Traffic guidance system for highway engineering construction

Abstract

The invention discloses a traffic command system for highway engineering construction, and the system comprises a sound wave reflection signal obtaining module which is used for obtaining a sound wave reflection signal of a highway construction section; the stress risk area identification module is used for determining the sound wave reflection source type of the sound wave reflection signal and the position of a stress risk area; the construction traffic control module is used for determining the maximum allowable passing speed of the construction traffic control area; the vehicle speed limit pre-adjusting module is used for determining a vehicle speed limit scheme in a future preset time period; the traffic density control module is used for implementing traffic density control measures on the construction traffic control area; the facility stability analysis module is used for evaluating the stability of construction facilities in the highway construction area; and the traffic control effect evaluation module is used for evaluating the traffic control effect and adjusting the vehicle speed limit and the heavy vehicle traffic density. The invention provides the method for dynamically optimizing the traffic control during the highway construction period, and the highway construction safety and the traffic smoothness are remarkably improved.

Description

Technical Field

[0001] The invention relates to the field of intelligent traffic management, and in particular to a traffic command system used in highway engineering construction. Background Art

[0002] With the rapid development of modern highway transportation networks and the continuous increase in traffic volume, the number and scale of highway construction projects are also expanding. During highway construction, the stress distribution of roadbed and pavement, the stability of construction facilities and the management of traffic flow have become key safety issues. Traditional traffic control methods mainly rely on static speed limits, simple traffic diversion and manual experience judgment, which are difficult to respond to the dynamically changing construction site conditions in real time and have many shortcomings. During highway construction, the bearing capacity of the roadbed and pavement changes due to construction operations and traffic pressure, which may lead to stress concentration or stress overload in local areas. If these high-stress areas cannot be identified and handled in time, they may cause serious consequences such as roadbed settlement, cracking or collapse, affecting the quality and service life of highway construction. Traditional methods mostly use regular manual inspections, which have limited detection frequency and accuracy, and cannot achieve real-time monitoring of stress distribution in the construction area, thereby increasing construction risks. Secondly, the traffic control schemes in highway construction areas are usually based on fixed speed limits and diversions, which are difficult to adapt to the ever-changing construction sites and changes in external conditions such as weather and traffic flow. Especially when traffic density suddenly increases or in bad weather conditions, fixed speed limit schemes may not be able to effectively prevent the impact of traffic pressure on the construction area. The lack of dynamic optimization of real-time data makes it difficult to balance traffic fluency and safety in the construction area, which may cause construction delays and traffic congestion. In addition, traditional traffic density control often relies on experience or manual intervention, lacks scientific evaluation based on real-time data, and the dynamic pressure of heavy vehicles on the roadbed is the main factor in the stress change of the roadbed in the construction area. If the traffic density of heavy vehicles cannot be managed in real time, it is easy to cause the accumulation of roadbed stress and eventually cause structural damage. Therefore, traditional traffic density control lacks accuracy for specific high-stress areas and cannot effectively disperse traffic pressure. In addition, highway construction sites usually have construction facilities such as roadblocks, warning signs, construction equipment and construction materials, and their stacking methods and stability directly affect the safety of the construction area. However, the traditional construction facility placement management relies on manual experience, and it is difficult to fully evaluate the impact of factors such as the stacking mode, weight distribution and support area of ​​the facilities on their stability. The identification and treatment of risks such as facility tilt and slippage often lag behind the occurrence of accidents, and safety problems cannot be effectively prevented. In order to solve the above problems, there is an urgent need for an intelligent traffic command method based on real-time data to dynamically adjust the construction traffic control plan and comprehensively evaluate and optimize the traffic flow command and management during highway construction. Summary of the invention

[0003] The present invention solves the problems existing in the above-mentioned prior art and provides a traffic control system for highway engineering construction, which mainly includes:

[0004] The acoustic wave reflection signal acquisition module is used to determine the acoustic wave emission parameters of the acoustic wave transmitter according to the acoustic wave reflection signal receiving effect of the highway in the construction section, and to obtain the acoustic wave reflection signal of the highway construction section;

[0005] A stress risk area identification module is used to extract its spectrum characteristics using a short-time Fourier transform algorithm to determine the type of the acoustic reflection source of the acoustic reflection signal and the location of the stress risk area;

[0006] The construction traffic control module is used to construct a vehicle speed adjustment model based on the traffic density, stress data and environmental factors in the construction traffic control area to determine the maximum allowable speed in the construction traffic control area;

[0007] A vehicle speed limit pre-adjustment module is used to determine a vehicle speed limit plan for a construction traffic control area in a future preset time period according to the temperature, humidity and rainfall in the construction traffic control area in the future preset time period;

[0008] Traffic density control module, used to implement traffic density control measures according to the heavy vehicle traffic density in the construction traffic control area and the real-time stress value of the stress risk area in the future preset time period;

[0009] The facility stability analysis module is used to evaluate the stability of construction facilities in the highway construction area according to the weight, support area and stacking mode of the construction facilities, and to formulate a real-time vehicle speed limit plan for the highway construction section based on the stability assessment results;

[0010] The traffic control effect evaluation module is used to evaluate the traffic control effect and adjust the vehicle speed limit and heavy vehicle traffic density based on the stress data and traffic data before and after the implementation of the vehicle speed limit scheme and traffic density control measures.

[0011] Furthermore, the acoustic wave reflection signal acquisition module is used to determine the acoustic wave emission parameters of the acoustic wave transmitter according to the acoustic wave reflection signal receiving effect of the highway in the construction section, and to acquire the acoustic wave reflection signal of the highway construction section, including:

[0012] The historical sound wave emission parameters of the sound wave transmitter, the material composition of the highway in the construction section corresponding to the detection time, the road surface humidity and the reflected signal receiving effect are obtained through the sound wave reflection monitoring database, and the recurrent neural network is used for model training to build a sound wave emission parameter prediction model for the sound wave transmitter, and the sound wave emission parameters of the sound wave transmitter with the best sound wave reflection signal receiving effect in the current highway construction section are determined. The emission parameters include emission frequency, amplitude and angle, and the material composition includes but is not limited to concrete and asphalt. The receiving effects include excellent, good and poor. A sound wave transmitter and a directional array sound wave receiver are set at the starting point of the highway construction section, and sound waves are emitted to the highway construction section by the sound wave transmitter according to the set sound wave emission parameters, and the sound wave reflection signal and the corresponding timestamp information are obtained by the directional array sound wave receiver; according to the sound wave reflection signal of the highway construction section, the sound wave reflection signal is separated by an independent component analysis algorithm to obtain the sound wave reflection signals of different sound wave reflection sources, and save them in the sound wave reflection monitoring database.

[0013] Furthermore, the stress risk area identification module is used to extract its spectrum characteristics using a short-time Fourier transform algorithm to determine the type of the acoustic reflection source of the acoustic reflection signal and the location of the stress risk area, including:

[0014] The short-time Fourier transform algorithm is used to convert the time-domain acoustic wave reflection signal into frequency-domain data, and its spectral characteristics are extracted, which include frequency, frequency offset and amplitude. The historical acoustic wave emission frequency, amplitude and spectral characteristics of the acoustic wave reflection signal are obtained through the acoustic wave reflection monitoring database, and the acoustic wave reflection source type is marked. The support vector machine algorithm is used for model training, and an acoustic wave reflection source type identification model is constructed to determine the acoustic wave reflection source type of the acoustic wave reflection signal. The acoustic wave reflection source type includes stress risk area reflection source and non-stress risk area reflection source. According to the echo time of the acquired acoustic wave reflection signal, the position of the stress risk area relative to the directional array acoustic wave receiver is calculated through time difference estimation to determine the position of the stress risk area. The construction traffic control area division unit is used to divide the construction traffic control area according to the position of the stress risk area.

[0015] It also includes a construction traffic control area division unit, which is used to divide the construction traffic control area according to the location of the stress risk area, specifically including:

[0016] The stress risk areas in the highway construction section are divided into construction traffic control areas; if the number of stress risk areas in the highway construction section is greater than 1, the location of each stress risk area in the highway construction section is obtained, and the distance between each stress risk area is determined. If the distance is less than the preset safety distance threshold, they are divided into the same construction traffic control area.

[0017] Furthermore, the construction traffic control module is used to construct a vehicle speed adjustment model according to the traffic density, stress data and environmental factors of the construction traffic control area to determine the maximum allowable speed of the construction traffic control area, including:

[0018] The temperature, humidity and rainfall in the construction traffic control area are obtained through real-time weather forecast, and the environmental factor formula is used Determine the environmental factors of the construction traffic control area, where H is relative humidity, T is temperature, and T max is the maximum temperature, R is the rainfall, R max is the maximum rainfall; strain gauge sensors are installed in the stress risk area of ​​the construction traffic control area to monitor the stress value of the stress risk area in real time; a vehicle speed adjustment model is constructed based on the traffic density, stress data and environmental factors of the construction traffic control area Determine the maximum permissible speed in the construction traffic control area, where V max is the maximum permissible speed of the vehicle, S is the stress value of the current stress risk area, S th is the preset stress safety threshold, D is the traffic density, α and β are the traffic density weight parameter and the environmental factor weight parameter respectively, which are used to adjust the influence of traffic density and environmental factors on the travel speed, and are obtained by fitting historical data. K is a constant coefficient, which is used to adjust the speed range and is set according to the maximum speed limit designed for the road section. E is the environmental factor, which represents the influence of the environment on the roadbed stress. If there are multiple stress risk areas in the construction traffic control area, the minimum value of the maximum allowable travel speeds in the multiple stress risk areas is selected as the vehicle speed limit in the construction traffic control area. According to the vehicle speed limit in the construction traffic control area, a vehicle speed limit plan for the construction traffic control area is formulated. If the stage of the highway construction project changes, the new stress risk area location is determined through the real-time acoustic wave reflection signal of the highway construction section, and the construction traffic control area is updated. According to the traffic density, stress data and environmental factors of the updated construction traffic control area, the vehicle speed limit of the updated construction traffic control area is determined using the speed adjustment model, and the vehicle speed limit plan for the construction traffic control area is adjusted.

[0019] Furthermore, the vehicle speed limit pre-adjustment module is used to determine the vehicle speed limit scheme for the construction traffic control area in the future preset time period according to the temperature, humidity and rainfall in the construction traffic control area in the future preset time period, including:

[0020] Through the traffic monitoring database, the historical traffic density data of the construction traffic control area is obtained, and the long short-term memory network is used for model training to predict the traffic density data of the construction traffic control area in the future preset time period, including the traffic density of heavy vehicles; the temperature, humidity and rainfall in the construction traffic control area in the future preset time period are obtained through weather forecasts, and the model is adjusted according to the vehicle speed to determine the vehicle speed limit plan in the construction traffic control area in the future preset time period.

[0021] Furthermore, the traffic density control module is used to implement traffic density control measures according to the heavy vehicle traffic density of the construction traffic control area and the real-time stress value of the stress risk area in the future preset time period, including:

[0022] If it is predicted that the heavy vehicle traffic density in the construction traffic control area in a preset time period in the future is greater than the preset density threshold, the time point when the heavy vehicle traffic density in the construction traffic control area in the future is greater than the preset density threshold is determined, and traffic density control measures are implemented in advance. The traffic density control measures include limiting the heavy vehicle traffic density in the construction traffic control area. When the upper limit of the number of heavy vehicle traffic is reached, new heavy vehicles are temporarily restricted from entering the area; the stress value of the stress risk area is monitored in real time through the strain gauge sensor installed in the stress risk area of ​​the construction traffic control area. If the real-time monitored stress data exceeds the preset stress safety threshold, traffic density control measures are implemented until the stress falls back to less than the preset stress safety threshold.

[0023] Furthermore, the facility stability analysis module is used to evaluate the stability of construction facilities in the highway construction area according to the weight, support area and stacking mode of the construction facilities, and to formulate a real-time vehicle speed limit plan for the highway construction section based on the stability evaluation results, including:

[0024] Through the camera of the highway construction section, the image of the construction facilities on the highway construction section is obtained; the Canny edge detection algorithm is used to identify the edge contours of the construction facilities on the highway construction section, and the shape and boundary information of the construction facilities are obtained through the findContours method in OpenCV to determine the size of the construction equipment; according to the image of the construction facility placement, a convolutional neural network is used for model training to determine the stacking mode and facility type of the construction facilities, and the weight and material of each facility are obtained according to the type of construction facilities. The facility types include but are not limited to roadblocks, warning signs, construction equipment and construction materials, and the stacking modes include but are not limited to stacking in rows, horizontal stacking, vertical stacking, staggered stacking, circular stacking and pyramid stacking; the minimum enclosing rectangle method is used to calculate the relative distance, angle and The system can identify the location of the construction facilities, obtain the spatial relationship between the facilities, and identify the actual supporting area of ​​the items during the stacking process; evaluate the stability of the construction facilities on the highway construction section in combination with the weight, supporting area and stacking pattern of the construction facilities, identify the risks of tilting, sliding or moving, and the stability includes excellent, good and poor; if the stability of the construction facilities on the highway construction section is good or poor, the highway section where the construction facilities are located will be divided into a construction traffic control area, and a vehicle speed limit plan will be implemented to limit the speed of vehicles on the highway construction section, and a warning message will be sent to the person in charge of the highway construction section; the camera on the highway construction section will continuously obtain the placement image of the construction facilities, and the Kalman filter will be used to judge the movement and change of the construction facilities over time, so as to obtain the dynamic update of the real-time stacking pattern of the construction facilities, and evaluate the stability of the construction facilities in the highway construction area in real time.

[0025] Furthermore, the traffic control effect evaluation module is used to evaluate the traffic control effect and adjust the vehicle speed limit and heavy vehicle traffic density based on the stress data and traffic data before and after the implementation of the vehicle speed limit scheme and traffic density control measures, including:

[0026] Obtain stress data and traffic data before and after the implementation of the vehicle speed limit scheme and traffic density control measures in the construction traffic control area, and evaluate the traffic control effect of the current vehicle speed limit scheme and traffic density control measures based on the stress recovery speed and amplitude and traffic efficiency of the construction traffic control area; if the traffic control effect is lower than expected, adjust the vehicle speed limit and heavy vehicle traffic density, and update the vehicle speed limit scheme and traffic density control measures in the construction traffic control area until the traffic control effect reaches the expected result.

[0027] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0028] The present invention provides a traffic command system for highway engineering construction. The present invention extracts the spectral characteristics of the sound wave reflection signal in real time to quickly identify the location of the stress risk area, effectively solving the problem that the traditional method cannot monitor the stress distribution in the construction area in real time. Based on the traffic density, stress data and environmental factors, the maximum allowable speed of the construction area is dynamically adjusted to fully adapt to traffic and weather changes and avoid the safety hazards and traffic congestion caused by the fixed speed limit scheme. The present invention combines future traffic density and environmental forecast data to optimize the speed limit and traffic density control scheme in the construction traffic control area, effectively manage the dynamic pressure of heavy vehicles on the roadbed, and prevent structural damage caused by stress accumulation. At the same time, the present invention uses the weight, support area and stacking mode of the construction facilities to evaluate the stability of the facilities, timely identify and warn of risks such as tilting and sliding, and ensure the safety of the construction site. Through real-time evaluation and optimization of traffic control effects, the present invention forms a dynamic closed-loop traffic command and management system during highway construction, which can continuously improve the traffic control effect of highway construction sections, optimize traffic efficiency while ensuring traffic safety, and fully meet the needs of modern highway construction for efficient and safe traffic management, thereby providing a strong guarantee for the stability of traffic order in the highway construction area and the smooth advancement of construction progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flow chart of a traffic control system used in highway engineering construction according to the present invention;

[0030] Figure 2 A schematic diagram of a traffic control system used in highway engineering construction according to the present invention;

[0031] Figure 3 This is another schematic diagram of a traffic control system used in highway engineering construction according to the present invention. DETAILED DESCRIPTION

[0032] In order to make the objectives, technical strategies and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] like Figure 1-3 In this embodiment, a traffic control system for highway engineering construction may specifically include:

[0034] Step S101, a sound wave reflection signal acquisition module is used to determine the sound wave emission parameters of the sound wave transmitter according to the sound wave reflection signal receiving effect of the highway construction section, and obtain the sound wave reflection signal of the highway construction section.

[0035] The historical sound wave emission parameters of the sound wave transmitter, the material composition of the construction section of the highway at the corresponding detection time, the road surface humidity and the reflected signal receiving effect are obtained through the sound wave reflection monitoring database. The recurrent neural network is used for model training to build a prediction model for the sound wave emission parameters of the sound wave transmitter, and the sound wave emission parameters of the sound wave transmitter with the best sound wave reflection signal receiving effect in the current highway construction section are determined. The emission parameters include emission frequency, amplitude and angle, and the material composition includes but is not limited to concrete and asphalt. The receiving effects include excellent, good and poor. A sound wave transmitter and a directional array sound wave receiver are set at the starting point of the highway construction section. The sound wave transmitter emits sound waves to the highway construction section according to the set sound wave emission parameters, and the directional array sound wave receiver obtains the sound wave reflection signal and the corresponding timestamp information. According to the sound wave reflection signal of the highway construction section, the sound wave reflection signal is separated by the independent component analysis algorithm to obtain the sound wave reflection signals of different sound wave reflection sources, and save them to the sound wave reflection monitoring database.

[0036] For example, on a highway construction section, the roadbed conditions of the section are monitored in real time through the acoustic reflection monitoring system, and the historical acoustic emission parameters and related environmental data of the section are obtained from the acoustic reflection monitoring database. When the acoustic emission frequency is set to 40kHz, the amplitude is set to 10Pa, and the emission angle is 15 degrees, the concrete pavement can obtain the best reflected signal reception effect under the condition of 50% humidity. When the acoustic emission frequency is set to 25kHz, the amplitude is 8Pa, and the emission angle is 10 degrees, the asphalt pavement can obtain the best reflected signal reception effect under the condition of about 60% humidity. According to the historical acoustic emission parameters and related environmental data of the section, a recurrent neural network is used for model training to construct an acoustic emission parameter prediction model for the acoustic wave transmitter. According to the current environmental conditions, the concrete pavement and the humidity are 50%, the emission parameters of the acoustic wave transmitter are automatically set to a frequency of 40kHz, an amplitude of 10Pa, and an emission angle of 15 degrees. At the starting point of the construction section, an acoustic wave transmitter and a directional array acoustic wave receiver are installed. The transmitter emits acoustic waves to the road section ahead according to the set parameters. The independent component analysis algorithm is used to process the received reflection signals, separate the acoustic wave reflection signals of multiple reflection sources that may exist on the construction section, such as water accumulation points and stress risk areas, and save the acoustic wave reflection signals of different acoustic wave reflection sources into the acoustic wave reflection monitoring database.

[0037] Step S102, a stress risk area identification module is used to extract the spectrum characteristics using a short-time Fourier transform algorithm to determine the type of the acoustic reflection source of the acoustic reflection signal and the location of the stress risk area.

[0038] The short-time Fourier transform algorithm is used to convert the time-domain acoustic wave reflection signal into frequency-domain data, and its spectral features are extracted, including frequency, frequency offset and amplitude. The historical acoustic wave emission frequency, amplitude and spectral features of the acoustic wave reflection signal are obtained through the acoustic wave reflection monitoring database, and the acoustic wave reflection source type is marked. The support vector machine algorithm is used for model training to build an acoustic wave reflection source type identification model to determine the acoustic wave reflection source type of the acoustic wave reflection signal. The acoustic wave reflection source type includes stress risk area reflection source and non-stress risk area reflection source. According to the echo time of the acquired acoustic wave reflection signal, the position of the stress risk area relative to the directional array acoustic wave receiver is calculated through time difference estimation to determine the position of the stress risk area. The construction traffic control area division unit is used to divide the construction traffic control area according to the location of the stress risk area.

[0039] Exemplarily, a group of acoustic wave signals with a frequency of 40kHz and an amplitude of 10Pa are emitted through an acoustic wave transmitter. After a short-time Fourier transform from the time domain to the frequency domain, a reflection signal with a frequency of 40kHz and an amplitude of about 8Pa, as well as a weak offset of a specific frequency appearing in the acoustic wave signal, is obtained. The acoustic wave reflection monitoring database calls historical data to obtain the historical acoustic wave emission frequency, amplitude, and spectral characteristics of the acoustic wave reflection signal, and marks the stress risk area reflection source or the non-stress risk area reflection source. Based on these historical data, the support vector machine algorithm is used for model training to construct an acoustic wave reflection source type recognition model, and acoustic wave reflection signals with different characteristics are classified as stress risk area reflection sources or non-stress risk area reflection sources. According to the acoustic wave reflection source type recognition model, it is identified that the spectral characteristics of the current signal meet the acoustic wave reflection characteristics of the stress risk area, so the acoustic wave reflection source type is judged to be a stress risk area reflection source. The time difference between the sound wave emission and the reflected signal reception was measured, and the time difference was converted to show that the stress risk area was about 300 meters away from the No. 1 receiver of the directional array acoustic wave receiver, 301 meters away from the No. 2 receiver, and 300.5 meters away from the No. 3 receiver. According to the source direction and distance of the sound wave reflection signal, the stress risk area was located somewhere in the central area of ​​the construction section. Therefore, through the construction traffic control area division unit, the 20 meters around the area was designated as the construction traffic control area to reduce the frequent passage of heavy-loaded vehicles in this high-stress area and avoid further aggravation of the roadbed pressure.

[0040] Among them, the construction traffic control area division unit is used to divide the construction traffic control area according to the location of the stress risk area.

[0041] The stress risk areas in the highway construction section are divided into construction traffic control areas. If the number of stress risk areas in the highway construction section is greater than 1, the location of each stress risk area in the highway construction section is obtained, and the distance between each stress risk area is determined. If the distance is less than the preset safety distance threshold, they are divided into the same construction traffic control area.

[0042] For example, in a highway construction section, the acoustic wave monitoring system detected two stress risk areas. The first stress risk area is located 200 meters from the construction section, and the second is located 220 meters from the construction section. If the preset safety distance threshold is 30 meters, it is used to determine whether adjacent stress risk areas are close enough to be merged into one construction traffic control area. After calculation, the distance between the two stress risk areas is 20 meters, which is lower than the preset 30-meter safety distance threshold. Therefore, the two stress risk areas are divided into the same construction traffic control area.

[0043] Step S103, a construction traffic control module is used to construct a vehicle speed adjustment model according to the traffic density, stress data and environmental factors in the construction traffic control area, and determine the maximum allowable speed in the construction traffic control area.

[0044] The temperature, humidity and rainfall in the construction traffic control area are obtained through real-time weather forecast, and the environmental factor formula is used Determine the environmental factors of the construction traffic control area, where H is relative humidity, T is temperature, and T max is the maximum temperature, R is the rainfall, R max is the maximum rainfall. Install strain gauge sensors in the stress risk area of ​​the construction traffic control area to monitor the stress value of the stress risk area in real time. Construct a vehicle speed adjustment model based on the traffic density, stress data and environmental factors of the construction traffic control area. Determine the maximum permissible speed in the construction traffic control area, where V max is the maximum permissible speed of the vehicle, S is the stress value of the current stress risk area, S this the preset stress safety threshold, D is the traffic density, α and β are the traffic density weight parameter and the environmental factor weight parameter, respectively, which are used to adjust the influence of traffic density and environmental factors on the speed, obtained by fitting historical data, K is a constant coefficient, which is used to adjust the speed range, and is set according to the maximum speed limit of the road section design, and E is the environmental factor, which represents the influence of the environment on the roadbed stress. If there are multiple stress risk areas in the construction traffic control area, the minimum value of the maximum allowable speed of multiple stress risk areas is selected as the vehicle speed limit of the construction traffic control area. According to the vehicle speed limit of the construction traffic control area, a vehicle speed limit plan for the construction traffic control area is formulated. If the stage of the highway construction project changes, the new stress risk area location is determined by the real-time acoustic wave reflection signal of the highway construction section, and the construction traffic control area is updated. According to the traffic density, stress data and environmental factors of the updated construction traffic control area, the vehicle speed limit of the updated construction traffic control area is determined using the speed adjustment model, and the vehicle speed limit plan for the construction traffic control area is adjusted.

[0045] For example, in a traffic control area of ​​a highway construction, the real-time weather forecast shows that the relative humidity H = 80, the temperature T = 25 ° C, and the rainfall R is 5 mm per hour. The highest historical temperature T max =40℃ and the highest rainfall in history R max =10mm. Use the environmental factor formula The environmental factor E of the construction traffic control area is determined to be 0.8064. Strain gauge sensors are installed in the stress risk area within the construction traffic control area, and the current stress value S=80MPa is monitored in real time. A speed adjustment model is constructed based on the traffic density, stress data and environmental factors of the construction traffic control area. If the safety threshold of stress S th =100MPa, current traffic density D = 50 vehicles / minute. Traffic density weight parameter α = 0.05, environmental factor weight parameter β = 0.3, constant coefficient K = 80, according to the maximum speed limit setting of the road section design, the maximum allowable speed V in the current construction traffic control area is calculated max=16.1km / h. If there are multiple stress risk areas in the construction traffic control area, the maximum allowable speed of each stress risk area is calculated, and the minimum value is taken as the overall speed limit of the area. For example, if the maximum allowable speed of other stress risk areas is calculated to be 15km / h and 18km / h, 15km / h is selected as the speed limit of the entire construction traffic control area. The vehicle speed limit scheme adjustment unit monitors the changes in the stage of highway construction in real time. If the stage of highway construction changes, the new location of the stress risk area is determined through the real-time sound wave reflection signal of the highway construction section, and the construction traffic control area is updated. According to the traffic density, stress data and environmental factors of the updated construction traffic control area, the vehicle speed limit of the updated construction traffic control area is determined using the speed adjustment model, and the vehicle speed limit scheme of the construction traffic control area is adjusted.

[0046] Step S104, a vehicle speed limit pre-adjustment module is used to determine a vehicle speed limit plan for the construction traffic control area in a future preset time period according to the temperature, humidity and rainfall in the construction traffic control area in the future preset time period.

[0047] Through the traffic monitoring database, the historical traffic density data of the construction traffic control area is obtained, and the long short-term memory network is used for model training to predict the traffic density data of the construction traffic control area in the future preset time period, including the traffic density of heavy vehicles. Through the weather forecast, the temperature, humidity and rainfall of the construction traffic control area in the future preset time period are obtained, and the model is adjusted according to the vehicle speed to determine the vehicle speed limit plan for the construction traffic control area in the future preset time period.

[0048] For example, in a highway construction traffic control area, the traffic density data of the past week was obtained through the traffic monitoring database, and the historical data was trained using the long short-term memory network model, and it was predicted that the traffic density from 7 to 9 tomorrow morning will reach 65 vehicles per minute, of which the heavy vehicle traffic density is 10 vehicles per minute. Through the weather forecast system, the weather conditions in the construction area tomorrow morning are obtained, including the relative humidity is expected to be 85%, the temperature is 30°C, and the rainfall is 5mm per hour. According to these predicted environmental data and traffic density data, they are substituted into the speed adjustment model, and the vehicle speed limit from 7 to 9 tomorrow morning is calculated to be 20km / h. Therefore, based on this prediction result, a vehicle speed limit plan for the construction traffic control area from 7 to 9 tomorrow morning is formulated. The construction management department will set up corresponding speed limit signs at the entrance of the construction section in advance, and publish speed limit information on the nearby traffic command center and navigation system to remind vehicles to slow down during this period to ensure the safety of the construction area and the smooth flow of traffic.

[0049] Step S105, a traffic density control module is used to implement traffic density control measures according to the heavy vehicle traffic density in the construction traffic control area and the real-time stress value of the stress risk area in a future preset time period.

[0050] If it is predicted that the heavy vehicle density in the construction traffic control area in the future preset time period is greater than the preset density threshold, the time point when the heavy vehicle density in the construction traffic control area is greater than the preset density threshold is determined, and traffic density control measures are implemented in advance. The traffic density control measures include limiting the heavy vehicle density in the construction traffic control area. When the upper limit of the number of heavy vehicles is reached, new heavy vehicles are temporarily restricted from entering the area. The stress value of the stress risk area is monitored in real time through the strain gauge sensor installed in the stress risk area of ​​the construction traffic control area. If the real-time monitored stress data exceeds the preset stress safety threshold, the traffic density control measures are implemented until the stress falls back to less than the preset stress safety threshold.

[0051] For example, if it is predicted that the heavy vehicle density in a highway construction traffic control area from 3:00 to 5:00 p.m. tomorrow will reach 25 vehicles per minute, which is greater than the preset density threshold of 20 vehicles per minute. In order to prevent the dense traffic from causing excessive pressure on the construction area, it is decided to implement traffic density control measures in advance before 3:00 p.m. tomorrow, including limiting the traffic density of heavy vehicles in the construction traffic control area, setting a maximum of 20 heavy vehicles per minute. Once the number of heavy vehicles reaches this upper limit, new heavy vehicles will be temporarily prohibited from entering the area until it is reopened in the next period. At the same time, strain gauge sensors are installed in the stress risk area of ​​the construction traffic control area to monitor the stress of the roadbed in real time. If the current stress safety threshold of the area is 100MPa, if the strain gauge sensor monitors that the stress value continues to rise while controlling the heavy vehicle traffic density and reaches 100MPa at 3:30, the traffic density control measures will be immediately strengthened. At this time, not only the passage of heavy vehicles is strictly restricted, but also the traffic density of all vehicles needs to be further reduced until the stress value is monitored to fall below the safety threshold.

[0052] Step S106, the facility stability analysis module is used to evaluate the stability of the construction facilities in the highway construction area according to the weight, support area and stacking mode of the construction facilities, and to formulate a real-time vehicle speed limit plan for the highway construction section based on the stability evaluation results.

[0053] The images of construction facilities on the highway construction section are obtained through the camera of the highway construction section. The Canny edge detection algorithm is used to identify the edge contours of the construction facilities on the highway construction section. The shape and boundary information of the construction facilities are obtained through the findContours method in OpenCV to determine the size of the construction equipment. According to the images of the construction facilities, the convolutional neural network is used for model training to determine the stacking mode and facility type of the construction facilities, and the weight and material of each facility are obtained according to the type of construction facilities. The facility types include but are not limited to roadblocks, warning signs, construction equipment and construction materials. The stacking mode includes but is not limited to stacking in rows, horizontal stacking, vertical stacking, staggered stacking, circular stacking and pyramid stacking. The minimum enclosing rectangle method is used to calculate the relative distance, angle and position between the facilities, obtain the spatial relationship between the facilities, and identify the actual support area of ​​the items during the stacking process. Combined with the weight, support area and stacking mode of the construction facilities, the stability of the construction facilities on the highway construction section is evaluated to identify the risks of tilting, sliding or moving. The stability includes excellent, good and poor. If the stability of the construction facilities in the highway construction section is good or poor, the highway section where the construction facilities are located will be divided into a construction traffic control area, and a vehicle speed limit plan will be implemented to limit the speed of vehicles in the highway construction section, and a warning message will be sent to the person in charge of the highway construction section. The camera of the highway construction section continuously obtains the placement image of the construction facilities, and the Kalman filter is used to judge the movement and change of the construction facilities over time, obtain the dynamic update of the real-time construction facility stacking mode, and evaluate the stability of the construction facilities in the highway construction area in real time.

[0054] For example, in a section of highway construction, a camera is used to capture images of facilities on the construction section, and various construction facilities are identified, including roadblocks, warning signs, construction equipment, and construction materials. The Canny edge detection algorithm and OpenCV's findContours method are used to extract the contour and boundary of each facility, and the roadblock is in the shape of a cuboid with an average size of 2 meters long, 0.5 meters wide, and 1 meter high. The shape of a certain construction equipment is a cuboid with a size of 1.5 meters long, 0.5 meters wide, and 1 meter high. According to the images of the construction facilities, the stacking mode and facility type of the construction facilities are identified through the convolutional neural network model. The stacking mode includes but is not limited to stacking in rows, horizontal stacking, vertical stacking, staggered stacking, circular stacking and pyramid stacking. The stacking mode of the facilities is staggered stacking, and they are all stacked within 2 meters outside the highway of the construction section. The identification results of the types of construction facilities are roadblocks, warning signs and construction equipment. The weight information of each facility is retrieved from the database, which is about 80 kilograms for roadblocks and about 150 kilograms for construction equipment carts, as well as material information. The relative distance, angle and position between the construction facilities are calculated using the minimum circumscribed rectangle method, and the average spacing between the roadblocks is 0.5 meters. Some other construction materials are also stacked next to the carts, including steel bars, cement bags, formwork, and steel pipes. The support area of ​​each facility is calculated based on the shape and size information of these construction facilities to evaluate the support effect during stacking. Combined with the weight, support area and row stacking mode of construction facilities, the stability of construction facilities in highway construction sections is evaluated, and the risks of tilting, sliding or moving are identified. The stability includes excellent, good and poor. The overall stability of the current construction facilities is poor, and some of the construction materials have the risk of sliding. The highway section where the construction facilities are located is divided into a construction traffic control area. To ensure safety, a warning message is automatically sent to the person in charge of the construction section, suggesting adjustments or reinforcements. At the same time, a vehicle speed limit plan is implemented in the section, reducing the speed limit from the original 40km / h to the preset speed threshold of 20km / h to reduce the vibration impact of vehicles on the facilities when driving. The placement of construction facilities is continuously monitored through the construction section camera, and the Kalman filter is used to track the location changes of the facilities. The stability of the construction facilities is evaluated every 5 minutes at a preset time interval. Through the dynamically updated stacking mode, the stability of the facilities is ensured to meet expectations during the subsequent construction process, and the safety management of the site is further optimized.

[0055] Step S107, a traffic control effect evaluation module is used to evaluate the traffic control effect and adjust the vehicle speed limit and heavy vehicle traffic density based on the stress data and traffic data before and after the implementation of the vehicle speed limit scheme and traffic density control measures.

[0056] Obtain stress data and traffic data before and after the implementation of the vehicle speed limit scheme and traffic density control measures in the construction traffic control area, and evaluate the traffic control effect of the current vehicle speed limit scheme and traffic density control measures based on the stress recovery speed and amplitude and traffic efficiency in the construction traffic control area. If the traffic control effect is lower than expected, adjust the vehicle speed limit and heavy vehicle traffic density, and update the vehicle speed limit scheme and traffic density control measures in the construction traffic control area until the traffic control effect reaches the expected result.

[0057] For example, in a highway construction traffic control area, the management department implemented a vehicle speed limit plan and traffic density control measures, set the vehicle speed limit to 20km / h, and limited the passage of a maximum of 20 heavy vehicles per minute. Before the implementation of the measures, the stress value of the stress risk area was 110MPa, while the stress safety threshold of the construction roadbed was 100MPa. Within 20 minutes after the implementation of the traffic density control measures and speed limit plan, the stress monitoring data showed that the stress dropped to 102MPa, and the traffic efficiency remained at 50 vehicles per minute. The evaluation results show that the current speed limit plan and traffic density control measures are less effective than expected, and the traffic efficiency has also decreased. In order to further improve the traffic control effect, the management department decided to further reduce the vehicle speed limit to 15km / h and adjust the upper limit of the traffic density of heavy vehicles to 15 vehicles per minute. After the implementation of the adjusted plan, the stress data and traffic data were recorded again. Under the new control measures, the stress value dropped to 90MPa in the next minute, reaching the safety threshold, and the traffic efficiency remained at 70 vehicles per minute, close to the expected level of the management department. Through this adjustment, an effective stress recovery speed and reasonable traffic efficiency were achieved, ensuring the safety of the construction area and smooth traffic flow. Therefore, the construction management department will continue to implement and monitor the adjusted speed limit and traffic density plan as a new traffic control measure to ensure that the roadbed in the construction area remains in a safe state during the construction period.

[0058] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the concept of the present application. For example, the above features are replaced with the technical features with similar functions disclosed in the present application (but not limited to) to form a technical solution.

Claims

1. A traffic control system for highway engineering construction, characterized in that: The system comprises: The acoustic wave reflection signal acquisition module is used to determine the acoustic wave emission parameters of the acoustic wave transmitter according to the acoustic wave reflection signal receiving effect of the highway in the construction section, and to obtain the acoustic wave reflection signal of the highway construction section; A stress risk area identification module is used to extract its spectrum characteristics using a short-time Fourier transform algorithm to determine the type of the acoustic reflection source of the acoustic reflection signal and the location of the stress risk area; The construction traffic control module is used to construct a vehicle speed adjustment model based on the traffic density, stress data and environmental factors in the construction traffic control area to determine the maximum allowable speed in the construction traffic control area; A vehicle speed limit pre-adjustment module is used to determine a vehicle speed limit plan for a construction traffic control area in a future preset time period according to the temperature, humidity and rainfall in the construction traffic control area in the future preset time period; Traffic density control module, used to implement traffic density control measures according to the heavy vehicle traffic density in the construction traffic control area and the real-time stress value of the stress risk area in the future preset time period; The facility stability analysis module is used to evaluate the stability of construction facilities in the highway construction area according to the weight, support area and stacking mode of the construction facilities, and to formulate a real-time vehicle speed limit plan for the highway construction section based on the stability assessment results; The traffic control effect evaluation module is used to evaluate the traffic control effect and adjust the vehicle speed limit and heavy vehicle traffic density based on the stress data and traffic data before and after the implementation of the vehicle speed limit scheme and traffic density control measures.

2. The system according to claim 1, wherein: The acoustic wave reflection signal acquisition module is used to determine the acoustic wave emission parameters of the acoustic wave transmitter according to the acoustic wave reflection signal receiving effect of the highway in the construction section, and to acquire the acoustic wave reflection signal of the highway construction section, including: The historical sound wave emission parameters of the sound wave transmitter, the material composition of the highway in the construction section corresponding to the detection time, the road surface humidity and the reflected signal receiving effect are obtained through the sound wave reflection monitoring database, and the recurrent neural network is used for model training to build a sound wave emission parameter prediction model for the sound wave transmitter, and the sound wave emission parameters of the sound wave transmitter with the best sound wave reflection signal receiving effect in the current highway construction section are determined. The emission parameters include emission frequency, amplitude and angle, and the material composition includes but is not limited to concrete and asphalt. The receiving effects include excellent, good and poor. A sound wave transmitter and a directional array sound wave receiver are set at the starting point of the highway construction section, and sound waves are emitted to the highway construction section by the sound wave transmitter according to the set sound wave emission parameters, and the sound wave reflection signal and the corresponding timestamp information are obtained by the directional array sound wave receiver; according to the sound wave reflection signal of the highway construction section, the sound wave reflection signal is separated by an independent component analysis algorithm to obtain the sound wave reflection signals of different sound wave reflection sources, and save them in the sound wave reflection monitoring database.

3. The system according to claim 1, wherein: The stress risk area identification module is used to extract its spectrum characteristics using a short-time Fourier transform algorithm. Determine the acoustic reflection source type and location of stress risk areas of acoustic reflection signals, including: The short-time Fourier transform algorithm is used to convert the time-domain acoustic wave reflection signal into frequency-domain data, and its spectral characteristics are extracted, which include frequency, frequency offset and amplitude. The historical acoustic wave emission frequency, amplitude and spectral characteristics of the acoustic wave reflection signal are obtained through the acoustic wave reflection monitoring database, and the acoustic wave reflection source type is marked. The support vector machine algorithm is used for model training, and an acoustic wave reflection source type identification model is constructed to determine the acoustic wave reflection source type of the acoustic wave reflection signal. The acoustic wave reflection source type includes stress risk area reflection source and non-stress risk area reflection source. According to the echo time of the acquired acoustic wave reflection signal, the position of the stress risk area relative to the directional array acoustic wave receiver is calculated through time difference estimation to determine the position of the stress risk area. The construction traffic control area division unit is used to divide the construction traffic control area according to the position of the stress risk area.

4. The system according to claim 3, wherein: The construction traffic control area division unit is used to divide the construction traffic control area according to the location of the stress risk area, and includes: The stress risk areas in the highway construction section are divided into construction traffic control areas; if the number of stress risk areas in the highway construction section is greater than 1, the location of each stress risk area in the highway construction section is obtained, and the distance between each stress risk area is determined. If the distance is less than the preset safety distance threshold, they are divided into the same construction traffic control area.

5. The system according to claim 1, wherein: The construction traffic control module is used to construct a vehicle speed adjustment model according to the traffic density, stress data and environmental factors of the construction traffic control area, and determine the maximum allowable speed of the construction traffic control area, including: The temperature, humidity and rainfall in the construction traffic control area are obtained through real-time weather forecast, and the environmental factor formula is used Determine the environmental factors of the construction traffic control area, where H is relative humidity, T is temperature, and T max is the maximum temperature, R is the rainfall, R max is the maximum rainfall; strain gauge sensors are installed in the stress risk area of ​​the construction traffic control area to monitor the stress value of the stress risk area in real time; a vehicle speed adjustment model is constructed based on the traffic density, stress data and environmental factors of the construction traffic control area Determine the maximum permissible speed in the construction traffic control area, where V max is the maximum permissible speed of the vehicle, S is the stress value of the current stress risk area, S th is the preset stress safety threshold, D is the traffic density, α and β are the traffic density weight parameter and the environmental factor weight parameter respectively, which are used to adjust the influence of traffic density and environmental factors on the travel speed, and are obtained by fitting historical data. K is a constant coefficient, which is used to adjust the speed range and is set according to the maximum speed limit designed for the road section. E is the environmental factor, which represents the influence of the environment on the roadbed stress. If there are multiple stress risk areas in the construction traffic control area, the minimum value of the maximum allowable travel speeds in the multiple stress risk areas is selected as the vehicle speed limit in the construction traffic control area. According to the vehicle speed limit in the construction traffic control area, a vehicle speed limit plan for the construction traffic control area is formulated. If the stage of the highway construction project changes, the new stress risk area location is determined through the real-time acoustic wave reflection signal of the highway construction section, and the construction traffic control area is updated. According to the traffic density, stress data and environmental factors of the updated construction traffic control area, the vehicle speed limit of the updated construction traffic control area is determined using the speed adjustment model, and the vehicle speed limit plan for the construction traffic control area is adjusted.

6. The system according to claim 1, wherein: The vehicle speed limit pre-adjustment module is used to determine the vehicle speed limit scheme for the construction traffic control area in the future preset time period according to the temperature, humidity and rainfall in the construction traffic control area in the future preset time period, including: Through the traffic monitoring database, the historical traffic density data of the construction traffic control area is obtained, and the long short-term memory network is used for model training to predict the traffic density data of the construction traffic control area in the future preset time period, including the traffic density of heavy vehicles; the temperature, humidity and rainfall in the construction traffic control area in the future preset time period are obtained through weather forecasts, and the model is adjusted according to the vehicle speed to determine the vehicle speed limit plan in the construction traffic control area in the future preset time period.

7. The system according to claim 1, wherein: The traffic density control module is used to implement traffic density control measures according to the heavy vehicle traffic density of the construction traffic control area and the real-time stress value of the stress risk area in the future preset time period, including: If it is predicted that the heavy vehicle traffic density in the construction traffic control area in a preset time period in the future is greater than the preset density threshold, the time point when the heavy vehicle traffic density in the construction traffic control area in the future is greater than the preset density threshold is determined, and traffic density control measures are implemented in advance. The traffic density control measures include limiting the heavy vehicle traffic density in the construction traffic control area. When the upper limit of the number of heavy vehicle traffic is reached, new heavy vehicles are temporarily restricted from entering the area; the stress value of the stress risk area is monitored in real time through the strain gauge sensor installed in the stress risk area of ​​the construction traffic control area. If the real-time monitored stress data exceeds the preset stress safety threshold, traffic density control measures are implemented until the stress falls back to less than the preset stress safety threshold.

8. The system according to claim 1, wherein: The facility stability analysis module is used to evaluate the stability of construction facilities in the highway construction area according to the weight, support area and stacking mode of the construction facilities, and to formulate a real-time vehicle speed limit plan for the highway construction section based on the stability evaluation results, including: Through the camera of the highway construction section, the image of the construction facilities on the highway construction section is obtained; the Canny edge detection algorithm is used to identify the edge contours of the construction facilities on the highway construction section, and the shape and boundary information of the construction facilities are obtained through the findContours method in OpenCV to determine the size of the construction equipment; according to the image of the construction facility placement, a convolutional neural network is used for model training to determine the stacking mode and facility type of the construction facilities, and the weight and material of each facility are obtained according to the type of construction facilities. The facility types include but are not limited to roadblocks, warning signs, construction equipment and construction materials, and the stacking modes include but are not limited to stacking in rows, horizontal stacking, vertical stacking, staggered stacking, circular stacking and pyramid stacking; the minimum enclosing rectangle method is used to calculate the relative distance, angle and The system can identify the location of the construction facilities, obtain the spatial relationship between the facilities, and identify the actual supporting area of ​​the items during the stacking process; evaluate the stability of the construction facilities on the highway construction section in combination with the weight, supporting area and stacking pattern of the construction facilities, identify the risks of tilting, sliding or moving, and the stability includes excellent, good and poor; if the stability of the construction facilities on the highway construction section is good or poor, the highway section where the construction facilities are located will be divided into a construction traffic control area, and a vehicle speed limit plan will be implemented to limit the speed of vehicles on the highway construction section, and a warning message will be sent to the person in charge of the highway construction section; the camera on the highway construction section will continuously obtain the placement image of the construction facilities, and the Kalman filter will be used to judge the movement and change of the construction facilities over time, so as to obtain the dynamic update of the real-time stacking pattern of the construction facilities, and evaluate the stability of the construction facilities in the highway construction area in real time.

9. The system according to claim 1, wherein: The traffic control effect evaluation module is used to evaluate the traffic control effect and adjust the vehicle speed limit and heavy vehicle traffic density based on the stress data and traffic data before and after the implementation of the vehicle speed limit scheme and traffic density control measures, including: Obtain stress data and traffic data before and after the implementation of the vehicle speed limit scheme and traffic density control measures in the construction traffic control area, and evaluate the traffic control effect of the current vehicle speed limit scheme and traffic density control measures based on the stress recovery speed and amplitude and traffic efficiency of the construction traffic control area; if the traffic control effect is lower than expected, adjust the vehicle speed limit and heavy vehicle traffic density, and update the vehicle speed limit scheme and traffic density control measures in the construction traffic control area until the traffic control effect reaches the expected result.

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