A municipal road and bridge structure health monitoring data acquisition system

Abstract

The application relates to the technical field of structural health monitoring, and discloses a municipal road and bridge structural health monitoring data collection system, which comprises a perception and decision layer and a cloud service and evolution layer; a plurality of sensor nodes of the perception and decision layer are embedded into the same structural modal field model and run an asynchronous event consensus protocol; after event consensus is reached through physical law verification based on the SMF model between the nodes, the event data packets are collected and uploaded in cooperation; the cloud service and evolution layer receives the data packets, identifies actual modal parameters online, generates an SMF differential update packet when model drift is monitored, and sends the SMF differential update packet to the nodes, so that adaptive updating of the model is realized. Through the mechanism of front-end intelligent consensus and cloud closed-loop evolution, the problems of high power consumption, data redundancy and poor anti-interference capability of the traditional monitoring system are solved, and the data quality and the effectiveness of long-term monitoring of the system are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of structural health monitoring technology, specifically to a data acquisition system for monitoring the structural health of municipal roads and bridges. Background Technology

[0002] As critical infrastructure for urban transportation, the real-time monitoring of the structural safety and service status of municipal roads and bridges is of paramount importance. Currently, bridge structural health monitoring systems typically deploy numerous sensors on bridges to collect data on physical quantities such as vibration and strain. However, existing data acquisition strategies face significant challenges in practical applications.

[0003] A commonly used strategy is continuous high-frequency sampling, but this generates massive amounts of redundant data, most of which only reflects the normal operating status of the structure or environmental noise, resulting in extremely low effective information density. This approach not only places extremely high demands on data storage and processing capabilities but also leads to huge energy consumption and communication bandwidth burdens on sensor nodes, making it particularly unsuitable for wireless monitoring systems that require long-term, low-power operation.

[0004] To reduce power consumption, another strategy is to use a simple amplitude threshold triggering mechanism. While this mechanism avoids continuous data acquisition, its judgment criteria are too singular, making it highly susceptible to unstructured local disturbances, such as instantaneous vehicle impacts and environmental noise from wind and rain, leading to frequent false alarms and invalid wake-ups. These false alarms also collect and transmit large amounts of useless data, failing to fundamentally solve the problem of data validity. Furthermore, most existing monitoring systems operate based on a fixed structural dynamics model established at the initial deployment stage, while the physical characteristics of bridges slowly evolve with material aging, damage accumulation, and environmental changes. This static model cannot adapt to long-term changes in structural conditions, causing the accuracy and reliability of the monitoring system to gradually decline over time. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a data acquisition system for monitoring the structural health of municipal roads and bridges. This system solves the problems commonly found in existing bridge monitoring systems, such as high power consumption, frequent false alarms, and the inability to adapt to long-term structural state evolution due to the lack of intelligence in data acquisition strategies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a municipal road bridge structural health monitoring data acquisition system, comprising: The perception and decision-making layer is deployed on the bridge structure. The perception and decision-making layer includes a plurality of sensor nodes, and each sensor node is embedded with the same structural modal field model that describes the overall dynamic characteristics of the bridge structure. The cloud service and evolution layer communicates and connects with the perception and decision-making layer. The sensor node is configured to: run an asynchronous event consensus protocol, perform physical law verification based on the SMF model among neighboring sensor nodes through the AEC protocol to achieve consensus on structural events, and perform collaborative data collection only after achieving event consensus, encapsulate the collected data into event data packets and upload them to the cloud service and evolution layer; The cloud service and evolution layer are configured to: receive the event data packet, use it to identify the current actual modal parameters of the bridge structure online, generate an SMF differential update package for updating the SMF model based on the actual modal parameters, and send the SMF differential update package to the sensor node to complete the adaptive update of the SMF model.

[0007] Preferably, the sensor node is configured to trigger the asynchronous event consensus protocol when the detected structural response signal exceeds a dynamic threshold.

[0008] Preferably, the physical law verification based on the SMF model includes: A query is initiated by an initiating node and sent to the verifying nodes in its neighborhood. The verification node calculates the theoretical response prediction value based on the response of the initiating node according to the mode coordinate values ​​in the SMF model stored locally. By comparing the actual response of the verification node with the theoretical response prediction value, it is determined whether the response relationship conforms to the physical laws described by the SMF model.

[0009] Preferably, reaching a consensus on structural events includes: After determining that the response relationship conforms to the physical laws described by the SMF model, the verification node broadcasts a confirmation vote; Within a preset consensus time window, when the received confirmation votes meet the preset consensus achievement conditions, the confirmation event consensus is achieved.

[0010] Preferably, the cloud service and evolution layer are configured to compare the actual modal parameters identified online with the SMF model parameters currently deployed in the sensor nodes to monitor the degree of model drift.

[0011] Preferably, the cloud service and evolution layer are configured to generate the SMF differential update package when the model drift exceeds a preset update threshold; the SMF differential update package only contains the parameter changes of the SMF model.

[0012] Preferably, the monitoring of the model drift is achieved by quantifying it through the calculation of modal confidence criteria and frequency deviation index.

[0013] Preferably, each of the sensor nodes in the system includes: Storage unit for storing the SMF model; A processing unit, connected to the storage unit, is used to execute the asynchronous event consensus protocol; A communication unit is used for data communication between the sensor nodes and with the cloud service and evolution layer.

[0014] Preferably, it further includes a network transport layer, the network transport layer comprising: An inter-node communication network is used to carry the challenge and voting signaling during the execution of the asynchronous event consensus protocol; A remote backhaul network is used to transmit the event data packets and the SMF differential update packets between the perception and decision-making layer and the cloud service and evolution layer.

[0015] A method for collecting structural health monitoring data for municipal road bridges includes the following steps: S1: Embed the same structural modal field model in multiple sensor nodes deployed on the bridge structure; S2: By running an asynchronous event consensus protocol through the sensor nodes, physical law verification based on the SMF model is performed among neighboring sensor nodes to achieve consensus on structural events; S3: Only after reaching an event consensus will the sensor nodes that participated in the consensus perform collaborative data acquisition and encapsulate the acquired data into an event data packet for uploading; S4: Receive the event data packet in the cloud and use it to identify the current actual modal parameters of the bridge structure online; S5: Generate an SMF differential update package for updating the SMF model based on the actual modal parameters, and send the SMF differential update package to the sensor node to complete the adaptive update of the SMF model.

[0016] This invention provides a data acquisition system for monitoring the structural health of municipal roads and bridges. It has the following beneficial effects: 1. This invention utilizes an event-driven and asynchronous event consensus protocol, enabling sensor nodes to remain in a low-power listening state most of the time. Only when a node's response exceeds a dynamic threshold and is confirmed as a genuine structural event through physical verification based on a structural modal field model among neighboring nodes, does the system perform high-fidelity data acquisition and uploading. This mechanism fundamentally filters out invalid data, avoiding the waste of energy and communication bandwidth caused by continuous acquisition or false alarms.

[0017] 2. This invention utilizes an SMF model embedded in each node to innovate the traditional single-point amplitude triggering mechanism into a multi-point event consensus based on the overall dynamic behavior of the structure. This physical law verification can effectively distinguish between global, highly correlated responses caused by actual structural vibrations and non-correlated responses caused by environmental noise, local impacts, etc., thereby greatly suppressing the false alarm rate, ensuring the authenticity and high signal-to-noise ratio of the collected data, and providing a high-quality data foundation for subsequent structural state assessment.

[0018] 3. This invention constructs a closed-loop adaptive system from front-end perception to cloud-based evolution. The cloud service and evolution layer utilizes collected high-fidelity event data to identify the current actual modal parameters of the bridge structure online and compares them with the SMF model deployed at the front-end nodes. Once model drift caused by factors such as structural aging and damage accumulation is detected, the system generates an SMF differential update package and sends it to the front-end nodes. This mechanism ensures long-term consistency between the monitoring model and the actual physical state of the structure, significantly improving the reliability and effectiveness of the monitoring system throughout its entire lifecycle. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see the appendix Figure 1 A municipal road and bridge structural health monitoring data acquisition system, comprising: The perception and decision-making layer is deployed on the bridge structure. The perception and decision-making layer includes multiple sensor nodes, each of which is embedded with the same structural modal field model that describes the overall dynamic characteristics of the bridge structure. The cloud service and evolution layer communicates and connects with the perception and decision-making layer; The sensor nodes are configured to run an asynchronous event consensus protocol, perform physical law verification based on the SMF model among neighboring sensor nodes through the AEC protocol to reach a consensus on structural events, and only perform collaborative data collection after reaching event consensus, encapsulate the collected data into event data packets and upload them to the cloud service and evolution layer. The cloud service and evolution layer are configured to: receive event data packets, use them to identify the current actual modal parameters of the bridge structure online, generate SMF differential update packets for updating the SMF model based on the actual modal parameters, and send the SMF differential update packets to the sensor nodes to complete the adaptive update of the SMF model. The network transport layer includes: The inter-node communication network is used to carry the challenge and voting signaling during the execution of the asynchronous event consensus protocol; The remote backhaul network is used to transmit event data packets and SMF differential update packets between the perception and decision-making layer and the cloud service and evolution layer.

[0022] The perception and decision-making layer is physically deployed on the monitored bridge structure and consists of multiple sensor nodes. This layer is the front-end execution unit of this invention, and its core function is to embed a structural modal field model describing the overall dynamic characteristics of the structure within each sensor node and run an asynchronous event consensus protocol. Through this protocol, the multiple sensor nodes can autonomously and collaboratively verify the physical laws of the structural response, thereby identifying real structural events and collaboratively capturing high-fidelity data only when an event occurs.

[0023] The network transport layer provides data communication support for the system. Specifically, this layer includes an inter-node communication network for low-latency, low-power communication between sensor nodes, primarily carrying challenge and voting signaling during the AEC protocol execution process. The network transport layer also includes a remote backhaul network for transmitting event data packets captured by the perception and decision layers to the cloud. This network is also responsible for distributing model update instructions generated in the cloud to the sensor nodes.

[0024] The cloud service and evolution layer is the central processing unit of the system, deployed on a remote server. This layer is responsible for receiving and parsing high-information-density event data packets uploaded by the perception and decision-making layers. Based on this data, the cloud service and evolution layer performs in-depth assessment and analysis of the structural health status. Furthermore, this layer runs an online modal field evolution engine, continuously monitoring and calibrating the accuracy of the SMF model currently deployed at the front end using real event data, and proactively generating update instructions to drive the adaptive evolution of the entire perception and decision-making layer.

[0025] The overall workflow of the system in this embodiment of the invention is as follows: First, in the system initialization phase, an initial structural modal field model is generated by finite element analysis of the bridge structure or on-site environmental excitation test, and the parameters of the model are embedded into all sensor nodes deployed at each measuring point of the bridge.

[0026] During system operation, each sensor node operates in a low-power listening state, continuously monitoring the structural response. When the response data detected by any sensor node exceeds a dynamically set threshold, the node is awakened and triggers the asynchronous event consensus protocol. This dynamic threshold... The calculation method is as follows: ; in, For dynamic wake-up threshold, This represents the historical average of the response signal over a certain period. is the historical standard deviation of the response signal during this period, and k is a coefficient preset according to the monitoring sensitivity requirements.

[0027] Upon triggering the AEC protocol, the awakened sensor nodes will challenge their physically neighboring nodes. Nodes receiving challenges will use their locally stored SMF models to verify whether the response relationships between multiple nodes conform to the inherent vibration modes of the structure. If, within the local network, the response relationships of a majority of nodes pass the modal verification, event consensus is reached, confirming that a genuine structural event has occurred.

[0028] Once the event is confirmed, all participating sensor nodes will collaboratively perform a high-frequency synchronous data acquisition, and aggregate and encapsulate the acquired data into an event data packet. This data packet is then uploaded to the cloud service and evolution layer via a remote backhaul network.

[0029] Upon receiving the event data packet, the cloud service and evolution layer utilize it for structural health assessment. Simultaneously, it uses this high-quality data as input to re-identify the actual modal parameters of the structure online. By comparing the newly identified parameters with the SMF model parameters currently deployed within the sensor nodes, it determines whether the model has drifted due to structural state changes or environmental factors. If significant drift is detected, the cloud service and evolution layer generates a lightweight SMF differential update packet and distributes it to all sensor nodes via the remote backhaul network. Upon receiving the update packet, each node updates its SMF model locally. This process constitutes a complete, data-driven, adaptive closed loop for the system.

[0030] Please see the appendix Figure 2 A method for collecting structural health monitoring data for municipal roads and bridges, comprising the following steps: S1: Embed the same structural modal field model in multiple sensor nodes deployed on the bridge structure; This step is the system initialization phase. First, by performing high-fidelity finite element analysis on the target bridge structure or by conducting initial field modal tests, its initial, several low-order main modal parameters are obtained, including the natural frequencies, damping ratios, and mode shape vectors of each mode.

[0031] The structural modal field model is essentially a lightweight data structure, the core of which is a mapping table. This table records the unique identifier of each sensor node and its corresponding normalized coordinate values ​​in the vectors of each major mode shape.

[0032] After generating the SMF model, it is burned into the non-volatile storage units of all sensor nodes deployed on the bridge structure via wireless or wired means. This operation ensures that every node in the network has an identical physical rulebook describing the overall dynamic characteristics of the structure.

[0033] S2: By running an asynchronous event consensus protocol through sensor nodes, physical law verification based on the SMF model is performed among neighboring sensor nodes to achieve consensus on structural events; After initialization, all sensor nodes enter a low-power continuous listening state. When the energy or amplitude of the structural response signal detected by any sensor node (hereinafter referred to as the initiating node) exceeds a dynamically adjusted threshold, the node is awakened and triggers the asynchronous event consensus protocol.

[0034] The specific implementation process of the AEC agreement is as follows: a) Initiating a challenge: The initiating node i broadcasts a challenge signaling message containing its own ID, the trigger time, and the response value at that time to its neighboring nodes within its communication range.

[0035] b) Neighborhood verification: After receiving the challenge signal, any node j in the neighborhood (hereinafter referred to as the verification node) extracts the mode shape coordinates of the initiating node i and itself under a certain key mode from its locally stored SMF model.

[0036] c) Physical law verification: Based on structural dynamics theory, the verification node j uses the stored mode shape coordinates to convert the actual response of the initiating node i according to the coordinate ratio of the two nodes under that mode shape, thereby obtaining the theoretical response prediction value of the verification node j itself. Subsequently, this prediction value is compared with the actual response value measured at the same moment.

[0037] d) Voting and Consensus: If the relative error between the predicted value and the actual measured value is less than a preset verification threshold, such as 5%, it indicates that the response relationship between the two nodes conforms to the physical laws described by the SMF model, and the verification node j will broadcast a confirmation vote. If the error is greater than the threshold, it is considered that the trigger may be a local interference, and the verification node remains silent. The initiating node collects votes within a preset consensus time window. When the number of received confirmation votes reaches the preset consensus achievement condition, such as at least two neighboring nodes confirming, the event consensus is achieved.

[0038] S3: Only after reaching an event consensus will the participating sensor nodes perform collaborative data collection and encapsulate the collected data into an event data packet for uploading; Once consensus is reached, it proves that the trigger originated from a real event that caused a structurally holistic response. At this point, the initiating node and all the verification nodes that cast confirmation votes, together forming a consensus node assembly, immediately initiate a high-frequency synchronous data acquisition mode. They will collect complete, high-fidelity response timeline data from a short period before the trigger to a period after the trigger.

[0039] After data collection is complete, a master node, typically the initiating node, is responsible for collecting the synchronization timeline data of all members within the consensus node set. This multi-channel data, along with metadata such as the event timestamp and the list of participating node IDs, is then encapsulated into a structured event data packet. Subsequently, this event data packet is uploaded to the cloud service and evolution layer via a remote backhaul network, such as 4G / 5G.

[0040] S4: Receive event data packets in the cloud and use them to identify the current actual modal parameters of the bridge structure online; After receiving event data packets, the cloud service and evolution layer servers parse them and extract the time histories of multi-channel synchronous vibration responses. Since these data represent real structural responses verified by front-end physical laws, they have a high signal-to-noise ratio and are ideally suited for running online modal parameter identification algorithms.

[0041] In this embodiment, the cloud can use output modal analysis methods such as random subspace identification to process the multi-channel time history data, thereby identifying the actual modal parameters of the bridge structure in the current state, including the actual natural frequencies, damping ratios, and mode shape vectors. S5: Generate an SMF differential update package based on the actual modal parameters to update the SMF model, and send the SMF differential update package to the sensor node to complete the adaptive update of the SMF model; The cloud compares the newly identified actual modal parameters with the SMF model parameters currently deployed in the sensor nodes to quantify the degree of model drift. Comparison metrics may include frequency deviation and modal confidence criterion values.

[0042] When the detected model drift exceeds a preset update threshold—for example, a change in a certain frequency exceeding 5% or a MAC value below 0.9—it indicates that the bridge structural characteristics have changed significantly, requiring an update to the front-end SMF model. In this case, the cloud service and evolution layer do not generate a complete model file, but instead generate an SMF differential update package. This update package only contains the changed parameters and their new values.

[0043] Finally, the cloud broadcasts or distributes this lightweight SMF differential update package to all sensor nodes in the perception and decision-making layers via a remote backhaul network. Upon receiving the update package, each node parses its contents and overwrites the corresponding old parameters of the SMF model in its local storage with the new parameters. This completes a full closed-loop adaptive update process. All sensor nodes will then perform the next round of event monitoring and consensus based on the updated, more accurate SMF model, thus ensuring the long-term effectiveness and accuracy of the entire monitoring system.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A municipal road bridge structure health monitoring data acquisition system, characterized in that, include: The perception and decision-making layer is deployed on the bridge structure. The perception and decision-making layer includes a plurality of sensor nodes, and each sensor node is embedded with the same structural modal field model that describes the overall dynamic characteristics of the bridge structure. The cloud service and evolution layer communicates and connects with the perception and decision-making layer. The sensor node is configured to: run an asynchronous event consensus protocol, perform physical law verification based on the structural modal field model among neighboring sensor nodes through the asynchronous event consensus protocol to achieve consensus on structural events, and perform collaborative data collection only after achieving event consensus, encapsulate the collected data into event data packets and upload them to the cloud service and evolution layer; The cloud service and evolution layer are configured to: receive the event data packet, use it to identify the current actual modal parameters of the bridge structure online, generate a structural modal field model differential update package for updating the structural modal field model based on the actual modal parameters, and send the structural modal field model differential update package to the sensor node to complete the adaptive update of the structural modal field model; The physical law verification based on the structural modal field model includes: A query is initiated by an initiating node and sent to the verifying nodes in its neighborhood. The verification node calculates the theoretical response prediction value based on the response of the initiating node according to the mode coordinate values ​​in the locally stored structural modal field model. By comparing the actual response of the verification node with the theoretical response prediction value, it is determined whether the response relationship conforms to the physical laws described by the structural modal field model.

2. The municipal road and bridge structure health monitoring data collection system according to claim 1, characterized in that, The sensor node is configured to trigger the asynchronous event consensus protocol when the detected structural response signal exceeds a dynamic threshold.

3. The municipal road and bridge structure health monitoring data collection system according to claim 1, characterized in that, The consensus reached on structural events includes: After determining that the response relationship conforms to the physical laws described by the structural modal field model, the verification node broadcasts a confirmation vote; Within a preset consensus time window, when the received confirmation votes meet the preset consensus achievement conditions, the confirmation event consensus is achieved.

4. The municipal road bridge structural health monitoring data acquisition system according to claim 1, characterized in that, The cloud service and evolution layer are configured to compare the actual modal parameters identified online with the structural modal field model parameters currently deployed within the sensor nodes to monitor the degree of model drift.

5. A municipal road bridge structural health monitoring data acquisition system according to claim 4, characterized in that, The cloud service and evolution layer are configured to generate a structural modal field model differential update package when the model drift exceeds a preset update threshold; the structural modal field model differential update package only contains the parameter changes of the structural modal field model.

6. The municipal road bridge structural health monitoring data acquisition system according to claim 4, characterized in that, The monitoring of the model drift is achieved by quantifying it through the calculation of modal confidence criteria and frequency deviation index.

7. The municipal road bridge structural health monitoring data acquisition system according to claim 1, characterized in that, Each of the sensor nodes in the system includes: Storage unit, used to store the structural modal field model; A processing unit, connected to the storage unit, is used to execute the asynchronous event consensus protocol; A communication unit is used for data communication between the sensor nodes and with the cloud service and evolution layer.

8. The municipal road bridge structural health monitoring data acquisition system according to claim 1, characterized in that, It also includes a network transport layer, which includes: An inter-node communication network is used to carry the challenge and voting signaling during the execution of the asynchronous event consensus protocol; A remote backhaul network is used to transmit the event data packets and the structural modal field model differential update packets between the perception and decision-making layer and the cloud service and evolution layer.

9. A method for collecting structural health monitoring data for municipal roads and bridges, used in a structural health monitoring data collection system for municipal roads and bridges according to any one of claims 1-8, characterized in that, Includes the following steps: S1: Embed the same structural modal field model in multiple sensor nodes deployed on the bridge structure; S2: By running an asynchronous event consensus protocol through the sensor nodes, physical law verification based on the structural modal field model is performed among neighboring sensor nodes to achieve consensus on structural events; S3: Only after reaching an event consensus will the sensor nodes that participated in the consensus perform collaborative data acquisition and encapsulate the acquired data into an event data packet for uploading; S4: Receive the event data packet in the cloud and use it to identify the current actual modal parameters of the bridge structure online; S5: Generate a structural modal field model differential update package based on the actual modal parameters to update the structural modal field model, and send the structural modal field model differential update package to the sensor node to complete the adaptive update of the structural modal field model.

Images