System for micro risk factor density counting based on image slicing and visual-language inference

Abstract

This invention discloses a hardware system for counting minute risk factors, addressing the physical bottlenecks of feature preservation and memory overflow in high-resolution image processing. The system includes: an optical imaging module with feature-locked illumination circuitry for image acquisition; an edge slicing unit with a built-in hardware slicing buffer, utilizing region extraction logic circuitry to extract sub-image regions to improve cache hit rate; a visual language inference unit, combining structured cue commands with a large-scale visual language model (VLM) hardware accelerator to perform visual inference; and an electromechanical and compliance control interface that converts quantified health indicators into electrical control commands to directly drive environmental control equipment (HVAC) or production line motors, and uses encrypted circuitry to generate an immutable blockchain-based ESG digital health compliance certificate compliant with FSMA 204 specifications.

Claims

1. A system for counting the density of minute risk factors based on image slices and visual language inference, characterized by comprising: An optical imaging module, configured in a monitoring field, has an image sensor for capturing high-resolution raw image data containing minute risk factors; An edge slicing processing unit, physically and electrically connected to the optical imaging module, has a built-in region extraction logic circuit and a hardware slicing buffer to define multiple sub-image region memory blocks in the original image data. The region extraction logic circuit includes a boundary integrity protection mechanism to ensure that the characteristics of minor risk factors are not truncated. A visual language inference unit, physically connected to the edge slicing processing unit, has a neural network accelerator to receive these sub-image region memory blocks. The visual language inference unit combines these sub-image regions with a pre-stored structured prompt instruction and inputs it into a built-in large visual language model (VLM) to perform visual semantic inference and generate a quantitative health indicator signal accordingly. An electromechanical and compliance control interface has a physical communication port connected to the visual language inference unit. The interface converts the quantitative health indicator signal into an electrical control instruction to directly drive a physical electromechanical device or trigger a physical alarm device.

2. The system as described in claim 1, wherein the edge slicing unit has a built-in hardware slicing buffer architecture; the edge slicing unit includes a memory controller that, before sending image data to the neural network accelerator, physically segments the original image data into independently addressed memory blocks in memory to improve the cache hit rate of the unit.

3. The system as described in claim 1, wherein the boundary integrity protection mechanism is a dynamically effective overlap operation logic; the edge slicing operation unit has a built-in arithmetic logic unit (ALU) that calculates the pixel width P_overlap of the overlap interval according to a feature dynamic coupling formula: P_overlap = ⌈(L_bio × 1.618) + (V_obj × T_exp × β)⌉+7 where, L_bio is the average feature pixel width of this small risk factor, 1.618 is a golden ratio integrity coefficient, V_obj is the target movement speed, T_exp is the exposure time parameter, β is the blur compensation coefficient, and constant 7 is the edge noise buffer value.

4. The system as described in claim 1, wherein the structured prompts stored in the visual language inference unit include a thought chain guide and a biometric exclusion rule based on preset geometric parameters; the large visual language model filters environmental noise according to the instructions, and the unit includes interactive verification hardware logic that maps the inference coordinates back to the original image data for pixel-level verification.

5. The system as claimed in claim 1, wherein the optical imaging module further includes a spectral feature-locked illumination circuit and a hardware synchronization trigger circuit; the illumination circuit includes a plurality of light-emitting elements physically arranged in a ring or array to emit a specific narrow-band wavelength corresponding to the reflectivity of the small risk factor; the hardware synchronization trigger circuit is physically connected to the shutter pin of the image sensor, forcing the image sensor to capture an image only within an exposure window during which the illumination circuit reaches a specific light intensity.

6. The system as described in claim 1, wherein the physical electromechanical equipment is an environmental control system (HVAC) or a production line drive motor; the electromechanical and compliance control interface has a built-in PID control chip, and when the quantitative hygiene indicator signal is lower than a preset physical safety threshold, the interface sends the electrical control command through the communication port to forcibly change the inverter output of the environmental control system, or control the opening and closing of a physical relay to physically cut off the power supply to the production line drive motor.

7. The system as described in claim 1, wherein the electromechanical and compliance control interface further includes a hardware authorization verification chip; the chip calculates a computing resource consumption value based on the total pixel area of ​​the original image data or the hardware operating time, and maintains high-resolution signal transmission only after verifying that the consumption value meets a preset authorization key; if the authorization key is lacking, the chip automatically triggers an internal bus switch to cut off the output of the high-resolution signal and enters hardware degradation mode.

8. The system as described in claim 7, wherein the electromechanical and compliance control interface includes a cryptographic hash engine; the engine combines the quantitative health indicator signal, the corresponding sub-image region, a timestamp, and a physical network interface card MAC address, executes a hash algorithm to generate a digital fingerprint, and anchors it to an external blockchain server through a network communication module to generate a legally valid digital health compliance certificate.

9. The system as described in claim 1, wherein the communication port of the electromechanical and compliance control interface is linked to an electronic procurement system server of an external retail channel; the interface generates a dynamic procurement priority weight parameter, and when the index is lower than a compliance threshold, it automatically sends an electronic packet via API to trigger the electronic procurement system server to execute a supplier freeze mechanism.

10. The system as described in claim 1, wherein the electromechanical and compliance control interface is further connected to a human-machine interface display device; the human-machine interface display device presents a bio-risk visualization interface, including a global heat map to display the spatial distribution of the quantitative health indicators; the human-machine interface display device includes a touch panel that, when receiving a touch signal on a specific area of ​​the global heat map, triggers the edge slicing computing unit to call from memory and display the corresponding high-resolution sub-image area, and writes the computing priority of the area into the scheduling register of the neural network accelerator.

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