Heterogeneous scalar trigger and timing alignment compensation method and system based on pre-recorded cache

Abstract

The application provides a method and system for heterogeneous scalar trigger and time alignment compensation based on pre-recording cache. The system comprises a nana-level micro-energy capture module (10), a master coprocessor (20), a controlled isolated power supply module (30), a heterogeneous non-imaging sensor (40), a volatile high-speed rolling cache medium (50), and a high-power main imaging module (60). The heterogeneous non-imaging sensor (40) captures a scalar mutation output hardware interrupt, activates the master coprocessor (20) to start the cache medium (50) for blind area pre-recording; after passing through dynamic confidence evaluation, the master coprocessor (20) closes the controlled isolated power supply module (30) to trigger the cold start of the main imaging module (60), and performs time axis physical alignment and residual image splicing when the first frame of image is output; after the event is ended, DMA is triggered to cooperate with TRNG to perform memory disorder overwrite, realizing seamless compensation of cold start blind area, nana-level standby, and physical-level privacy irreversible destruction.

Description

Technical Field

[0001] This invention belongs to the field of low-power edge computing and intelligent security monitoring technology, specifically involving a method and system for low-power fast wake-up, video anti-missed capture prediction compensation, and hardware-level privacy destruction based on multimodal heterogeneous sensor collaboration. Background Technology

[0002] In low-power security monitoring, smart healthcare, and passive IoT monitoring scenarios, edge monitoring devices typically rely heavily on micro-batteries or micro-energy trapping modules (such as nanowatt-level piezoelectric trapping, micro-photovoltaics, and radio frequency micro-energy trapping) for power. The energy supply for such devices is extremely limited, making it impossible to support 24 / 7 continuous operation of high-definition visual imaging systems that include high-power CMOS image sensors and ISPs (image signal processors).

[0003] Currently, the industry mainly adopts the following two traditional modes for power management of low-power monitoring devices, but both have significant technical bottlenecks: First, the "hot standby" mode. The system's main control SoC and visual imaging module are in a low-power suspension or hot standby state for a long time. Although the response speed is fast, the normal maintenance power consumption is usually above the milliwatt level, which can easily lead to rapid battery depletion and is completely unsuitable for the energy supply capabilities of nanowatt-level micro-energy harvesting systems.

[0004] Second, the "Passive Infrared (PIR) Wake-up" mode. During the sleep period, the system cuts off power to the main vision system, retaining only the front-facing passive infrared sensor for monitoring. When an anomaly is detected, the sensor triggers the main control SoC to power on and initialize the image sensor (cold start). However, from the time the sensor outputs the trigger signal to the main vision module completing hardware power-on, register configuration, automatic white balance / automatic exposure stabilization, and outputting the first high-definition image, there is objectively a 1-2 second physical startup delay (time gap / blind spot). In highly dynamic transient events such as door breaking, objects thrown from heights, and sudden falls, the most critical and valuable first-frame images will inevitably be missed, leading to the loss of crucial evidence.

[0005] Third, frequent false alarms are caused by environmental noise. Single non-imaging sensors (such as traditional PIR) are highly susceptible to interference from environmental clutter such as flying insects, sudden changes in light and shadow, and wind blowing curtains. Frequent invalid false triggers cause the main vision system to continuously execute an invalid cycle of "cold start-image output-false alarm judgment-shutdown", which causes the average power consumption of the entire system to rise sharply and severely shortens the equipment's battery life.

[0006] Fourth, privacy compliance risks. Traditional solutions often employ cloud-based continuous caching or local storage for long-form, continuous loop recording of videos to compensate for missed moments. This results in the continuous recording of everyday private moments outside of event cycles, making them highly vulnerable to cyberattacks or local physical extraction, and failing to meet stringent international privacy compliance standards such as GDPR or HIPAA. Summary of the Invention

[0007] The purpose of this invention is to overcome the aforementioned deficiencies of the prior art and provide a heterogeneous scalar triggering and timing alignment compensation method and system based on pre-recorded cache. Through a strongly coupled hardware and software architecture of "micro-energy collaboration + heterogeneous scalar triggering + hardware-level rolling pre-recording + dynamic confidence to prevent false alarms + cross-modal timing alignment + hardware-level DMA and true random number (TRNG) out-of-order overwriting," it achieves zero-power maintenance under complete hard power failure of the main visual system during sleep, zero-missed-shot prediction compensation for cold-start blind zone images, and hardware-level irreversible privacy destruction after the event ends.

[0008] To achieve the above objectives, the present invention provides a heterogeneous scalar triggering and timing alignment compensation method based on pre-recorded cache, which is applied to a system including a main control coprocessor, a heterogeneous non-imaging sensor and a main imaging module, including steps S1 to S5.

[0009] Step S1, Heterogeneous Spatial Scalar Feature Residency and Hardware-Level Triggering: When the system is in deep sleep mode, the high-power main imaging module achieves physical hard power-off through the controlled isolated power supply module, and the whole machine is in a nanowatt-level ultra-low power consumption state; the ultra-low power heterogeneous non-imaging sensor continuously monitors the scalar changes in space. When the scalar change is detected to exceed the environmental background baseline, the heterogeneous non-imaging sensor instantly outputs a hardware interrupt signal to wake up the underlying main control coprocessor.

[0010] Step S2, Hardware-level High-Speed ​​Rolling Cache and Dynamic Confidence Assessment: After the main control coprocessor responds to the interrupt, it activates the volatile high-speed rolling cache medium to record the afterimage data of the heterogeneous sensor environment before and after the wake-up start time T0 at a high sampling rate, forming a pre-recorded cache stream; at the same time, it calls the lightweight edge computing model to perform dynamic confidence assessment. If the overall confidence is lower than the preset threshold, the cache area is cleared and the main imaging module is kept in power-off sleep mode; if it is higher than or equal to the preset threshold, it is determined to be a true alarm.

[0011] Step S3, Controlled hard start of the main imaging module: The main control coprocessor controls the controlled isolated power supply module to conduct the main power supply circuit of the main imaging module by outputting an enable signal. The high-power vision system starts to perform cold start initialization. During the physical time difference Delta T of the cold start initialization, the volatile high-speed rolling buffer medium continues to record heterogeneous image data without interruption.

[0012] Step S4, Physical alignment of time axis and prediction and compensation of afterimage stitching: When the main imaging module completes cold start and outputs the first high-definition image at time T1 (i.e., T0 + Delta T), read the heterogeneous afterimage data stream from T0 to T1 stored in the volatile high-speed rolling buffer medium; using the hardware-level real-time clock cycle of the main control coprocessor as the absolute time anchor, the physical alignment algorithm of time axis is used to perform cross-modal clock drift compensation, linear interpolation and phase anchoring on the discrete time series of non-imaging sensors and the absolute timestamps of each frame of the image output by the main imaging module, to generate a continuous spatiotemporal event recording stream.

[0013] Step S5, Physical-level Closed-Loop Destruction and System Reset: After the event log is encrypted and reported or stored locally, the main control coprocessor disconnects the controlled isolation power supply module to cut off the power supply to the main vision module. The system re-enters deep sleep and then directly calls the Direct Memory Access (DMA) channel through the underlying hardware instructions to coordinate with the hardware True Random Number Generator (TRNG) to perform multiple rounds of pseudo-random number out-of-order overwriting on the physical storage units in the volatile high-speed rolling cache medium within one machine cycle.

[0014] Preferably, the heterogeneous non-imaging sensor includes at least one of the following: a low-power radar Doppler frequency shift scalar sensor, a piezoelectric or piezoresistive stress-strain voltage scalar sensor, a micro-pressure pressure change scalar sensor, or an electromagnetic field stress change scalar sensor.

[0015] Preferably, the environmental background baseline mentioned in step S1 is updated in real time using a first-order exponential moving average algorithm. The formula is as follows: the environmental background baseline is equal to the smoothing factor multiplied by the current real-time measured scalar value plus parentheses 1 minus the smoothing factor multiplied by the environmental background baseline at the previous moment; when it is determined that the absolute value of the current real-time measured scalar value minus the environmental background baseline is greater than the preset safety threshold, the hardware interrupt signal is triggered.

[0016] As a preferred embodiment, the specific implementation mechanism of the dynamic confidence assessment in step S2 includes: extracting the peak frequency and trough amplitude feature vectors from the afterimage data using a sliding window, and using threshold comparison logic, Kalman filtering smoothing, or an embedded TinyML neural network to perform similarity matching and scoring calculation between the pre-prepared feature vectors and a preset real anomaly sample library.

[0017] Preferably, the linear interpolation and phase anchoring calculation method of the time axis physical alignment algorithm in step S4 is described as follows: Let the wake-up time be T0, the cold start delay be Delta T, the image output time be T1 (T1 = T0 + Delta T), the discrete sampling time be $t_i$ and the corresponding scalar value be $S_i$, and the main imaging module output the timestamps of the first frame and subsequent frames; firstly, calculate the cross-modal phase offset, which is equal to the clock drift coefficient multiplied by the absolute time anchor point plus the fixed-sequence phase offset; perform linear interpolation on the discrete scalar data within the blind zone Delta T to obtain the continuous compensation scalar value, which is equal to the starting scalar value plus the current timestamp minus the starting timestamp divided by the ending timestamp minus the starting timestamp, multiplied by the ending scalar value minus the starting scalar value, thereby eliminating the sampling rate time difference and clock drift of the cross-modal heterogeneous sensors. If the RTC counter overflows and resets during acquisition, the system automatically superimposes a fixed-period modulus through the hardware overflow interrupt capture flag to ensure continuous timestamp calculation.

[0018] Preferably, the physical-level memory out-of-order overwrite in step S5 includes: the main control coprocessor, through the direct memory access (DMA) channel, in coordination with the hardware true random number generator (TRNG), sequentially writing the first specific codeword 0x55, the second specific codeword 0xAA, and the true random code generated by the TRNG to the physical address space of the volatile high-speed rolling cache medium, performing at least three rounds of hardware-level concurrent overwrite.

[0019] The present invention also provides a heterogeneous scalar triggering and timing alignment compensation system based on pre-recorded cache, comprising: a nanowatt-level micro-energy capture module (10) for providing micro-energy power support for the system; a heterogeneous non-imaging sensor (40) for continuously monitoring spatial scalar changes and generating hardware interrupts and scalar data during abrupt changes; a controlled isolated power supply module (30) including a MOSFET physical hard-shutdown switch for cutting off the main power supply circuit of the high-power main imaging module (60) during sleep; a main control coprocessor (20) connected to the heterogeneous non-imaging sensor (40) for responding to hardware interrupts, controlling cache pre-recording, and performing dynamic confidence assessment; a volatile high-speed rolling cache medium (50) embedded or externally connected to the storage unit for high-sampling-rate rolling recording of heterogeneous afterimage data and performing physical-level out-of-order overwriting after data reporting; and a high-power main imaging module (60) including a CMOS image sensor and an ISP image processor for performing cold start and outputting high-definition visual image stream after real alarm wake-up.

[0020] Preferably, the volatile high-speed rolling cache medium (50) adopts a rolling SRAM FIFO cache area and is connected to the main control coprocessor (20) via QSPI or HyperBus high-speed storage bus.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) It achieves physical unification of zero power consumption during sleep and zero missed shots during startup. By physically powering down the main vision module during sleep through the controlled isolated power supply module, the static power consumption during standby is eliminated; at the same time, by pre-triggering the heterogeneous scalar sensor and pre-recording the underlying layer of the high-speed rolling buffer medium, the physical blank period Delta T of 1 to 2 seconds during the cold start stage is completely filled, ensuring zero loss of images during critical dynamic moments such as breaking down doors and falling down. (2) It has efficient anti-false alarm and power consumption reduction capabilities. The dynamic confidence assessment based on TinyML or Kalman filtering is placed before the main imaging module is powered on. The high-power main imaging module is only turned on after the lightweight micro data stream verification passes (determined to be a real alarm), which effectively filters out false triggers caused by environmental noise such as wind and flying insects, and greatly extends the battery life of the edge device. (3) It meets physical-level privacy compliance requirements. After the event is processed, the system directly calls the DMA-coordinated hardware true random number generator (TRNG) through low-level hardware instructions to perform multiple rounds of pseudo-random number out-of-order overwriting on the volatile high-speed rolling cache medium (SRAM FIFO), completely eliminating residual charge and traces in the physical storage unit, eliminating the risk of privacy leakage from the physical source, and perfectly complying with GDPR and HIPAA international standards. Attached Figure Description

[0022] Figure 1 is a hardware topology diagram of the system provided in an embodiment of the present invention.

[0023] Figure 2 is a flowchart of the operation of the method provided in the embodiment of the present invention.

[0024] Figure 3 is a schematic diagram of cold start time axis physical alignment and afterimage compensation splicing in an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] Referring to Figure 1, this embodiment provides a heterogeneous scalar triggering and timing alignment compensation system based on pre-recorded cache, including: a nanowatt-level low-energy capture module (10), a heterogeneous non-imaging sensor (40), a main control coprocessor (20), a volatile high-speed rolling SRAM FIFO cache medium (50) embedded or externally connected via QSPI / HyperBus high-speed bus, a controlled isolated power supply module (30) containing MOSFET physical hard isolation switches, and a high-power main imaging module (60) containing a CMOS image sensor and an ISP image processor.

[0027] In terms of physical hardware connection topology: the power output terminal of the nanowatt-level micro-energy harvesting module (10) is connected to the input terminals of the main control coprocessor (20) and the controlled isolated power supply module (30) respectively, providing them with nanowatt-level continuous micro-energy support; the interrupt output pin of the heterogeneous non-imaging sensor (40) is directly connected to the external interrupt input pin (EXTI / INT) of the main control coprocessor (20), and the data bus (I2C / SPI) is connected to the data interface of the main control coprocessor (20); the general purpose input / output pin (GPIO) of the main control coprocessor (20) is directly connected to the gate control terminal of the MOSFET in the controlled isolated power supply module (30); the power input terminal (VCC) of the high-power main imaging module (60) is connected in series with the drain and source of the MOSFET in the controlled isolated power supply module (30) in the main power supply circuit; the data output bus (MIPI-CSI / Parallel) of the high-power main imaging module (60) is connected to the high-speed image interface of the main control coprocessor (20).

[0028] Referring to Figures 2 and 3, this embodiment provides a heterogeneous scalar triggering and timing alignment compensation method based on pre-recorded cache. The specific execution process includes steps S1 to S5.

[0029] Step S1: The system is in deep sleep mode. The main control coprocessor (20) outputs a low level to turn off the MOSFET switch. The main power supply circuit (VCC) of the high-power main imaging module (60) is completely physically disconnected. The main imaging module (60) is in a zero-power power-off state, and the whole machine maintains a static power consumption of nanowatt level. The ultra-low power heterogeneous non-imaging sensor (40) continuously monitors the physical scalar changes of the surrounding environment (such as Doppler frequency shift, stress strain, or micro-pressure change) with extremely low current. The sensor maintains an environmental background adaptive baseline, which is updated in real time according to the aforementioned first-order exponential moving average rule. When the absolute value of the difference between the real-time scalar and the background baseline is detected at time T0 exceeds the preset safety threshold, the heterogeneous non-imaging sensor (40) instantly pulls the hardware interrupt pin high / low and sends an interrupt signal to the main control coprocessor (20).

[0030] Step S2: The main control coprocessor (20) is instantly awakened by a hardware interrupt and immediately activates the volatile high-speed rolling SRAM FIFO buffer medium (50) to continuously record the scalar time-series situation vector before and after the start time T0 at a high sampling rate (e.g., 1kHz). At the same time, the main control coprocessor (20) calls the built-in lightweight edge computing model to perform dynamic confidence assessment. During the assessment, the peak frequency and trough amplitude feature vectors in the afterimage data are extracted using a sliding window, Kalman filtering is used for smoothing and denoising, and the embedded TinyML decision tree classifier is used to score the similarity with the pre-stored real anomaly sample feature library. If the overall confidence score obtained by the scoring is lower than the preset threshold, it is determined to be a false alarm caused by wind, flying insects, etc. The main control coprocessor (20) immediately clears the pointer of the SRAM FIFO buffer area (50), keeps the main imaging module (60) powered off and re-enters deep sleep; if the confidence score is higher than or equal to the preset threshold, it is determined to be a real alarm event and immediately proceeds to step S3.

[0031] Step S3: The main control coprocessor (20) outputs a high-level enable signal to the controlled isolated power supply module (30) via GPIO, the MOSFET hard switch closes, and the main power supply circuit (VCC) of the main imaging module (60) is turned on. The main imaging module (60) begins to perform cold start initialization (including chip power-on, register configuration, ISP image parameter convergence and autofocus, etc., this cold start process has an objective physical time difference Delta T, the typical value is between 800ms and 1500ms). During the cold start time Delta T, the SRAM FIFO (50) continues to continuously record the foreign source image data to ensure that the data in the physical blind zone is completely preserved.

[0032] Step S4: At time T1 (T1 = T0 + Delta T) when the main imaging module (60) completes its cold start and outputs the first high-definition visual image, the system reads the heterogeneous afterimage data stream from T0 to T1 stored in the SRAM FIFO (50). The system calls the time axis physical alignment algorithm: using the high-precision counter period of the hardware-level real-time clock (RTC) inside the main control coprocessor (20) as the absolute time anchor. For the discrete time series generated by the non-imaging sensor (40) under low-power asynchronous sampling and the absolute timestamp of the high-definition video frame output by the main imaging module (60), clock drift compensation is performed through the aforementioned cross-modal phase offset, and linear interpolation is performed on the discrete scalar data in the blind zone Delta T to generate a continuous situational compensation stream that is strictly phase aligned with the video frame rate. If the RTC counter overflows and resets during acquisition, the system automatically superimposes a fixed period modulus through the hardware overflow interrupt capture flag to ensure that the timestamp calculation is continuous and without discontinuities. By precisely stitching together blind spot afterimage data with high-definition visual video streams, a complete and continuous spatiotemporal event record stream is generated from the start of the T0 event to its end.

[0033] Step S5: After the continuous spatiotemporal event recording stream is encrypted and uploaded to local or cloud storage via the wireless module, the main control coprocessor (20) pulls down the GPIO enable signal, the controlled isolation power supply module (30) is disconnected, and the main vision module (60) is physically powered off again. Subsequently, the main control coprocessor (20) calls the direct memory access (DMA) channel and coordinates with the hardware true random number generator (TRNG) through the underlying hardware instructions to continuously write three rounds of hardware-level concurrent overwrite to the physical memory address space of the SRAM FIFO (50): the first round writes the full grid code 0x55, the second round writes the anti-grid code 0xAA, and the third round writes the true random number stream generated by the hardware TRNG. The multi-round out-of-order overwrite is completed instantly within one machine cycle, realizing the irreversible erasure of data at the physical level. Then the main control coprocessor (20) shuts down its non-essential peripherals and resets to re-enter deep sleep state.

[0034] The embodiments of this invention also include the following specific application scenarios: In smart home privacy and anti-theft scenarios, piezoelectric thin-film stress sensors sense high-frequency stress waveforms from prying open doors and trigger an interrupt. After the pre-recorded waveform is confirmed with confidence, the camera is activated, achieving a unified approach to privacy and preventing peeping while securing irrefutable evidence of forced entry. In smart elderly care fall monitoring scenarios, 60GHz millimeter-wave radar monitors point cloud trajectories. When an elderly person suddenly falls, the coprocessor is awakened, a pre-recorded spatial trajectory scalar is generated, and TinyML determines the confidence level before the fall and then activates the camera, generating a spliced ​​stream of the radar trajectory before the fall and the image after the fall. After the event is reported, DMA performs multiple rounds of overwriting and destruction, fully complying with the HIPAA Medical Privacy Act. In outdoor fire prevention / perimeter security passive IoT scenarios, nanowatt-level piezoelectric sensors and micro-photovoltaic arrays provide power, and PIR... In conjunction with a micro-pressure sensor, dynamic confidence assessment combines air pressure and infrared waveforms to comprehensively determine environmental clutter and clear the buffer, keeping the thermal imager powered off and significantly improving device battery life. In high-impact scenarios such as financial vaults, micro-pressure change and electromagnetic field stress sensors immediately trigger an interrupt and start SRAM rolling pre-recording when an intruder damages the ventilation opening or releases electromagnetic interference. Even if the main cable of the camera is physically cut, the front-end scalar situation has been encrypted, captured, and uploaded, building a physical bottom-line defense.

[0035] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A heterogeneous scalar triggering and timing alignment compensation method based on pre-recorded buffers, characterized in that, Includes the following steps: S1. Heterogeneous spatial scalar feature dwell and hardware-level triggering: When the system is in deep sleep, the high-power main imaging module (60) achieves physical hard power-off through the controlled isolation power supply module (30), and the whole machine is in a nanowatt-level ultra-low power consumption state. The ultra-low power heterogeneous non-imaging sensor (40) continuously monitors the scalar changes in the space. When the scalar change is detected to exceed the environmental background baseline, a hardware interrupt signal is output to wake up the underlying main control coprocessor (20). S2, Hardware-level high-speed rolling cache and dynamic confidence assessment: After the main control coprocessor (20) responds to the interrupt, it activates the volatile high-speed rolling cache medium (50) and records the residual data of the heterogeneous sensor environment before and after the wake-up start time T0 at a high sampling rate to form a pre-recorded cache stream; at the same time, it calls the lightweight edge computing model to perform dynamic confidence assessment. If the overall confidence is lower than the preset threshold, the cache area is cleared and the main imaging module (60) is kept in power-off sleep. If it is higher than or equal to the preset threshold, it is determined to be a true alarm. S3, Controlled hard start of main imaging module: The main control coprocessor (20) controls the controlled isolated power supply module (30) to turn on the main power supply circuit of the main imaging module (60) by outputting an enable signal. The high power vision system starts to perform cold start initialization. During the physical time difference Delta T of cold start initialization, the rolling buffer medium (50) continues to record the heterogeneous image data without interruption. S4. Physical alignment of time axis and image retention prediction compensation stitching: When the main imaging module (60) completes cold start and outputs the first high-definition image at time T1 (i.e. T0 + Delta T), read the heterogeneous image retention data stream stored in the rolling buffer medium (50) from T0 to T1. Using the hardware-level real-time clock cycle of the main control coprocessor (20) as the absolute time anchor point, the physical alignment algorithm of time axis is used to perform cross-modal clock drift compensation, linear interpolation and phase anchoring on the discrete time series of non-imaging sensors and the absolute timestamps of each frame of the main imaging module (60) to generate a continuous spatiotemporal event recording stream. S5. Physical-level closed-loop destruction and system reset: After the event record is encrypted and reported or stored locally, the main control coprocessor (20) disconnects the controlled isolation power supply module (30) to cut off the power supply of the main vision module. Then, through the underlying hardware instructions, it directly calls the direct memory access (DMA) channel to coordinate the hardware true random number generator (TRNG) to perform multiple rounds of pseudo-random number reordering on the physical storage units in the volatile high-speed rolling cache medium (50) within one machine cycle.

2. The method according to claim 1, characterized in that, The heterogeneous non-imaging sensor (40) includes at least one of the following: a low-power radar Doppler frequency shift scalar sensor, a piezoelectric or piezoresistive stress-strain voltage scalar sensor, a low-pressure gas pressure change scalar sensor, or an electromagnetic field stress change scalar sensor.

3. The method according to claim 1, characterized in that, The specific implementation mechanism of dynamic confidence assessment in step S2 includes: extracting the peak frequency and trough amplitude feature vectors from the afterimage data using a sliding window, and using threshold comparison logic, Kalman filtering smoothing or embedded TinyML neural network to perform similarity matching and scoring calculation between the pre-prepared feature vectors and the preset real abnormal sample library.

4. The method according to claim 1, characterized in that, The physical alignment algorithm of the time axis described in step S4 is specifically expressed as follows: taking the hardware-level real-time clock cycle of the main control coprocessor (20) as the absolute time anchor point, for the discrete time series of the non-imaging sensor (40) under low-power asynchronous sampling, cross-modal clock drift compensation, linear interpolation and phase anchoring are performed with the absolute timestamps of each frame of the image output by the main imaging module (60).

5. The method according to claim 1, characterized in that, The physical-level memory out-of-order overwrite described in step S5 is performed concurrently by the hardware direct memory access (DMA) channel and the hardware-level true random number generator (TRNG) within one machine cycle. The physical storage units in the volatile high-speed rolling cache medium (50) are sequentially and continuously overwritten with full grid code 0x55, anti-grid code 0xAA and hardware true random numbers in multiple rounds. This ensures that the front-end heterogeneous image situation data is physically and irreversibly erased within one machine cycle after the transmission is completed, meeting global privacy compliance standards.

6. A heterogeneous scalar triggering and timing alignment compensation system based on pre-recorded buffers, characterized in that, include: A nanowatt-level micro-energy capture module (10) is used to provide micro-energy power support for the system; a heterogeneous non-imaging sensor (40) is used to continuously monitor spatial scalar changes and generate hardware interrupts and scalar data when abrupt changes occur; a controlled isolated power supply module (30) includes a MOSFET physical hard-shutdown switch, which is used to cut off the main power supply circuit of the high-power main imaging module (60) during the sleep period; a main control coprocessor (20) is connected to the heterogeneous non-imaging sensor (40) and is used to respond to hardware interrupts, control cache pre-recording, and perform dynamic confidence assessment; a volatile high-speed rolling cache medium (50) is embedded or externally connected to the storage unit and is used to record heterogeneous afterimage data at a high sampling rate and perform physical-level out-of-order overwriting after data reporting; a high-power main imaging module (60) includes a CMOS image sensor and an ISP image processor, which is used to perform cold start and output high-definition visual image stream after real alarm wake-up.

7. The system according to claim 6, characterized in that, The volatile high-speed rolling cache medium (50) adopts a rolling SRAM FIFO cache area and is connected to the main control coprocessor (20) via QSPI or HyperBus high-speed storage bus.

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