Municipal fire hydrant reuse type high-frequency pressure monitoring method and system

Abstract

The embodiment of the invention provides a municipal fire hydrant reuse type high-frequency pressure monitoring method and system, and relates to the technical field of technologies. The method comprises the following steps: acquiring a pressure sensing signal; in response to the pressure sensing signal, executing event pre-identification in a preset simulation domain to generate a filtered signal; generating a hardware wake-up interrupt instruction based on a comparison result of the filtered signal and a preset voltage threshold; and in response to the hardware wake-up interruption, waking up a main processor from a sleep mode, and executing high-frequency data acquisition and event confirmation on the pressure sensing signal through the wakened main processor. According to the invention, the problem of high standby power consumption under the condition of lossless capture of the high-frequency event is solved, so that the effect of reducing the standby power consumption under the condition of lossless capture of the high-frequency event is achieved.

Description

Technical Field

[0001] This invention relates to the field of municipal pipeline safety monitoring technology, specifically to a reusable high-frequency pressure monitoring method and system for municipal fire hydrants. Background Technology

[0002] As a crucial component of urban water supply networks, real-time monitoring of the internal pressure of municipal fire hydrants is of paramount importance for ensuring fire safety, providing early warning of pipeline leaks, and detecting unauthorized water use. Traditional pressure monitoring equipment typically employs a microcontroller-based digital sampling scheme, analyzing pressure data through periodic wake-ups, analog-to-digital conversion (ADC), and digital signal processing.

[0003] However, to accurately capture sudden pressure events (e.g., instantaneous pipe rupture due to external impact or rapid unauthorized opening of fire hydrants), existing technologies require monitoring systems to acquire data at very high frequencies (e.g., above kilohertz). Otherwise, the most critical waveform characteristics at the initial stage of the event will be lost, making it impossible to accurately determine the nature and timing of the event. On the other hand, high-frequency wake-up and data processing, especially the execution of frequency domain analysis algorithms such as Fourier transform (FFT), generate average operating current in the milliampere (mA) range. This is an unacceptable power consumption burden for monitoring nodes that rely on battery power and require long-term unattended deployment. Therefore, how to achieve lossless capture of high-frequency events while minimizing system standby power consumption has become a pressing technical problem in this field. Summary of the Invention

[0004] This invention provides a reusable high-frequency pressure monitoring method and system for municipal fire hydrants, which at least solves the problem of high standby power consumption in the case of non-destructive capture of high-frequency events in related technologies.

[0005] According to an embodiment of the present invention, a reusable high-frequency pressure monitoring method for municipal fire hydrants is provided, comprising: acquiring a pressure sensing signal; responding to the pressure sensing signal, performing event pre-identification within a preset analog domain to generate a filtered signal; generating a hardware wake-up interrupt instruction based on a comparison result between the filtered signal and a preset voltage threshold; and responding to the hardware wake-up interrupt, waking up the main processor from sleep mode, and performing high-frequency data acquisition and event confirmation on the pressure sensing signal through the woken-up main processor.

[0006] In one exemplary embodiment, prior to performing continuous event pre-identification, the method further includes:

[0007] Based on the preset acoustic energy characteristics of the target event, the transfer function of the analog matched filter is configured so that the amplitude-frequency response characteristics of the analog matched filter match the spectral shape of the acoustic energy characteristics.

[0008] In an exemplary embodiment, the step of performing event pre-identification within a preset analog domain in response to the pressure sensing signal to generate a filtered signal includes:

[0009] The pressure sensing signal is continuously input to the analog matched filter to perform real-time convolution filtering on the pressure sensing signal, thereby generating the filtered signal.

[0010] In an exemplary embodiment, generating a hardware wake-up interrupt instruction based on the comparison result between the filtered signal and a preset voltage threshold includes:

[0011] The voltage amplitude of the filtered signal is continuously compared with the preset voltage threshold using a hardware comparator.

[0012] When the voltage amplitude of the filtered signal is greater than the preset voltage threshold, the hardware comparator outputs a level transition signal and uses the level transition signal as the hardware wake-up interrupt instruction.

[0013] In one exemplary embodiment, the step of performing high-frequency data acquisition on the pressure sensing signal by the awakened main processor includes:

[0014] The main processor controls the switching of the signal path, guiding the pressure sensing signal to the high-frequency data acquisition module;

[0015] The main processor starts the high-frequency data acquisition module to sample the pressure sensing signal at a preset high sampling rate and writes the sampled data into a circular buffer.

[0016] In one exemplary embodiment, the step of performing event confirmation on the pressure sensing signal via the awakened main processor includes:

[0017] The main processor performs digital domain signal processing on the sampled data in the circular buffer for secondary confirmation;

[0018] Upon receiving secondary confirmation, the main processor packages the data in the circular buffer and reports it via the communication module.

[0019] According to another embodiment of the present invention, a reusable high-frequency pressure monitoring system for municipal fire hydrants is provided, comprising:

[0020] Pressure sensor module, used to generate pressure sensing signals;

[0021] The analog domain event pre-identification front end is electrically connected to the pressure sensor module and is used to perform continuous event pre-identification in the analog domain in response to the pressure sensing signal, so as to generate a filtered signal and generate a hardware wake-up interrupt instruction based on the comparison result of the filtered signal and a preset voltage threshold.

[0022] The main processing and control module is electrically connected to the analog domain event pre-identification front end and is used to be woken up from the sleep mode when the hardware wake-up interrupt is received;

[0023] The high-frequency data acquisition and storage module, controlled by the main processing and control module, is used to perform high-frequency data acquisition of the pressure sensing signal after the main processing and control module is awakened.

[0024] In one exemplary embodiment, the simulated domain event pre-identification front-end includes:

[0025] An analog matched filter is used to perform real-time convolution filtering on the pressure sensing signal to generate the filtered signal. The analog matched filter includes a transfer function that matches the amplitude-frequency response characteristics of the analog matched filter with the spectral shape of the preset acoustic energy characteristics of the target event.

[0026] According to yet another embodiment of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0027] According to yet another embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0028] This invention achieves zero-system-latency capture of abnormal pipeline pressure events by performing continuous event pre-identification within the analog domain and directly triggering hardware interrupts on the main processor using the identification results. This allows the main processor to remain in deep sleep most of the time, thus enabling the capture of abnormal pipeline pressure events. Therefore, it resolves the inherent contradiction between the high-frequency real-time monitoring requirements and the ultra-low power consumption limitations in battery-powered scenarios, achieving the beneficial effect of reducing system standby power consumption by several orders of magnitude without sacrificing the integrity of event capture. Attached Figure Description

[0029] Figure 1 This is a structural block diagram of a municipal fire hydrant reusable high-frequency pressure monitoring system according to an embodiment of the present invention;

[0030] Figure 2This is a flowchart of a reusable high-frequency pressure monitoring method for municipal fire hydrants according to an embodiment of the present invention;

[0031] Figure 3 This is a simulation diagram of the amplitude-frequency response curve according to an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0033] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0034] Furthermore, in this application, directional terms such as "upper," "lower," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and may change accordingly depending on the orientation of the components in the accompanying drawings.

[0035] In this application, unless otherwise expressly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a direct connection or an indirect connection through an intermediate medium. Furthermore, the term "coupled" can refer to an electrical connection that enables signal transmission.

[0036] As used herein, “about,” “approximately,” or “approximately” includes the stated value and the average value within an acceptable range of deviation from the given value, wherein the acceptable range of deviation is determined by a person skilled in the art taking into account the measurement under discussion and the error associated with the measurement of the given quantity (i.e., the limitations of the measurement system).

[0037] Reference Figure 1 The system includes a pressure sensor module 10, an analog domain event pre-identification front-end 20, a main processing and control module 30, and a high-frequency data acquisition and storage module 40. In some embodiments, the system may also include a data communication module 50 and an analog switch, which is not limited here.

[0038] The pressure sensor module 10 converts the fluid pressure in the internal pipes of municipal fire hydrants into an analog voltage signal, i.e., a pressure sensing signal. This module typically employs a high-sensitivity piezoresistive or piezoelectric pressure sensor, which is capable of responding to broadband signals ranging from static pressure to high-frequency pressure fluctuations of several kilohertz, and outputs a pressure sensing signal. The voltage amplitude is directly proportional to the instantaneous pressure.

[0039] The analog domain event pre-identification front-end 20 is electrically connected to the pressure sensor module 10 for continuous processing of pressure sensing signals. This module performs signal processing in the analog domain, so its internal signal processing process does not depend on the digital clock drive, and the core components only consume a static bias current in the nanoampere (nA) range. It integrates an analog matched filter and a hardware comparator. Without waking up the main processing and control module 30, this front-end can autonomously determine whether the input pressure sensing signal contains the acoustic characteristics of a specific event. If and only if a matching feature is identified, it generates a high-level or low-level transition as a hardware wake-up interrupt signal or hardware wake-up interrupt instruction, which directly drives the interrupt pin of the main processing and control module 30.

[0040] The main processing and control module 30 is typically an ultra-low-power microcontroller (MCU) or processor (MPU). Under normal system monitoring conditions (i.e., when no events are detected), this module is in deep sleep mode, with its internal clock oscillator and most peripherals disabled, resulting in power consumption as low as microamps. It can even reach the nanoampere (nA) level. One of its external interrupt pins is electrically connected to the output of the analog domain event pre-identification front-end 20; the module will only be woken up and resume full-speed operation when the pin receives a level transition generated by the front-end 20 (i.e., a hardware wake-up interrupt instruction), and take over the subsequent data acquisition, analysis and reporting tasks.

[0041] The high-frequency data acquisition and storage module 40 is controlled by the main processing and control module 30. After the main processor is woken up, this module immediately performs high-speed analog-to-digital conversion on the raw pressure sensing signal. This module typically includes a high-speed analog-to-digital converter (ADC) and a memory for temporarily storing sampled data (e.g., a segment of static random access memory (SRAM) configured as a circular buffer). Since the wake-up process is triggered by a hardware interrupt with almost no software delay, this module can start capturing data from the exact moment the event occurs, ensuring the integrity of the event's initiation information.

[0042] Preferably, the transfer function of the analog matched filter inside the analog domain event pre-identification front-end 20 is programmable. Specifically, the analog matched filter can be composed of a series of switched-capacitor filter units or tunable operational amplifier circuits, which allows the system to adapt to different types of monitoring tasks. For example, the pressure wave acoustic characteristics (i.e., spectral distribution) generated by two different events, "minor pipeline leak" and "illegal opening of fire hydrants," are different. The system can pre-store the acoustic energy characteristics corresponding to these two events, and according to the current task requirements, the main processing and control module 30 configures the circuit parameters (such as equivalent resistance and capacitance values) of the analog matched filter during the initialization phase, so that its amplitude-frequency response curve accurately matches the spectral shape of the target event, thereby enhancing the system's flexibility and identification accuracy.

[0043] Preferably, the circular buffer in the high-frequency data acquisition and storage module 40 can provide background pressure data for a period of time prior to the event after the event is confirmed. The working mechanism of the circular buffer is that new sampled data continuously overwrites the oldest data. When a hardware wake-up interrupt occurs, the main processing and control module 30 stops writing data. At this time, the buffer not only stores the data from the time of the interrupt, but also naturally retains several sampling points before the time of the interrupt.

[0044] Example 2

[0045] This embodiment provides a reusable high-frequency pressure monitoring method for municipal fire hydrants based on the aforementioned system. (Refer to...) Figure 2 The method includes:

[0046] S100: System Initialization and Acoustic Feature Configuration

[0047] In this embodiment, the standardized acoustic energy characteristic data of the target event is loaded into the non-volatile memory of the main processing and control module 30 of the monitoring system. The acoustic energy characteristic is the energy distribution characteristic that can uniquely identify the event after the pressure wave generated in the fluid by a specific physical event (e.g., a pipe leak at a specific size and pressure) is analyzed by spectrum. Mathematically, it can be represented as a discretized vector consisting of a set of key frequency points and the corresponding normalized energy values ​​at these frequency points.

[0048] For example, assuming the target event is a typical micro-pipeline leak, after extensive experimental data calibration, it was found that the acoustic signal energy generated is mainly concentrated near two characteristic frequency bands; therefore, the acoustic energy characteristics of this leak event... It can be defined as: In a specific numerical instance, this feature can be ;in, and These are two key frequency points, measured in kilohertz (kHz). and This is the corresponding normalized energy value, which is used here. The energy at that location is based on 1.0; it is easy to see that the energy distribution of the target event in the frequency domain is non-uniform and has a recognizable pattern.

[0049] After acquiring the acoustic energy characteristics, the main processing and control module 30 will, based on these characteristics, precisely configure the transfer function of the analog matched filter in the analog domain event pre-identification front-end 20, so that the amplitude-frequency response characteristics of the analog matched filter approximate the spectral shape of the acoustic energy characteristics to the greatest extent possible. In terms of circuit implementation, this can be achieved by controlling a set of digital potentiometers or a programmable capacitor array, which together determine the pole and zero positions of the filter circuit.

[0050] For example, in order to match features with two energy peaks An analog matched filter can be a cascaded structure of two second-order bandpass filters. The main processor will set the core parameters of these two filters respectively: center frequency. Quality Factor and gain For example, the parameters of the first-stage filter are set to: center frequency. Quality factor Gain The parameters of the second-stage filter are set as follows: center frequency Quality factor Gain .

[0051] With such configuration, such as Figure 3 The final amplitude-frequency response curve of the entire filter will be as described above. and Two significant gain peaks are formed at the point, and the ratio of their peak heights corresponds to the energy ratio in the feature. A higher Q value will form a sharper resonance peak, thus having higher selectivity for the target frequency and being able to suppress out-of-band noise more effectively. However, it may also make the filter more sensitive to center frequency drift caused by temperature or component aging. Therefore, the selection of Q value needs to take into account both the recognition accuracy and the robustness of the system throughout its entire life cycle. The specific adjustment can be made according to actual needs, which will not be elaborated here.

[0052] Finally, the main processing and control module 30 will set a trigger threshold voltage for the internal hardware comparator of the analog domain event pre-identification front-end 20. This threshold determines the sensitivity of event recognition; a lower threshold increases sensitivity but may increase the false alarm rate, and vice versa. In this embodiment, the threshold voltage... Can be set to (Unit: Volt)

[0053] After all configurations are completed, the main processing and control module 30 will actively enter a deep sleep mode, and the system will then enter a state of continuous nanowatt-level power consumption monitoring.

[0054] S200: Perform simulated domain event pre-identification

[0055] After the system enters passive monitoring mode, the pressure sensor module 10 generates a real-time and continuous analog pressure sensing signal. The signal will be continuously fed into the pre-configured analog domain event pre-identification front-end 20. Inside this front-end, the signal first flows through the analog matched filter, which has extremely low quiescent power consumption. The quiescent current of its core operational amplifier and other active devices can be controlled within tens of nanoamps, and the total power consumption is in the nanowatt range.

[0056] Then, the matched filter is simulated for the input. The signal undergoes real-time analog domain convolution filtering to output its impulse response as a convolution of the input signal; since the filter's transfer function matches the acoustic characteristics of the target event, it selectively responds to the spectral content of the input signal.

[0057] Specifically, when the input pressure sensing signal When a signal contains only normal fluid pressure fluctuations or random background noise, its power spectral density is typically relatively flat or lacks specific prominent features across the entire frequency band. When such a signal passes through an analog matched filter with specific narrowband pass characteristics, most of its energy is suppressed, with only a very small portion of the energy falling within the passband passing through. Therefore, the filter's output signal... The voltage amplitude will remain at a low baseline level.

[0058] For example, in a scenario with only background noise, although the input signal There may be fluctuations of tens of millivolts, but after processing by the aforementioned filter, its output signal... The average amplitude can be stably controlled within To evaluate the filter's ability to suppress non-target signals, an interference source can be added, such as a heavy vehicle passing by on a nearby road, creating a source where the main energy is concentrated. Low-frequency pressure waves; due to The two passband centers far from the filter ( and The interference signal will be significantly attenuated, and the voltage it generates at the filter output may only be a few millivolts, far from enough to trigger the threshold.

[0059] Conversely, when a certain acoustic feature is consistent with the preset acoustic characteristics When a matching real leak event occurs, the input pressure sensing signal Lieutenant General included and When a signal with a significantly enhanced energy component in the vicinity passes through a matched filter, a resonance-like effect occurs. In this case, frequency components within the filter's passband not only pass through without attenuation but are also amplified by the filter's gain. Due to the high degree of matching between the signal's spectral shape and the filter's response curve, this amplification effect is coherently superimposed, resulting in a significant increase in the filter's output signal. The voltage amplitude experiences a dramatic and significant jump at the instant the event occurs.

[0060] For example, when a conforms to When a characteristic stress event occurs, even if it is in the original signal The amplitude in the signal is not huge, but after resonant amplification by the matched filter, its output signal... The peak amplitude can jump instantaneously to .

[0061] Subsequently, within the analog domain event pre-identification front-end 20, a nanoampere-level hardware voltage comparator continuously applies the filtered voltage signal. The instantaneous amplitude and the set threshold voltage Compare them.

[0062] S300: Generates zero-system-delay wake-up interrupt instructions based on hardware comparison results.

[0063] In this embodiment, the generation of instructions is entirely driven by hardware logic and does not involve any software or clock cycles, so its response is instantaneous.

[0064] In terms of hardware selection, to match the overall front-end's nanowatt-level power consumption target, the comparator can have a quiescent current of less than [missing information]. A nanoampere comparator; at its two inputs, one is dynamically changing. The other end is a fixed reference threshold. ;if only The voltage value is less than or equal to The comparator output will remain at a stable logic level, such as logic low (0V).

[0065] For example, in the aforementioned background noise scenario, much smaller Therefore, the comparator output will remain stably at a low level and will not cause any interference to the main processor.

[0066] If and only if a matching event occurs, resulting in The voltage amplitude instantaneously exceeded When the comparator's internal circuitry flips, the level of its output pin will undergo an extremely fast and near-vertical transition, for example, an instantaneous transition from logic low (0V) to logic high (e.g., 3.3V). This level transition signal itself is the hardware wake-up interrupt instruction INT_WAKEUP. The electrical characteristics of this instruction (such as rise time) are typically on the nanosecond level, thus enabling reliable triggering of the interrupt pin.

[0067] To further improve the robustness of the system, preferably, the hardware comparator is configured as a comparator with hysteresis, i.e., a Schmitt trigger. Hysteresis means that the rising edge of the comparator triggers the threshold (e.g., ) and falling edge trigger threshold (e.g. The input signal is different, thus effectively preventing it from being affected by the input signal. When the amplitude of the signal fluctuates slightly around the threshold, the comparator output generates a high-frequency oscillation, thereby avoiding invalid and repeated wake-up interrupts to the main processor and enhancing the stability of the decision.

[0068] Because the entire process (from the pressure wave reaching the sensor, to the signal passing through the analog filter, and then to the comparator flipping) is a continuous physical process, its total delay is only the circuit propagation delay, typically in the range of nanoseconds (ns) to microseconds (µs). The system latency is at the zero level, which can be considered as zero system latency relative to the system response.

[0069] S400: Performs lossless high-frequency data capture and event confirmation after wake-up.

[0070] In this embodiment, the process includes the following sub-steps:

[0071] S410: When the external interrupt pin of the main processing and control module 30 receives the rising edge (or falling edge, depending on the configuration) of the INT_WAKEUP instruction output by the comparator, it will be immediately woken up from the deep sleep mode, its internal high-speed clock oscillator will be started, and the processor will begin to execute the preset interrupt service routine.

[0072] S420: Upon waking, one of the primary tasks of the main processor is to immediately control the analog switch to change the path of the pressure sensing signal. In sleep mode, this analog switch connects the output of the pressure sensor module 10 to the analog domain event pre-identification front-end 20. Upon waking, the main processor immediately controls the switch to disconnect the output of the sensor module 10 from the front-end 20 and instead connect it to the input of the high-frequency data acquisition and storage module 40, ensuring that the subsequently acquired signal is the raw, unfiltered pressure sensing signal.

[0073] S430: Simultaneously or after switching the signal path, the main processor activates the high-speed ADC in the high-frequency data acquisition and storage module 40. The ADC will sample the original pressure sensing signal at a preset high sampling rate. Sampling is performed, and the sampling rate must be chosen to satisfy the Nyquist sampling theorem and be much higher than the highest characteristic frequency of the target event.

[0074] For example, considering that the highest feature frequency of the target event is In order to record the waveform completely, the sampling rate... It can be set to (Unit: kilohertz); Each conversion result of the ADC (a digitized sample point) is written to a circular buffer via the direct memory access (DMA) controller.

[0075] For example, the size of the circular buffer It can be set to 8192 sampling points; At this sampling rate, this buffer can record a total duration of seconds (i.e.) The continuous pressure waveform data; since the wake-up is zero-latency, this buffer will completely record all high-frequency waveform information from the moment the event occurs, thus achieving lossless capture of the event start information.

[0076] S440: After acquiring sufficient data (e.g., when the circular buffer is filled once), the main processor performs digital domain signal processing on the sampled data in the buffer to perform secondary confirmation of the event. This is to eliminate false triggering caused by factors such as extremely low probability strong electromagnetic interference from the analog front end. Secondary confirmation may include performing a Fast Fourier Transform (FFT) on the acquired data and then checking whether its spectrum is also within the range of... and Significant energy peaks exist nearby, and their energy ratios are verified to match pre-defined characteristics. Consistent.

[0077] S450: After the event passes the secondary confirmation in the digital domain, the main processor recognizes it as a genuine and valid stress anomaly event. At this point, it packages the 8192 data points of the complete waveform recorded in the circular buffer, along with the timestamp of the event, device ID, and other information, into a data frame. Subsequently, the main processor activates the data communication module 50 (e.g., an NB-IoT or LoRa module) and reports the data frame to the remote monitoring center via the wireless network.

[0078] S460: If, during the secondary confirmation, the collected data is found to be inconsistent with the characteristics of the target event, the main processor will determine that this wake-up is a false trigger. In this case, it will discard the data in the circular buffer, shut down all unnecessary peripherals, and re-control the analog switch to switch the signal path back to the analog domain event pre-identification front end 20. Then it will re-enter the deep sleep mode and wait for the next hardware wake-up interrupt to arrive, and so on.

[0079] Example 3

[0080] To avoid the problem of missed or false alarms that may occur when the fixed threshold is used in the face of changing background noise levels, this embodiment, based on embodiment two, sets up an adaptive voltage threshold adjustment mechanism.

[0081] In a specific implementation, the mechanism consists of an algorithm flow executed by the main processing and control module 30 at specific times, with the main processor periodically or event-driven dynamically updating the voltage threshold.

[0082] The execution process of this mechanism may include the following steps:

[0083] Background noise sampling: The main processor is configured to periodically and briefly wake itself up via its internal real-time clock (RTC), in addition to responding to event interrupts. For example, it wakes up once every 24 hours at 3:00 AM (typically a period with minimal pipeline pressure fluctuations and human activity); after waking up, the main processor only samples the raw pressure sensing signal for a short period, for example, using... The sampling rate is set to collect data for 1 second, resulting in 40,000 background noise samples.

[0084] Noise level quantization: The main processor performs energy analysis on the acquired background noise sample; for example, assume the acquired noise sample sequence is... , The formula for calculating its RMS value is: Here, we assume the calculation was done on a certain day. .

[0085] Threshold dynamic calculation: The main processor calculates the new threshold voltage based on the quantized noise level using a preset decision function. The decision function aims to maintain a constant signal-to-noise ratio detection threshold:

[0086]

[0087] in, The signal-to-noise ratio factor represents the ratio of the desired event signal strength to the background noise. It is a fixed offset voltage (in volts) used to prevent the threshold from being too low when the noise is extremely low.

[0088] For example, assume preset parameters , Based on the calculated noise level The new threshold is: If, in another cycle, the background noise level increases due to nearby construction, the calculated... The new threshold will then be automatically adjusted to: And so on.

[0089] Threshold update: Calculate the new threshold voltage Afterward, the main processor updates the voltage applied to the reference input of the hardware comparator through a digital-to-analog converter (DAC) or a variable resistor voltage divider network controlled by digital signals. After the update is completed, the main processor enters deep sleep again.

[0090] Through the aforementioned adaptive adjustment mechanism, this system can maintain its detection sensitivity at an optimal level relative to the current environmental noise without significantly increasing overall power consumption (due to the extremely low adjustment frequency), thus maintaining excellent performance with high detection success rate and low false alarm rate in various practical deployment scenarios.

[0091] Example 4

[0092] To further broaden the scope of abnormal event detection, based on the above embodiments, the simulated domain event pre-identification front-end 20 of this embodiment includes:

[0093] Simulated feature extraction unit

[0094] This unit is used to extract several key analog features of the pressure sensing signal in different dimensions in parallel. These features together constitute a low-dimensional analog feature vector describing the current signal state.

[0095] For example, the unit may include the following parallel signal processing paths:

[0096] Pathway 1 (Low-frequency energy characteristics) ):

[0097] raw pressure sensing signal First, through a center frequency of Quality factor is An analog bandpass filter is used, and its output is then fed into an analog RMS DC-DC converter, whose output is a quasi-DC voltage. Its amplitude is proportional to the energy of the signal near the 800Hz frequency band.

[0098] Pathway 2 (High-frequency energy characteristics) ):

[0099] The signal passes through another center frequency of Quality factor is The analog bandpass filter, also passed through an RMS-to-DC converter, outputs a quasi-DC voltage. It represents the energy of the signal in the high-frequency region.

[0100] Pathway 3 (Transient Characteristics) ):

[0101] The signal passes through a high-pass filter to remove DC and slowly varying components, and then is fed into an analog envelope detector circuit whose output voltage... It is used to reflect the rapid rate of change of signal amplitude in order to capture transient events such as sudden pressure changes.

[0102] At some point Assume the analog characteristic voltages output by the three channels are respectively , ,as well as This set of voltages [0.1, 0.4, 0.05] constitutes a real-time simulated feature vector. And so on.

[0103] Programmable analog fusion unit

[0104] This unit receives multiple feature voltages generated by the analog feature extraction unit and linearly combines them into an analog anomaly score according to a set of programmable weights. This unit is typically implemented using an adder circuit consisting of a low-power operational amplifier. The gain (i.e., weight) of each input path is controlled by the main processor via a digital potentiometer or a programmable resistor array. Specifically:

[0105]

[0106] in, These are the weight coefficients corresponding to the three features, which together form the weight vector. ;in It is an adjustable bias voltage used to set the basic trigger threshold.

[0107] Simulated anomaly scoring It is directly fed into the hardware comparator and compared with a reference threshold (e.g. ) for comparison, when When this occurs, a hardware wake-up interrupt is generated.

[0108] This calibration is performed each time the main processor is successfully woken up and a real event is confirmed. The calibration includes the following steps:

[0109] S500: Initialization and Generalization Weight Loading

[0110] When the system is deployed for the first time, the main processor loads a set of initial weight vectors for the programmable analog fusion unit. The initial weights can be set to... This means that the system initially gives equal attention to all types of feature changes.

[0111] S600: Perform simulated domain feature fusion and anomaly scoring

[0112] During dormancy monitoring, the simulated domain event pre-identification front-end continuously executes the aforementioned feature extraction and weighted fusion process, performing real-time calculations. and with Compare.

[0113] S700: Wake-up and High-Dimensional Digital Feature Confirmation

[0114] When an event leads to If the threshold is exceeded, the main processor is awakened and performs high-frequency data acquisition, followed by digital domain analysis to classify the event in a refined manner and extract its digital feature vector. .

[0115] For example, the main processor performs detailed spectral analysis on the 8192 waveform data points collected, calculating the true energy ratio in the low-frequency band (around 800Hz) and the high-frequency band (around 3.5kHz), and using the waveform kurtosis as a quantitative indicator of the transient degree. Assuming the analysis results are: low-frequency energy accounts for 20%, high-frequency energy accounts for 70%, and transient characteristics account for 10%, then the ground-based true digital feature vector of this event is normalized as follows: .

[0116] S800: Perform online weight vector update

[0117] In obtaining digital feature vectors Then, the main processor will perform an online update of the weight vector to adjust the weight vector of the simulated front end. Adjustments should be made in a direction that better highlights the characteristics of such events:

[0118]

[0119] in, It is the weight vector before the update. It is the updated weight vector; It is the learning rate, used to control the step size of each update; It is a scaling factor used to match the dimensions and dynamic range of digital feature vectors with simulated weights.

[0120] For example, suppose the current weight is Learning rate , scale factor The ground's true feature vector is The target weight direction is The new weight vector is calculated as follows:

[0121]

[0122] The main processor then assigns this new set of weight values. The updated weights are written into the registers of the analog fusion core. This is easily understood because this event has significant high-frequency characteristics. The low-frequency weights have been improved. The decline means that analog front-ends will become more sensitive to events with similar high-frequency characteristics in the future.

[0123] Through the above process, the method and system provided in this application achieve ultra-low power standby at the nanowatt level while ensuring lossless capture of key event information, which greatly improves the deployment feasibility and economy of battery-powered monitoring nodes in practical applications.

[0124] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0125] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0126] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0127] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0128] Embodiments of the present invention also provide an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0129] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0132] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A reusable high-frequency pressure monitoring method for municipal fire hydrants, characterized in that, include: Acquire pressure sensing signals; In response to the pressure sensing signal, event pre-identification is performed within a preset analog domain to generate a filtered signal; Based on the comparison result between the filtered signal and the preset voltage threshold, a hardware wake-up interrupt instruction is generated; In response to the hardware wake-up interrupt, the main processor is woken up from sleep mode, and the woken main processor performs high-frequency data acquisition and event confirmation on the pressure sensing signal.

2. The method according to claim 1, characterized in that, Prior to performing continuous event pre-identification, the method further includes: Based on the preset acoustic energy characteristics of the target event, the transfer function of the analog matched filter is configured so that the amplitude-frequency response characteristics of the analog matched filter match the spectral shape of the acoustic energy characteristics.

3. The method according to claim 2, characterized in that, The step of performing event pre-identification within a preset analog domain in response to the pressure sensing signal to generate a filtered signal includes: The pressure sensing signal is continuously input to the analog matched filter to perform real-time convolution filtering on the pressure sensing signal, thereby generating the filtered signal.

4. The method according to claim 1, characterized in that, The step of generating a hardware wake-up interrupt instruction based on the comparison result between the filtered signal and a preset voltage threshold includes: The voltage amplitude of the filtered signal is continuously compared with the preset voltage threshold using a hardware comparator. When the voltage amplitude of the filtered signal is greater than the preset voltage threshold, the hardware comparator outputs a level transition signal and uses the level transition signal as the hardware wake-up interrupt instruction.

5. The method according to claim 1, characterized in that, The step of performing high-frequency data acquisition on the pressure sensing signal through the awakened main processor includes: The main processor controls the switching of the signal path, guiding the pressure sensing signal to the high-frequency data acquisition module; The main processor starts the high-frequency data acquisition module to sample the pressure sensing signal at a preset high sampling rate and writes the sampled data into a circular buffer.

6. The method according to claim 5, characterized in that, The process of acknowledging the pressure sensor signal via the awakened main processor includes: The main processor performs digital domain signal processing on the sampled data in the circular buffer for secondary confirmation; Upon receiving secondary confirmation, the main processor packages the data in the circular buffer and reports it via the communication module.

7. A reusable high-frequency pressure monitoring system for municipal fire hydrants, characterized in that, include: Pressure sensor module, used to generate pressure sensing signals; The analog domain event pre-identification front end is electrically connected to the pressure sensor module and is used to perform continuous event pre-identification in the analog domain in response to the pressure sensing signal, so as to generate a filtered signal and generate a hardware wake-up interrupt instruction based on the comparison result of the filtered signal and a preset voltage threshold. The main processing and control module is electrically connected to the analog domain event pre-identification front end and is used to be woken up from the sleep mode when the hardware wake-up interrupt is received; The high-frequency data acquisition and storage module, controlled by the main processing and control module, is used to perform high-frequency data acquisition of the pressure sensing signal after the main processing and control module is awakened.

8. The system according to claim 7, characterized in that, The simulated domain event pre-identification front-end includes: An analog matched filter is used to perform real-time convolution filtering on the pressure sensing signal to generate the filtered signal. The analog matched filter includes a transfer function that matches the amplitude-frequency response characteristics of the analog matched filter with the spectral shape of the preset acoustic energy characteristics of the target event.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to perform the method described in any one of claims 1 to 6 when executed.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method as described in any one of claims 1 to 6.

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