Road surface monitoring and early warning system

Abstract

The invention relates to a road surface monitoring and early warning system, which belongs to the technical field of road surface monitoring, and comprises a matrix type optical fiber sensing network embedded in a road surface; the medial strip digital controller is used for transmitting a first optical signal to the matrix type optical fiber sensing network and demodulating a second optical signal transmitted back by the matrix type optical fiber sensing network; the matrix neural network algorithm module is used for identifying the demodulated second optical signal through a preset neural network model to obtain pavement disease information; the plurality of terminal devices are used for displaying the pavement disease information in a linkage manner; wherein the plurality of terminal devices comprise a digital management and control platform, a driver intelligent terminal, a roadside horn, a digital medial strip anti-glare panel and a roadside variable information sign. According to the invention, all-weather monitoring can be carried out on the road surface, multiple terminal devices are integrated, and timely and accurate technical support can be provided for road maintenance and driving safety.

Description

Technical Field

[0001] This invention relates to the field of road surface monitoring technology, and in particular to a road surface monitoring and early warning system. Background Technology

[0002] In recent years, safety accidents caused by road surface damage and collapse have occurred frequently in many places. In particular, how to effectively prevent cracks, damage, potholes, settlement and collapse in high embankment sections and sections with poor drainage in service has always been a difficult problem.

[0003] Currently, the commonly used detection and early warning methods mainly include annual national inspections, routine intelligent road patrols, and image-based automatic detection algorithm systems. Although national inspections and routine intelligent road patrols are highly accurate and equipped with technologies such as 3D ground-penetrating radar and 3D laser detection, they are limited by the frequency of inspections and are considered passive monitoring methods and static detection solutions. They are more focused on detection and evaluation and cannot provide dynamic, real-time, all-weather effective early warnings.

[0004] Image-based automatic detection algorithms mainly collect road surface images by setting up monitoring equipment or using drones for inspection. After image processing, detection, and comparative analysis are performed based on neural network models, the results are returned to the road information monitoring platform. Although they have a certain early warning capability, they have disadvantages such as low system integration, great susceptibility to weather and environmental influences, and poor noise resistance, and cannot meet the needs of rapid, all-weather real-time early warning. Summary of the Invention

[0005] In view of this, it is necessary to provide a road surface monitoring and early warning system to solve the problems of low system integration, great susceptibility to weather and environment, and poor noise resistance of existing road surface monitoring methods.

[0006] To address the aforementioned problems, in a first aspect, the present invention provides a road surface monitoring and early warning system, comprising: A matrix-type fiber optic sensor network, buried in the road surface; A central divider digital controller is used to transmit a first optical signal to the matrix fiber optic sensor network and to demodulate a second optical signal transmitted back from the matrix fiber optic sensor network. The matrix neural network algorithm module is used to identify the demodulated second optical signal through a preset neural network model to obtain road surface defect information; Multiple terminal devices are used to display the road surface defect information in a coordinated manner; wherein, the multiple terminal devices include a digital management and control platform, a driver's intelligent terminal, a roadside horn, a digital central median anti-glare panel, and a roadside variable message sign.

[0007] In one possible implementation, the matrix-type fiber optic sensing network includes: a self-adhesive unidirectional fiberglass geogrid, multiple optical fibers, a displacement sensor, and a photosensitive sensor; wherein the multiple optical fibers are mounted on the self-adhesive unidirectional fiberglass geogrid, and the displacement sensor and the photosensitive sensor are arranged diagonally on different optical fibers in the same grid.

[0008] In one possible implementation, the digital median controller is connected to the digital median anti-glare panel, and a solar panel is provided on one side of the digital median anti-glare panel for supplying power to the digital median controller and the digital median anti-glare panel.

[0009] In one possible implementation, the preset neural network model includes a spatial feature extraction module based on a convolutional neural network, a temporal feature extraction module based on a bidirectional long short-term memory neural network, a feature fusion module based on an attention mechanism, and a disease information output module based on a deep neural network.

[0010] In one possible implementation, the pavement distress information includes distress type, distress severity, and distress location; the distress type includes cracks, settlement, and collapse.

[0011] In one possible implementation, the central median digital controller is configured to generate driving suggestions based on the road surface distress information and send the road surface distress information and the driving suggestions to the driver's smart terminal.

[0012] In one possible implementation, the central median digital controller is used to generate pavement maintenance recommendations based on the pavement distress information, and send the pavement distress information and the pavement maintenance recommendations to the digital management and control platform.

[0013] In one possible implementation, the central median digital controller is used to generate voice playback content based on the road surface damage information and determine the playback frequency based on the degree of damage, and control the roadside loudspeakers to play the voice playback content at the playback frequency.

[0014] In one possible implementation, the central median digital controller is used to determine a warning icon based on the road surface defect information and control the digital central median anti-glare panel to display the warning icon.

[0015] In one possible implementation, the central median digital controller is used to determine the text warning content based on the road surface damage information, determine the text color and text flashing frequency based on the degree of damage, and control the roadside variable message signs to display the text warning content according to the text color and text flashing frequency.

[0016] The beneficial effects of this invention are: This invention deploys a matrix-type fiber optic sensor network on the road surface and a digital controller in the central median. The central median controller controls the transmission and demodulation of optical signals within the matrix-type fiber optic sensor network. A matrix neural network algorithm module, based on a preset neural network model, identifies the demodulated optical signals to obtain road surface defect information. This enables real-time, all-weather monitoring of the road surface, and this monitoring method is less affected by weather and environmental factors compared to image-based monitoring methods. Furthermore, this invention integrates a digital management platform, driver smart terminals, roadside horns, digital central median anti-glare panels, and roadside variable message signs. When road surface defects are detected, these terminals can collaboratively display the information, providing technical support for road maintenance and driving safety. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0018] Figure 1 This is a schematic diagram of a structure of an embodiment of the road surface monitoring and early warning system provided by the present invention; Figure 2 A schematic diagram of a self-adhesive unidirectional fiberglass geogrid laying method provided by the present invention; Figure 3 A schematic diagram of a sensor mounting section provided by the present invention; Figure 4 A diagram of a central median digital controller architecture is provided by the present invention. Figure 5 A schematic diagram of the integration of a central divider and a sensing matrix provided by the present invention; Figure 6 This is a schematic diagram of a photosensitive sensor; Figure 7 This is a schematic diagram of an FBG displacement sensor; Among them, 1-unidirectional fiberglass geogrid; 2-optical fiber; 3-data port; 4-FBG displacement sensor; 5-photosensitive sensor; 6-digital controller; 7-central divider; 8-digital central divider anti-glare panel; 9-photosensitive receiving element; 10-photosensitive motherboard; 11-optical fiber interface; 12-encapsulation protective cover; 13-photosensitive photomask; 14-displacement sensing electrode; 15-displacement sensing emitter. Detailed Implementation

[0019] The technical solutions of 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 a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] In the description of the embodiments of this invention, unless otherwise stated, "a plurality of" means two or more. The terms "first," "second," etc., used in the embodiments of this invention are used to distinguish similar objects, and are not used to describe a specific order or sequence, nor to indicate or imply their relative importance or implicitly specify the number of indicated technical features. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, and the number of objects is not limited; for example, a first object can be one or more.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] Reference Figure 1 The diagram shows a structural schematic of an embodiment of the road surface monitoring and early warning system provided by the present invention. The system 100 includes: A matrix-type fiber optic sensor network 110 is buried in the road surface; The central divider digital controller 120 is used to transmit a first optical signal to the matrix fiber optic sensor network and demodulate the second optical signal transmitted back from the matrix fiber optic sensor network. The matrix neural network algorithm module 130 is used to identify the demodulated second optical signal through a preset neural network model to obtain road surface defect information; 140 terminal devices are used to display road surface defect information in a coordinated manner; these terminal devices include a digital management and control platform, driver smart terminals, roadside horns, digital median strip anti-glare panels, and roadside variable message signs.

[0023] In this embodiment, the matrix fiber optic sensor network 110, i.e., the fiber optic sensor network buried in a matrix, is the sensing layer of system 100; the matrix neural network algorithm module 130, i.e., the algorithm module integrating multiple neural networks, is the road surface defect identification layer of system 100; the multiple terminal devices are the early warning response layer of system 100; and the central median digital controller 120 is the signal processing center of system 100. It is electrically connected to the matrix fiber optic sensor network 110 and the matrix neural network algorithm module 130, and communicatively connected to the multiple terminal devices 140. It is used to transmit a first optical signal to the matrix fiber optic sensor network 110, demodulate the second optical signal transmitted back from the matrix fiber optic sensor network 110, send the demodulated second optical signal to the matrix neural network algorithm module 130, receive the road surface defect information identified by the matrix neural network algorithm module 130, and control the multiple terminal devices 140 to perform linkage response early warning based on the road surface defect information.

[0024] In summary, this embodiment deploys a matrix-type fiber optic sensor network on the road surface and a digital controller in the central median. The central median controller controls the transmission and demodulation of optical signals within the matrix-type fiber optic sensor network. A matrix neural network algorithm module, based on a preset neural network model, identifies the demodulated optical signals to obtain road surface defect information. This enables real-time, all-weather monitoring of the road surface, and this monitoring method is less affected by weather and environmental factors compared to image-based monitoring methods. Furthermore, this embodiment integrates a digital management and control platform, driver smart terminals, roadside horns, digital central median anti-glare panels, and roadside variable message signs. When road surface defects are detected, these terminal devices can collaboratively display the information, providing technical support for road maintenance and driving safety.

[0025] In some embodiments of the present invention, the aforementioned system 100 can be installed on high-fill sections and sections with inherently poor drainage of existing highways for digital micro-renovation. This embodiment combines the renovation with the needs of major and medium road repairs, avoiding engineering waste and redundant construction, and achieving digitalization of existing highways through a small amount of engineering renovation.

[0026] In some embodiments of the present invention, the matrix fiber optic sensing network 110 includes: a self-adhesive unidirectional fiberglass geogrid, multiple optical fibers, a displacement sensor, and a photosensitive sensor; wherein, the multiple optical fibers are mounted on the self-adhesive unidirectional fiberglass geogrid, and the displacement sensor and the photosensitive sensor are arranged diagonally on different optical fibers in the same grid.

[0027] In this embodiment, the laying method of the self-adhesive unidirectional fiberglass geogrid is as follows: Figure 2It is laid between the asphalt surface layer and the asphalt surface layer. The material properties of the self-adhesive unidirectional fiberglass geogrid can meet the GF / BK80-100 standard (tensile strength ≥100kN / m, elongation ≤3%). After the self-adhesive unidirectional fiberglass geogrid is laid, its "self-adhesive layer-road surface interface bonding" can enhance the interlayer tensile stiffness between the geogrid and the road surface (interlayer shear stress reduced by more than 20%), while providing a "high-stability, low-creep" mounting base for sensors, avoiding false signals generated by the sensors due to carrier deformation.

[0028] The displacement sensor can be a fiber Bragg grating (FBG) based displacement sensor. The period of the FBG... With effective refractive index n cff Determines the center wavelength of its reflected light , Satisfies the Bragg equation: Displacement sensors can be used to monitor road surface settlement / collapse.

[0029] Photosensitive sensors can monitor road surface cracks / damage. Under normal road conditions, the light emitted from the optical fiber is reflected / scattered by the road surface, and the light intensity I at the receiving end remains stable. When cracks exist, the light transmits into the cracks or the scattering characteristics change, causing the received light intensity to attenuate, satisfying the light intensity attenuation model:

[0030] In the formula, I 0 represents the emitted light intensity. a The light absorption coefficient of the fractured medium. ω The width of the crack. θ The fiber exit angle is given. The crack width can be assessed using the attenuation ratio of I / I0.

[0031] For the deployment methods of the two types of sensors, please refer to [link / reference]. Figure 3 The sensors can be arranged in an M×N grid (corresponding to a 20mm×300mm monitoring section) to form a row-column coordinate-based sensing matrix, achieving multi-dimensional signal acquisition with "area coverage (matrix) + point precision (node)". FBG motion sensors 4 are deployed longitudinally along the road to monitor sensitive settlement deformation, while photosensitive sensors 5 are deployed laterally to capture the direction of crack extension. This layout allows for a 90% overlap in the spatial response radii of the two sensors, solving the spatiotemporal registration error problem of traditional separate arrangements (reducing the positioning error from 0.3m to 0.15m).

[0032] The two types of sensors are deployed on different optical fibers, and multiple different optical fibers are integrated in the same network for transmission via a shared cable. When one of the optical fibers breaks, it can also send an alarm signal to the central divider digital controller 120, indicating that there is a hazard on the road surface and that the hazard caused the optical fiber to break.

[0033] The demodulation techniques for the two sensors can employ a dual-band time-division multiplexing strategy: the FBG displacement sensor uses the 1530nm±0.5nm band, and the photosensitive sensor uses the 850nm band. The signal isolation transmission of the same optical fiber link is achieved through a wavelength division multiplexer, avoiding the spectral crosstalk problem in the existing technology, with spectral crosstalk ≤-38dBm.

[0034] In some embodiments of the present invention, the central divider digital controller 120 is connected to the digital central divider anti-glare panel, and a solar panel is provided on one side of the digital central divider anti-glare panel. The solar panel is used to supply power to the central divider digital controller and the digital central divider anti-glare panel.

[0035] In some embodiments of the present invention, reference is made to... Figure 4 The central median digital controller 120 includes: a power supply module, a data acquisition module, a detection module, an optical modulator, an FBG demodulator, a light intensity demodulator, a coupler, a transmitter / receiver, and a signal transmission module. The optical modulator emits probe light of a specific wavelength to the FBG displacement sensor and probe light of constant intensity to the photosensitive sensor. The FBG demodulator converts the wavelength shift signal (converted to displacement) of the light signal reflected from the FBG displacement sensor into a digital electrical signal. The light intensity demodulator converts the light intensity attenuation signal (converted to light intensity percentage) of the light signal reflected from the photosensitive sensor into a digital electrical signal. The transmitter / receiver transmits information to or receives information from multiple terminal devices.

[0036] In some embodiments of the present invention, the central median digital controller 120 is also used to filter (remove interference such as vehicle vibration and ambient light fluctuations) and normalize (unify signal dimensions) the digital electrical signal to obtain a three-dimensional dataset of "spatial coordinates-time-signal value", and transmit the three-dimensional dataset to the matrix neural network algorithm module 130.

[0037] In some embodiments of the present invention, the preset neural network model includes a spatial feature extraction module based on a convolutional neural network (CNN), a temporal feature extraction module based on a bidirectional long short-term memory neural network (Bi-LSTM), a feature fusion module based on an attention mechanism, and a disease information output module based on a deep neural network (DNN).

[0038] Specifically, the matrix neural network algorithm module 130 automatically analyzes the data collected by the central median digital controller 120 and outputs pavement distress information. This information includes distress type, distress severity, and distress location; distress types include cracks, settlement, and subsidence. The analysis process uses different activation functions to achieve the following three types of decisions: Disease type determination: Softmax is activated, and the probability distribution of "crack", "settlement", "collapse" and "normal" is output. The result is the one with the highest probability.

[0039] Location of defects: Through the "coordinate decoding layer", the "row-column coordinates of the activation region" in the CNN feature map are mapped to the road mileage marker + lateral position (such as "KX+XXX, 1 / 3 width of the left lane").

[0040] Disease severity assessment: Sigmoid activation (for "relative severity", such as crack width ratio) or linear activation (for "absolute severity", such as settlement in mm), output indicators such as "crack width range (0-5mm / 5-10mm / ≥10mm)" and "settlement (0-1cm / 1-5cm / ≥5cm)".

[0041] In some embodiments of the present invention, the central median digital controller 120 is used to generate driving suggestions based on road surface defect information and send the road surface defect information and driving suggestions to the driver's smart terminal.

[0042] For example, the central median digital controller 120 uses vehicle-to-everything (V2X) technology to push driving suggestions to the driver's smart terminal, such as "There is a road defect X meters ahead. It is recommended to reduce speed to v km / h (v is dynamically calculated by the central median digital controller. For example, if the crack width is ≥10mm, it is recommended to reduce speed to 40 km / h)", in order to achieve accurate navigation and obstacle avoidance.

[0043] In some embodiments of the present invention, the central median digital controller 120 is used to generate road maintenance suggestions based on road surface distress information and send the road surface distress information and road surface maintenance suggestions to the digital management and control platform.

[0044] For example, the central median digital controller 120 generates road maintenance suggestions with "disease type, precise location (error ≤ 0.3m), severity level, and recommended treatment priority", which triggers the automatic generation of maintenance work orders on the digital management and control platform (such as "KX+XXX left lane, crack width Xmm, recommended to fill crack within 12 hours").

[0045] In some embodiments of the present invention, the central median digital controller 120 is used to generate voice playback content based on road surface damage information and determine the playback frequency based on the degree of damage, and control the roadside loudspeakers to play the voice playback content according to the playback frequency.

[0046] For example, the digital controller 120 in the central divider controls the roadside loudspeakers to play the voice message "800 meters ahead, crack in the left lane, please be careful." The higher the severity, the faster the broadcast frequency, such as once per second in the event of a collapse.

[0047] In some embodiments of the present invention, the central median digital controller 120 is used to determine warning icons based on road surface defect information and control the digital central median anti-glare panel to display the warning icons.

[0048] For example, a "warning" graphic is displayed on the LED matrix screen of the anti-glare panel in the digital central divider, and a bright red flashing signal (1 time / second) is given as a warning.

[0049] In some embodiments of the present invention, the central median digital controller 120 is used to determine the text warning content based on road surface damage information, determine the text color and text flashing frequency based on the degree of damage, and control the roadside variable message signs to display the text warning content according to the text color and text flashing frequency.

[0050] For example, the digital controller 120 in the central median controls the roadside variable message signs to dynamically display "800 meters ahead, road surface damaged, slow down / detour", with the text color (yellow → orange → red) and flashing frequency (1 time / second → 2 times / second → 3 times / second) increasing with the severity of the damage.

[0051] In some embodiments of the present invention, the digital controller for the central median is powered by a solar panel on the back of the digital central median during the day and by a battery at night, enabling uninterrupted 24-hour information display. The power supply for roadside variable message signs and roadside loudspeakers can be solved using external solar panels.

[0052] The following combination Figure 5 The invention will be described in its entirety as follows: I. Construction of the carrier: For high-fill sections, poorly drained sections, and special sections requiring early warning on existing highways, the top 4cm of asphalt is milled, and residual asphalt and debris on the milled surface are cleaned to ensure a smooth surface. A polymer-modified chloroprene rubber (PCR) emulsified asphalt tack coat is applied at a rate of 0.3–0.6 L / m². Then, a unidirectional fiberglass geogrid 1 is laid in the direction of traffic. Adjacent fiberglass geogrids in both the longitudinal and transverse directions are connected as a whole through data interfaces 3. Specifically, each neural network geogrid has four socket-type data interfaces in both the longitudinal and transverse directions, facilitating the connection of adjacent neural networks into a single unit and allowing for individual replacement of damaged modules, reducing the difficulty of later maintenance. The material properties of the uniaxial fiberglass geogrid 1 conform to the GF / BK80-100 standard (tensile strength ≥100kN / m, elongation ≤3%). The geogrid mesh spacing is 20mm×300mm, providing a carrier for the sensing matrix. Optical fibers 2, FBG displacement sensors 4, and photosensitive sensors 5 are mounted on the uniaxial fiberglass geogrid 1 to form a row-column coordinate-based sensing matrix. Then, a 4cm thick layer of AC-13C fine-grained styrene-butadiene-styrene (SBS) modified asphalt concrete is laid and statically compacted with a road roller to ensure the bonding strength between the geogrid and the road surface. It is connected to the digital controller 6 (i.e., the central median digital controller 120) in the central divider 7 via data interface 3, forming a multi-terminal digital monitoring and early warning system. The digital controller 6 is powered through the digital central divider anti-glare panel 8.

[0053] II. Sensor Matrix Deployment: Photosensitive sensor 5: Distributed on the same grid but different transmission layer as FBG displacement sensor 4, with a spacing of 80cm × 120cm. The pigtail of photosensitive sensor 5 is also fused to the trunk optical cable, using space division multiplexing for transmission on the same cable as the FBG displacement sensor. See also Figure 6 The diagram shows a schematic of a photosensitive sensor. The photosensitive sensor includes a photosensitive receiving element 9, a photosensitive mainboard 10, an optical fiber interface 11, a protective enclosure 12, and a photosensitive photomask 13.

[0054] FBG displacement sensor 4: Layout in an 80cm × 120cm grid, the FBG sensors are fitted onto the grid reinforcement strips. The sensor pigtails are spliced ​​to the main optical cable using a fiber optic fusion splicer. The main optical cable is then led to the digital controller 6. Multiple FBG displacement sensors are connected in series on a single optical fiber using wavelength division multiplexing (WDM) or time division multiplexing (TDM) technology to achieve distributed displacement monitoring. See also... Figure 7, showing a schematic diagram of a FBG displacement sensor. The FBG displacement sensor includes a fiber optic interface 11, a packaging protective cover 12, a displacement sensing induction pole 14, and a displacement sensing emitter 15.

[0055] The dual-band anti-interference sensing matrix uses co-location packaging of "FBG displacement sensor + photosensitive sensor" and transmits through dual-band time-division multiplexing of "1525 - 1565nm continuous wave / 800 - 900nm pulsed wave", with spectral crosstalk ≤ -38dBm.

[0056] Installation of the three median digital controller: Install the digital controller 6 box inside the median 7. The digital controller 6 is the signal processing and command center, integrating a power supply module, a data acquisition module, a detection module, an optical modulator, an FBG demodulator, an optical intensity demodulator, a coupler, a transmitter / receiver, and a signal publishing module. Connect the optical fiber 2 to the coupler of the controller to complete the optical signal splitting / combining configuration.

[0057] Optical modulator: Set the detection optical wavelength range of the FBG sensor (such as 1525 - 1565nm) and set the detection optical intensity of the photosensitive sensor (such as 10mW).

[0058] Demodulator: Calibrate the FBG wavelength demodulation accuracy (1pm) and calibrate the optical intensity demodulation accuracy of the photosensitive sensor (≤1%).

[0059] The digital controller 6 is powered by the solar panel on the back of the digitalized median anti-glare board 8 during the day and by the battery at night to achieve 24-hour uninterrupted information display throughout the day. The roadside variable message sign and the roadside high-volume horn can solve the power supply problem through an external solar panel.

[0060] The digital controller 6 can achieve multi-terminal linkage warning through communication technologies such as 5G network, vehicle-to-everything network, Internet of Things, and vehicle-road collaborative V2X technology. It conducts all-weather real-time monitoring of the pavement condition index, structural strength index, ride quality index, and pavement deformation index, and conducts sub-second linkage warning with the command center, the driver's mobile phone terminal, the roadside high-volume horn, the digitalized median anti-glare board, and the roadside variable message sign, and alarms about abnormal pavement conditions in the first time to improve driving safety.

[0061] IV. Matrix neural network training and deployment: Adopt a multi-neural network integration architecture of "CNN (spatial feature extraction) + Bi-LSTM (temporal feature extraction) + attention mechanism (feature fusion) + DNN (decision output)".

[0062] V. Multi-terminal warning trigger: (1) If the decision is "crack (width ≥ 5mm)": Command center terminal: A warning window pops up saying "KX+XXX left side, crack width Xmm, it is recommended to fill the crack within 12 hours".

[0063] Driver App: Pushes a message "Road damage is present X meters ahead, it is recommended to reduce speed to v km / h", and displays the warning location in conjunction with navigation.

[0064] Roadside variable sign: "Slow down if there is a crack on the left side ahead XXm ahead".

[0065] (2) If the decision is "settlement (≥3cm)": Command center terminal: Emergency warning pops up: KX+XX right lane, settlement Xcm, requires action within 24 hours.

[0066] Roadside loudspeakers: repeatedly broadcasting "Road subsidence XX meters ahead, please be careful."

[0067] Digital anti-glare panel: Displays a red "settlement warning" icon.

[0068] Roadside variable message signs: dynamically display "Road surface damaged 800 meters ahead, slow down / detour", with text color (yellow → orange → red) and flashing frequency (1 time / second → 2 times / second → 3 times / second) increasing with severity.

[0069] (3) If the decision is "collapse (depth ≥ 10cm)": Command center terminal: Triggers audible and visual alarms and automatically generates a "road closure application" process.

[0070] All terminals: Push the highest level warning (APP pop-up, sign display "Road collapse XXm ahead, please detour", loudspeaker emergency broadcast).

[0071] In summary, this embodiment has the following beneficial effects: (1) Synergistic enhancement of structure: The integrated construction of self-adhesive fiberglass geogrid and road surface not only provides a highly stable carrier for sensors, but also allows for the minimum degree of digital micro-renovation of high embankment sections and poorly drained sections of in-service highways in conjunction with major and medium road repairs, thereby extending the fatigue life of the road surface by ≥30%, avoiding engineering waste and repeated construction, and achieving the dual benefits of "monitoring function and structural enhancement", which is different from the traditional system that "only monitors and does not enhance".

[0072] (2) High integration of the system: The end-to-end collaborative design of "sensor matrix - algorithm hub - multiple terminals" makes the signal transmission delay ≤0.5 seconds and the early warning response speed ≥3 orders of magnitude faster than manual inspection, meeting the real-time early warning requirements of expressways and highways.

[0073] (3) Strong generalization of the algorithm: The attention mechanism dynamically weights the spatial-temporal features, making the false alarm rate of the system ≤2% under conditions of "multiple disease coupling (such as cracks accompanied by slight settlement)" and "complex environment (such as rainy night, strong light)", which solves the pain point of "frequent false alarms under environmental interference" in the traditional threshold method based on image analysis.

[0074] (4) Accuracy of multidimensional sensing: By linking the “wavelength-deformation” of FBG with the “light intensity-crack” of photosensitive sensor in multiple dimensions, and combining the deep mining of “spatial-temporal” features by multiple neural networks, the disease identification accuracy is ≥98%, the position error is ≤0.3m, and the severity assessment error is ≤5%, which is far superior to the existing technology (identification accuracy ≤90%, position error ≥1m).

[0075] (5) 24 / 7 monitoring and early warning: Unlike traditional passive monitoring methods that are limited by the frequency of inspections and image-based defect identification technology, the embodiments of this application connect sensors into a network of monitoring, command transmission devices and alarm devices through optical fiber, which can realize dynamic real-time 24 / 7 effective early warning.

[0076] (6) Multi-terminal linkage early warning: Real-time monitoring of road condition index, structural strength index, driving quality index and road deformation index around the clock, and linkage early warning with command center, driver mobile terminal, roadside loudspeaker, digital median anti-glare panel and roadside variable information sign, and alarm for abnormal road conditions in the first time to improve driving safety.

[0077] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0078] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A road surface monitoring and early warning system, characterized in that, include: A matrix-type fiber optic sensor network, buried in the road surface; A central divider digital controller is used to transmit a first optical signal to the matrix fiber optic sensor network and to demodulate a second optical signal transmitted back from the matrix fiber optic sensor network. The matrix neural network algorithm module is used to identify the demodulated second optical signal through a preset neural network model to obtain road surface defect information; Multiple terminal devices are used to display the road surface defect information in a coordinated manner; wherein, the multiple terminal devices include a digital management and control platform, a driver's intelligent terminal, a roadside horn, a digital central median anti-glare panel, and a roadside variable message sign.

2. The road surface monitoring and early warning system according to claim 1, characterized in that, The matrix-type fiber optic sensing network includes: a self-adhesive unidirectional fiberglass geogrid, multiple optical fibers, a displacement sensor, and a photosensitive sensor; wherein, the multiple optical fibers are mounted on the self-adhesive unidirectional fiberglass geogrid, and the displacement sensor and the photosensitive sensor are arranged diagonally on different optical fibers in the same grid.

3. The road surface monitoring and early warning system according to claim 1, characterized in that, The digital controller for the central divider is connected to the digital anti-glare panel for the central divider. A solar panel is provided on one side of the digital anti-glare panel for supplying power to the digital controller for the central divider and the digital anti-glare panel for the central divider.

4. The road surface monitoring and early warning system according to claim 1, characterized in that, The preset neural network model includes a spatial feature extraction module based on a convolutional neural network, a temporal feature extraction module based on a bidirectional long short-term memory neural network, a feature fusion module based on an attention mechanism, and a disease information output module based on a deep neural network.

5. The road surface monitoring and early warning system according to claim 1, characterized in that, The pavement distress information includes distress type, distress severity, and distress location; the distress types include cracks, settlement, and collapse.

6. The road surface monitoring and early warning system according to claim 5, characterized in that, The central median digital controller is used to generate driving suggestions based on the road surface defect information, and send the road surface defect information and the driving suggestions to the driver's smart terminal.

7. The road surface monitoring and early warning system according to claim 5, characterized in that, The central median digital controller is used to generate road maintenance suggestions based on the road surface distress information, and send the road surface distress information and the road surface maintenance suggestions to the digital management and control platform.

8. The road surface monitoring and early warning system according to claim 5, characterized in that, The central median digital controller is used to generate voice playback content based on the road surface damage information and determine the playback frequency based on the degree of damage, and control the roadside loudspeakers to play the voice playback content at the playback frequency.

9. The road surface monitoring and early warning system according to claim 5, characterized in that, The digital controller for the central median strip is used to determine a warning icon based on the road surface defect information and to control the digital anti-glare panel of the central median strip to display the warning icon.

10. The road surface monitoring and early warning system according to claim 5, characterized in that, The central median digital controller is used to determine the text warning content based on the road surface damage information, determine the text color and text flashing frequency based on the degree of damage, and control the roadside variable message signs to display the text warning content according to the text color and text flashing frequency.

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