Infrastructure safety detection system and method based on moire fringes

Abstract

The invention discloses an infrastructure safety detection system and method based on moire fringes, and relates to the technical field of infrastructure safety monitoring, the system comprises a sensing node, a cloud server and an intelligent early warning module, the sensing node is composed of a double-grating structure, a monochromatic light source and the like, and the reference grating and the sensing grating are respectively fixed at a stable part and a displacement sensitive part of the infrastructure. During detection, the monochromatic light source irradiates the double gratings to generate moire fringes, the moire fringes are collected by the camera, displacement data are calculated by the processor and then uploaded to the cloud, the cloud analyzes structural performance by combining a finite element model, and the intelligent early warning module triggers an alarm according to a preset rule. The system achieves high-precision non-contact measurement, has the advantages of being waterproof, dustproof, high in weather resistance and the like, can be matched with various infrastructures such as bridges and ancient buildings, supports long-term automatic monitoring, and effectively overcomes the defects of a traditional measurement method.

Description

Technical Field

[0001] This invention relates to the field of infrastructure safety monitoring technology, specifically an infrastructure safety detection system and method based on moiré patterns. Background Technology

[0002] The safe and stable operation of infrastructure is crucial to economic and social development. If hidden dangers such as bridge cracking, damage to ancient buildings, landslides, and tunnel deformation are not monitored and warned of in a timely manner, they may lead to serious safety accidents. Accurate measurement of minute displacements and deformations is the core of infrastructure safety monitoring. Traditional measurement methods have many limitations: contact measurements introduce additional stress, affecting accuracy and potentially damaging special structures such as cultural relics; GNSS measurements have limited accuracy in areas with signal obstruction, such as deep valleys and forests; total station measurements are greatly affected by weather and cannot achieve continuous automated monitoring; optical measurement equipment such as Michelson interferometers are sensitive to environmental vibrations and are costly, making large-scale outdoor application difficult.

[0003] Moiré fringe technology, with its optical magnification effect and high sensitivity, shows promise in the field of micro-displacement measurement. When two gratings move relative to each other, the superimposed moiré fringes change significantly, magnifying sub-millimeter deformations into easily detectable fringe movements. Current moiré fringe-based measurement techniques are mostly used in laboratory environments and still have shortcomings in long-term stable outdoor operation, multi-scenario adaptability, and intelligent data analysis, making it difficult to meet the actual needs of infrastructure safety monitoring.

[0004] Based on this, an infrastructure security inspection system and method based on moiré stripes are now provided. Summary of the Invention

[0005] The purpose of this invention is to provide an infrastructure safety inspection system and method based on moiré patterns to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An infrastructure security detection system based on moiré stripes includes several sensor nodes, a cloud server, and an intelligent early warning module; The sensing node includes a sealed housing, a dual-grating structure, a monochromatic light source, a diffused screen, a miniature industrial camera, an embedded processor, and a wireless communication module. The dual-grating structure consists of a reference grating and a sensing grating. The reference grating is fixed to a stable part of the infrastructure, and the sensing grating is fixed to a displacement-sensitive part of the infrastructure. The monochromatic light source, dual grating structure, diffused screen, and miniature industrial camera are arranged sequentially along the optical path. The miniature industrial camera is electrically connected to the embedded processor, which communicates with the cloud server through a wireless communication module. The intelligent early warning module is connected to the cloud server.

[0007] Based on the above technical solutions, the present invention also provides the following optional technical solutions: In one alternative: the sealed housing is designed to be waterproof and dustproof, and the wireless communication module is a 4G / 5G module or a WIFI module.

[0008] In one alternative: the dual grating structures have the same grating constant and the grating pitch is 0.5-1mm; the monochromatic light source is a laser or an LED light source, and the wavelength of the laser is 632nm.

[0009] In one alternative: the embedded processor is configured with an image processing algorithm for identifying the center of the moiré fringe, calculating the fringe spacing and position offset, and calculating the linear displacement of the sensing grating relative to the reference grating in the X and Y directions and the rotation angle around the Z axis according to the geometric optics formula.

[0010] In one alternative: the cloud server integrates a finite element model, which is used to correlate and compare the geometric displacement data obtained by optical measurement with the structural mechanical state data; The cloud server also has the functions of multi-node data time synchronization and spatial correlation analysis, and can reconstruct a three-dimensional displacement field.

[0011] In one alternative: the intelligent early warning module is preset with displacement thresholds and abnormal trend judgment rules. When the monitored value exceeds the threshold or the displacement development trend is abnormal, an alarm is automatically triggered and sent to relevant personnel.

[0012] A method for infrastructure security inspection based on moiré patterns includes the following steps: S1: Based on the type of object being monitored, reference gratings and sensing gratings are installed at stable and displacement-sensitive parts of the infrastructure, respectively, to form sensing nodes and construct a monitoring network; S2: Moiré fringes generated by illuminating the dual grating structure with a monochromatic light source are imaged on a diffuse screen. A miniature industrial camera periodically acquires the fringe images and transmits them to the embedded processor. S3: The embedded processor analyzes image data using image processing algorithms, calculates precise displacement information, and uploads it to the cloud server via a wireless communication module. S4: The cloud server performs synchronous processing and correlation analysis on data from multiple nodes, and combines the finite element model to achieve structural performance evaluation; S5: The intelligent early warning module monitors data in real time and triggers an alarm when the early warning conditions are met.

[0013] In one alternative: the objects to be detected in step S1 include bridges, ancient buildings, landslides, settlement areas, tunnels, and underground works; For bridge inspection, sensor nodes are deployed at key monitoring sections such as mid-span, quarter-span, and pier top; For the inspection of ancient buildings, the sensor nodes are miniaturized and concealed and installed on both sides of the cracks and in key stress areas; For geological disaster detection, sensor nodes are combined with inclinometer tubes and deep anchoring points to form an integrated monitoring profile of the surface and deep areas; For tunnel inspection, sensor nodes are placed at the joints of the lining segments and at potentially deformable sections.

[0014] In one alternative: the image processing algorithm in step S3 includes image grayscale processing, stripe recognition and fitting, and pixel calibration and conversion steps, with the actual pixel size calibrated to 16.036μm.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: Based on the optical magnification effect of moiré fringes, this invention achieves accurate measurement of linear displacement of 0.01 mm and angular displacement of 0.016°, with submicron-level detection potential, meeting the needs of monitoring minute deformations in infrastructure.

[0016] This invention employs a non-contact measurement method, avoiding the additional stress caused by contact measurement, and the installation of the sensing nodes does not affect the appearance of the infrastructure (especially suitable for ancient buildings and other cultural relic protection scenarios), with no risk of damage.

[0017] The sensing node of this invention adopts a waterproof, dustproof, and weather-resistant design, and is resistant to electromagnetic interference. It can work stably for a long time in complex environments such as outdoors and underground. It is not limited by factors such as GNSS signal obstruction and weather conditions, thus making up for the environmental limitations of traditional measurement methods.

[0018] This invention enables automatic data acquisition, automatic calculation, wireless transmission, and intelligent early warning, requiring no manual intervention and allowing for long-term continuous monitoring; the combination of spatial correlation analysis and finite element model on the cloud server enables in-depth evaluation of structural performance.

[0019] This invention, through its flexible deployment methods and node design, can be adapted to the safety monitoring of various infrastructures such as bridges, ancient buildings, geological disasters, and tunnels, and has a wide range of applications. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the process of the present invention.

[0021] Figure 2 This is a schematic diagram illustrating the geometric principle of the moiré fringes of the present invention.

[0022] Figure 3 The variation of the stripe spacing and orientation angle with the grating angle in this invention is shown. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0024] In one embodiment, such as Figures 1-3 As shown, an infrastructure security detection system based on moiré stripes includes several sensor nodes, a cloud server, and an intelligent early warning module. The sensing node includes a sealed housing, a dual-grating structure, a monochromatic light source, a diffused screen, a miniature industrial camera, an embedded processor, and a wireless communication module. The dual-grating structure consists of a reference grating and a sensing grating. The reference grating is fixed to a stable part of the infrastructure, and the sensing grating is fixed to a displacement-sensitive part of the infrastructure. The monochromatic light source, dual grating structure, diffused screen, and miniature industrial camera are arranged sequentially along the optical path. The miniature industrial camera is electrically connected to the embedded processor, which communicates with the cloud server through a wireless communication module. The intelligent early warning module is connected to the cloud server.

[0025] The sensing node is the core detection unit of the system, featuring a sealed, waterproof, and dustproof design for weather resistance in outdoor applications. Internally, it integrates a dual-grating structure, a monochromatic light source, a diffuser screen, a miniature industrial camera, an embedded processor, and a wireless communication module. The dual-grating structure consists of a reference grating and a sensing grating. The reference grating is fixed to a stable part of the infrastructure (such as the stronger side of a crack in an ancient building or a stable bedrock location in geological monitoring), while the sensing grating is fixed to a displacement-sensitive part (such as the other side of a crack in an ancient building or the surface of a landslide), ensuring that the two gratings can move relative to each other with structural deformation. The monochromatic light source uses a laser (wavelength 632nm) or LED to provide stable monochromatic illumination; the diffuser screen is used to receive moiré fringe imaging; the miniature industrial camera periodically captures fringe images and transmits them to the embedded processor. The embedded processor has a built-in image processing algorithm that can automatically identify the fringe center, calculate the fringe spacing and positional offset, and, combined with geometric optics formulas, calculate the linear displacement in the X and Y directions and the rotation angle around the Z-axis, achieving a measurement accuracy of 0.01mm and 0.016°, respectively. The wireless communication module uses a 4G / 5G or WIFI module to realize the wireless transmission of displacement data to the cloud server.

[0026] The geometric optics formulas used include: When the grating constant d is fixed, a change in the angle θ between two gratings will cause a change in the moiré fringe spacing ω, and the fringe orientation angle φ will also change accordingly. Detailed derivations of the formulas for calculating the moiré fringe spacing ω and orientation angle φ in relation to the grating angle θ are provided in Appendix A. Typically, experiments are conducted using two gratings with the same grating constant d, and the resulting moiré fringe relationship can be expressed as:

[0027]

[0028] When the angle θ between the two gratings remains constant, moving one grating perpendicularly to the grating line of the other will cause the resulting moiré fringes to shift along the grating line of the fixed grating. When the grating undergoes a displacement of one grating constant d, the moiré fringes will shift by a distance equal to the fringe spacing ω. Therefore, the magnification K is...

[0029] Therefore, the relationship between the grating translation distance Δx and the moiré fringe translation distance ΔX can be derived as follows:

[0030] The cloud server handles data storage, analysis, and structural performance evaluation. Its integrated infrastructure finite element model can correlate and compare "geometric displacement" data obtained from optical measurements with the structure's "mechanical state" (such as strain and load effects) to achieve in-depth performance evaluation. Simultaneously, the cloud server possesses multi-node data time synchronization and spatial correlation analysis capabilities. By analyzing the displacement differences between different nodes, it can calculate beam deflection curves, pier inclination, or reconstruct the three-dimensional displacement field of a landslide, accurately determining the location and rate of the sliding surface.

[0031] The intelligent early warning module is pre-set with displacement thresholds and abnormal trend judgment rules, and monitors the data processed by the cloud server in real time. When the monitored value exceeds the preset threshold, or when the displacement trend shows abnormal changes, the module automatically triggers an alarm and sends it to maintenance personnel and managers through relevant means, achieving early warning. The infrastructure security detection method based on the above system includes the following steps: (1) Monitoring Network Deployment: Select the appropriate installation method according to the type of object being monitored. When monitoring bridges, sensor nodes are arranged at key monitoring sections such as mid-span, quarter-span, and pier top; when monitoring ancient buildings, miniaturized and concealed installation methods are adopted to fix sensor nodes to key parts such as both sides of cracks, beam joints, and column bases; when monitoring geological hazards (landslides, settlement), sensor nodes are combined with inclinometer tubes and deep anchoring points to form an integrated monitoring profile of the surface and deep areas; when monitoring tunnels, nodes are arranged at the joints of lining segments and potential deformation sections. The reference gratings of all nodes are fixed at stable locations, and the sensing gratings are fixed at displacement-sensitive locations.

[0032] (2) Stripe Image Acquisition: The sensing node is turned on, and a monochromatic light source continuously illuminates the dual-grating structure. The two gratings move relative to each other due to the deformation of the infrastructure, and the resulting moiré fringes are imaged on the diffuse screen. The miniature industrial camera takes stripe images at preset frequencies to ensure complete capture of the displacement change process.

[0033] (3) Displacement data calculation: After receiving the image data, the embedded processor first performs grayscale processing to eliminate the influence of uneven illumination. Then, it uses an image recognition algorithm to fit and identify the stripes. Combined with the screen calibration grid (5mm corresponds to 311.8 pixels, i.e., 1 pixel = 16.036μm), it performs pixel conversion to calculate the stripe spacing, direction angle and translation amount. Finally, it calculates the accurate displacement information based on the geometric optics formula and uploads it to the cloud server through the wireless communication module.

[0034] (4) Comprehensive data analysis: The cloud server performs time synchronization and spatial correlation analysis on the data uploaded by each node. For bridges, the deflection curve and curvature are calculated by the displacement difference of different nodes in the same beam segment to assess the structural bearing capacity; for ancient buildings, environmental data such as temperature, humidity and vibration are combined to distinguish between periodic deformation caused by thermal expansion and contraction and irreversible displacement caused by structural damage; for geological disasters, the three-dimensional displacement field is reconstructed to determine the location and slip rate of the sliding surface; for tunnels, the lining convergence deformation and joint opening are monitored to assess the structural stability.

[0035] (5) Intelligent early warning and feedback: The intelligent early warning module monitors and analyzes the results in real time. When the displacement value exceeds the preset threshold or the displacement development trend shows an accelerated abnormal situation, an alarm is automatically triggered and sent to relevant personnel. At the same time, the cloud server stores complete data to provide a basis for subsequent maintenance, repair and engineering management.

[0036] Furthermore, this method can be combined with environmental sensor data (temperature, humidity, vibration, rainfall, groundwater level, etc.) for collaborative analysis, further improving the accuracy of monitoring results and the scientific nature of the assessment.

[0037] Example 1: Bridge structural health monitoring: Sensing nodes are deployed at key monitoring sections of the bridge, such as mid-span, quarter-span, and pier top. Reference gratings are fixed to the stable area at the bottom of the piers, while sensing gratings are fixed to displacement-sensitive parts at the bottom of the beams and the top of the piers. The nodes are equipped with waterproof and dustproof housings and transmit data via 4G / 5G or WIFI modules.

[0038] During monitoring, when the bridge experiences slight deflection under load, it causes the sensing grating to move relative to the reference grating, creating moiré fringes that are periodically captured by a miniature industrial camera. An embedded processor processes the images, calculating the linear displacement and rotation data in the X and Y directions, and uploads the data to a cloud server.

[0039] The cloud server synchronizes data from each node in time, calculates the beam deflection curve and curvature by analyzing the displacement difference between the mid-span and quarter-span nodes, and assesses pier tilt and settlement by comparing the displacement between the pier top and bottom nodes. Combined with the bridge's finite element model, the displacement data is correlated with beam strain and load effects to determine the bridge's structural health. When the monitored beam deflection exceeds a preset threshold, the intelligent early warning module immediately sends an alarm to maintenance personnel.

[0040] Example 2: Monitoring cracks in ancient buildings: Cracks in key areas of ancient wooden or brick-and-stone buildings, such as beam joints, column bases, or hollow areas of murals, are monitored using miniaturized sensor nodes concealed on both sides of the crack. A reference grating is fixed to a sturdy component on one side of the crack, while the sensing grating is fixed on the other side.

[0041] The system continuously monitors the relative displacement on both sides of the crack. When changes in ambient temperature and humidity cause thermal expansion and contraction, the system captures periodic minute displacements. When the crack undergoes tension or displacement due to structural fatigue, the system records irreversible displacement changes. The embedded processor, combined with environmental sensor data such as temperature, humidity, and vibration, automatically distinguishes between the two types of displacement. The cloud server stores the data long-term, forming a crack development trend curve, providing a basis for preventive protection and precise repair.

[0042] Example 3: Landslide Deformation Monitoring Several sensing nodes were deployed in the potential landslide area. Some nodes were installed at stable bedrock locations as reference points, while the remaining nodes were placed on the landslide surface and at deep anchoring points. The nodes were fitted with weather-resistant shells to adapt to outdoor environments.

[0043] When a landslide undergoes creep, the sensing grating shifts relative to the reference grating, causing changes in the spacing and position of the moiré fringes. These changes are captured by the camera, which then calculates the displacement data. A cloud server performs spatial correlation analysis on the data from multiple nodes to reconstruct the three-dimensional displacement field of the landslide, accurately determining the location, direction, and rate of the sliding surface. Combined with data from sensors such as rainfall and groundwater levels, the intelligent early warning module sends a warning to relevant departments when the sliding rate exceeds a preset value.

[0044] Example 4: Tunnel convergence deformation monitoring: Sensing nodes are installed at the joints of the tunnel lining segments and at potential deformation sections. The nodes are moisture-resistant, electromagnetic interference-resistant, and adaptable to the underground environment. The reference grating is fixed in the stable area in the middle of the segment, and the sensing grating is fixed on both sides of the joint.

[0045] During monitoring, changes in surrounding rock pressure cause the tunnel segments to converge and deform, which in turn moves the induction grating. The system captures changes in moiré fringes and calculates the joint opening and segment convergence. When an abnormality is found in the segment convergence of a certain area, the system immediately sends an early warning to the tunnel maintenance personnel, prompting them to strengthen patrols in that area.

[0046] The above embodiments are only some application scenarios of the present invention. The protection scope of the present invention is not limited to the above embodiments. All equivalent transformations made based on the technical solutions of the present invention shall fall within the protection scope of the present invention.

[0047] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An infrastructure safety inspection system based on moiré patterns, characterized in that, It includes several sensor nodes, cloud servers, and intelligent early warning modules; The sensing node includes a sealed housing, a dual-grating structure, a monochromatic light source, a diffused screen, a miniature industrial camera, an embedded processor, and a wireless communication module. The dual-grating structure consists of a reference grating and a sensing grating. The reference grating is fixed to a stable part of the infrastructure, and the sensing grating is fixed to a displacement-sensitive part of the infrastructure. The monochromatic light source, dual grating structure, diffused screen, and miniature industrial camera are arranged sequentially along the optical path. The miniature industrial camera is electrically connected to the embedded processor, which communicates with the cloud server through a wireless communication module. The intelligent early warning module is connected to the cloud server.

2. The infrastructure safety inspection system based on moiré patterns according to claim 1, characterized in that, The sealed housing is designed to be waterproof and dustproof, and the wireless communication module is a 4G / 5G module or a WIFI module.

3. The infrastructure safety inspection system based on moiré patterns according to claim 2, characterized in that, The dual-grating structures have the same grating constant and the grating pitch is 0.5-1mm; the monochromatic light source is a laser or an LED light source, and the wavelength of the laser is 632nm.

4. The infrastructure safety inspection system based on moiré patterns according to claim 1, characterized in that, The embedded processor is equipped with an image processing algorithm for identifying the center of the moiré fringe, calculating the fringe spacing and position offset, and calculating the linear displacement of the sensing grating relative to the reference grating in the X and Y directions and the rotation angle around the Z axis according to the geometric optics formula.

5. The infrastructure safety inspection system based on moiré patterns according to claim 1, characterized in that, The cloud server integrates a finite element model, which is used to correlate and compare the geometric displacement data obtained by optical measurement with the structural mechanical state data; The cloud server also has the functions of multi-node data time synchronization and spatial correlation analysis, and can reconstruct a three-dimensional displacement field.

6. The infrastructure safety inspection system based on moiré patterns according to claim 1, characterized in that, The intelligent early warning module is preset with displacement thresholds and abnormal trend judgment rules. When the monitored value exceeds the threshold or the displacement development trend is abnormal, an alarm is automatically triggered and sent to relevant personnel.

7. An infrastructure security detection method based on the system described in any one of claims 1-6, characterized in that, Includes the following steps: S1: Based on the type of object being monitored, reference gratings and sensing gratings are installed at stable and displacement-sensitive parts of the infrastructure, respectively, to form sensing nodes and construct a monitoring network; S2: Moiré fringes generated by illuminating the dual grating structure with a monochromatic light source are imaged on a diffuse screen. A miniature industrial camera periodically acquires the fringe images and transmits them to the embedded processor. S3: The embedded processor analyzes image data using image processing algorithms, calculates precise displacement information, and uploads it to the cloud server via a wireless communication module. S4: The cloud server performs synchronous processing and correlation analysis on data from multiple nodes, and combines the finite element model to achieve structural performance evaluation; S5: The intelligent early warning module monitors data in real time and triggers an alarm when the early warning conditions are met.

8. The infrastructure safety inspection method according to claim 7, characterized in that, The objects to be detected in step S1 include bridges, ancient buildings, landslides, settlement areas, tunnels, and underground works; For bridge inspection, sensor nodes are deployed at key monitoring sections such as mid-span, quarter-span, and pier top; For the inspection of ancient buildings, the sensor nodes are miniaturized and concealed and installed on both sides of the cracks and in key stress areas; For geological disaster detection, sensor nodes are combined with inclinometer tubes and deep anchoring points to form an integrated monitoring profile of the surface and deep areas; For tunnel inspection, sensor nodes are placed at the joints of the lining segments and at potentially deformable sections.

9. The infrastructure safety inspection method according to claim 7, characterized in that, The image processing algorithm in step S3 includes image grayscale processing, stripe recognition and fitting, and pixel calibration and conversion steps. The actual pixel size is calibrated to 16.036μm.

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