A low power intelligent sensor device

Abstract

The utility model relates to sensor technical field provides a kind of low-power intelligent sensor device, comprising: monitoring module, obtains the monitoring signal in measured environment;Self-energy module, vibration energy in environment is converted into electric energy to store, and power supply is provided for device;Communication module, the communication of device and external equipment is realized;Main control module, the wake-up / sleep state of switching device is compared to obtain fault judgment result with the monitoring signal and preset fault data, and whether control communication module sends the fault judgment result is judged in combination with the current remaining power of self-energy module.The utility model realizes device long-term self-support operation by environment self-energy power supply mode;Intelligent wake-up and energy-information dual decision mechanism ensure that only when there is fault and power is sufficient, wireless transmission is started, the average power consumption of whole machine is low, the endurance time is long, simultaneously maintain high identification precision and real-time alarm ability, significantly reduce the maintenance cost and shutdown risk of industrial sensor.

Description

Technical Field

[0001] This utility model relates to the field of sensor technology, specifically to a low-power intelligent sensor device. Background Technology

[0002] With the rapid development of the Internet of Things (IoT), edge computing, and smart manufacturing technologies, wireless sensors, as key nodes for information sensing and transmission, have been widely applied in many fields such as industrial automation, environmental monitoring, smart homes, healthcare, and traffic management. Wireless sensors can not only detect various physical quantities such as temperature, pressure, humidity, vibration, and sound in real time, but also intelligently process, preliminarily judge, and self-calibrate the collected data through built-in microprocessors, thereby significantly improving the accuracy, reliability, and stability of measurement systems.

[0003] However, with the continuous expansion of application scenarios, especially the rapid rise of emerging fields such as wearable devices, portable medical devices, and intelligent industrial monitoring, higher requirements are being placed on wireless sensor nodes. These application scenarios typically require sensor nodes to have extremely low power consumption, extremely small size, and a high level of intelligence. Since such devices are usually battery-powered and are required to operate continuously for months or even years without battery replacement, minimizing system power consumption, especially static power consumption in standby mode, has become one of the core problems that engineers in the field of wireless sensor design urgently need to solve.

[0004] Therefore, developing a wireless sensor solution that can effectively reduce power consumption, extend battery life, and maintain a high level of intelligence is of great practical significance and has broad market prospects. Utility Model Content

[0005] Based on the above description, this utility model provides a low-power intelligent sensor device to solve the problems of high power consumption and short battery life of sensor devices.

[0006] The technical solution of this utility model to solve the above-mentioned technical problems is as follows: A low-power smart sensor device, comprising: The monitoring module is used to acquire monitoring signals in the environment under test; The self-powered module is used to convert vibration energy in the environment into electrical energy for storage and to power the device. The communication module is used to enable communication between the device and external devices; The main control module is used to switch the wake-up / sleep state of the device according to preset conditions, compare the monitoring signal with preset fault data to obtain the fault judgment result, and determine whether to control the communication module to send the fault judgment result based on the current remaining power of the self-powered module.

[0007] Compared with the prior art, the technical solution of this application has the following beneficial technical effects: This invention provides a low-power intelligent sensor device that converts vibration signal energy from the measured environment into electrical energy to continuously power the device. When wake-up conditions are met, the device wakes up from sleep mode to collect physical quantities of the measured environment (such as temperature, sound, vibration, etc.) to determine whether a fault has occurred. If a fault is detected, the device further determines whether to report the fault for secondary calculation based on the remaining power. For example, if a fault is detected but the remaining power is insufficient, the fault is not reported temporarily, and the device continues to store electrical energy converted from vibration energy. When the stored electrical energy is sufficient, or when the device is woken up again, the device reports the fault. This device uses vibration signals already present during machine operation to generate and store electricity, eliminating the need for an additional charging power source, making installation simpler and more versatile. Combined with environmental self-powering and wake-up functions, it significantly reduces device power consumption and effectively extends the device's battery life.

[0008] Based on the above technical solution, the present invention can be further improved as follows.

[0009] Preferably, the self-powered module includes a piezoelectric vibration energy harvester or an electromagnetic vibration energy harvester, which is used to collect vibration signals in the measured environment and convert them into electrical energy. It also includes a battery for storing the converted electrical energy; It also includes a power detection circuit, which is used to detect the remaining power in the battery.

[0010] Preferably, the monitoring module includes a sensor and a signal processing circuit, and the sensor is connected to the main control module through the signal processing circuit.

[0011] Preferably, the sensor includes any one or more of a vibration sensor, a temperature sensor, and a sound sensor.

[0012] Preferably, when the sensor includes a vibration sensor, the signal processing circuit connected to the vibration sensor includes a first operational amplifier, a first low-pass filter, and an analog-to-digital converter connected in sequence, wherein: The vibration sensor is used to collect simulated vibration signals in the environment under test; The first operational amplifier is used to amplify the acquired vibration simulation signal; The first low-pass filter is used to filter out noise from the amplified vibration analog signal; The analog-to-digital converter is connected to the main control module and is used to convert the filtered vibration analog signal into a vibration digital signal.

[0013] Preferably, when the sensor includes a temperature sensor, the signal processing circuit connected to the temperature sensor includes a second operational amplifier, a second low-pass filter, and a first comparator connected in sequence, wherein: The temperature sensor is used to collect simulated temperature signals from the environment being measured. The second operational amplifier is used to amplify the acquired temperature analog signal; The second low-pass filter is used to filter out noise from the amplified temperature analog signal; The first comparator is connected to the main control module and is used to compare the filtered temperature analog signal with the temperature threshold, and output a temperature fault judgment signal based on the comparison result.

[0014] Preferably, when the sensor includes a sound sensor, the signal processing circuit connected to the sound sensor includes a third operational amplifier and a second comparator connected in sequence, wherein: The sound sensor is used to collect analog sound signals from the environment under test; The third operational amplifier is used to amplify the acquired analog sound signal; The second comparator is connected to the main control module and is used to compare the amplified analog sound signal with the sound threshold, and output a sound fault judgment signal based on the comparison result.

[0015] Preferably, the main control module includes a main control ARM core, and a main control AI core, a storage unit, and multiple bus interfaces respectively connected to the main control ARM core, wherein: The bus interface is used to connect to the monitoring module to receive monitoring signals; The main control AI core is used to compare the monitoring signal with various preset fault data to obtain fault judgment results; The main control ARM core is used to compare the current remaining power of the self-powered module with the power threshold: when the current remaining power is not less than the power threshold and the fault judgment result indicates that there is a fault, a fault reporting command is sent to the communication module; when the current remaining power is less than the power threshold or the fault judgment result indicates that there is no fault, no fault reporting command is sent. The storage unit is used to store the monitoring signals, various fault data, and fault judgment results.

[0016] Preferably, the communication module includes a wireless communication chip, a power amplifier, and an antenna connected in sequence, wherein: The wireless communication chip is connected to the main control module and is used to send a fault reporting signal according to the fault reporting instruction. The power amplifier is used to amplify the fault reporting signal; The antenna is used to transmit the amplified fault reporting signal as radio waves.

[0017] Preferably, the preset conditions for the wake-up device are timed wake-up or the monitoring signal reaching the wake-up threshold. Attached Figure Description

[0018] Figure 1 A schematic diagram of the structure of a low-power intelligent sensor device provided in a certain embodiment of this utility model; Figure 2 This is a schematic diagram of the structure of the analog circuit part of the device provided in another embodiment of the present invention; Figure 3 This is a schematic diagram of the digital circuit structure of a device provided in another embodiment of the present invention.

[0019] The attached diagram lists the components represented by each number as follows: 1. Monitoring Module; 11. Sensors; 1101. Vibration Sensor; 1102. Temperature Sensor; 1103. Sound Sensor; 12. Signal Processing Circuit; 1201. First Operational Amplifier; 1202. First Low-Pass Filter; 1203. Analog-to-Digital Converter; 1204. Second Operational Amplifier; 1205. Second Low-Pass Filter; 1206. First Comparator; 1207. Third Operational Amplifier; 1208. Second Comparator; 2. Main Control Module; 3. Communication Module; 301. Wireless Communication Chip; 302. Power Amplifier; 303. Antenna; 4. Self-Powered Module. Detailed Implementation

[0020] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0022] It is understood that spatial relation terms such as "below," "under," "below," "below," "above," "over," etc., can be used here to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, the element or feature described as "below" or "under" or "below" of other elements or features will be oriented "over" of other elements or features. Therefore, the exemplary terms "below" and "under" can include both upper and lower orientations. Furthermore, the device may also include other orientations (e.g., rotated 90 degrees or other orientations), and the spatial descriptive terms used herein will be interpreted accordingly.

[0023] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. In the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have the transmission of electrical signals or data between them.

[0024] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” or “having,” etc., specify the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0025] Figure 1 This paper presents a block diagram illustrating the overall structure of a low-power smart sensor device according to a certain embodiment of the present invention.

[0026] like Figure 1 As shown, this embodiment provides a low-power intelligent sensor device, including a main control module 2, and a monitoring module 1, a communication module 3, and a self-powered module 4, all connected to the main control module 2, wherein: 1. Monitoring module 1, used to acquire monitoring signals in the environment under test, such as physical quantities such as vibration signals, temperature signals, and sound signals during the operation of machinery and equipment by various sensors 11.

[0027] 2. Self-powered module 4, used to convert vibration energy in the environment into electrical energy for storage and to power the device.

[0028] The self-powered module 4 converts low-frequency vibration energy into electrical energy, primarily through triboelectric nanogeneration, piezoelectricity, and electromagnetic mechanisms. Since the intelligent sensor device provided in this embodiment is often used in long-term fault monitoring scenarios during machine operation, the machine's operation generates vibration signals. The energy conversion function of the self-powered module 4 provides real-time power to the device and stores excess power for the long-term operation of the sensor module 11. This design allows the sensor device to operate for extended periods without relying on an external power source, and is not limited by the external power supply installation structure, making it suitable for various applications without an external power source.

[0029] 3. Communication module 3, used to enable communication between the device and external devices.

[0030] The communication module 3 preferably adopts wireless communication, which does not require an external communication interface, simplifies the device structure, and facilitates the miniaturization and portability of the device.

[0031] 4. The main control module 2 is used to switch the device's wake-up / sleep state according to preset conditions. For example, the device is in sleep state most of the time and wakes up once according to a preset cycle (e.g., 3 hours), or triggers a wake-up action when the monitored signal (e.g., vibration, temperature) exceeds a preset value. In the wake-up state, the main control module 2 compares the monitored signal with preset fault data to obtain a fault judgment result, and determines whether to control the communication module 3 to send the fault judgment result based on the current remaining power of the self-powered module 4.

[0032] It is understood that the low-power intelligent sensor device provided in this embodiment converts the vibration signal energy in the measured environment into electrical energy to continuously power the device. When the wake-up conditions are met, the device wakes up from its sleep state to collect physical quantities (such as temperature, sound, vibration, etc.) of the measured environment to determine whether a fault has occurred. If a fault is determined, the device further determines whether to report the fault for secondary calculation based on the remaining power. For example, if a fault is determined to exist and the remaining power is insufficient, the fault is not reported temporarily to avoid the device running out of power and crashing; at this time, the device continues to use the electrical energy converted from vibration energy for energy storage. When the stored electrical energy is sufficient, or when the device is woken up again, the device reports the fault. This device uses the vibration signal already present during machine operation to generate and store electricity, eliminating the need for an additional charging power source. The device is less affected by the external installation structure, making installation simpler and more applicable. Combined with environmental self-powering and sleep / wake-up functions, the device power consumption is significantly reduced, effectively extending the device's battery life.

[0033] Based on the above technical solution, this embodiment can be further improved as follows.

[0034] In one possible implementation, the self-powered module 4 includes a piezoelectric vibration energy harvester or an electromagnetic vibration energy harvester, which is used to collect vibration signals in the measured environment and convert them into electrical energy. It also includes a battery for storing the converted electrical energy; It also includes a power detection circuit, which is used to detect the remaining power in the battery.

[0035] Understandably, a more suitable self-harvesting module type 4 can be selected based on the frequency characteristics of the vibration signal in the measured environment. For example, when the vibration signal frequency is 50–150 Hz, a piezoelectric vibration energy harvester, such as model KCF VH-1, is preferred; when the vibration signal frequency is 10–30 Hz, an electromagnetic vibration energy harvester, such as model MicroStrain MVEH or Perpetuum PMG, is preferred.

[0036] To collect more vibration energy, piezoelectric or electromagnetic vibration energy harvesters can be attached to the device under test. The vibration energy generated during device operation is collected and converted into electrical energy, which is prioritized to power the sensor device. Excess power is stored in the battery for backup. Compared to other power-consuming modules, the communication module 3 requires more power to send communication signals. Therefore, in the device's wake-up state, a power detection circuit monitors the remaining battery power. The communication module 3 only issues a fault diagnosis when the remaining power is sufficient. If the power is insufficient, the piezoelectric or electromagnetic vibration energy harvester continuously charges the battery until sufficient power is available.

[0037] In one possible implementation, such as Figure 1 As shown, the monitoring module 1 includes a sensor 11 and a signal processing circuit 12. The sensor 11 is connected to the main control module 2 through the signal processing circuit 12. More specifically, the sensor 11 includes any one or more of a vibration sensor 1101, a temperature sensor 1102, and a sound sensor 1103.

[0038] Understandably, the raw signals output by sensor 11 (such as vibration, temperature, and sound) typically have small amplitudes, high noise levels, and impedance mismatches. Signal processing circuit 12 (through amplification, filtering, comparison, ADC, etc.) conditions the analog signals into a format that the main control module 2 can recognize and process, improving the accuracy of subsequent algorithm judgments. Front-end signal processing can complete threshold judgments in the analog domain (such as the comparator output for temperature / sound). The main control module 2 does not need to continuously sample; it only activates the ADC or processor for complex processing under abnormal or wake-up conditions, reducing unnecessary wake-ups and computational power consumption.

[0039] This embodiment achieves signal front-end adaptation, reduces main control power consumption, and improves recognition reliability through the modular structure design of "sensor 11 + signal processing circuit 12". It also provides a technical foundation for the subsequent access and intelligent judgment of multiple types of sensors 11 and is a key link supporting the overall low-power intelligent monitoring function.

[0040] In one possible implementation, such as Figure 2 As shown, when the sensor 11 includes a vibration sensor 1101, the signal processing circuit 12 connected to the vibration sensor 1101 includes a first operational amplifier 1201, a first low-pass filter 1202, and an analog-to-digital converter 1203 connected in sequence, wherein: The vibration sensor 1101 is used to collect simulated vibration signals in the measured environment; The first operational amplifier 1201 is used to amplify the acquired vibration simulation signal; The first low-pass filter 1202 is used to filter out noise from the amplified vibration analog signal; The analog-to-digital converter 1203 is connected to the main control module 2 and is used to convert the filtered vibration analog signal into a vibration digital signal.

[0041] Understandably, the vibration sensor 1101 converts mechanical vibration into an analog voltage signal, which is typically weak in amplitude and contains high-frequency noise. The first operational amplifier 1201 linearly amplifies the weak analog vibration signal, increasing the signal amplitude to a range that subsequent circuits can process. The first low-pass filter 1202 suppresses high-frequency interference (such as electromagnetic noise and structural resonance) while retaining effective low-frequency vibration characteristics. The analog-to-digital converter 1203 converts the filtered analog signal into a digital signal for feature extraction and fault identification by the main control module 2. The main control module 2 receives vibration-related digital signals and matches them with a fault database to determine whether any vibration anomalies exist.

[0042] In this embodiment, the "amplification + filtering" structure effectively suppresses noise and enhances the signal-to-noise ratio, ensuring that the main control module 2 receives high-fidelity vibration characteristic data. This provides a clean and stable input source for vibration fault analysis, improving the accuracy of fault identification. Amplification and filtering are performed in the analog domain, requiring no continuous involvement from the main control module 2. The main control module 2 / communication module 3 is only awakened when an abnormal feature is successfully matched, reducing unnecessary power consumption and aligning with the overall low-power design goal.

[0043] In one possible implementation, such as Figure 2As shown, when the sensor 11 includes a temperature sensor 1102, the signal processing circuit 12 connected to the temperature sensor 1102 includes a second operational amplifier 1204, a second low-pass filter 1205, and a first comparator 1206 connected in sequence, wherein: The temperature sensor 1102 is used to collect simulated temperature signals in the measured environment; The second operational amplifier 1204 is used to amplify the acquired temperature analog signal; The second low-pass filter 1205 is used to filter out noise from the amplified temperature analog signal; The first comparator 1206 is connected to the main control module 2 and is used to compare the filtered temperature analog signal with the temperature threshold and output a temperature fault judgment signal based on the comparison result.

[0044] Understandably, the temperature sensor 1102 converts the ambient temperature into an analog voltage signal, which has a small output amplitude and requires amplification. The second operational amplifier 1204 linearly amplifies the temperature signal to bring it into the voltage range recognizable by the comparator. The second low-pass filter 1205 filters out high-frequency interference (such as power supply ripple and EMI), retains the effective low-frequency signal of temperature changes, effectively suppresses transient interference (such as proximity of heat sources and electromagnetic coupling), and prevents the first comparator 1206 from erroneously flipping. The first comparator 1206 compares the filtered temperature voltage with a preset temperature threshold voltage and outputs a high or low level signal that can be recognized by the main control module 2. The main control module 2 receives the output of the first comparator 1206, records the duration of the high level, and if it exceeds the set time threshold, it is determined to be a temperature anomaly / fault. The main control module 2 uses a "duration judgment" mechanism to avoid short-term fluctuations triggering false alarms and improve detection accuracy.

[0045] In this embodiment, the first comparator 1206 completes the threshold judgment in the analog domain. The main control module 2 does not need to continuously sample or run the ADC. The main control is only woken up after a preset wake-up time or after the temperature exceeds the standard and continues for a certain period of time, which greatly reduces the system power consumption and realizes fast and low-power judgment of temperature faults.

[0046] In one possible implementation, such as Figure 2 As shown, when the sensor 11 includes a sound sensor 1103, the signal processing circuit 12 connected to the sound sensor 1103 includes a third operational amplifier 1207 and a second comparator 1208 connected in sequence, wherein: The sound sensor 1103 is used to collect analog sound signals in the environment under test; The third operational amplifier 1207 is used to amplify the acquired analog sound signal; The second comparator 1208 is connected to the main control module 2 and is used to compare the amplified analog sound signal with the sound threshold and output a sound fault judgment signal based on the comparison result.

[0047] Understandably, the sound sensor 1103 converts ambient sound waves into an analog voltage signal. The output analog sound signal has a small amplitude and requires amplification. The third operational amplifier 1207 linearly amplifies the analog sound signal to bring it within the voltage range recognizable by the second comparator 1208. The second comparator 1208 compares the amplified sound voltage with a preset sound threshold voltage and outputs high and low level signals. The main control module 2 receives the output of the second comparator 1208 and records the duration of the high level. If the duration exceeds the set time threshold, it is determined to be a sound abnormality / fault. By completing the sound threshold judgment in the analog domain through the "amplification-comparison" path, and combining it with duration logic, low-power, high-reliability sound fault identification is achieved, providing the system with a simple and efficient sound monitoring capability.

[0048] It should be noted that the monitored analog sound signal has complex components and a wide frequency range, including both high-frequency and low-frequency signals. The signal processing circuit 12 in this embodiment does not include a low-pass filter because, to improve the accuracy of sound fault identification, sound signals from high to low frequencies must be involved in fault identification.

[0049] In one possible implementation, such as Figure 3 As shown, the main control module 2 includes a main control ARM core, a main control AI core, a storage unit, and multiple bus interfaces respectively connected to the main control ARM core, wherein: The bus interface is used to connect to the monitoring module 1, communicate with the corresponding sensor 11 through a specific communication format, and obtain the monitoring signal of the sensor 11. The main control AI core is used to compare the monitoring signal with various preset fault data to obtain fault judgment results; The main control ARM core is used to compare the current remaining power of the self-powered module 4 with the power threshold: when the current remaining power is not less than the power threshold and the fault judgment result indicates that there is a fault, a fault reporting command is sent to the communication module 3; when the current remaining power is less than the power threshold or the fault judgment result indicates that there is no fault, no fault reporting command is sent. The storage unit is used to store the monitoring signals, various fault data, and fault judgment results.

[0050] Understandably, the main control module 2 adopts an "ARM+AI" heterogeneous dual-core structure. Its bus interface (I2C, SPI, I2S, etc.) communicates with the monitoring module 1 via a specific communication format to obtain data from sensor 11. This data is then directly stored in the storage unit (external PSRAM), and retrieved after data transmission is complete. The data is then transmitted to the main control AI core via an internal pipeline. The main control AI core matches the preprocessed monitoring data with the fault database and outputs the closest fault type. In one possible implementation, the main control AI core internally performs FFT operations to obtain the feature distribution in the frequency domain. It then first feeds the FFT results back to the main control ARM core via an internal pipeline for storage in the external PSRAM for backup. The main control AI core then extracts features based on the FFT calculation results using a multiply-accumulate network. The extracted feature values ​​are then fused using a convolutional neural network model to calculate a fault result, which is then reported to the main control ARM core. The main control ARM core is responsible for determining whether to report the calculation results to the server for secondary calculations based on the fault result and the remaining self-generated power. It should be noted that the FFT operation, feature extraction through the multiply-accumulate network, and the fusion of feature values ​​based on the convolutional neural network model to obtain the classification results all use existing technologies, which will not be elaborated here.

[0051] This implementation example Figure 3 As shown, the chip-level implementation integrates the "ARM+AI" heterogeneous dual-core, high-efficiency wireless communication, and dual-port buffer into the same energy budget, truly achieving "local intelligent diagnosis, energy self-sufficiency judgment, and zero-loss data upload," making the low-power, long-life intelligent sensor 11 a reality that can be mass-produced from a paper concept.

[0052] In one possible implementation, such as Figure 3 As shown, the communication module 3 includes a wireless communication chip 301 (e.g., nRF52833), a power amplifier 302 (e.g., nRF21540), and an antenna 303 connected in sequence, wherein: The wireless communication chip 301 is connected to the main control module 2 and is used to send a fault reporting signal according to the fault reporting instruction. The power amplifier 302 is used to amplify the fault reporting signal; The antenna 303 is used to transmit the amplified fault reporting signal in the form of radio waves.

[0053] It is understandable that the wireless communication chip 301 preferably uses the nRF52833, which has a built-in Zigbee protocol stack and is responsible for baseband modulation, frequency synthesis, and data packetization, outputting a 0 dBm RF signal. The power amplifier 302 preferably uses the nRF21540, which amplifies the 0 dBm signal to +20 dBm to improve the link budget and compensate for shielding losses in industrial environments. The antenna 303 converts the amplified high-frequency current into electromagnetic waves and transmits them to the base station / gateway; it also receives downlink commands (such as parameter updates and wake-up commands). In this embodiment, the communication module 3, through a three-level RF architecture of "low-power SoC (wireless communication chip 301) + switchable PA (power amplifier 302) + standard antenna 303", can achieve a peak transmission capability of 20 dBm at an average power consumption of milliwatts, realizing long-distance, highly reliable, and debug-free wireless communication for the energy self-sufficient sensor 11, providing core link assurance for the engineering implementation of the entire self-sufficient energy monitoring system.

[0054] Now Figure 3 The overall workflow of the device will be illustrated using the architecture as an example.

[0055] The self-powered module 4 converts environmental micro-vibrations into electrical energy, continuously trickling-charging the battery; the power detection circuit outputs the simulated value of the remaining battery power, Vcap, in real time. By self-powering vibration energy, the energy source is unlimited, and the system does not require battery replacement; the power information is used for subsequent "energy-information" joint judgment to avoid system crashes due to low power.

[0056] The sensors 11 of monitoring module 1 collect physical signals from the environment. The vibration signal monitoring link connects to the SPI interface of the main control module 2, eliminating the need for an external ADC through digital output, resulting in low noise; its SPI clock is only turned on during the sampling window, reducing dynamic current. The temperature signal monitoring link outputs temperature voltage once per second through the I2C interface; the on-chip second op-amp + comparator compares it with the temperature threshold, generating a "temperature over-limit" flag; threshold judgment is completed in the analog domain, allowing the ARM to know whether the temperature is over-limit without activating the ADC, saving power. The sound signal monitoring link outputs a PDM bitstream through the I2S interface; the on-chip third op-amp + comparator provides a "sound pressure over-limit" flag; threshold judgment is completed in the analog domain, reducing main control computation; the PDM bitstream can be directly used for subsequent AI core voiceprint analysis.

[0057] The main control module 2 receives monitoring signals from each sensor 11 in the monitoring module 1 through various bus interfaces. These monitoring signals have been preprocessed in the previous stage signal processing circuit 12.

[0058] The main control ARM core transfers the raw vibration data to the external PSRAM APS6404 cache via the AXI-to-AHB bridge and triggers an interrupt in the main control AI core, handing over the data pointer. The ARM core only handles data transfer and flow control, with a short runtime, and can immediately enter sleep mode. The main control AI core reads the PSRAM → performs FFT → writes the frequency domain energy distribution back to the PSRAM, extracts frequency domain features through a multiply-accumulate network, and provides a fault similarity score through an AI model (e.g., a convolutional neural network) → returns to the main control ARM core. Through the synergistic effect of the main control AI core and the ARM core, hardware acceleration improves the efficiency of monitoring data processing while only increasing the peak current by a very small amount (approximately 3 mA). Verification shows that the AI ​​core's power consumption is about 1 / 10 of that of a comparable MCU software solution.

[0059] In the main control ARM core, a joint energy-information decision is made, comparing two conditions: ① Fault similarity > set threshold; ② Remaining battery power (determined by Vcap) > battery power threshold. If both conditions are met, the "fault reporting flag" is set. This dual-criteria decision avoids forced transmission when the battery is low, which could lead to system power loss; it ensures that critical faults are sent first, while non-critical data is delayed in uploading, thus extending battery life.

[0060] After receiving the "fault reporting flag," the wireless communication chip 301 (nRF52833) starts its Zigbee protocol stack, reads data packets from the PSRAM, amplifies them to +20 dBm via power amplifier 302 (nRF21540), and then transmits them through antenna 303. During this process, if battery power is insufficient or the channel is busy, the main control ARM core writes the data to the Flash (external FLASHW25Q64JV) queue, and retransmits it in batches when power is sufficient or upon the next wake-up. During sleep mode, the communication module 3 consumes very little power, ensuring no data loss even when power is off, implementing a "store-and-forward" mechanism to guarantee data integrity.

[0061] Task completed. The main control ARM core turns off the power switch of monitoring module 1, the main control ARM core itself enters RTC+low power RAM retention mode, the main control AI core automatically clocks, and the SoC of wireless communication module 3 enters System OFF.

[0062] The entire process described above achieves high-precision fault diagnosis and reliable data reporting within a milliwatt-level energy budget, fully demonstrating the core advantages of the invention: "low power consumption, long lifespan, and intelligence".

[0063] This invention provides a low-power intelligent sensor device that converts vibration signal energy from the measured environment into electrical energy to continuously power the device. When wake-up conditions are met, the device wakes up from sleep mode to collect physical quantities of the measured environment (such as temperature, sound, vibration, etc.) to determine whether a fault has occurred. If a fault is detected, the device further determines whether to report the fault for secondary calculation based on the remaining power. For example, if a fault is detected but the remaining power is insufficient, the fault is not reported temporarily, and the device continues to store electrical energy converted from vibration energy. When the stored electrical energy is sufficient, or when the device is woken up again, the device reports the fault. This device uses vibration signals already present during machine operation to generate and store electricity, eliminating the need for an additional charging power source, making installation simpler and more versatile. Combined with environmental self-powering and wake-up functions, it significantly reduces device power consumption and effectively extends the device's battery life.

[0064] The above description is only a preferred embodiment of the present utility model and is not intended to limit the present utility model. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present utility model should be included within the protection scope of the present utility model.

Claims

1. A low-power intelligent sensor device, characterized in that, include: The monitoring module (1) is used to acquire monitoring signals in the environment under test; The self-powered module (4) is used to convert vibration energy in the environment into electrical energy for storage and to power the device. The communication module (3) is used to enable communication between the device and external devices; The main control module (2) is used to switch the wake-up / sleep state of the device according to preset conditions, compare the monitoring signal with the preset fault data to obtain the fault judgment result, and determine whether to control the communication module (3) to issue the fault judgment result based on the current remaining power of the self-powered module (4).

2. The low-power intelligent sensor device according to claim 1, characterized in that, The self-powered module (4) includes a piezoelectric vibration energy collector or an electromagnetic vibration energy collector, which is used to collect vibration signals in the measured environment and convert them into electrical energy. It also includes a battery for storing the converted electrical energy; It also includes a power detection circuit, which is used to detect the remaining power in the battery.

3. The low-power intelligent sensor device according to claim 1, characterized in that, The monitoring module (1) includes a sensor (11) and a signal processing circuit (12). The sensor (11) is connected to the main control module (2) through the signal processing circuit (12).

4. The low-power intelligent sensor device according to claim 3, characterized in that, The sensor (11) includes any one or more of the vibration sensor (1101), temperature sensor (1102), and sound sensor (1103).

5. A low-power intelligent sensor device according to claim 4, characterized in that, When the sensor (11) includes a vibration sensor (1101), the signal processing circuit (12) connected to the vibration sensor (1101) includes a first operational amplifier (1201), a first low-pass filter (1202), and an analog-to-digital converter (1203) connected in sequence, wherein: The vibration sensor (1101) is used to collect simulated vibration signals in the environment under test; The first operational amplifier (1201) is used to amplify the acquired vibration simulation signal; The first low-pass filter (1202) is used to filter out noise from the amplified vibration analog signal; The analog-to-digital converter (1203) is connected to the main control module (2) and is used to convert the filtered vibration analog signal into a vibration digital signal.

6. A low-power intelligent sensor device according to claim 4, characterized in that, When the sensor (11) includes a temperature sensor (1102), the signal processing circuit (12) connected to the temperature sensor (1102) includes a second operational amplifier (1204), a second low-pass filter (1205), and a first comparator (1206) connected in sequence, wherein: The temperature sensor (1102) is used to collect simulated temperature signals in the measured environment; The second operational amplifier (1204) is used to amplify the acquired temperature analog signal; The second low-pass filter (1205) is used to filter out noise from the amplified temperature analog signal; The first comparator (1206) is connected to the main control module (2) and is used to compare the filtered temperature analog signal with the temperature threshold and output a temperature fault judgment signal based on the comparison result.

7. A low-power intelligent sensor device according to claim 4, characterized in that, When the sensor (11) includes a sound sensor (1103), the signal processing circuit (12) connected to the sound sensor (1103) includes a third operational amplifier (1207) and a second comparator (1208) connected in sequence, wherein: The sound sensor (1103) is used to collect analog sound signals in the environment under test; The third operational amplifier (1207) is used to amplify the acquired analog sound signal; The second comparator (1208) is connected to the main control module (2) and is used to compare the amplified analog sound signal with the sound threshold and output a sound fault judgment signal based on the comparison result.

8. A low-power intelligent sensor device according to claim 1, characterized in that, The main control module (2) includes a main control ARM core, and a main control AI core, a storage unit, and multiple bus interfaces respectively connected to the main control ARM core, wherein: The bus interface is used to connect to the monitoring module (1) to receive monitoring signals; The main control AI core is used to compare the monitoring signal with various preset fault data to obtain fault judgment results; The main control ARM core is used to compare the current remaining power of the self-powered module (4) with the power threshold: when the current remaining power is not less than the power threshold and the fault judgment result indicates that there is a fault, a fault reporting instruction is sent to the communication module (3); when the current remaining power is less than the power threshold or the fault judgment result indicates that there is no fault, no fault reporting instruction is sent. The storage unit is used to store the monitoring signals, various fault data, and fault judgment results.

9. A low-power intelligent sensor device according to claim 8, characterized in that, The communication module (3) includes a wireless communication chip (301), a power amplifier (302), and an antenna (303) connected in sequence, wherein: The wireless communication chip (301) is connected to the main control module (2) and is used to send a fault reporting signal according to the fault reporting instruction; The power amplifier (302) is used to amplify the fault reporting signal; The antenna (303) is used to transmit the amplified fault reporting signal in the form of radio waves.

10. A low-power intelligent sensor device according to claim 1, characterized in that, The preset conditions for the wake-up device are timed wake-up or wake-up when the monitoring signal reaches the wake-up threshold.

Images