Distributed building vibration monitoring method and system

Abstract

The invention relates to a distributed building vibration monitoring method and system. The method comprises the following steps: step 1, detecting node hardware model selection; step 2, sink node hardware model selection; 3, deploying system hardware; step 4, detecting initialization and operation of node software; 5, sink node software data processing; step 6, executing a positioning algorithm; 7, testing and calibrating the positioning precision; and step 8, testing data transmission stability. The method has the beneficial effects that (1) the method aims at solving the problem of signal blind areas of the traditional wireless technology in a complex building environment; and (2) the sensor deployment density is reduced, the project hardware cost is controlled, the positioning efficiency is improved, and the positioning precision is far better than that of a traditional acoustic positioning scheme. And (3) information loss possibly caused by temporary interruption of data transmission is reduced, the reliability of the system is improved, and the long-term unattended monitoring scene requirement of the building is well met.

Description

Technical Field

[0001] This invention relates to the fields of vibration source location and wireless detection, specifically to a distributed building vibration monitoring method and system. Background Technology

[0002] In modern building management, noise pollution control and source tracing are key requirements for improving the comfort of living and working environments. Current building noise location technologies largely rely on acoustic sensor arrays or single-dimensional detection methods, which suffer from several technical bottlenecks: Firstly, traditional acoustic sensors are easily affected by ambient background noise. Sound signals attenuate rapidly when propagating through the air and are easily blocked and reflected by walls, leading to signal distortion and making it difficult to accurately distinguish the floor and room of the noise source, resulting in significant location errors. Secondly, existing wireless transmission methods mostly use Bluetooth and WiFi. Bluetooth has a short transmission distance and weak wall penetration, failing to meet the needs of cross-floor transmission. WiFi has poor anti-interference capabilities and a high packet loss rate in dense buildings, making it difficult to guarantee the real-time and completeness of the data required for location. Furthermore, existing location methods often involve a one-time, dense deployment of sensors across the entire area, creating a significant contradiction between location accuracy and deployment cost. They also cannot achieve the hierarchical, efficient location logic of "first coarsely locating the floor, then finely locating the specific area," resulting in poor practicality. Summary of the Invention

[0003] To address the aforementioned technical deficiencies, the technical problem this invention aims to solve is to provide a distributed building vibration monitoring method and system, which possesses three core advantages: First, it uses vibration sensors instead of traditional acoustic sensors to capture wall vibration signals caused by noise. Vibration propagation in solids is highly stable, its attenuation is predictable, and it is unaffected by background air noise. Combined with the high precision of the sensors, it can accurately preserve the signal characteristics of the noise source, effectively solving the problems of weak anti-interference and rapid signal attenuation. Second, it uses StarFlash technology as the core of wireless transmission. As a new short-range wireless communication standard, StarFlash technology has microsecond-level response, can stably penetrate four solid walls, is suitable for cross-floor transmission, has excellent anti-interference capabilities, supports concurrent connections of multiple devices, and its microsecond-level response speed ensures real-time data transmission without packet loss, overcoming the bottleneck of traditional wireless transmission. Third, it adopts a hierarchical positioning strategy, combined with a Time Difference of Arrival (TDOA) positioning algorithm. A small number of nodes are first deployed on each floor to complete coarse positioning, and then nodes are deployed on the target floor for fine positioning. This avoids the high cost of full-area deployment and accurately locates the vibration source through the algorithm, balancing positioning accuracy and cost, and achieving efficient positioning.

[0004] This invention aims to solve four core problems existing in current building noise location technology: 1. Poor wireless transmission stability, with severe signal attenuation and high packet loss rate when transmitting across floors and through walls, making it impossible to guarantee real-time and reliable transmission of location data; 2. Insufficient positioning accuracy, making it difficult to achieve precise layered positioning from floors to specific areas, and easily affected by environmental interference leading to positioning deviations; 3. High sensor deployment costs and high power consumption, with poor economic efficiency of dense deployment schemes across the entire area, making them unsuitable for long-term monitoring; 4. Simple data processing and positioning logic, unable to adapt to the complex spatial structure of multi-story and multi-room buildings, resulting in low positioning efficiency.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: a distributed building vibration monitoring method, comprising:

[0006] Step 1, Hardware selection for detection nodes: By selecting a triaxial vibration sensor, a low-power embedded microcontroller that supports analog-to-digital conversion and drives the detection node star flash module, the detection node star flash module and local storage module as the core hardware, and equipping them with lithium battery power, the detection node hardware with vibration signal acquisition, preprocessing, transmission and local backup functions is obtained.

[0007] Step 2, selection of aggregation node hardware: Based on the detection node hardware obtained in Step 1, a high-performance embedded microcontroller, an aggregation node star flash module of the same model as the detection node star flash module, and a host computer are selected to obtain aggregation node hardware with data reception, preliminary processing, uploading and positioning calculation support functions.

[0008] Step 3, System Hardware Deployment: Based on the detection nodes obtained in Step 1 and the aggregation node obtained in Step 2, the detection nodes are deployed in two phases: In the first phase, one detection node is deployed on the wall of the stairwell on each floor of the target building; in the second phase, after the target floor is subsequently identified, three detection nodes are deployed at multiple key locations on the target floor to form an array layout that can cover the entire monitoring area, ensuring that each detection node maintains an effective working distance; at the same time, the aggregation node is fixed in the central control room for networking, thus obtaining the complete system hardware for building a cross-floor communication network;

[0009] Step 4, Initialization and operation of detection node software: Based on the detection node hardware deployed in Step 3, the sensor, detection node star flash module and local storage module are initialized after the system is powered on. The low-power monitoring and data acquisition, preprocessing, transmission and local backup process is started to obtain the preprocessed vibration data with timestamps and send it to the aggregation node.

[0010] Step 5, Data processing of the aggregation node software: Based on the vibration data sent to the aggregation node in step 4, the data is received by the aggregation node star flash module and forwarded to the high-performance embedded microcontroller. After CRC verification and timestamp synchronization by the high-performance embedded microcontroller, the valid vibration data is uploaded to the host computer.

[0011] Step 6, Positioning Algorithm Execution: Based on the effective vibration data obtained in Step 5 and the data in the storage module, the host computer first compares the signal strength of each floor node to lock the target floor, then extracts the trigger time of the target floor array detection node in Step 3, and obtains the floor where the noise source is located and the specific vibration source location by calculating the time difference and distance difference and solving the hyperbola equation.

[0012] Step 7, Positioning accuracy test and calibration: Based on the positioning results obtained in Step 6, standard vibration signals are generated on each floor and in each area. The positioning results are recorded and the sensor trigger threshold and positioning algorithm parameters are adjusted to obtain the calibrated parameters and positioning results with the required accuracy.

[0013] Step 8, Data Transmission Stability Test: Based on the system calibrated in Step 7, a 72-hour uninterrupted operation test is conducted to monitor the cross-floor data transmission status and obtain a data transmission stability verification report, verifying that the system meets the real-time positioning operation requirements.

[0014] Based on the above technical solution, the present invention can be further improved as follows:

[0015] Further, step 4 specifically involves: after the system is powered on, the low-power embedded microcontroller automatically completes the initialization configuration of the vibration sensor, the detection node star flash module, and the local storage module; the vibration sensor enters a low-power monitoring mode, and when the intensity of the vibration signal detected by the vibration sensor exceeds a preset threshold, data acquisition is triggered; the raw data acquired by the vibration sensor is filtered, amplified, and preprocessed by analog-to-digital conversion by the low-power embedded microcontroller, and a node timestamp is added. The system attempts to transmit the data to the aggregation node through the detection node star flash module. Data that fails to transmit is synchronously stored in the local storage module. After the data transmission is completed, the detection node returns to the low-power mode.

[0016] Furthermore, in step 5, the microcontroller completes timestamp synchronization through the clock synchronization function of the aggregation node star flash module.

[0017] Furthermore, step 6 specifically involves:

[0018] Step 6.1: Based on the valid vibration data obtained in Step 5 and the data in the storage module, the host computer first compares the signal strength, vibration trigger time and characteristic frequency of the detection nodes on each floor to lock the target floor with the strongest vibration signal.

[0019] Step 6.2: Extract the vibration trigger times t1, t2, and t3 from the data of the three detection nodes on the target floor, calculate the time difference Δt12 = t2 - t1 and Δt13 = t3 - t1, and combine it with the preset wall vibration propagation speed v to calculate the distance difference Δd12 = v × Δt12 and Δd13 = v × Δt13.

[0020] Step 6.3: With the two detection nodes as the focus, construct two hyperbola equations with the corresponding distance difference as the real axis length, solve for the intersection point, and combine it with the floor layout map to map the specific area number. After correcting the error, display the result visually.

[0021] Another object of the present invention is to provide a system for applying the above-described distributed building vibration monitoring method, comprising:

[0022] Vibration sensors are used to capture vibration signals in building walls caused by noise.

[0023] A low-power embedded microcontroller is responsible for vibration signal preprocessing, extracting feature parameters, controlling the star flash module of the detection node to transmit data, and managing the storage module.

[0024] The detection node star flash module enables wireless communication with the aggregation node, and has the characteristics of wall penetration, anti-interference and microsecond-level response to ensure real-time data transmission;

[0025] The local storage module backs up the original data and preprocessed results in case of failed transmission, thus preventing data loss.

[0026] The aggregation node star-flash module receives data from each detection node and forwards it to the embedded microcontroller, establishing a communication link between the detection nodes and the aggregation node;

[0027] A high-performance embedded microcontroller performs CRC verification and timestamp synchronization on the received data, completing the initial data integration.

[0028] The host computer, with its built-in positioning algorithm and data visualization module, is responsible for data storage, analysis, positioning calculation, result correction and visualization, and is also used for system testing and parameter calibration.

[0029] The beneficial effects of this invention are:

[0030] (1) The Star Flash module is used as the core technology for wireless transmission. It is integrated between the detection node and the aggregation node. Relying on the characteristics of this technology, it can pass through 4 walls without attenuation, ultra-long distance transmission and strong anti-interference. It can effectively resist the electromagnetic interference generated by various electronic devices in the building. In the project, it can realize zero packet loss data transmission across floors and multiple detection nodes. It is committed to solving the signal blind zone problem of traditional wireless technology in complex building environments and provides core support for real-time reliable transmission of positioning data.

[0031] (2) A two-step hierarchical positioning logic combined with the TDOA (Time Difference of Arrival) positioning algorithm is used, working in conjunction with high-precision vibration sensors. In project applications, this technology achieves a "coarse-to-fine" positioning process: first, coarse positioning of the floor is completed by utilizing the difference in vibration intensity to quickly locate the floor where the noise source is located; then, on the target floor, the TDOA algorithm is used, combined with the precise vibration signals captured by the sensors, to calculate the time difference of the signal arrival at different nodes to complete the fine positioning of the specific area. This approach reduces the density of sensor deployment, improves positioning efficiency while controlling project hardware costs, and achieves positioning accuracy far exceeding traditional acoustic positioning solutions, accurately tracing back to specific areas.

[0032] (3) An ultra-low power vibration sensor is used in conjunction with a low power embedded microcontroller management strategy, and an SD card local storage technology is integrated to construct the detection node. In the project application, the vibration sensor, together with the low power scheduling logic of the embedded microcontroller, enables the detection node to operate for a long time by battery power; the SD card local storage function can synchronously back up the original vibration data and preprocessing results, reduce the information loss that may be caused by temporary interruption of data transmission, improve system reliability, and well adapt to the monitoring scenario requirements of buildings that are unattended for a long time. Attached Figure Description

[0033] Figure 1 This is a flowchart of the method of the present invention;

[0034] Figure 2 This is a system structure diagram of the present invention.

[0035] The attached diagram lists the components represented by each number as follows: Detailed Implementation

[0036] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0037] like Figure 1 As shown, the distributed building vibration monitoring method includes:

[0038] Step 1, Detect node hardware selection

[0039] By selecting a triaxial vibration sensor, a low-power embedded microcontroller supporting analog-to-digital conversion and driving the detection node's star-flash module, the detection node's star-flash module, and a local storage module (SD card) as the core hardware, and integrating them with a lithium battery power supply, a detection node hardware with vibration signal acquisition, preprocessing, transmission, and local backup functions is obtained. The vibration sensor is used to collect vibration signals generated by noise in building walls, and its ultra-low power consumption characteristics are suitable for long-term unattended operation. The low-power embedded microcontroller is responsible for preprocessing the vibration signal (filtering, amplification, analog-to-digital conversion), extracting vibration characteristic parameters, controlling the detection node's star-flash module to transmit data, and storing the raw data and preprocessing results that failed to transmit to the SD card for backup. The detection node's star-flash module adopts an industrial-grade star-flash module, which has the ability to transmit through walls, resist interference, and has microsecond-level response capabilities, realizing stable wireless communication with the aggregation node.

[0040] Step 2, Selection of aggregation node hardware

[0041] Based on the detection node hardware obtained in step 1 (the core being the detection node's flash module model), a high-performance embedded microcontroller, a convergence node flash module of the same model as the detection node flash module, and a host computer are selected to form the convergence node hardware, which has the functions of data reception, preliminary processing, uploading, and positioning calculation support. The convergence node flash module is matched according to the detection node flash module model. A high-performance embedded microcontroller with stronger computing power is selected, paired with a host computer with data processing, algorithm execution, and visualization functions to form the convergence node hardware system. The convergence node flash module of the same model as the detection node flash module ensures communication compatibility between the detection node and the convergence node. The high-performance embedded microcontroller meets the requirements of multi-node parallel data processing, and the host computer provides a hardware and software platform for subsequent positioning algorithm execution and result display.

[0042] Step 3, System Hardware Deployment

[0043] Based on the detection nodes obtained in step 1 and the aggregation node obtained in step 2, the detection nodes are deployed in two phases: In the first phase, one detection node is deployed on the wall of the stairwell on each floor of the target building; in the second phase, after the target floors are subsequently identified, three detection nodes are deployed at multiple key locations on the target floors to form an array layout that can cover the entire monitoring area, ensuring that each detection node maintains an effective working distance. Simultaneously, the aggregation node is fixed in the central control room for networking, thus completing the hardware for the entire system to establish a cross-floor communication network.

[0044] For example, in a three-story building scenario, in the first stage, one detection node is deployed on the walls of the stairwells on floors 1-3 for coarse floor positioning. In the second stage, after the target floor is located, three detection nodes (spaced ≥ 5m apart, forming an equilateral triangle) are deployed in the east, south, and west corners of that floor for fine area positioning.

[0045] Layered deployment aligns with the "coarse first, then fine" positioning logic. Single-node deployment on each floor can cover vibration signals across the entire floor, array deployment enables room-wide monitoring without blind spots, and central control room deployment of aggregation nodes ensures communication link stability and data transmission efficiency.

[0046] Step 4: Initialize and run the detection node software

[0047] Based on the detection node hardware deployed in step 3, after the system is powered on, the low-power embedded microcontroller automatically completes the initialization configuration of the vibration sensor, the detection node strobe module, and the local storage module. The vibration sensor enters low-power monitoring mode, and when the intensity of the vibration signal detected by the vibration sensor exceeds a preset threshold, data acquisition is triggered. The raw data acquired by the vibration sensor is filtered, amplified, and preprocessed by analog-to-digital conversion by the low-power embedded microcontroller, and a node timestamp is appended. An attempt is made to transmit the data to the aggregation node (aggregation node strobe module) via the detection node strobe module. Data that fails to transmit is synchronously stored in the local storage module. After the data transmission is completed, the detection node returns to low-power mode.

[0048] Software initialization enables hardware components to work together, low-power monitoring mode reduces energy consumption in non-working states, data preprocessing purifies signal features, timestamps provide a time reference for the positioning algorithm, and local storage avoids information loss due to data transmission interruptions.

[0049] Step 5, Data processing in aggregation node software

[0050] Based on the vibration data sent to the aggregation node in step 4, the aggregation node's star-flash module continuously receives data transmitted from each detection node (detection node star-flash module) and forwards it to the high-performance embedded microcontroller in real time. The high-performance embedded microcontroller performs CRC checks on the received data, discarding invalid data. Simultaneously, relying on the clock synchronization function of the aggregation node's star-flash module, it completes the timestamp synchronization of all detection node data. The synchronized valid data is then uploaded to the host computer via serial port for storage.

[0051] CRC checksums ensure data integrity and prevent invalid data from consuming processing resources. Timestamp synchronization eliminates time discrepancies caused by hardware differences among detection nodes, providing accurate and consistent time data support for subsequent positioning algorithms.

[0052] Step 6, localization algorithm execution

[0053] Based on the valid vibration data obtained in step 5 and the data in the storage module, the host computer first compares the signal strength of the nodes on each floor to locate the target floor. Then, it extracts the trigger time of the target floor array detection node from step 3. Through time difference and distance difference calculations and solving the hyperbolic equation, the floor error (±1 floor) of the noise source and the specific vibration source location (error ≤ 1m) are obtained. Details are as follows:

[0054] Step 6.1: Based on the valid vibration data obtained in Step 5 and the data in the storage module, the host computer first analyzes the valid vibration data of each floor detection node, compares the signal strength, trigger time and characteristic frequency, and locks the floor with the strongest vibration signal as the target floor.

[0055] Step 6.2: Extract the vibration signal trigger times t1, t2, and t3 from the valid data of the three detection nodes on the target floor in Step 3. The host computer calculates the time difference Δt12 = t2 - t1 and Δt13 = t3 - t1 based on the TDOA positioning algorithm. Combined with the preset wall vibration propagation speed v (e.g., the longitudinal wave propagation speed of concrete walls is about 3000 m / s), the distance difference Δd12 = v × Δt12 and Δd13 = v × Δt13 are obtained.

[0056] Step 6.3: With the two detection nodes as the focus, construct two hyperbola equations with the corresponding distance difference as the real axis length, solve for the intersection of the two hyperbolas, and combine them with the floor and room layout map to obtain the specific area number of the noise source. After error correction of the positioning results, the results are visualized and displayed on the host computer.

[0057] Taking advantage of the significant attenuation of vibrations propagating across floors in solid walls, coarse floor location is achieved through signal strength comparison. The TDOA positioning algorithm constructs a hyperbolic positioning model based on the relationship between the time difference of signal arrival at different nodes and the propagation speed. The intersection of the hyperbolas indicates the location of the noise source. The microsecond-level transmission capability of the star-flash module ensures the accuracy of time difference measurement, thereby improving positioning accuracy.

[0058] Step 7, Positioning accuracy testing and calibration

[0059] Based on the positioning results obtained in step 6, standard vibration signals are generated on each floor and in each area. The positioning results are recorded and the sensor trigger threshold and positioning algorithm parameters are adjusted to obtain calibrated parameters and positioning results with satisfactory accuracy.

[0060] After system deployment and software debugging are completed, standard vibration signals (such as those from knocking on walls) are generated sequentially on each floor and in each area of ​​the building. The positioning results of each test are recorded by a host computer. The recorded positioning results are compared with the actual positions of the standard vibration signals to analyze the causes of positioning deviations. The sensor trigger thresholds and relevant parameters in the positioning algorithm are adjusted. The testing and adjustment process is repeated until the requirements of 100% floor positioning accuracy and area positioning error ≤1m are met.

[0061] Standard vibration signals provide a test benchmark with known real positions. By comparing the measured positioning results with the real positions, the positioning parameters can be optimized to improve the system's positioning accuracy and ensure that practical application requirements are met.

[0062] Step 8, Data Transmission Stability Test

[0063] Based on the system (hardware + software + parameters) calibrated in step 7, a 72-hour uninterrupted operation test was conducted to monitor the cross-floor data transmission status and obtain a data transmission stability verification report (packet loss rate of 0, transmission delay ≤5ms), verifying that the system meets the real-time positioning operation requirements.

[0064] After calibrating the complete system, conduct a continuous 72-hour uninterrupted operation test, focusing on monitoring the cross-floor data transmission status and recording key indicators such as data transmission packet loss rate and transmission latency. After the test, analyze the recorded data to verify whether the packet loss rate of the StarShine module communication link is 0 and whether the data transmission latency is ≤5ms.

[0065] Long-term, uninterrupted operation tests simulate actual building monitoring scenarios, fully verifying the wall-penetrating and anti-interference characteristics of the StarShock module, ensuring that data transmission remains lossless and low-latency in complex building environments, thus providing a guarantee for the long-term stable real-time positioning of the system.

[0066] A system for applying the above-described distributed building vibration monitoring method includes:

[0067] Vibration sensor: The core data acquisition component, capturing vibration signals generated by noise in building walls, with ultra-low power consumption suitable for long-term monitoring.

[0068] Low-power embedded microcontroller: The control core, responsible for vibration signal preprocessing (filtering, amplification, analog-to-digital conversion), extracting feature parameters, controlling the star flash module to transmit data, and managing the local storage module (SD card).

[0069] Detection node star flash module: a communication component that enables wireless communication with the aggregation node. It features wall penetration, anti-interference, and microsecond-level response characteristics to ensure real-time data transmission.

[0070] Local storage module: A storage component that backs up raw data and preprocessed results in case of failed transmission, preventing data loss.

[0071] Aggregation Node Star Flash Module: A communication component that receives data from each detection node and forwards it to the embedded microcontroller, establishing a communication link between the detection nodes and the aggregation node.

[0072] High-performance embedded microcontroller: Data processing unit, which performs CRC check (removes invalid data) and timestamp synchronization (based on the clock synchronization function of the aggregation node star flash module) on the received data, and completes the initial data integration.

[0073] Host computer: The core processing and display component, with a built-in positioning algorithm (TDOA algorithm) and data visualization module. It is responsible for data storage, analysis, positioning calculation, result correction and visualization display, and is also used for system testing and parameter calibration.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A distributed building vibration monitoring method, characterized in that, include: Step 1, Hardware selection for detection nodes: By selecting a triaxial vibration sensor, a low-power embedded microcontroller that supports analog-to-digital conversion and drives the detection node star flash module, the detection node star flash module and local storage module as the core hardware, and equipping them with lithium battery power, the detection node hardware with vibration signal acquisition, preprocessing, transmission and local backup functions is obtained. Step 2, selection of aggregation node hardware: Based on the detection node hardware obtained in Step 1, a high-performance embedded microcontroller, an aggregation node star flash module of the same model as the detection node star flash module, and a host computer are selected to obtain aggregation node hardware with data reception, preliminary processing, uploading and positioning calculation support functions. Step 3, System Hardware Deployment: Based on the detection nodes obtained in Step 1 and the aggregation node obtained in Step 2, the detection nodes are deployed in two phases: In the first phase, one detection node is deployed on the wall of the stairwell on each floor of the target building; in the second phase, after the target floor is subsequently identified, three detection nodes are deployed at multiple key locations on the target floor to form an array layout that can cover the entire monitoring area, ensuring that each detection node maintains an effective working distance; at the same time, the aggregation node is fixed in the central control room for networking, thus obtaining the complete system hardware for building a cross-floor communication network; Step 4, Initialization and operation of detection node software: Based on the detection node hardware deployed in Step 3, the vibration sensor, detection node star flash module and local storage module are initialized after the system is powered on. The low-power monitoring and data acquisition, preprocessing, transmission and local backup process is started to obtain the preprocessed vibration data with timestamps and send it to the aggregation node. Step 5, Data processing of the aggregation node software: Based on the vibration data sent to the aggregation node in step 4, the data is received by the aggregation node star flash module and forwarded to the high-performance embedded microcontroller. After CRC verification and timestamp synchronization by the high-performance embedded microcontroller, the valid vibration data is uploaded to the host computer. Step 6, Positioning Algorithm Execution: Based on the effective vibration data obtained in Step 5 and the data in the storage module, the host computer first compares the signal strength of each floor node to lock the target floor, then extracts the trigger time of the target floor array detection node in Step 3, and obtains the floor where the noise source is located and the specific vibration source location by calculating the time difference and distance difference and solving the hyperbola equation. Step 7, Positioning accuracy test and calibration: Based on the positioning results obtained in Step 6, standard vibration signals are generated on each floor and in each area. The positioning results are recorded and the sensor trigger threshold and positioning algorithm parameters are adjusted to obtain the calibrated parameters and positioning results with the required accuracy. Step 8, Data Transmission Stability Test: Based on the system calibrated in Step 7, a 72-hour uninterrupted operation test is conducted to monitor the cross-floor data transmission status and obtain a data transmission stability verification report, verifying that the system meets the real-time positioning operation requirements.

2. The distributed building vibration monitoring method according to claim 1, characterized in that, Step 4 is as follows: After the system is powered on, the low-power embedded microcontroller automatically completes the initialization configuration of the vibration sensor, the detection node star flash module, and the local storage module; the vibration sensor enters the low-power monitoring mode, and when the vibration signal intensity detected by the vibration sensor exceeds the preset threshold, data acquisition is triggered; the raw data acquired by the vibration sensor is filtered, amplified, and preprocessed by the low-power embedded microcontroller through analog-to-digital conversion, and a node timestamp is added. It attempts to transmit the data to the aggregation node through the detection node star flash module. Data that fails to transmit is synchronously stored in the local storage module. After the data transmission is completed, the detection node returns to the low-power mode.

3. The distributed building vibration monitoring method according to claim 1, characterized in that, In step 5, the microcontroller completes timestamp synchronization through the clock synchronization function of the aggregation node star flash module.

4. The distributed building vibration monitoring method according to claim 1, characterized in that, Step 6 specifically involves: Step 6.1: Based on the valid vibration data obtained in Step 5 and the data in the storage module, the host computer first compares the signal strength, vibration trigger time and characteristic frequency of the detection nodes on each floor to lock the target floor with the strongest vibration signal. Step 6.2: Extract the vibration trigger times t1, t2, and t3 from the data of the three detection nodes on the target floor, calculate the time difference Δt12 = t2 - t1 and Δt13 = t3 - t1, and combine it with the preset wall vibration propagation speed v to calculate the distance difference Δd12 = v × Δt12 and Δd13 = v × Δt13. Step 6.3: With the two detection nodes as the focus, construct two hyperbola equations with the corresponding distance difference as the real axis length, solve for the intersection point, and combine it with the floor layout map to map the specific area number. After correcting the error, display the result visually.

5. A distributed building vibration monitoring system, used to apply the distributed building vibration monitoring method as described in any one of claims 1-4, characterized in that, include: Vibration sensors are used to capture vibration signals in building walls caused by noise. A low-power embedded microcontroller is responsible for vibration signal preprocessing, extracting feature parameters, controlling the star flash module of the detection node to transmit data, and managing the storage module. The detection node star flash module enables wireless communication with the aggregation node, and has the characteristics of wall penetration, anti-interference and microsecond-level response to ensure real-time data transmission; The local storage module backs up the original data and preprocessed results in case of failed transmission, thus preventing data loss. The aggregation node star-flash module receives data from each detection node and forwards it to the embedded microcontroller, establishing a communication link between the detection nodes and the aggregation node; A high-performance embedded microcontroller performs CRC verification and timestamp synchronization on the received data, completing the initial data integration. The host computer, with its built-in positioning algorithm and data visualization module, is responsible for data storage, analysis, positioning calculation, result correction and visualization, and is also used for system testing and parameter calibration.

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