Intelligent road infrastructure health state prediction method

Abstract

The invention discloses an intelligent road infrastructure health state prediction method, and relates to the field related to intelligent traffic, and the method comprises the steps: calculating a road strain distribution matrix at an edge calculation node based on a quantum entanglement photon pair signal; constructing a road digital twinborn body by fusing road three-dimensional scanning information, road surface pressure distribution information and a road strain distribution matrix; calculating a road structure health index according to the digital twin of the road; processing the road structure health index by adopting a gated quantum recurrent neural network to obtain a predicted health decline rate; when the predicted health decline rate exceeds a preset rate threshold value, triggering a self-repairing microcapsule rupture instruction, releasing an asphalt regenerant to fill micro cracks, and automatically repairing the road. The technical problem that existing intelligent road infrastructure health state prediction is insufficient in real-time performance and accuracy is solved, and the technical effect of improving the real-time performance and accuracy of road infrastructure health state prediction is achieved.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation, and particularly to a method for predicting the health status of intelligent road infrastructure. Background Art

[0002] Predicting and managing the health status of intelligent road infrastructure is crucial for ensuring road safety, extending service life, and improving traffic efficiency. Currently, the main methods to solve this problem rely on traditional road detection and maintenance means, such as regular manual inspections and using sensors to monitor road conditions, and these methods combine data analysis to evaluate the health status of roads. However, the current methods are limited by the real-time performance and accuracy of data processing, as well as the insufficient sensitivity to minor damages and potential structural changes, resulting in difficulties in timely and effectively detecting and predicting the downward trend of road health, which may cause potential safety hazards and an increase in maintenance costs.

[0003] In the current related technologies, there are technical problems of insufficient real-time performance and accuracy in predicting the health status of intelligent road infrastructure. Summary of the Invention

[0004] By providing a method for predicting the health status of intelligent road infrastructure, this application calculates the road strain matrix using quantum signals at the edge node, fuses three-dimensional scanning and pressure distribution information to construct a road digital twin, calculates the health index based on this, predicts the health decline rate through a quantum neural network, and triggers a self-repair instruction to release a regenerant to repair the road when the speed limit is exceeded, thereby achieving the technical effect of improving the real-time performance and accuracy of predicting the health status of road infrastructure.

[0005] This application provides a method for predicting the health status of intelligent road infrastructure, including: at the edge computing node, calculating the road strain distribution matrix based on the quantum entangled photon pair signal; constructing a road digital twin by fusing the road three-dimensional scanning information, the road surface pressure distribution information, and the road strain distribution matrix; calculating the road structure health index according to the road digital twin; processing the road structure health index using a gated quantum recurrent neural network to obtain the predicted health decline rate; when the predicted health decline rate exceeds a preset rate threshold, triggering a self-repair microcapsule rupture instruction to release asphalt regenerant to fill microcracks for automatic road repair.

[0006] In a possible implementation, at the edge computing node, based on the quantum entangled photon pair signal, the road strain distribution matrix is calculated, and the following processing is performed: An FBG array is deployed inside the road asphalt layer, and a quantum entangled photon pair generator is deployed beside the road; the quantum entangled photon pair generator generates a first photon beam and a second photon beam, the first photon beam is injected into the FBG array to obtain a first photon signal, and the second photon beam is transmitted to the edge computing node to obtain a second photon signal; according to the first photon signal and the second photon signal, the road strain distribution matrix is calculated.

[0007] In a possible implementation, for calculating the road strain distribution matrix according to the first photon signal and the second photon signal, the following processing is performed: Measure the coincidence counting rate of the first photon signal and the second photon signal, and calculate the road strain distribution matrix through a formula, and the formula is as follows: where, ∑ is the road strain distribution matrix, K is the material strain coefficient, ΔC is the coincidence counting rate, ΔC0 is the reference coincidence count, α is the temperature attenuation factor, T is the asphalt layer temperature, and n is the empirical coefficient.

[0008] In a possible implementation, for constructing a road digital twin by fusing the road three-dimensional scan information, the road surface pressure distribution information, and the road strain distribution matrix, the following processing is performed: Represent the road three-dimensional scan information as a road three-dimensional geometric model; represent the road surface pressure distribution information as a road pressure distribution map; represent the road strain distribution matrix as a road stress distribution map; fuse the road three-dimensional geometric model, the road pressure distribution map, and the road stress distribution map to obtain a road comprehensive feature tensor; construct a road digital twin according to the road comprehensive feature tensor.

[0009] In a possible implementation, for calculating the road structure health index according to the road digital twin, the following processing is performed: Collect historical road health status data, including the historical road comprehensive feature tensor and the corresponding historical road structure health index under known health statuses; according to the historical road comprehensive feature tensor and the historical road structure health index, supervise and train a health index calculation model; extract the current road comprehensive feature tensor from the road digital twin and input it into the health index calculation model to obtain the road structure health index.

[0010] In a possible implementation, when using a gated quantum recurrent neural network to process the road structure health index to obtain a predicted health decline rate, the following processing is performed: Collect time series data of historical road structure health indexes, including historical road structure health index sequences and corresponding historical health decline rate sequences under known health states; Train a gated quantum recurrent neural network according to the historical road structure health index sequences and the historical health decline rate sequences; Form a time series input vector by combining the current road structure health index and the road structure health indexes at the previous M time points, input the formed time series input vector into the trained gated quantum recurrent neural network, and calculate and output the predicted health decline rate.

[0011] In a possible implementation, when the predicted health decline rate exceeds a preset rate threshold, a self-repair microcapsule rupture instruction is triggered to release asphalt rejuvenator to fill microcracks for automatic road repair, and the following processing is also performed: Establish a meteorology-road health state association model according to the association between meteorological conditions and road health states; Input real-time meteorological parameters into the meteorology-road health state association model to obtain a road health expected change coefficient; Dynamically adjust the preset rate threshold according to the road health expected change coefficient.

[0012] In a possible implementation, the following processing is performed: The edge computing node includes a quantum entanglement strain calculation module, a road digital twin construction module, a health index calculation module, a health state prediction module, and a self-repair instruction trigger module.

[0013] It is intended to use the intelligent road infrastructure health state prediction method proposed in this application. First, at the edge computing node, based on quantum entanglement photon pair signals, calculate the road strain distribution matrix. Then, by fusing the road three-dimensional scanning information, the road surface pressure distribution information, and the road strain distribution matrix, construct a road digital twin. Then, according to the road digital twin, calculate the road structure health index. Next, use a gated quantum recurrent neural network to process the road structure health index to obtain a predicted health decline rate. Finally, when the predicted health decline rate exceeds a preset rate threshold, trigger a self-repair microcapsule rupture instruction to release asphalt rejuvenator to fill microcracks for automatic road repair. The technical effect of improving the real-time performance and accuracy of road infrastructure health state prediction is achieved. Description of the Drawings

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the methods according to the embodiments of this application. It should be understood that the operations before or below do not necessarily need to be precisely executed in sequence. On the contrary, as needed, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0015] Figure 1 It is a schematic flowchart of the method for predicting the health status of intelligent road infrastructure provided by the embodiments of this application.

[0016] Figure 2 It is a schematic flowchart of dynamically adjusting the preset rate threshold in the method for predicting the health status of intelligent road infrastructure provided by the embodiments of this application. Detailed implementation manners

[0017] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically lists the detailed implementation manners of this application.

[0018] In order to make the purpose, technical solutions and advantages of this application clearer, the following will further describe this application in detail in conjunction with the accompanying drawings. The described embodiments should not be regarded as limitations of this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0019] In the following descriptions, "some embodiments" are involved, which describe subsets of all possible embodiments. However, it can be understood that "some embodiments" can be the same subsets or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first\second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0020] The embodiments of this application provide a method for predicting the health status of intelligent road infrastructure, as Figure 1As shown, the method includes:

[0021] Step S100, at an edge computing node, calculate a road strain distribution matrix based on a quantum entangled photon pair signal.

[0022] Specifically, using the characteristics of quantum entanglement, entangled photon pairs are generated through a specific quantum communication device. One of the photons is sent to a road monitoring device, and the other remains at the edge computing node. When the photon on the road monitoring device is affected by road strain, its state changes and affects the photon remaining at the edge computing node through quantum entanglement. Among them, the edge computing node is a computing device deployed near the road for real-time processing of data collected from the road monitoring device. The road strain distribution matrix is a strain distribution matrix formed by calculating the strain conditions of various parts of the road using the output signal of the quantum communication device in combination with a specific algorithm. For example, the road is 100 meters long and 10 meters wide, divided into 1000 monitoring points. The strain data of each monitoring point is in microstrain (με) units, ranging from 0 to 1000 με. The strain distribution matrix is a two-dimensional array of 100x10, and each element represents the strain value of the corresponding monitoring point.

[0023] In a possible implementation manner, the step of calculating the road strain distribution matrix based on the quantum entangled photon pair signal at the edge computing node in step S100 further includes step S110, deploying an FBG array inside the road asphalt layer and deploying a quantum entangled photon pair generator beside the road. Specifically, a fiber Bragg grating (FBG) is a device that can reflect light of a specific wavelength, and its reflected wavelength has a linear relationship with the strain received by the FBG. Deploying an FBG array inside the road asphalt layer means burying a series of FBGs along different positions of the road to monitor the strain conditions of each point on the road. These FBGs are connected by optical fibers to form a network that can transmit optical signals. Quantum entanglement is a phenomenon in quantum mechanics where two or more particles are correlated in such a way that the state of one particle can instantaneously affect the state of another particle, regardless of how far apart they are. Deploying a quantum entangled photon pair generator beside the road, which can generate a pair of entangled photons, namely the first photon beam and the second photon beam.

[0024] Step S120: The quantum entanglement photon pair generator generates a first photon beam and a second photon beam. The first photon beam is injected into the FBG array to obtain a first photon signal, and the second photon beam is transmitted to the edge computing node to obtain a second photon signal. Specifically, the quantum entanglement photon pair generator generates a pair of entangled photons, which are respectively labeled as the first photon beam and the second photon beam. The first photon beam is injected into the FBG array deployed in the road asphalt layer through an optical fiber. Due to the strain sensitivity of the FBG, it will change the wavelength of the first photon beam according to the strain condition of the road, thereby generating a first photon signal containing road strain information. The second photon beam is directly transmitted to the edge computing node through another optical fiber as a reference signal.

[0025] Step S130: According to the first photon signal and the second photon signal, a road strain distribution matrix is calculated. Specifically, at the edge computing node, the quantum states of the first photon signal and the second photon signal are analyzed by quantum measurement techniques (such as quantum state tomography) to extract road strain information. Using the extracted road strain information and combining with the layout of the FBG array, a road strain distribution matrix is constructed. Each element of this matrix represents the strain value at the corresponding position on the road. This implementation method utilizes the combination of FBG and quantum entanglement technology to achieve high-precision real-time monitoring of road strain and improve the accuracy of road health status prediction. The introduction of the edge computing node makes data processing more efficient, enabling the calculation of the road strain distribution matrix to be completed in real time on-site, providing timely data support for subsequent prediction and repair.

[0026] In a possible implementation, for the step of calculating the road strain distribution matrix according to the first photon signal and the second photon signal in step S130, step S130 further includes step S131: measuring the coincidence counting rate of the first photon signal and the second photon signal, and calculating the road strain distribution matrix through the formula as follows:

[0027]

[0028] where ∑ is the road strain distribution matrix, K is the material strain coefficient, ΔC is the coincidence counting rate, ΔC0 is the reference coincidence counting, α is the temperature attenuation factor, T is the asphalt layer temperature, and n is the empirical coefficient.

[0029] Specifically, at the edge computing node, photon detectors are set to receive the first photon signal (transmitted from the FBG array) and the second photon signal (transmitted directly from the quantum entanglement photon pair generator) respectively. The photon detectors convert the photon signals into electrical signals, and the counter records the number of coincidence events. A coincidence event refers to an event where two detectors receive photons simultaneously or almost simultaneously within a certain time window. By measuring multiple times and calculating the proportion of coincidence events in the total events, the coincidence counting rate is obtained. This ratio reflects the quantum entanglement degree of the first photon signal and the second photon signal, and indirectly reflects the road strain information. A given formula is used to calculate the road strain distribution matrix, where K is the material strain coefficient, which is a constant determined in advance according to the characteristics of the road material. This coefficient is used to relate the quantum measurement result to the actual road strain. The reference coincidence count is the coincidence counting rate measured when there is no road strain (or the strain is very small and negligible), serving as a reference benchmark. The temperature attenuation factor is a function related to the asphalt layer temperature T, reflecting the influence of temperature on the quantum signal and road strain measurement. This function is obtained by fitting experimental data. T is the actual temperature of the asphalt layer, measured in real time by a temperature sensor. n is an empirical coefficient used to adjust the influence degree of the temperature attenuation factor on the coincidence counting rate, and this coefficient is optimized through experimental data. This implementation method uses the coincidence counting rate of quantum entanglement photon pairs to indirectly measure road strain, which can improve the measurement accuracy and sensitivity.

[0030] In a possible implementation, step S100 further includes step S140. The edge computing node includes a quantum entanglement strain calculation module, a road digital twin construction module, a health index calculation module, a health status prediction module, and a self-repair instruction trigger module.

[0031] Specifically, the quantum entanglement strain calculation module is the module responsible for calculating the road strain distribution matrix. It receives the first photon signal and the second photon signal transmitted from the quantum entanglement photon pair generator, and measures the coincidence counting rate between them. Inside the module, there are integrated photon detectors, counters, and coincidence logic circuits. The photon detectors convert the photon signals into electrical signals, the counters record the number of coincidence events, and the coincidence logic circuits judge whether the two detectors receive photons simultaneously according to the preset time window, so as to calculate the coincidence counting rate.

[0032] The road digital twin construction module is the module responsible for fusing the road three-dimensional scan information, the road surface pressure distribution information, and the road strain distribution matrix obtained by the quantum entanglement strain calculation module to construct a road digital twin. Inside the module, there are integrated data fusion algorithms and three-dimensional modeling software. The data fusion algorithms integrate data from different sources, and the three-dimensional modeling software constructs a three-dimensional model of the road, that is, the digital twin, according to the fused data.

[0033] The health index calculation module is responsible for calculating the road structure health index. The health index calculation algorithm is integrated inside the module. Based on information such as the road geometry, material properties, and strain distribution in the digital twin, this algorithm comprehensively evaluates the health status of the road and gives a quantified health index.

[0034] The health status prediction module is responsible for predicting the rate of road health decline. The model and training algorithm of the gated quantum recurrent neural network are integrated inside the module. This neural network can learn the relationship between historical health index data and the rate of health decline, so as to predict the future rate of health decline. This module receives historical health index data as training samples and current health index data as prediction inputs.

[0035] The self - repair instruction trigger module is responsible for triggering the self - repair micro - capsule rupture instruction to release asphalt rejuvenator to fill micro - cracks when the predicted rate of health decline exceeds the preset rate threshold. The comparison logic and instruction sending function are integrated inside the module. The comparison logic is responsible for comparing the predicted rate of health decline with the preset rate threshold. If it exceeds the threshold, the instruction sending function sends a rupture instruction to the self - repair system. This implementation method divides the edge computing node into multiple functional modules, which can process and analyze data related to the road health status more efficiently. Each module is responsible for a specific task, which not only improves the processing accuracy and speed but also facilitates system maintenance and upgrade.

[0036] Step S200: Construct a road digital twin by fusing the road three - dimensional scanning information, road surface pressure distribution information, and the road strain distribution matrix.

[0037] Specifically, use devices such as 3D laser scanners to scan the road to obtain the three - dimensional geometric information of the road. Through pressure sensors arranged on the road surface, the pressure distribution on the road surface is obtained in real - time. The road three - dimensional scanning information, road surface pressure distribution information, and road strain distribution matrix are fused to form the basic data of the road digital twin. Use visualization technology or virtual reality technology to construct the virtual environment of the road digital twin.

[0038] In a possible implementation, to construct a road digital twin by fusing road three-dimensional scanning information, road surface pressure distribution information, and the road strain distribution matrix, step S200 further includes step S210 of representing the road three-dimensional scanning information as a road three-dimensional geometric model. Specifically, a high-precision three-dimensional scanner is used to scan the road to obtain point cloud data of the road surface. Then, three-dimensional modeling software (such as AutoCAD, Blender, etc.) is used to process the point cloud data to construct a three-dimensional geometric model of the road, which accurately reflects the shape, size, and geometric features of the road.

[0039] Step S220 of representing the road surface pressure distribution information as a road pressure distribution map. Specifically, a pressure sensor array is arranged on the road surface to monitor the pressure distribution on the road surface in real time. The pressure data collected by the sensors is converted into a two-dimensional image form, that is, a road pressure distribution map, which visually shows the pressure magnitudes at different positions on the road surface.

[0040] Step S230 of representing the road strain distribution matrix as a road stress distribution map. Specifically, the road strain distribution matrix calculated using quantum entangled photon pair signals is converted into a road stress distribution map by means of color mapping or contour maps, etc., which shows the strain or stress states at different positions inside the road.

[0041] Step S240 of fusing the road three-dimensional geometric model, the road pressure distribution map, and the road stress distribution map to obtain a road comprehensive feature tensor. Specifically, tensor fusion technology is adopted to fuse the road three-dimensional geometric model, the road pressure distribution map, and the road stress distribution map. First, these three data sources are converted into tensor forms with the same size and resolution. Then, methods such as tensor decomposition, tensor multiplication, or tensor splicing are used to fuse them into a comprehensive feature tensor, which contains comprehensive information such as the three-dimensional geometric features, surface pressure distribution, and internal stress state of the road.

[0042] Step S250 of constructing a road digital twin according to the road comprehensive feature tensor. Specifically, using three-dimensional modeling software and simulation technology, a road digital twin is constructed using the road comprehensive feature tensor. This digital twin is a virtual road model that contains all the geometric features, surface pressure distribution, and internal stress state of the road. By updating the road comprehensive feature tensor in real time, the consistency between the digital twin and the real road state is maintained. Through the digital twin, the health state of the road can be monitored and predicted in real time. This implementation method improves the accuracy and reliability of road health state monitoring by integrating information from multiple data sources.

[0043] Step S300: Calculate the road structure health index based on the road digital twin.

[0044] Specifically, use an algorithm or software module to process and analyze the data in the road digital twin. Based on the analysis results, calculate the road structure health index. The road structure health index is an indicator that comprehensively reflects the health status of the road structure and is calculated based on geometric information, mechanical information, damage information, etc. in the road digital twin.

[0045] In a possible implementation, the step of calculating the road structure health index based on the road digital twin, step S300, further includes step S310: Collect historical road health status data, including historical road comprehensive feature tensors in known health states and corresponding historical road structure health indexes. Specifically, collect a large amount of historical road health status data, which includes road comprehensive feature tensors in known health states and corresponding historical road structure health indexes. The historical data can be sourced from the long-term records of the road monitoring system, and the collected data needs to be organized into a structured format. Among them, the road comprehensive feature tensor contains three-dimensional geometric information, pressure distribution information, and stress distribution information of the road, and these information are extracted and integrated from the original data using the same method as in steps S210 - S250. The historical road structure health index is a quantitative assessment of the road health condition, which is labeled by road maintenance experts according to the actual condition of the road. For example, there is a dataset containing 1000 historical records, and each record contains a road comprehensive feature tensor and the corresponding road structure health index. These feature tensors are four-dimensional arrays, where three dimensions represent the length, width, and different physical quantities (such as geometric height, pressure value, strain value) of the road respectively, and the fourth dimension is used to store the measured values of these physical quantities at different time points or different positions. The road structure health index is a floating-point number between 0 and 1, indicating the health condition of the road.

[0046] Step S320: Supervise and train a health index calculation model based on the historical road comprehensive feature tensor and the historical road structure health index. Specifically, use the collected historical data to supervise and train a health index calculation model. The goal of this model is to predict the road structure health index based on the input road comprehensive feature tensor. Deep learning models such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs) can be selected to process this data with spatial and temporal dependencies. Use the road comprehensive feature tensors in the historical dataset as inputs and the historical road structure health indexes as outputs to train the model. During the training process, adjust the parameters of the model by minimizing the error between the predicted value and the actual value. Use methods such as cross-validation to evaluate the performance of the model, and adjust the model architecture or parameters as needed to optimize the prediction results.

[0047] Step S330: Extract the current road comprehensive feature tensor from the road digital twin and input it into the health index calculation model to obtain the road structure health index. Specifically, through a specific algorithm or interface, extract the current road comprehensive feature tensor from the real-time constructed road digital twin and input it into the trained health index calculation model. The model predicts and outputs the current road structure health index based on the learned knowledge. This implementation method improves the efficiency and accuracy of calculating the road structure health index by collecting historical data, supervising and training the health index calculation model, and extracting features from the road digital twin for prediction.

[0048] Step S400: Process the road structure health index using a gated quantum recurrent neural network to obtain the predicted health decline rate.

[0049] Specifically, the gated quantum recurrent neural network is a deep learning model that combines the characteristics of quantum computing and recurrent neural networks, used to process time series data and predict future change trends. Use a quantum machine learning framework or deep learning software to build a gated quantum recurrent neural network model. Take the road structure health index as the input data and train the model to predict the future health decline rate.

[0050] In a possible implementation, the step of using a gated quantum recurrent neural network to process the road structure health index to obtain the predicted health decline rate, step S400 further includes step S410: Collect the time series data of historical road structure health indices, including the historical road structure health index sequence under known health states and the corresponding historical health decline rate sequence. Specifically, collect a large amount of time series data of historical road structure health indices through a road monitoring system. These data include the historical road structure health index sequence under known health states and the corresponding historical health decline rate sequence. The historical road structure health index sequence is a time series, where each time point corresponds to a road structure health index. The historical health decline rate sequence is calculated based on these health indices and represents the rate of change of the road health condition over time.

[0051] Step S420: Train a gated quantum recurrent neural network based on the historical road structure health index sequence and the historical health decline rate sequence. Specifically, use the collected historical data to train a gated quantum recurrent neural network. The goal of this network is to predict the future health decline rate based on the input time series of road structure health indices. The gated quantum recurrent neural network combines the advantages of quantum computing and recurrent neural networks and can exhibit higher efficiency and accuracy when processing time series data. The specific architecture of the model includes an input layer, a quantum layer, a recurrent layer, an output layer, etc. Use the historical road structure health index sequence as the input and the historical health decline rate sequence as the output to train the gated quantum recurrent neural network. During the training process, optimize the model's parameters through the quantum gradient descent method. Use a cross-validation strategy to avoid overfitting and adjust the model's hyperparameters according to the performance of the validation set.

[0052] Step S430: Compose the current road structure health index and the road structure health indices at the previous M time points into a time series input vector and input it into the trained gated quantum recurrent neural network to calculate and output the predicted health decline rate. Specifically, according to the current time point, obtain the current road structure health index from the road monitoring system and compose it with the health indices at the previous M time points to form an input vector. Input this vector into the gated quantum recurrent neural network for prediction, and the model predicts and outputs the future health decline rate based on the learned knowledge. This implementation method improves the accuracy and efficiency of prediction by collecting historical data, training the gated quantum recurrent neural network model, and inputting the current data in real time for prediction.

[0053] Step S500: When the predicted health decline rate exceeds the preset rate threshold, trigger the instruction to rupture the self-repair microcapsules, release the asphalt rejuvenator to fill the microcracks, and perform automatic road repair.

[0054] Specifically, the rate of health decline of the road is monitored and predicted, and compared with a preset rate threshold (a preset threshold used to determine whether the rate of health decline of the road has reached the level that requires triggering automatic repair). When the predicted rate of health decline exceeds the preset rate threshold, a trigger instruction is sent to the control system of the self-healing microcapsules through wireless communication technology to trigger their rupture and release the asphalt rejuvenator. Among them, the self-healing microcapsules are microcapsules containing asphalt rejuvenator and are embedded in the road materials. When microcracks appear on the road, the microcapsules will rupture and release the asphalt rejuvenator to fill the cracks. In the embodiment of the present application, quantum signals are used at the edge node to calculate the road strain matrix, and three-dimensional scanning and pressure distribution information are fused to construct a road digital twin. Based on this, the health index is calculated, the rate of health decline is predicted through a quantum neural network, and when it exceeds the speed limit, a self-healing instruction is triggered to release the rejuvenator to repair the road. By these technical means, the technical effects of improving the real-time performance and accuracy of the prediction of the health status of road infrastructure are achieved.

[0055] As Figure 2 shown, in a possible implementation manner, when the predicted rate of health decline exceeds the preset rate threshold and triggers the rupture instruction of the self-healing microcapsules to release the asphalt rejuvenator to fill the microcracks for automatic road repair, step S500 further includes step S510 of establishing a meteorological-road health status association model according to the association between meteorological conditions and the road health status. Specifically, a large amount of historical data is collected, including road health monitoring data (such as crack width, depth, distribution, road surface flatness, material strength, etc.) and synchronous meteorological data (such as temperature, humidity, rainfall, wind speed, etc.). The collected data is preprocessed to extract key features, such as the daily average value, maximum value, minimum value, etc. of meteorological parameters, and related indicators of the road health status. A machine learning algorithm (such as random forest) is used to train the model, with the input being the meteorological data and road health status data processed by feature engineering, and the output being the change trend or predicted value of the road health status. The performance of the model is evaluated through methods such as cross-validation, and the model parameters are adjusted according to the evaluation results to optimize the model.

[0056] Step S520, input the real-time meteorological parameters into the meteorological-road health status association model to obtain the expected change coefficient of road health. Specifically, real-time meteorological parameters, including temperature, humidity, rainfall, etc., are obtained through a meteorological monitoring station or a meteorological API. The real-time meteorological parameters are input into the trained meteorological-road health status association model to obtain the expected change coefficient of road health. The expected change coefficient of road health represents the expected degree or rate of change of the road health status under specific meteorological conditions.

[0057] Step S530: Dynamically adjust the preset rate threshold according to the road health expected change coefficient. Specifically, calculate an adjustment coefficient according to the road health expected change coefficient to adjust the preset rate threshold. For example, if the expected road health status will deteriorate significantly (such as the crack propagation accelerating under high temperature and dry conditions), increase the adjustment coefficient to lower the preset rate threshold, making it easier to trigger automatic repair. Multiply the preset rate threshold by the adjustment coefficient to obtain a new threshold. Compare the new threshold with the predicted health decline rate. If the predicted health decline rate exceeds the new threshold, trigger the instruction for the rupture of the self-repair microcapsules. This implementation method can more accurately predict the change trend of the road health status by considering the influence of meteorological conditions on the road health status, thereby improving the triggering accuracy of automatic repair.

[0058] The above specific implementation manners do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for predicting the health status of intelligent road infrastructure, characterized in that, Including: At the edge computing node, based on the quantum entangled photon pair signal, calculate the road strain distribution matrix; Construct a road digital twin by fusing the road three-dimensional scanning information, the road surface pressure distribution information, and the road strain distribution matrix; Calculate the road structure health index according to the road digital twin; Use a gated quantum recurrent neural network to process the road structure health index to obtain the predicted health decline rate; When the predicted health decline rate exceeds the preset rate threshold, trigger the self-repair microcapsule rupture instruction to release the asphalt regenerant to fill the microcracks for automatic road repair.

2. The method for predicting the health status of the intelligent road infrastructure according to claim 1, wherein The step of calculating the road strain distribution matrix based on the quantum entangled photon pair signal at the edge computing node includes: Deploy an FBG array inside the road asphalt layer and deploy a quantum entangled photon pair generator beside the road; The quantum entangled photon pair generator generates a first photon beam and a second photon beam. The first photon beam is injected into the FBG array to obtain a first photon signal, and the second photon beam is transmitted to the edge computing node to obtain a second photon signal; Calculate the road strain distribution matrix according to the first photon signal and the second photon signal.

3. The method for predicting the health status of the intelligent road infrastructure according to claim 2, wherein The step of calculating the road strain distribution matrix according to the first photon signal and the second photon signal includes: Measure the coincidence counting rate of the first photon signal and the second photon signal, and calculate the road strain distribution matrix through the formula. The formula is as follows: Where, ∑ is the road strain distribution matrix, K is the material strain coefficient, ΔC is the coincidence counting rate, ΔC0 is the reference coincidence counting, α is the temperature attenuation factor, T is the asphalt layer temperature, and n is the empirical coefficient.

4. The method for predicting the health status of intelligent road infrastructure according to claim 1, characterized in that, The step of constructing a road digital twin by fusing the road three-dimensional scanning information, the road surface pressure distribution information, and the road strain distribution matrix includes: Represent the road three-dimensional scanning information as a road three-dimensional geometric model; Represent the road surface pressure distribution information as a road pressure distribution map; Represent the road strain distribution matrix as a road stress distribution map; Fuse the road three-dimensional geometric model, the road pressure distribution map, and the road stress distribution map to obtain a road comprehensive feature tensor; Construct a road digital twin according to the road comprehensive feature tensor.

5. The method for predicting the health status of the intelligent road infrastructure according to claim 4, wherein The step of calculating the road structure health index according to the road digital twin includes: Collect historical road health status data, including historical road comprehensive feature tensors and corresponding historical road structure health indices under known health statuses; Supervise and train a health index calculation model according to the historical road comprehensive feature tensors and the historical road structure health indices; Extract the current road comprehensive feature tensor from the road digital twin and input it into the health index calculation model to obtain the road structure health index.

6. The method for predicting the health status of the intelligent road infrastructure according to claim 1, wherein, The step of using a gated quantum recurrent neural network to process the road structure health index to obtain the predicted health decline rate includes: Collect time series data of historical road structure health indices, including historical road structure health index sequences and corresponding historical health decline rate sequences under known health statuses; Train a gated quantum recurrent neural network based on the historical road structure health index sequence and the historical health decline rate sequence; Form a time series input vector by combining the current road structure health index and the road structure health indices at the previous M time points, and input it into the trained gated quantum recurrent neural network to calculate and output the predicted health decline rate.

7. The method for predicting the health status of the intelligent road infrastructure according to claim 1, wherein, When the predicted health decline rate exceeds the preset rate threshold, trigger a self-repair microcapsule rupture instruction to release asphalt rejuvenator to fill microcracks for automatic road repair, and it further includes: Establish a meteorology-road health status association model based on the association between meteorological conditions and road health status; Input real-time meteorological parameters into the meteorology-road health status association model to obtain the expected change coefficient of road health; Dynamically adjust the preset rate threshold according to the expected change coefficient of road health.

8. The method for predicting the health status of the intelligent road infrastructure according to claim 1, wherein, The edge computing node includes a quantum entanglement strain calculation module, a road digital twin construction module, a health index calculation module, a health status prediction module, and a self-repair instruction trigger module.

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