Intelligent municipal garden road laying method

Abstract

The invention discloses an intelligent municipal garden road laying method. Comprising the following steps: S1, collecting basic information of a road; s2, receiving data information acquired by a sensor; s3, inputting the digital information into the BIM for modeling; s4, laying a municipal garden road by automatic equipment; s5, the municipal garden road laying process is adjusted through PID control; the roadbed is scanned and detected through the sensor, various parameter information of the roadbed can be conveniently obtained, various sensor devices are adopted to obtain different parameter information, and the three-dimensional modeling accuracy of the roadbed is improved; basic data are processed through preprocessing, and the accuracy of digital information is kept; when the automatic equipment runs, the thickness and the width of a laid material are detected through a laser detector, detected data information is compared with parameter information generated by BIM modeling, the laying condition is adjusted through PID control, and automatic municipal garden road laying is completed.

Description

Technical Field

[0001] The invention belongs to the technical field of road paving, and more specifically, particularly relates to an intelligent municipal garden road paving method. Background Art

[0002] Municipal garden road paving involves design and construction to meet aesthetic and functional requirements. First of all, the design includes road layout, material selection and drainage system planning. The construction steps start with clearing vegetation, preparing the base soil, then laying the base layer (such as crushed stone or gravel), and finally paving. Commonly used materials include permeable bricks, stone slabs, etc., which take into account both durability and landscape effects. After completion, the setting of greening and drainage systems is key, aiming to enhance the beauty of the road and prevent water accumulation problems. Regular maintenance ensures the long-term stability and beauty of the road.

[0003] Intelligence refers to the property of things that can meet various human needs with the support of technologies such as computer networks, big data, the Internet of Things and artificial intelligence. Intelligence is the trend of modern human civilization development. With the vigorous promotion of intelligent manufacturing, the intelligence of manufacturing will improve the production efficiency and technological innovation capabilities of manufacturing enterprises and become a new driving force for my country's economic growth. However, the impact mechanism of intelligence on economic growth is still unclear, and its impact extent needs to be empirically tested. Intelligent technology represented by industrial robots has gradually penetrated into all aspects of enterprise production and operation, improving enterprise production efficiency and promoting economic growth. Specifically, first, as a new production factor, intelligent technology is conducive to improving production efficiency; second, intelligence improves production efficiency by improving the skill level of the labor force and promoting the optimal allocation of production factors.

[0004] Intelligent construction represents the modernization progress of the construction industry. It improves construction efficiency and quality through advanced technology and equipment. Through intelligent construction, engineering projects can be completed in a shorter time and meet higher construction standards, bringing significant efficiency and quality improvements to road construction and maintenance.

[0005] However, there are some problems with the existing technology: for building construction, the premise of intelligence is to realize the construction model, and the premise of building the construction model is to collect construction parameters, and in the process of intelligent construction, control and adjustment are particularly important. In the existing technology, intelligent construction only uses automated equipment for operation, and the collection and processing of various data information and the control of the construction process are relatively simple, and even manual control and adjustment are still required. Therefore, we propose an intelligent municipal garden road paving method. Summary of the invention

[0006] In view of the problems existing in the prior art, the purpose of the present invention is to provide an intelligent municipal garden road paving method, which collects data information through sensors and processes the data information to facilitate construction and control based on the data information, thereby improving the accuracy of construction and facilitating automated control and adjustment.

[0007] To achieve the above object, the present invention provides the following technical solution: an intelligent municipal garden road paving method, comprising the following steps:

[0008] S1. Collect basic information of roads: collect basic data of municipal garden roads through sensors, and collect data information of municipal garden roads through laser scanners, photogrammetry systems or ground radars;

[0009] S2. Receiving data information collected by the sensor: the computer receives the basic data collected by the sensor through the communication device, and pre-processes the received basic data;

[0010] The preprocessing of basic data includes the following steps:

[0011] S201, performing analog-to-digital conversion on the received basic data information, converting the analog information into digital information;

[0012] S202, performing anomaly detection on digital information, removing abnormal digital information, and then filling in the information using the average;

[0013] S203, filtering the converted digital information to improve the accuracy of the digital information;

[0014] S3. Input digital information into BIM for modeling: According to BIM modeling, simulate the municipal garden road paving parameters, and transmit the parameter information generated by BIM modeling to the automation equipment through the remote control system;

[0015] S4. Automated equipment paves municipal garden roads: After receiving the parameter information generated by BIM modeling, the automated equipment automatically paves the municipal garden roads, and during the paving process, the thickness and width of the municipal garden roads are detected by a laser detector;

[0016] S5. PID control and adjustment of the municipal garden road paving process: the thickness and width parameters of the municipal garden road paving detected by the laser detector are compared with the parameter information generated by BIM modeling, and then the parameters of the municipal garden road paving of the automated equipment are adjusted through feedback, so that the municipal garden road paved by the automated equipment conforms to the parameter information generated by BIM modeling.

[0017] Preferably, the steps for installing and using the laser scanner in S1 are as follows:

[0018] Install laser scanners on both sides of the municipal garden roads, and install laser scanners on the outer circle of the corners of the municipal garden roads to ensure that the entire road surface is covered;

[0019] When the device is turned on, the laser scanner will emit a laser beam, measure the time it takes for the laser beam to reflect back, and calculate the height and shape of the road surface;

[0020] Data processing: transmit the scanning results to the computer to generate high-precision laser 3D point cloud data and analyze the road conditions;

[0021] The steps for using the photogrammetry system are as follows:

[0022] Set up multiple cameras at different angles on the municipal garden roads to cover the entire road surface area;

[0023] The camera takes multiple overlapping high-resolution photos simultaneously;

[0024] Image processing, inputting the photos into computer software to generate detailed road surface models through image matching and 3D reconstruction algorithms;

[0025] The steps for using ground radar are as follows:

[0026] The ground radar device is installed on a road paving vehicle or a cart, and the road paving vehicle or the cart is driven on the path to be paved;

[0027] Perform radar scanning, where the radar transmits electromagnetic waves to penetrate the road surface and receives the reflected signals;

[0028] Data analysis: Process the received reflected signal data to analyze the pavement structure, layer thickness and potential defects.

[0029] Preferably, the 3D reconstruction algorithm is used to convert the high-resolution photos taken by the camera into 3D point cloud data of the picture, and fuse the 3D point cloud data of the picture with the 3D point cloud data of the laser to obtain accurate 3D point cloud data of the municipal garden road;

[0030] The steps of converting the high-resolution photo into three-dimensional point cloud data are as follows:

[0031] S101, acquiring images: capturing multiple perspective images of a municipal garden road from two or more cameras;

[0032] S102, image matching: using a feature matching algorithm to find the same feature points in images from different perspectives;

[0033] S103, depth calculation: by comparing the disparity, that is, the position difference of the same point in the two perspective images, the depth information of each point is calculated;

[0034] S104, constructing a point cloud: combining the depth information with the two-dimensional image coordinates to generate a three-dimensional point cloud;

[0035] S105, reconstructing the model: fusing the image 3D point cloud data with the laser 3D point cloud data, creating a 3D mesh model, and performing smoothing and optimization.

[0036] Preferably, the depth information of each point in the perspective image is calculated as follows:

[0037] Assume that two cameras shoot the same position, namely the left camera and the right camera. The optical axes of the two cameras are parallel. The distance between the cameras is B, that is, the baseline length. In the left image and the right image, the parallax of a certain point is d. The depth Z is calculated by the following formula:

[0038] Where Z is the depth of the calculated point, that is, the distance from the camera to the calculated point in the scene; B is the baseline distance between the two cameras, that is, the distance between the cameras; f is the focal length of the camera, and f e is the focal length error, and f e The calculation formula is p x and p y is the size of the pixel in the x and y directions, f·f e is the corrected focal length, d c is the corrected and filtered disparity value to reduce the influence of noise and artifacts, d b is the parallax deviation, which is used to compensate for the system error or measurement error. Δd is the dynamic adjustment value according to the local parallax change. A is the error caused by the environment and the environmental adaptability factor. N is the noise model term, which is used to correct the sensor noise.

[0039] Preferably, the anomaly detection in S2 determines whether it is an anomaly by the ease with which the digital information point is isolated by the isolation forest algorithm;

[0040] The expected path length E(h(x)) is calculated as follows:

[0041] Among them, h(x) is the path length of a digital information point x from the root node to its leaf node. The path length refers to the number of segmentation steps passed. n is the total number of samples of digital information. c is the depth of the isolation tree and is related to the total number of samples n. The calculation formula of n is as follows:

[0042]

[0043] is a factor used for adjustment, β is an adjustment coefficient used to control the impact of data volume on the expected path length, and β is set to a constant of 0.5; is the adjustment factor for the sample size, taking into account the size of the data set and the relationship between samples, where γ is the Euler constant, set to 0.5772;

[0044] Isolation forest performs anomaly detection by calculating the anomaly score of each digital information point. The anomaly score s(x) is calculated by the following formula:

[0045] The range of anomaly scores is [0, 1]. The closer the score is to 1, the more likely the digital information point is to be abnormal.

[0046] Preferably, after the abnormality detection, the abnormal digital information points are removed, and the vacant positions after the removal are filled by averaging;

[0047] And the average is calculated as follows:

[0048]

[0049] Among them, D is the digital information filled in after calculation, w is the weight value, x i is the non-abnormal digital information, m is the total number of normal digital information, and λ is the introduced error value;

[0050] And the calculation formula of the error value λ is as follows:

[0051] in, is the total number of calculations under the weight of normal digital information, is the total number of calculations under the weight of the abnormal numerical information, and n is the total number of samples of the numerical information.

[0052] Preferably, the laser detector in S4 detects and calculates the thickness and width of the paved municipal garden road as follows:

[0053] The calculation of the thickness and width of the municipal garden road is as follows:

[0054]

[0055] When measuring thickness, set the thickness before laying as H. q , the thickness after laying is H h , then the paving thickness is H = H q -H h ;

[0056] Similarly, when measuring the width, set the data of the left edge after paving as L z , after laying, the data on the right is L y , then the paving width is L = L z -Ly .

[0057] Preferably, the calculation formula of the PID control adjustment in S5 is as follows:

[0058]

[0059] Among them, K p (t) is the proportional gain, K i (t) is the integral gain. The integral part accumulates past errors and eliminates steady-state errors. K d (t) is the differential gain. The differential part predicts the change in error and reduces system oscillations. e(t) is the current error and is calculated using the following formula:

[0060] e(t)=e1(t)-e2(t), where e1(t) is the width or thickness data collected by the laser detector after paving, and e2(t) is the parameter information generated by BIM modeling;

[0061] Add an adaptive algorithm to the controller to automatically adjust the PID parameters according to the system response, and use optimization algorithms or machine learning methods to dynamically adjust:

[0062]

[0063] in, and It is a function that dynamically adjusts the PID gain based on the error and the error change rate.

[0064] Preferably, the steps of laying municipal garden roads by the automated equipment in S5 are as follows:

[0065] Preparation: clean and level the construction area, mark the road alignment;

[0066] Laying the base layer: using automated equipment to lay and compact the base material;

[0067] Laying the middle layer: Automated equipment lays the middle layer material to ensure uniform thickness;

[0068] Compaction: Use automated rollers to compact materials to ensure a smooth road surface;

[0069] Finishing: Use automated equipment to finish and repair the road surface, and handle corners and joints;

[0070] Road markings and signs: Automated equipment sprays road markings and sets traffic signs.

[0071] Preferably, when laying the base layer, corresponding modifications are made according to the pavement structure, layer thickness and potential defects collected by ground radar equipment, including settlement and water accumulation of the pavement base layer;

[0072] Improve the pavement base settlement through soil improvement and base layer design and construction:

[0073] Soil reinforcement: Use chemical stabilizers or physical methods to reinforce the base soil; chemical stabilizers include lime and asphalt emulsions, and physical methods include deep mixing;

[0074] Thickness optimization: According to the design requirements and geological conditions, the thickness of the base layer is reasonably set to enhance the stability of the structure;

[0075] Compaction control: ensuring that the base material is adequately compacted during construction to reduce settlement potential, using automated compaction equipment for uniform compaction;

[0076] Layered construction: Lay and compact the base material in layers, and control the thickness of each layer within a reasonable range to ensure the uniformity and stability of each layer;

[0077] Improve the pavement base through drainage improvement and drainage system construction

[0078] Drainage improvement: Improve the drainage capacity of the soil to prevent sedimentation caused by excessive water, and improve the soil structure by setting up a drainage system or soil modification technology;

[0079] Set up drainage pipes: Install drainage pipes and water collection wells to prevent water accumulation and cause subsidence;

[0080] Filter layer and drainage layer: A filter layer or drainage layer is set between the base layer and the surface layer to effectively drain the accumulated water.

[0081] Technical effects and advantages of the present invention:

[0082] The present invention realizes scanning and detection of the roadbed through sensors, so as to obtain various parameter information of the roadbed, and adopts multiple sensor devices to obtain different parameter information, especially to perform three-dimensional modeling of the roadbed, adopts a laser scanner and a photogrammetry system to respectively obtain three-dimensional point cloud data and image data, and converts the image data into three-dimensional point cloud data, and then fuses the two three-dimensional point cloud data to improve the accuracy of three-dimensional modeling of the roadbed, and analyzes the pavement structure, layer thickness and potential defects through ground radar, so as to obtain the internal situation of the roadbed and facilitate repair and improvement during paving;

[0083] In order to improve the accuracy of basic data, the basic data is processed through preprocessing, especially the anomaly detection of digital information, and the anomaly of digital information is determined through isolation forest. When performing anomaly detection on digital information by isolation forest, an adjustment factor is introduced to facilitate the accurate calculation of the anomaly of digital information, and for the eliminated abnormal digital information, the accuracy of the digital information is maintained by inserting the mean value;

[0084] When the automated equipment is running, the thickness and width of the paved material are detected by a laser detector, and the detected data information is compared with the parameter information generated by BIM modeling. The paving situation is adjusted through PID control to complete the automated paving of municipal garden roads.

[0085] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is a schematic diagram of the steps provided by the present invention;

[0087] Figure 2 It is a schematic diagram of the preprocessing process of basic data provided by the present invention;

[0088] Figure 3 It is a schematic diagram of the process of converting a high-resolution photo provided by the present invention into a three-dimensional point cloud data of a picture;

[0089] Figure 4 The present invention provides a schematic diagram of the process of laying municipal garden roads using the automated equipment provided by the present invention. DETAILED DESCRIPTION

[0090] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0091] like Figures 1 to 4 As shown, an intelligent municipal garden road paving method provided by an embodiment of the present invention includes the following steps:

[0092] S1. Collect basic information of roads: collect basic data of municipal garden roads through sensors, and collect data information of municipal garden roads through laser scanners, photogrammetry systems or ground radars;

[0093] Specifically, the steps for installing and using the laser scanner in S1 are as follows:

[0094] Install laser scanners on both sides of the municipal garden roads, and install laser scanners on the outer circle of the corners of the municipal garden roads to ensure that the entire road surface is covered;

[0095] When the device is turned on, the laser scanner will emit a laser beam, measure the time it takes for the laser beam to reflect back, and calculate the height and shape of the road surface;

[0096] Data processing: transmit the scanning results to the computer to generate high-precision laser 3D point cloud data and analyze the road conditions;

[0097] The steps for using the photogrammetry system are as follows:

[0098] Set up multiple cameras at different angles on the municipal garden roads to cover the entire road surface area;

[0099] The camera takes multiple overlapping high-resolution photos simultaneously;

[0100] Image processing, inputting the photos into computer software to generate detailed road surface models through image matching and 3D reconstruction algorithms;

[0101] The steps for using ground radar are as follows:

[0102] The ground radar device is installed on a road paving vehicle or a cart, and the road paving vehicle or the cart is driven on the path to be paved;

[0103] Perform radar scanning, where the radar transmits electromagnetic waves to penetrate the road surface and receives the reflected signals;

[0104] Data analysis: Process the received reflected signal data to analyze the pavement structure, layer thickness and potential defects.

[0105] Furthermore, the 3D reconstruction algorithm is used to convert the high-resolution photos taken by the camera into 3D point cloud data of the picture, and fuse the 3D point cloud data of the picture with the 3D point cloud data of the laser to obtain accurate 3D point cloud data of the municipal garden road;

[0106] The steps of converting the high-resolution photo into three-dimensional point cloud data are as follows:

[0107] S101, acquiring images: capturing multiple perspective images of a municipal garden road from two or more cameras;

[0108] S102, image matching: using a feature matching algorithm to find the same feature points in images from different perspectives;

[0109] S103, depth calculation: by comparing the disparity, that is, the position difference of the same point in the two perspective images, the depth information of each point is calculated;

[0110] S104, constructing a point cloud: combining the depth information with the two-dimensional image coordinates to generate a three-dimensional point cloud;

[0111] S105, reconstructing the model: fusing the image 3D point cloud data with the laser 3D point cloud data, creating a 3D mesh model, and performing smoothing and optimization.

[0112] Furthermore, the depth information of each point in the perspective image is calculated as follows:

[0113] Assume that two cameras shoot the same position, namely the left camera and the right camera. The optical axes of the two cameras are parallel. The distance between the cameras is B, that is, the baseline length. In the left image and the right image, the parallax of a certain point is d. The depth Z is calculated by the following formula:

[0114] Where Z is the depth of the calculated point, that is, the distance from the camera to the calculated point in the scene; B is the baseline distance between the two cameras, that is, the distance between the cameras; f is the focal length of the camera, and f e is the focal length error, and f e The calculation formula is p x and p y is the size of the pixel in the x and y directions, f·f e is the corrected focal length, d c is the corrected and filtered disparity value to reduce the influence of noise and artifacts, d b is the parallax deviation, which is used to compensate for the system error or measurement error, Δd is the dynamic adjustment value according to the local parallax change, A is the error caused by the environment and the environmental adaptability factor, and N is the noise model term, which is used to correct the sensor noise;

[0115] It should be noted that the roadbed is scanned and processed by multiple sensors, which is convenient for collecting different types of data information, and the laser 3D point cloud data is generated by the collected data information, and the pictures taken by the camera are converted into image 3D point cloud data, and the image 3D point cloud data and the laser 3D point cloud data are fused to obtain accurate 3D point cloud data of the municipal garden road, so as to improve the 3D model of the roadbed of the eczema garden road, and in order to realize the fusion of pictures taken by multiple cameras, that is, a detailed road surface model is generated through image matching and 3D reconstruction algorithms, which is convenient for converting into image 3D point cloud data, and realizing fusion processing with laser 3D point cloud data to obtain accurate 3D point cloud data of the municipal garden road, so as to facilitate the subsequent accurate 3D model construction, and when processing pictures taken by multiple cameras, parallax deviation, environmental errors, environmental adaptability factors and noise model items are introduced, and various parameters are corrected to improve the accuracy of image processing.

[0116] S2. Receiving data information collected by the sensor: the computer receives the basic data collected by the sensor through the communication device, and pre-processes the received basic data;

[0117] The preprocessing of basic data includes the following steps:

[0118] S201, performing analog-to-digital conversion on the received basic data information, converting the analog information into digital information;

[0119] S202, performing anomaly detection on digital information, removing abnormal digital information, and then filling in the information using the average;

[0120] Specifically, the anomaly detection in S2 determines whether it is an anomaly by the ease with which the isolation forest algorithm isolates digital information points;

[0121] The expected path length E(h(x)) is calculated as follows:

[0122] Among them, h(x) is the path length of a digital information point x from the root node to its leaf node. The path length refers to the number of segmentation steps passed. n is the total number of samples of digital information. c is the depth of the isolation tree and is related to the total number of samples n. The calculation formula of n is as follows:

[0123]

[0124] is a factor used for adjustment, β is an adjustment coefficient used to control the impact of data volume on the expected path length, and β is set to a constant of 0.5; is the adjustment factor for the sample size, taking into account the size of the data set and the relationship between samples, where γ is the Euler constant, set to 0.5772;

[0125] Isolation forest performs anomaly detection by calculating the anomaly score of each digital information point. The anomaly score s(x) is calculated by the following formula:

[0126] The range of anomaly scores is [0, 1]. The closer the score is to 1, the more likely the digital information point is to be abnormal.

[0127] Furthermore, after the anomaly detection, the abnormal digital information points are removed, and the vacant positions after the removal are filled by averaging;

[0128] And the average is calculated as follows:

[0129]

[0130] Among them, D is the digital information filled in after calculation, w is the weight value, x i is the non-abnormal digital information, m is the total number of normal digital information, and λ is the introduced error value;

[0131] And the calculation formula of the error value λ is as follows:

[0132] in, is the total number of calculations under the weight of normal digital information, is the total number of calculations under the weight of the abnormal numerical information, and n is the total number of samples of the numerical information;

[0133] S203, filtering the converted digital information to improve the accuracy of the digital information;

[0134] It should be noted that in order to improve the accuracy of basic data and facilitate subsequent processing of basic data, the basic data information is converted into digital signals through analog-to-digital conversion, so that the basic data information can be in a unified format for subsequent calculation and processing. In order to eliminate outliers in digital information and improve the accuracy of data information, outlier detection is performed through the isolation forest algorithm, and an adjustment factor is introduced into the existing isolation forest algorithm to facilitate the control of the impact of data volume on the expected path length. Abnormal digital information can be effectively and accurately detected, and the anomalies of digital information points are judged and displayed through anomaly scores to improve observability. The eliminated digital information is filled to maintain the integrity of the digital information. When using mean filling, weight values ​​and error values ​​are introduced to improve the accuracy of digital information when calculating the mean, so that the filled digital information can be more consistent with the real data and eliminate the influence of nonlinear data.

[0135] S3. Input digital information into BIM for modeling: According to BIM modeling, simulate the municipal garden road paving parameters, and transmit the parameter information generated by BIM modeling to the automation equipment through the remote control system;

[0136] It should be noted that the fusion processing of image 3D point cloud data and laser 3D point cloud data can obtain accurate 3D point cloud data of municipal garden roads, which is convenient for improving the 3D model of the roadbed of municipal garden roads. By building a 3D model of the roadbed through BIM modeling, it is possible to integrate data from various stages such as design, construction, and operation, improve the collaborative efficiency and information consistency of the project, support the whole process management of buildings from design to maintenance, and optimize operation and maintenance efficiency.

[0137] S4. Automated equipment paves municipal garden roads: After receiving the parameter information generated by BIM modeling, the automated equipment automatically paves the municipal garden roads, and during the paving process, the thickness and width of the municipal garden roads are detected by a laser detector;

[0138] Specifically, the detection calculation of the thickness and width of the paved municipal garden road by the laser detector in S4 is as follows:

[0139] The calculation of the thickness and width of the municipal garden road is as follows:

[0140]

[0141] When measuring thickness, set the thickness before laying as H. q , the thickness after laying is H h , then the paving thickness is H = H q -H h ;

[0142] Similarly, when measuring the width, set the data of the left edge after paving as L z , after laying, the data on the right is L y , then the paving width is L = L z -L y ;

[0143] It should be noted that in order to realize the inspection of municipal garden roads during the paving process and facilitate the comparison with the basic data of BIM modeling, it is possible to realize reverse adjustment and control of the automated equipment so that the paving of municipal garden roads conforms to the original design parameters.

[0144] S5. PID control and adjustment of the municipal garden road paving process: the thickness and width parameters of the municipal garden road paving detected by the laser detector are compared with the parameter information generated by BIM modeling, and then the parameters of the municipal garden road paving of the automated equipment are adjusted by feedback, so that the municipal garden road paved by the automated equipment meets the parameter information generated by BIM modeling;

[0145] Specifically, the calculation formula of the PID control adjustment in S5 is as follows:

[0146]

[0147] Among them, K p (t) is the proportional gain, K i (t) is the integral gain. The integral part accumulates past errors and eliminates steady-state errors. K d (t) is the differential gain. The differential part predicts the change in error and reduces system oscillations. e(t) is the current error and is calculated using the following formula:

[0148] e(t)=e1(t)-e2(t), where e1(t) is the width or thickness data collected by the laser detector after paving, and e2(t) is the parameter information generated by BIM modeling;

[0149] Add an adaptive algorithm to the controller to automatically adjust the PID parameters according to the system response, and use optimization algorithms or machine learning methods to dynamically adjust:

[0150]

[0151] in, and It is a function that dynamically adjusts the PID gain based on the error and the error change rate.

[0152] Specifically, the steps of laying municipal garden roads by the automated equipment in S5 are as follows:

[0153] Preparation: clean and level the construction area, mark the road alignment;

[0154] Laying the base layer: using automated equipment to lay and compact the base material;

[0155] Furthermore, when the base layer is laid, corresponding modifications are made according to the pavement structure, layer thickness and potential defects collected by ground radar equipment, including the settlement and water accumulation of the pavement base layer;

[0156] Improve the pavement base settlement through soil improvement and base layer design and construction:

[0157] Soil reinforcement: Use chemical stabilizers or physical methods to reinforce the base soil; chemical stabilizers include lime and asphalt emulsions, and physical methods include deep mixing;

[0158] Thickness optimization: According to the design requirements and geological conditions, the thickness of the base layer is reasonably set to enhance the stability of the structure;

[0159] Compaction control: ensuring that the base material is adequately compacted during construction to reduce settlement potential, using automated compaction equipment for uniform compaction;

[0160] Layered construction: Lay and compact the base material in layers, and control the thickness of each layer within a reasonable range to ensure the uniformity and stability of each layer;

[0161] Improve the pavement base through drainage improvement and drainage system construction

[0162] Drainage improvement: Improve the drainage capacity of the soil to prevent sedimentation caused by excessive water, and improve the soil structure by setting up a drainage system or soil modification technology;

[0163] Set up drainage pipes: Install drainage pipes and water collection wells to prevent water accumulation and cause subsidence;

[0164] Filter layer and drainage layer: set filter layer or drainage layer between base layer and surface layer to effectively drain water;

[0165] Laying the middle layer: Automated equipment lays the middle layer material to ensure uniform thickness;

[0166] Compaction: Use automated rollers to compact materials to ensure a smooth road surface;

[0167] Finishing: Use automated equipment to finish and repair the road surface, and handle corners and joints;

[0168] Road markings and signs: Automated equipment sprays road markings and sets traffic signs.

[0169] It should be noted that after detecting the paved data information, it is compared with the original data of BIM modeling, and the automatic equipment is reversely adjusted through PID control to make the paving of municipal garden roads meet the original design parameters. In order to adjust the PID parameters, an adaptive algorithm is added to the controller to dynamically adjust the PID parameters to eliminate errors and error change rates in the PID control process and improve the accuracy of calculations. When paving municipal garden roads, it is particularly important to lay the base layer. In the early stage, the pavement structure, layer thickness and potential defects are detected and collected through ground radar equipment. If they need to be repaired when laying the base layer, the long-term use of municipal garden roads can be improved.

[0170] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent municipal garden road paving method, characterized in that: The steps include: S1. Collect basic information of roads: collect basic data of municipal garden roads through sensors, and collect data information of municipal garden roads through laser scanners, photogrammetry systems or ground radars; S2. Receiving data information collected by the sensor: the computer receives the basic data collected by the sensor through the communication device, and pre-processes the received basic data; The preprocessing of basic data includes the following steps: S201, performing analog-to-digital conversion on the received basic data information, converting the analog information into digital information; S202, performing anomaly detection on digital information, removing abnormal digital information, and then filling in the information using the average; S203, filtering the converted digital information to improve the accuracy of the digital information; S3. Input digital information into BIM for modeling: According to BIM modeling, simulate the municipal garden road paving parameters, and transmit the parameter information generated by BIM modeling to the automation equipment through the remote control system; S4. Automated equipment paves municipal garden roads: After receiving the parameter information generated by BIM modeling, the automated equipment automatically paves the municipal garden roads, and during the paving process, the thickness and width of the municipal garden roads are detected by a laser detector; S5. PID control and adjustment of the municipal garden road paving process: the thickness and width parameters of the municipal garden road paving detected by the laser detector are compared with the parameter information generated by BIM modeling, and then the parameters of the municipal garden road paving of the automated equipment are adjusted through feedback, so that the municipal garden road paved by the automated equipment conforms to the parameter information generated by BIM modeling.

2. The intelligent municipal garden road paving method according to claim 1 is characterized in that: The steps for installing and using the laser scanner in S1 are as follows: Install laser scanners on both sides of the municipal garden roads, and install laser scanners on the outer circle of the corners of the municipal garden roads to ensure that the entire road surface is covered; When the device is turned on, the laser scanner will emit a laser beam, measure the time it takes for the laser beam to reflect back, and calculate the height and shape of the road surface; Data processing: transmit the scanning results to the computer to generate high-precision laser 3D point cloud data and analyze the road conditions; The steps for using the photogrammetry system are as follows: Set up multiple cameras at different angles on the municipal garden roads to cover the entire road surface area; The camera takes multiple overlapping high-resolution photos simultaneously; Image processing, inputting the photos into computer software to generate detailed road surface models through image matching and 3D reconstruction algorithms; The steps for using ground radar are as follows: The ground radar device is installed on a road paving vehicle or a cart, and the road paving vehicle or the cart is driven on the path to be paved; Perform radar scanning, where the radar transmits electromagnetic waves to penetrate the road surface and receives the reflected signals; Data analysis: Process the received reflected signal data to analyze the pavement structure, layer thickness and potential defects.

3. The intelligent municipal garden road paving method according to claim 2 is characterized in that: The 3D reconstruction algorithm is used to convert the high-resolution photos taken by the camera into 3D point cloud data of the picture, and fuse the 3D point cloud data of the picture with the 3D point cloud data of the laser to obtain accurate 3D point cloud data of the municipal garden road; The steps of converting the high-resolution photo into three-dimensional point cloud data are as follows: S101, acquiring images: capturing multiple perspective images of a municipal garden road from two or more cameras; S102, image matching: using a feature matching algorithm to find the same feature points in images from different perspectives; S103, depth calculation: by comparing the disparity, that is, the position difference of the same point in the two perspective images, the depth information of each point is calculated; S104, constructing a point cloud: combining the depth information with the two-dimensional image coordinates to generate a three-dimensional point cloud; S105, reconstructing the model: fusing the image 3D point cloud data with the laser 3D point cloud data, creating a 3D mesh model, and performing smoothing and optimization.

4. The intelligent municipal garden road paving method according to claim 3 is characterized by: The depth information of each point in the perspective image is calculated as follows: Assume that two cameras shoot the same position, namely the left camera and the right camera. The optical axes of the two cameras are parallel. The distance between the cameras is B, that is, the baseline length. In the left image and the right image, the parallax of a certain point is d. The depth Z is calculated by the following formula: Where Z is the depth of the calculated point, that is, the distance from the camera to the calculated point in the scene; B is the baseline distance between the two cameras, that is, the distance between the cameras; f is the focal length of the camera, and f e is the focal length error, and f e The calculation formula is p x and p y is the size of the pixel in the x and y directions, f·f e is the corrected focal length, d c is the corrected and filtered disparity value to reduce the influence of noise and artifacts, d b is the parallax deviation, which is used to compensate for the system error or measurement error, Δd is the dynamic adjustment value according to the local parallax change, A is the error caused by the environment and the environmental adaptability factor, and N is the noise model term, which is used to correct the sensor noise.

5. The intelligent municipal garden road paving method according to claim 1 is characterized in that: The anomaly detection in S2 determines whether it is an anomaly by the ease with which the digital information point is isolated by the isolation forest algorithm; The expected path length E(h(x)) is calculated as follows: Among them, h(x) is the path length of a digital information point x from the root node to its leaf node. The path length refers to the number of segmentation steps passed, n is the total number of samples of digital information; c is the depth of the isolation tree, and is related to the total number of samples n, and the calculation formula of n is as follows: is a factor used for adjustment, β is an adjustment coefficient used to control the impact of data volume on the expected path length, and β is set to a constant of 0.5; is the adjustment factor for the sample size, taking into account the size of the data set and the relationship between samples, where γ is the Euler constant, set to 0.5772; Isolation forest performs anomaly detection by calculating the anomaly score of each digital information point. The anomaly score s(x) is calculated by the following formula: The range of anomaly scores is [0, 1]. The closer the score is to 1, the more likely the digital information point is to be abnormal.

6. The intelligent municipal garden road paving method according to claim 5 is characterized in that: After the abnormality detection, the abnormal digital information points are removed, and the vacant positions after the removal are filled by averaging; And the average is calculated as follows: Among them, D is the digital information filled in after calculation, w is the weight value, x i is the non-abnormal digital information, m is the total number of normal digital information, and λ is the introduced error value; And the calculation formula of the error value λ is as follows: in, is the total number of calculations under the weight of normal digital information, is the total number of calculations under the weight of the abnormal numerical information, and n is the total number of samples of the numerical information.

7. The intelligent municipal garden road paving method according to claim 1 is characterized in that: The laser detector in S4 detects and calculates the thickness and width of the paved municipal garden road as follows: The calculation of the thickness and width of the municipal garden road is as follows: When measuring thickness, set the thickness before laying as H. q , the thickness after laying is H h , then the paving thickness is H = H q -H h ; Similarly, when measuring the width, set the data of the left edge after paving as L z , after laying, the data on the right is L y , then the paving width is L = L z -L y .

8. The intelligent municipal garden road paving method according to claim 1 is characterized by: The calculation formula of the PID control adjustment in S5 is as follows: Among them, K p (t) is the proportional gain, K i (t) is the integral gain. The integral part accumulates past errors and eliminates steady-state errors. K d (t) is the differential gain. The differential part predicts the change in error and reduces system oscillations. e(t) is the current error and is calculated using the following formula: e(t)=e1(t)-e2(t), where e1(t) is the width or thickness data collected by the laser detector after paving, and e2(t) is the parameter information generated by BIM modeling; Add an adaptive algorithm to the controller to automatically adjust the PID parameters according to the system response, and use optimization algorithms or machine learning methods to dynamically adjust: in, It is a function that dynamically adjusts the PID gain based on the error and the error change rate.

9. The intelligent municipal garden road paving method according to claim 2 is characterized in that: The steps of laying municipal garden roads by the automated equipment in S5 are as follows: Preparation: clean and level the construction area, mark the road alignment; Laying the base layer: using automated equipment to lay and compact the base material; Laying the middle layer: Automated equipment lays the middle layer material to ensure uniform thickness; Compaction: Use automated rollers to compact materials to ensure a smooth road surface; Finishing: Use automated equipment to finish and repair the road surface, and handle corners and joints; Road markings and signs: Automated equipment sprays road markings and sets traffic signs.

10. The intelligent municipal garden road paving method according to claim 9, characterized in that: When laying the base layer, corresponding modifications are made according to the pavement structure, layer thickness and potential defects collected by ground radar equipment, including settlement and water accumulation of the pavement base layer; Improve the pavement base settlement through soil improvement and base layer design and construction: Soil reinforcement: Use chemical stabilizers or physical methods to reinforce the base soil; chemical stabilizers include lime and asphalt emulsions, and physical methods include deep mixing; Thickness optimization: According to the design requirements and geological conditions, the thickness of the base layer is reasonably set to enhance the stability of the structure; Compaction control: ensuring that the base material is adequately compacted during construction to reduce settlement potential, using automated compaction equipment for uniform compaction; Layered construction: Lay and compact the base material in layers, and control the thickness of each layer within a reasonable range to ensure the uniformity and stability of each layer; Improve the pavement base through drainage improvement and drainage system construction Drainage improvement: Improve the drainage capacity of the soil to prevent sedimentation caused by excessive water, and improve the soil structure by setting up a drainage system or soil modification technology; Set up drainage pipes: Install drainage pipes and water collection wells to prevent water accumulation and cause subsidence; Filter layer and drainage layer: A filter layer or drainage layer is set between the base layer and the surface layer to effectively drain the accumulated water.

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