IoT-based intelligent monitoring system for building module data interfaces and its deployment method

Abstract

This invention relates to an intelligent monitoring system and deployment method for building module data interfaces based on the Internet of Things (IoT), belonging to the field of building information monitoring technology. The system consists of distributed sensing units, an edge computing gateway, a cloud platform, and a visualization terminal. The edge computing gateway incorporates an adaptive filtering algorithm and an anomaly pattern recognition model. The cloud platform uses a time-series database to construct a multi-dimensional data warehouse and performs structural health assessment by fusing LSTM neural networks and random forest algorithms. The deployment method includes: optimizing sensor placement strategies based on BIM models, establishing a wireless mesh self-organizing network communication architecture, and configuring a hierarchical early warning mechanism and fault tracing function. The innovation lies in proposing a dynamic threshold adjustment algorithm and an interface performance degradation prediction model to achieve real-time monitoring and lifespan prediction of building module connection status. This system has the advantages of flexible deployment, high detection accuracy, and low maintenance costs, effectively improving the intelligent level of building structural safety monitoring.

Description

Technical Field

[0001] This invention relates to the field of building information monitoring technology, and in particular to an intelligent monitoring system and deployment method for building module data interfaces based on the Internet of Things. Background Technology

[0002] With the acceleration of urbanization and the widespread application of prefabricated buildings, the reliability monitoring of modular building interfaces has become crucial for ensuring structural safety. Traditional building structure monitoring technologies mainly rely on wired sensor networks and manual inspections, which suffer from problems such as complex deployment, insufficient real-time performance, long cycles, high maintenance costs, and significant data response delays. In recent years, the introduction of IoT technology has improved monitoring efficiency to some extent, but it still faces technical bottlenecks such as poor sensor deployment flexibility, high data processing and response delays, static threshold settings, low fault diagnosis accuracy, and passive maintenance strategies.

[0003] Referring to the Chinese patent "A Prefabricated Building Structural Health Monitoring System," a monitoring scheme based on pre-set sensor nodes in a BIM model is disclosed, with centralized data processing via a cloud server. While this scheme optimizes sensor layout using the BIM model, it has significant limitations: the sensor node positions are fixed, making it impossible to dynamically adjust the monitoring density according to the actual stress distribution at the building module interfaces; data processing relies entirely on the cloud, resulting in a real-time filtering delay of over 200ms for high-frequency data such as vibration signals; and the use of a static threshold alarm mechanism (e.g., a fixed displacement threshold of ±5mm) fails to consider baseline drift caused by material creep, temperature deformation, etc., leading to a false alarm rate as high as 18%.

[0004] Based on existing technologies, the following problems urgently need to be addressed in this field:

[0005] Insufficient dynamic monitoring capabilities: Existing sensor deployment strategies rely on preset models and cannot adapt to changes in stress gradients at building interfaces, resulting in monitoring blind spots;

[0006] Low data processing efficiency: The local preprocessing capability for high-frequency vibration signals (≥500Hz) is lacking, and centralized cloud computing cannot meet the real-time requirements;

[0007] Rigid threshold setting: Fixed thresholds cannot distinguish between environmental disturbances and actual damage, especially in long-term monitoring where the false alarm rate increases significantly.

[0008] Inefficient use of multi-source data: Independent analysis of data such as displacement, stress, and vibration fails to uncover the coupling and correlation characteristics of multiple parameters, affecting the accuracy of fault tracing.

[0009] To address the above issues, this invention integrates multidisciplinary technologies (BIM modeling, edge computing, dynamic threshold algorithms, and hybrid machine learning models) to overcome the limitations of traditional monitoring systems, achieving a technological leap from "passive response" to "proactive early warning" and from "experience-based judgment" to "data-driven" approaches, thus providing reliable protection for the safety management of building structures throughout their entire lifecycle. Summary of the Invention

[0010] In view of the aforementioned existing problems, the present invention is proposed.

[0011] Therefore, this invention provides an intelligent monitoring system and deployment method for building module data interfaces based on the Internet of Things, which solves the problems of insufficient dynamic monitoring capabilities, low data processing efficiency, rigid threshold settings, and inefficient utilization of multi-source data.

[0012] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0013] In a first aspect, the present invention provides an intelligent monitoring system for building module data interfaces based on the Internet of Things, which consists of a distributed sensing unit, an edge computing gateway, a cloud platform and a visualization terminal;

[0014] The distributed sensing unit collects displacement, stress, temperature, humidity, and vibration data from the building module interface through a multi-mode sensor array. Its sampling frequency must satisfy the extended conditions of the Nyquist sampling theorem.

[0015]

[0016] in The sampling frequency of the vibration signal. This is the minimum period of the vibration signal (characteristic period of high-frequency vibration). The critical time interval for stress change (material elastic deformation response time) is used to ensure the integrity of data acquisition.

[0017] The edge computing gateway communicates with the sensing unit via the LoRaWAN or ZigBee 3.0 protocol and is configured with a data cache queue to cope with network fluctuations;

[0018] The system uses a dynamic threshold adjustment algorithm to calibrate abnormal thresholds in real time, enabling real-time monitoring of the connection status of building modules.

[0019] As a preferred embodiment of the IoT-based intelligent monitoring system for building module data interfaces described in this invention, the multi-mode sensor array employs an entropy weight fusion algorithm, specifically including:

[0020] Information entropy was calculated for four types of sensor data: displacement, stress, temperature and humidity, and vibration.

[0021]

[0022] in The probability distribution of the data;

[0023] Dynamically allocate fusion weights using the entropy method:

[0024]

[0025] Fusion weights The real-time update cycle is no more than 10 seconds, and when the entropy value of a certain type of sensor... A fault alarm is triggered when the value is greater than 0.9.

[0026] As a preferred embodiment of the IoT-based intelligent monitoring system for building module data interfaces described in this invention, the adaptive Kalman filter algorithm of the edge computing gateway is implemented as follows:

[0027] Define state equations With observation equation ,

[0028] in Here is the state transition matrix. To control the input matrix, To control the input vector, For process noise, To observe the noise, The observation matrix;

[0029] Based on real-time signal-to-noise ratio Adjustment process noise covariance and observation noise covariance :

[0030] ,

[0031] in The initial process noise covariance, For the initial observation noise covariance, For signal power, Let λ be the noise power and λ be the attenuation coefficient, with a value range of [0.1, 0.5].

[0032] As a preferred embodiment of the IoT-based intelligent monitoring system for building module data interfaces described in this invention, the hybrid health assessment model of the cloud platform operates according to the following steps:

[0033] The input to the LSTM neural network is time-series data {X} t Given a string t = 1, 2, ..., T, output the health score S. LSTM ∈[0,1];

[0034] The random forest model takes a feature vector {mean, variance, peak factor} as input and outputs a score S. RF ∈[0,1];

[0035] The final health score is obtained through weighted fusion:

[0036]

[0037] The weight α is optimized every 24 hours through cross-validation, with the optimization objective being to minimize the prediction error MSE.

[0038] As a preferred embodiment of the IoT-based intelligent monitoring system for building module data interfaces described in this invention, the implementation of the dynamic threshold adjustment algorithm includes:

[0039] A sliding window variance analysis was used, with the window length N dynamically set according to the sampling frequency of the vibration signal.

[0040]

[0041] in The threshold update frequency (default 1Hz) The sampling frequency of the vibration signal;

[0042] The system achieves real-time monitoring of the connection status of building modules by dynamically adjusting abnormal thresholds using a dynamic threshold adjustment algorithm. Calculate the mean of the sliding window statistic with standard deviation And satisfy:

[0043]

[0044] in Let k be the arithmetic mean of the data within the first N time windows at time point k. Let be the standard deviation of the data within the first N time windows at time point k, and β be the risk coefficient, which is obtained by training based on historical failure data and has a value range of [2.5, 3.5].

[0045] As a preferred embodiment of the IoT-based intelligent monitoring system for building module data interfaces described in this invention, the system includes the following steps:

[0046] S1. Analysis of the stress gradient field of interface nodes based on BIM model ( ) and strain energy density gradient field ( The sensor placement coordinates P(x,y,z) are determined using the gradient maximization algorithm, satisfying:

[0047]

[0048] And the distance between adjacent sensors Δd ≤ 13λ max , where λ max To monitor the maximum wavelength of the vibration signal;

[0049] S2. Deploy a multi-mode sensor array, configure displacement gauges, strain gauges, temperature and humidity sensors, and vibration accelerometers, and set the sampling frequency according to the signal type:

[0050] Vibration signal sampling frequency Stress signal sampling frequency ,

[0051] in The frequency of the vibration peak;

[0052] S3. Construct a wireless mesh self-organizing network architecture and dynamically adjust the node transmit power. :

[0053]

[0054] in The path loss index is set to 2.5 for indoor use or 3.0 for outdoor use, depending on the environment type. Maximum node transmit power, ≤20dBm; The minimum signal-to-noise ratio required by the receiver. Let d be the noise power spectral density, and d be the straight-line distance between the transmitting and receiving nodes. For transmit antenna gain, For receiving antenna gain;

[0055] S4. Configure an adaptive Kalman filter algorithm in the edge computing gateway to dynamically update the noise covariance matrix. and :

[0056] ,

[0057] Where λ∈[0.1,0.5], and SNR is the real-time signal-to-noise ratio;

[0058] S5. Deploy a hybrid health assessment model on the cloud platform, and optimize the weights α of LSTM and Random Forest daily:

[0059] and ;

[0060] in, This indicates the actual health score;

[0061] A model for predicting interface performance degradation is used to predict lifetime.

[0062] S6. Configure tiered early warning rules in the visualization terminal:

[0063] Level 1 warning criteria: Health score S < 0.8S0 and degradation rate ;

[0064] Level 2 warning criteria: Health score S < 0.6S0 or dynamic threshold T k >3σ hist ;

[0065] Level 3 early warning condition: Predicted remaining lifespan L remain <30 days;

[0066] Where S0 is the initial health score, σ hist The historical standard deviation;

[0067] S7. During the system integration and debugging phase, inject white noise to verify the filtering performance, requiring a signal-to-noise ratio improvement of ≥15dB, and test the sensor response delay of ≤100ms;

[0068] S8. Periodically update the model and calibration equipment, retrain the hybrid health assessment model every quarter, and calibrate the sensors every 6 months using a laser rangefinder and a standard force source.

[0069] As a preferred embodiment of the IoT-based intelligent monitoring system for building module data interfaces described in this invention, in step 2, a multi-mode sensor array (displacement gauge, strain gauge, temperature and humidity sensor, vibration accelerometer) is installed at the planned location and fixed by magnetic attraction or bolts; IP67 protection level encapsulation is adopted to ensure waterproof and shockproof performance; in step 3, ZigBee 3.0 (short-range low power consumption) or LoRaWAN (long-range wide coverage) protocol is used to network the edge computing gateway and the sensing unit, and the network path is dynamically maintained through the AODV routing protocol to ensure that the packet loss rate is <1%.

[0070] As a preferred embodiment of the IoT-based intelligent monitoring system for building module data interfaces described in this invention, step 4 integrates an entropy weight fusion algorithm to update sensor weights every 10 seconds. Set up a circular data cache queue (capacity ≥ 1 hour of raw data) to cope with network interruption scenarios; in step 6, overlay real-time data (displacement, stress) and health heatmap on the BIM model; support historical data backtracking and fault event annotation; trigger emergency response and push alarm information via SMS / email / API interface.

[0071] As a preferred embodiment of the IoT-based intelligent monitoring system for building module data interfaces described in this invention, step 5 involves constructing a time-series database (InfluxDB) and storing it according to the following rules:

[0072] Raw data: Retained for 30 days (sampling period ≤ 1 minute);

[0073] Feature data (mean, variance): retained for 5 years;

[0074] Hybrid health assessment model: Training an LSTM neural network (Input: Time series data, Output: ∈[0,1]);

[0075] Simultaneous running of the random forest model (input: feature vector, output: ∈[0,1]);

[0076] Interface performance degradation prediction model: Deploy an improved ARIMA model, with exogenous variables including the temperature-stress coupling factor C(T). k , ),in The standard deviation is denoted as .

[0077] As a preferred embodiment of the IoT-based intelligent monitoring system for building module data interfaces described in this invention, the following steps are performed: Step 7 simulates interface displacement by applying a step signal of Δx=5mm to verify that the sensor response delay is ≤100ms; white noise is injected to test the Kalman filter noise reduction effect (signal-to-noise ratio improvement ≥15dB); long-term stability testing involves continuous operation for 30 days, with statistical analysis of false alarm rate (≤2%) and false negative rate (≤0.5%); Step 8 involves retraining the hybrid health assessment model quarterly for model updates, incorporating newly collected fault case data; dynamically adjusting the sampling frequency and transmission interval based on node battery life (estimated ≥3 years) to optimize the network; and when the fault probability... When the value is >0.7, a fault analysis report is automatically generated and pushed to the maintenance terminal for fault tracing.

[0078] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the Internet of Things-based building module data interface deployment method described in the first aspect of the present invention.

[0079] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the Internet of Things-based building module data interface deployment method as described in the first aspect of the present invention.

[0080] The beneficial effects of this invention are: improved monitoring efficiency and accuracy.

[0081] Dynamic adaptive monitoring: By using BIM model gradient analysis and sensor optimization, the monitoring density is matched with the interface stress distribution, the blind zone coverage is reduced to ≤3% (about 15% with traditional methods), and the data acquisition resolution is improved to 0.01mm (displacement) / 0.1MPa (stress).

[0082] Real-time local processing: The edge computing gateway integrates an adaptive Kalman filter algorithm, which has a filtering delay of ≤20ms for high-frequency vibration signals (≥500Hz) (traditional cloud processing delay ≥200ms), and improves the signal-to-noise ratio by ≥15dB;

[0083] Intelligent threshold adjustment: The dynamic threshold algorithm combines sliding window statistics with historical baselines, reducing the false alarm rate to ≤2% (the false alarm rate of traditional fixed threshold is ≥15%).

[0084] Resource optimization and cost saving

[0085] Wireless deployment flexibility: Based on the dynamic power adjustment technology of Mesh self-organizing network, the node deployment time is reduced by 60% (wired solutions require pre-installed cabling), and the battery life is extended to ≥3 years (traditional node life ≤1.5 years).

[0086] Data fusion reduces redundancy: the entropy weight fusion algorithm reduces invalid data transmission by up to 40%, and cloud platform storage costs by 35%;

[0087] Predictive maintenance: By using a remaining life prediction model (error ≤10%), maintenance can be planned in advance, reducing the frequency of manual inspections by 50% and saving ≥200,000 yuan / 10,000 square meters in annual maintenance costs.

[0088] Upgrading intelligent decision-making capabilities

[0089] Multi-model collaborative evaluation: The LSTM and random forest hybrid model achieves an accuracy of 92% in evaluating interface health (compared to ≤85% for a single model), and improves the early detection rate of progressive damage (such as fatigue cracks) by 30%.

[0090] Precise fault tracing: Multi-source spatiotemporal correlation analysis improves fault location accuracy to 88% (traditional methods ≤70%), and supports BIM model visualization annotation;

[0091] Adaptive learning capability: The model is iterated and optimized every quarter, and the response time for identifying new failure modes is shortened to ≤24 hours (traditional systems require ≥1 week).

[0092] This invention employs IP67 protective encapsulation and dynamic power adjustment technology, enabling the system to operate reliably in extreme environments such as -40℃ to 85℃ and humidity ≥95% (the failure probability of traditional equipment is ≥25%). It supports various modular structures such as prefabricated buildings, bridges, and pipe corridors, and its interface protocol is compatible with mainstream BIM software. This system achieves a warning response time of ≤5 seconds for sudden interface displacement exceeding limits (≥8mm); a bolt loosening fault tracing accuracy rate of 91.5%; and a 28.7% reduction in annual maintenance costs compared to previous versions. Attached Figure Description

[0093] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0094] Figure 1 This is a schematic diagram of an IoT-based intelligent monitoring system for building module data interfaces, as shown in Example 1.

[0095] Figure 2 This is a flowchart of a method for deploying a building module data interface based on the Internet of Things in Example 2. Detailed Implementation

[0096] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0097] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0098] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0099] Example 1, referring to Figure 1 This is the first embodiment of the present invention. This embodiment provides an intelligent monitoring system for building module data interfaces based on the Internet of Things, which consists of a distributed sensing unit 101, an edge computing gateway 102, a cloud platform 103, and a visualization terminal 104.

[0100] The distributed sensing unit 101 collects displacement, stress, temperature, humidity, and vibration data of the building module interface through a multi-mode sensor array. Its sampling frequency must satisfy the extended conditions of the Nyquist sampling theorem.

[0101]

[0102] in The sampling frequency of the vibration signal. This is the minimum period of the vibration signal (characteristic period of high-frequency vibration). The critical time interval for stress change (material elastic deformation response time) is used to ensure the integrity of data acquisition.

[0103] The edge computing gateway 102 communicates with the sensing unit via the LoRaWAN or ZigBee 3.0 protocol and is configured with a data buffer queue to cope with network fluctuations;

[0104] The system uses a dynamic threshold adjustment algorithm to calibrate abnormal thresholds in real time, enabling real-time monitoring of the connection status of building modules.

[0105] The multi-mode sensor array employs an entropy weight fusion algorithm, specifically including:

[0106] Information entropy was calculated for four types of sensor data: displacement, stress, temperature and humidity, and vibration.

[0107]

[0108] in The probability distribution of the data;

[0109] Dynamically allocate fusion weights using the entropy method:

[0110]

[0111] Fusion weights The real-time update cycle is no more than 10 seconds, and when the entropy value of a certain type of sensor... A fault alarm is triggered when the value is greater than 0.9.

[0112] The adaptive Kalman filter algorithm of the edge computing gateway 102 is implemented as follows: Define the state equation. With observation equation ,

[0113] in Here is the state transition matrix. To control the input matrix, To control the input vector, For process noise, To observe the noise, The observation matrix;

[0114] Based on real-time signal-to-noise ratio Adjustment process noise covariance and observation noise covariance :

[0115] ,

[0116] in The initial process noise covariance, For the initial observation noise covariance, For signal power, Let λ be the noise power and λ be the attenuation coefficient, with a value range of [0.1, 0.5].

[0117] The hybrid health assessment model for cloud platform 103 operates according to the following steps:

[0118] The input to the LSTM neural network is time-series data {X} t Given a string t = 1, 2, ..., T, output the health score S. LSTM ∈[0,1];

[0119] The random forest model takes a feature vector {mean, variance, peak factor} as input and outputs a score S. RF ∈[0,1];

[0120] The final health score is obtained through weighted fusion:

[0121]

[0122] The weight α is optimized every 24 hours through cross-validation, with the optimization objective being to minimize the prediction error MSE.

[0123] The implementation of the dynamic threshold adjustment algorithm includes: using sliding window variance analysis, with the window length N dynamically set according to the vibration signal sampling frequency.

[0124]

[0125] in The threshold update frequency (default 1Hz) The sampling frequency of the vibration signal;

[0126] The system achieves real-time monitoring of the connection status of building modules by dynamically adjusting abnormal thresholds using a dynamic threshold adjustment algorithm. Calculate the mean of the sliding window statistic with standard deviation And satisfy:

[0127]

[0128] in Let k be the arithmetic mean of the data within the first N time windows at time point k. Let be the standard deviation of the data within the first N time windows at time point k, and β be the risk coefficient, which is obtained by training based on historical failure data and has a value range of [2.5, 3.5].

[0129] Example 2, refer to Figure 2 This is the second embodiment of the present invention, which provides a method for deploying building module data interfaces based on the Internet of Things, including the following steps:

[0130] S1. Analysis of the stress gradient field of interface nodes based on BIM model ( ) and strain energy density gradient field ( The sensor placement coordinates P(x,y,z) are determined using the gradient maximization algorithm, satisfying:

[0131]

[0132] And the distance between adjacent sensors Δd ≤ 13λ max , where λ max To monitor the maximum wavelength of the vibration signal;

[0133] 1. BIM Model Data Extraction and Preprocessing

[0134] Model import: Import the BIM model (format: IFC / RVT) of the building module into dedicated analysis software (such as ANSYS, Revit Structural Analysis) to extract the geometric topology and material properties (elastic modulus E, Poisson's ratio ν) of the interface nodes.

[0135] Load configuration: Define the design load combination (dead load, live load, wind load, seismic action), and set the partial factors according to the "Code for Design of Building Structures" GB50009; for special working conditions (such as typhoon, equipment impact), add dynamic load spectrum according to actual engineering needs.

[0136] Finite Element Analysis (FEA): Performs static and modal analyses to generate stress distribution field σ(x,y,z) and strain energy density field ε(x,y,z);

[0137] Output format: CSV or JSON, containing the coordinates of each node and the corresponding mechanical parameters.

[0138] 2. Gradient field calculation and high gradient region identification

[0139] Gradient calculation: For each interface node, calculate the stress gradient. and strain energy density gradient ;

[0140] Gradient magnitude calculation:

[0141]

[0142] Threshold filtering: Set the gradient threshold σ th =0.3σ yield (σ) yield (For material yield strength), screening to meet the requirements or The high gradient region.

[0143] Region clustering: The DBSCAN algorithm is used to cluster nodes with high gradients and merge adjacent regions to avoid overly dense clustering.

[0144] 3. Sensor placement coordinate optimization

[0145] Gradient Maximization Algorithm:

[0146] Objective function: Maximize the overall gradient strength of the covered region.

[0147]

[0148] Constraints:

[0149] Spacing between adjacent sensors ,in = v is the vibration wave velocity in the material. To monitor the lowest frequency of vibration signals (such as in bridge monitoring) =1Hz); Boundary avoidance distance ≥0.5m (avoiding non-uniform areas such as bolt holes and welds).

[0150] Optimization Solution: The objective function is solved using a genetic algorithm (GA) or particle swarm optimization (PSO), and the output sensor coordinate set {P1, P2, ..., P} is obtained. n}

[0151] Environmental adaptation correction: In corrosive environments (such as chemical plants), at P i Add redundant sensors around the perimeter (spacing reduced to) In areas with high electromagnetic interference, shielded sensor nodes must be used for labeling.

[0152] 4 Output and Verification

[0153] Sensor placement scheme generation: Output a BIM model with sensor coordinates (format: NWD / NWC), indicating the installation method (magnetic / bolt) and protection level (IP67 / IP69K).

[0154] Simulation verification:

[0155] Simulate the removal of 10% of sensor nodes in FEA software to verify the remaining nodes' impact on critical areas. The coverage rate is still ≥95%;

[0156] Vibration monitoring wavelength verification: If λ max =10m, then the maximum allowable distance between adjacent sensors is Δd=3.33m.

[0157] Implementation Case: A prefabricated steel structure factory building (span 60m, height 24m)

[0158] BIM model analysis: 12 high-stress gradient regions were extracted (∥) (∥>15MPa / m);

[0159] Optimized sensor placement: The coordinates of 48 sensors were determined using the PSO algorithm, with a spacing Δd = 2.8m. λ max =3.3m;

[0160] Implementation results: The coverage rate of monitoring blind spots was reduced from 18% in the traditional scheme to 2.5%; stress mutation caused by wind vibration of the roof was successfully captured (gradient change rate ≥20% / s), and the early warning response time was ≤3 seconds.

[0161] It should be noted that the gradient maximization algorithm ensures that the sensor placement is strictly matched with the mechanical properties, reducing redundant nodes by more than 30%; the spacing constraint ensures complete acquisition of vibration signals (Nyquist sampling theorem satisfaction rate ≥ 99%); and the BIM model is dynamically corrected to be compatible with complex building structures and harsh environmental requirements.

[0162] S2. Deploy a multi-mode sensor array, configure displacement gauges, strain gauges, temperature and humidity sensors, and vibration accelerometers, and set the sampling frequency according to the signal type:

[0163] Vibration signal sampling frequency Stress signal sampling frequency ,

[0164] in The frequency of the vibration peak;

[0165] In step 2, a multi-mode sensor array (displacement gauge, strain gauge, temperature and humidity sensor, vibration accelerometer) is installed at the planned location and fixed by magnetic attraction or bolts; it is encapsulated with an IP67 protection rating to ensure waterproof and shockproof performance;

[0166] 1. Hardware Installation and Fixing

[0167] Sensor selection and configuration:

[0168] Displacement monitoring: A laser displacement sensor (range ±50mm, resolution 0.001mm) or an LVDT linear displacement sensor (accuracy ±0.1%FS) is used.

[0169] Stress monitoring: Deploy fiber optic strain gauges (wavelength range 1525~1565nm, sensitivity 1.2pm / ) ) or resistance strain gauge (grid length 5mm, sensitivity coefficient K=2.05);

[0170] Vibration monitoring: Install a triaxial MEMS accelerometer (range ±50g, bandwidth 0~2000Hz);

[0171] Temperature and humidity monitoring: Integrated digital temperature and humidity sensor (temperature range -40~85℃, accuracy ±0.5℃; humidity range 0~100%RH, accuracy ±2%RH).

[0172] Installation method:

[0173] Magnetic fixing: Neodymium iron boron permanent magnet base (magnetic force ≥50N) is used for steel structure interfaces, and the installation time is ≤30 seconds / node;

[0174] Bolt fixing: Expansion bolts (M6×40mm) are used for concrete modules, with pre-drilled threaded holes aligned with the BIM model coordinates, and torque controlled at 5N·m±0.3N·m;

[0175] Protective Encapsulation: The sensor housing meets the IP67 protection rating, and a PTFE anti-corrosion coating is added for corrosive environments.

[0176] 2. Dynamic configuration of sampling frequency

[0177] Vibration signal sampling:

[0178] Based on the dominant frequency of vibration (Obtained through pre-sampling FFT analysis), the vibration signal sampling frequency is set according to the Nyquist theorem. :

[0179] (Safety margin)

[0180] If the dominant frequency of bridge vibration is detected =200Hz, then ≥480Hz.

[0181] Stress signal sampling:

[0182] Based on stress response time (Typical value for carbon steel: 0.05~0.2s), set the stress signal sampling frequency. :

[0183]

[0184] like =0.1s, then ≥100Hz.

[0185] Temperature and humidity sampling: fixed low-frequency sampling =0.1Hz, triggers adaptive upsampling (up to 1Hz) when there is a sudden change.

[0186] 3. Data Synchronization and Calibration

[0187] Clock synchronization: IEEE 1588 Precision Time Protocol (PTP) is adopted, and the clock deviation between nodes is ≤1ms; unconnected nodes are synchronized through GPS module (time synchronization accuracy ±100ns).

[0188] On-site calibration:

[0189] Displacement sensor: Baseline calibration is performed using a laser interferometer (such as Renishaw XL-80), with an error ≤ ±0.005mm;

[0190] Strain gauge: Apply a standard weight load (e.g., 1000N) and verify that the linearity error is ≤0.5%FS;

[0191] Vibration sensor: Frequency response curve calibration is performed using a standard vibration table (frequency 5~2000Hz, acceleration 1~50g).

[0192] Data preprocessing: An FIR low-pass filter (cutoff frequency = 0.8 × 10⁻⁶) is built into the sensor. / 2), suppressing high-frequency noise; performing temperature compensation on temperature and humidity data (compensation coefficient α). T =0.003% / ℃).

[0193] 4. Network fluctuation fault tolerance mechanism

[0194] Edge caching: Each sensing unit is configured with 8MB of local storage (circular queue), supporting resume transmission after network outage for ≥24 hours;

[0195] Add timestamps and CRC checks (polynomial 0x04C11DB7) to data packets.

[0196] Dynamic frequency reduction: When the network latency is >500ms, the sampling frequency is automatically reduced to 50% of the base value to ensure continuous acquisition of key parameters (stress, displacement).

[0197] Implementation Case: Monitoring of a Cross-Sea Bridge Tower Module

[0198] Sensor installation: 24 fiber optic strain gauges (bolted) and 32 triaxial accelerometers (magnetically fixed) are installed at the steel-concrete interface of the tower.

[0199] The node spacing is Δd = 2.5m (satisfying λ). max =7.5m constraint).

[0200] Sampling configuration: Vibration dominant frequency =12Hz (vortex vibration), setting =30Hz;

[0201] The stress response time Δt = 0.15s is set. =67Hz.

[0202] Implementation results: Data synchronization error ≤1.2ms (PTP protocol);

[0203] The completeness of stress acquisition under sudden wind load is 100%, and the error after temperature and humidity drift compensation is ≤0.3%RH.

[0204] It should be noted that the displacement resolution reaches 0.001 mm, and the stress sensitivity reaches 1. IP67 protection and dynamic frequency reduction enable the system to operate reliably in typhoon weather (wind speed ≥35m / s); the calibration cycle is extended to 6 months (the traditional solution requires 3 months), and labor costs are reduced by 50%.

[0205] S3. Construct a wireless mesh self-organizing network architecture and dynamically adjust the node transmit power. :

[0206]

[0207] in The path loss index is set to 2.5 for indoor use or 3.0 for outdoor use, depending on the environment type. Maximum node transmit power, ≤20dBm; The minimum signal-to-noise ratio required by the receiver. Let d be the noise power spectral density, and d be the straight-line distance between the transmitting and receiving nodes. For transmit antenna gain, For receiving antenna gain;

[0208] In step 3, a ZigBee 3.0 (short-range, low-power) or LoRaWAN (long-range, wide-coverage) protocol is used to network the edge computing gateway and the sensing unit. The network path is dynamically maintained through the AODV routing protocol to ensure that the packet loss rate is less than 1%.

[0209] 1. Communication Protocol and Network Topology Configuration

[0210] Protocol selection:

[0211] For short-distance scenarios (node ​​spacing ≤ 100m): ZigBee 3.0 protocol (2.4GHz band, 250kbps rate) is adopted, supporting low-power star / tree topologies;

[0212] Long-distance scenarios (node ​​spacing > 100m): LoRaWAN protocol (470MHz band, SF=12, bandwidth 125kHz) is adopted to support wide area Mesh networks.

[0213] Mesh networking rules:

[0214] Node role definition: EndDevice: only collects data, no routing function, sleep power consumption ≤10μA; Router: forwards data, dynamically maintains the neighbor table, maximum number of child nodes ≤15;

[0215] Coordinator: Connects to the cloud platform, is unique across the entire network, and is deployed in the building control center.

[0216] Network density control: Deploy 1 routing node per 100㎡, and the ratio of terminal nodes to routing nodes should be ≤5:1.

[0217] 2. Implementation of Dynamic Power Adjustment Algorithm

[0218] Power adjustment formula:

[0219]

[0220] Parameter definition:

[0221] Target signal-to-noise ratio (10dB indoors, 15dB outdoors);

[0222] Noise power spectral density (-174dBm / Hz + environmental interference correction value);

[0223] d: Node spacing (measured by RSSI, accuracy ±1m);

[0224] Antenna gain (2dBi for omnidirectional antenna, 8dBi for directional antenna);

[0225] Path loss index (2.5 indoors, 3.0 outdoors, 3.5 in environments with dense obstacles).

[0226] Dynamic adjustment logic:

[0227] Initial power: based on maximum power P max =20dBm to establish a connection;

[0228] Periodic optimization: Power is updated every 5 minutes and adjusted based on real-time channel conditions (SNR, packet loss rate). and ;

[0229] Error handling: If three consecutive transmissions fail, trigger power increment (in 2dBm steps) until P. max .

[0230] 3. Routing Protocols and Anti-interference Strategies

[0231] Routing protocol configuration: AODV protocol (AdhocOn-DemandDistanceVector):

[0232] Path discovery: Broadcast RREQ (RouteRequest) packets and select the path with the fewest hops and the highest SNR;

[0233] Maintenance mechanism: If the link is interrupted, the number of local repair attempts will be ≤3, and global rerouting will occur after the timeout.

[0234] Routing table update: Synchronize the status of neighboring nodes (signal strength, remaining battery power) every 30 seconds.

[0235] Anti-interference measures:

[0236] Adaptive frequency hopping: 16 channels are divided in the 2.4GHz band, and the channels are dynamically switched according to the Channel Busy / Idle Index (CBI);

[0237] Forward error correction: Reed-Solomon (255,223) encoding is used, with error correction capability ≥ 16 bytes / data packet;

[0238] Conflict avoidance: CSMA / CA mechanism, random backoff time t backoff ∈[0,15]×SlotTime (SlotTime=320μs).

[0239] 4. Network Performance Verification and Optimization

[0240] Packet loss rate test: Send 1000 test packets (128 bytes in length). Requirements: Packet loss rate ≤1% in indoor scenes (actual value 0.8%); Packet loss rate ≤3% in outdoor scenes (actual value 2.5%).

[0241] Energy management:

[0242] Node sleep rate ≥ 80% (average power consumption of ZigBee nodes ≤ 1mW);

[0243] Router node battery life ≥ 3 years (2 AA lithium batteries, 3400mAh).

[0244] Real-time verification: End-to-end data transmission latency ≤200ms (including edge computing processing time).

[0245] Implementation Case: A large commercial complex (building area of ​​200,000 square meters)

[0246] Network deployment: 120 ZigBee nodes (terminal:router = 4:1) and 6 LoRa gateways to cover the underground parking garage;

[0247] Dynamic power range: P t ∈[8dBm,20dBm], average power consumption 12dBm.

[0248] Interference resistance test: In areas with dense WiFi coverage (2.4GHz band CBI≥70%), the channel collision rate was reduced from 25% to 5% by frequency hopping; the number of data packet retransmissions was ≤2 times / hour.

[0249] Implementation results:

[0250] The network has an average packet loss rate of 0.9% and a maximum end-to-end latency of 180ms; the annual energy consumption of routing nodes is ≤15% of battery capacity.

[0251] It should be noted that dynamic power adjustment improves signal coverage to 99.5% (compared to ≤95% for traditional fixed power solutions); Mesh networks support automatic access for new nodes (registration time ≤10 seconds); and adaptive frequency hopping and error correction coding ensure a communication success rate of ≥98% in complex electromagnetic environments.

[0252] S4. Configure an adaptive Kalman filter algorithm in the edge computing gateway 102 to dynamically update the noise covariance matrix. and :

[0253] ,

[0254] Where λ∈[0.1,0.5], and SNR is the real-time signal-to-noise ratio;

[0255] In step 4, an entropy weight fusion algorithm is integrated to update the sensor weights every 10 seconds. Set up a circular data buffer queue (capacity ≥ 1 hour of raw data) to handle network interruption scenarios;

[0256] 1 Hardware Configuration and Interface Definition

[0257] Gateway hardware selection: Core processor: ARM Cortex-A72 quad-core (clock speed ≥ 1.8GHz, computing power ≥ 20KDMIPS);

[0258] Memory configuration: 4GB LPDDR4 (bandwidth 34.1GB / s), storage 64GB eMMC;

[0259] Communication interfaces: dual-band WiFi (2.4 / 5GHz), RS-485 industrial bus, LoRa module (SX1276 chip).

[0260] Peripheral interface management:

[0261] Sensor interfaces: Supports Modbus RTU (baud rate 9600~115200bps) and CAN2.0B (1Mbps).

[0262] Power management: Wide input voltage (DC9~36V), power consumption ≤5W (standby ≤0.5W).

[0263] 2. Implementation of Adaptive Kalman Filter Algorithm

[0264] Algorithm initialization: Define the state vector ,

[0265] Observation vector ;

[0266] Initial noise covariance:

[0267] ,

[0268] Dynamic noise adjustment:

[0269] Real-time signal-to-noise ratio calculation:

[0270]

[0271] Covariance update formula:

[0272] ,

[0273] Filtering execution process:

[0274] Prediction --> Update --> Output Clean Data

[0275] 3. Deployment of the entropy weight fusion algorithm

[0276] Weighting calculation rules:

[0277] Data standardization: Z-score standardization was performed on displacement, stress, vibration, and temperature / humidity data respectively;

[0278] Information entropy calculation:

[0279]

[0280] in For displacement, stress, vibration, temperature and humidity data, This represents the minimum values ​​of displacement, stress, vibration, temperature, and humidity data. This represents the maximum values ​​of displacement, stress, vibration, and temperature / humidity data.

[0281] Dynamic weight allocation:

[0282]

[0283] Fusion execution logic:

[0284] When the entropy value of a certain type of sensor When the value is greater than 0.9, a fault alarm is triggered and the data source is temporarily removed.

[0285] Fusion Output: ,

[0286] in For initial data, This is the data after fusion and output.

[0287] 4. Data caching and resume download after network outage

[0288] Caching mechanism: A circular queue is used to store raw data (capacity ≥ 1 hour, ≥ 360,000 records are required when the sampling rate is 100Hz); Data packet structure: timestamp (Unix milliseconds) + sensor ID (4 bytes) + data value (float32×4).

[0289] Network outage handling strategy:

[0290] Network status detection: Ping the cloud platform every 5 seconds; three consecutive timeouts are considered a network outage.

[0291] Degradation mode: Local storage for incremental data (overwrites the oldest data when storage is full); sampling rate for key parameters (stress, displacement) is reduced to 50%.

[0292] 5. Gateway verification test

[0293] Real-time performance test: Input step signal (displacement increases abruptly from 0 to 10mm), filter delay ≤20ms; average processing time of 10,000 filter iterations ≤15μs / item.

[0294] Accuracy verification:

[0295] Comparing the outputs of a high-precision oscilloscope (Keysight DSOX1204G) and a gateway, the requirements are:

[0296] Displacement error ≤ 0.02 mm (confidence level 95%);

[0297] Vibration frequency detection error ≤ 0.5Hz (50Hz reference signal).

[0298] Implementation Case: A Subway Tunnel Segment Monitoring Project

[0299] Gateway configuration:

[0300] Processor: Rockchip RK3399 (dual-core A72 + quad-core A53);

[0301] An adaptive Kalman filter (λ=0.3) is deployed, with an entropy weight fusion period Δt=8s.

[0302] Implementation results:

[0303] The vibration signal signal-to-noise ratio was improved from 12dB to 28dB;

[0304] Data integrity rate was 100% during a 12-hour internet outage.

[0305] The false alarm rate dropped from 9.7% for traditional gateways to 1.3%.

[0306] It should be noted that the 20ms latency meets the requirements for real-time monitoring of high-frequency vibration; the signal-to-noise ratio is improved by ≥15dB (measured average 18.6dB); offline transmission resumes after network outages and supports ≥72 hours of offline operation (1Hz sampling rate); edge computing reduces cloud data volume by 65% ​​and saves ≥120,000 yuan in annual bandwidth costs.

[0307] S5. Cloud Platform 103 deploys a hybrid health assessment model, optimizing the weights α of LSTM and Random Forest daily:

[0308] and ;

[0309] in, This indicates the actual health score;

[0310] A model for predicting interface performance degradation is used to predict lifetime.

[0311] In step 5, a time series database (InfluxDB) is built and stored according to the following rules:

[0312] Raw data: Retained for 30 days (sampling period ≤ 1 minute);

[0313] Feature data (mean, variance): retained for 5 years;

[0314] Hybrid health assessment model: Training an LSTM neural network (Input: Time series data, Output: ∈[0,1]); Simultaneously run the random forest model (input: feature vector, output: ∈[0,1]);

[0315] Interface performance degradation prediction model:

[0316] Deploy an improved ARIMA model with exogenous variables including the temperature-stress coupling factor C(T). k , ),in The standard deviation is denoted as .

[0317] 1. Hybrid Model Architecture Design

[0318] LSTM Neural Network Module:

[0319] Input: Standardized time series data X t ={x t−T+1 ,x t (Time window T = 24 hours, sampling interval 10 minutes);

[0320] Structure: 2-layer LSTM (128 units per layer, Dropout=0.2) + fully connected layer (output SLSTM∈[0,1]);

[0321] Training: The Adam optimizer was used (learning rate 0.001), and the loss function was weighted MAE.

[0322]

[0323] Random Forest module:

[0324] Input features:

[0325] Statistics: mean μ, variance σ 2 kurtosis K, peak factor ;

[0326] Environmental factors: moving average of temperature and humidity

[0327] Model configuration: 100 decision trees (maximum depth 10, minimum leaf samples 5), output S RF ∈[0,1].

[0328] Weight fusion mechanism:

[0329]

[0330] 2. Dynamic Weight Optimization Algorithm

[0331] Cross-validation process:

[0332] Data partitioning: Data from the previous 30 days is extracted at 0:00 each day and divided into training and validation sets in a 7:3 ratio;

[0333] Grid search: Traverse the grid α∈{0,0.1,...,1.0} and select the optimization weight α that minimizes the MSE of the validation set. opt :

[0334]

[0335] Constraints: If |α new α old If |>0.2, then smooth update of α is used. final =0.8α old +0.2α new ;

[0336] Where α new For the new weights, α old For the old weights, α final To smooth out the updated weights.

[0337] Online learning:

[0338] When a new failure mode occurs (actual S < 0.5 but predicted S > 0.7), incremental model training is triggered:

[0339] LSTM: Freeze the first layer, fine-tune the fully connected layer (learning rate 0.0001).

[0340] Random Forest: Add 10 trees (training only on new data).

[0341] 3 Improved ARIMA Prediction Model

[0342] Introduction of exogenous variables:

[0343] Define the temperature-stress coupling factor:

[0344] in =0.003 / ℃ is the material's temperature sensitivity coefficient. =20℃ is the reference temperature.

[0345] ARIMA(p,d,q) modeling: The difference order d is determined by the ADF test (to ensure the sequence is stationary);

[0346] Parameter selection: Search for p∈[0,5] and q∈[0,5] based on the AIC minimization criterion;

[0347] Improved equation:

[0348]

[0349] Remaining lifespan prediction: When health S < 0.6, initiate ARIMA rolling prediction (7-day step); Failure threshold setting: =0.3, remaining lifetime .

[0350] 4. Implementation Process

[0351] Data preparation: Receive filtered data from the edge gateway and align the timestamps at 5-minute intervals; perform missing value imputation (linear interpolation) and outlier removal (3σ principle).

[0352] Model training and deployment:

[0353] Daily task (executed from 00:00 to 02:00): Update LSTM and Random Forest model parameters; optimize weight α and reload the model;

[0354] Real-time tasks:

[0355] Health score S is calculated every 10 minutes; when S < 0.8, ARIMA lifespan prediction is triggered.

[0356] Implementation Case: Monitoring of the Core Tube of a Super High-Rise Building

[0357] Model configuration: LSTM input: 24-hour displacement time series (144 points);

[0358] Random Forest Features: Peak Factor C f =3.2, Temperature sliding average =28℃;

[0359] Initial weight α = 0.6, optimized α opt =0.73.

[0360] Implementation results:

[0361] Health assessment error: MAE=0.05 (single model LSTMMAE=0.08, RFMAE=0.07);

[0362] Remaining life prediction: 35 days' advance warning of fatigue failure of steel components (actual failure occurred on the 38th day after prediction);

[0363] Computing resource consumption: Daily average GPU utilization ≤15% (NVIDIA T4).

[0364] It should be noted that the hybrid model improves the accuracy of health scores to 93% (compared to ≤85% for a single model); daily optimization of weight α reduces the model's adaptation error to seasonal changes by 40%; the improved ARIMA controls the remaining life expectancy prediction error within ±10 days (90% confidence); and incremental model training reduces cloud computing costs by 50%.

[0365] S6. Configure hierarchical early warning rules in the visualization terminal 104:

[0366] Level 1 warning criteria: Health score S < 0.8S0 and degradation rate ;

[0367] Level 2 warning criteria: Health score S < 0.6S0 or dynamic threshold T k >3σ hist ;

[0368] Level 3 early warning condition: Predicted remaining lifespan L remain <30 days;

[0369] Where S0 is the initial health score, σ hist The historical standard deviation;

[0370] Step 6 involves overlaying real-time data (displacement, stress) and a health heatmap onto the BIM model; supporting historical data backtracking and fault event annotation; and triggering emergency response and pushing alarm information via SMS / email / API interface.

[0371] 1. Setting of Level-Based Early Warning Conditions

[0372] Level 1 Warning (Yellow, Mild Abnormality):

[0373] Triggering conditions:

[0374]

[0375] Threshold basis:

[0376] This serves as the initial health baseline (average value for the first 30 days after deployment).

[0377] Degradation rate threshold Based on the material fatigue curve (SN curve).

[0378] Response measures:

[0379] Automatically generate a preliminary diagnostic report;

[0380] Mark abnormal areas in the BIM model and notify the inspection personnel to check within 72 hours.

[0381] Level 2 Warning (Orange, Moderate Abnormality):

[0382] Triggering conditions:

[0383] or

[0384] Parameter description:

[0385] Historical standard deviation (calculated from long-term stable operating condition data);

[0386] Dynamic threshold ( ).

[0387] Response measures: Trigger fault source analysis; limit the load on the relevant area (shut down some equipment through the intelligent building system).

[0388] Level 3 Warning (Red, Severe Anomaly):

[0389] Triggering conditions:

[0390] Predicting remaining lifespan ;

[0391] Sudden large deformation ,in The yield strain of the material.

[0392] Response measures: Activate audible and visual alarms and evacuate personnel; automatically push to the emergency management platform (API interface complies with GB / T22239-2019).

[0393] 2. Alarm push notifications and visual configuration

[0394] Multi-channel alarm push notifications:

[0395] Priority division:

[0396] Warning level Push method Response time limit Level 1 SMS + Internal Work Order System ≤72 hours Level 2 Phone + Email ≤24 hours Level 3 Audible and visual alarms + emergency platform API ≤10 minutes

[0397] Log recording: All alarm events are stored on the blockchain (HyperledgerFabric) to ensure immutability.

[0398] Visual terminal design:

[0399] BIM Integration:

[0400] Health heatmap: color mapping S∈[0,1] (red→green);

[0401] Real-time data overlay: displacement vector arrows, stress cloud diagrams, and vibration spectrum diagrams.

[0402] Historical review: Slide the timeline to view data for any time period; supports fault event annotation and report export (PDF / A format).

[0403] 3. Dynamic threshold optimization and false alarm handling

[0404] Threshold adaptive adjustment:

[0405] Periodic calibration: every quarter based on the latest σ hist Update the Level 3 early warning threshold;

[0406] Machine learning assistance: Detecting outlier data using the Isolation Forest algorithm and dynamically adjusting k1 and β.

[0407] False alarm suppression mechanism:

[0408] Multi-source verification: When a single sensor triggers an alarm, data from at least two types of sensors must be cross-verified (e.g., displacement + vibration).

[0409] Delayed confirmation: An alarm is triggered only after three consecutive samples exceed the threshold (the sampling interval can be configured from 1 to 60 seconds).

[0410] 4. Verification Testing and Effect Evaluation

[0411] Simulation test: Injecting stepped degradation data (S linearly decreases from 1.0 to 0.4) verifies that the alarm triggering timing of each level meets expectations;

[0412] Simulate sudden impact The response time for a Level 3 alarm is required to be ≤5 seconds.

[0413] On-site testing:

[0414] In a steel structure factory, a bolt loosening fault was deliberately created to verify:

[0415] Level 1 alarm trigger time: 8 days after the start of degradation (S=0.78);

[0416] Level 2 alarm trigger time: Day 15 (S=0.57);

[0417] Fault location accuracy: 89.3%.

[0418] Implementation Case: Cable Stress Monitoring of a Cross-River Cable-Stayed Bridge

[0419] Early warning configuration: =0.95 (initial health) =0.12;

[0420] Dynamic threshold (24-hour sliding window).

[0421] Implementation results:

[0422] Level 1 Alarm: A slight slack in the stay cable was detected (S=0.76, degradation rate -0.06 / hour). Manual confirmation is required 24 hours after triggering.

[0423] Level 3 Alarm: During a typhoon, the cable force suddenly increases (Δε=5.8%), and traffic control will be initiated within 10 seconds;

[0424] False alarm rate: reduced from 12% in the traditional approach to 1.8%.

[0425] It should be noted that the three-level early warning system improves the response speed to major risks by 80% (the average delay of traditional single-level alarms is ≥30 minutes); the visualization terminal supports one-click generation of maintenance plans (CBR case reasoning technology).

[0426] S7. During the system integration and debugging phase, inject white noise to verify the filtering performance, requiring a signal-to-noise ratio improvement of ≥15dB, and test the sensor response delay of ≤100ms;

[0427] In step 7, a step signal of Δx=5mm is applied to simulate the interface displacement to verify that the sensor response delay is ≤100ms; white noise is injected to test the Kalman filter noise reduction effect (signal-to-noise ratio improvement ≥15dB); long-term stability test is performed by running continuously for 30 days, and the false alarm rate (≤2%) and false alarm rate (≤0.5%) are statistically analyzed.

[0428] 1. Benchmarking

[0429] Sensor response verification:

[0430] Step signal test: Apply a displacement step (e.g., 0→5mm→0) using a laser rangefinder (accuracy ±0.001mm) to verify the sensor response delay. :

[0431]

[0432] in For sensor response time, Used as the base time.

[0433] Force source test: Apply stepped load (step size 500N) using a standard hydraulic loading device to verify that the linearity error of the strain gauge is ≤0.5%FS.

[0434] Filtering performance test: Inject Gaussian white noise (SNR=5dB), and the required SNR after adaptive Kalman filtering is ≥20dB;

[0435] Vibration signal spectrum analysis: passband (0~1000Hz) fluctuation ≤±1dB.

[0436] Network performance verification:

[0437] End-to-end latency: When sending 1000 128-byte data packets, the average latency is ≤200ms (including edge computing time).

[0438] Network outage recovery test: Simulate a 24-hour network outage and verify that the data transmission integrity rate is 100%.

[0439] 2. Long-term stability test

[0440] False alarm / false negative rate statistics:

[0441] False Alarm Rate (FAR):

[0442] Missed Detection Rate (MDR):

[0443] in Number of false alarms Total number of alarms This represents the actual number of failures.

[0444] Environmental adaptability testing:

[0445] Temperature and humidity cycling: -40℃ (4h) → 85℃ (4h) cycled 20 times to verify sensor drift ≤0.1%FS;

[0446] Vibration durability: Random vibration test (5~500Hz, 0.1g² / Hz, 1 hour) was performed according to IEC60068-2-6 standard.

[0447] 3. On-site calibration and standardization

[0448] Displacement sensor calibration:

[0449] Tools: Laser interferometer (such as Renishaw XL-80) with a precision displacement stage;

[0450] Method: Calibrate at 1mm intervals within the range of 0~10mm, and the linear fitting residual is ≤±0.005mm.

[0451] Strain gauge calibration:

[0452] Equipment: Standard force sensor (HBMU10M, accuracy class 0.02) + loading frame;

[0453] Procedure: Apply a cyclic load of 0→1000N→0 and calculate the sensitivity coefficient. ;

[0454] Repeatability error ≤ 0.3%.

[0455] Vibration sensor frequency response calibration:

[0456] Apparatus: Electromagnetic vibration table (frequency range 5~2000Hz, acceleration 0.1~50g);

[0457] Specifications: Amplitude-frequency response fluctuation ≤ ±3% (reference frequency 50Hz); transverse sensitivity ratio ≤ 5%.

[0458] Implementation Case: Acceptance of Steel Structure Monitoring System for a High-Speed ​​Railway Station

[0459] Benchmarking:

[0460] Displacement step test: average delay 82ms, maximum residual 0.004mm;

[0461] White noise filtering: SNR increased from 8dB to 23dB.

[0462] Long-term test (60 days):

[0463] The false alarm rate was 1.7%, and the false negative rate was 0% (including 8 types of faults such as loose injection bolts and cracked welds).

[0464] The maximum temperature drift at the node is 0.08%RH / ℃.

[0465] On-site calibration:

[0466] The strain gauge sensitivity coefficient K = 2.03 / N, repeatability error 0.27%;

[0467] The frequency response curve of the vibration sensor conforms to the ISO16063-21 standard.

[0468] It should be noted that the system can operate continuously for 30 days without downtime, with an MTBF (Mean Time Between Failures) of ≥100,000 hours; the annual sensor drift is ≤0.1%, and the calibration cycle is extended to 12 months.

[0469] S8. Periodically update the model and calibration equipment, retrain the hybrid health assessment model every quarter, and calibrate the sensors every 6 months using a laser rangefinder and a standard force source.

[0470] In step 8, the hybrid health assessment model is retrained and updated quarterly, incorporating newly collected failure case data; the sampling frequency and transmission interval are dynamically adjusted based on node battery life (estimated ≥3 years) to optimize the network; when the failure probability... When the value is >0.7, a fault analysis report is automatically generated and pushed to the maintenance terminal for fault tracing.

[0471] 1. Iterative updates to the health assessment model

[0472] Incremental learning mechanism:

[0473] Data filtering: Collect data on newly added fault cases every quarter. <0.5 or Events >0.7 were removed, along with duplicate and noisy samples (data with a signal-to-noise ratio (SNR) <15dB).

[0474] Model fine-tuning:

[0475] LSTM: Preserve the bottom feature extraction layer (freeze the first 2 LSTM layers), only update the parameters of the fully connected layer (learning rate 0.0001);

[0476] Random Forest: Add decision trees (number = number of new fault categories × 5), maximum depth ≤ original model setting;

[0477] Validation process: Divide the training set and the test set in a 7:3 ratio, and require that the updated model improves its MAE on the test set by ≥5%.

[0478] Degradation prediction model optimization:

[0479] Exogenous variable extension: introduction of environmental corrosion factors (t is time, The average humidity. (for temperature)

[0480] Parameter recalibration: ARIMA model coefficients are updated every 6 months using maximum likelihood estimation (MLE). , Ensure that the prediction error is ≤10%.

[0481] 2. Dynamic optimization of wireless networks

[0482] Energy balance strategy:

[0483] Node sleep scheduling: based on remaining power Dynamically adjust wake-up cycle :

[0484]

[0485] in The total power consumption is the current status. The base period is used as the reference period.

[0486] Routing priority: Nodes with remaining power > 80% are given priority to undertake routing functions to prevent low-power nodes from failing prematurely.

[0487] Adaptive communication parameters: Channel switching rules: Dynamically select the optimal frequency band based on the Channel Quality Index (CQI).

[0488]

[0489] Power fine-tuning: Real-time updates of the path loss index α based on environmental changes (e.g., α during the rainy season). new =α+0.2).

[0490] 3. Fault tracing and equipment calibration

[0491] Multi-source data tracing analysis:

[0492] Spatiotemporal correlation matrix: Defines the spatiotemporal correlation coefficient between fault event Fj and sensor data Di:

[0493]

[0494] Failure probability calculation:

[0495]

[0496] Visual positioning: Highlighted in the BIM model For nodes with a confidence level greater than 0.7, a fault analysis report (including confidence level and maintenance recommendations) is generated.

[0497] Regular equipment calibration:

[0498] Displacement sensor: Full-field calibration is performed every 6 months using a laser tracker (such as Leica AT960). Error compensation formula:

[0499] ( (Temperature and humidity compensation coefficient)

[0500] Where T is temperature and RH is relative humidity.

[0501] Strain gauge: Zero drift is calibrated using the bridge balancing method, and the zero stability is required to be ≤0.1με / 24h.

[0502] 4. Maintenance effect verification

[0503] Model update validation:

[0504] Accuracy comparison: Compare the F1-score of the new and old models on the same test set, requiring an improvement of ≥3%;

[0505] Resource consumption: GPU memory usage increase ≤10% (NVIDIA T4 benchmark).

[0506] Network optimization verification:

[0507] Battery life: Average battery life ≥ 3 years (2 AA lithium-ion batteries, 3400mAh).

[0508] Packet loss rate: The optimized network packet loss rate is ≤1.5% (72-hour stress test).

[0509] Implementation Case: Maintenance of a Modular Building Complex for a Data Center

[0510] Model iteration:

[0511] Two new types of corrosion fault data (a total of 1200 records) were added. After the update, the MAE of the LSTM model decreased from 0.07 to 0.063; the ARIMA prediction error decreased from 12% to 8.5%.

[0512] Network optimization:

[0513] The node hibernation strategy reduces overall energy consumption by 35% and extends battery life to 3.8 years;

[0514] The rainy season channel switching strategy reduced the packet loss rate from 4.2% to 1.1%.

[0515] Calibration results: Annual drift of displacement sensor ≤0.008mm (from 0.02mm); Fault location accuracy improved from 85% to 93%.

[0516] It should be noted that the model is updated quarterly, which improves the accuracy of health assessment by an average of 7% per year; the dynamic network optimization strategy extends equipment life by more than 30%; the accuracy of fault tracing is >90%, and maintenance costs are reduced by 40%.

[0517] This embodiment also provides a computer device applicable to a building module data interface deployment method based on the Internet of Things (IoT), comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the building module data interface deployment method based on the IoT proposed in the above embodiment.

[0518] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0519] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a method for deploying a building module data interface based on the Internet of Things as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0520] In summary, this invention achieves a match between monitoring density and interface stress distribution through BIM model gradient analysis and optimized sensor placement, reducing blind zone coverage to ≤3% (compared to approximately 15% with traditional methods) and improving data acquisition resolution to 0.01mm (displacement) / 0.1MPa (stress). The edge computing gateway integrates an adaptive Kalman filter algorithm, reducing the filtering delay for high-frequency vibration signals (≥500Hz) to ≤20ms (compared to ≥200ms with traditional cloud processing), and improving the signal-to-noise ratio by ≥15dB. The dynamic threshold algorithm, combined with sliding window statistics and historical baselines, reduces the false alarm rate to ≤2% (compared to ≥15% with traditional fixed thresholds). Dynamic power adjustment technology based on Mesh self-organizing network reduces node deployment time by 60% (wired solutions require pre-installed cabling) and extends battery life to ≥3 years (traditional node life ≤1.5 years); entropy weight fusion algorithm reduces invalid data transmission by 40% and cloud platform storage costs by 35%; maintenance is planned in advance through the remaining life prediction model (error ≤10%), reducing manual inspection frequency by 50% and saving ≥200,000 RMB / 10,000 square meters in annual maintenance costs. The LSTM and random forest hybrid model achieves 92% accuracy in assessing interface health (single model ≤85%), especially improving the early detection rate of progressive damage (such as fatigue cracks) by 30%; multi-source spatiotemporal correlation analysis improves fault location accuracy to 88% (traditional methods ≤70%) and supports BIM model visualization annotation; quarterly model iteration optimization shortens the response time for identifying new fault modes to ≤24 hours (traditional systems require ≥1 week). This invention employs IP67 protective encapsulation and dynamic power adjustment technology, enabling the system to operate reliably in extreme environments such as -40℃ to 85℃ and humidity ≥95% (the failure probability of traditional equipment is ≥25%). It supports various modular structures such as prefabricated buildings, bridges, and pipe corridors, and its interface protocol is compatible with mainstream BIM software. This system achieves a warning response time of ≤5 seconds for sudden interface displacement exceeding limits (≥8mm); a bolt loosening fault tracing accuracy rate of 91.5%; and a 28.7% reduction in annual maintenance costs compared to previous versions.

[0521] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An intelligent monitoring system for building module data interfaces based on the Internet of Things, characterized in that, It consists of a distributed sensing unit (101), an edge computing gateway (102), a cloud platform (103), and a visualization terminal (104); The sensor placement coordinates P(x,y,z) of the distributed sensing unit (101) are based on the stress gradient field of the interface nodes analyzed by the BIM model. and strain energy density gradient field Determined by the gradient maximization algorithm, it satisfies: ， And the distance between adjacent sensors Δd ≤ λ max , where λ max To monitor the maximum wavelength of the vibration signal, The distributed sensing unit (101) collects displacement, stress, temperature, humidity, and vibration data of the building module interface through a multi-mode sensor array. Its sampling frequency must satisfy the extended conditions of the Nyquist sampling theorem. ， in The sampling frequency of the vibration signal. This refers to the minimum period of the vibration signal, i.e., the characteristic period of high-frequency vibration. This is the critical time interval for stress change, i.e., the elastic deformation response time of the material, to ensure the integrity of data acquisition; The edge computing gateway (102) communicates with the sensing unit via the LoRaWAN or ZigBee 3.0 protocol and is configured with a data cache queue to cope with network fluctuations; The system uses a dynamic threshold adjustment algorithm to calibrate abnormal thresholds in real time, enabling real-time monitoring of the connection status of building modules. The implementation of the dynamic threshold adjustment algorithm includes: A sliding window variance analysis was used, with the window length N dynamically set according to the sampling frequency of the vibration signal. ， in The threshold update frequency is set to 1Hz by default. The sampling frequency of the vibration signal; The system achieves real-time monitoring of the connection status of building modules by dynamically adjusting abnormal thresholds using a dynamic threshold adjustment algorithm. Calculate the mean of the sliding window statistic with standard deviation And satisfy: ， in Let k be the arithmetic mean of the data within the first N time windows at time point k. Let be the standard deviation of the data within the first N time windows at time point k, and β be the risk coefficient, which is obtained by training based on historical failure data and has a value range of [2.5, 3.5].

2. The intelligent monitoring system for building module data interfaces based on the Internet of Things as described in claim 1, characterized in that, The multi-mode sensor array employs an entropy weight fusion algorithm, specifically including: Information entropy was calculated for four types of sensor data: displacement, stress, temperature and humidity, and vibration. ， in The probability distribution of the data; Dynamically allocate fusion weights using the entropy method: ， Fusion weights The real-time update cycle is no more than 10 seconds, and when the entropy value of a certain type of sensor... A fault alarm is triggered when the value is greater than 0.

9.

3. The intelligent monitoring system for building module data interfaces based on the Internet of Things as described in claim 2, characterized in that, The adaptive Kalman filter algorithm of the edge computing gateway (102) is implemented as follows: Define state equations With observation equation , in Here is the state transition matrix. To control the input matrix, To control the input vector, For process noise, To observe the noise, The observation matrix; Based on real-time signal-to-noise ratio Adjustment process noise covariance and observation noise covariance : ， ， in The initial process noise covariance, For the initial observation noise covariance, For signal power, Let λ be the noise power and λ be the attenuation coefficient, with a value range of [0.1, 0.5].

4. The intelligent monitoring system for building module data interfaces based on the Internet of Things as described in claim 3, characterized in that, The hybrid health assessment model of the cloud platform (103) operates according to the following steps: The input to the LSTM neural network is time-series data {X} t Given a string t = 1, 2, ..., T, output the health score S. LSTM ∈[0,1]; The random forest model takes a feature vector {mean, variance, peak factor} as input and outputs a score S. RF ∈[0,1]; The final health score is obtained through weighted fusion: ， The weight α is optimized every 24 hours through cross-validation, with the optimization objective being to minimize the prediction error MSE.

5. A method for deploying building module data interfaces based on the Internet of Things (IoT), which is implemented based on the intelligent monitoring system for building module data interfaces based on the IoT as described in claim 4, characterized in that, Includes the following steps: S1. Stress gradient field analysis of interface nodes based on BIM model and strain energy density gradient field The sensor placement coordinates P(x,y,z) are determined using the gradient maximization algorithm, satisfying: ， And the distance between adjacent sensors Δd ≤ λ max , where λ max To monitor the maximum wavelength of the vibration signal; S2. Deploy a multi-mode sensor array, configure displacement gauges, strain gauges, temperature and humidity sensors, and vibration accelerometers, and set the sampling frequency according to the signal type: Vibration signal sampling frequency Stress signal sampling frequency , in The frequency of the vibration peak; S3. Construct a wireless mesh self-organizing network architecture and dynamically adjust the node transmit power. : ， in The path loss index is set to 2.5 for indoor use or 3.0 for outdoor use, depending on the environment type. Maximum node transmit power, ≤20dBm; The minimum signal-to-noise ratio required by the receiver. Let d be the noise power spectral density, and d be the straight-line distance between the transmitting and receiving nodes. For transmit antenna gain, For receiving antenna gain; S4. Configure an adaptive Kalman filter algorithm in the edge computing gateway (102) to dynamically update the noise covariance matrix. and : , ， Where λ∈[0.1,0.5], and SNR is the real-time signal-to-noise ratio; S5. Deploy a hybrid health assessment model on the cloud platform (103), and optimize the weights α of LSTM and random forest daily: and ; in, This indicates the actual health score; A model for predicting interface performance degradation is used to predict lifetime. S6. Configure hierarchical early warning rules in the visualization terminal (104): Level 1 warning criteria: Health score S < 0.8S0 and degradation rate ; Level 2 warning criteria: Health score S < 0.6S0 or abnormal threshold T k >3σ hist ; Level 3 early warning condition: Predicted remaining lifespan L remain <30 days; Where S0 is the initial health score, σ hist The historical standard deviation; S7. During the system integration and debugging phase, inject white noise to verify the filtering performance, requiring a signal-to-noise ratio improvement of ≥15dB, and test the sensor response delay of ≤100ms; S8. Periodically update the model and calibration equipment, retrain the hybrid health assessment model every quarter, and calibrate the sensors every 6 months using a laser rangefinder and a standard force source.

6. The method for deploying building module data interfaces based on the Internet of Things as described in claim 5, characterized in that, In step S2, a multi-mode sensor array is installed at the planned location, and displacement gauges, strain gauges, temperature and humidity sensors, and vibration accelerometers are configured and fixed by magnetic attraction or bolts; IP67 protection level encapsulation is adopted to ensure waterproof and shockproof performance; in step S3, ZigBee 3.0 short-range low power or LoRaWAN long-range wide coverage protocol is used to network between the edge computing gateway and the sensing unit, and the network path is dynamically maintained through AODV routing protocol to ensure that the packet loss rate is <1%.

7. The method for deploying building module data interfaces based on the Internet of Things as described in claim 5, characterized in that, In step S4, an entropy weight fusion algorithm is integrated to update the sensor weights every 10 seconds. ; Set up a circular data cache queue with a capacity of ≥1 hour of raw data to cope with network interruption scenarios; in step S6, overlay real-time data displacement, stress and health heatmaps on the BIM model; support historical data backtracking and fault event annotation; trigger emergency response and push alarm information via SMS / email / API interface.

8. The method for deploying building module data interfaces based on the Internet of Things as described in claim 5, characterized in that, In step S5, the time series database InfluxDB is built and stored according to the following rules: Raw data: retained for 30 days, sampling period ≤ 1 minute; Mean, variance: retained for 5 years; Hybrid health assessment model: Training an LSTM neural network: Input: Time series data; Output: ∈[0,1]; Simultaneously run the random forest model, input: feature vector, output: ∈[0,1]; Interface performance degradation prediction model: Deploy an improved ARIMA model with exogenous variables including the temperature-stress coupling factor C(T). k , ),in The standard deviation is denoted as .

9. The method for deploying building module data interfaces based on the Internet of Things as described in claim 5, characterized in that, In step S7, a step signal of Δx=5mm is applied to simulate interface displacement to verify that the sensor response delay is ≤100ms; white noise is injected to test the Kalman filter noise reduction effect; long-term stability testing is conducted by running continuously for 30 days, with a false alarm rate ≤2% and a false alarm rate ≤0.5%; in step S8, the hybrid health assessment model is retrained quarterly for model updates, incorporating newly collected fault case data; the sampling frequency and transmission interval are dynamically adjusted according to the node battery life to optimize the network; when the fault probability... When the value is >0.7, a fault analysis report is automatically generated and pushed to the maintenance terminal for fault tracing.

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