An intelligent data wireless transmission system based on low-power technology

Abstract

The application discloses a kind of intelligent data wireless transmission systems based on low-power technology, and the application relates to the technical field of intelligent data transmission, including sensor acquisition module, edge gateway, hybrid power supply module, dynamic power management module, alarm module and cloud platform, the advantages of the application are that: through thermoelectric conversion unit and piezoelectric energy harvesting unit respectively utilize underground pipe network temperature difference heat energy and vehicle rolling vibration mechanical energy to generate electricity, realize the continuous collection of multi-source environmental energy, cooperate with low self-discharge lithium battery, greatly prolong the autonomous operation time of system, reduce battery replacement frequency and operation and maintenance cost, suitable for large-scale distributed deployment, through dynamic power management module, intelligent switching of sleep mode and working mode is realized, system enters deep sleep state when no event triggers, under the premise of guaranteeing monitoring effectiveness, system power consumption is reduced to very low level.

Description

Technical Field

[0001] This invention relates to the field of intelligent data transmission technology, specifically to an intelligent wireless data transmission system based on low-power technology. Background Technology

[0002] With the rapid development of IoT technology, intelligent data wireless transmission systems are widely used in industrial monitoring, underground pipelines, smart agriculture, remote area monitoring and other fields, becoming the core support for realizing device interconnection and data interoperability. At present, most wireless transmission systems have obvious technical defects and are difficult to adapt to the requirements of distributed deployment and long-term stable operation.

[0003] Common intelligent wireless data transmission systems rely heavily on external power supplies or conventional lithium batteries, resulting in a single energy source, inability to provide continuous power autonomously, short battery life, frequent replacements, high maintenance costs, and difficulty in large-scale distributed deployment. Furthermore, traditional equipment fails to effectively recover and utilize idle thermal energy and vibration energy from the environment, leading to low energy utilization. In addition, most systems lack refined dynamic power consumption management, resulting in high power consumption and continued operation even without events, leading to energy waste and short autonomous operation time. These shortcomings severely restrict the long-term stability and intelligent application of pipeline monitoring systems. Therefore, we propose an intelligent wireless data transmission system based on low-power technology. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent wireless data transmission system based on low-power technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent data wireless transmission system based on low-power technology, comprising:

[0006] The sensor acquisition module is deployed on the manhole cover to collect data on the tilt angle and vibration frequency of the manhole cover.

[0007] An edge gateway, deployed at the end of the manhole cover, is connected to the sensor acquisition module via the LoRa wireless communication protocol. The edge gateway has a built-in lightweight decision tree model, which is used to determine abnormal states locally based on the received tilt angle data and vibration frequency data.

[0008] A hybrid power supply module is used to power the sensor acquisition module and the edge gateway. The hybrid power supply module includes a thermoelectric conversion unit, a piezoelectric energy harvesting unit and a low self-discharge lithium battery. The thermoelectric conversion unit and the piezoelectric energy harvesting unit respectively store the acquired electrical energy in the low self-discharge lithium battery through the power management unit.

[0009] A dynamic power management module is used to control the switching between sleep mode and working mode of the sensor acquisition module and the edge gateway;

[0010] The alarm module is communicatively connected to the edge gateway and is used to send real-time alarms to surrounding pedestrians and vehicles when the edge gateway determines that the manhole cover is in an abnormal state.

[0011] The cloud platform connects to the edge gateway via a mobile communication network to receive data uploaded by the edge gateway and provide remote operation and maintenance management services.

[0012] As a further aspect of the present invention: the thermoelectric conversion unit is a thermoelectric power generation module, which is installed at the connection between the manhole cover and the underground pipe network, and uses the temperature difference between the underground pipe network and the ground surface to generate electricity continuously.

[0013] The piezoelectric energy harvesting unit is a piezoelectric energy harvesting device installed inside the pressure-bearing structure layer of the manhole cover, which converts the mechanical energy of vibration generated when a vehicle runs over the manhole cover into electrical energy.

[0014] As a further aspect of the present invention: the hybrid power supply module further includes a solar micro-panel, which is embedded in the edge of the manhole cover and is used to convert solar energy into electrical energy and store it in the low self-discharge lithium battery via the power management unit.

[0015] As a further aspect of the present invention: when no event is triggered, the dynamic power consumption management module puts the sensor acquisition module and the edge gateway into a sleep mode, and the total current consumption of the system in the sleep mode is 3 microamps.

[0016] When the sensor acquisition module detects a preset trigger event, the dynamic power consumption management module switches the system to the working mode;

[0017] The dynamic power consumption management module adjusts the self-test cycle according to the acquired weather status information. It adopts a first self-test cycle in rainy weather and a second self-test cycle in sunny weather. The first self-test cycle is shorter than the second self-test cycle.

[0018] As a further aspect of the present invention: the anomaly determination logic of the lightweight decision tree model is as follows: when the tilt angle data exceeds a preset tilt angle threshold and the vibration frequency data exceeds a preset vibration frequency threshold, the edge gateway determines the manhole cover status as an abnormal state and triggers the alarm module to issue an alarm.

[0019] As a further aspect of the present invention: the edge gateway supports OTA remote model update function, the cloud platform packages and sends the updated decision tree model parameters to the edge gateway, the edge gateway receives and loads the updated model parameters, and completes the online iteration of the decision tree model.

[0020] As a further aspect of the present invention: the system further includes a visual inspection module, which includes a camera and an image recognition unit. The camera is deployed on a fixed bracket on a municipal inspection vehicle. The image recognition unit uses a lightweight deep learning model based on an improved YOLO architecture to analyze the collected manhole cover images, identify the damaged state, missing state, settlement state, and tilted state of the manhole cover, and send the recognition results to the edge gateway.

[0021] As a further aspect of the present invention: the sensor acquisition module and the visual detection module constitute a dual-mode verification and monitoring system, and the edge gateway performs fusion and judgment on the sensing data of the sensor acquisition module and the recognition result of the visual detection module;

[0022] When the results of the two detection methods are consistent and both are abnormal, the manhole cover is confirmed to be in an abnormal state and an alarm is triggered; when the results of the two detection methods are inconsistent, the monitoring frequency is increased and the data is reported to the cloud platform.

[0023] As a further aspect of the present invention: the alarm module is deployed on a smart light pole and includes a display screen and a voice broadcasting device. The display screen is used to display abnormal alarm information and location information of the manhole cover, and the voice broadcasting device is used to broadcast voice alarm prompts to the surrounding area.

[0024] Compared with the prior art, the beneficial effects of the present invention by adopting the above technical solution are as follows:

[0025] 1. This invention utilizes the thermal energy from the temperature difference in the underground pipeline network and the mechanical energy from the vibration of vehicles to generate electricity through a thermoelectric conversion unit and a piezoelectric energy harvesting unit, respectively. This enables continuous harvesting of multi-source environmental energy. Combined with a low self-discharge lithium battery, it significantly extends the system's autonomous operation time, reduces battery replacement frequency and maintenance costs, and is suitable for large-scale distributed deployment.

[0026] 2. This invention achieves intelligent switching between sleep mode and working mode through a dynamic power consumption management module. When no event is triggered, the system enters a deep sleep state with a total current consumption of only 3 microamps. It also adaptively adjusts the self-test cycle according to weather information, reducing the system power consumption to an extremely low level while ensuring the effectiveness of monitoring, thus achieving refined power consumption management.

[0027] 3. This invention enables local intelligent decision-making at the sensor node by embedding a lightweight decision tree model in the edge gateway. It can quickly determine abnormal states and trigger alarms without uploading all raw data to the cloud, which significantly reduces data communication volume and power consumption, and greatly shortens the response time from the occurrence of an anomaly to the issuance of an alarm.

[0028] 4. This invention establishes a dual-mode verification and monitoring system by combining a sensor acquisition module and a vision detection module. The fusion of these two heterogeneous detection methods effectively reduces the probability of false alarms and missed alarms in complex environments by using a single detection method, thereby improving the accuracy and reliability of manhole cover status monitoring.

[0029] 5. This invention supports OTA remote model update function through edge gateway. The cloud platform can remotely send the optimized decision tree model parameters to the edge gateway to realize online iterative upgrade of the model, so that the system's judgment capability can be continuously optimized with the accumulation of running data without the need for on-site manual intervention. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the system flow in an embodiment of the present invention. Detailed Implementation

[0031] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0032] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0033] Please see the appendix Figure 1 The present invention discloses an intelligent wireless data transmission system based on low-power technology, comprising:

[0034] The sensor acquisition module is deployed on the manhole cover to collect data on the tilt angle and vibration frequency of the manhole cover.

[0035] The edge gateway, deployed at the end of the manhole cover, connects to the sensor acquisition module via the LoRa wireless communication protocol. The edge gateway has a built-in lightweight decision tree model, which is used to determine abnormal states locally based on the received tilt angle data and vibration frequency data.

[0036] The hybrid power supply module is used to power the sensor acquisition module and the edge gateway. The hybrid power supply module includes a thermoelectric conversion unit, a piezoelectric energy harvesting unit and a low self-discharge lithium battery. The thermoelectric conversion unit and the piezoelectric energy harvesting unit respectively store the collected electrical energy into the low self-discharge lithium battery through the power management unit.

[0037] The dynamic power management module is used to control the switching between sleep mode and working mode of the sensor acquisition module and the edge gateway;

[0038] The alarm module communicates with the edge gateway and is used to send real-time alarms to surrounding pedestrians and vehicles when the edge gateway determines that the manhole cover is in an abnormal state.

[0039] The cloud platform connects to the edge gateway via a mobile communication network to receive data uploaded by the edge gateway and provide remote operation and maintenance management services.

[0040] Example 1: Deployment and operation of the sensor acquisition module;

[0041] The sensor acquisition module is deployed on the manhole cover body. This module includes a MEMS triaxial accelerometer and a vibration frequency sensor. The MEMS triaxial accelerometer is installed at the center of the inner surface of the manhole cover. By detecting the projection changes of the gravitational acceleration component on the three coordinate axes, the current tilt angle data of the manhole cover is calculated.

[0042] When the manhole cover is in a normal horizontal position, the Z-axis of the sensor is aligned with the direction of gravity, and the tilt angle data is close to zero.

[0043] When a manhole cover tilts or shifts, the components of gravitational acceleration on each axis change, which can be used to accurately calculate the tilt angle of the manhole cover from the horizontal plane.

[0044] Vibration frequency sensors are also installed on the inner surface of the manhole cover to continuously detect the vibration characteristics of the manhole cover surface. Under normal conditions, the vibration generated by vehicles running over the manhole cover exhibits periodic and low amplitude characteristics. When the manhole cover becomes loose or cracked, the vibration frequency and amplitude will show significant abnormal changes.

[0045] The sensor acquisition module transmits the acquired tilt angle data and vibration frequency data to the edge gateway via the LoRa wireless communication protocol. LoRa communication uses spread spectrum modulation technology, operates in the 470 to 510 MHz frequency band, and has a transmission power configuration of 17 dBm. In densely built-up urban environments, the effective communication distance is 3 to 5 kilometers. The power consumption of a single data transmission is extremely low, making it suitable for long-distance, low-speed data transmission in battery-powered scenarios.

[0046] Example 2: Deployment and Intelligent Decision-Making Mechanism of Edge Gateway

[0047] The edge gateway is deployed in a protective enclosure at the end of the manhole cover. The enclosure has an IP67 protection rating, which can resist rainwater immersion and dust intrusion.

[0048] The core hardware of the edge gateway is an ultra-low power ARM Cortex-M series microcontroller, which has built-in flash memory to store the parameters and operating logic of the lightweight decision tree model;

[0049] The anomaly detection logic of the lightweight decision tree model is as follows:

[0050] After receiving the tilt angle data and vibration frequency data uploaded by the sensor acquisition module, the edge gateway first compares the tilt angle data with the preset tilt angle threshold.

[0051] In this embodiment, the preset tilt angle threshold is set to 15 degrees, that is, when the tilt angle of the manhole cover deviating from the horizontal plane exceeds 15 degrees, it is considered to be an abnormal tilt angle.

[0052] If the tilt angle data does not exceed the preset tilt angle threshold, the manhole cover is determined to be in a normal state, and the current monitoring cycle remains unchanged.

[0053] If the tilt angle data exceeds the preset tilt angle threshold, the vibration frequency data will be further compared with the preset vibration frequency threshold.

[0054] In this embodiment, the preset vibration frequency threshold is set to 50 Hz;

[0055] When the tilt angle data exceeds the preset tilt angle threshold and the vibration frequency data also exceeds the preset vibration frequency threshold, the edge gateway will determine the manhole cover status as abnormal, trigger the alarm module to issue a real-time alarm, and report the abnormal event data to the cloud platform through the NB-IoT network.

[0056] This decision logic based on dual-parameter joint judgment can effectively distinguish between transient vibrations caused by normal vehicle rolling and continuous state changes caused by real anomalies in the manhole cover, thereby reducing the probability of false alarms.

[0057] In this embodiment, the parameter storage space of the decision tree model only occupies about 2KB of flash memory, and the model inference calculation can be completed in just a few milliseconds. It has extremely low requirements for the computing power of the microcontroller and is very suitable for resource-constrained embedded edge computing scenarios.

[0058] Example 3: Composition and Working Principle of Hybrid Power Supply Module

[0059] The hybrid power supply module includes a thermoelectric conversion unit, a piezoelectric energy harvesting unit, a power management unit, and a low self-discharge lithium battery;

[0060] The thermoelectric conversion unit is a thermoelectric power generation module, which is installed at the connection between the manhole cover and the underground pipeline network;

[0061] There is a stable temperature difference between the temperature of the medium flowing in the underground pipe network and the surface ambient temperature;

[0062] In summer, the temperature of underground pipe networks is lower than that of the ground surface;

[0063] In winter, the temperature of underground pipe networks is higher than that of the ground surface.

[0064] The thermoelectric power generation module utilizes the Seebeck effect to continuously generate DC power driven by temperature difference;

[0065] Under a temperature difference of 10 degrees Celsius, the output power of a single thermoelectric power generation module is about tens of milliwatts. Although the output power is low at one time, the continuous existence of the temperature difference day and night enables uninterrupted energy harvesting.

[0066] The piezoelectric energy harvesting unit is a piezoelectric energy harvesting device installed inside the pressure-bearing structure layer of the manhole cover;

[0067] When a vehicle runs over a manhole cover, the dynamic load on the manhole cover causes the piezoelectric ceramic sheet embedded in it to deform, generating alternating charges based on the positive piezoelectric effect.

[0068] On main urban roads, manhole covers are subjected to hundreds to thousands of vehicle passes every day. The instantaneous electrical energy generated by each pass can be rectified and accumulated into a considerable amount of electricity.

[0069] In a preferred embodiment, the hybrid power supply module further includes a solar micropane;

[0070] The solar micro-panel is embedded in the edge of the manhole cover. It uses amorphous silicon thin-film solar cells and is covered with a wear-resistant and light-transmitting protective layer to convert solar energy into electrical energy.

[0071] Solar micro-panels serve as a third energy source, supplementing power supply during the daytime when sunlight is abundant, further improving the system's energy harvesting redundancy.

[0072] The power management unit integrates a multi-input rectifier circuit, a boost / buck converter, and a charge / discharge control circuit.

[0073] The DC power from the thermoelectric conversion unit and the AC power from the piezoelectric energy harvesting unit are rectified and regulated respectively before being fed into the charging path of the power management unit to trickle charge the low self-discharge lithium battery.

[0074] The power management unit is also equipped with overcharge protection and over-discharge protection to prevent the lithium battery from being damaged by overcharging and over-discharging.

[0075] When solar micro-panels are connected, the power management unit simultaneously manages the convergence and scheduling of three energy inputs;

[0076] As the core energy storage component of the system, the low self-discharge lithium battery has an annual self-discharge rate of no more than 2%, which can provide a stable and reliable power supply to the system during periods when the energy harvesting source is insufficient.

[0077] With continuous replenishment from multiple energy sources, the lifespan of low self-discharge lithium batteries can be significantly extended, reducing the frequency of battery replacement and maintenance costs.

[0078] Example 4: Working Mechanism of Dynamic Power Management Module

[0079] The core function of the dynamic power management module is to intelligently switch between sleep mode and working mode based on the system's operating status and external environmental conditions, so as to keep the system power consumption at an extremely low level.

[0080] Under normal circumstances where no events are triggered, the dynamic power management module puts the sensor acquisition module and the edge gateway into sleep mode;

[0081] In hibernation mode, the microcontroller enters a deep sleep state, shutting down the main clock and most peripherals, leaving only the real-time clock module and interrupt detection pin active.

[0082] In hibernation mode, the total system current consumption is 3 microamps. This extremely low static power consumption allows the system to maintain standby time for several years using only the low self-discharge lithium battery without external energy replenishment.

[0083] When the sensor acquisition module detects a preset trigger event, the dynamic power management module quickly wakes up the system and switches it to working mode through a hardware interrupt signal;

[0084] The preset trigger events include: the accelerometer detecting that the change in the tilt angle of the manhole cover exceeds a preset change threshold, and the vibration sensor detecting that the vibration amplitude exceeds a preset amplitude threshold;

[0085] After the system is awakened and enters working mode, the sensor acquisition module performs high-precision data acquisition, the edge gateway starts the decision tree model to determine anomalies, and after completing data processing and necessary communication tasks, the system re-enters sleep mode.

[0086] The dynamic power management module also has an adaptive self-test cycle adjustment function based on weather conditions;

[0087] The edge gateway obtains weather status information of the current deployment area through the cloud platform, and the dynamic power management module adjusts the system's timed self-test cycle according to the weather status information.

[0088] In rainy weather, the risk of manhole covers shifting or lifting increases because rainfall may cause water to accumulate on the road and wash away the manhole covers. Therefore, the system adopts a shorter first self-check cycle, which is set to 5 minutes in this embodiment.

[0089] Under clear weather conditions, the risk of manhole cover abnormalities is relatively low, and the system adopts a longer second self-inspection cycle. In this embodiment, the second self-inspection cycle is set to 30 minutes.

[0090] This adaptive adjustment strategy based on environmental risk levels optimizes the system's average power consumption while ensuring the effectiveness of monitoring.

[0091] Example 5: Deployment and Operation of the Alarm Module

[0092] The alarm module is deployed on the smart light pole, geographically adjacent to the abnormal manhole cover. The alarm module includes a display screen and a voice broadcast device.

[0093] When the edge gateway determines that the manhole cover is in an abnormal state, the edge gateway sends an alarm command to the alarm module through the wireless communication link;

[0094] After receiving the alarm command, the alarm module displays the abnormal alarm information and location information of the manhole cover in real time on the display screen, providing visual warnings to pedestrians and drivers in the form of text and icons;

[0095] At the same time, the voice broadcasting device broadcasts voice alarms to the surrounding area, reminding pedestrians and vehicles to pay attention and avoid the area, ensuring the safety of people and vehicles on site.

[0096] The advantage of deploying alarm modules on smart light poles is that smart light poles have independent mains power supply systems, and alarm modules can be powered by the power of smart light poles without the need for additional independent power supplies.

[0097] Meanwhile, the height and location of smart light poles help to expand the coverage of alarm information, enabling pedestrians and vehicles in a wider area to receive alarm information.

[0098] Example 6: Cloud Platform Functions and Data Interaction

[0099] The cloud platform establishes a two-way data communication link with the edge gateway through the NB-IoT mobile communication network;

[0100] In the uplink direction, the edge gateway uploads data to the cloud platform in the following two situations:

[0101] First, at the end of each self-inspection cycle, the edge gateway uploads the sensor status summary data and battery power information for this cycle to the cloud platform, so that maintenance personnel can keep track of the operating status of each monitoring point.

[0102] Secondly, when the edge gateway determines that the manhole cover is in an abnormal state, it immediately uploads detailed data of the abnormal event to the cloud platform, including the abnormality type, tilt angle value, vibration frequency value, occurrence time and equipment location information.

[0103] In the downlink direction, the cloud platform can issue operation and maintenance management instructions to the edge gateway, including adjusting monitoring parameters, updating decision tree model parameters, and pushing weather status information;

[0104] The edge gateway supports OTA remote model update functionality;

[0105] As the system runs for a long time, the cloud platform uses a large amount of historical sensor data and abnormal event samples to train and optimize the decision tree model offline, generating new version model parameters with higher accuracy.

[0106] The cloud platform packages the updated decision tree model parameters and sends them to the edge gateway via the NB-IoT network. The edge gateway receives and loads the updated model parameters into the flash memory of the microcontroller, replacing the original model parameters and completing the online iterative upgrade of the decision tree model.

[0107] The entire OTA update process does not require maintenance personnel to go to the site for manual operation, reducing the labor costs of model maintenance.

[0108] Example 7: Visual Inspection Module and Dual-Mode Verification and Monitoring System

[0109] In a preferred embodiment, the system further includes a visual detection module;

[0110] The visual inspection module includes a camera and an image recognition unit;

[0111] The camera is mounted on a fixed bracket on a municipal inspection vehicle and continuously captures images of manhole covers on the road while the vehicle is in motion.

[0112] The image recognition unit uses a lightweight deep learning model based on an improved YOLO architecture to perform real-time analysis and processing of the collected manhole cover images, identify the damaged, missing, settled, and tilted states of the manhole covers, and send the recognition results to the corresponding edge gateway via wireless communication.

[0113] The lightweight deep learning model based on the improved YOLO architecture has been specifically optimized for the manhole cover scenario;

[0114] In terms of the feature extraction backbone network, depthwise separable convolution is used to replace standard convolution operations, which significantly reduces the number of model parameters and computational cost while maintaining detection accuracy.

[0115] In the detection head section, a special anchor frame size configuration is designed for the geometric characteristics of the manhole cover, which improves the detection adaptability to manhole covers of different sizes and shapes.

[0116] The model was trained using a large number of real manhole cover scene images. The training data covered manhole cover image samples under different lighting conditions, different weather conditions, and different road surface materials.

[0117] The sensor acquisition module and the vision inspection module constitute a dual-mode verification and monitoring system;

[0118] The edge gateway fuses and determines the sensor data output by the sensor acquisition module and the recognition results output by the vision detection module;

[0119] When the results of the two detection methods are consistent and both point to an abnormality, the edge gateway confirms that the manhole cover is in an abnormal state and immediately triggers the alarm module to issue an alarm, while also reporting to the cloud platform.

[0120] When the judgment results of the two detection methods are inconsistent, that is, one method judges it as abnormal while the other method judges it as normal, the edge gateway will increase the monitoring frequency of the system to encrypted monitoring mode, shorten the data collection interval, and report the raw data and judgment results of the two methods to the cloud platform for further confirmation by the cloud platform's operation and maintenance personnel or a higher-precision analysis model.

[0121] The advantage of this dual-mode verification mechanism is that the sensor acquisition module can achieve uninterrupted continuous monitoring 24 / 7, covering time periods that cannot be reached by manual inspection or vehicle inspection.

[0122] The visual inspection module can intuitively acquire the appearance image information of the manhole cover and has a stronger ability to detect visually identifiable abnormalities such as damage or missing parts on the surface of the manhole cover.

[0123] The complementary advantages and cross-validation of the two heterogeneous detection methods effectively reduce the false alarm rate and false negative rate of a single detection method in complex urban environments.

[0124] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Any variations and modifications can be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the invention, fall within the protection scope defined by the claims of the present invention.

Claims

1. A smart data wireless transmission system based on low-power technology, characterized in that, include: The sensor acquisition module is deployed on the manhole cover to collect data on the tilt angle and vibration frequency of the manhole cover. An edge gateway, deployed at the end of the manhole cover, is connected to the sensor acquisition module via the LoRa wireless communication protocol. The edge gateway has a built-in lightweight decision tree model, which is used to determine abnormal states locally based on the received tilt angle data and vibration frequency data. A hybrid power supply module is used to power the sensor acquisition module and the edge gateway. The hybrid power supply module includes a thermoelectric conversion unit, a piezoelectric energy harvesting unit and a low self-discharge lithium battery. The thermoelectric conversion unit and the piezoelectric energy harvesting unit respectively store the acquired electrical energy in the low self-discharge lithium battery through the power management unit. A dynamic power management module is used to control the switching between sleep mode and working mode of the sensor acquisition module and the edge gateway; The alarm module is communicatively connected to the edge gateway and is used to send real-time alarms to surrounding pedestrians and vehicles when the edge gateway determines that the manhole cover is in an abnormal state. The cloud platform connects to the edge gateway via a mobile communication network to receive data uploaded by the edge gateway and provide remote operation and maintenance management services.

2. The intelligent data wireless transmission system based on low-power technology according to claim 1, characterized in that: The thermoelectric conversion unit is a thermoelectric power generation module, which is installed at the connection between the manhole cover and the underground pipe network, and uses the temperature difference between the underground pipe network and the ground surface to generate electricity continuously. The piezoelectric energy harvesting unit is a piezoelectric energy harvesting device installed inside the pressure-bearing structure layer of the manhole cover, which converts the mechanical energy of vibration generated when a vehicle runs over the manhole cover into electrical energy.

3. The intelligent data wireless transmission system based on low-power technology according to claim 1, characterized in that: The hybrid power supply module also includes a solar micro-panel, which is embedded in the edge of the manhole cover and is used to convert solar energy into electrical energy and store it in the low self-discharge lithium battery via the power management unit.

4. The intelligent data wireless transmission system based on low-power technology according to claim 1, characterized in that: The dynamic power management module puts the sensor acquisition module and the edge gateway into sleep mode when no event is triggered. In sleep mode, the total current consumption of the system is 3 microamps. When the sensor acquisition module detects a preset trigger event, the dynamic power consumption management module switches the system to the working mode; The dynamic power consumption management module adjusts the self-test cycle according to the acquired weather status information. It adopts a first self-test cycle in rainy weather and a second self-test cycle in sunny weather. The first self-test cycle is shorter than the second self-test cycle.

5. The intelligent data wireless transmission system based on low-power technology according to claim 1, characterized in that, The anomaly detection logic of the lightweight decision tree model is as follows: when the tilt angle data exceeds the preset tilt angle threshold and the vibration frequency data exceeds the preset vibration frequency threshold, the edge gateway determines the manhole cover status as an abnormal state and triggers the alarm module to issue an alarm.

6. The intelligent data wireless transmission system based on low-power technology according to claim 1, characterized in that: The edge gateway supports OTA remote model update functionality. The cloud platform packages and sends the updated decision tree model parameters to the edge gateway, which receives and loads the updated model parameters to complete the online iteration of the decision tree model.

7. The intelligent data wireless transmission system based on low-power technology according to claim 1, characterized in that: The system also includes a visual inspection module, which includes a camera and an image recognition unit. The camera is deployed on a fixed bracket on a municipal inspection vehicle. The image recognition unit uses a lightweight deep learning model based on an improved YOLO architecture to analyze the collected manhole cover images, identify the damaged, missing, settled, and warped states of the manhole covers, and send the recognition results to the edge gateway.

8. The intelligent data wireless transmission system based on low-power technology according to claim 7, characterized in that: The sensor acquisition module and the vision detection module constitute a dual-mode verification and monitoring system. The edge gateway fuses and determines the sensing data of the sensor acquisition module and the recognition results of the vision detection module. When the results of the two detection methods are consistent and both are abnormal, the manhole cover is confirmed to be in an abnormal state and an alarm is triggered; when the results of the two detection methods are inconsistent, the monitoring frequency is increased and the data is reported to the cloud platform.

9. The intelligent data wireless transmission system based on low-power technology according to claim 1, characterized in that: The alarm module is deployed on the smart light pole and includes a display screen and a voice broadcasting device. The display screen is used to display abnormal alarm information and location information of the manhole cover, and the voice broadcasting device is used to broadcast voice alarm prompts to the surrounding area.

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