Digital Intelligent Control and Optimized Construction Method for Prefabricated Road Surface Plate Replacement

Abstract

The present application discloses a digital intelligent control and optimized construction method for prefabricated pavement slab replacement. By introducing innovative technologies such as adaptive zonal interpolation method, reinforcement learning, deep neural network, and temporal convolutional network, high-precision control and quality monitoring during the construction process of the pavement slab are achieved. Compared with traditional methods, the present invention optimizes the intelligent processing of various links such as construction preparation, prefabrication quality inspection, hoisting, and leveling, improving construction efficiency and quality. Through data-driven automatic control, human intervention is reduced, construction risks are lowered, and the precision and stability of pavement slab paving are ensured. In addition, hypergraph algorithms and ARIMA models are used for health monitoring and prediction, effectively improving the monitoring precision and response speed during the operation and maintenance stage.

Description

Technical Field

[0001] This application relates to the field of intelligent construction technology, and particularly to a digital intelligent control and optimized construction method for replacing slabs of assembled pavements. Background Art

[0002] With the rapid development of urbanization and transportation, the road traffic volume is increasing day by day, posing higher requirements for pavement performance and service life. During the long-term service process of traditional asphalt and cement concrete pavements, diseases such as cracks, potholes, and ruts are likely to occur, which not only affect driving comfort and safety but also increase road maintenance costs and traffic congestion. Traditional pavement repair methods usually require long-term road closure construction, causing serious interference to traffic. At the same time, pollution such as noise and dust generated during the construction process also has an adverse impact on the surrounding environment.

[0003] As a new type of pavement structure, assembled pavement has the advantages of fast construction speed, reusability, good environmental protection performance, etc., and shows great potential in meeting the increasing traffic demand and sustainable development. Its core lies in prefabricating the pavement into block units. When maintenance is required, only the damaged pavement slabs need to be quickly replaced to restore the pavement function, greatly shortening the construction period and reducing traffic interference.

[0004] Currently, the existing assembled pavement technology still faces some challenges in practical applications: for example, how to ensure the leveling accuracy during the rapid replacement process to guarantee pavement flatness; how to improve the durability of precast slabs, extend their service life, and reduce the replacement frequency; and how to achieve intelligent control and optimization of the construction process to improve construction efficiency and quality.

[0005] Especially in the field of highways, due to factors such as large traffic volume, heavy load, and complex environment, higher performance requirements are imposed on assembled pavements. Although the existing assembled technology for airport pavements is mature, its precast slab size is large and its self-weight is heavy, which is not suitable for highway scenarios. Therefore, developing a rapid replacement technology for small and lightweight precast slabs suitable for highways and realizing intelligent control and optimized construction in combination with Internet of Things technology is of great significance for improving highway maintenance levels and ensuring smooth road safety. Based on this, researching an intelligent control and optimized construction method for replacing slabs of assembled pavements based on the Internet of Things has important theoretical value and practical significance for promoting the popularization and application of assembled pavement technology in the highway field. Summary of the Invention

[0006] To solve the above technical problems, the embodiments of this application provide a digital intelligent control and optimized construction method for replacing slabs of assembled pavements, promoting the wide application of assembled pavement technology in the highway field.

[0007] The embodiment of the present application provides a digital intelligent control and optimized construction method for assembled pavement slab replacement, including the following steps:

[0008] Step S1) Intelligent construction preparation: Obtain the full-range scanning data of the construction site, generate a high-precision digital terrain model through three-dimensional modeling technology, introduce an adaptive partition interpolation method, dynamically adjust the interpolation accuracy, optimize the construction design to ensure that the construction plan adapts to complex terrains, and obtain the layout design of the pavement slabs;

[0009] Step S2) Intelligent quality inspection of precast pavement slabs: Construct a convolutional neural network model for defect identification, and generate an adversarial network to classify defect images;

[0010] Step S3) Intelligent hoisting of pavement slabs: Combine reinforcement learning and particle swarm optimization algorithms for hoisting path planning, and use the Kalman filter algorithm to control the hoisting accuracy in real time to ensure that the hoisting equipment avoids obstacles;

[0011] Step S4) Intelligent quality inspection after pavement slab paving: Construct an LSTM model to perform real-time monitoring and time-series analysis on the flatness and contact quality during the paving process to ensure the continuous stability of the paving quality;

[0012] Step S5) Intelligent leveling of pavement slabs: Automatically identify the grouting hole positions through a laser scanner or machine vision technology, combine the elevation deviation analysis with the previous data, control the grouting equipment to perform automatic grouting, and cooperate with vibration and pressure sensors to monitor the grouting process in real time to ensure the accuracy of the slurry ratio and filling, and optimize the leveling effect;

[0013] Step S6) Intelligent gap filling and sealing detection: Identify the geometric parameters of the gaps through a machine vision camera, use an improved extreme learning machine algorithm to predict the filling requirements, and use modified asphalt mortar and early-strength cement mortar for gap filling to ensure the uniformity and compressive strength of the gaps;

[0014] Step S7) Intelligent post-operation maintenance: Collect the strain, displacement, and vibration data of the pavement slabs, use the hypergraph algorithm and the ARIMA model for health monitoring and prediction, and formulate a maintenance plan;

[0015] Step S8) Global data management and optimization: Integrate and manage all data during the construction process through the Internet of Things platform, optimize the construction plan and process in real time to ensure the construction quality and efficiency, and dynamically adjust the construction strategy to achieve the best construction effect.

[0016] Further, in the step S1), when introducing the adaptive partition interpolation method in the process of constructing the digital terrain model, the interpolation accuracy is dynamically adjusted according to the terrain complexity of different regions to generate the digital terrain model: , where, is the interpolation result at position x, where x represents the coordinates on the digital terrain model. represents the weight function corresponding to the i-th known data point at position x. is the value of the known data point i, and n is the number of known data points in the space.

[0017] Furthermore, step S1) specifically includes:

[0018] The layout design is optimized through the constructed digital terrain model and on-site parameters, and using a reinforcement learning algorithm model: aiming at the flatness, material utilization rate, and time cost of the panel layout, an optimal solution is designed, and continuous evaluation and feedback are carried out through the policy iteration algorithm: , where is the optimal policy, is the immediate reward for each step, is the discount factor, is the expected operation, is to select the policy that maximizes the expected cumulative reward , t is the time step, are the state and action at time step t, respectively;

[0019] Based on the completed layout design, the base flatness is detected: a flatness analysis algorithm based on wavelet transform is adopted. , where is the original data of the signal in the time domain; is the complex conjugate of the mother wavelet; is the mother wavelet function; is the translation factor, and s is the scale factor.

[0020] Furthermore, step S2) specifically includes:

[0021] Adopt a machine vision system and combine it with an improved convolutional neural network for defect detection: , where is the activation value of the output layer, representing the detection result of cracks or damages; is the activation function; is the convolution kernel weight; is the feature of the input image; is the bias term; R is the number of feature dimensions;

[0022] Use a deep neural network for defect recognition, and generate an adversarial network to perform fusion analysis on multi-source data to generate a higher-quality defect image classification model , where represents the sample e sampled from the true data distribution, Let \(z\) denote the input sampled from a random noise distribution, and \(E\) denote the expectation operation, i.e., the average loss calculation of the samples. denotes the loss of the real samples, i.e., the logarithmic loss of the classification probability of the discriminator for the real data. denotes the loss of the generated samples, i.e., the logarithmic loss of the classification probability of the discriminator for the generated data; \(G\) are the parameters of the generator, and the generator optimizes these parameters to generate data close to the real data; \(D\) are the parameters of the discriminator, and the discriminator optimizes these parameters to accurately distinguish between real and generated data.

[0023] For internal quality inspection, non-destructive testing techniques are adopted and combined with the temporal convolutional network in deep learning for data modeling and analysis to dynamically track the change trend of the road slab during the production process. Each convolutional kernel will obtain the weighted features at each time step in the time series through the convolutional operation with the input features. After processing these features through a multi-layer convolutional network, a result evaluating the current internal quality state of the road slab will be finally output: , where is the predicted value at time step \(t\); is the convolutional kernel weight; TCN is the temporal convolutional operation to extract the data features at this moment; is the input feature at time step \(c - 1\); \(N\) is the length of the time series;

[0024] All the detected data will generate the quality score of the road slab through a multi-dimensional weighted evaluation model and generate an electronic tag with complete quality information: , where is the comprehensive quality score of the road slab; 、 are the weights of each quality index; is the score of the \(p\)th quality index; is the score of the \(q\)th depth feature; \(m\) is the number of quality indexes; \(v\) is the number of depth features.

[0025] Furthermore, the hoisting path planning in step S3) specifically includes:

[0026] S31: Through a multi-objective optimization method combining reinforcement learning and the particle swarm optimization algorithm, considering the movement trajectory of the hoisting equipment, the size, weight of the road slab, and the distribution of on-site obstacles, optimize the hoisting path to ensure that the equipment avoids obstacles and reduces the energy consumption and time of the equipment movement: , where represents the minimum distance between the hoisting equipment and the obstacle in the \(u\)th hoisting trajectory; 、 are the lateral and longitudinal offsets of the \(u\)th equipment path respectively; The optimization objective function adjusts the weight coefficient To balance different optimization objectives and achieve the optimal adjustment of the path; T is the number of hoisting trajectory segments;

[0027] S32: Establish a virtual 3D model synchronized with the construction site using digital twin technology, and use the Monte Carlo method to conduct risk assessment and uncertainty analysis of the hoisting process. The Monte Carlo method evaluates risks through random simulation of multiple scenarios: , where, is the estimated probability of potential risk; is the result of the m-th simulation; is the defined failure area, indicates that it is 1 when belongs to the failure area, otherwise it is 0; where the failure area represents the area exceeding any one of the maximum allowable tilt angle, maximum allowable displacement error, and maximum force on the hoisting rope; O represents the total number of simulations;

[0028] S33: For hoisting accuracy control, adopt the Kalman filter algorithm combined with RTK or UWB positioning system and high-precision sensors to fuse sensor data in real time to accurately estimate the position and attitude of the pavement slab: , where, is the estimated value at time k; is the Kalman gain; is the current observation value, is the observation matrix, and the initial observation matrix = ; , indicating that the initial pavement slab is located at a certain position in the site, with the xy axes of its three-dimensional coordinates being 0 and the initial height being 3 meters; in addition also includes setting the initial attitude of the pavement slab to , indicating that the pavement slab has no tilt initially, and the initial Kalman gain ;

[0029] During the hoisting process, the real-time monitoring system combines the anomaly detection algorithm to dynamically identify potential hoisting risks. When the sensor data corresponding to the force on the hoisting rope and the attitude of the pavement slab exceed the safety threshold, the hoisting risk is evaluated through a hybrid model of support vector machine and anomaly detection algorithm, , where, is the prediction result of anomaly detection; is the current sensor data; is the mean value of the data; is the preset safety threshold; when the difference between the current sensor data and the mean value exceeds the threshold , it indicates that there is an anomaly and an early warning is immediately issued.

[0030] Furthermore, the specific content of step S4) is as follows:

[0031] After the pavement slab paving is completed, a laser scanner or a structured light sensor carried by a mobile robot is used to quickly scan the pavement slab and intelligently detect key indicators; the key indicators at least include flatness, elevation difference, and the width of the gap between slabs.

[0032] Through a long short-term memory network, temporal analysis is performed on the scanned data to capture the dynamic change trend of the pavement slab flatness over time, and predict and optimize the future paving quality:

[0033]

[0034] Among them, is the forget gate, which determines the proportion of information to be discarded currently;

[0035] is the input gate, which determines the proportion of information to be updated currently;

[0036] is the output gate, which determines the proportion of information read from the cell state at the current moment;

[0037] is the cell state, which updates the cell state according to the outputs of the forget gate and the input gate;

[0038] is the hidden state, which is the network output at the current moment and is used to be passed to the LSTM cell at the next moment;

[0039] represents the hidden state at the previous moment and the input data at the current moment;

[0040] is the forget gate bias vector, which is used to adjust the output of the forget gate;

[0041] is the input gate bias vector, which determines the information to be updated into the cell state at the current moment;

[0042] is the output gate bias vector, which determines which information in the current cell state will be passed to the hidden state at the next moment;

[0043] is the cell state bias vector, which is used to update the current cell state;

[0044] , , , The weight matrices corresponding to the update of the forget gate, input gate, output gate, and cell state;

[0045] In terms of contact quality detection, the acoustic signals collected by knocking on the road slab are obtained, and the decision tree algorithm is used to analyze the propagation characteristics of sound waves to intelligently identify the contact state between the road slab and the base layer;

[0046] The decision tree algorithm realizes classification by continuously dividing the feature space and constructs a tree-like decision model , where is the sample set of the current node, is the number of categories, is the probability that the sample belongs to the th category;

[0047] By training the decision tree model, the phenomenon of hollowing or virtual adhesion between the road slab and the base layer is automatically identified based on the acoustic signal. Then, a combination of laser scanning, acoustic detection, and image recognition technologies is adopted, and the neural network algorithm is used to comprehensively analyze multi-source data. The optimization goal is to minimize the error, and the parameters are updated through the backpropagation algorithm to comprehensively evaluate the paving quality of the road slab and generate a detection report: , where is the loss function; is the learning rate; are the network parameters (weights and biases).

[0048] Furthermore, the specific content of step S5) is as follows:

[0049] During the leveling process of the road slab, first, the reserved grouting hole positions are automatically identified through a laser scanner or image recognition technology, and the elevation deviation of each area is analyzed in combination with the previous detection data;

[0050] Based on this, the intelligent grouting equipment automatically performs the proportioning, stirring, and transportation of the grout to ensure the uniformity and accuracy of grouting;

[0051] The vibration sensor and pressure sensor monitor the grout diffusion and filling conditions in real time, and dynamically optimize the grouting model according to the monitoring data to ensure the high efficiency and stability of the grouting process;

[0052]

[0053] Among them, is the real-time grouting height; is the target filling height; is the real-time diffusion radius; is the maximum allowable diffusion radius; , are the change amounts of flow rate and pressure respectively; , , , are the weight coefficients.

[0054] Further, the specific steps of step S6) are as follows:

[0055] Automatically identify and measure the width and depth of the gap through machine vision technology, predict the filling requirements using an improved extreme learning machine algorithm, and automatically calculate the most suitable filling material ratio and dosage; the extreme learning machine algorithm accelerates the training process by randomly generating hidden layer nodes and optimizing the output layer weights:

[0056] ,

[0057] where, is the output matrix of the hidden layer; , , …, are the input samples; is the output of the rd node of the hidden layer, is the number of samples; the output of each node is determined by the activation function ; is the output layer weight, is 's transpose matrix, is the identity matrix, is the regularization parameter, is the expected output, that is, the filling requirement;

[0058] During the filling process, the system uses fuzzy adaptive control technology to dynamically adjust the grouting flow rate, pressure, and slurry ratio; the fuzzy controller adjusts according to real-time sensor data: , where, is the grouting control output, is the fuzzy membership function, is the fuzzy control rule gain coefficient, is the change amount of the control input, and the fuzzy adaptive control ensures the precise control of the grouting process by dynamically adjusting the rules and gain coefficients; is the number of fuzzy rules (set to 5);

[0059] After the filling is completed, the filling effect of the gap is monitored in real time through an optical rheometer and laser measurement technology to ensure filling uniformity and quality; and image recognition technology is used to check for defects such as air bubbles and cracks.

[0060] Further, the specific steps of step S7) are as follows:

[0061] During the later operation and maintenance process of the road slab, the intelligent sensor network is responsible for collecting health monitoring data of strain, displacement, and vibration, and transmitting it to the cloud platform in real time through a wireless network;

[0062] Based on the hypergraph algorithm and multi-modal information fusion technology, the sensor data is deeply analyzed, , where, represents the total relationship between nodes; represents the node set; represents the edge set, representing the relationship between nodes; represents the hyperedge set;

[0063] An autoregressive integrated moving average model is adopted for prediction. By means of autoregression and moving average, the changing trend of the slab state is accurately captured, potential structural problems are predicted, and early warnings are issued in a timely manner: , where, are the model parameters at the current moment, is the gradient of the loss function at the current moment, is the learning rate.

[0064] Advantages of the present invention:

[0065] By introducing innovative technologies such as adaptive partition interpolation method, reinforcement learning, deep neural network (DNN), and temporal convolutional network (TCN), high-precision control and quality monitoring of the slab construction process are realized. Compared with traditional methods, the present invention optimizes the intelligent processing of each link such as construction preparation, precast quality inspection, hoisting, and leveling, improves construction efficiency and quality. Through data-driven automatic control, human intervention is reduced, construction risks are lowered, and the accuracy and stability of slab paving are ensured. In addition, health monitoring and prediction are carried out using the hypergraph algorithm and ARIMA model, effectively improving the monitoring accuracy and response speed in the operation and maintenance stage. The present invention has significant technological innovation and promotes the wide application of assembled pavement technology in the highway field. Description of the Drawings

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0067] Figure 1 is the flowchart of the present application. Detailed Embodiments

[0068] To make the application purpose, features, and advantages of this application more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the embodiments described below are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0069] The following further clarifies the present invention with reference to the accompanying drawings and specific embodiments.

[0070] In the description of this application, it should be understood that the orientation or positional relationships indicated by the terms "upper", "lower", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the accompanying drawings. These are only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to this application.

[0071] The following describes this application with specific embodiments:

[0072] This embodiment proposes an intelligent control and optimized construction method for prefabricated pavement slab replacement based on the Internet of Things. The specific operation steps are as follows:

[0073] Step S1: Intelligent construction preparation:

[0074] Use a DJI Matrice 300 RTK drone equipped with a Velodyne VLP-16 lidar to perform on-site terrain scanning, generate a high-precision digital terrain model (DTM), and introduce an adaptive zoning interpolation method to dynamically adjust the interpolation accuracy according to different terrain complexities, optimize the construction design, and ensure that the construction plan adapts to complex terrains and improves the design accuracy.

[0075] As a specific embodiment,

[0076] First, use a drone equipped with a lidar or an oblique photography camera to perform a full-range rapid scan of the construction site, and generate a high-precision digital terrain model (DTM) through three-dimensional modeling technology.

[0077] For the model generation process, traditional methods often rely on interpolation algorithms with fixed parameters, such as the Kriging method. However, they often face problems of low accuracy and poor adaptability when dealing with complex terrains, especially in areas with large terrain variations, and cannot effectively improve the interpolation accuracy. In contrast, the present invention innovatively introduces an adaptive partition interpolation method. This algorithm dynamically adjusts the interpolation accuracy according to the terrain complexity of different regions, generates a more accurate and adaptable digital terrain model, significantly overcomes the limitations of traditional methods, and improves the overall accuracy and computational efficiency: , where is the interpolation result at position x, represents the weight function corresponding to the i-th known data point at position x, is the value of the known data point i, and n is the number of known data points in the space.

[0078] Based on this model and on-site parameters, a reinforcement learning algorithm is used to optimize the layout design of the pavement slabs. The reinforcement learning model aims at the flatness, material utilization rate, and time cost of the slab layout, designs the optimal scheme, and continuously evaluates and feeds back through the policy iteration algorithm: , where is the optimal policy, is the immediate reward at each step, is the discount factor, is the expected operation, is to select the policy that maximizes the expected cumulative reward , t is the time step, are the state and action at time step t respectively;

[0079] During the subgrade flatness detection process, a flatness analysis algorithm based on wavelet transform is adopted,

[0080]

[0081] where is the original data of the signal in the time domain; is the complex conjugate of the mother wavelet; is the mother wavelet function; is the translation factor, and s is the scale factor.

[0082] This algorithm has higher accuracy and multi-scale characteristics compared with traditional methods. Traditional methods can only analyze signals of a single frequency and are difficult to comprehensively capture the multi-dimensional characteristics of flatness. In contrast, the algorithm based on wavelet transform can accurately extract different frequency components through multi-scale analysis and comprehensively reflect the local and overall flatness of the subgrade.

[0083] Step S2: Intelligent quality inspection of precast pavement slabs:

[0084] Integrate an automated production line with the Keyence CV-X series machine vision system and the LMI Technologies Gocator 2490 laser measurement system to perform real-time inspection on the road slabs and ensure production quality. Use a convolutional neural network (CNN) model under the TensorFlow framework for defect recognition, and combine it with a generative adversarial network (GAN) to classify defect images, further improving the inspection accuracy and ensuring that each road slab meets the design quality requirements. During the production process, use C50 high-strength concrete with a compressive strength ≥50 MPa to ensure the durability and structural safety of the slabs.

[0085] As a specific embodiment,

[0086] During the prefabrication of road slabs, the intelligent system integrates machine vision and laser measurement technologies through an automated production line to monitor and automatically adjust various key parameters in the production process in real time, ensuring the stability of prefabrication quality. Compared with the traditional manual monitoring and detection by a single measurement tool, which cannot achieve real-time feedback and automatic adjustment, the present invention introduces a machine vision system and combines it with an improved convolutional neural network (CNN) for defect detection, overcoming the problems of low accuracy and many missed detections in traditional manual visual inspection and simple image processing methods, and significantly improving the accuracy and efficiency of defect recognition: , where is the activation value of the output layer, representing the detection result of cracks or damages; is the activation function; is the convolutional kernel weight; is the feature of the input image; is the bias term; R is the number of feature dimensions.

[0087] In the quality inspection after production, use the generative adversarial network (GAN) in the deep neural network (DNN) to perform fusion analysis on multi-source data and generate a higher-quality defect image classification model:

[0088] , where, represents a sample e sampled from the true data distribution, represents an input z sampled from the random noise distribution, E represents the expectation operation, that is, the average loss calculation of the samples, represents the loss of the true sample, that is, the logarithmic loss of the classification probability of the discriminator for the true data, represents the loss of the generated sample, that is, the logarithmic loss of the classification probability of the discriminator for the generated data; G is the parameter of the generator, and the generator optimizes these parameters to generate data close to the true data; D is the parameter of the discriminator, and the discriminator optimizes these parameters to accurately distinguish between true and generated data;

[0089] Internal quality inspection uses non-destructive testing techniques such as ultrasonic or ray testing, and combines the Temporal Convolutional Network (TCN) in deep learning for data modeling and analysis to dynamically track the changing trends of the road slabs during the production process, thereby accurately identifying potential defects. Each convolutional kernel will obtain weighted features for each time step in the time series through convolution operations with the input features. After being processed by multiple layers of convolutional networks, these features finally output a result for evaluating the current internal quality status of the road slab (such as voids, density, etc.): , where is the predicted value at time step t; is the convolutional kernel weight; TCN is the temporal convolution operation to extract the data features at this moment; is the input feature at time step c - 1; N is the length of the time series;

[0090] Finally, all inspection data will be integrated into the intelligent quality rating system, and a quality score for the road slab will be generated through a multi-dimensional weighted evaluation model, generating an electronic label with complete quality information to achieve traceability and control of quality problems, and further strengthening the quality management in the subsequent construction stage.

[0091] Generate a quality score for the road slab through a multi-dimensional weighted evaluation model for all inspection data, generating an electronic label with complete quality information: , where is the comprehensive quality score of the road slab; , are the weights of each quality index; is the score of the p-th quality index (such as crack width, surface flatness, etc.); is the score of the q-th depth feature (such as internal density, void defect, etc.); m is the number of quality indexes; v is the number of depth features. By monitoring the quality score, re-production is carried out for the parts that do not reach the preset cut-off score.

[0092] Step S3: Intelligent lifting of road slabs:

[0093] Use a KUKA KR Quantec robot to cooperate with reinforcement learning and the Particle Swarm Optimization (PSO) algorithm for lifting path planning to ensure that the lifting equipment can avoid obstacles and reduce energy consumption. Combine the Siemens Simatic S7-1500 PLC and the CalmanFilter algorithm to real-time control the lifting accuracy through digital twin technology to ensure the efficiency and precision of the lifting operation process.

[0094] During hoisting, use 8.8-grade high-strength steel screws (screw diameter 20 mm) to ensure the stability and safety during hoisting. The screws are made of high-strength alloy steel materials to withstand large loads and maintain fatigue resistance. The hoisting process combines digital twin technology and Kalman filtering algorithm to achieve precise control of hoisting accuracy, avoid equipment collisions and minimize energy consumption to the greatest extent.

[0095] As a specific embodiment,

[0096] S31: For hoisting path planning, first, a multi-objective optimization method combining Reinforcement Learning (RL) and Particle Swarm Optimization (PSO) is used. Considering the movement trajectory of the hoisting equipment, the size, weight of the road slab, and the distribution of on-site obstacles, optimize the hoisting path to ensure equipment obstacle avoidance and reduce the energy consumption and time of equipment movement:

[0097] , where, represents the minimum distance between the hoisting equipment and the obstacle in the u-th hoisting trajectory; 、 are the lateral and longitudinal offsets of the u-th equipment path respectively; The optimization objective function adjusts the weight coefficient to balance different optimization objectives and achieve the optimal adjustment of the path; T is the number of hoisting trajectory segments.

[0098] S32: Based on this, use digital twin technology to establish a virtual three-dimensional model synchronized with the construction site, and use the Monte Carlo method for risk assessment and uncertainty analysis of the hoisting process. The Monte Carlo method evaluates risks through random simulations of multiple scenarios: , where, is the estimated probability of potential risks; is the result of the m-th simulation; is the defined failure area, represents 1 when belongs to the failure area, otherwise 0; The failure area represents the area exceeding any one of the maximum allowable tilt angle, maximum allowable displacement error, and maximum rope force; O represents the total number of simulations.

[0099] In terms of hoisting accuracy control, the Kalman filtering algorithm is combined with RTK or UWB positioning system and high-precision sensors to fuse sensor data in real time to accurately estimate the position and attitude of the road slab: , where, is the estimated value at time k; is the Kalman gain; is the current observation value, is the observation matrix, and the initial observation matrix = ; , indicating that the initial slab is located at a certain position in the site, with the xy axes of its three-dimensional coordinates being 0 and the initial height being 3 meters; in addition also includes setting the initial attitude of the slab to , indicating that initially the slab has no inclination and the initial Kalman gain .

[0100] During the hoisting process, the real-time monitoring system combined with the anomaly detection algorithm can identify potential hoisting risks. When the sensor data such as the force on the hoisting rope and the attitude of the slab exceed the safety threshold (in this embodiment, the safety threshold is set to 80% of the maximum load of the hoisting equipment), the system evaluates the hoisting risk through a hybrid model of support vector machine and anomaly detection algorithm.

[0101] , where is the prediction result of anomaly detection; is the current sensor data; is the mean value of the data; is the preset safety threshold; when the difference between the current sensor data and the mean value exceeds the threshold , it indicates that there is an anomaly and an early warning is immediately issued.

[0102] Step S4: Quality inspection after intelligent paving of the slab:

[0103] Use the Clearpath Robotics Jackal mobile robot equipped with the FARO Focus S70 laser scanner to monitor the flatness and contact quality of the slab in real time. Use the long short-term memory network (LSTM) under the TensorFlow framework to perform time series analysis on the scanned data to predict the change of paving quality and ensure the continuous stability of quality during the paving operation. Compared with the traditional static quality inspection method, the LSTM network can effectively process time series data, realize the prediction and optimization of paving quality, so as to identify potential problems in advance and provide solutions. In addition, the system combines the decision tree analysis method of acoustic signals to identify the contact state between the slab and the base layer, automatically detect the phenomenon of hollowing or loose adhesion, and further improve the accuracy and intelligence level of paving quality inspection.

[0104] Prefabricated slab specifications: The standard prefabricated slab size is 3750mm×3000mm×80mm, using C50 high-strength concrete, and the compressive strength ≥ 50MPa. The laser positioning system ensures that the level error is controlled within ±3mm.

[0105] As a specific embodiment

[0106] After the pavement of the road slab is completed, a mobile robot equipped with a laser scanner or a structured light sensor is used to quickly scan the road slab and intelligently detect key indicators such as flatness, height difference, and the width of the gap between slabs.

[0107] Next, through the long short-term memory network (LSTM), temporal analysis is performed on the scanned data. The LSTM network can effectively capture the dynamic change trend of the flatness of the road slab over time, so as to predict and optimize the future paving quality.

[0108] , where is the forget gate (determining the proportion of information to be discarded currently), is the input gate (determining the proportion of information to be updated currently), is the output gate (determining the proportion of information read from the cell state at the current moment), is the cell state (updating the cell state according to the outputs of the forget gate and the input gate), is the hidden state (the output of the network at the current moment, used to be passed to the LSTM cell at the next moment), represents the hidden state at the previous moment and the input data at the current moment, is the forget gate bias vector, used to adjust the output of the forget gate;

[0109] is the input gate bias vector, determining the information to be updated into the cell state at the current moment;

[0110] is the output gate bias vector, determining which information in the current cell state will be passed to the hidden state at the next moment;

[0111] is the cell state bias vector, used to update the current cell state;

[0112] , , , The weight matrices corresponding to the updates of the forget gate, input gate, output gate, and cell state.

[0113] In terms of contact quality detection, the system analyzes the propagation characteristics of sound waves by using the decision tree algorithm on the acoustic signals collected by knocking on the road slab, and intelligently identifies the contact state between the road slab and the base layer. The basic principle of the decision tree algorithm is to achieve classification by continuously dividing the feature space and constructing a tree-like decision model: , where is the sample set of the current node, is the number of categories, is that the sample belongs to the Probability of the class.

[0114] By training a decision tree model, the system can automatically identify the phenomenon of hollowing or loose adhesion between the slab and the base course based on acoustic signals. Subsequently, a combination of laser scanning, acoustic detection, and image recognition technologies is adopted, and the neural network (NN) algorithm is used to comprehensively analyze multi-source data. The optimization objective is to minimize the error, and the parameters are updated through the backpropagation algorithm. The paving quality of the slab is comprehensively evaluated, and a detailed inspection report is generated to provide key data support for subsequent intelligent grouting and leveling work: , where is the loss function; is the learning rate; are the network parameters (weights and biases).

[0115] Step S5: Intelligent slab leveling:

[0116] Automatically identify the reserved grouting holes through a FARO Focus S70 laser scanner or a Basler acA2000-50gm machine vision camera, and analyze the elevation deviation of each area in combination with the previous detection data.

[0117] Use a Graco Fusion Air Purge grouting device for automatic grouting to ensure the accurate proportioning, mixing, and transportation of the grout. Vibration sensors (such as PCB Piezotronics vibration sensors) and pressure sensors (such as Honeywell pressure sensors) monitor the grouting diffusion and filling process in real time, and the system dynamically adjusts the grouting parameters according to the sensor feedback data to optimize the grouting process.

[0118] Select modified cement slurry as the grouting material. The ratio is P.O4.25 cement: water: medium sand = 1: 2.5: 0.4, the compressive strength is ≥ 30 MPa, and the final setting time is about 2 hours. The grouting flow rate is controlled at 150 - 250 L / h, and the pressure is controlled at 0.6 - 0.9 MPa to ensure that the elevation deviation area is fully filled without excessive diffusion. After completion, use laser scanning technology to monitor the leveling effect to ensure a uniform layer, no air bubbles and cracks, and guarantee the construction quality.

[0119] As a specific embodiment, during the slab leveling process, first automatically identify the reserved grouting holes through a laser scanner or image recognition technology, and analyze the elevation deviation of each area in combination with the previous detection data. Based on this, the intelligent grouting equipment automatically performs the proportioning, mixing, and transportation of the grout to ensure the uniformity and accuracy of grouting. Vibration sensors and pressure sensors monitor the grouting diffusion and filling conditions in real time, and the system dynamically optimizes the grouting model according to the monitoring data to ensure the high efficiency and stability of the grouting process.

[0120] , where is the real-time grouting height; is the target filling height; is the real-time diffusion radius; is the maximum allowable diffusion radius; , are the change amounts of flow rate and pressure respectively; , , , are weight coefficients.

[0121] Step S6: Intelligent gap filling and sealing detection:

[0122] Identify the gap geometric parameters through a Basler acA2000-50gm machine vision camera, and use an improved extreme learning machine (ELM) algorithm to predict the filling requirements to ensure accurate matching. The filling materials are selected as modified asphalt mortar (compressive strength ≥ 25 MPa, ductility ≥ 10%) and early-strength cement mortar (initial setting time ≤ 2 hours, compressive strength ≥ 35 MPa). The modified asphalt mortar is used in the upper 1 / 2 area, and the early-strength cement mortar is used in the lower 1 / 2 area. The slurry flow rate is controlled at 200 - 300 L / h, and the pressure is 0.5 - 0.8 MPa to ensure uniformity and quality. After filling, lay a stress absorption layer (thickness 10 mm), and lay a protective layer through an automatic laying machine. The construction speed is 20 square meters per hour, and the layer thickness uniformity is controlled within ±1 mm.

[0123] As a specific embodiment:

[0124] During the process of treating the slab gap, the system automatically identifies and measures the width and depth of the gap through machine vision technology, providing accurate data support for subsequent intelligent filling. Based on these geometric parameters, the system uses an improved extreme learning machine (ELM) algorithm to predict the filling requirements, and automatically calculates the most suitable filling material ratio and dosage. The ELM algorithm accelerates the training process by randomly generating hidden layer nodes and optimizing the output layer weights:

[0125] ,

[0126] where is the hidden layer output matrix; , , …, are the input samples; is the output of the th node of the hidden layer, is the number of samples; the output of each node is determined by the activation function to determine; is the output layer weight, is the transpose matrix of is the identity matrix, is the regularization parameter, is the expected output, i.e., the filling requirement. This optimization formula obtains the optimal weight by minimizing the error, thus accelerating the training process and reducing the calculation time.

[0127] During the filling process, the system uses the fuzzy adaptive control (FAC) technology to dynamically adjust the grouting flow rate, pressure, and slurry ratio. The fuzzy controller makes adjustments based on real-time sensor data (such as pressure and flow rate):

[0128] , where is the grouting control output, is the fuzzy membership function, is the fuzzy control rule gain coefficient, is the change amount of the control input, is the number of fuzzy rules. Fuzzy adaptive control ensures precise control of the grouting process by dynamically adjusting the rules and gain coefficients.

[0129] After the filling is completed, the system uses an optical rheometer and laser measurement technology to monitor the filling effect of the gap in real time to ensure filling uniformity and quality. Finally, image recognition technology is used to check for defects such as bubbles and cracks to ensure the final compliance of the filling quality.

[0130] Step S7: Intelligent post-operation and maintenance:

[0131] In the post-operation and maintenance stage, by collecting health data such as the strain, displacement, and vibration of the road slab, and conducting in-depth analysis and prediction by combining the hypergraph algorithm and the ARIMA model, long-term monitoring of the road surface health condition is achieved. Based on the prediction results, the system can formulate an optimized maintenance plan, thereby effectively extending the service life of the road slab. Compared with the traditional graph neural network (GNN), the present invention uses the hypergraph algorithm, which can handle the high-order relationships between nodes, exceeding the limitation of only second-order relationships, and can effectively model the interaction between different types of sensors and the influence of environmental factors on the health state of the road slab. In addition, the system combines the autoregressive integrated moving average (ARIMA) model for health state prediction, uses historical monitoring data for incremental update, optimizes the model structure, and ensures accurate prediction of the health state of the road slab.

[0132] As a specific embodiment,

[0133] During the later operation and maintenance process of the road slab, the intelligent sensor network is responsible for collecting health monitoring data such as strain, displacement, and vibration, and transmitting it to the cloud platform in real time through a wireless network. Based on the hypergraph algorithm and multi-modal information fusion technology, the system deeply analyzes the sensor data. Compared with the traditional graph neural network (GNN), the hypergraph algorithm can not only handle the second-order relationships between nodes, but also deal with complex higher-order associations, such as the interaction between different types of sensors and the impact of environmental factors on the health of the road slab, improving the accuracy of health status assessment.

[0134] , where represents the node set (sensors, structural units, etc.); represents the edge set (representing the relationships between nodes); represents the hyperedge set (higher-order structural relationships, such as the time dependence of sensor data and environmental factors).

[0135] At the same time, the system uses the autoregressive integrated moving average (ARIMA) model for prediction. This model combines historical monitoring data and accurately captures the changing trend of the road slab state through autoregression and moving average, so as to more accurately predict potential structural problems and issue early warnings in a timely manner.

[0136]

[0137] where are the model parameters at the current moment, is the gradient of the loss function at the current moment, is the learning rate. This algorithm optimizes the model through continuous incremental updates to ensure the sensitivity of the model to new data.

[0138] Step S8: Global data management and optimization:

[0139] Integrate and manage all construction process data through the Siemens MindSphere Internet of Things platform, optimize the construction plan and process in real time, and ensure construction quality and efficiency. Through data analysis and feedback mechanisms, dynamically adjust construction strategies to ensure that each link achieves the best results.

[0140] As a specific embodiment:

[0141] During the entire intelligent construction process, the data collected in all links will be integrated and managed to form a complete construction process data chain. The system monitors the construction progress and quality in real time, compares and analyzes them with the plan, and dynamically optimizes the construction plan and process parameters through data feedback to ensure construction quality and efficiency. At the same time, the key information of the construction process is visually displayed in the form of 3D models and charts, and managers can retrieve and analyze historical data at any time to optimize future construction management processes.

[0142] The intelligent control and optimized construction method for prefabricated pavement slab replacement based on the Internet of Things proposed in the present invention realizes high-precision control and quality monitoring of the pavement slab construction process by introducing innovative technologies such as adaptive zonal interpolation method, reinforcement learning, deep neural network (DNN), and temporal convolutional network (TCN). Compared with traditional methods, the present invention optimizes the intelligent processing of each link such as construction preparation, prefabrication quality inspection, hoisting, and leveling, improving construction efficiency and quality. Through data-driven automatic control, it reduces human intervention, reduces construction risks, and ensures the accuracy and stability of pavement slab paving. In addition, the use of hypergraph algorithms and ARIMA models for health monitoring and prediction effectively improves the monitoring accuracy and response speed in the operation and maintenance stage. The present invention has significant technological innovation and promotes the wide application of prefabricated pavement technology in the highway field.

[0143] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other.

[0144] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations (such as quantity, shape, position, etc.) can be made to the technical solution of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. An assembled pavement slab replacement digital intelligent control and optimized construction method, characterized in that: It includes the following steps: Step S1) Intelligent construction preparation: Obtain the all-round scanning data of the construction site, generate a high-precision digital terrain model through 3D modeling technology, introduce the adaptive partition interpolation method, dynamically adjust the interpolation accuracy, optimize the construction design to ensure that the construction plan adapts to complex terrains, and obtain the layout design of the pavement slabs; Step S2) Intelligent quality inspection of precast pavement slabs: Construct a convolutional neural network model for defect identification, and generate an adversarial network to classify the defect images; Step S3) Intelligent hoisting of pavement slabs: Combine reinforcement learning and particle swarm optimization algorithm for hoisting path planning, and use the Kalman filter algorithm to control the hoisting accuracy in real time to ensure that the hoisting equipment avoids obstacles; Step S4) Intelligent quality inspection after pavement slab paving: Construct an LSTM model to perform real-time monitoring and time series analysis on the flatness and contact quality during the paving process to ensure the continuous stability of the paving quality; Step S5) Intelligent leveling of pavement slabs: Automatically identify the grouting hole positions through a laser scanner or machine vision technology, combine with the previous data analysis of elevation deviation, control the grouting equipment to perform automatic grouting, cooperate with vibration and pressure sensors to monitor the grouting process in real time, ensure the accuracy of slurry proportioning and filling, and optimize the leveling effect; Step S6) Intelligent gap filling and sealing detection: Identify the geometric parameters of the gaps through a machine vision camera, use an improved extreme learning machine algorithm to predict the filling requirements, and use modified asphalt mortar and early-strength cement mortar for gap filling to ensure the uniformity and compressive strength of the gaps; Step S7) Intelligent post-operation maintenance: Collect the strain, displacement, and vibration data of the pavement slabs, use the supergraph algorithm and ARIMA model for health monitoring and prediction, and formulate a maintenance plan; Step S8) Global data management and optimization: Integrate and manage all data during the construction process through the Internet of Things platform, optimize the construction plan and process in real time, ensure the construction quality and efficiency, and dynamically adjust the construction strategy to achieve the best construction effect.

2. The digital intelligent control and optimized construction method for replacing slabs of assembled pavement according to claim 1, characterized in that, In the step S1), when introducing the adaptive partition interpolation method in the process of constructing the digital terrain model, the interpolation accuracy is dynamically adjusted according to the terrain complexity of different regions to generate the digital terrain model: , where is the interpolation result at position x, x represents the coordinate on the digital terrain model, represents the weight function corresponding to the i-th known data point at position x, is the value of the known data point i, and n is the number of known data points in the space.

3. The digital intelligent control and optimized construction method for replacing slabs of assembled pavement according to claim 1, characterized in that The specific content of the said Step S1) includes: The layout design is optimized and formed through the constructed digital terrain model and on-site parameters, and by using a reinforcement learning algorithm model: with the flatness of the panel layout, material utilization rate, and time cost as the objectives, an optimal solution is designed, and continuous evaluation and feedback are carried out through the policy iteration algorithm: , where is the optimal policy, is the immediate reward for each step, is the discount factor, is the expected operation, is to select the policy that maximizes the expected cumulative reward , t is the time step, are the state and action at time step t, respectively; Perform base layer flatness detection based on the completed layout design: Use the flatness analysis algorithm based on wavelet transform, , where, is the original data of the signal in the time domain, is the complex conjugate of the mother wavelet, is the mother wavelet function, is the translation factor, and s is the scale factor.

4. The digital intelligent control and optimized construction method for replacing slabs of assembled pavement according to claim 1, characterized in that, The specific content of the said Step S2) includes: Defect detection is carried out by using a machine vision system and combining it with an improved convolutional neural network: , where is the activation value of the output layer, representing the detection result of cracks or damages; is the activation function; is the convolutional kernel weight; is the feature of the input image; is the bias term; R is the number of feature dimensions; Defect recognition is carried out using a deep neural network, and a generative adversarial network is used to perform fusion analysis on multi-source data to generate a higher-quality defect image classification model , where represents a sample e sampled from the true data distribution, represents an input z sampled from a random noise distribution, and E represents the expectation operation, that is, the average loss calculation of the samples, represents the loss of the true sample, that is, the logarithmic loss of the classification probability of the discriminator for the true data, represents the loss of the generated sample, that is, the logarithmic loss of the classification probability of the discriminator for the generated data; G is the parameter of the generator, and the generator optimizes these parameters to generate data close to the true data; D is the parameter of the discriminator, and the discriminator optimizes these parameters to accurately distinguish between true and generated data; For internal quality inspection, non-destructive testing technology is adopted and combined with the temporal convolutional network in deep learning for data modeling and analysis to dynamically track the change trend of the road slab during the production process. Each convolutional kernel will obtain the weighted features of each time step in the time series through the convolutional operation with the input features. After processing these features through multiple convolutional networks, a result evaluating the current internal quality state of the road slab will be finally output: , where is the predicted value at time step t; is the convolutional kernel weight; TCN is the temporal convolutional operation to extract the data features at that moment; is the input feature at time step c - 1; N is the length of the time series; Generate the quality score of the road slab through a multi-dimensional weighted evaluation model for all the detected data, and generate an electronic tag with complete quality information: , where is the comprehensive quality score of the road slab; , are the weights of each quality index; is the score of the p-th quality index; is the score of the q-th depth feature; m is the number of quality indexes; v is the number of depth features.

5. The digital intelligent control and optimized construction method for replacing slabs of assembled pavement according to claim 1, characterized in that, The specific content of the hoisting path planning in the said Step S3) includes: S31: A multi-objective optimization method combining reinforcement learning and particle swarm optimization algorithm, which considers the movement trajectory of the hoisting equipment, the size and weight of the pavement slab, and the distribution of on-site obstacles, optimizes the hoisting path to ensure obstacle avoidance of the equipment and reduce the energy consumption and time of the equipment movement: , where represents the minimum distance between the hoisting equipment and the obstacle in the u-th hoisting trajectory; , are the lateral and longitudinal offsets of the u-th equipment path respectively; the optimization objective function adjusts the weight coefficient to balance different optimization objectives and achieve the optimal adjustment of the path; T is the number of segments of the hoisting trajectory; S32: Establish a virtual 3D model synchronized with the construction site using digital twin technology, and use the Monte Carlo method to conduct risk assessment and uncertainty analysis of the hoisting process. The Monte Carlo method evaluates risks through random simulation of multiple scenarios: , where is the estimated probability of potential risk; is the result of the m-th simulation; is the defined failure region, indicates that when belongs to the failure region, it is 1, otherwise it is 0; where the failure region represents the region where any one of the maximum allowable tilt angle, maximum allowable displacement error, and maximum stress on the lifting rope is exceeded; O represents the total number of simulations; S33: For the hoisting precision control, the Kalman filter algorithm is combined with the RTK or UWB positioning system and high-precision sensors to fuse the sensor data in real time to accurately estimate the position and attitude of the pavement slab: , where is the estimated value at time k; is the Kalman gain; is the current observation value, is the observation matrix, and the initial observation matrix = ; , indicating that the initial pavement slab is located at a certain position in the site, with the xy axes of its three-dimensional coordinates being 0 and the initial height being 3 meters; in addition also includes setting the initial attitude of the pavement slab to , indicating that the pavement slab has no inclination initially, and the initial Kalman gain ; During the hoisting process, the real-time monitoring system combines anomaly detection algorithms to dynamically identify potential hoisting risks. When the sensor data corresponding to the force on the hoisting rope and the attitude of the road slab exceeds the safety threshold, the hoisting risk is evaluated through a hybrid model of support vector machine and anomaly detection algorithm. , where is the prediction result of anomaly detection; is the current sensor data; is the mean value of the data; is the preset safety threshold; when the difference between the current sensor data and the mean value exceeds the threshold , it indicates that there is an anomaly and an early warning is immediately issued.

6. The digital intelligent control and optimized construction method for replacing slabs of assembled pavement according to claim 1, wherein, The specific content of the said Step S4) is: After the pavement slab paving is completed, use a laser scanner or structured light sensor carried by a mobile robot to quickly scan the pavement slab and intelligently detect the key indicators; The said key indicators at least include flatness, elevation difference, and the width of the gaps between the slabs; Perform time series analysis on the scanning data through a long short-term memory network, capture the dynamic change trend of the pavement slab flatness over time, and predict and optimize the future paving quality: ; Among them, is the forget gate, which determines the proportion of information to be discarded currently; is an input gate that determines the proportion of information that needs to be updated currently; is the output gate, which determines the proportion of information read from the cell state at the current time; For the cell state, update the cell state according to the outputs of the forget gate and the input gate; In the hidden state, it is the network output at the current moment and is used to be passed to the LSTM cell at the next moment; represent the hidden state at the previous moment and the input data at the current moment; is the bias vector of the forget gate, which is used to adjust the output of the forget gate; is the input gate bias vector, which determines the information to be updated into the cell state at the current time; is the output gate bias vector, which determines which information in the current cell state will be passed to the hidden state at the next time step; is the unit state bias vector, which is used to update the current unit state; , , , weight matrices corresponding to the forget gate, input gate, output gate, and cell state update In terms of contact quality detection, obtain the acoustic signals collected by knocking on the pavement slab, use the decision tree algorithm to analyze the propagation characteristics of the sound waves, and intelligently identify the contact state between the pavement slab and the base layer; The decision tree algorithm realizes classification by continuously dividing the feature space and constructs a tree-like decision model , where is the sample set of the current node, is the number of classes, is the probability that the sample belongs to the th class; Automatically identify the phenomenon of hollowing or loose adhesion between the pavement slab and the base course based on acoustic signals by training a decision tree model. Then, combine laser scanning, acoustic detection, and image recognition technologies, and use a neural network algorithm to comprehensively analyze multi-source data. The optimization objective is to minimize the error, and the parameters are updated through the backpropagation algorithm to comprehensively evaluate the paving quality of the pavement slab and generate a detection report: , where is the loss function; is the learning rate; are the network parameters.

7. The digital intelligent control and optimized construction method for replacing slabs of the prefabricated pavement according to claim 1, characterized in that, The specific content of the said Step S5) is: During the pavement slab leveling process, first automatically identify the reserved grouting hole positions through a laser scanner or image recognition technology, and combine with the previous detection data to analyze the elevation deviation of each area; Based on this, the intelligent grouting equipment automatically performs slurry proportioning, stirring, and transportation to ensure the uniformity and accuracy of grouting; Vibration sensors and pressure sensors monitor the grouting diffusion and filling conditions in real time, and dynamically optimize the grouting model according to the monitoring data to ensure the high efficiency and stability of the grouting process; ; Among them, is the real-time grouting height; is the target filling height; is the real-time diffusion radius; is the maximum allowable diffusion radius; 、 are the change amounts of flow rate and pressure respectively; 、 、 、 are weight coefficients.

8. The digital intelligent control and optimized construction method for replacing slabs of assembled pavement according to claim 1, characterized in that, The specific content of step S6) is as follows: Automatically identify and measure the width and depth of the gap through machine vision technology, use an improved extreme learning machine algorithm to predict the filling requirements, and automatically calculate the most suitable filling material ratio and dosage; the extreme learning machine algorithm accelerates the training process by randomly generating hidden layer nodes and optimizing the output layer weights: , ; Among them, is the output matrix of the hidden layer; , ,…, are input samples; is the output of the th node in the hidden layer, is the number of samples; the output of each node is determined by the activation function ; is the weight of the output layer, is the transpose matrix of, is the identity matrix, is the regularization parameter, is the expected output, that is, the filling requirement;​ During the filling process, the system uses fuzzy adaptive control technology to dynamically adjust the grouting flow rate, pressure, and slurry ratio; the fuzzy controller is adjusted based on real-time sensor data: , where is the grouting control output, is the fuzzy membership function, is the fuzzy control rule gain coefficient, is the change in the control input, is the number of fuzzy rules; After filling, use an optical rheometer and laser measurement technology to monitor the filling effect of the gap in real time to ensure filling uniformity and quality; and use image recognition technology to check for defects such as air bubbles and cracks.

9. The digital intelligent control and optimized construction method for replacing slabs of assembled pavement according to claim 1, characterized in that The specific content of step S7) is as follows: During the later operation and maintenance process of the road slab, the intelligent sensor network is responsible for collecting health monitoring data of strain, displacement, and vibration, and transmitting it to the cloud platform in real time through a wireless network; Based on the hypergraph algorithm and multi-modal information fusion technology, deeply analyze the sensor data, , where, represents the total relationship between nodes; represents the node set; represents the edge set, representing the relationship between nodes; represents the hyperedge set; Use the autoregressive integrated moving average model for prediction, accurately capture the changing trend of the slab state through autoregression and moving average, predict potential structural problems, and issue early warnings in a timely manner: , where is the model parameter at the current moment, is the gradient of the loss function at the current moment, is the learning rate.

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