Asphalt paving construction digital model measurement method

Abstract

The invention relates to the technical field of construction management, and discloses an asphalt paving construction digital model measurement method, which comprises the following steps: carrying out three-dimensional point position measurement on a construction site to obtain topographic data of the construction site, and constructing a high-precision digital model of a whole pavement based on the topographic data; and calling a preset design model for the construction site, comparing the high-precision digital model with the design model, generating elevation correction information based on each measurement point location based on a comparison result, transmitting the elevation correction information to a control host of the paver, analyzing the elevation correction information by the paver, and sending the analyzed elevation correction information to the control host of the paver. Adjusting the height of the screed plate of the paver based on the analysis result; in the whole construction process, real-time monitoring data of paving quality on a construction site are obtained, corresponding quality evaluation is carried out based on the real-time monitoring data, a corresponding quality evaluation result is obtained, and paving parameters are adjusted according to the quality evaluation result. According to the invention, high-precision management in the construction process can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of construction management, and in particular to a method for measuring a digital model of asphalt paving construction. Background Art

[0002] In the field of asphalt paving construction, traditional construction methods mainly rely on manual layout, wire drawing, and the experience and judgment of on-site personnel. This method is not only inefficient, but also easily affected by human factors, resulting in low construction accuracy and difficulty in meeting the high quality and high efficiency requirements of modern road construction. In recent years, with the development of digital technology, some digital model measurement methods have begun to be introduced into asphalt paving construction. However, the digital model measurement methods in the prior art still have some limitations, such as limited measurement accuracy, insufficient real-time performance, and low degree of automation.

[0003] Specifically, the digital model measurement methods in the existing technology usually use equipment such as laser scanners or total stations for measurement, but the measurement accuracy of these devices in complex environments is often limited. In addition, the existing methods usually rely on static design drawings and the experience and judgment of on-site personnel for construction control, which lacks real-time and dynamic characteristics and makes it difficult to ensure accuracy and efficiency during the construction process.

[0004] From the above, we can see that how to achieve high-precision management during the construction process still needs to be solved. Summary of the invention

[0005] In order to achieve high-precision management during the construction process, the present application provides a digital model measurement method for asphalt paving construction.

[0006] In the first aspect, the present application provides a method for measuring a digital model of asphalt paving construction, which adopts the following technical solution:

[0007] A method for measuring a digital model of asphalt paving construction comprises: performing three-dimensional point measurement on a construction site to obtain terrain data of the construction site, constructing a high-precision digital model of the entire road surface based on the terrain data, wherein the terrain data includes terrain undulations and slope changes, and the high-precision digital model reflects in detail the geometric shape, elevation distribution and possible terrain changes of the road surface; calling a design model preset for the construction site, comparing the high-precision digital model with the design model, and generating elevation correction information based on each measurement point based on the comparison result, transmitting the elevation correction information to a control host of a paver, the paver receiving and parsing the elevation correction information through a built-in real-time three-dimensional positioning device, and adjusting the height of the paver ironing plate based on the parsing result; obtaining real-time monitoring data of paving quality at the construction site during the entire construction process, performing corresponding quality assessment based on the real-time monitoring data, obtaining corresponding quality assessment results, and adjusting paving parameters according to the quality assessment results, wherein the real-time monitoring data includes paving temperature, elevation, thickness, flatness and paving speed.

[0008] By adopting the above technical solution, through three-dimensional point measurement and high-precision digital model construction, this method not only accurately reflects the terrain characteristics of the construction site, but also generates detailed elevation correction information by comparing with the preset design model, ensuring that the paver can adjust the screed height in real time. During the entire construction process, real-time monitoring data is used for quality assessment, and paving parameters are dynamically adjusted according to the assessment results, realizing refined and intelligent management of the entire process from measurement to construction, significantly improving construction quality and efficiency.

[0009] Optionally, before constructing the high-precision digital model, the method also includes: retrieving external reference points corresponding to the terrain data; determining local error areas where large errors may exist based on the external reference points; re-surveying the local error areas using high-precision local measurement equipment, and performing multiple rounds of re-survey and correction.

[0010] By adopting the above technical solution, we can call external reference points to identify local areas where large errors may exist, and use high-precision measurement equipment to re-measure and calibrate these areas multiple times to ensure the absolute accuracy of terrain data. This can effectively improve the reliability of subsequent models and construction quality.

[0011] Optionally, during the real-time monitoring data transmission process, the method also includes: identifying potential measurement errors of the real-time monitoring data through a built-in diagnostic algorithm; determining the corresponding error type based on the potential measurement error, the error type includes systematic error, random error and environmental impact error; if the error type is a systematic error, using a preset correction coefficient for correction; if the error type is a random error, using filtering technology to smooth the data; if the error type is an environmental impact error, introducing environmental factors for dynamic adjustment; feeding back the real-time monitoring data after compensation for potential measurement errors to the paver control host in real time, continuously monitoring the actual situation of the real-time monitoring data after compensation, and dynamically adjusting the compensation parameters according to the actual situation.

[0012] By adopting the above technical solutions, during the real-time monitoring data transmission process, potential measurement errors are identified through the built-in diagnostic algorithm, and automatic compensation is performed using correction coefficients, filtering technology and environmental factors according to the error type (systematic, random, environmental impact). The compensated real-time monitoring data will be fed back to the paver control host, and the system will continuously monitor and dynamically adjust the compensation parameters to ensure data accuracy and construction quality.

[0013] Optionally, after adjusting the height of the paving machine ironing plate, the method further includes: using a high-precision sensor to obtain actual height change data of the ironing plate, and transmitting the actual height change data back to the control host; the control host evaluates whether the height of the ironing plate meets the expected elevation correction information based on the received height data, and if the monitored actual height deviates from the expected one, the control host immediately starts a secondary correction mechanism; the actual effect after each adjustment of the ironing plate height is collected to form a historical database, and the correction algorithm is optimized based on the historical database.

[0014] By adopting the above technical solution, high-precision sensors are used to obtain actual height change data and feed it back to the control host. The control host evaluates whether the height meets expectations. If there is any deviation, a secondary correction is made immediately. The system also records the effect of each adjustment to form a historical database to optimize the correction algorithm and ensure the consistency and accuracy of the paving quality.

[0015] Optionally, the method also includes: dividing the construction area of ​​the construction site into different types of terrain sections, wherein the terrain sections use different measurement grid densities, and the terrain sections include flat areas, sloping areas, and complex terrain areas; in the actual measurement process, determining the corresponding real-time terrain sections in real time based on the terrain data, and judging whether there are terrain changes in the real-time terrain sections, and if so, dynamically adjusting the grid density; and optimizing the corresponding grid adjustment strategy in combination with the real-time terrain sections and quality assessment results.

[0016] By adopting the above technical solution, the construction site is divided into different types of terrain sections, such as flat areas, slope areas and complex terrain areas, and different measurement grid densities are used. In actual measurement, the grid density is dynamically adjusted according to real-time terrain data, and the grid adjustment strategy is optimized in combination with quality assessment results to ensure measurement accuracy and efficiency.

[0017] Optionally, before actual construction, the method also includes: constructing a three-dimensional digital model corresponding to the construction site based on terrain data, and creating a virtual construction scene based on the three-dimensional digital model in combination with VR technology; planning the driving path of the paver in the virtual construction scene, and setting the working parameters of the paver for simulation exercises; identifying potential problems that may arise through multiple simulation exercises, and adjusting the construction plan or equipment configuration based on the potential problems, wherein potential problems include equipment collision, insufficient materials, and operational errors.

[0018] By adopting the above technical solutions, a three-dimensional digital model is built based on terrain data, and VR technology is used to create a virtual construction scene. Through simulation exercises, the paver's driving path is planned and working parameters are set, potential problems (such as equipment collision, insufficient materials, and operational errors) are identified and resolved, and construction plans and equipment configurations are optimized.

[0019] In the second aspect, the present application provides a digital model measurement system for asphalt paving construction, which adopts the following technical solution:

[0020] A digital model measurement system for asphalt paving construction, including but not limited to:

[0021] A high-precision digital model building module performs three-dimensional point measurement on the construction site to obtain terrain data of the construction site, and uses the terrain data to build a high-precision digital model of the entire road surface, wherein the terrain data includes terrain undulations and slope changes. The high-precision digital model reflects the geometric shape, elevation distribution and possible terrain changes of the road surface in detail;

[0022] The elevation correction information generation module retrieves the design model preset for the construction site, compares the high-precision digital model with the design model, and generates elevation correction information based on each measuring point based on the comparison result, and transmits the elevation correction information to the control host of the paver. The paver receives and analyzes the elevation correction information through a built-in real-time three-dimensional positioning device, and adjusts the height of the paver screed plate based on the analysis result;

[0023] The quality assessment result acquisition module obtains real-time monitoring data of paving quality at the construction site during the entire construction process, performs corresponding quality assessment based on the real-time monitoring data, and is used to obtain corresponding quality assessment results, and adjusts paving parameters according to the quality assessment results, wherein the real-time monitoring data includes paving temperature, elevation, thickness, flatness and paving speed.

[0024] In a third aspect, the present application provides a method for measuring a digital model of asphalt paving construction, which adopts the following technical solution:

[0025] A method for measuring a digital model of asphalt paving construction includes a processor in which a program of any one of the above-mentioned methods for measuring a digital model of asphalt paving construction runs.

[0026] In a fourth aspect, the present application provides a storage medium, which adopts the following technical solution:

[0027] A storage medium stores a program of a digital model measurement method for asphalt paving construction as described in any one of the above.

[0028] In summary, the present application includes at least one of the following beneficial technical effects:

[0029] Through three-dimensional point measurement and high-precision digital model construction, this method ensures the absolute accuracy of terrain data and provides a reliable foundation for subsequent construction. Before building the model, external reference points are retrieved and multiple rounds of re-measurement and correction are carried out to effectively improve the accuracy of terrain data. This process not only accurately reflects the terrain characteristics of the construction site, but also generates detailed elevation correction information by comparing with the preset design model, ensuring that the paver can adjust the screed height in real time to achieve precise construction.

[0030] During the construction process, the system uses real-time monitoring data to conduct quality assessments and dynamically adjusts paving parameters such as temperature, thickness and speed based on the assessment results to ensure construction quality and efficiency. The built-in diagnostic algorithm identifies potential measurement errors and automatically compensates for them based on the error type (systematic, random, environmental impact), feeds back the compensated data to the control host, continuously monitors and optimizes the compensation parameters to ensure data accuracy and construction consistency. The closed-loop feedback system further ensures paving quality, and immediately activates the secondary correction mechanism if there is any deviation.

[0031] In order to optimize the construction plan and equipment configuration, this method also builds a three-dimensional digital model based on terrain data before actual construction, and combines VR technology to create a virtual construction scene. Through simulation exercises to plan the paver's driving path and set working parameters, potential problems (such as equipment collision, insufficient materials, and operational errors) can be discovered and solved in advance to ensure smooth actual construction. Intelligent grid division technology dynamically adjusts the measurement grid density according to the complexity of the terrain, improves measurement efficiency and accuracy, and realizes refined management of the construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The present invention is a flow chart of a method for measuring a digital model of asphalt paving construction according to an exemplary embodiment.

[0033] Figure 2 It is a structural block diagram of a digital model measurement system for asphalt paving construction according to an exemplary embodiment. DETAILED DESCRIPTION

[0034] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0035] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0036] The present application embodiment discloses a method for measuring a digital model of asphalt paving construction, referring to Figure 1 ,include:

[0037] S100, three-dimensional point measurement is performed on the construction site to obtain terrain data of the construction site, and a high-precision digital model of the entire road surface is constructed based on the terrain data.

[0038] Among them, a variety of high-precision sensors (such as laser scanners, GNSS receivers, total stations, etc.) are deployed at the construction site to ensure that the entire construction area can be covered; these sensors are used to perform preliminary three-dimensional point measurements to obtain detailed terrain data, including terrain undulations, slope changes and other information; data from different sources (such as laser scanning data, GNSS data) are fused, and professional software is used to process and optimize these data to generate the final high-precision terrain data set.

[0039] Based on the processed terrain data, a high-precision digital model of the entire road surface is constructed using modeling software. The model should reflect the road surface's geometric shape, elevation distribution, and possible terrain changes in detail.

[0040] In addition, for terrain data, before building a high-precision digital model, the method also includes:

[0041] S101, retrieve the external reference point corresponding to the terrain data.

[0042] Among them, according to the specific location of the construction site, appropriate external reference points (such as GNSS base stations, national geodetic control points, etc.) should be selected. These reference points should have known and highly precise coordinate information and be located in or close to the construction area; it is also necessary to ensure that the paver and measuring equipment can establish a stable communication connection with the selected external reference points.

[0043] Perform preliminary verification on the selected external reference points to ensure their stability and accuracy, either by comparing them with the data of multiple reference points or by re-verifying them using independent high-precision measurement equipment (such as a total station).

[0044] S102, determining a local error area where a large error may exist based on an external reference point.

[0045] Among them, the initially collected terrain data is compared and analyzed with the high-precision coordinate data provided by external reference points to identify possible global position errors. Statistical analysis methods (such as standard deviation and variance analysis) can be used to evaluate the distribution of errors and determine which areas have large error fluctuations, focusing on areas with complex terrain and difficult measurement, such as slopes, gullies, and near buildings.

[0046] Combined with 3D visualization software, the error distribution map is displayed intuitively to help operators quickly identify local error areas that need further attention; key points that may have large errors are marked in the error distribution map as the focus of subsequent re-measurement, and the specific locations of these points and the expected correction targets are recorded.

[0047] Through detailed comparison of the initial data and analysis of the error distribution, this step accurately identifies local areas where large errors may exist.

[0048] S103, using high-precision local measurement equipment to remeasure the local area of ​​the error, and perform multiple rounds of remeasurement and correction.

[0049] Among them, for different types of local error areas, appropriate high-precision measurement equipment is selected. For example, GNSS receivers can be used for flat areas, while higher-level measurement equipment such as laser scanners, total stations or precision levels are used for complex terrain areas.

[0050] Set up multiple measuring points in the local error area, use the selected high-precision measuring equipment to carry out the first re-measurement, and ensure that each point is measured in detail; import the data obtained from the first re-measurement into professional software for processing, and compare it with the high-precision coordinate data provided by the external reference point to evaluate whether the re-measurement results meet the expected accuracy requirements.

[0051] If the first retest results still have large errors, multiple rounds of iterative correction process will be initiated, and the error situation will be re-evaluated after each iteration, and the cumulative error will be gradually reduced until the predetermined accuracy standard is reached. After completing all retests and corrections, an independent verification method (such as a third-party measurement agency or higher-precision measurement equipment) will be used to conduct a final verification of the key points to ensure that the data in all local error areas have reached the highest accuracy.

[0052] First, reliable external reference points are retrieved to provide an absolute positioning benchmark for subsequent error correction; second, detailed data comparison and error distribution analysis are performed based on external reference points to accurately identify local areas where large errors may exist; finally, these areas are re-measured and corrected multiple times using high-precision local measurement equipment to ensure the absolute accuracy of the terrain data. This series of measures effectively improves the reliability and accuracy of terrain data.

[0053] In addition, in the embodiment of the present application, the method further includes:

[0054] S104, dividing the construction area of ​​the construction site into different types of terrain sections.

[0055] Among them, different measurement grid densities are used in terrain sections, and preliminary rough measurement data (such as drone aerial photography and satellite images) are used to quickly assess the overall terrain of the construction site. These data should include elevation maps, slope analysis and other information to help identify different terrain features; the construction area is divided into different types of terrain sections. Common terrain sections include flat areas, slope areas and complex terrain areas (such as gullies, near buildings, etc.), which can be dynamically divided according to preset rules (such as slope threshold, elevation change rate, etc.).

[0056] Finally, the division results are verified through field surveys or the use of high-precision sensors (such as laser scanners) to ensure that the classification of each segment is accurate. If necessary, the division results are manually adjusted according to the actual situation.

[0057] S105, during the actual measurement process, the corresponding real-time terrain segment is determined in real time based on the terrain data, and it is determined whether there is a terrain change in the real-time terrain segment. If so, the grid density is dynamically adjusted.

[0058] Among them, in the actual measurement process, high-precision sensors (such as laser rangefinders, ultrasonic sensors, GNSS receivers) are used to continuously collect terrain data. These sensors should be installed on pavers or other mobile platforms to transmit the real-time collected terrain data back to the control host. The control host quickly processes and classifies the received data to determine the current terrain section type (flat area, slope area, complex terrain area).

[0059] In an embodiment of the present application, a terrain change detection algorithm is introduced, which can compare the current terrain data with the existing digital model in real time, identify new terrain features or changes (such as unforeseen uneven areas), and if significant changes are found, the area is marked as a key section requiring special attention; once a terrain change is detected, the system will immediately issue an alarm to the operator and provide detailed terrain change information so that corresponding measures can be taken in time.

[0060] S106, optimizing the corresponding grid adjustment strategy in combination with the real-time terrain segment and the quality assessment result.

[0061] In addition, according to the results of the intelligent partitioning algorithm, the initial measurement grid density is set for each terrain section. Sparse grids are used in flat areas to save computing resources, and dense grids are used in complex terrain areas to ensure data integrity. When terrain changes are detected or feedback from operators is received, the system automatically starts a dynamic adjustment mechanism. For newly emerging complex terrain features, the system will increase the grid density in the area; for flat areas that have been confirmed to be stable, the grid density can be appropriately reduced.

[0062] After each adjustment, the system will perform a quality assessment based on real-time monitoring data to ensure that the adjusted grid density can meet the accuracy requirements. If the evaluation results show that there are still errors or deficiencies, the system will continue to adjust until the optimal state is reached.

[0063] First, the construction area is divided into different types of terrain sections through an intelligent partitioning algorithm, which provides a basis for subsequent measurements. Second, during the actual measurement process, the measurement strategy is dynamically adjusted based on real-time terrain data to ensure high-precision measurements even when the terrain changes. Finally, the efficiency and accuracy of the measurement are ensured by dynamically adjusting the grid density and optimizing the adjustment strategy in combination with quality assessment results.

[0064] S110, retrieve the preset design model for the construction site, compare the high-precision digital model with the design model, and generate elevation correction information based on each measuring point based on the comparison result, and transmit the elevation correction information to the control host of the paver. The paver receives and analyzes the elevation correction information through the built-in real-time three-dimensional positioning device, and adjusts the height of the paver ironing plate based on the analysis result.

[0065] Among them, the design model corresponding to the construction site is retrieved from the preset design database. The design model contains information such as the expected pavement geometry and elevation distribution. The high-precision digital model is compared with the design model in detail to identify the differences between the two, especially the deviation in elevation.

[0066] Based on the comparison results, detailed elevation correction information is generated for each measurement point. This information should be specific to the elevation adjustment amount of each point. The generated elevation correction information is transmitted to the control host of the paver. The paver receives and analyzes this information through the built-in real-time three-dimensional positioning device (such as GNSS receiver, inertial navigation system). According to the analysis results, the control host automatically adjusts the height of the paver's ironing board to ensure that it performs paving operations in accordance with the design requirements.

[0067] After adjusting the height of the paver screed, the method also includes:

[0068] S111, uses a high-precision sensor to obtain actual height change data of the ironing plate, and transmits the actual height change data back to the control host.

[0069] Among them, select suitable high-precision sensors (such as laser rangefinders, ultrasonic sensors, tilt sensors, etc.) to ensure that these sensors can accurately measure the height changes of the ironing plate. The sensors should be installed at key positions of the ironing plate to cover the entire working area and provide comprehensive data.

[0070] When the paver adjusts the height of the screed, the sensor continuously collects the actual height change data of the screed, which includes the absolute height, relative height change rate, tilt angle and other information of the screed. During this period, the data collected by the sensor usually contains noise or instantaneous fluctuations, so preliminary data preprocessing is required. This includes filtering, smoothing and removing outliers to ensure the quality of data transmitted to the control host; at the same time, the processed actual height change data is transmitted back to the control host in real time using a high-speed communication interface (such as CAN bus, Ethernet, wireless communication module).

[0071] S112, the control host evaluates the received height data to see whether the height of the ironing plate meets the expected height correction information. If the actual height monitored deviates from the expected one, the control host immediately starts a secondary correction mechanism.

[0072] Among them, after the control host receives the altitude data transmitted by the sensor, the control host will conduct a detailed comparative analysis of the actual altitude data received and the pre-set altitude correction information. The altitude correction information should be specific to the expected altitude value of each point to ensure the accuracy of the comparison results.

[0073] At this time, the deviation detection algorithm can quickly identify the difference between the actual height and the expected height. If the deviation is found to exceed the preset threshold, the area is marked as a key section that needs to be corrected. Once the height deviation is detected, the control host will immediately start the secondary correction mechanism. According to the specific situation of the deviation, the hydraulic system or electric actuator of the paver can be automatically adjusted to fine-tune the height of the ironing board to ensure that its height is always in the best state.

[0074] During the secondary correction process, the control host continuously monitors the adjustment effect and dynamically adjusts the correction parameters according to the actual situation to ensure that the screed height gradually approaches the expected value. Through detailed evaluation of the actual height data and immediate activation of the secondary correction mechanism, this step ensures that the screed height always meets the expected elevation correction information, improving the consistency and accuracy of construction.

[0075] S113, collecting actual results after adjusting the ironing plate height each time to form a historical database, and optimizing the correction algorithm based on the historical database.

[0076] Each time the height of the screed is adjusted, the control host will automatically record the actual effect before and after the adjustment, including height change, adjustment time, adjustment parameters and other information. These data will be completely saved in the historical database for subsequent analysis. By using data analysis tools and machine learning algorithms to deeply mine the data in the historical database, we can analyze the laws of adjustment effects under different construction conditions and identify the key factors that affect the adjustment effect (such as environmental conditions, operating parameters, etc.).

[0077] Based on the results of data analysis, the correction algorithm is continuously optimized, for example, by adjusting the algorithm's parameter settings, introducing new correction strategies, or improving the sensitivity and accuracy of the deviation detection algorithm. Through continuous iterative optimization, the adaptability and efficiency of the correction algorithm are improved.

[0078] With the accumulation of historical data, the system gradually acquires the ability to self-learn and automatically apply the best correction strategy in similar situations, further improving construction quality and efficiency. At the same time, the optimized correction algorithm can be regularly evaluated, feedback from operators can be collected, and adjustments and improvements can be made based on actual construction results to ensure that the algorithm always maintains optimal performance.

[0079] S120, during the entire construction process, obtain real-time monitoring data on the paving quality at the construction site, perform corresponding quality assessment based on the real-time monitoring data, obtain corresponding quality assessment results, and adjust paving parameters according to the quality assessment results.

[0080] Among them, a variety of real-time monitoring equipment (such as temperature sensors, laser rangefinders, ultrasonic sensors, etc.) are deployed on the paver and construction site to monitor key parameters such as paving temperature, elevation, thickness, flatness and paving speed; the real-time monitoring equipment continuously collects various data during the paving process and transmits the data back to the control host through high-speed communication interfaces (such as CAN bus, wireless communication module).

[0081] The control host quickly processes and analyzes the real-time monitoring data it receives, evaluates whether the current paving quality meets the expected standards, and feeds back the evaluation results to the operator; based on the quality evaluation results, the control host automatically adjusts the paving parameters (such as paving speed, thickness, temperature control) to ensure the consistency and stability of construction quality.

[0082] Through multiple simulation drills, we can identify possible problems (such as equipment collision, insufficient materials, operational errors), and adjust the construction plan or equipment configuration in a timely manner to ensure the smooth progress of actual construction. At the same time, we can collect the actual effects after each adjustment to form a historical database, use machine learning algorithms to analyze these data, and continuously optimize and correct the algorithms to improve the system's adaptability and accuracy.

[0083] The absolute accuracy of terrain data was ensured through three-dimensional point measurement and high-precision digital model construction; precise adjustment of the paver was achieved through comparison of the high-precision digital model with the design model; high-precision management and improvement of construction quality were ensured during the construction process through real-time monitoring of paving quality and dynamic adjustment of paving parameters. This series of measures significantly improved the overall efficiency and quality of asphalt paving construction.

[0084] It should be noted here that, in the embodiment of the present application, during the real-time monitoring of data transmission, the method further includes:

[0085] S121, real-time monitoring data is used to identify potential measurement errors through a built-in diagnostic algorithm.

[0086] Among them, real-time monitoring equipment (such as temperature sensors, laser rangefinders, ultrasonic sensors, etc.) continuously collects data on the working parameters and environmental conditions of the paver, and transmits these data to the control host through high-speed communication interfaces (such as CAN bus, Ethernet). The control host performs preliminary preprocessing on the received data, including format conversion, noise filtering and outlier detection.

[0087] A special diagnostic algorithm is integrated into the control host. The diagnostic algorithm can comprehensively analyze the real-time monitoring data and identify potential measurement errors. The diagnostic algorithm should have high sensitivity and accuracy, and be able to quickly and accurately detect abnormal data points in complex and changing construction environments.

[0088] Diagnostic algorithms extract characteristics of potential errors from real-time monitoring data through a variety of methods (such as statistical analysis, pattern recognition, machine learning, etc.), for example, identifying sudden changes in the data, trend changes, or inconsistencies with other relevant parameters, which are all indicative signals of potential errors.

[0089] S122, determining a corresponding error type based on potential measurement errors, where the error types include systematic errors, random errors, and environmental impact errors.

[0090] If the diagnostic algorithm identifies that the error has a fixed deviation or trend (such as sensor drift, equipment calibration deviation), it is classified as a systematic error; systematic errors are usually caused by inherent equipment problems or long-term use. For systematic errors, preset correction factors are used for correction. These correction factors are pre-calculated based on historical data and laboratory test results, and can effectively eliminate fixed deviations, for example, adjusting the zero point or gain of the sensor, or applying mathematical models for compensation.

[0091] If the error shows irregular changes or fluctuations (such as short-term noise, instantaneous interference), it is classified as a random error. Random errors are usually caused by environmental factors or short-term improper operation. For random errors, filtering techniques (such as Kalman filtering and moving average filtering) are used to smooth the data. Filtering technology can reduce the impact of noise and make the data more stable and smooth. It is crucial to choose the right filter and parameter settings to ensure that the noise can be effectively removed without over-smoothing the useful information.

[0092] For random errors, filtering techniques (such as Kalman filtering and moving average filtering) are used to smooth the data. Filtering technology can reduce the impact of noise and make the data more stable and smooth. It is crucial to select appropriate filters and parameter settings to ensure that noise can be effectively removed without over-smoothing useful information. For environmental impact errors, environmental factors are introduced for dynamic adjustment. This requires integrating an environmental monitoring module into the control system to obtain environmental conditions (such as temperature, humidity, wind speed, etc.) in real time and automatically adjust compensation parameters according to these conditions. For example, the asphalt content is appropriately adjusted under high temperature conditions and the drainage performance is optimized under high humidity environments.

[0093] S123, feeding back the real-time monitoring data after potential measurement error compensation to the paver control host in real time, continuously monitoring the actual situation of the real-time monitoring data after compensation, and dynamically adjusting the compensation parameters according to the actual situation.

[0094] Among them, the real-time monitoring data after error identification and correction is fed back to the control host of the paver in real time. The control host will immediately analyze the data and adjust the working parameters of the paver (such as ironing plate height, paving speed, thickness control, etc.) according to the analysis results to ensure that the construction quality meets expectations.

[0095] The control host continuously monitors the actual situation of the real-time monitoring data after compensation to ensure that the adjusted parameters are always in the best state. The monitoring content includes the stability and consistency of the data and whether there are still new signs of errors.

[0096] According to the actual situation, the control host dynamically adjusts the compensation parameters. For example, if it is found that the error in some areas is still large, the correction coefficient or filter parameter is further optimized; if the environmental conditions change, the weight of the environmental factor is updated in time. If an abnormal situation is found (such as the error exceeds the preset threshold), the system will trigger an alarm and notify the operator to check. At the same time, all adjustment records and abnormal situations will be recorded in detail to facilitate subsequent quality tracing and problem analysis.

[0097] First, the built-in diagnostic algorithm identifies potential measurement errors, providing a basis for subsequent error classification and compensation. Second, targeted correction measures are taken based on the error type, significantly improving the accuracy and stability of real-time monitoring data. Finally, by providing real-time feedback of compensated monitoring data and continuously monitoring and dynamically adjusting compensation parameters, the accuracy and real-time nature of parameter adjustment during construction are ensured. This series of measures not only improves construction quality, but also enhances the system's adaptability and flexibility, and realizes refined management of the entire process from measurement to construction.

[0098] Finally, before actual construction, the method also includes:

[0099] S1201, constructing a three-dimensional digital model corresponding to the construction site based on the terrain data, and creating a virtual construction scene based on the three-dimensional digital model in combination with VR technology.

[0100] The measurement data from different sources are processed and integrated to eliminate noise and outliers to ensure data consistency and integrity. Professional software is used to optimize the processed data to generate an accurate terrain data set. Based on the processed terrain data, a three-dimensional digital model of the entire construction site is constructed using modeling software (such as AutoCAD Civil 3D, Bentley MicroStation, etc.). The model should reflect the geometry of the road surface, elevation distribution, and possible terrain changes in detail.

[0101] Introduce virtual reality (VR) technology and use VR development platforms (such as Unity and Unreal Engine) to convert three-dimensional digital models into immersive virtual construction scenes. The scenes should have highly realistic visual effects, allowing users to browse and operate freely to simulate the real construction environment.

[0102] Integrating interactive functions into virtual construction scenes enables users to adjust viewing angles in real time, view different levels of information (such as underground pipelines and road structure layers), and interact with other virtual objects (such as pavers and auxiliary equipment).

[0103] S1202: planning a driving path of the paver in the virtual construction scene, and setting working parameters of the paver for simulation exercises.

[0104] In the virtual construction scene, the paver's driving path is preliminarily planned according to the design drawings and construction requirements. The path should take into account factors such as terrain complexity, underground facility location, and traffic flow to ensure that the paver can complete the operation under safe and efficient conditions. Set the initial working parameters for the paver (such as paving speed, thickness, and temperature control). These parameters should be based on design standards and best practices. At the same time, set the working parameters of other auxiliary equipment (such as the number of rolling times of the roller and the water volume control of the sprinkler) to cooperate with the operation of the paver.

[0105] Start the simulation program and run the paver and other auxiliary equipment in the virtual environment according to the planned path and set parameters. During the simulation, the detailed data of each operation should be recorded, including the driving trajectory, changes in working parameters, etc. Conduct multiple simulations, evaluate the construction effect after each simulation, and identify potential problems or room for improvement. Adjust the path planning and working parameters according to the evaluation results, and gradually optimize the construction plan.

[0106] S1203, through multiple simulation exercises, identify potential problems that may arise and adjust the construction plan or equipment configuration based on the potential problems.

[0107] In each simulation exercise, the system automatically detects and records possible problems, such as equipment collision, insufficient materials, and operating errors. These problems can be discovered in time through the alarm mechanism of the real-time monitoring system, or can be manually marked by operators. The identified potential problems are classified and analyzed in detail to determine their causes and scope of impact. For example, equipment collision may be caused by unreasonable path planning or improper operation; insufficient materials require checking supply chain management or budget arrangements.

[0108] Develop specific solutions for each potential problem. For equipment collision, re-plan the path or adjust the equipment spacing; for material shortage, optimize the material supply plan or increase reserves; for operational errors, strengthen operational training or introduce automated control systems.

[0109] According to the solution, adjust the construction plan or equipment configuration, for example, modify the paver's driving path, adjust working parameters, increase the number of auxiliary equipment, etc., to ensure that the new plan can effectively avoid the occurrence of potential problems. Then conduct simulation exercises again to verify whether the adjusted construction plan has solved the original potential problems. If there are still unresolved problems, continue to optimize until the optimal state is reached. Collect the effect data of each adjustment to form a historical database to provide reference for similar projects in the future.

[0110] First, by building a high-precision three-dimensional digital model and combining VR technology to create a virtual construction scene, a realistic virtual environment is provided for subsequent path planning and simulation exercises; secondly, the driving path is planned in the virtual construction scene and the working parameters are set for simulation exercises, which optimizes the construction path and parameter settings and provides training opportunities for the construction team; finally, through multiple simulation exercises, potential problems are identified and solved, the rationality of the construction plan and equipment configuration is ensured, and the risks in actual construction are significantly reduced. This series of measures not only improves the success rate and efficiency of construction, but also enhances the safety and quality assurance of construction.

[0111] The present application embodiment discloses a digital model measurement system for asphalt paving construction, referring to Figure 2 , including but not limited to:

[0112] The high-precision digital model building module 200 performs three-dimensional point measurement on the construction site to obtain terrain data of the construction site, and uses the terrain data to build a high-precision digital model of the entire road surface, wherein the terrain data includes terrain undulations and slope changes, and the high-precision digital model reflects the geometric shape, elevation distribution and possible terrain changes of the road surface in detail;

[0113] The elevation correction information generating module 210 retrieves the design model preset for the construction site, compares the high-precision digital model with the design model, and generates elevation correction information based on each measuring point based on the comparison result, and transmits the elevation correction information to the control host of the paver. The paver receives and analyzes the elevation correction information through a built-in real-time three-dimensional positioning device, and adjusts the height of the paver screed plate based on the analysis result;

[0114] The quality assessment result acquisition module 220 obtains real-time monitoring data of the paving quality at the construction site during the entire construction process, performs corresponding quality assessment based on the real-time monitoring data, and is used to obtain corresponding quality assessment results, and adjusts paving parameters according to the quality assessment results, wherein the real-time monitoring data includes paving temperature, elevation, thickness, flatness and paving speed.

[0115] An embodiment of the present application further discloses a method for measuring a digital model of asphalt paving construction, comprising a processor in which a program for measuring a digital model of asphalt paving construction as described in any one of the above is run.

[0116] The embodiment of the present application also discloses a storage medium storing a program of a digital model measurement method for asphalt paving construction as described in any one of the above.

[0117] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A digital model measurement method for asphalt paving construction, characterized in that: include: Conduct three-dimensional point measurement on the construction site to obtain terrain data of the construction site, and build a high-precision digital model of the entire road surface based on the terrain data, wherein the terrain data includes terrain undulations and slope changes, and the high-precision digital model reflects the geometric shape, elevation distribution and possible terrain changes of the road surface in detail; Retrieving a preset design model for the construction site, comparing the high-precision digital model with the design model, and generating elevation correction information based on each measuring point based on the comparison result, transmitting the elevation correction information to the control host of the paver, and the paver receives and analyzes the elevation correction information through a built-in real-time three-dimensional positioning device, and adjusts the height of the paver screed based on the analysis result; During the entire construction process, real-time monitoring data of paving quality is obtained at the construction site, corresponding quality assessment is performed based on the real-time monitoring data, and corresponding quality assessment results are obtained. Paving parameters are adjusted according to the quality assessment results, wherein the real-time monitoring data includes paving temperature, elevation, thickness, flatness and paving speed.

2. The method for measuring a digital model of asphalt paving construction according to claim 1, characterized in that: Before building a high-precision digital model, the method also includes: Retrieve external reference points corresponding to terrain data; Determine the local error area where there may be a large error based on the external reference point; The local error area is remeasured using high-precision local measurement equipment, and multiple rounds of remeasurement and correction are performed.

3. The method for measuring a digital model of asphalt paving construction according to claim 1, characterized in that: During real-time monitoring of data transmission, the method further includes: Passing the real-time monitoring data through a built-in diagnostic algorithm to identify potential measurement errors; Determine the corresponding error type based on the potential measurement error, the error type includes systematic error, random error and environmental impact error; if the error type is a systematic error, use a preset correction coefficient to correct it; if the error type is a random error, use filtering technology to smooth the data; if the error type is an environmental impact error, introduce environmental factors for dynamic adjustment; The real-time monitoring data after compensation for potential measurement errors is fed back to the paver control host in real time, and the actual situation of the real-time monitoring data after compensation is continuously monitored, and the compensation parameters are dynamically adjusted according to the actual situation.

4. The method for measuring a digital model of asphalt paving construction according to claim 1, characterized in that: After said adjusting the height of the paving machine screed, the method further comprises: Using a high-precision sensor to obtain actual height change data of the ironing plate, and transmitting the actual height change data back to the control host; The control host evaluates the received height data to see whether the height of the screed plate meets the expected height correction information. If the actual height monitored deviates from the expected one, the control host will immediately start the secondary correction mechanism. The actual effect after each adjustment of the ironing plate height is collected to form a historical database, and the correction algorithm is optimized based on the historical database.

5. The method for measuring a digital model of asphalt paving construction according to claim 1, characterized in that: The method also includes: The construction area of ​​the construction site is divided into different types of terrain sections, where different measurement grid densities are used for the terrain sections, including flat areas, slope areas, and complex terrain areas; In the actual measurement process, the corresponding real-time terrain section is determined in real time based on the terrain data, and it is judged whether there is terrain change in the real-time terrain section. If so, the grid density is dynamically adjusted; The corresponding grid adjustment strategy is optimized in combination with the real-time terrain segment and the quality assessment result.

6. The method for measuring a digital model of asphalt paving construction according to claim 1, characterized in that: Before actual construction, the method also includes: Build a 3D digital model of the construction site based on terrain data, and create a virtual construction scene based on the 3D digital model combined with VR technology; Plan the paver's driving path in the virtual construction scene and set the paver's working parameters for simulation exercises; Through multiple simulation drills, potential problems that may arise are identified, and the construction plan or equipment configuration is adjusted based on the potential problems. Potential problems include equipment collision, insufficient materials, and operational errors.

7. A digital model measurement system for asphalt paving construction, characterized in that: include: A high-precision digital model building module performs three-dimensional point measurement on the construction site to obtain terrain data of the construction site, and uses the terrain data to build a high-precision digital model of the entire road surface, wherein the terrain data includes terrain undulations and slope changes. The high-precision digital model reflects the geometric shape, elevation distribution and possible terrain changes of the road surface in detail; The elevation correction information generation module retrieves the design model preset for the construction site, compares the high-precision digital model with the design model, and generates elevation correction information based on each measuring point based on the comparison result, and transmits the elevation correction information to the control host of the paver. The paver receives and analyzes the elevation correction information through a built-in real-time three-dimensional positioning device, and adjusts the height of the paver screed plate based on the analysis result; The quality assessment result acquisition module obtains real-time monitoring data of paving quality at the construction site during the entire construction process, performs corresponding quality assessment based on the real-time monitoring data, and is used to obtain corresponding quality assessment results, and adjusts paving parameters according to the quality assessment results, wherein the real-time monitoring data includes paving temperature, elevation, thickness, flatness and paving speed.

8. A digital model measurement method for asphalt paving construction, characterized in that: It comprises a processor, in which runs a program of a digital model measurement method for asphalt paving construction as claimed in any one of claims 1 to 6.

9. A storage medium, characterized in that: A program for measuring a digital model of asphalt paving construction as described in any one of claims 1 to 6 is stored.

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