Asphalt pavement engineering quality control platform and method

Abstract

The embodiment of the present invention relates to the technical field of asphalt construction management, and specifically discloses an asphalt pavement engineering quality control platform and method. The embodiment of the present invention obtains multi-source monitoring data by performing quality control planning and multi-source monitoring; performs video monitoring of multiple key construction areas; performs quality control and registration records of materials entering and leaving the site; performs equipment performance monitoring, status monitoring, and work monitoring; performs asphalt mixing monitoring and asphalt test monitoring; and performs intelligent compaction monitoring and completion acceptance monitoring. It is capable of generating control planning data, performing multi-source monitoring, and displaying real-time monitoring videos, visual management data of materials entering and leaving the site, visual data of equipment monitoring, visual data of asphalt monitoring, and visual data of compaction test acceptance monitoring. It is capable of realizing automatic and precise tracking and strict management of asphalt pavement engineering projects, and the engineering quality control is comprehensive and detailed, and it is capable of realizing remote and real-time engineering supervision.

Description

Technical Field

[0001] The present invention belongs to the technical field of asphalt construction management, and in particular relates to an asphalt pavement engineering quality control platform and method. Background Art

[0002] Asphalt construction management refers to a series of management activities such as planning, organizing, directing, coordinating, controlling and supervising construction activities during the asphalt pavement construction process, aiming to ensure the smooth progress of construction and to ensure that the requirements of project quality, safety, progress and cost are effectively met.

[0003] In the prior art, the application with patent number CN117115749A discloses a global construction quality detection method and system, which performs video monitoring on the construction area through the video monitoring equipment deployed in the construction area to form an initial monitoring video corresponding to the construction area, and then performs a monitoring parameter determination operation on the construction area based on the initial monitoring video to output video monitoring parameters corresponding to the video monitoring equipment corresponding to the construction area. The video monitoring equipment is used to perform video monitoring on the corresponding construction area based on the video monitoring parameters to form a corresponding target monitoring video.

[0004] Therefore, the application can realize the determination of monitoring parameters for construction project areas.

[0005] The application with patent number CN109302487A discloses a highway engineering quality management information system based on a cloud platform. It creates a universal data source template for universal machines, presses and mixing plant equipment, uses a relational database as a data container, and manages the data in combination with a spatial database engine. The spatial database engine acts as a conversion tool for data in and out of the relational database in this application, thereby achieving cross-database platform access, making database access faster, more functional, and making spatial data applications more flexible.

[0006] Therefore, the main purpose of this application is to achieve the problem of integrated and unified management of multi-source equipment.

[0007] The above existing technologies achieve the effect of video surveillance in multiple areas. However, in the process of regional monitoring, if high-definition and high-frame-rate video streams are used, the network bandwidth requirements will increase significantly. The quality control platform needs to monitor multiple areas in real time. Therefore, the transmission process will lead to the risk of network congestion, and storage resources and computing resources will increase accordingly. Although the use of lower resolution and angle frame rate will reduce the risk of network congestion, it will affect the observation of details, and facial features of people, license plate numbers, etc. cannot be recognized. Summary of the invention

[0008] The purpose of the embodiments of the present invention is to provide an asphalt pavement engineering quality control platform and method, aiming to solve the problems raised in the background technology.

[0009] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0010] A method for quality control of asphalt pavement engineering, the method specifically comprising the following steps:

[0011] Acquire construction design data of the asphalt pavement project, conduct quality control planning, generate control planning data, and conduct multi-source monitoring according to the control planning data to obtain multi-source monitoring data;

[0012] Conduct video monitoring of multiple key construction areas according to the multi-source monitoring data, and obtain and display real-time monitoring videos;

[0013] According to the multi-source monitoring data, quality control and registration records of materials entering and leaving the site are carried out, and visual management data of materials entering and leaving the site is generated and displayed;

[0014] According to the multi-source monitoring data, equipment performance monitoring, status monitoring and operation monitoring are performed, and equipment monitoring visualization data is generated and displayed;

[0015] According to the multi-source monitoring data, asphalt mixing monitoring and asphalt test monitoring are performed, and asphalt monitoring visualization data is generated and displayed;

[0016] According to the multi-source monitoring data, intelligent compaction monitoring is carried out, and after the asphalt pavement project is completed, completion acceptance monitoring is carried out to generate and visualize compaction acceptance monitoring visualization data.

[0017] As a further limitation of the technical solution of the embodiment of the present invention, the method of obtaining the construction design data of the asphalt pavement project, performing quality control planning, generating control planning data, and performing multi-source monitoring according to the control planning data, and obtaining the multi-source monitoring data specifically includes the following steps:

[0018] Acquiring construction design data of an asphalt pavement project, wherein the construction design data includes the type, proportion, technical requirements and construction specifications of the asphalt mixture;

[0019] Carry out quality control planning according to the construction design data of the project and generate control planning data;

[0020] According to the control planning data, arrange the sensors and transmission plan for multi-source monitoring and create a multi-source monitoring system;

[0021] Based on the multi-source monitoring system, multi-source monitoring processing is performed to obtain multi-source monitoring data.

[0022] As a further limitation of the technical solution of the embodiment of the present invention, the video monitoring of multiple key construction areas according to the multi-source monitoring data, and obtaining and displaying the real-time monitoring video specifically include the following steps:

[0023] According to the multi-source monitoring data, a plurality of key construction areas are determined, wherein the plurality of key construction areas include an asphalt mixing plant, a paver operation area, and a roller operation area;

[0024] Generate video surveillance instructions;

[0025] According to the video monitoring instructions, multiple key construction areas are monitored to obtain real-time monitoring videos;

[0026] The real-time monitoring video is displayed in real time.

[0027] As a further limitation of the technical solution of the embodiment of the present invention, the quality control and registration records of materials entering and leaving the site according to the multi-source monitoring data, and the generation and display of visual management data of materials entering and leaving the site specifically include the following steps:

[0028] According to the multi-source monitoring data, conduct needle penetration, softening point, ductility, specification and strength inspections on materials entering and leaving the site, and record material inspection data;

[0029] According to the multi-source monitoring data, register the entry time, batch, and quantity of materials entering and leaving the site, and record the material registration data;

[0030] Combining the material inspection data and the material registration data, generating visual management data for material entry and exit;

[0031] The visual management data of the materials entering and leaving the site is displayed visually.

[0032] As a further limitation of the technical solution of the embodiment of the present invention, the performing of equipment performance monitoring, status monitoring and operation monitoring according to the multi-source monitoring data, and generating and displaying equipment monitoring visualization data specifically includes the following steps:

[0033] According to the multi-source monitoring data, multiple construction equipment including an asphalt mixing plant, a paver and a road roller are identified;

[0034] Performing equipment performance monitoring on the plurality of construction equipment to obtain performance monitoring data;

[0035] Performing equipment status monitoring on the plurality of construction equipment to obtain status monitoring data;

[0036] Performing equipment operation monitoring on the plurality of construction equipment to obtain operation monitoring data;

[0037] Combining the performance monitoring data, the status monitoring data and the operation monitoring data to generate equipment monitoring visualization data;

[0038] The device monitoring visualization data is visualized.

[0039] As a further limitation of the technical solution of the embodiment of the present invention, the asphalt mixing monitoring and asphalt test monitoring are performed according to the multi-source monitoring data, and the generation and display of asphalt monitoring visualization data specifically include the following steps:

[0040] According to the multi-source monitoring data, monitor the temperature, mix ratio and output of asphalt mixing to obtain mixing monitoring data;

[0041] According to the multi-source monitoring data, monitor the softening point, penetration, ductility, rutting test and fatigue test of the asphalt test to obtain the test monitoring data;

[0042] Combining the mixing monitoring data and the test monitoring data to generate asphalt monitoring visualization data;

[0043] The asphalt monitoring visualization data is visualized.

[0044] As a further limitation of the technical solution of the embodiment of the present invention, the intelligent compaction monitoring is performed according to the multi-source monitoring data, and after the asphalt pavement project is completed, the completion acceptance monitoring is performed, and the generation and visualization of the compaction acceptance monitoring visualization data specifically includes the following steps:

[0045] According to the multi-source monitoring data, the vibration frequency, amplitude and temperature of intelligent compaction are monitored to obtain compaction monitoring data;

[0046] After the asphalt pavement project is completed, completion acceptance monitoring is carried out to obtain acceptance monitoring data;

[0047] Combining the compaction monitoring data and the acceptance monitoring data to generate compaction test acceptance monitoring visualization data;

[0048] The pressure test acceptance monitoring visualization data is visualized.

[0049] An asphalt pavement engineering quality control platform, the platform includes a control planning processing unit, a video monitoring display unit, a material entry and exit visualization unit, an equipment monitoring visualization unit, an asphalt monitoring visualization unit and a pressure test acceptance monitoring visualization unit, wherein:

[0050] A control planning processing unit is used to obtain the construction design data of the asphalt pavement project, perform quality control planning, generate control planning data, and perform multi-source monitoring according to the control planning data to obtain multi-source monitoring data;

[0051] A video monitoring display unit, used to perform video monitoring of multiple key construction areas according to the multi-source monitoring data, and to obtain and display real-time monitoring videos;

[0052] A material entry and exit visualization unit is used to perform quality control and registration records of material entry and exit according to the multi-source monitoring data, and to generate and display material entry and exit visualization management data;

[0053] An equipment monitoring visualization unit, used to perform equipment performance monitoring, status monitoring and operation monitoring according to the multi-source monitoring data, and generate and display equipment monitoring visualization data;

[0054] An asphalt monitoring visualization unit, used to perform asphalt mixing monitoring and asphalt test monitoring according to the multi-source monitoring data, and generate and display asphalt monitoring visualization data;

[0055] The compaction test acceptance monitoring visualization unit is used to perform intelligent compaction monitoring according to the multi-source monitoring data, and to perform completion acceptance monitoring after the asphalt pavement project is completed, and to generate and visualize compaction test acceptance monitoring visualization data.

[0056] As a further limitation of the technical solution of the embodiment of the present invention, the control planning processing unit specifically includes:

[0057] A design data acquisition module, used to acquire construction design data of an asphalt pavement project, wherein the construction design data includes the type, proportion, technical requirements and construction specifications of the asphalt mixture;

[0058] The control and planning module is used to carry out quality control planning according to the construction design data of the project and generate control and planning data;

[0059] A multi-source monitoring planning module, used to carry out sensor layout and transmission planning for multi-source monitoring according to the control planning data, and to create a multi-source monitoring system;

[0060] The multi-source monitoring processing module is used to perform multi-source monitoring processing based on the multi-source monitoring system to obtain multi-source monitoring data.

[0061] As a further limitation of the technical solution of the embodiment of the present invention, the video monitoring display unit specifically includes:

[0062] A key area determination module, used to determine a plurality of key construction areas according to the multi-source monitoring data, wherein the plurality of key construction areas include an asphalt mixing plant, a paver operation area, and a roller operation area;

[0063] An instruction generation module, used for generating video monitoring instructions;

[0064] A video monitoring module, used to perform video monitoring of multiple key construction areas according to the video monitoring instructions and obtain real-time monitoring videos;

[0065] The monitoring display module is used to display the real-time monitoring video in real time.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] 1. The embodiment of the present invention acquires multi-source monitoring data by performing quality control planning and multi-source monitoring; performs video monitoring of multiple key construction areas; performs quality control and registration records of materials entering and leaving the site; performs equipment performance monitoring, status monitoring, and work monitoring; performs asphalt mixing monitoring and asphalt test monitoring; and performs intelligent compaction monitoring and completion acceptance monitoring. It is able to generate control planning data, perform multi-source monitoring, and display real-time monitoring videos, visual management data of materials entering and leaving the site, visual data of equipment monitoring, visual data of asphalt monitoring, and visual data of compaction test acceptance monitoring. It is able to realize automatic and precise tracking and strict management of asphalt pavement engineering projects, and the engineering quality control is comprehensive and detailed, and it is able to realize remote and real-time engineering supervision.

[0068] 2. In the embodiment of the present invention, the video stream is analyzed in real time to identify potential abnormal events, and the monitoring strategy is adjusted in real time based on the detection results. For example, monitoring is performed at a lower resolution and frame rate at ordinary times. When the model predicts that there are risks in one or more key construction areas, the video frame rate and resolution of the corresponding key construction areas are automatically increased to capture more details, and the corresponding priority is increased to detect problems in a timely manner. In addition, by dynamically adjusting the resolution and frame rate of each monitoring, system resources can be effectively managed to ensure that the most important cameras receive the maximum resource allocation. While ensuring accurate monitoring, the risk of network congestion is reduced, and subsequent storage resources and computing resources can also be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0070] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0071] Figure 2 A flow chart of multi-source monitoring processing in the method provided by an embodiment of the present invention is shown.

[0072] Figure 3 A flow chart of displaying real-time monitoring video in the method provided by an embodiment of the present invention is shown.

[0073] Figure 4 A flow chart showing visual management data of materials entering and leaving a site in a method provided by an embodiment of the present invention is shown.

[0074] Figure 5 A flow chart for displaying visual data of equipment monitoring in the method provided by an embodiment of the present invention is shown.

[0075] Figure 6 A flow chart showing visual data of asphalt monitoring in the method provided by an embodiment of the present invention is shown.

[0076] Figure 7 A flow chart showing visualization data of pressure test reception monitoring in the method provided by an embodiment of the present invention is shown.

[0077] Figure 8 The application architecture diagram of the platform provided by the embodiment of the present invention is shown.

[0078] Fig. 9 The structure block diagram of the management and control planning processing unit in the platform provided by the embodiment of the present invention is shown.

[0079] Fig.10 The structure block diagram of the video monitoring display unit in the platform provided by the embodiment of the present invention is shown. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0081] It is understandable that the existing technology lacks an efficient and comprehensive digital quality control platform for asphalt construction management to achieve detailed tracking and strict management of asphalt pavement projects. Instead, it mainly relies on manual means to supervise and control the quality of the project. It is often difficult to achieve full coverage and in-depth and detailed control of the project quality, resulting in insufficient comprehensive and detailed project quality control, and inability to achieve remote and real-time project supervision.

[0082] To solve the above problems, the embodiment of the present invention obtains the construction design data of the asphalt pavement project, carries out quality control planning, generates control planning data, and carries out multi-source monitoring according to the control planning data to obtain multi-source monitoring data; according to the multi-source monitoring data, video monitoring is carried out on multiple key construction areas to obtain and display real-time monitoring videos; according to the multi-source monitoring data, quality control and registration records of materials entering and leaving the site are carried out, and visual management data of materials entering and leaving the site is generated and displayed; according to the multi-source monitoring data, equipment performance monitoring, status monitoring and operation monitoring are carried out, and visual equipment monitoring data is generated and displayed; according to the multi-source monitoring data, asphalt mixing monitoring and asphalt test monitoring are carried out, and visual asphalt monitoring data is generated and displayed; according to the multi-source monitoring data, intelligent compaction monitoring is carried out, and after the asphalt pavement project is completed, completion acceptance monitoring is carried out, and compaction acceptance monitoring visual data is generated and displayed visually. It can generate management and control planning data, conduct multi-source monitoring, display real-time monitoring video, material entry and exit visual management data, equipment monitoring visual data, asphalt monitoring visual data and compaction test acceptance monitoring visual data. It can realize automatic and precise tracking and strict management of asphalt pavement projects, and the project quality control is comprehensive and meticulous, and can realize remote and real-time project supervision.

[0083] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0084] Specifically, a method for controlling the quality of an asphalt pavement project comprises the following steps:

[0085] Step S101, obtaining construction design data of the asphalt pavement project, performing quality control planning, generating control planning data, and performing multi-source monitoring according to the control planning data to obtain multi-source monitoring data.

[0086] In an embodiment of the present invention, by obtaining construction design data such as the type, proportion, technical requirements and construction specifications of the asphalt mixture of the asphalt pavement project, quality control planning is performed on the asphalt pavement project according to the construction design data of the project, and control planning data is generated. Then, according to the control planning data, sensor layout and transmission planning for multi-source monitoring are performed to create a multi-source monitoring system, and then based on the multi-source monitoring system, multi-source monitoring processing is performed to obtain multi-source monitoring data.

[0087] Specifically, Figure 2 A flow chart of multi-source monitoring processing in the method provided by an embodiment of the present invention is shown.

[0088] Among them, in the preferred embodiment provided by the present invention, the construction design data of the asphalt pavement project is obtained, quality control planning is performed, control planning data is generated, and multi-source monitoring is performed according to the control planning data. The acquisition of multi-source monitoring data specifically includes the following steps:

[0089] Step S1011, obtaining construction design data of an asphalt pavement project, wherein the construction design data includes the type, proportion, technical requirements and construction specifications of the asphalt mixture;

[0090] Step S1012, performing quality control planning according to the construction design data of the project, and generating control planning data;

[0091] Step S1013, performing sensor layout and transmission planning for multi-source monitoring according to the control planning data, and creating a multi-source monitoring system;

[0092] Step S1014: Based on the multi-source monitoring system, multi-source monitoring processing is performed to obtain multi-source monitoring data.

[0093] Among them, in the preferred implementation mode provided by the present invention, according to the control planning data, the sensor arrangement and transmission planning of multi-source monitoring are performed, and the creation of a multi-source monitoring system specifically includes the following steps:

[0094] Obtain monitoring areas, target focus areas, signal interference areas and sensor coverage density in control planning data

[0095] The number of sensors is obtained based on the size of the monitoring area, the number of target areas of interest, and the sensor coverage density;

[0096] The target focus area is used as the key point, and the key points are divided into several clusters. The center point of each cluster is used as the initial position of a sensor.

[0097] The total transmission distance between sensors, the coverage of each key point, the energy consumption of sensors and signal interference are used as optimization targets, and the size of the monitoring area is used as a constraint to perform multi-objective optimization.

[0098] After the optimization is completed, the optimal layout position of the sensor is obtained.

[0099] In the embodiment of the present invention, the initial position of the sensor is obtained by clustering, thereby improving the quality of the initial solution and improving the efficiency and results of subsequent optimization. In addition, multiple factors considered in the optimization process can ensure that all key points are covered, the transmission distance and energy consumption are minimized, and high interference areas are avoided as much as possible. Furthermore, the asphalt pavement engineering quality control method also includes the following steps:

[0100] Step S102: Perform video monitoring on multiple key construction areas according to the multi-source monitoring data, and obtain and display real-time monitoring videos.

[0101] In an embodiment of the present invention, multiple key construction areas such as an asphalt mixing plant, a paver operating area, and a roller operating area in an asphalt pavement project are determined according to multi-source monitoring data, and video monitoring instructions are generated. According to the video monitoring instructions, video monitoring is performed on the multiple key construction areas, real-time monitoring videos are obtained, and a monitoring video display window is constructed. In the monitoring video display window, the real-time monitoring videos are displayed in real time.

[0102] Specifically, Figure 3 A flow chart of displaying real-time monitoring video in the method provided by an embodiment of the present invention is shown.

[0103] Among them, in the preferred embodiment provided by the present invention, the video monitoring of multiple key construction areas according to the multi-source monitoring data, and obtaining and displaying the real-time monitoring video specifically include the following steps:

[0104] Step S1021, determining multiple key construction areas according to the multi-source monitoring data, wherein the multiple key construction areas include an asphalt mixing plant, a paver operation area, and a roller operation area;

[0105] Step S1022, generating a video monitoring instruction;

[0106] Step S1023, performing video monitoring on multiple key construction areas according to the video monitoring instruction to obtain real-time monitoring video;

[0107] Step S1024: display the real-time monitoring video in real time.

[0108] Among them, in the preferred implementation mode provided by the present invention, according to the video monitoring instruction, video monitoring is performed on multiple key construction areas, and obtaining real-time monitoring video specifically includes the following steps:

[0109] Obtain historical monitoring video streams and current monitoring video streams of multiple key construction areas, and perform noise reduction processing on the historical monitoring video streams and current monitoring video streams;

[0110] Build a deep learning model, extract static frames of the historical surveillance video stream after noise reduction as training frames, and use the corresponding annotated events as data annotations for training to obtain a trained prediction model;

[0111] The static frame of the current surveillance video stream after denoising is extracted and input into the trained prediction model as a test frame to obtain the prediction results of multiple key construction areas;

[0112] According to the prediction results of multiple key construction areas, the sensor monitoring parameters and priorities of different key construction areas are dynamically adjusted to obtain real-time monitoring videos.

[0113] In an embodiment of the present invention, the present invention performs real-time analysis on the video stream, identifies potential abnormal events, and adjusts the monitoring strategy in real time based on the detection results. For example, monitoring is performed at a lower resolution and frame rate at ordinary times. When the model predicts that there are risks and hidden dangers in one or more key construction areas, the video frame rate and resolution of the corresponding key construction areas are automatically increased to capture more details, and the corresponding priority is increased to detect problems in a timely manner. In addition, by dynamically adjusting the resolution and frame rate of each monitoring, system resources can be effectively managed to ensure that the most important cameras receive the maximum resource allocation.

[0114] Moreover, after improving the video frame rate and resolution of the corresponding key construction area, the corresponding video stream can also be used as the input of the model. The high-resolution and high-frame-rate video can provide more detailed parameters, which can effectively improve the accuracy of the model prediction results. Among them, in the preferred embodiment provided by the present invention, the noise reduction processing of the monitoring video stream specifically includes the following steps:

[0115] Video frames are extracted from the surveillance video stream, and Fourier transform is performed on the video frames to convert the pixel intensity representation into the representation of the frequency component to obtain the spectrum. The corresponding process has the following relationship:

[0116] ;

[0117] in, Represents a video stream, represents a video frame extracted from a video stream, represents the Fourier transform operation, represents the spectrum;

[0118] The spectrum is centralized so that the low-frequency components are concentrated in the center of the spectrum to obtain a centralized spectrum. The corresponding process has the following relationship:

[0119] ;

[0120] in, represents the centralized spectrum, represents spectrum centering operation;

[0121] Calculate the amplitude of each component in the centralized spectrum and perform logarithmic transformation on the amplitude to obtain the amplitude spectrum. The corresponding process has the following relationship:

[0122] ;

[0123] in, represents the amplitude of the centered spectrum, represents the logarithmic transformation operation, represents the amplitude spectrum;

[0124] Calculate the median of the amplitude spectrum, and construct a low-pass filter based on the median of the amplitude spectrum. Use the low-pass filter to filter the amplitude spectrum to remove noise, and then perform inverse Fourier transform to obtain a preliminary denoised image. The corresponding process has the following relationship:

[0125] ;

[0126] ;

[0127] in, represents a low-pass filter, represents the median of the amplitude spectrum, represents the spectrum after low-pass filtering, represents the inverse Fourier transform operation, represents the preliminary denoised image;

[0128] The preliminary denoised image is subjected to multi-scale processing using the Laplacian pyramid to obtain a multi-scale processed image, and then the multi-scale processed image is fused with the preliminary denoised image in an average weighted manner to enhance the structural information and edge details of the image, and a multi-scale denoised image is obtained. The corresponding process has the following relationship:

[0129] ;

[0130] ;

[0131] in, represents the Laplacian pyramid multi-scale processing operation, represents a multi-scale image, represents the first weight, represents a multi-scale denoised image, represents the average weighted fusion operation;

[0132] The image after multi-scale denoising is subjected to nonlinear transformation to enhance the contrast and detail of the image and obtain an enhanced image. The video frame and the enhanced image are fused in a linear weighted manner to obtain the final denoised image. The corresponding process has the following relationship:

[0133] ;

[0134] ;

[0135] in, represents the enhanced image, represents the enhanced image, represents the final denoised image, Represents two different linear weighting coefficients.

[0136] In the embodiment of the present invention, during the noise reduction process of the surveillance video stream, the median of the amplitude spectrum is calculated based on the input image, and different images have different medians. A low-pass filter is constructed based on the median to automatically adjust the filter strength according to the frequency characteristics of the image itself, so that the processing is more flexible and can adapt to different noise levels. In addition, by finally mixing the denoised image with the original image, it can be ensured that the naturalness and important details of the image are not lost in the noise reduction. Weighted mixing can adjust the noise reduction strength according to specific needs. The balance between noise reduction effect and image quality can be achieved by adjusting the mixing parameters according to the needs of different scenes.

[0137] Among them, in the preferred embodiment provided by the present invention, the static frame of the current monitoring video stream after noise reduction is extracted and input into the trained prediction model as a test frame, and the prediction results of multiple key construction areas are obtained, which specifically includes the following steps:

[0138] A pre-trained neural network is used as the basic model, and the pre-trained neural network is expanded into a multi-branch convolutional neural network architecture. Each branch processes different types of features. The multi-branch convolutional neural network architecture includes a first branch, a second branch, and a third branch.

[0139] The test frame is input into the first branch, and the test frame is passed through multiple convolutional layers to gradually extract higher-level texture information to obtain deep texture information features. The corresponding process has the following relationship:

[0140] ;

[0141] in, represents multiple layers of convolution, Represents deep texture information features;

[0142] The deep texture information features are subjected to the maximum pooling operation to obtain the texture features. The corresponding process has the following relationship:

[0143] ;

[0144] in, represents the maximum pooling operation, Represents texture features;

[0145] The texture feature map is subjected to batch normalization and nonlinear activation operations in sequence to obtain the final texture feature. The corresponding process has the following relationship:

[0146] ;

[0147] in, Represents the final texture feature, represents the batch normalization operation, represents a nonlinear activation operation;

[0148] The test frame is input into the second branch, and the test frame is passed through multiple convolutional layers to capture the overall shape information and obtain the shape information features. The corresponding process has the following relationship:

[0149] ;

[0150] in, Represents shape information features;

[0151] Perform a global pooling operation on the shape information features to obtain the shape features. The corresponding process has the following relationship:

[0152] ;

[0153] in, Represents shape characteristics, Represents a global pooling operation;

[0154] The shape feature map is reduced in size by strided convolution to obtain the downsampled shape feature map. The corresponding process has the following relationship:

[0155] ;

[0156] in, represents the shape feature map after downsampling, represents strided convolution;

[0157] The downsampled shape feature map is residually connected with the test frame to obtain the final shape feature. The corresponding process has the following relationship:

[0158] ;

[0159] in, represents the final shape feature, represents residual connection;

[0160] The test frame is input into the third branch, converted into color space, and passed through multiple convolutional layers to extract higher-level color features layer by layer to obtain color information features. The corresponding process has the following relationship:

[0161] ;

[0162] ;

[0163] in, Represents a color conversion operation, represents a color space image, Represents color information features;

[0164] The color information features are averaged and pooled to reduce the spatial dimension and aggregate the color features to obtain the color features. The corresponding process has the following relationship:

[0165] ;

[0166] in, represents the average pooling operation, Representing color characteristics

[0167] The color feature map is subjected to batch normalization and nonlinear activation operations in sequence to obtain the final color feature. The corresponding process has the following relationship:

[0168] ;

[0169] in, Represents the final color characteristics

[0170] The final texture features, final shape features, and final color features are respectively calculated using the channel attention mechanism to obtain texture feature maps, shape feature maps, and color feature maps. The corresponding processes have the following relationship:

[0171] ;

[0172] ;

[0173] ;

[0174] in, represents the channel attention mechanism, represents the texture feature map, represents the shape feature map, Represents a color feature map;

[0175] The texture feature map, shape feature map and color feature map are spliced, and then the multi-head self-attention mechanism is used to perform attention calculation to calculate the correlation between the features and obtain the final feature map. The corresponding process has the following relationship:

[0176] ;

[0177] ;

[0178] in, Represents a splicing operation, Represents the splicing result, represents the multi-head attention mechanism, Represents the final feature map;

[0179] The final feature map is sent to the fully connected layer through batch normalization and activation operations to obtain the prediction results of multiple key construction areas. The corresponding process has the following relationship:

[0180] ;

[0181] in, represents the fully connected layer, Represents the prediction results for critical construction areas.

[0182] In an embodiment of the present invention, the multi-branch model of the present invention processes the input data in parallel on multiple branches, each branch uses an independent convolutional layer and neurons, and focuses on extracting different types of features. It performs particularly well when processing complex and diverse features and can analyze the input data from multiple angles, thereby improving the robustness and generalization ability of the model to different input variations.

[0183] Furthermore, the asphalt pavement engineering quality control method further includes the following steps:

[0184] Step S103, according to the multi-source monitoring data, quality control and registration records of materials entering and leaving the site are performed, and visual management data of materials entering and leaving the site is generated and displayed.

[0185] In an embodiment of the present invention, according to multi-source monitoring data, the penetration, softening point, ductility, specification and strength of materials entering and leaving the site are inspected, and the material inspection data is recorded. In addition, according to the multi-source monitoring data, the entry time, batch and quantity of materials entering and leaving the site are registered, and the material registration data is recorded. By comprehensively arranging the material inspection data and the material registration data, visual management data of materials entering and leaving the site is generated, and then the visual management data of materials entering and leaving the site is visualized.

[0186] Specifically, Figure 4 A flow chart showing visual management data of materials entering and leaving a site in a method provided by an embodiment of the present invention is shown.

[0187] Among them, in the preferred embodiment provided by the present invention, the quality control and registration records of materials entering and leaving the site are performed according to the multi-source monitoring data, and the visual management data of materials entering and leaving the site are generated and displayed, which specifically includes the following steps:

[0188] Step S1031, according to the multi-source monitoring data, the penetration, softening point, ductility, specification and strength of the materials entering and leaving the site are inspected, and the material inspection data is recorded;

[0189] Step S1032, registering the time, batch, and quantity of materials entering and leaving the site according to the multi-source monitoring data, and recording the material registration data;

[0190] Step S1033, combining the material inspection data and the material registration data to generate material entry and exit visualization management data;

[0191] Step S1034, visually displaying the material entry and exit visual management data.

[0192] Furthermore, the asphalt pavement engineering quality control method further includes the following steps:

[0193] Step S104, performing equipment performance monitoring, status monitoring and operation monitoring according to the multi-source monitoring data, and generating and displaying equipment monitoring visualization data.

[0194] In an embodiment of the present invention, multiple construction equipment such as multiple asphalt mixing plants, multiple pavers and multiple rollers are determined according to multi-source monitoring data, and performance monitoring data is obtained by performing equipment performance monitoring on the multiple construction equipment, and equipment status monitoring is performed on the multiple construction equipment to obtain status monitoring data, and equipment operation monitoring is performed on the multiple construction equipment to obtain operation monitoring data, and then the performance monitoring data, status monitoring data and operation monitoring data are comprehensively organized to generate equipment monitoring visualization data, and then the equipment monitoring visualization data is visualized and displayed.

[0195] Specifically, Figure 5 A flow chart for displaying visual data of equipment monitoring in the method provided by an embodiment of the present invention is shown.

[0196] Among them, in the preferred implementation mode provided by the present invention, the equipment performance monitoring, status monitoring and operation monitoring are performed according to the multi-source monitoring data, and the generation and display of equipment monitoring visualization data specifically include the following steps:

[0197] Step S1041, determining multiple construction equipment according to the multi-source monitoring data, including an asphalt mixing plant, a paver, and a road roller;

[0198] Step S1042, performing equipment performance monitoring on the plurality of construction equipment to obtain performance monitoring data;

[0199] Step S1043, performing equipment status monitoring on the plurality of construction equipment to obtain status monitoring data;

[0200] Step S1044, performing equipment operation monitoring on the plurality of construction equipment to obtain operation monitoring data;

[0201] Step S1045, integrating the performance monitoring data, the state monitoring data and the work monitoring data to generate equipment monitoring visualization data;

[0202] Step S1046: Visually display the device monitoring visualization data.

[0203] Furthermore, the asphalt pavement engineering quality control method further includes the following steps:

[0204] Step S105: Perform asphalt mixing monitoring and asphalt test monitoring according to the multi-source monitoring data, and generate and display asphalt monitoring visualization data.

[0205] In an embodiment of the present invention, the temperature, ratio and output of asphalt mixing are monitored according to multi-source monitoring data to obtain mixing monitoring data, and the softening point, penetration, ductility, rutting test and fatigue test of asphalt tests are monitored according to multi-source monitoring data to obtain test monitoring data, and the mixing monitoring data and the test monitoring data are comprehensively collated to generate asphalt monitoring visualization data, which is then visualized.

[0206] Specifically, Figure 6 A flow chart showing visual data of asphalt monitoring in the method provided by an embodiment of the present invention is shown.

[0207] Among them, in the preferred embodiment provided by the present invention, the asphalt mixing monitoring and asphalt test monitoring are performed according to the multi-source monitoring data, and the generation and display of asphalt monitoring visualization data specifically include the following steps:

[0208] Step S1051, monitoring the temperature, mix ratio and output of asphalt mixing according to the multi-source monitoring data, and obtaining mixing monitoring data;

[0209] Step S1052, monitoring the softening point, penetration, ductility, rutting test and fatigue test of the asphalt test according to the multi-source monitoring data, and obtaining test monitoring data;

[0210] Step S1053, combining the mixing monitoring data and the test monitoring data to generate asphalt monitoring visualization data;

[0211] Step S1054: Visually display the asphalt monitoring visualization data.

[0212] Furthermore, the asphalt pavement engineering quality control method further includes the following steps:

[0213] Step S106, performing intelligent compaction monitoring according to the multi-source monitoring data, and performing completion acceptance monitoring after the asphalt pavement project is completed, generating and visually displaying compaction acceptance monitoring visualization data.

[0214] In an embodiment of the present invention, the vibration frequency, amplitude and temperature of intelligent compaction are monitored according to multi-source monitoring data to obtain compaction monitoring data. After the asphalt pavement project is completed, completion acceptance monitoring is carried out to obtain acceptance monitoring data. By comprehensively arranging the compaction monitoring data and the acceptance monitoring data, compaction test acceptance monitoring visualization data is generated, and then the compaction test acceptance monitoring visualization data is visualized.

[0215] Specifically, Figure 7 A flow chart showing visualization data of pressure test reception monitoring in the method provided by an embodiment of the present invention is shown.

[0216] Among them, in the preferred embodiment provided by the present invention, the intelligent compaction monitoring is performed according to the multi-source monitoring data, and after the asphalt pavement project is completed, the completion acceptance monitoring is performed, and the generation and visualization of the compaction acceptance monitoring visualization data specifically includes the following steps:

[0217] Step S1061, monitoring the vibration frequency, amplitude, and temperature of intelligent compaction according to the multi-source monitoring data, and acquiring compaction monitoring data;

[0218] Step S1062, after the asphalt pavement project is completed, completion acceptance monitoring is performed to obtain acceptance monitoring data;

[0219] Step S1063, integrating the compaction monitoring data and the acceptance monitoring data to generate compaction test acceptance monitoring visualization data;

[0220] Step S1064: Visually display the pressure test acceptance monitoring visualization data.

[0221] Furthermore, Figure 8 The application architecture diagram of the platform provided by the embodiment of the present invention is shown.

[0222] Among them, in another preferred embodiment provided by the present invention, an asphalt pavement engineering quality control platform includes:

[0223] The control planning processing unit 101 is used to obtain the construction design data of the asphalt pavement project, perform quality control planning, generate control planning data, and perform multi-source monitoring according to the control planning data to obtain multi-source monitoring data.

[0224] In an embodiment of the present invention, the control planning processing unit 101 obtains the construction design data such as the type, proportion, technical requirements and construction specifications of the asphalt mixture of the asphalt pavement project, and performs quality control planning for the asphalt pavement project according to the construction design data, generates control planning data, and then performs sensor layout and transmission planning for multi-source monitoring according to the control planning data, creates a multi-source monitoring system, and then performs multi-source monitoring processing based on the multi-source monitoring system to obtain multi-source monitoring data.

[0225] Specifically, Fig. 9 It shows a structural block diagram of the management and control planning processing unit 101 in the platform provided by an embodiment of the present invention.

[0226] Among them, in the preferred embodiment provided by the present invention, the control planning processing unit 101 specifically includes:

[0227] The design data acquisition module 1011 is used to acquire the construction design data of the asphalt pavement project, wherein the construction design data includes the type, proportion, technical requirements and construction specifications of the asphalt mixture;

[0228] The control planning module 1012 is used to perform quality control planning according to the construction design data of the project and generate control planning data;

[0229] A multi-source monitoring planning module 1013 is used to perform sensor layout and transmission planning for multi-source monitoring according to the control planning data, and to create a multi-source monitoring system;

[0230] The multi-source monitoring processing module 1014 is used to perform multi-source monitoring processing based on the multi-source monitoring system to obtain multi-source monitoring data.

[0231] Furthermore, the asphalt pavement engineering quality control platform also includes:

[0232] The video monitoring display unit 102 is used to perform video monitoring on multiple key construction areas according to the multi-source monitoring data, and obtain and display real-time monitoring videos.

[0233] In an embodiment of the present invention, the video monitoring display unit 102 determines multiple key construction areas such as the asphalt mixing plant, the paver operation area, and the roller operation area in the asphalt pavement project according to the multi-source monitoring data, and generates a video monitoring instruction. According to the video monitoring instruction, the multiple key construction areas are monitored, real-time monitoring video is obtained, and a monitoring video display window is constructed. In the monitoring video display window, the real-time monitoring video is displayed in real time.

[0234] Specifically, Fig.10 It shows a structural block diagram of the video monitoring display unit 102 in the platform provided by the embodiment of the present invention.

[0235] Among them, in the preferred embodiment provided by the present invention, the video monitoring display unit 102 specifically includes:

[0236] The key area determination module 1021 is used to determine a plurality of key construction areas according to the multi-source monitoring data, wherein the plurality of key construction areas include an asphalt mixing plant, a paver operation area, and a roller operation area;

[0237] An instruction generation module 1022 is used to generate video monitoring instructions;

[0238] The video monitoring module 1023 is used to perform video monitoring on multiple key construction areas according to the video monitoring instructions to obtain real-time monitoring videos;

[0239] The monitoring display module 1024 is used to display the real-time monitoring video in real time.

[0240] Furthermore, the asphalt pavement engineering quality control platform also includes:

[0241] The material entry and exit visualization unit 103 is used to perform quality control and registration records of material entry and exit according to the multi-source monitoring data, and generate and display material entry and exit visualization management data.

[0242] In an embodiment of the present invention, the material entry and exit visualization unit 103 performs needle penetration, softening point, ductility, specification and strength inspections of materials entering and exiting the site according to multi-source monitoring data, records material inspection data, and registers the entry time, batch and quantity of materials entering and exiting the site according to multi-source monitoring data, records material registration data, and generates material entry and exit visualization management data by comprehensively collating the material inspection data and the material registration data, and then visualizes the material entry and exit visualization management data.

[0243] The equipment monitoring visualization unit 104 is used to perform equipment performance monitoring, status monitoring and operation monitoring according to the multi-source monitoring data, and to generate and display equipment monitoring visualization data.

[0244] In an embodiment of the present invention, the equipment monitoring visualization unit 104 determines multiple construction equipment such as multiple asphalt mixing plants, multiple pavers and multiple rollers according to multi-source monitoring data, obtains performance monitoring data by performing equipment performance monitoring on the multiple construction equipment, obtains status monitoring data by performing equipment status monitoring on the multiple construction equipment, obtains status monitoring data by performing equipment operation monitoring on the multiple construction equipment, and then comprehensively organizes the performance monitoring data, status monitoring data and operation monitoring data to generate equipment monitoring visualization data, which is then visualized and displayed.

[0245] The asphalt monitoring visualization unit 105 is used to perform asphalt mixing monitoring and asphalt test monitoring according to the multi-source monitoring data, and generate and display asphalt monitoring visualization data.

[0246] In an embodiment of the present invention, the asphalt monitoring visualization unit 105 monitors the temperature, ratio and output of asphalt mixing according to multi-source monitoring data, and obtains mixing monitoring data, and monitors the softening point, penetration, ductility, rutting test and fatigue test of asphalt tests according to multi-source monitoring data, and obtains test monitoring data, and generates asphalt monitoring visualization data by comprehensively collating the mixing monitoring data and the test monitoring data, and then visualizes the asphalt monitoring visualization data.

[0247] The compaction test acceptance monitoring visualization unit 106 is used to perform intelligent compaction monitoring according to the multi-source monitoring data, and to perform completion acceptance monitoring after the asphalt pavement project is completed, and to generate and visualize compaction test acceptance monitoring visualization data.

[0248] In an embodiment of the present invention, the compaction test acceptance monitoring visualization unit 106 performs vibration frequency, amplitude, and temperature monitoring of intelligent compaction according to multi-source monitoring data to obtain compaction monitoring data. After completing the asphalt pavement project, completion acceptance monitoring is performed to obtain acceptance monitoring data. By comprehensively arranging the compaction monitoring data and the acceptance monitoring data, compaction test acceptance monitoring visualization data is generated, and then the compaction test acceptance monitoring visualization data is visualized.

[0249] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0250] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0251] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0252] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0253] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for quality control of asphalt pavement engineering, characterized in that: The method specifically comprises the following steps: Acquire construction design data of the asphalt pavement project, conduct quality control planning, generate control planning data, and conduct multi-source monitoring according to the control planning data to obtain multi-source monitoring data; Conduct video monitoring of multiple key construction areas according to the multi-source monitoring data, and obtain and display real-time monitoring videos; According to the multi-source monitoring data, quality control and registration records of materials entering and leaving the site are carried out, and visual management data of materials entering and leaving the site is generated and displayed; According to the multi-source monitoring data, equipment performance monitoring, status monitoring and operation monitoring are performed, and equipment monitoring visualization data is generated and displayed; According to the multi-source monitoring data, asphalt mixing monitoring and asphalt test monitoring are performed, and asphalt monitoring visualization data is generated and displayed; According to the multi-source monitoring data, intelligent compaction monitoring is performed, and after the asphalt pavement project is completed, completion acceptance monitoring is performed to generate and visualize compaction acceptance monitoring visualization data; The method of obtaining the construction design data of the asphalt pavement project, performing quality control planning, generating control planning data, and performing multi-source monitoring according to the control planning data, and obtaining the multi-source monitoring data specifically includes the following steps: Acquiring construction design data of an asphalt pavement project, wherein the construction design data includes the type, proportion, technical requirements and construction specifications of the asphalt mixture; Carry out quality control planning according to the construction design data of the project and generate control planning data; According to the control planning data, arrange the sensors and transmission plan for multi-source monitoring and create a multi-source monitoring system; Based on the multi-source monitoring system, multi-source monitoring processing is performed to obtain multi-source monitoring data; The method of performing video monitoring on multiple key construction areas according to the multi-source monitoring data, and obtaining and displaying real-time monitoring videos specifically includes the following steps: According to the multi-source monitoring data, a plurality of key construction areas are determined, wherein the plurality of key construction areas include an asphalt mixing plant, a paver operation area, and a roller operation area; Generate video surveillance instructions; According to the video monitoring instructions, multiple key construction areas are monitored to obtain real-time monitoring videos; Displaying the real-time monitoring video in real time; Among them, according to the video monitoring instruction, multiple key construction areas are monitored by video, and obtaining real-time monitoring video specifically includes the following steps: Obtain historical monitoring video streams and current monitoring video streams of multiple key construction areas, and perform noise reduction processing on the historical monitoring video streams and current monitoring video streams; Build a deep learning model, extract static frames of the historical surveillance video stream after noise reduction as training frames, and use the corresponding annotated events as data annotations for training to obtain a trained prediction model; The static frame of the current surveillance video stream after denoising is extracted and input into the trained prediction model as a test frame to obtain the prediction results of multiple key construction areas; According to the prediction results of multiple key construction areas, the sensor monitoring parameters and priorities of different key construction areas are dynamically adjusted to obtain real-time monitoring videos; The noise reduction process for the surveillance video stream specifically includes the following steps: Video frames are extracted from the surveillance video stream, and Fourier transform is performed on the video frames to convert the pixel intensity representation into the representation of the frequency component to obtain the spectrum. The corresponding process has the following relationship: ； in, Represents a video stream, represents a video frame extracted from a video stream, represents the Fourier transform operation, represents the spectrum; The spectrum is centralized so that the low-frequency components are concentrated in the center of the spectrum to obtain a centralized spectrum. The corresponding process has the following relationship: ； in, represents the centralized spectrum, represents spectrum centering operation; Calculate the amplitude of each component in the centralized spectrum and perform logarithmic transformation on the amplitude to obtain the amplitude spectrum. The corresponding process has the following relationship: ； in, represents the amplitude of the centered spectrum, represents the logarithmic transformation operation, represents the amplitude spectrum; Calculate the median of the amplitude spectrum, and construct a low-pass filter based on the median of the amplitude spectrum. Use the low-pass filter to filter the amplitude spectrum to remove noise, and then perform inverse Fourier transform to obtain a preliminary denoised image. The corresponding process has the following relationship: ； ； in, represents a low-pass filter, represents the median of the amplitude spectrum, represents the spectrum after low-pass filtering, represents the inverse Fourier transform operation, represents the preliminary denoised image; The preliminary denoised image is subjected to multi-scale processing using the Laplacian pyramid to obtain a multi-scale processed image, and then the multi-scale processed image is fused with the preliminary denoised image in an average weighted manner to enhance the structural information and edge details of the image, and a multi-scale denoised image is obtained. The corresponding process has the following relationship: ； ； in, represents the Laplacian pyramid multi-scale processing operation, represents a multi-scale image, represents the first weight, represents a multi-scale denoised image, represents the average weighted fusion operation; The image after multi-scale denoising is subjected to nonlinear transformation to enhance the contrast and detail of the image and obtain an enhanced image. The video frame and the enhanced image are fused in a linear weighted manner to obtain the final denoised image. The corresponding process has the following relationship: ； ； in, represents the enhanced image, represents the enhanced image, represents the final denoised image, Represents two different linear weighting coefficients.

2. A method for controlling the quality of asphalt pavement engineering according to claim 1, characterized in that: According to the control planning data, the sensor layout and transmission planning of multi-source monitoring are carried out, and the creation of a multi-source monitoring system specifically includes the following steps: Obtain monitoring areas, target focus areas, signal interference areas and sensor coverage density in control planning data; The number of sensors is obtained based on the size of the monitoring area, the number of target areas of interest, and the sensor coverage density; The target focus area is used as the key point, and the key points are divided into several clusters. The center point of each cluster is used as the initial position of a sensor. The total transmission distance between sensors, the coverage of each key point, the energy consumption of sensors and signal interference are used as optimization targets, and the size of the monitoring area is used as a constraint to perform multi-objective optimization. After the optimization is completed, the optimal layout position of the sensor is obtained.

3. The asphalt pavement engineering quality control method according to claim 1, characterized in that: The static frame of the current surveillance video stream after noise reduction is extracted and input into the trained prediction model as a test frame to obtain the prediction results of multiple key construction areas. The specific steps include: A pre-trained neural network is used as the basic model, and the pre-trained neural network is expanded into a multi-branch convolutional neural network architecture. Each branch processes different types of features. The multi-branch convolutional neural network architecture includes a first branch, a second branch, and a third branch. The test frame is input into the first branch, and the test frame is passed through multiple convolutional layers to gradually extract higher-level texture information to obtain deep texture information features. The corresponding process has the following relationship: ； in, represents multiple layers of convolution, Represents deep texture information features; The deep texture information features are subjected to the maximum pooling operation to obtain the texture features. The corresponding process has the following relationship: ； in, represents the maximum pooling operation, Represents texture features; The texture feature map is subjected to batch normalization and nonlinear activation operations in sequence to obtain the final texture feature. The corresponding process has the following relationship: ； in, Represents the final texture feature, represents the batch normalization operation, represents a nonlinear activation operation; The test frame is input into the second branch, and the test frame is passed through multiple convolutional layers to capture the overall shape information and obtain the shape information features. The corresponding process has the following relationship: ； in, Represents shape information features; Perform a global pooling operation on the shape information features to obtain the shape features. The corresponding process has the following relationship: ； in, Represents shape characteristics, Represents a global pooling operation; The shape feature map is reduced in size by strided convolution to obtain the downsampled shape feature map. The corresponding process has the following relationship: ； in, represents the shape feature map after downsampling, represents strided convolution; The downsampled shape feature map is residually connected with the test frame to obtain the final shape feature. The corresponding process has the following relationship: ； in, represents the final shape feature, represents residual connection; The test frame is input into the third branch, converted into color space, and passed through multiple convolutional layers to extract higher-level color features layer by layer to obtain color information features. The corresponding process has the following relationship: ； ； in, Represents a color conversion operation, represents a color space image, Represents color information features; The color information features are averaged and pooled to reduce the spatial dimension and aggregate the color features to obtain the color features. The corresponding process has the following relationship: ； in, represents the average pooling operation, Indicates color characteristics; The color feature map is subjected to batch normalization and nonlinear activation operations in sequence to obtain the final color feature. The corresponding process has the following relationship: ； in, Represents the final color characteristics; The final texture features, final shape features, and final color features are respectively calculated using the channel attention mechanism to obtain texture feature maps, shape feature maps, and color feature maps. The corresponding processes have the following relationship: ； ； ； in, represents the channel attention mechanism, represents the texture feature map, represents the shape feature map, Represents a color feature map; The texture feature map, shape feature map and color feature map are spliced, and then the multi-head self-attention mechanism is used to perform attention calculation to calculate the correlation between the features and obtain the final feature map. The corresponding process has the following relationship: ； ； in, Represents a splicing operation, Indicates the splicing result, represents the multi-head attention mechanism, Represents the final feature map; The final feature map is sent to the fully connected layer through batch normalization and activation operations to obtain the prediction results of multiple key construction areas. The corresponding process has the following relationship: ； in, represents the fully connected layer, Represents the prediction results for critical construction areas.

4. A method for controlling the quality of asphalt pavement engineering according to claim 3, characterized in that: The method of performing quality control and registration of materials entering and leaving the site according to the multi-source monitoring data, and generating and displaying visual management data of materials entering and leaving the site specifically includes the following steps: According to the multi-source monitoring data, conduct needle penetration, softening point, ductility, specification and strength inspections on materials entering and leaving the site, and record material inspection data; According to the multi-source monitoring data, register the entry time, batch, and quantity of materials entering and leaving the site, and record the material registration data; Combining the material inspection data and the material registration data, generating visual management data for material entry and exit; The visual management data of the materials entering and leaving the site is displayed visually.

5. A method for controlling the quality of asphalt pavement engineering according to claim 4, characterized in that: The step of performing equipment performance monitoring, status monitoring and operation monitoring according to the multi-source monitoring data, and generating and displaying equipment monitoring visualization data specifically includes the following steps: According to the multi-source monitoring data, multiple construction equipment including an asphalt mixing plant, a paver and a road roller are identified; Performing equipment performance monitoring on the plurality of construction equipment to obtain performance monitoring data; Performing equipment status monitoring on the plurality of construction equipment to obtain status monitoring data; Performing equipment operation monitoring on the plurality of construction equipment to obtain operation monitoring data; Combining the performance monitoring data, the status monitoring data and the operation monitoring data to generate equipment monitoring visualization data; The device monitoring visualization data is visualized.

6. A method for controlling the quality of asphalt pavement engineering according to claim 5, characterized in that: The asphalt mixing monitoring and asphalt test monitoring are performed according to the multi-source monitoring data, and the generation and display of asphalt monitoring visualization data specifically include the following steps: According to the multi-source monitoring data, monitor the temperature, mix ratio and output of asphalt mixing to obtain mixing monitoring data; According to the multi-source monitoring data, monitor the softening point, penetration, ductility, rutting test and fatigue test of the asphalt test to obtain the test monitoring data; Combining the mixing monitoring data and the test monitoring data to generate asphalt monitoring visualization data; The asphalt monitoring visualization data is visualized.

7. A method for controlling the quality of asphalt pavement engineering according to claim 6, characterized in that: The method of performing intelligent compaction monitoring according to the multi-source monitoring data, and performing completion acceptance monitoring after completing the asphalt pavement project, and generating and visually displaying compaction acceptance monitoring visualization data specifically includes the following steps: According to the multi-source monitoring data, the vibration frequency, amplitude and temperature of intelligent compaction are monitored to obtain compaction monitoring data; After the asphalt pavement project is completed, completion acceptance monitoring is carried out to obtain acceptance monitoring data; Combining the compaction monitoring data and the acceptance monitoring data to generate compaction test acceptance monitoring visualization data; The pressure test acceptance monitoring visualization data is visualized.

8. An asphalt pavement engineering quality control platform, the platform is used to implement the asphalt pavement engineering quality control method according to any one of claims 1 to 7, characterized in that: The platform includes a control planning processing unit, a video monitoring display unit, a material entry and exit visualization unit, an equipment monitoring visualization unit, an asphalt monitoring visualization unit, and a pressure test acceptance monitoring visualization unit, wherein: A control planning processing unit is used to obtain the construction design data of the asphalt pavement project, perform quality control planning, generate control planning data, and perform multi-source monitoring according to the control planning data to obtain multi-source monitoring data; A video monitoring display unit, used to perform video monitoring of multiple key construction areas according to the multi-source monitoring data, and to obtain and display real-time monitoring videos; A material entry and exit visualization unit is used to perform quality control and registration records of material entry and exit according to the multi-source monitoring data, and to generate and display material entry and exit visualization management data; An equipment monitoring visualization unit, used to perform equipment performance monitoring, status monitoring and operation monitoring according to the multi-source monitoring data, and generate and display equipment monitoring visualization data; An asphalt monitoring visualization unit, used to perform asphalt mixing monitoring and asphalt test monitoring according to the multi-source monitoring data, and generate and display asphalt monitoring visualization data; The compaction test acceptance monitoring visualization unit is used to perform intelligent compaction monitoring according to the multi-source monitoring data, and to perform completion acceptance monitoring after the asphalt pavement project is completed, and to generate and visualize compaction test acceptance monitoring visualization data.

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