Energy-based dual-function ultra-low power detection method and system

Abstract

This invention provides an energy-based dual-function ultra-low-power detection method and system, comprising: isolating and suppressing the received radio frequency signal through a high-Q bandpass filter; extracting the envelope voltage of the filtered signal through a passive envelope detector; multiplexing the envelope voltage signal and hard-wired it into two parallel paths for synchronous processing; wherein, the first path inputs a comparator with adaptive boundary circuitry, using dynamic level as the decision boundary to demodulate and output a digital bit stream; the second path synchronously inputs a low-speed analog-to-digital converter, combined with a moving average filter in a low-power processor, to calculate and output a received signal strength indication value in real time. This invention enables low-power IoT devices to simultaneously achieve downlink OOK signal demodulation and continuous real-time quantization monitoring of channel energy with only microwatt-level power consumption, without relying on the high-power low-noise amplifiers and mixers in traditional radio frequency chips.

Description

Technical Field

[0001] This invention relates to the field of low-power Internet of Things and low-power wireless communication technology, specifically to an energy-based dual-function ultra-low-power detection method and system. Background Technology

[0002] With the deepening development of IoT technology, hundreds of millions of IoT tags have been deployed in a wide range of scenarios such as smart warehousing, environmental monitoring, and healthcare. Traditional wireless communication technologies (such as Wi-Fi, Bluetooth, and ZigBee) typically consume tens of milliwatts (>10mW) or even hundreds of milliwatts during communication due to the use of high-power radio frequency components such as built-in low-noise amplifiers, local oscillators, and high-frequency mixers. This level of power consumption greatly limits the battery life and deployment of low-power IoT devices that are powered without batteries or rely on environmental micro-energy harvesting (such as radio frequency or solar power).

[0003] To maintain long-term operation, low-power IoT devices typically require extremely tight microwatt-level power budgets. While existing commercial active RF chips (such as the Semtech SX1276) possess high-sensitivity signal strength detection capabilities, their current consumption remains high, even when only the listening function is enabled, resulting in consistently high overall power consumption. However, existing purely passive tags (such as traditional backscatter devices) prioritize near-zero power consumption in their downlink RF front-end design, focusing on demodulating downlink data packets with near-zero power. The classic low-power downlink RF front-end paradigm is the downlink receiver module of a traditional RFID tag. When the transmitter sends an OOK-modulated signal to the near-zero-power receiver, the receiver circuit uses a purely passive diode envelope detection method to demodulate the OOK signal. The output DC baseband signal is then compared with a threshold by a comparator to obtain the demodulated bitstream.

[0004] Subsequent research has followed a similar design. In 2022, Fengyuan Zhu et al. published "Towards Ultra-Low Power OFDMA Downlink Demodulation" in SenSys, proposing a parallel demodulation architecture based on analog filtering to reduce terminal power consumption in multi-user access scenarios. Xiuzhen Guo et al. published "Saiyan: Design and Implementation of a Low-power Demodulator for LoRa Backscatter Systems" in NSDI, designing a low-power LoRa demodulator based on a surface acoustic wave filter, converting the frequency-modulated signal into an amplitude-modulated signal, and then achieving chirped symbol demodulation through envelope detection and dual threshold comparison. In 2025, Bingbing Wang et al. published "Wook: Enabling High-Throughput Wi-Fi Downlink with Ultra-Low Power" in MobiCom, proposing a high-speed OOK transmission mechanism based on sub-symbol-level modulation, which significantly improves the downlink data rate compared to traditional symbol-level OOK downlink demodulation. However, existing research lacks the hardware capability for continuous, real-time, and accurate quantization of current channel energy, and its methods focus on high-rate demodulation of various wireless signals with low power consumption. Therefore, designing a low-power hardware architecture and method that can effectively demodulate downlink signals and continuously and in real-time quantize ambient RF energy within a strict microwatt-level power budget is a critical challenge that urgently needs to be addressed in the field of low-power IoT.

[0005] Patent document CN210927653U discloses an automatic power consumption testing system for a narrowband Internet of Things (IoT) module, comprising: a signal acquisition module, a data preprocessing module, a protocol analysis module, a data processing module, and an automatic report export module; the signal acquisition module is connected to a DC power supply and the IoT module under test. The IoT module is electrically connected; the data preprocessing module is electrically connected to both the signal acquisition module and the data processing module; the protocol analysis module is electrically connected to the NB under test. The IoT module and the data processing module are electrically connected; the data processing module is electrically connected to the automatic report export module. However, this patent cannot completely solve the existing technical problems, nor can it meet the needs of this invention. Summary of the Invention

[0006] In view of the shortcomings of the prior art, the purpose of this invention is to provide an energy-based dual-function ultra-low power detection method and system.

[0007] The energy-based dual-function ultra-low power detection method provided by the present invention includes: Step S1: Physically isolate and suppress interference of the received RF signal by using a high-Q bandpass filter to obtain the filtered RF signal; Step S2: Extract the envelope of the filtered radio frequency signal using a passive envelope detector and output an analog envelope voltage signal; Step S3: Multiplex the analog envelope voltage signal and divide it into two parallel paths for synchronous processing to simultaneously achieve downlink signal demodulation and real-time channel energy monitoring, and output the demodulated bit stream and energy sampling sequence; Step S4: Input the demodulated bit stream and energy sampling sequence into the processor, and perform sliding window filtering on the energy sampling sequence output by the preset low-speed ADC through the processor to obtain the channel energy indication.

[0008] Preferably, step S1 includes: Step S1.1: Capture radio frequency signals in the environment through the device antenna and transmit them to a single-ended impedance matching network; Step S1.2: The RF signal after impedance matching enters a high-Q bandpass filter to filter out interference noise outside the target frequency band and output a clean filtered RF signal.

[0009] Preferably, step S2 includes: Step S2.1: Input the filtered radio frequency signal into a nonlinear detection circuit composed of Schottky diodes; Step S2.2: Utilize the envelope detection characteristics of the diode to filter out the high-frequency carrier under low power or zero static power conditions, and generate an analog envelope voltage signal that fluctuates with the baseband signal.

[0010] Preferably, step S3 includes: Step S3.1: Hard-wire the single analog envelope voltage signal output by the envelope detector to form a first signal and a second signal that are physically connected in parallel on the hardware circuit. Step S3.2: Input the first signal into the demodulation branch, dynamically capture the envelope mean value as a reference boundary through the envelope tracking RC network, compare the first signal with the adaptive reference boundary through the comparator, and output a bit stream with alternating high and low levels; Step S3.3: Synchronously connect the second signal to the low-speed analog-to-digital converter, perform multi-bit precision quantization on the amplitude of the analog envelope voltage according to the set low-speed sampling rate, and generate an energy sampling sequence.

[0011] Preferably, step S4 includes: Step S4.1: The processor continuously reads the energy sampling sequence output by the low-speed analog-to-digital converter via the digital communication bus; Step S4.2: Apply a moving average filter to perform low-pass smoothing on the energy sampling sequence using the processor; Step S4.3: Based on the high and low frequency noise characteristics of the current channel, the processor dynamically configures and adjusts the window size of the moving average filter to output the final stable and reliable channel energy indication value.

[0012] The energy-based dual-function ultra-low power detection system provided by the present invention includes: Module M1: Physically isolates and suppresses interference in the received RF signal using a high-Q bandpass filter to obtain a filtered RF signal; Module M2: Extracts the envelope of the filtered radio frequency signal using a passive envelope detector and outputs an analog envelope voltage signal; Module M3: Multiplexes the analog envelope voltage signal, divides it into two parallel paths for synchronous processing, so as to simultaneously realize downlink signal demodulation and real-time channel energy monitoring, and outputs the demodulated bit stream and energy sampling sequence; Module M4: Inputs the demodulated bit stream and energy sampling sequence into the processor. The processor performs sliding window filtering on the energy sampling sequence output by the preset low-speed ADC to obtain the channel energy indication.

[0013] Preferably, the module M1 includes: Module M1.1: Captures radio frequency signals in the environment via the device antenna and transmits them to a single-ended impedance matching network; Module M1.2: The RF signal after impedance matching enters the high-Q bandpass filter, which filters out interference noise outside the target frequency band and outputs a clean filtered RF signal.

[0014] Preferably, the module M2 includes: Module M2.1: Inputs the filtered RF signal into a nonlinear detection circuit composed of Schottky diodes; Module M2.2: Utilizes the envelope detection characteristics of diodes to filter out high-frequency carrier waves under low power or zero static power conditions, generating an analog envelope voltage signal that fluctuates with the baseband signal.

[0015] Preferably, the module M3 includes: Module M3.1: Hard-wires the single analog envelope voltage signal output by the envelope detector to form a first and second signal that are physically connected in parallel on the hardware circuit. Module M3.2: Inputs the first signal into the demodulation branch, dynamically captures the envelope mean value as a reference boundary through an envelope tracking RC network, compares the first signal with this adaptive reference boundary through a comparator, and outputs a bit stream with alternating high and low levels; Module M3.3: Synchronously connects the second signal to the low-speed analog-to-digital converter, performs multi-bit precision quantization on the amplitude of the analog envelope voltage according to the set low-speed sampling rate, and generates an energy sampling sequence.

[0016] Preferably, the module M4 includes: Module M4.1: The processor continuously reads the energy sampling sequence output by the low-speed analog-to-digital converter via the digital communication bus; Module M4.2: Applies a moving average filter to perform low-pass smoothing on the energy sampling sequence via the processor; Module M4.3: Based on the high and low frequency noise characteristics of the current channel, the processor dynamically configures and adjusts the window size of the moving average filter to output a final stable and reliable channel energy indicator value.

[0017] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention abandons the low-noise amplifier and local oscillator mixer components that consume a lot of power in traditional radio frequency receivers, and uses only passive devices combined with low-speed ADC to realize continuous radio frequency energy sensing under extremely low power consumption conditions. (2) This invention innovatively multiplexes the output of the envelope detector simultaneously for the two tasks of "signal demodulation" and "energy quantization", avoiding the high hardware overhead and additional power consumption brought about by dual-channel ADC or independent RF monitoring module; (3) The present invention introduces an RC adaptive boundary circuit in the demodulation branch, eliminating the need to use a high-power dual-channel ADC to determine the level; and introduces a moving average filter in the energy detection branch, whose configurable window size can effectively cope with complex dynamic environmental noise and output a stable RSSI value. Attached Figure Description

[0018] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart of the low-power IoT dual-function detection method used in this invention; Figure 2 This is a circuit diagram of a low-power IoT dual-function detection system used in this invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0020] Example 1 In view of the shortcomings of existing technologies, such as the excessive power consumption of traditional radio frequency chips and the lack of channel energy sensing capability of passive tags, the purpose of this invention is to provide an energy-based dual-function ultra-low power detection method and system to solve the problem that ultra-low power devices cannot simultaneously perform downlink signal demodulation and continuous energy monitoring.

[0021] The energy-based dual-function ultra-low power detection method provided by the present invention includes: Step S1: Physically isolate and suppress interference of the received RF signal by using a high-Q bandpass filter to obtain the filtered RF signal; The high Q value refers to the filter having a high quality factor, a steep out-of-band attenuation characteristic, and the ability to achieve narrowband high-frequency selective filtering. The bandpass filter is preferably a surface acoustic wave filter, which can passively operate at the front end of the receiving link and rely on physical frequency band isolation to significantly suppress out-of-band interference noise; Step S2: Extract the envelope of the filtered radio frequency signal using a passive envelope detector and output an analog envelope voltage signal; The passive envelope detector is constructed using a Schottky diode with extremely low forward voltage drop (e.g., 0.3V) and a matching RC network, requiring no additional power supply. The envelope extraction refers to the process by which the high-frequency carrier component of the radio frequency signal is filtered out by a nonlinear device after passing through the detector, and the low-frequency process that fluctuates with the amplitude of the baseband signal is extracted. The simulated envelope voltage signal is a continuous simulated voltage signal output by a passive envelope detector that fluctuates with the baseband signal. Step S3: Multiplex the analog envelope voltage signal and divide it into two parallel paths for synchronous processing to simultaneously achieve downlink signal demodulation and real-time channel energy monitoring, and output the demodulated bit stream and energy sampling sequence; The downlink signal demodulation refers to the process of accurately restoring the downlink OOK digital signal sent by the environmental gateway by converting analog voltage into high and low levels. The real-time channel energy monitoring refers to the process of continuously quantifying the energy amplitude in the current radio frequency environment while maintaining microwatt-level power consumption; The bit stream refers to the TTL-compatible digital square wave sequence representing the baseband signal output by the voltage comparator. The energy sampling sequence refers to the set of discrete digital signals output by the low-speed ADC after performing analog-to-digital conversion on the analog envelope voltage; Step S4: The demodulated bit stream and energy sampling sequence are input into the processor. The processor performs sliding window filtering on the energy sampling sequence output by the low-speed ADC to obtain the channel energy indication.

[0022] The processor refers to a low-power microprocessor, preferably a microcontroller (MCU), which is used to receive external signals through a hardware digital interface and perform baseband protocol parsing and firmware software algorithm calculations; The sliding window filtering refers to maintaining a data buffer inside the processor and performing moving average calculations on continuously incoming sampling points, acting as a low-pass filter to smooth out and eliminate high-frequency environmental disturbances and ADC sampling glitches. The channel energy indicator, or Received Signal Strength Indicator (RSSI) value, is used to characterize the current energy level of the radio frequency channel and serves as the physical basis for ultra-low power devices to evaluate communication link quality and switch operating modes.

[0023] Preferably, step S1 includes: Step S1.1: Capture radio frequency signals in the environment through the device antenna and transmit them to a single-ended impedance matching network; Step S1.2: The RF signal after impedance matching enters the high-Q surface acoustic wave filter to filter out interference noise outside the target frequency band and output a clean filtered RF signal.

[0024] Preferably, step S2 includes: Step S2.1: Input the filtered radio frequency signal into a nonlinear detection circuit composed of Schottky diodes; Step S2.2: Utilize the envelope detection characteristics of the diode to filter out the high-frequency carrier under low power or zero static power conditions, and generate an analog envelope voltage signal that fluctuates with the baseband signal.

[0025] Preferably, step S3 includes: Step S3.1: Hard-wire the single analog envelope voltage signal output by the envelope detector to form a first signal and a second signal that are physically connected in parallel on the hardware circuit. Step S3.2: Input the first signal into the demodulation branch, dynamically capture the envelope mean value as a reference boundary through the envelope tracking RC network, and compare the first signal with the adaptive reference boundary to output a bit stream with alternating high and low levels; Step S3.3: Synchronously connect the second signal to the low-speed analog-to-digital converter, perform multi-bit precision quantization on the amplitude of the analog envelope voltage according to the set low-speed sampling rate, and generate an energy sampling sequence.

[0026] Preferably, step S4 includes: Step S4.1: The processor continuously reads the energy sampling sequence output by the low-speed analog-to-digital converter through the digital communication bus; Step S4.2: The processor firmware applies a moving average filter to perform low-pass smoothing on the energy sampling sequence; Step S4.3: The processor dynamically configures and adjusts the window size of the moving average filter based on the high and low frequency noise characteristics of the current channel, and outputs the final stable and reliable channel energy indication value.

[0027] The energy-based dual-function ultra-low power detection system provided by the present invention includes: Module M1: Used to physically isolate and suppress interference in the received RF signal through a high-Q bandpass filter to obtain a filtered RF signal; Module M2: Used to extract the envelope of the filtered radio frequency signal using a passive envelope detector and output an analog envelope voltage signal; Module M3: Used to multiplex the analog envelope voltage signal and divide it into two parallel paths for synchronous processing, so as to simultaneously realize downlink signal demodulation and real-time channel energy monitoring; The module M3 specifically includes: Module M3.1: Used to input the first analog envelope voltage signal into the comparator, convert it into digital high and low levels based on the adaptively extracted dynamic level as the decision boundary, demodulate and output the baseband digital bit stream; Module M3.2: Used to synchronously input the second analog envelope voltage signal into a low-speed analog-to-digital converter for analog-to-digital conversion to obtain an energy sampling sequence.

[0028] Module M4: Used to acquire baseband digital bit stream and process energy sampling sequences to obtain channel energy indication.

[0029] Preferably, the processor module is used to apply a moving average filter to the energy sampling sequence for low-pass filtering to smooth out high-frequency noise interference, and the moving average filter is deployed in the processor's firmware, supporting dynamic reconfiguration of the window size.

[0030] Example 2 This embodiment details a dual-function low-power detection method, the core of which lies in achieving parallel processing of demodulation and energy sensing through hardware branch multiplexing. For example... Figure 1 As shown, the specific steps include: Step S1: Physical isolation and filtering of radio frequency signals.

[0031] The device antenna captures radio frequency signals in the air, which are first input to a high-Q bandpass filter through a single-ended impedance matching network. In this embodiment, a surface acoustic wave (SAW) filter is selected, which has a steep out-of-band attenuation characteristic and can effectively suppress out-of-band interference noise.

[0032] Step S2: Extraction of the simulated envelope signal.

[0033] The filtered RF signal is input to a passive envelope detector. This detector is constructed using a Schottky diode with a low forward voltage drop (e.g., 0.3V). After passing through the detector, the high-frequency carrier is filtered out, and the output is an analog envelope voltage signal (Vpp) that fluctuates with the amplitude of the baseband signal.

[0034] Step S3: Envelope signal multiplexing and dual-channel synchronous processing.

[0035] The analog envelope voltage signal output above is hardwired and simultaneously input into two parallel processing branches: 1. Demodulation Branch Processing: The first analog envelope voltage signal is input to the non-inverting input of the voltage comparator. Simultaneously, an adaptive boundary circuit containing an RC integrator is used to extract the average signal level, which is then used as the dynamic decision boundary input to the inverting input of the comparator. The comparator compares the instantaneous envelope voltage with the decision boundary to output a digital bit stream representing the baseband OOK signal.

[0036] 2. Energy Detection Branch Processing: The second analog envelope voltage signal is synchronously connected to a low-speed analog-to-digital converter (ADC). This ADC performs analog-to-digital conversion on the voltage amplitude with microwatt-level power consumption (e.g., a sampling rate of 100kSPS) to obtain the energy sampling sequence.

[0037] Step S4: Processor back-end smoothing.

[0038] The demodulated digital bitstream and energy sampling sequence are input to a low-power processor via an interface. The processor uses a built-in moving average filter to perform low-pass filtering on the energy sampling sequence, smoothing out sampling glitches and high-frequency environmental disturbances, and finally outputting a high-precision received signal strength indication value.

[0039] Example 3 This embodiment illustrates the hardware implementation of the corresponding system. For example... Figure 2 As shown, the system includes the following core modules: Module M1: Filtering module; It includes impedance matching networks and SAW filters (such as TA0692A) to physically purify the electromagnetic environment and ensure that only signals in the target frequency band enter the subsequent circuitry.

[0040] Module M2: Envelope Detection Module; Passive detection is achieved using a Schottky diode (such as the SMS7630) in conjunction with an external RC network. Since no external power supply is required, this module can convert RF energy into a processable envelope-level signal with near-zero power consumption.

[0041] Module M3: Dual-channel synchronous processing module, including: Module M3.1: Demodulation Submodule: Contains a voltage comparator and an adaptive boundary unit. This unit consists of an RC integrator circuit, which can automatically adjust the decision boundary according to the strength of the input signal, giving the demodulation process the characteristics of being resistant to channel fluctuations.

[0042] Module M3.2: Energy Detection Submodule: Contains a low-power, low-speed ADC (such as ADS7042). The ADC resolution is set to 12 bits to ensure the accuracy of energy sensing.

[0043] Module M4: Processor module; An ultra-low-power microcontroller (such as the MSP430 series) is employed. This processor performs two core tasks: first, MAC layer parsing of the digital bitstream input to the demodulation submodule; and second, calculation of the moving average of the digital quantization value input to the energy detection submodule.

[0044] Preferably, the processor can dynamically adjust the window size of the moving average algorithm according to the statistical characteristics of channel noise in the current environment, thereby achieving an optimal balance between detection response speed and data smoothness.

[0045] In practical low-power IoT deployments (such as warehouse inventory management), the workflow of the system of this invention is as follows: In standby mode, the device shuts down all high-power components, retaining only the dual-function detection branch. When the environmental gateway sends a downlink query command, module M3.1 demodulates the command bitstream in real time to wake up the main control chip. Simultaneously, module M3.2 continuously records the channel RSSI value. If the RSSI value undergoes a significant step change, the processor can infer whether the device has entered a new coverage area or is experiencing physical obstruction, thus assisting the device in autonomously determining its subsequent communication strategy.

[0046] Those skilled in the art will understand that, in addition to implementing the system, apparatus, and their modules provided by this invention in purely computer-readable program code, the same program can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system, apparatus, and their modules provided by this invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; alternatively, modules for implementing various functions can be considered both software programs implementing the method and structures within the hardware component.

[0047] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. An energy-based dual-function ultra-low power consumption detection method, characterized in that, include: Step S1: Physically isolate and suppress interference of the received RF signal by using a high-Q bandpass filter to obtain the filtered RF signal; Step S2: Extract the envelope of the filtered radio frequency signal using a passive envelope detector and output an analog envelope voltage signal; Step S3: Multiplex the analog envelope voltage signal and divide it into two parallel paths for synchronous processing to simultaneously achieve downlink signal demodulation and real-time channel energy monitoring, and output the demodulated bit stream and energy sampling sequence; Step S4: Input the demodulated bit stream and energy sampling sequence into the processor, and perform sliding window filtering on the energy sampling sequence output by the preset low-speed ADC through the processor to obtain the channel energy indication.

2. The energy-based dual-function ultra-low power consumption detection method according to claim 1, characterized in that, Step S1 includes: Step S1.1: Capture radio frequency signals in the environment through the device antenna and transmit them to a single-ended impedance matching network; Step S1.2: The RF signal after impedance matching enters a high-Q bandpass filter to filter out interference noise outside the target frequency band and output a clean filtered RF signal.

3. The energy-based dual-function ultra-low power consumption detection method according to claim 1, characterized in that, Step S2 includes: Step S2.1: Input the filtered radio frequency signal into a nonlinear detection circuit composed of Schottky diodes; Step S2.2: Utilize the envelope detection characteristics of the diode to filter out the high-frequency carrier under low power or zero static power conditions, and generate an analog envelope voltage signal that fluctuates with the baseband signal.

4. The energy-based dual-function ultra-low power consumption detection method according to claim 1, characterized in that, Step S3 includes: Step S3.1: Hard-wire the single analog envelope voltage signal output by the envelope detector to form a first signal and a second signal that are physically connected in parallel on the hardware circuit. Step S3.2: Input the first signal into the demodulation branch, dynamically capture the envelope mean value as a reference boundary through the envelope tracking RC network, compare the first signal with the adaptive reference boundary through the comparator, and output a bit stream with alternating high and low levels; Step S3.3: Synchronously connect the second signal to the low-speed analog-to-digital converter, perform multi-bit precision quantization on the amplitude of the analog envelope voltage according to the set low-speed sampling rate, and generate an energy sampling sequence.

5. The energy-based dual-function ultra-low power consumption detection method according to claim 1, characterized in that, Step S4 includes: Step S4.1: The processor continuously reads the energy sampling sequence output by the low-speed analog-to-digital converter via the digital communication bus; Step S4.2: Apply a moving average filter to perform low-pass smoothing on the energy sampling sequence using the processor; Step S4.3: Based on the high and low frequency noise characteristics of the current channel, the processor dynamically configures and adjusts the window size of the moving average filter to output the final stable and reliable channel energy indication value.

6. An energy-based dual-function ultra-low power detection system, characterized in that, include: Module M1: Physically isolates and suppresses interference in the received RF signal using a high-Q bandpass filter to obtain a filtered RF signal; Module M2: Extracts the envelope of the filtered radio frequency signal using a passive envelope detector and outputs an analog envelope voltage signal; Module M3: Multiplexes the analog envelope voltage signal, divides it into two parallel paths for synchronous processing, so as to simultaneously realize downlink signal demodulation and real-time channel energy monitoring, and outputs the demodulated bit stream and energy sampling sequence; Module M4: Inputs the demodulated bit stream and energy sampling sequence into the processor. The processor performs sliding window filtering on the energy sampling sequence output by the preset low-speed ADC to obtain the channel energy indication.

7. The energy-based dual-function ultra-low power detection system according to claim 6, characterized in that, The module M1 includes: Module M1.1: Captures radio frequency signals in the environment via the device antenna and transmits them to a single-ended impedance matching network; Module M1.2: The RF signal after impedance matching enters the high-Q bandpass filter, which filters out interference noise outside the target frequency band and outputs a clean filtered RF signal.

8. The energy-based dual-function ultra-low power detection system according to claim 6, characterized in that, The module M2 includes: Module M2.1: Inputs the filtered RF signal into a nonlinear detection circuit composed of Schottky diodes; Module M2.2: Utilizes the envelope detection characteristics of diodes to filter out high-frequency carrier waves under low power or zero static power conditions, generating an analog envelope voltage signal that fluctuates with the baseband signal.

9. The energy-based dual-function ultra-low power detection system according to claim 6, characterized in that, The module M3 includes: Module M3.1: Hard-wires the single analog envelope voltage signal output by the envelope detector to form a first and second signal that are physically connected in parallel on the hardware circuit. Module M3.2: Inputs the first signal into the demodulation branch, dynamically captures the envelope mean value as a reference boundary through an envelope tracking RC network, compares the first signal with this adaptive reference boundary through a comparator, and outputs a bit stream with alternating high and low levels; Module M3.3: Synchronously connects the second signal to the low-speed analog-to-digital converter, performs multi-bit precision quantization on the amplitude of the analog envelope voltage according to the set low-speed sampling rate, and generates an energy sampling sequence.

10. The energy-based dual-function ultra-low power detection system according to claim 6, characterized in that, The module M4 includes: Module M4.1: The processor continuously reads the energy sampling sequence output by the low-speed analog-to-digital converter via the digital communication bus; Module M4.2: Applies a moving average filter to perform low-pass smoothing on the energy sampling sequence via the processor; Module M4.3: Based on the high and low frequency noise characteristics of the current channel, the processor dynamically configures and adjusts the window size of the moving average filter to output a final stable and reliable channel energy indicator value.

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