software bionic perception architecture that decouples software and hardware and is based on distributed neural data flow across the entire domain. It also lacks standardized expansion interfaces to connect to subsequent sensory modules such as hearing and taste. Summary of the Invention

[0004] Purpose of the invention Overcoming the shortcomings of existing sensing systems, such as slow response, high hardware-software coupling, and poor scalability, this solution provides a pure software edge sensing scheme based on biomimetic neural data streams. By relying on edge nodes to analyze neural data streams in real time, it achieves microsecond-level pain avoidance reflexes and adaptive temperature control, and reserves standardized data interfaces to enable multi-sensory modular expansion. Technical solution

[0005] 1. Real-time monitoring of bionic neural data stream The underlying daemon process collects three types of data from the underlying medium at microsecond intervals through a global biomimetic neural data bus: photon cloud density distribution matrix, neural topology connectivity data, and microscopic particle oscillation frequency matrix.

[0006] 2. Sharp feature edge calculation (pain trigger) Edge computing nodes load gradient calculation models to analyze data streams in real time; when the regional deformation gradient exponent soars and the data distribution presents a stress cone shape corresponding to flesh puncture, the characteristics of sharp object penetration are marked.

[0007] 3. Temperature gradient feature extraction (triggered by hot and cold sensation) By performing spatial difference calculations on the energy thermogram using a heat conduction model, local particle oscillation frequencies shift directionally and exceed preset hot and cold thresholds, generating temperature anomaly labels.

[0008] 4. Emergency control of pain reflex bypass If a puncture feature or a sharp drop in neural topology data (equivalent tissue damage) is detected, the underlying program triggers a hardware interrupt, bypassing the top-level central control thread, and directly outputs commands for reverse body contraction and emergency avoidance.

[0009] 5. Heating and Cooling and Adaptive Thermal Balance Temperature gradients are nonlinearly mapped to generate standardized cold and hot digital sensing signals; under extreme high and low temperature conditions, local energy balance commands are issued, and the body's autonomous thermal balance regulation is completed through energy flow via neural links.

[0010] 6. Asynchronous reporting of data packet encapsulation After completing the underlying risk avoidance and temperature control actions, the stimulus coordinates, stimulus intensity, and topological damage range are encapsulated into a data packet according to a unified neural protocol and asynchronously uploaded to the global central cognitive module for upper-level intelligent decision-making. Beneficial effects

[0011] (1) Excellent reflection speed: The bypass interruption architecture realizes microsecond-level instinctive retraction, replicates the instantaneous pain reflex of biological skin, and greatly reduces the probability of equipment damage; (2) High versatility: It is free from the constraints of dedicated sensor hardware and runs solely on bionic neural data streams, making it compatible with all types of bionic bodies and humanoid robots. (3) Excellent scalability: With a unified data encapsulation protocol, this module can be plugged and used, facilitating seamless integration of subsequent auditory, olfactory, and gustatory bionic perception. Attached Figure Description

[0012] Appendix Figure 1 System three-tier architecture and data flow diagram Three-layer structure: Bottom layer: Global bionic neural data bus; Middle layer: Edge bionic neural perception and computing unit (the core of this invention, which includes a sharp calculation module and a temperature gradient calculation module); Top layer: Central cognitive module.

[0013] Data flow: The bottom layer outputs photon cloud density and particle oscillation frequency data upwards; the middle layer outputs emergency contraction commands and energy balancing commands downwards; the middle layer reports pain / heat perception data packets upwards.

[0014] Attached diagram: This diagram shows the overall architecture and the entire data flow.

[0015] Appendix Figure 2 Flowchart of sharp stimulus recognition and pain bypass reflex Process: Start → Acquire raw data of bionic nerve → Determine gradient exponential surge + stress cone feature → Puncture mark → Bypass interruption, skipping the main control issuing risk avoidance command → Packaging data and reporting; if the threshold is not met, collect data in a loop.

[0016] Figure description: This figure is a flowchart of the pain perception and emergency reflex control algorithm.

[0017] Appendix Figure 3 Cold and heat sensing and adaptive thermal balance logic diagram Input: Microscopic particle oscillation frequency matrix; Intermediate: Spatial difference operation + threshold judgment; Branch 1: Nonlinear mapping to generate hot and cold digital signals; Branch 2: Output local energy balance temperature control command.

[0018] Figure description: This figure is a schematic diagram of the principle of cold and hot feature extraction and autonomous constant temperature control.

Claims

1. A method for edge perception and instinctive reflex control based on biomimetic neural data stream, characterized in that, Includes the following steps S1 to S6: S1. Real-time monitoring of bionic neural data stream: The underlying daemon process acquires the output data of the underlying medium through the global bionic neural data bus at a microsecond acquisition cycle. The data includes: photon cloud density distribution matrix, neural topology connectivity state data, and microscopic particle oscillation frequency matrix. S2, Sharp Feature Edge Calculation: Edge computing nodes are equipped with gradient calculation models to analyze the incoming bionic neural data stream in real time. When the deformation gradient exponent of a local area suddenly increases and the data distribution forms a stress cone shape corresponding to bionic tissue puncture, the sharp object puncture feature is marked. S3. Temperature gradient feature extraction: Based on the heat conduction model, spatial difference calculation is performed on the energy thermogram. If the oscillation frequency of local micro particles shifts in a specific direction and exceeds the preset high temperature threshold or low temperature threshold, a temperature anomaly label is generated. S4, Pain Reflex Bypass Emergency Control: When a sharp piercing feature is detected, or when the neural topology connection data drops sharply or the equivalent bionic tissue is damaged, the underlying program triggers a hardware interrupt, bypassing the top-level central main control operation thread, and directly sends a risk avoidance drive command for body reverse contraction and emergency avoidance to the drive end. S5. Temperature gradient and adaptive thermal balance: The extracted temperature gradient is nonlinearly mapped to generate standardized cold and heat digital sensing signals; when extreme high and low temperature conditions are detected, a local energy balance command is issued through the bionic neural link to complete the autonomous thermal balance regulation of the bionic body. S6. Asynchronous Reporting of Perception Data Packets: After the bottom layer completes the avoidance action and thermal balance regulation, it encapsulates the stimulus coordinates, stimulus intensity, and neural topology damage range into perception data packets according to a unified neural communication protocol and asynchronously uploads them to the top-level central cognitive module for use by upper-level intelligent decision-making.

2. The control method according to claim 1, characterized in that: The global bionic neural data bus is a fully distributed data transmission link that standardizes the output format of multi-source sensor data at the underlying level, enabling standardized data output from bionic body hardware of different materials and structures.

3. The control method according to claim 1, characterized in that: The bypass emergency control is a hardware interrupt priority scheduling mechanism, with interrupt priority higher than the central master control regular scheduling thread, ensuring that risk avoidance instructions are output with priority at the microsecond level.

4. A control system for edge perception and instinctive reflexes based on biomimetic neural data streams, characterized in that, The architecture comprises three layers: a bottom-layer global bionic neural data bus, a middle-layer edge bionic neural perception and computing unit, and a top-layer central cognitive module. The global bionic neural data bus is used to collect and transmit three types of raw data streams in real time: photon cloud density distribution matrix, neural topology connectivity state, and microscopic particle oscillation frequency matrix. The edge bionic neural perception and computing unit integrates a sharpness calculation module, a temperature gradient calculation module, a bypass reflection control module, a thermal balance regulation module, and a data encapsulation and reporting module. The sharpness calculation module is used for gradient calculation, puncture stress cone feature recognition, and puncture marking. The temperature gradient calculation module is used for differential calculation of thermal maps and determination of hot and cold anomalies; the bypass reflection control module is used to trigger hardware interrupts and issue body avoidance commands across the main controller; the thermal balance regulation module is used for quantification of hot and cold signals and local energy balance control; the data encapsulation and reporting module is used to package and asynchronously upload sensing data according to a unified protocol; the top-level central cognition module is used to receive sensing data packets reported by the middle layer and realize global intelligent decision-making and multi-sensory collaborative scheduling; among them, the middle-level edge bionic neural sensing and computing unit outputs emergency contraction commands and energy balance commands downward through the neural bus.

5. The control system according to claim 4, characterized in that: The edge-inspired bionic neural perception computing unit reserves a standardized extended communication interface, which is used to connect to external bionic hearing, smell, and taste perception subsystems to realize multi-sensory modular plug-and-play expansion.

6. The control system according to claim 4, characterized in that: The system adopts a hardware-software decoupled architecture. All sensing logic is implemented through software algorithms based on bionic neural data flow, without the need for dedicated custom sensing hardware.

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