An intelligent analysis system for safety assessment of highway bridges for vehicle traffic

Abstract

The present invention discloses an intelligent safety assessment analysis system for vehicle traffic on highway bridges, which relates to the field of data analysis and includes a bridge operation and maintenance management platform, which includes a bridge monitoring module, a vehicle acquisition module, a driving analysis module, a risk assessment module and a traffic operation and maintenance module; the bridge monitoring module is used to obtain vehicle driving status data, environmental monitoring data and bridge diagnostic data on the bridge, and set a three-dimensional virtual image for operation status assessment based on the obtained data; the vehicle acquisition module is used to obtain vehicle basic data and vehicle driving data; the driving analysis module obtains the basic type of vehicle; the risk assessment module is used to obtain corresponding vehicle risk assessment prediction data; the traffic operation and maintenance module performs comprehensive analysis based on the three-dimensional virtual image for operation status assessment, the prediction status assessment model and the vehicle risk assessment prediction data to generate traffic operation and maintenance data; the present invention improves the safety of vehicles in the process of passing highway bridges to a certain extent.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an intelligent analysis system for safety assessment of highway bridges for vehicle traffic. Background Art

[0002] As a key component of transportation infrastructure, highway bridges bear the task of carrying a large number of vehicles. Their safety is directly related to the smoothness of transportation and the safety of public life and property. With the development of the economy and the continuous increase in traffic flow, highway bridges are facing increasingly severe safety challenges. Once a bridge has safety problems, it may lead to serious consequences such as traffic interruption and vehicle accidents, causing huge economic losses and adverse effects to society.

[0003] A search revealed an invention patent in China with the number CN116415507A, which discloses an intelligent assessment system for large-scale transport bridges based on big data and cloud computing. This system, which relates to the field of safe bridge passage, includes a bridge information parameter collection module, a bridge passability assessment module, a cloud-based remote human-computer interaction module, and a convolutional neural network data learning module. Both the bridge information parameter collection module and the bridge passability assessment module are three-stage modules with three data integration points. In the present invention, the bridge passability assessment module is used to establish passability standards based on the vehicle and large-scale object parameter data collected by the bridge information parameter collection module at the three data integration points for each vehicle crossing the bridge.

[0004] Compared with the existing technology, this invention patent with Chinese patent number CN116415507A can evaluate the driving status of crossing vehicles on the bridge through three data integration points, and judge whether there are any abnormalities on the corresponding bridge based on the real-time evaluation results, thereby improving the safety of vehicles and bridges.

[0005] However, in actual use, the above system monitors the vehicle driving data on the bridge through the data integration point. When an anomaly is detected, it may have caused a certain degree of impact on the safety of the bridge. Therefore, how to predict the risk of anomalies before the vehicle enters the bridge and avoid the impact of abnormal situations on the highway bridge is a problem we need to solve. Summary of the Invention

[0006] The purpose of the present invention is to solve the shortcoming of safety assessment hysteresis in the prior art and to propose an intelligent analysis system for safety assessment of highway bridges for vehicle traffic.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] An intelligent safety assessment and analysis system for vehicle traffic on highway bridges includes a bridge operation and maintenance management platform, wherein the bridge operation and maintenance management platform is provided with a bridge monitoring module, a vehicle collection module, a driving analysis module, a risk assessment module, and a traffic operation and maintenance module;

[0009] The bridge monitoring module is used to monitor the vehicle driving status data, environmental monitoring data and bridge diagnostic data corresponding to the highway bridge in real time, analyze and process the obtained data, obtain a three-dimensional virtual image of the current highway bridge operation status assessment, and build a corresponding predictive status assessment model based on historical monitoring data;

[0010] The vehicle collection module is used to collect the driving process of vehicles that have not entered the highway bridge and obtain vehicle basic data and vehicle driving data;

[0011] The driving analysis module is used to classify vehicles according to vehicle basic data and obtain the vehicle basic type corresponding to the corresponding vehicle;

[0012] The risk assessment module is used to perform risk warning assessment based on the basic type of vehicles that have not entered the highway bridge according to the prediction state assessment model, and obtain corresponding vehicle risk assessment prediction data;

[0013] The traffic operation and maintenance module is used to comprehensively analyze the risk assessment prediction data of vehicles that have not entered the highway bridge based on the current corresponding operating status evaluation three-dimensional virtual image, determine whether the corresponding vehicles are allowed to enter the corresponding highway bridge, and generate traffic operation and maintenance data.

[0014] The above technical solution further includes: the process of real-time monitoring of vehicle driving status data, environmental monitoring data and bridge diagnostic data corresponding to the highway bridge includes:

[0015] Setting up bridge driving monitoring unit, bridge environment monitoring unit and bridge diagnosis monitoring unit;

[0016] The bridge travel monitoring unit, the bridge environment monitoring unit and the bridge diagnosis monitoring unit are respectively connected to a plurality of vehicle travel monitoring devices, environment monitoring devices and bridge diagnosis monitoring devices provided at corresponding positions on the highway bridge;

[0017] Obtain corresponding vehicle driving status data through vehicle driving monitoring equipment;

[0018] Obtain corresponding environmental monitoring data through environmental monitoring equipment;

[0019] Obtain corresponding bridge diagnostic data through bridge diagnostic monitoring equipment.

[0020] Furthermore, the process of obtaining the three-dimensional virtual image corresponding to the current highway bridge operation status assessment includes:

[0021] Perform bridge segmentation processing based on the location information of vehicle driving monitoring equipment, environmental monitoring equipment, and bridge diagnostic monitoring equipment on highway bridges, and obtain corresponding bridge monitoring nodes based on the segmentation processing results;

[0022] Obtaining bridge diagnostic data corresponding to the corresponding bridge monitoring node, and constructing a bridge diagnostic data set corresponding to the corresponding bridge monitoring node according to the data type corresponding to the bridge diagnostic data; performing feature extraction on the obtained bridge diagnostic data set to obtain evaluation feature data corresponding to the bridge diagnostic data set;

[0023] Preset the evaluation index system at the bridge monitoring node corresponding to the corresponding highway bridge, compare and analyze the obtained evaluation feature data with the corresponding evaluation index system, and obtain the corresponding indicator safety status evaluation data;

[0024] The obtained indicator safety status assessment data is comprehensively processed to obtain the safety status assessment data corresponding to the corresponding bridge monitoring nodes. The safety status assessment data corresponding to each bridge monitoring node is visualized to construct a three-dimensional virtual image of the operation status assessment.

[0025] Furthermore, the process of constructing the corresponding prediction state assessment model includes:

[0026] Obtain historical vehicle driving status data, historical environmental monitoring data, and historical bridge diagnostic data corresponding to the corresponding bridge monitoring node, set the historical vehicle driving status data and historical environmental monitoring data as independent variable data, and set the historical bridge diagnostic data as dependent variable data, perform single variable extraction on the data information corresponding to the independent variable data in turn, obtain the corresponding single variable evaluation data set, set the corresponding independent variable elements according to the different data types corresponding to the independent variable data, and set the single variable evaluation data subset;

[0027] Obtaining the correlation coefficients corresponding to the independent variable elements and the corresponding dependent variable data of the corresponding single variable evaluation data subset based on the Pearson correlation coefficient algorithm, normalizing the obtained correlation coefficients, and obtaining the weight coefficients corresponding to the corresponding independent variable elements;

[0028] An initial assessment state model is set up based on historical environmental monitoring data, corresponding correlation coefficients, weight coefficients, and corresponding historical bridge diagnostic data. A historical assessment data set is set up based on the output results of the initial assessment state model and the corresponding historical vehicle driving state data. The historical assessment data set is analyzed and trained based on a deep learning algorithm to construct a predictive state assessment model corresponding to the corresponding bridge monitoring node.

[0029] Furthermore, the process of obtaining vehicle basic data and vehicle driving data includes:

[0030] Set up corresponding common monitoring sections on both sides of the highway bridge, and set up corresponding vehicle travel data collection terminals in the common monitoring sections;

[0031] The basic vehicle data and vehicle driving data of the corresponding vehicle are obtained through the corresponding vehicle travel collection terminal. The basic vehicle data includes license plate number, vehicle type, vehicle size, approved load capacity and vehicle weight monitoring data. The vehicle driving data includes driving characteristic data corresponding to the driving process of the corresponding vehicle.

[0032] Furthermore, the process of obtaining the vehicle basic type corresponding to the corresponding vehicle includes:

[0033] Obtain vehicle basic data and vehicle driving data corresponding to the corresponding license plate number;

[0034] The corresponding basic vehicle classification and evaluation standards are set according to the currently obtained environmental monitoring data. The basic vehicle classification and evaluation standards include the size evaluation data and weight evaluation data corresponding to ordinary vehicles and large vehicles respectively. If the evaluation results corresponding to the basic vehicle classification and evaluation standards are consistent, the corresponding basic vehicle type is obtained. Otherwise, the basic vehicle classification and evaluation standards are dynamically refined and adjusted to obtain the corresponding basic vehicle type.

[0035] Furthermore, the process of obtaining corresponding vehicle risk assessment prediction data includes:

[0036] Obtain the vehicle basic type and corresponding environmental monitoring data of the corresponding vehicle obtained in the ordinary monitoring section, input the obtained environmental monitoring data into the initial assessment state model corresponding to the corresponding bridge monitoring node, and output the correlation coefficient and corresponding weight coefficient corresponding to the current environmental monitoring data and the corresponding bridge diagnosis data;

[0037] Dynamically adjust the correlation coefficient and weight coefficient of the independent variable factors corresponding to the vehicle basic type corresponding to the corresponding vehicle and the environmental monitoring data;

[0038] According to the corresponding dynamic adjustment results, the obtained correlation coefficient, weight coefficient and corresponding vehicle driving data are input into the prediction state assessment model, and the vehicle risk assessment prediction data of the corresponding vehicle at the bridge monitoring node corresponding to the highway bridge is output and sent to the traffic operation and maintenance module.

[0039] Furthermore, the process of generating traffic operation and maintenance data includes:

[0040] Obtaining vehicle driving data corresponding to the corresponding vehicle and a corresponding three-dimensional virtual image of the operating status evaluation, and combining the corresponding vehicle driving data to obtain a predicted time interval corresponding to the vehicle entering the corresponding bridge monitoring node of the highway bridge;

[0041] Based on the safety status assessment data of the corresponding bridge monitoring nodes in the three-dimensional virtual image of the operating status assessment and the predicted time interval of vehicles that have not entered the highway bridge passing the corresponding bridge monitoring nodes, the maximum risk value of the vehicle risk assessment prediction data corresponding to each bridge monitoring node is obtained;

[0042] Preset the risk assessment threshold corresponding to each bridge monitoring node, compare and analyze the corresponding maximum risk value with the corresponding risk assessment threshold, determine whether the corresponding vehicle is allowed to pass through the highway bridge, and generate traffic operation and maintenance data based on the judgment results.

[0043] The present invention has the following beneficial effects:

[0044] 1. In the present invention, by analyzing and processing the vehicle driving status data, environmental monitoring data, and bridge diagnostic data on the bridge at the current moment, a corresponding three-dimensional image of the operating status assessment is obtained. Traffic operation and maintenance processing is performed based on the three-dimensional image of the operating status assessment and the vehicle driving data corresponding to the corresponding vehicle. This ensures the safety of the highway bridge to the greatest extent possible, avoids safety accidents that occur after vehicles enter the highway bridge, and thus improves the safety of vehicles passing through the highway bridge. By conducting risk assessment in advance, unnecessary losses caused by the lag in safety assessment are avoided.

[0045] 2. In the present invention, by setting up multiple bridge monitoring nodes and taking into account the different effects of environmental monitoring data and vehicle driving data corresponding to different bridge monitoring nodes on bridge safety data, a dynamically adjustable data evaluation process is set up. The maximum risk data of a vehicle when passing a highway bridge is predicted and evaluated in combination with the vehicle data type and vehicle driving data, and it is determined whether the corresponding vehicle is allowed to pass the corresponding highway bridge. While ensuring the safety of the highway bridge to the greatest extent, the safety of vehicles driving on the highway bridge is also improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a structural diagram of an intelligent analysis system for safety assessment of highway bridges for vehicle traffic proposed by the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] Example 1

[0049] like Figure 1 As shown, the present invention proposes an intelligent analysis system for safety assessment of highway bridges for vehicle traffic, including a bridge operation and maintenance management platform, in which a bridge monitoring module, a vehicle collection module, a driving analysis module, a risk assessment module and a traffic operation and maintenance module are provided.

[0050] In this embodiment, the bridge operation and maintenance management platform is used to evaluate and process the driving status data of corresponding vehicles on the highway bridge, obtain safety status assessment data corresponding to the corresponding highway bridge, perform predictive analysis on the risk assessment data corresponding to vehicles that have not entered the highway bridge entering the current highway bridge based on the corresponding safety status assessment data, and determine whether to allow the corresponding vehicles to enter the corresponding highway bridge based on the predictive analysis results, thereby performing a safety assessment on the highway bridge. The specific implementation process includes:

[0051] The bridge monitoring module is used to monitor the vehicle driving status data, environmental monitoring data, and bridge diagnostic data corresponding to the highway bridge in real time, analyze and process the obtained data, obtain a three-dimensional virtual image of the current highway bridge operation status assessment, and build a corresponding predictive status assessment model based on historical monitoring data. The specific implementation process includes:

[0052] Set up bridge driving monitoring unit, bridge environment monitoring unit, bridge diagnosis monitoring unit and bridge status assessment unit;

[0053] The bridge driving monitoring unit is used to monitor the corresponding vehicle driving status data on the highway bridge in real time;

[0054] Multiple vehicle driving monitoring devices are installed on the corresponding highway bridges, and corresponding vehicle driving video data at corresponding positions on the highway bridges are obtained through each vehicle driving monitoring device. The obtained vehicle driving video data are analyzed and processed, and the vehicle driving video data are marked according to the position information of the corresponding vehicle driving monitoring device;

[0055] According to the location information of the corresponding highway bridge to which the vehicle driving monitoring equipment belongs, corresponding driving evaluation nodes are sequentially set, and the set driving evaluation nodes are set as driving analysis links;

[0056] According to the driving analysis link, corresponding forward evaluation sub-links and reverse evaluation sub-links are set respectively;

[0057] The forward evaluation sub-link and the reverse evaluation sub-link are respectively used to monitor vehicles on the highway bridge in the relative travel direction, and the process includes:

[0058] At the driving evaluation nodes corresponding to the beginning and the end of the driving analysis link, vehicle features are collected from the corresponding vehicle driving video data, the vehicle driving video data corresponding to the corresponding driving evaluation node is obtained, the vehicle driving video data is decomposed to obtain a corresponding image sequence, and the corresponding video image frames in the obtained image sequence are enhanced;

[0059] Randomly obtain the corresponding video image frames in the image sequence, analyze and process the video image frames based on the target detection algorithm, detect the location information and bounding box of the corresponding vehicle, and obtain vehicle image data;

[0060] Performing vehicle feature extraction on the obtained vehicle image data, performing image preprocessing on the obtained vehicle image data according to corresponding pixel values ​​to obtain a corresponding grayscale image, performing texture feature extraction on the grayscale image, calculating the occurrence frequencies corresponding to pixels of different grayscale values ​​in corresponding directions and distances in the grayscale image, constructing a grayscale co-occurrence matrix according to the corresponding occurrence frequencies, and extracting corresponding texture features based on the grayscale co-occurrence matrix;

[0061] Marking corresponding vehicles according to corresponding texture features in vehicle image data;

[0062] The corresponding driving evaluation node in the driving analysis link analyzes and processes the corresponding vehicle according to the corresponding marked vehicle, and the specific implementation process includes:

[0063] When the driving evaluation nodes corresponding to the first and last parts of the corresponding driving analysis link obtain the corresponding vehicle tag, the corresponding vehicle storage file is set according to the corresponding vehicle tag result. The corresponding vehicle storage file is transmitted in sequence by the corresponding driving evaluation node according to the vehicle driving path, and the corresponding vehicle driving result is input into the corresponding vehicle storage file;

[0064] The corresponding driving evaluation node obtains the vehicle driving video data corresponding to the vehicle tag in the corresponding vehicle storage file, analyzes and processes the image sequence corresponding to the corresponding driving evaluation node in the vehicle driving video data, and extracts corresponding evaluation index data based on the change relationship between each image element in the image sequence. The evaluation index data includes but is not limited to driving speed index, driving behavior index and driving environment index, among which:

[0065] Driving speed indicators include average speed and speeding frequency data;

[0066] Driving behavior indicators include the frequency of sudden braking, lane changing, and sharp turns;

[0067] Driving environment indicators include road condition information and climate impact data corresponding to the corresponding highway bridges;

[0068] Comprehensively analyze the obtained evaluation index data to obtain the node vehicle driving status data corresponding to the corresponding marked vehicle in the corresponding driving evaluation node, and store the obtained node vehicle driving status data in the corresponding vehicle storage file and mark it;

[0069] Obtain the vehicle driving video data corresponding to the next driving evaluation node in the driving analysis link, use the node vehicle driving status data corresponding to the corresponding driving evaluation node in the vehicle storage file as the evaluation basis, and evaluate and analyze the node vehicle driving status data corresponding to the next driving evaluation node based on the corresponding evaluation basis;

[0070] Mark the time information corresponding to the node vehicle driving status data corresponding to each driving evaluation node in the corresponding vehicle storage file;

[0071] Visualize the analysis results in the storage files of each vehicle in the driving analysis link on the corresponding highway bridge to obtain the vehicle driving status data corresponding to each position on the current highway bridge;

[0072] The bridge environment monitoring unit is used to monitor the corresponding environmental monitoring data on the highway bridge in real time, and the process includes:

[0073] Corresponding environmental monitoring equipment is installed in the corresponding highway bridges and the highways connected to the highway bridges. The environmental monitoring equipment includes meteorological monitoring equipment and hydrological monitoring equipment, wherein:

[0074] The meteorological monitoring equipment is used to collect meteorological monitoring data corresponding to the corresponding location, and the meteorological monitoring data includes wind speed data, rainfall data, temperature data, humidity data and sunshine data;

[0075] The hydrological monitoring equipment is used to collect hydrological monitoring data corresponding to the corresponding highway bridge, and the hydrological monitoring data includes water level data and water flow velocity data;

[0076] Marking the meteorological monitoring data and hydrological monitoring data obtained from the corresponding environmental monitoring equipment according to the corresponding location information, and uniformly marking the meteorological monitoring data and hydrological monitoring data as the corresponding environmental monitoring data according to the marking results;

[0077] The bridge diagnosis monitoring unit is used to monitor the corresponding bridge diagnosis data on the highway bridge in real time, and the process includes:

[0078] Bridge diagnostic monitoring equipment is installed at corresponding positions in corresponding highway bridges, and the bridge diagnostic monitoring equipment includes strain monitoring equipment, displacement monitoring equipment and crack monitoring equipment, wherein:

[0079] The strain monitoring equipment is used to obtain stress change data corresponding to the corresponding positions of the highway bridge;

[0080] The displacement monitoring equipment is used to obtain the displacement change data corresponding to the corresponding position of the highway bridge;

[0081] The crack monitoring equipment is used to obtain crack signal data corresponding to the corresponding position of the highway bridge;

[0082] Marking the stress change data, displacement change data, and crack signal data corresponding to the corresponding positions of the highway bridge according to the corresponding position information, and generating bridge diagnostic data corresponding to the corresponding positions of the highway bridge according to the marking processing results;

[0083] The bridge state assessment unit is used to analyze and process vehicle driving state data, environmental monitoring data, and bridge diagnostic data obtained on the current highway bridge, obtain a three-dimensional virtual image of the operation state assessment corresponding to the current highway bridge, and construct a corresponding predictive state assessment model based on historical monitoring data. The process includes:

[0084] Constructing a virtual bridge image corresponding to the corresponding highway bridge based on the location information of the corresponding monitoring equipment within the highway bridge and the construction image of the highway bridge, performing mapping processing within the virtual bridge image based on the location information of the highway bridge corresponding to the corresponding monitoring equipment, and obtaining a running three-dimensional virtual visualization image;

[0085] Perform bridge segmentation based on the position information of the corresponding monitoring equipment in the running three-dimensional virtual visualization image, obtain the corresponding bridge monitoring nodes in the running three-dimensional virtual visualization image, and mark the corresponding bridge monitoring nodes according to the order of the highway bridge;

[0086] The bridge safety assessment is performed based on the marking results corresponding to the corresponding bridge monitoring nodes and the data information corresponding to the corresponding monitoring equipment. The process includes:

[0087] Obtain the bridge diagnostic data corresponding to the corresponding bridge monitoring node location, evaluate and process the bridge diagnostic data, integrate the corresponding bridge diagnostic data, construct the corresponding bridge diagnostic data set, and perform data cleaning and data standardization on the bridge diagnostic data set;

[0088] Feature extraction is performed on the bridge diagnosis dataset that has completed data cleaning and data standardization. The data information collected by different data types in the bridge diagnosis dataset is obtained, and the data information subsets corresponding to the corresponding data types are marked as x. i (t), where i is the corresponding data type and t is the collection time of the corresponding data information;

[0089] Perform data signal decomposition processing on the corresponding bridge diagnosis data set based on wavelet transform to obtain the corresponding decomposition layer number J;

[0090] In the j-th layer decomposition, the low-pass filter Analyze and process with high-pass filter g(n) to obtain the corresponding approximate coefficient sim j (m) and detail coefficient det j (m);

[0091] Analyze and process the corresponding data information subset according to the corresponding approximation coefficient and detail coefficient to obtain the corresponding mean data, variance data and energy data;

[0092] Obtain corresponding time domain features, frequency domain features and frequency characteristics respectively according to the mean data, variance data and energy data of the corresponding data information subset;

[0093] The time domain features, frequency domain features and frequency characteristics corresponding to the corresponding data information subset;

[0094] Perform correlation analysis on the obtained time domain features, frequency domain features, and frequency characteristics with the preset bridge condition evaluation indicators to obtain the evaluation feature data corresponding to the bridge diagnosis data set;

[0095] Establish an evaluation index system for corresponding highway bridges based on the expert experience in the field of bridge engineering and relevant specifications and standards;

[0096] Compare and analyze the obtained evaluation feature data with the corresponding evaluation index system to obtain the indicator safety status evaluation data corresponding to the corresponding bridge monitoring node;

[0097] Comprehensively process the obtained indicator safety status assessment data to obtain the safety status assessment data corresponding to the corresponding bridge monitoring node, and perform visualization processing on the corresponding operating three-dimensional virtual image based on the safety status assessment data corresponding to the corresponding bridge monitoring node to obtain the operating status assessment three-dimensional virtual image corresponding to the current moment of the highway bridge;

[0098] Obtain historical vehicle driving status data, historical environmental monitoring data, and historical bridge diagnostic data obtained by the corresponding bridge monitoring node, perform single variable extraction based on the historical environmental monitoring data, historical vehicle driving status data, and historical bridge diagnostic data, and construct a corresponding single variable evaluation data set, wherein the corresponding historical environmental monitoring data and historical vehicle driving status data in the single variable evaluation data set are respectively corresponding independent variable data, and the historical bridge diagnostic data are corresponding dependent variable data;

[0099] Analyze and process each obtained single variable evaluation data set, set the variable type corresponding to the corresponding independent variable data in the single variable evaluation data set to the corresponding independent variable element, set a corresponding single variable evaluation data subset based on the corresponding independent variable element, and obtain historical bridge diagnostic data corresponding to the corresponding dependent variable data in the single variable evaluation data subset based on the change of the corresponding independent variable element and the condition that other independent variable elements remain unchanged;

[0100] Based on the Pearson correlation coefficient algorithm, the correlation coefficient between the independent variable element and the corresponding dependent variable data of the corresponding single variable evaluation data subset is obtained. :

[0101] , where n is the number of corresponding data points in the corresponding single variable evaluation data subset, X and Y correspond to the corresponding independent variable data and dependent variable data, respectively. and For each single variable, evaluate the i-th data point corresponding to the independent variable data and the dependent variable data in the data subset. and The corresponding means are respectively, and k is the corresponding independent variable element;

[0102] The correlation coefficient corresponding to the corresponding single variable evaluation data subset Perform integration, normalize the integration results, and obtain the weight coefficients corresponding to the independent variable elements ,in:

[0103] , where K is the number of independent variable elements, k is the corresponding label, M is the amount of data corresponding to the independent variable element with the corresponding label k, and j is the label of the corresponding independent variable element. is the basic deviation coefficient corresponding to the independent variable element;

[0104] A condition assessment model is constructed based on the correlation data and weight coefficients corresponding to the historical bridge diagnosis data of the historical environmental monitoring data and the historical vehicle driving status data;

[0105] Setting an initial evaluation state model according to historical environmental monitoring data, and setting a prediction state evaluation model corresponding to historical bridge diagnostic data according to corresponding historical vehicle driving state data based on the output of the initial evaluation state model;

[0106] The constructed initial evaluation state model and the corresponding prediction state evaluation model are sequentially connected and stored according to the sequential connection results.

[0107] The vehicle collection module is used to collect the driving process of vehicles that do not enter the highway bridge and obtain vehicle basic data and vehicle driving data. The specific implementation process includes:

[0108] Set up corresponding common monitoring sections on both sides of the highway bridge, and set up corresponding vehicle travel data collection terminals in the common monitoring sections;

[0109] The vehicle travel data collection terminal includes an RFID reader / writer terminal, a dynamic weighing sensor, and a vehicle travel video monitoring device, wherein:

[0110] The RFID reader / writer terminal is used to identify the corresponding license plate number, vehicle type, body color, vehicle size and approved load capacity, etc. The vehicle types include cars, trucks, buses and other types;

[0111] The dynamic weighing sensor is used to obtain vehicle weight monitoring data corresponding to the corresponding vehicle;

[0112] Mark the data information obtained by the RFID reading and writing terminal and the dynamic weighing sensor as the vehicle basic data corresponding to the vehicle with the corresponding license plate number;

[0113] The vehicle driving video monitoring device is used to obtain vehicle driving video data, analyze and process the vehicle driving video data, and obtain vehicle driving data corresponding to the corresponding vehicle;

[0114] Obtain corresponding vehicle driving data and perform feature extraction on the corresponding vehicle driving data, including average speed data, maximum acceleration data, and braking frequency data;

[0115] A driving evaluation model is preset, and the corresponding feature extraction results are set as the driving data input set corresponding to the corresponding type of vehicle. The obtained driving data input set is input into the driving evaluation model, and the corresponding driving evaluation model evaluates the driving state of the corresponding vehicle driving data and outputs the corresponding state evaluation type. The state evaluation type includes safe driving type, abnormal driving type and dangerous driving type. Vehicles of the safe driving type are sent to the driving analysis module.

[0116] The driving analysis module is used to classify vehicles according to vehicle basic data and obtain the vehicle basic type corresponding to the corresponding vehicle. The specific implementation process includes:

[0117] Obtain the basic vehicle data and vehicle driving data corresponding to the corresponding license plate number, and analyze and process the basic vehicle data and vehicle driving data obtained from the current common monitoring section. The process includes:

[0118] Obtain the corresponding vehicle size data, approved load data and vehicle weight monitoring data in the vehicle basic data;

[0119] Setting corresponding basic vehicle classification and assessment standards based on currently acquired environmental monitoring data, wherein the basic vehicle classification and assessment standards include size assessment data and weight assessment data corresponding to ordinary vehicles and large vehicles respectively;

[0120] Comparing and analyzing the vehicle size data and vehicle weight monitoring data with the corresponding vehicle basic classification assessment standards, respectively, and obtaining the vehicle basic type according to the corresponding comparative analysis results, wherein the vehicle basic type is divided into two types: ordinary vehicle and large vehicle;

[0121] If the vehicle base types corresponding to the size assessment data and the weight assessment data are consistent, marking the obtained vehicle base type as the corresponding vehicle base type;

[0122] If the vehicle basic types corresponding to the size assessment data and the weight assessment data are inconsistent, the corresponding vehicle basic classification assessment standard is dynamically adjusted according to the corresponding vehicle type, and the vehicle basic type corresponding to the corresponding size assessment data and the weight assessment data is obtained according to the dynamic adjustment result of the vehicle basic classification assessment standard;

[0123] If the vehicle base types corresponding to the corresponding size assessment data and weight assessment data are consistent, marking the obtained vehicle base type as the corresponding vehicle type;

[0124] If the vehicle base types corresponding to the size assessment data and the weight assessment data are still inconsistent, the obtained vehicle base types are marked as corresponding anomalies;

[0125] Abnormal vehicles are marked, and the classification results corresponding to other vehicle types are stored and sent to the risk assessment module.

[0126] The risk assessment module is used to perform risk warning assessment based on the basic type of vehicles that have not entered the highway bridge according to the prediction state assessment model, and obtain corresponding vehicle risk assessment prediction data. The specific implementation process includes:

[0127] Obtain the classification results of the corresponding vehicles obtained in the ordinary monitoring section, and perform risk assessment and prediction based on the classification results of the corresponding vehicles. The process includes:

[0128] Obtain environmental monitoring data corresponding to the current highway bridge, input the obtained environmental monitoring data into the corresponding initial assessment state model, output the correlation coefficient and corresponding weight coefficient corresponding to the current environmental monitoring data and the corresponding bridge diagnostic data, and dynamically adjust the correlation coefficient and weight coefficient of the independent variable factors corresponding to the environmental monitoring data corresponding to the vehicle basic type corresponding to the corresponding vehicle;

[0129] Obtain the corresponding vehicle mass data F, and mark the effect coefficients corresponding to large vehicles and ordinary vehicles as and , the correlation coefficient and weight coefficient of the corresponding independent variable factors after dynamic adjustment are marked as and ,in:

[0130] ;

[0131] ;

[0132] According to the corresponding dynamic adjustment results, the obtained correlation coefficient, weight coefficient and corresponding vehicle driving data are input into the prediction state assessment model, and the vehicle risk assessment prediction data of the corresponding vehicle at the corresponding bridge monitoring node of the highway bridge is output;

[0133] The risk assessment prediction data of vehicles that have not entered the highway bridge at each bridge monitoring node is sent to the traffic operation and maintenance module.

[0134] The traffic operation and maintenance module is used to comprehensively analyze the risk assessment prediction data of vehicles that have not entered the highway bridge based on the current corresponding operating status evaluation three-dimensional virtual image, determine whether the corresponding vehicle is allowed to enter the corresponding highway bridge, and generate traffic operation and maintenance data. The specific implementation process includes:

[0135] Obtaining a three-dimensional virtual image of the operating status assessment corresponding to the current highway bridge and vehicle risk assessment prediction data corresponding to each bridge monitoring node within the highway bridge;

[0136] Obtain vehicle driving data corresponding to the corresponding vehicle, and obtain a predicted time interval corresponding to the vehicle entering the corresponding bridge monitoring node of the highway bridge in combination with the vehicle driving data;

[0137] An integrated analysis is performed based on the three-dimensional virtual images of the operating status evaluation corresponding to each bridge monitoring node and the predicted time interval for vehicles that have not entered the highway bridge to pass through the bridge monitoring node;

[0138] Obtain the maximum risk value of the vehicle risk assessment prediction data corresponding to each bridge monitoring node;

[0139] Preset the risk assessment threshold corresponding to each bridge monitoring node, and compare and analyze the corresponding maximum risk value with the corresponding risk assessment threshold:

[0140] If the maximum risk value is less than the risk assessment threshold, the corresponding bridge monitoring node is allowed to pass;

[0141] If the maximum risk value is greater than or equal to the risk assessment threshold, the corresponding bridge monitoring node is not allowed to pass;

[0142] Conduct statistical analysis on the traffic conditions allowed at each bridge monitoring node within the highway bridge:

[0143] If all bridge monitoring nodes in the highway bridge allow passage, the corresponding vehicle is allowed to enter the highway bridge. If there is a bridge monitoring node that does not allow passage, the corresponding vehicle is not allowed to enter the highway bridge. Traffic operation and maintenance data is generated based on the judgment result, and the traffic operation and maintenance data includes the analysis result of whether the corresponding vehicle is allowed to enter the highway bridge;

[0144] Based on the traffic operation and maintenance data, the corresponding vehicle entering the highway bridge process is fed back to the bridge monitoring module, and the corresponding operation status evaluation 3D virtual image is updated in real time;

[0145] It should be further explained that, during the specific implementation process, vehicles whose vehicle types are judged to be abnormal need to be marked and sent to the corresponding management personnel based on the marking results, and the corresponding management personnel will manage the corresponding vehicles.

[0146] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent safety assessment and analysis system for road bridges used for vehicle traffic, including a bridge operation and maintenance management platform, characterized in that: The bridge operation and maintenance management platform is equipped with a bridge monitoring module, a vehicle collection module, a driving analysis module, a risk assessment module and a traffic operation and maintenance module; The bridge monitoring module is used to monitor the vehicle driving status data, environmental monitoring data and bridge diagnostic data corresponding to the highway bridge in real time, analyze and process the obtained data, obtain a three-dimensional virtual image of the current highway bridge operation status assessment, and build a corresponding predictive status assessment model based on historical monitoring data; The vehicle collection module is used to collect the driving process of vehicles that have not entered the highway bridge and obtain vehicle basic data and vehicle driving data; The driving analysis module is used to classify vehicles according to vehicle basic data and obtain the vehicle basic type corresponding to the corresponding vehicle; The risk assessment module is used to perform risk warning assessment based on the basic type of vehicles that have not entered the highway bridge according to the prediction state assessment model, and obtain corresponding vehicle risk assessment prediction data; The traffic operation and maintenance module is used to comprehensively analyze the risk assessment prediction data of vehicles that have not entered the highway bridge based on the current corresponding operating status evaluation three-dimensional virtual image, determine whether the corresponding vehicle is allowed to enter the corresponding highway bridge, and generate traffic operation and maintenance data; The process of real-time monitoring of vehicle driving status data, environmental monitoring data, and bridge diagnostic data on highway bridges includes: Setting up bridge driving monitoring unit, bridge environment monitoring unit and bridge diagnosis monitoring unit; The bridge travel monitoring unit, the bridge environment monitoring unit and the bridge diagnosis monitoring unit are respectively connected to a plurality of vehicle travel monitoring devices, environment monitoring devices and bridge diagnosis monitoring devices provided at corresponding positions on the highway bridge; Obtain corresponding vehicle driving status data through vehicle driving monitoring equipment; Obtain corresponding environmental monitoring data through environmental monitoring equipment; Obtain corresponding bridge diagnostic data through bridge diagnostic monitoring equipment; The process of obtaining a three-dimensional virtual image corresponding to the current highway bridge operation status assessment includes: Perform bridge segmentation processing based on the location information of vehicle driving monitoring equipment, environmental monitoring equipment, and bridge diagnostic monitoring equipment on highway bridges, and obtain corresponding bridge monitoring nodes based on the segmentation processing results; Obtaining bridge diagnostic data corresponding to the corresponding bridge monitoring node, and constructing a bridge diagnostic data set corresponding to the corresponding bridge monitoring node according to the data type corresponding to the bridge diagnostic data; performing feature extraction on the obtained bridge diagnostic data set to obtain evaluation feature data corresponding to the bridge diagnostic data set; Preset the evaluation index system at the bridge monitoring node corresponding to the corresponding highway bridge, compare and analyze the obtained evaluation feature data with the corresponding evaluation index system, and obtain the corresponding indicator safety status evaluation data; Comprehensively process the obtained indicator safety status assessment data to obtain the safety status assessment data corresponding to the corresponding bridge monitoring nodes, visualize the safety status assessment data corresponding to each bridge monitoring node, and construct a three-dimensional virtual image of the operation status assessment; The process of building a corresponding predictive state assessment model includes: Obtain historical vehicle driving status data, historical environmental monitoring data, and historical bridge diagnostic data corresponding to the corresponding bridge monitoring node, set the historical vehicle driving status data and historical environmental monitoring data as independent variable data, and set the historical bridge diagnostic data as dependent variable data, perform single variable extraction on the data information corresponding to the independent variable data in turn, obtain the corresponding single variable evaluation data set, set the corresponding independent variable elements according to the different data types corresponding to the independent variable data, and set the single variable evaluation data subset; Obtaining the correlation coefficients corresponding to the independent variable elements and the corresponding dependent variable data of the corresponding single variable evaluation data subset based on the Pearson correlation coefficient algorithm, normalizing the obtained correlation coefficients, and obtaining the weight coefficients corresponding to the corresponding independent variable elements; An initial assessment state model is set based on historical environmental monitoring data, corresponding correlation coefficients, weight coefficients, and corresponding historical bridge diagnostic data. A historical assessment dataset is set based on the output of the initial assessment state model and the corresponding historical vehicle driving state data. The historical assessment dataset is analyzed and trained based on a deep learning algorithm to construct a predictive state assessment model corresponding to the corresponding bridge monitoring node. The process of obtaining vehicle basic data and vehicle driving data includes: Set up corresponding common monitoring sections on both sides of the highway bridge, and set up corresponding vehicle travel data collection terminals in the common monitoring sections; Obtain basic vehicle data and vehicle driving data of the corresponding vehicle through the corresponding vehicle travel collection terminal. The basic vehicle data includes license plate number, vehicle type, vehicle size, approved load capacity, and vehicle weight monitoring data. The vehicle driving data includes driving characteristic data corresponding to the driving process of the corresponding vehicle; The process of obtaining the vehicle base type corresponding to the corresponding vehicle includes: Obtain vehicle basic data and vehicle driving data corresponding to the corresponding license plate number; Based on the currently acquired environmental monitoring data, the corresponding basic vehicle classification and assessment standards are set. The basic vehicle classification and assessment standards include the size assessment data and weight assessment data corresponding to ordinary vehicles and large vehicles, respectively. If the assessment results corresponding to the basic vehicle classification and assessment standards are consistent, the corresponding basic vehicle type is obtained. Otherwise, the basic vehicle classification and assessment standards are dynamically refined and adjusted to obtain the corresponding basic vehicle type; The process of obtaining the corresponding vehicle risk assessment prediction data includes: Obtain the vehicle basic type and corresponding environmental monitoring data of the corresponding vehicle obtained in the ordinary monitoring section, input the obtained environmental monitoring data into the initial assessment state model corresponding to the corresponding bridge monitoring node, and output the correlation coefficient and corresponding weight coefficient corresponding to the current environmental monitoring data and the corresponding bridge diagnosis data; Dynamically adjust the correlation coefficient and weight coefficient of the independent variable factors corresponding to the vehicle basic type corresponding to the corresponding vehicle and the environmental monitoring data; According to the corresponding dynamic adjustment results, the obtained correlation coefficient, weight coefficient and corresponding vehicle driving data are input into the prediction state assessment model, and the vehicle risk assessment prediction data of the corresponding vehicle at the bridge monitoring node corresponding to the highway bridge is output and sent to the traffic operation and maintenance module.

2. The intelligent safety assessment and analysis system for vehicle-passing highway bridges according to claim 1 is characterized in that: The process of generating traffic operation and maintenance data includes: Obtaining vehicle driving data corresponding to the corresponding vehicle and a corresponding three-dimensional virtual image of the operating status evaluation, and combining the corresponding vehicle driving data to obtain a predicted time interval corresponding to the vehicle entering the corresponding bridge monitoring node of the highway bridge; Based on the safety status assessment data of the corresponding bridge monitoring nodes in the three-dimensional virtual image of the operating status assessment and the predicted time interval of vehicles that have not entered the highway bridge passing the corresponding bridge monitoring nodes, the maximum risk value of the vehicle risk assessment prediction data corresponding to each bridge monitoring node is obtained; Preset the risk assessment threshold corresponding to each bridge monitoring node, compare and analyze the corresponding maximum risk value with the corresponding risk assessment threshold, determine whether the corresponding vehicle is allowed to pass through the highway bridge, and generate traffic operation and maintenance data based on the judgment results.

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