Sensor network layout and self-starting implementation method for monitoring long-term service performance of highway subgrade and pavement

Abstract

The invention discloses a sensor network layout and self-starting implementation method for monitoring the long-term service performance of a highway subgrade and pavement, and the method comprises the following steps: S1, analyzing the type and position of a disease based on the historical detection data of a highway, and constructing a subgrade and pavement state evaluation model; constructing a road monitoring sensor network probability model based on different types of sensor parameters; s2, road network level monitoring scheme decision optimization is carried out; s3, analyzing the gridding position and granularity of the sensor field under different working conditions in combination with the roadbed and pavement state evaluation model, deducing a monitoring probability threshold value, and establishing a genetic algorithm training data set; s4, optimizing the layout of the road monitoring sensor network through a multi-objective optimization genetic algorithm; s5, performing self-awakening control on the road monitoring sensor based on the game theory model; and S6, based on the traffic environment and the monitoring data, establishing a highway long-term service performance risk early warning mechanism. According to the invention, real-time early warning of the long-term service performance risk of the road can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of road engineering, and in particular to a sensor network deployment and self-starting implementation method for long-term service performance monitoring of highway roadbed and pavement. Background Art

[0002] With the rapid development of social economy, the construction of expressway network in my country has been rapid, the road traffic volume has been increasing, and the long-term service performance monitoring of highways has become increasingly important. Especially in complex traffic environment, the structural health of roadbed and pavement directly affects the service life and driving safety of highways.

[0003] At present, the monitoring of the health status of roadbed and pavement mainly relies on periodic manual inspection, which makes it difficult to achieve real-time monitoring and early warning of highway status. At the same time, the deployment of existing sensor networks is mostly guided by the monitoring area coverage model theory, and the monitoring accuracy cannot meet the needs. The monitoring redundancy rate is high, the energy consumption is large, the cost is high, and it is difficult to promote and apply. In addition, the current sensor network for highway monitoring is always in the state of sending data, which consumes energy quickly, the sensor replacement frequency is high, and the cost is high. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a sensor network deployment and self-starting implementation method for long-term service performance monitoring of highway roadbed and pavement, to achieve low-energy consumption and high-efficiency monitoring under different working conditions, and to make up for the problems of insufficient accuracy, excessive energy consumption and excessive cost of traditional monitoring methods.

[0005] Technical solution: A sensor network deployment and self-starting implementation method for long-term service performance monitoring of highway roadbed and pavement, including the following steps:

[0006] S1, based on the historical highway detection data, analyze the types and locations of diseases and build a roadbed and pavement condition assessment model; based on the parameters of different types of sensors, build a highway monitoring sensor network probability model;

[0007] S2, based on the improved weighted random forest algorithm, optimize the decision-making of road network-level monitoring scheme;

[0008] S3, combined with the roadbed and pavement condition assessment model, uses simulation software to analyze the location and granularity of the sensor field grid under different working conditions, deduce the monitoring probability threshold, and establish a genetic algorithm training data set;

[0009] S4, using the genetic algorithm dataset, optimizes the layout of highway monitoring sensor networks through multi-objective optimization genetic algorithm;

[0010] S5, based on the game theory model, realizes the self-wake-up control of highway monitoring sensors;

[0011] S6. Establish a risk warning mechanism for long-term highway service performance based on traffic environment and monitoring data.

[0012] Furthermore, in step S1, the implementation steps of establishing the roadbed and pavement condition assessment model are as follows:

[0013] SA1, divide the road network to be monitored into several sections, and regularly use the drop weight deflectometer and ground penetrating radar to detect and evaluate the subgrade and pavement conditions in each section, and obtain the subgrade and pavement deflection basin change data and the subgrade and pavement GPR data change trends respectively;

[0014] SA2 uses the law of large numbers and Gaussian distribution to quantify the anomaly of the data in the roadbed and pavement condition assessment of each road section, and uses deep neural networks to mark and interpolate the missing data in the deflection basin data and GPR data; at the same time, based on DNN, feature extraction is performed on the deflection basin data and GPR data, and the structural modulus E of each structural layer of the roadbed and pavement is calculated, and the dielectric constant ε is extracted. r and reflection waveform characteristics;

[0015] SA3, uses the YOLO network to determine the weak locations of the detected road sections and obtain the specific types of diseases that often occur;

[0016] SA4, combined with historical inspection data, spatially aligns the processed GPR data and FWD data to verify whether the bearing capacity meets the design standards; and evaluates the degradation trend of the bearing capacity through historical inspection data to identify potential voids, voids or subsidence areas in the roadbed; verifies the severity of the disease in combination with the abnormal area of ​​the FWD deflection basin, evaluates the structural strength, bearing capacity and disease conditions of the roadbed and pavement, and establishes a roadbed and pavement condition assessment model for this section.

[0017] Furthermore, in step S1, the implementation process of constructing the highway monitoring sensor network probability model is as follows:

[0018] The monitoring probability model of different types of sensors is calculated according to the following formula:

[0019] P(d)=P0·e -αd

[0020] Where P(d) is the detection probability, P0 is the detection probability of the sensor at zero distance, α is the attenuation factor, and d is the distance between the sensor and the target;

[0021] A type of sensor is selected, and the monitoring probability at different distances is measured experimentally. The target is placed at different distances from the sensor, the number of successful monitoring times of the sensor is recorded, the monitoring probability is calculated, and saved in the computer; after obtaining a sufficient amount of experimental data, the attenuation factor α is fitted using the least squares method to obtain the monitoring probability model of this type of sensor; and the monitoring probability model of all types of sensors is fitted using the same method as the highway monitoring sensor network probability model.

[0022] Furthermore, the road network level monitoring solution decision optimization includes the following steps:

[0023] S21, combining the actual situation of the specific road section, weighting the extracted feature data; if the road section has a specific disease risk, increasing the weight of the feature data corresponding to the specific disease;

[0024] S22, using Gini impurity minimization as the growth principle of the decision tree, accumulating the probability density functions of the monitoring schemes under the same indicator, and dividing the subtrees of similar sizes; taking the Gini value growth rate less than the threshold as the growth stop condition, and obtaining the classifier model based on the training sample;

[0025] S23, perform out-of-band data detection on the generated decision tree, remove and reconstruct unqualified decision trees, and ensure the accuracy of the final synthesized classifier; use the layer analysis method to calculate the sensitivity of the decision tree to different features, and determine the weight of the sub-classifier according to the feature sensitivity, and form the total classifier of the random forest according to the weight combination of each sub-classifier;

[0026] S24, based on the characteristic data of the roadbed and pavement in the road section, manually annotate and produce a data set according to the roadbed and pavement condition assessment model of the road section, and divide the data into different monitoring degree labels according to the monitoring degree of the road section: no monitoring required-label 0, ordinary monitoring-label 1, key monitoring-label 2;

[0027] S25, using the feature data as an input feature vector and the monitoring degree label as an output vector to create a data set; and using the data set to train the overall classifier.

[0028] Furthermore, the analysis of sensor field gridding and deduction of monitoring probability thresholds under different working conditions include the following steps:

[0029] S31, selecting the sensor type and the layout location based on the roadbed and pavement condition assessment model; selecting the sensor installation location according to the possible disease types and weak locations in the roadbed and pavement condition assessment model;

[0030] S32, according to the different sensor types, weak locations and possible diseases, use MATLAB to perform grid simulation analysis of the sensor field of the designated road section based on the optimized road network level monitoring scheme;

[0031] S33, combining the roadbed and pavement condition assessment model with the gridded sensor field of a specific road section, analyzes the most unfavorable position of the roadbed and pavement structure, and sets the monitoring probability threshold to 90%; for non-key monitoring areas, the monitoring probability threshold is set to 70%; a multi-sensor joint monitoring probability model is established in MATLAB, and the monitoring probability of the grid point is calculated as follows when multiple sensors jointly monitor:

[0032] P miss =(1-P1)(1-P2)...(1-P n )

[0033] P detect =1-P miss

[0034] Where P miss is the probability of monitoring failure, P n is the monitoring probability of the nth sensor of the same type to a specific point, P detect is the probability of joint monitoring;

[0035] Among them, the most unfavorable positions include positions where diseases may occur and positions where the bearing capacity decays fastest.

[0036] Further, in step S4, the optimization objectives of the genetic algorithm are determined to be: the number of sensors and the sensor monitoring coverage. By weighted combination of the two optimization objectives, a comprehensive objective function is obtained:

[0037] minF(n,P(d))=ω1n-ω2Q P

[0038] Where minF(n,P(d)) is the objective function, which decreases during the iteration process; P(d) is the monitoring probability model of this type of sensor; n is the number of sensors; Q P is the sensor monitoring coverage, with a value between 0 and 1; ω1 and ω2 are weight parameters;

[0039] Select the initial population. Each individual is usually encoded in the form of a binary string or integer array to represent the deployment status of sensors at different locations. Based on the probabilistic coverage model of sensor monitoring capabilities, the differences in requirements of different damage areas of the structure are considered. Under the condition of meeting the reliability, the monitoring capabilities required for each monitoring point under different environmental conditions are quantified as the optimization boundary, and the initial population selection is guided.

[0040] The objective function is used to comprehensively evaluate the coverage monitoring probability reliability and the number of sensors. The multi-objective optimization iterative process is used to integrate the sensor collaborative monitoring probability reliability and eliminate redundant deployment. The number of sensors and deployment locations of the monitoring system are gradually determined. After iteration, the Pareto optimal solution set is obtained, which is used to characterize the optimal deployment location of sensors on this road section.

[0041] Further, in step S5, the steps of implementing the self-wake-up control of the monitoring sensor based on the game theory model are as follows:

[0042] S51, establish the sensor energy consumption model, the total energy consumption C of the i-th sensor i Calculate according to the following formula:

[0043] C i =E s (S i )+E p (S i )+E t (S i )

[0044] Among them, E p (S i ) represents the sensing energy of the i-th sensor, E s (S i ) represents the processing energy of the i-th sensor, E t (S i ) represents the communication energy of the i-th sensor, S i is the i-th sensor in the sensor network;

[0045] S52, the switching of the sensor between the awake state and the dormant state is regarded as a game problem, and the profit function is defined according to the number of data successfully transmitted by the sensor in the awake state; the sensor node comprehensively considers the profit function and the amount of energy consumption to make a trade-off, and its game space is: {from awake to dormant, from awake to not enter dormant}, and the game model GT of the game party is defined as follows:

[0046] GT={N,K,{u i}}

[0047] In the formula, N represents the game party, which is all sensor nodes in the network; K is the strategy space adopted; u i is the utility function;

[0048] Iterative game model, in this process, the dynamic sleep threshold of the sensor node is determined based on the utility function. In one round of game, when the utility function value is greater than the sleep threshold, the sensor node remains awake; when the utility function value is less than the sleep threshold, the sensor enters the sleep state;

[0049] S53, introduces the sensor node penalty mechanism. When a sensor node always chooses to enter a dormant state to save its own energy consumption, it is called the selfish behavior of the sensor node. When a sensor node takes selfish behavior, the sensor network will mark the node and force the sensor node to stay awake in the subsequent M rounds. The M-th round of the game is defined as follows:

[0050] u i (s i ,s -i )=U i (s i ,s -i )-C i (s i ,s -i )

[0051] Among them, U i (s i ,s -i ) is the profit function of the ith sensor game in the sensor network; C i (s i ,s -i ) is the energy consumption function of the ith sensor game in the sensor network; s i The decision to wake up the i-th sensor in the sensor network; s -i Take the sleep decision for the i-th sensor in the sensor network.

[0052] Further, in step S6, the implementation process of establishing a highway long-term service performance risk early warning mechanism is as follows:

[0053] After determining the layout location and self-starting conditions, according to the monitoring data: vertical stress σ z , stress σ along the vehicle travel direction x , stress perpendicular to the vehicle's driving direction σ y , vertical strain ε z , strain ε along the vehicle travel direction x , strain perpendicular to the vehicle's direction of travel ε y , the temperature T and humidity H of each structural layer, and use DNN to calculate the pavement structure modulus E as a characterization indicator of the health of the roadbed and pavement structure. The calculation process is as follows:

[0054] In the formula, is the structural modulus output prediction vector, E i is the structural modulus of the i-th layer of the pavement structure; A=[σ z ,σ x ,σ y ,ε z ,ε x ,εy ,T,H]; θ is the parameter in the prediction model, including weights and biases;

[0055] The structural strength and bearing capacity of each structural layer of the roadbed and pavement are evaluated by the changes in the structural modulus E of each layer;

[0056] Use automatic data collection technology to monitor and record the traffic parameters and environmental parameters of a designated road section in real time, and evaluate the traffic and environmental conditions of the road section;

[0057] By combining monitoring data and traffic environment models, the downward trend in the bearing capacity of the roadbed and pavement structure, the location and development trend of cracks, voids, interlayer delamination, and subsidence can be predicted, ultimately achieving real-time early warning of the long-term service performance risks of the highway.

[0058] Compared with the prior art, the present invention has the following significant effects:

[0059] 1. Based on historical detection data and improved optimization algorithms, the present invention constructs a roadbed and pavement condition assessment model based on historical monitoring data, a probability coverage model based on sensor parameters and actual highway conditions, integrates the reliability of sensor collaborative monitoring probability through a multi-objective optimization iteration process, eliminates redundant deployment, gradually determines the number and deployment locations of sensors in the monitoring system, and establishes a monitoring basis that can be dynamically adjusted to ensure that the monitoring needs of key areas are met and the monitoring accuracy is further improved; it makes up for the problems of insufficient accuracy, high energy consumption, and high cost of traditional monitoring methods, and can monitor the roadbed and pavement structure status in real time;

[0060] 2. Based on the game theory model, the present invention optimizes the system cooperation strategy under the premise of ensuring the monitoring reliability, gradually determines the self-starting strategy of each node sensor in the network, ensures the monitoring accuracy of key areas while meeting the reliability and reducing the system power consumption, thereby realizing efficient and low-redundancy monitoring of the structural health status, and providing technical support and theoretical basis for improving the long-term service performance and intelligent management of highways. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flowchart of the invention;

[0062] Figure 2 The present invention is a flow chart of the self-awakening control of the highway monitoring sensor based on the game theory model. DETAILED DESCRIPTION

[0063] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0064] like Figure 1As shown, the present invention proposes a sensor network deployment and self-starting strategic optimization method for long-term service performance monitoring of highway subgrade and pavement. The method uses a strategic optimization method to build a subgrade and pavement state evaluation model, extract structural state characteristic parameters, iterate genetic algorithms, and iterate game theory to analyze the optimal sensor deployment scheme and self-starting strategy under different working conditions, so as to achieve real-time early warning of long-term service performance risks of highways and provide suggestions for subsequent subgrade and pavement detection schemes. The method specifically includes the following steps:

[0065] Step 1: Based on the historical highway detection data, analyze the types and locations of defects and build a roadbed and pavement condition assessment model; based on the parameters of different types of sensors, build a highway monitoring sensor network probability model;

[0066] (i) Based on the historical data of FWD (Falling Weight Deflectometer) and ground penetrating radar detection of the road section, DNN (Deep Neural Networks) was used for feature extraction, and the YOLO network was used to analyze the type and location of the disease, and the information was integrated to establish a roadbed and pavement condition assessment model;

[0067] The implementation steps of establishing the roadbed and pavement condition assessment model are as follows:

[0068] Step A1, divide the road network to be monitored into several sections, and use the drop weight deflectometer and ground penetrating radar to detect and evaluate the roadbed and pavement conditions in each section every month; measure the deflection of each point on the road surface by using the drop weight deflectometer, and comprehensively compare the deflection basin data of the roadbed and pavement at different times to obtain the deflection basin change data of the roadbed and pavement; detect the roadbed and pavement by ground penetrating radar to obtain the GPR (Ground Penetrating Radar) raw data at a certain moment, comprehensively compare the ground penetrating radar detection results at different times, and analyze the change trend of the GPR data of the roadbed and pavement.

[0069] Step A2: Use the law of large numbers and Gaussian distribution to perform quantitative evaluation of the data in the roadbed and pavement condition assessment of each road section, and use deep neural networks to mark and interpolate the data with abnormalities in the deflection basin data and GPR data; at the same time, based on DNN, extract features from the deflection basin data and GPR data, and inversely calculate the structural modulus E of each structural layer of the roadbed and pavement, reflecting the changing trend of the bearing capacity of the roadbed and pavement structure; extract the dielectric constant ε r , reflection waveform characteristics and other characteristic data are used to characterize the development trend of cracks, voids, interlayer delamination, settlement and other defects in the roadbed and pavement structure.

[0070] Step A3, using the YOLO network to determine the weak position of the detected road section and obtain the specific types of diseases that often occur;

[0071] Step A4, in combination with the above-mentioned historical detection data, the processed GPR data and FWD data are spatially aligned to check whether the bearing capacity meets the design standard; and the degradation trend of the bearing capacity is evaluated through the historical detection data to identify potential cavities, voids or subsidence areas in the roadbed; in combination with the abnormal area of ​​the FWD deflection basin, the severity of the disease is verified, the structural strength, bearing capacity and disease condition of the roadbed and pavement are evaluated, and a roadbed and pavement condition evaluation model for the section is established.

[0072] (II) By analyzing the parameters of different types of sensors and conducting experiments, a probability model of the highway monitoring sensor network is established using experimental data and an exponential decay model. The implementation process is as follows:

[0073] In the laboratory, install one different type of sensor required for road monitoring, and connect the sensor to the computer wirelessly. Turn on the computer to ensure that the computer and the sensor are connected normally. Calibrate different sensors and prepare to collect data. Select the exponential decay model to describe the influence of distance on the monitoring probability. The model probability is calculated according to formula (1):

[0074] P(d)=P0·e -αd (1)

[0075] Where P(d) is the detection probability, P0 is the detection probability of the sensor at zero distance (contact monitoring), α is the attenuation factor, and d is the distance between the sensor and the target.

[0076] A type of sensor is selected, and the monitoring probability at different distances is measured experimentally. The target is placed at different distances from the sensor, the number of successful monitoring times of the sensor is recorded, the monitoring probability is calculated, and saved in the computer; after obtaining a sufficient amount of experimental data, the attenuation factor α in the exponential attenuation model is fitted using the least squares method to obtain the monitoring probability model of this type of sensor; and the monitoring probability model of all types of sensors is fitted using the same method as the highway monitoring sensor network probability model.

[0077] Step 2: Optimization of road network-level monitoring scheme decision based on improved weighted random forest algorithm;

[0078] The decision optimization of the road network level monitoring solution includes the following steps:

[0079] Step 21, introduce a weighting mechanism to generate a decision tree: Combined with the actual situation of a specific road section, extract the dielectric constant ε from the extracted feature data structure modulus E r , reflection waveform characteristics A, signal attenuation rate α r, two-way travel time t, and signal phase difference φ are weighted. If the road section has no disease risk in historical detection, the weight of the structural modulus E is increased; if the road section has a specific disease risk, the weight of the characteristic data corresponding to the disease is increased, thereby increasing the influence weight of the characteristic indicators that characterize the disease and highlighting the important disease characteristic indicators;

[0080] Step 22, using Gini impurity minimization as the growth principle of the decision tree, the probability density functions of the monitoring schemes under the same indicator are accumulated and divided into subtrees of similar size; the Gini value growth rate less than the threshold is used as the growth stop condition to obtain a classifier model based on the training sample. The process is shown in formulas (2) and (3).

[0081]

[0082]

[0083] In the formula, D represents the divided training sample set; is the number of decision subtrees; β is the partition feature of the training sample set; GiniIndex(D,β) is the sum of the Gini values ​​of the current partitioned sample training set; N is the number of samples in set D; N v is the vth set D v The number of samples; d is the number of different samples in set D, p i is the proportion of the i-th sample in the set D to all samples; p(β) is the probability density coincidence distribution function of feature β, which is the probability set of the partition points of feature β in random sampling; is the probability density coincidence distribution function of the partition point set

[0084] Step 23, generating a high-precision classifier based on the random forest algorithm: performing out-of-band data detection on the generated decision tree, removing and reconstructing unqualified decision trees, and ensuring the accuracy of the final synthesized classifier; using the analytic hierarchy process to calculate the sensitivity of the decision tree to different features according to formula (4), and determining the weight of the sub-classifier according to the feature sensitivity, and forming the total classifier of the random forest according to the weight combination of each sub-classifier;

[0085]

[0086] In the formula, The roadbed and pavement condition evaluation feature d (the feature index extracted in step 1, i.e., the distance between the sensor and the target) is used to evaluate the decision tree J i Sensitivity; T OOB is the classification accuracy of the decision tree classifier; T OOB_d is the decision tree J after deleting feature d i Out-Of-Bag Accuracy

[0087] Step 24, set the road section monitoring intensity label: extract the dielectric constant ε based on the structural modulus E of the roadbed and pavement in the road section r , reflection waveform characteristics A, signal attenuation rate α r , two-way travel time t, signal phase difference φ feature data, refer to the road section roadbed and pavement condition assessment model for manual annotation to produce a data set, and divide it into: no monitoring required (label 0), ordinary monitoring (label 1), and key monitoring (label 2) according to the different monitoring levels of the road section;

[0088] Step 25, training model to select monitoring scheme: extract dielectric constant ε from structural modulus E r , reflection waveform characteristics A, signal attenuation rate α r , two-way travel time t, signal phase difference φ and other feature data are used as input feature vectors, and the monitoring degree label is used as the output vector to make a data set. The data set is used to train the overall classifier, and finally a data-driven monitoring solution is obtained. The process is shown in formula (5).

[0089]

[0090] In the formula, For classification labels The predicted probability, the final classification label is the largest probability category, where c = 0, 1, 2; To extract feature data based on the original detection data, T is the temperature of each structural layer; H is the humidity of each structural layer; is the predicted probability of the kth tree for category c.

[0091] Step 3: Combined with the roadbed and pavement condition assessment model, simulation software is used to analyze the location and granularity of the sensor field grid under different working conditions, deduce the monitoring probability threshold, and establish a genetic algorithm training data set;

[0092] Sensor field gridding and monitoring probability threshold analysis include the following steps:

[0093] Step 31, based on the roadbed and pavement condition assessment model, select the sensor type and layout location; in this embodiment, the sensor types selected are stress sensors, strain sensors, temperature sensors and humidity sensors, which are used to respectively detect the three-dimensional stress (vertical stress σ z , stress σ along the vehicle travel direction x , stress perpendicular to the vehicle's driving direction σ y ), three-dimensional strain (vertical strain ε z , strain ε along the vehicle travel direction x , strain perpendicular to the vehicle's direction of travel εy ), the temperature T and humidity H of each structural layer are monitored; according to the possible disease types and weak positions in the roadbed and pavement condition assessment model, the sensor installation positions are selected. For the positions where fatigue cracking of the asphalt layer may occur, the tensile strain at the bottom of the asphalt layer is monitored; for the positions where fatigue cracking of the inorganic binder stabilization layer may occur, the horizontal tensile stress at the bottom of the base layer is monitored; for the positions where subsidence may occur, the compressive strain on the top surface of the roadbed is monitored; the temperature sensors and humidity sensors are mainly installed at the bottom of each structural layer.

[0094] Step 32, divide the sensor field granularity based on the monitoring scheme and the roadbed and pavement condition assessment model: according to the sensor type, weak position and possible disease, use MATLAB based on the optimized road network-level monitoring scheme to perform grid simulation analysis on the sensor field of the specified section. The specific method is as follows: according to the type and characteristics of the roadbed and pavement structure, the main installation position of the monitoring sensor, the geometric model of the grid division of the specific area is selected as three-dimensional, and each grid point uses (x, y, z) coordinates to represent its position; the granularity of the grid division is determined according to the importance of the monitoring scheme, the trend of bearing capacity decline, the possible disease type and the monitoring accuracy requirements; define the geometric boundary of the monitoring area, generate three-dimensional grid points through the meshgrid function, and define the probabilistic monitoring model of the sensor in MATLAB; considering the actual situation, in the specified section, create a Boolean matrix to simulate the existence of obstacles, mark whether each grid point is blocked by obstacles, and obtain a suitable grid sensor field for the section to provide data support for the establishment of the probability threshold.

[0095] Step 33, simulation and deduction of sensor coverage probability threshold: Combine the roadbed and pavement condition assessment model with the gridded sensor field of a specific road section, analyze the most unfavorable position of the roadbed and pavement structure (including the possible disease position and the position with the fastest bearing capacity decay), set the monitoring probability threshold to 90%, and set the monitoring probability threshold to 70% for non-key monitoring areas; establish a multi-sensor joint monitoring probability model in MATLAB, and when multiple sensors jointly monitor the grid points, the monitoring probability is calculated according to equations (6) and (7). For non-key areas, a higher overall coverage rate can be achieved by appropriately reducing the monitoring probability threshold of a single point; determine the appropriate threshold by continuously adjusting the sensor distribution position and the monitoring probability threshold to ensure that the sensors are adequately covered while the monitoring redundancy is as small as possible; organize the data obtained during the adjustment process into a genetic algorithm data set for further optimization.

[0096] P miss =(1-P1)(1-P2)...(1-P n ) (6)

[0097] P detect=1-P miss (7)

[0098] Where P miss is the probability of monitoring failure, P n is the monitoring probability of the nth sensor of the same type to a specific point, P detect is the probability of joint monitoring.

[0099] Step 4, using the established genetic algorithm data set, optimize the layout of the highway monitoring sensor network through a multi-objective optimization genetic algorithm;

[0100] The non-dominated sorting genetic algorithm (NSG-II) with elite strategy based on multi-objective optimization is used. First, the optimization objectives and boundary conditions of the genetic algorithm are determined. There are two optimization objectives: the number of sensors and the sensor monitoring coverage rate. By weighted combination of the two optimization objectives, the comprehensive objective function is obtained as shown in formula (8). Taking the key monitoring area as an example, the boundary conditions are shown in formula (9). The initial population is selected. Each individual is usually encoded in the form of a binary string or an integer array, indicating the deployment status of the sensor at different locations. Based on the sensor monitoring capability probabilistic coverage model, the differences in the requirements of different structural damage areas are considered. Under the condition of meeting the reliability, the monitoring capability required by each monitoring point under different environmental conditions is quantified as the optimization boundary, and guides the selection of the initial population. The objective function is used to comprehensively evaluate the coverage monitoring probability reliability and the number of sensors. The multi-objective optimization iterative process integrates the sensor collaborative monitoring probability reliability and eliminates redundant deployment. The number of sensors and deployment locations of the monitoring system are gradually determined, and a monitoring basis that can be dynamically adjusted is established. After iteration, the Pareto optimal solution set is obtained, which is used to characterize the optimal deployment location of the sensor on the road section.

[0101] minF(n,P(d))=ω1n-ω2Q P (8)

[0102] n>0,Q P >0.9 (9)

[0103] Where minF(n,P(d)) is the objective function, which decreases during the iteration process; P(d) is the monitoring probability model of this type of sensor; n is the number of sensors; Q P is the sensor monitoring coverage, with a value between 0 and 1; ω1 and ω2 are weight parameters.

[0104] Step 5: Establish a game model in PyCharm according to the determined sensor layout plan, and realize sensor self-wake-up control while ensuring the reliability of roadbed and pavement structure monitoring;

[0105] The steps to implement the self-wake-up control of the monitoring sensor based on the game theory model are as follows:

[0106] Step 51, first establish a sensor energy consumption model: the energy consumed by the sensor during the detection process is related to many factors, including the sensing energy E p (S i ), processing energy E s (S i ), communication energy E t (S i ), the above three energy consumptions are determined by the sensor itself, so the total energy consumption of a single sensor is defined according to formula (10);

[0107] Step 52, then establish a sensor network game model: the rational switching of the sensor between the awake state and the sleep state can be regarded as a game problem, and the game model is expressed as formula (10); the profit function is defined according to the number of data successfully transmitted by the sensor in the awake state, and the sensor node comprehensively considers the profit function and the amount of energy consumption to make a trade-off, and its game space is: {from awake to sleep, from awake not to sleep}, and the game model GT of the game party is defined as shown in formula (11); iterate the game model, in this process, determine the dynamic sleep threshold of the sensor node based on the utility function. In a round of game, when the utility function value is greater than the sleep threshold, the sensor node remains awake, and when the utility function value is less than the sleep threshold, the sensor enters the sleep state;

[0108] Step 53, finally introduce the sensor node penalty mechanism: when the sensor node always chooses to enter the dormant state to save its own energy consumption, it is called the selfish behavior of the sensor node; when the sensor node takes selfish behavior, the sensor network will mark the node and force the sensor node to stay awake in the subsequent M rounds, where the M-th round of game is defined according to formula (12), so as to ensure that the sensor node can wake up in time in the subsequent game process, and finally the entire sensor network enters the Nash equilibrium, realizing the self-starting control of sensor network monitoring when the deployment plan is determined and the reliability is met. The process is as follows Figure 2 shown.

[0109] C i =E s (S i )+E p (S i )+E t (S i ) (10)

[0110] In the formula, S i is the i-th sensor in the sensor network; C i is the total energy consumed by the sensor.

[0111] GT={N,K,{u i}} (11)

[0112] u i (s i ,s -i )=U i (s i ,s -i )-C i (s i ,s -i ) (12)

[0113] In the formula, N represents the game party. In this step, every node in the sensor network participates in sending sensor information, so the game party is all sensor nodes in the network; K is the strategy space adopted; u i is the utility function; U i (s i ,s -i ) is the profit function of the ith sensor game in the sensor network; C i (s i ,s -i ) is the energy consumption function of the ith sensor game in the sensor network; s i The decision to wake up the i-th sensor in the sensor network; s -i Take the sleep decision for the i-th sensor in the sensor network.

[0114] Step 6: Establish a risk warning mechanism for long-term highway service performance based on traffic environment and monitoring data; perform modulus back calculation based on real-time monitoring data, and combine the roadbed and pavement status assessment model and traffic environment information to provide guidance for subsequent monitoring and maintenance of roadbed and pavement diseases;

[0115] The implementation process of establishing a highway long-term service performance risk early warning mechanism based on traffic environment and monitoring data is as follows:

[0116] After determining the deployment location and self-starting conditions, DNN is used to calculate the vertical stress σ according to the monitoring data. z , stress σ along the vehicle travel direction x , stress perpendicular to the vehicle's driving direction σ y , vertical strain ε z , strain ε along the vehicle travel direction x , strain perpendicular to the vehicle's direction of travel ε y, the temperature T and humidity H of each structural layer are used to calculate the pavement structure modulus E, which is used as a characterization indicator of the health of the roadbed and pavement structure. The calculation process is shown in formula (13). The structural strength and bearing capacity of each structural layer of the roadbed and pavement are evaluated by the change of the structural modulus E of each layer. Ring coils, microwave sensors, video monitoring systems, thermometers and hygrometers are installed on the designated road sections, and the traffic parameters (traffic volume, traffic composition, vehicle axle load information) and environmental parameters (temperature, humidity) of the designated road sections are monitored and recorded in real time using automatic acquisition technology to evaluate the traffic and environmental conditions of the road sections. Combined with monitoring data and traffic environment models, the downward trend of the bearing capacity of the roadbed and pavement structure, the location and development trend of cracks, voids, interlayer peeling, subsidence and other diseases are predicted, and subsequent inspections and maintenance are guided, and finally a real-time early warning of the long-term service performance risks of the highway is achieved.

[0117]

[0118] In the formula, is the structural modulus output prediction vector, Where E i is the structural modulus of the i-th layer of the pavement structure; A=[σ z ,σ x ,σ y ,ε z ,ε x ,ε y ,T,H]; θ is the parameter in the prediction model, including weights and biases.

[0119] The examples described in the present invention are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, various modifications and improvements made to the technical solutions of the present invention by engineers and technicians in this field should all fall within the protection scope of the present invention.

Claims

1. A sensor network deployment and self-starting implementation method for long-term service performance monitoring of highway roadbed and pavement, characterized in that: The steps include: S1, based on the historical highway detection data, analyze the types and locations of diseases and build a roadbed and pavement condition assessment model; based on the parameters of different types of sensors, build a highway monitoring sensor network probability model; S2, based on the improved weighted random forest algorithm, optimize the decision-making of road network-level monitoring scheme; S3, combined with the roadbed and pavement condition assessment model, uses simulation software to analyze the location and granularity of the sensor field grid under different working conditions, deduce the monitoring probability threshold, and establish a genetic algorithm training data set; S4, using the genetic algorithm dataset, optimizes the layout of highway monitoring sensor networks through multi-objective optimization genetic algorithm; S5, based on the game theory model, realizes the self-wake-up control of highway monitoring sensors; S6. Establish a risk warning mechanism for long-term highway service performance based on traffic environment and monitoring data.

2. The sensor network deployment and self-starting implementation method for long-term service performance monitoring of highway roadbed and pavement according to claim 1 is characterized in that: In step S1, the implementation steps of establishing the roadbed and pavement condition assessment model are as follows: SA1, divide the road network to be monitored into several sections, and regularly use the drop weight deflectometer and ground penetrating radar to detect and evaluate the subgrade and pavement conditions in each section, and obtain the subgrade and pavement deflection basin change data and the subgrade and pavement GPR data change trends respectively; SA2 uses the law of large numbers and Gaussian distribution to quantify the anomaly of the data in the roadbed and pavement condition assessment of each road section, and uses deep neural networks to mark and interpolate the missing data in the deflection basin data and GPR data; at the same time, based on DNN, feature extraction is performed on the deflection basin data and GPR data, and the structural modulus E of each structural layer of the roadbed and pavement is calculated, and the dielectric constant ε is extracted. r and reflection waveform characteristics; SA3, uses the YOLO network to determine the weak locations of the detected road sections and obtain the specific types of diseases that often occur; SA4, combined with historical inspection data, spatially aligns the processed GPR data and FWD data to verify whether the bearing capacity meets the design standards; and evaluates the degradation trend of the bearing capacity through historical inspection data to identify potential voids, voids or subsidence areas in the roadbed; verifies the severity of the disease in combination with the abnormal area of ​​the FWD deflection basin, evaluates the structural strength, bearing capacity and disease conditions of the roadbed and pavement, and establishes a roadbed and pavement condition assessment model for this section.

3. The sensor network deployment and self-starting implementation method for long-term service performance monitoring of highway roadbed and pavement according to claim 1 is characterized in that: In step S1, the implementation process of constructing the highway monitoring sensor network probability model is as follows: The monitoring probability model of different types of sensors is calculated according to the following formula: P(d)=P0·e -αd Where P(d) is the detection probability, P0 is the detection probability of the sensor at zero distance, α is the attenuation factor, and d is the distance between the sensor and the target; A type of sensor is selected, and the monitoring probability at different distances is measured experimentally. The target is placed at different distances from the sensor, the number of successful monitoring times of the sensor is recorded, the monitoring probability is calculated, and saved in the computer; after obtaining a sufficient amount of experimental data, the attenuation factor α is fitted using the least squares method to obtain the monitoring probability model of this type of sensor; and the monitoring probability model of all types of sensors is fitted using the same method as the highway monitoring sensor network probability model.

4. The sensor network deployment and self-starting implementation method for long-term service performance monitoring of highway roadbed and pavement according to claim 1 is characterized in that: The decision optimization of the road network level monitoring solution includes the following steps: S21, combining the actual situation of the specific road section, weighting the extracted feature data; if the road section has a specific disease risk, increasing the weight of the feature data corresponding to the specific disease; S22, using Gini impurity minimization as the growth principle of the decision tree, accumulating the probability density functions of the monitoring schemes under the same indicator, and dividing the subtrees of similar sizes; taking the Gini value growth rate less than the threshold as the growth stop condition, and obtaining the classifier model based on the training sample; S23, perform out-of-band data detection on the generated decision tree, remove and reconstruct unqualified decision trees, and ensure the accuracy of the final synthesized classifier; use the layer analysis method to calculate the sensitivity of the decision tree to different features, and determine the weight of the sub-classifier according to the feature sensitivity, and form the total classifier of the random forest according to the weight combination of each sub-classifier; S24, based on the characteristic data of the roadbed and pavement in the road section, manually annotate and produce a data set according to the roadbed and pavement condition assessment model of the road section, and divide the data into different monitoring degree labels according to the monitoring degree of the road section: no monitoring required-label 0, ordinary monitoring-label 1, key monitoring-label 2; S25, using the feature data as an input feature vector and the monitoring degree label as an output vector to create a data set; and using the data set to train the overall classifier.

5. The sensor network deployment and self-starting implementation method for long-term service performance monitoring of highway roadbed and pavement according to claim 4 is characterized in that: Analyzing the sensor field gridding and deducing the monitoring probability threshold under different working conditions includes the following steps: S31, selecting the sensor type and the layout location based on the roadbed and pavement condition assessment model; selecting the sensor installation location according to the possible disease types and weak locations in the roadbed and pavement condition assessment model; S32, according to the different sensor types, weak locations and possible diseases, use MATLAB to perform grid simulation analysis of the sensor field of the designated road section based on the optimized road network level monitoring scheme; S33, combining the roadbed and pavement condition assessment model with the gridded sensor field of a specific road section, analyzes the most unfavorable position of the roadbed and pavement structure, and sets the monitoring probability threshold to 90%; for non-key monitoring areas, the monitoring probability threshold is set to 70%; a multi-sensor joint monitoring probability model is established in MATLAB, and the monitoring probability of the grid point is calculated as follows when multiple sensors jointly monitor: P miss =(1-P1)(1-P2)...(1-P n ) P detect =1-P miss Where P miss is the probability of monitoring failure, P n is the monitoring probability of the nth sensor of the same type to a specific point, P detect is the probability of joint monitoring; Among them, the most unfavorable positions include positions where diseases may occur and positions where the bearing capacity decays fastest.

6. The sensor network deployment and self-starting implementation method for long-term service performance monitoring of highway roadbed and pavement according to claim 1 is characterized in that: In step S4, the optimization objectives of the genetic algorithm are determined to be: the number of sensors and the sensor monitoring coverage. By weighted combination of the two optimization objectives, a comprehensive objective function is obtained: minF(n,P(d))=ω1n-ω2Q P Where minF(n,P(d)) is the objective function, which decreases during the iteration process; P(d) is the monitoring probability model of this type of sensor; n is the number of sensors; Q P is the sensor monitoring coverage, with a value between 0 and 1; ω1 and ω2 are weight parameters; Select the initial population. Each individual is usually encoded in the form of a binary string or integer array to represent the deployment status of sensors at different locations. Based on the probabilistic coverage model of sensor monitoring capabilities, the differences in requirements of different damage areas of the structure are considered. Under the condition of meeting the reliability, the monitoring capabilities required for each monitoring point under different environmental conditions are quantified as the optimization boundary, and the initial population selection is guided. The objective function is used to comprehensively evaluate the coverage monitoring probability reliability and the number of sensors. The multi-objective optimization iterative process is used to integrate the sensor collaborative monitoring probability reliability and eliminate redundant deployment. The number of sensors and deployment locations of the monitoring system are gradually determined. After iteration, the Pareto optimal solution set is obtained, which is used to characterize the optimal deployment location of sensors on this road section.

7. The sensor network deployment and self-starting implementation method for long-term service performance monitoring of highway roadbed and pavement according to claim 1 is characterized in that: In step S5, the steps of implementing the self-wake-up control of the monitoring sensor based on the game theory model are as follows: S51, establish the sensor energy consumption model, the total energy consumption C of the i-th sensor i Calculate according to the following formula: C i =E s (S i )+E p (S i )+E t (S i ) Among them, E p (S i ) represents the sensing energy of the i-th sensor, E s (S i ) represents the processing energy of the i-th sensor, E t (S i ) represents the communication energy of the i-th sensor, S i is the i-th sensor in the sensor network; S52, the switching of the sensor between the awake state and the dormant state is regarded as a game problem, and the profit function is defined according to the number of data successfully transmitted by the sensor in the awake state; the sensor node comprehensively considers the profit function and the amount of energy consumption to make a trade-off, and its game space is: {from awake to dormant, from awake to not enter dormant}, and the game model GT of the game party is defined as follows: GT={N,K,{u i }} In the formula, N represents the game party, which is all sensor nodes in the network; K is the strategy space adopted; u i is the utility function; Iterative game model, in this process, the dynamic sleep threshold of the sensor node is determined based on the utility function. In one round of game, when the utility function value is greater than the sleep threshold, the sensor node remains awake; when the utility function value is less than the sleep threshold, the sensor enters the sleep state; S53, introduces the sensor node penalty mechanism. When a sensor node always chooses to enter a dormant state to save its own energy consumption, it is called the selfish behavior of the sensor node. When a sensor node takes selfish behavior, the sensor network will mark the node and force the sensor node to stay awake in the subsequent M rounds. The M-th round of the game is defined as follows: u i (s i ,s -i )=U i (s i ,s -i )-C i (s i ,s -i ) Among them, U i (s i ,s -i ) is the profit function of the i-th sensor game in the sensor network; C i (s i ,s -i ) is the energy consumption function of the ith sensor game in the sensor network; s i The decision to wake up the i-th sensor in the sensor network; s -i Take the sleep decision for the i-th sensor in the sensor network.

8. The sensor network deployment and self-starting implementation method for long-term service performance monitoring of highway roadbed and pavement according to claim 1 is characterized in that: In step S6, the implementation process of establishing a highway long-term service performance risk early warning mechanism is as follows: After determining the layout location and self-starting conditions, according to the monitoring data: vertical stress σ z , stress σ along the vehicle travel direction x , stress perpendicular to the vehicle's travel direction σ y , vertical strain ε z , strain ε along the vehicle travel direction x , strain perpendicular to the vehicle's travel direction ε y , the temperature T and humidity H of each structural layer, and use DNN to calculate the pavement structure modulus E as a characterization indicator of the health of the roadbed and pavement structure. The calculation process is as follows: In the formula, is the structural modulus output prediction vector, E i is the structural modulus of the i-th layer of the pavement structure; A=[σ z ,σ x ,σ y ,ε z ,ε x ,ε y ,T,H]; θ is the parameter in the prediction model, including weights and biases; The structural strength and bearing capacity of each structural layer of the roadbed and pavement are evaluated by the changes in the structural modulus E of each layer; Use automatic data collection technology to monitor and record the traffic parameters and environmental parameters of a designated road section in real time, and evaluate the traffic and environmental conditions of the road section; By combining monitoring data and traffic environment models, the downward trend in the bearing capacity of the roadbed and pavement structure, the location and development trend of cracks, voids, interlayer delamination, and subsidence can be predicted, ultimately achieving real-time early warning of the long-term service performance risks of the highway.

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