Asphalt pavement paving quality management system

Abstract

The invention relates to the technical field of measurement and monitoring of road engineering variables, in particular to an asphalt pavement paving quality management system, which comprises a data acquisition module configured to acquire real-time meteorological data, mixture temperature parameters and pavement quality monitoring data; the viscosity analysis module is used for determining the dynamic viscosity adjustment range of the asphalt material based on the real-time meteorological data and the temperature parameters of the mixture; the temperature control module is used for regulating and controlling the mixing temperature according to the viscosity change of the asphalt material and generating a target paving temperature curve; the power optimization module is used for matching a dynamic adjustment strategy of the heating power of the screed of the paver based on the target paving temperature curve; by means of the system, meteorological and construction data are monitored in real time, parameters such as asphalt viscosity and screed power are dynamically regulated and controlled, closed-loop optimization is achieved, the paving quality is improved, and intelligent construction is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of measurement and monitoring of road engineering variables, and in particular to an asphalt pavement paving quality management system. Background Art

[0002] The asphalt pavement paving quality management system mainly involves ensuring the consistency and stability of paving quality by precisely controlling key parameters during the construction process.

[0003] The system combines real-time data monitoring and dynamic adjustment technology to monitor changes in mixture temperature, compaction, and viscosity during the paving process, and optimizes equipment operation and material properties based on this data.

[0004] However, the system still has some challenges that need to be addressed: First, how to precisely control the heating power of the paver screed according to the mixture temperature curve to avoid uneven pavement compaction caused by temperature differences; second, how to use real-time meteorological data to adjust the viscosity of the asphalt material during construction under high-temperature conditions to reduce wheel marks and ensure paving smoothness. Solving these issues will help further improve the quality control level of paving construction. Summary of the Invention

[0005] In order to solve the problems raised by the above background technology, the present application provides an asphalt pavement paving quality management system.

[0006] The present application provides an asphalt pavement paving quality management system, which adopts the following technical solution: an asphalt pavement paving quality management system, comprising: Data acquisition module: configured to obtain real-time meteorological data, mixture temperature parameters and pavement quality monitoring data; Viscosity analysis module: determines the dynamic viscosity adjustment range of the asphalt material based on the real-time meteorological data and mixture temperature parameters; Temperature control module: regulates mixing temperature according to the viscosity change of asphalt material and generates target paving temperature curve; Power optimization module: Based on the target paving temperature curve, it matches the dynamic adjustment strategy of the screed heating power of the paver; The execution feedback module applies the adjusted heating power to the paving operation, monitors the road surface compaction and wheel track conditions, and feeds back to the temperature control module and the power optimization module to optimize the parameters.

[0007] In a specific embodiment, the data acquisition module further includes: Weather sensor unit: configured to obtain the current ambient temperature and relative humidity ; Mixture temperature sensor unit: configured to record the initial mixture temperature And collect the mixture temperature in real time ; Wind speed monitoring unit: configured to obtain real-time wind speed parameters ; Road surface quality detection unit: configured to collect road surface compaction , wheel track depth and surface flatness data.

[0008] In one embodiment, the viscosity analysis module further comprises: Parameter calculation unit: configured to use formula Calculate the viscosity adjustment factor for asphalt materials, where and Empirical parameters set based on historical data regression analysis, The current relative temperature is calculated as the ratio of the ambient temperature to the reference temperature. is the current relative humidity; Viscosity prediction unit: configured to update the target mixing temperature based on the viscosity adjustment factor , and combined with wind speed parameters Generate viscosity prediction models, is the base mixing temperature, is the viscosity-temperature conversion coefficient, Indicates the viscosity adjustment factor of asphalt material; Deviation judgment unit: configured to compare the viscosity value measured in real time With pre-set target viscosity , to determine whether there is a deviation , is the viscosity deviation threshold.

[0009] In one embodiment, the temperature control module further comprises: Temperature curve generation unit: configured to draw the mixture temperature decay curve , represents the predicted temperature of the mixture at paving time t; Alert judgment unit: configured based on formula Determine whether the mixture has reached the warning temperature condition; Temperature compensation unit: When the mixture temperature is lower than the warning value, it generates a temperature compensation instruction and adjusts the sample temperature. ,in is the temperature regulation coefficient, The mixture temperature decay constant, where For paving time, The current temperature of the mixture, is the initial temperature of the mixture, is the ambient temperature, is the mixing temperature increment that needs to be adjusted, is the target mixture temperature.

[0010] In a specific embodiment, the power optimization module includes: a temperature distribution acquisition unit configured to acquire the actual temperature distribution of multiple measurement points in real time through a temperature sensor array; , calculate the average value of the measurement , n represents the number of measuring points of the temperature sensor; and based on the mixture temperature attenuation curve of the temperature control module Generate a temperature prediction sequence for future control periods; Power adjustment unit: configured to adjust the power of the system based on the target temperature value in each control cycle. With the predicted temperature series, by optimizing the objective function Solving the future Step power adjustment command, only execute the first step command ,in is the proportional gain factor, is the power smoothing weight coefficient; k is the current control cycle, m is the number of prediction steps; Global optimization unit: configured to detect temperature deviation exceeding the threshold The measuring points are compensated by independent Generate local compensation power and generate global paving power adjustment table based on 2D coordinates , to achieve regional differentiated power collaborative optimization.

[0011] In a specific embodiment, the execution feedback module further includes: Power execution unit: configured to apply the adjusted heating power to the paver screed; quality monitoring unit: configured to collect the road surface compaction , wheel track depth and surface flatness Data; Feedback optimization unit: configured to calculate the compaction deviation based on the quality monitoring data and flatness deviation , is the target compaction degree, The target flatness is achieved, and parameter optimization suggestions are generated and fed back to the temperature control module and the power optimization module.

[0012] In a specific embodiment, a high temperature environment special control module is also included, which is configured to: identify that the external temperature is higher than the critical high temperature value Entering a special regulation state; Using the modified power regulation model Calculate, among which For sensitivity tuning parameters, is the current ambient temperature, is the basic heating power, For real-time measurement of asphalt viscosity, is the base viscosity, High temperature reference temperature.

[0013] In a specific embodiment, the invention further includes a wheel track correction control module configured to: If the continuous wheel track length exceeds the predetermined length In the case of a fault, an alarm signal is triggered; the working angle of the ironing board is reset ,in is the initial angle, is the path sensitivity coefficient, Adjust the cycle for paving.

[0014] In a specific embodiment, it further includes a microstructure detection module configured to: Collect microscopic particle shape distribution data on asphalt surface and calculate surface roughness ,in is the height of the surface profile deviating from the mean line, and n is the number of road surface roughness detection points; Using the formula Determine whether the particle irregularity exceeds the standard, To allow the maximum roughness, is the sampling length; When the limit is exceeded, the warning program is triggered and a local enhanced thermal compensation instruction is generated , is the local thermal compensation power for surface roughness deviation, Surface roughness adjustment factor, Target surface roughness.

[0015] In a specific embodiment, it also includes an intelligent decision support module configured to: establish a comprehensive rating index system ,in is the weight coefficient, is each quality indicator, n represents the quality indicators in the comprehensive rating index system I Generate an intelligent recommendation strategy database based on historical data; continuously iterate and optimize key system parameters through machine learning algorithms , generate complete construction parameter documents and archive them.

[0016] In summary, this application includes at least one of the following beneficial technical effects: The embodiments disclosed in this application provide an asphalt pavement paving quality management system that integrates real-time data monitoring and dynamic adjustment technology, overcoming the limitations of traditional processes, significantly improving construction quality and reducing maintenance costs. To address the issue of mixture temperature affecting compaction, sensors and a three-dimensional mapping database are used to precisely control screed heating power, avoiding local temperature anomalies and effectively improving pavement compaction quality. During high-temperature construction, the dynamic viscosity adjustment range of asphalt is calculated based on meteorological data and the mixing temperature is adjusted. Combined with the screed power adjustment, the viscosity and plasticity of the mixture are improved and the formation of wheel marks is suppressed. At the same time, high-frequency detection instruments are used to collect data, and through machine learning and digital twin technology analysis and deduction, a closed-loop feedback optimization mechanism is established to automatically optimize key construction parameters, improve parameter adjustment efficiency, and ensure construction quality. In addition, the integration of multiple technologies such as blockchain, ZigBee, and BIM ensures data security, realizes real-time transmission and intelligent decision-making, reduces dependence on manual experience, and promotes the intelligent and refined development of paving construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Module flow chart of asphalt pavement paving quality management system. DETAILED DESCRIPTION

[0018] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings.

[0019] Throughout this specification, reference to the terms "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is 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. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0020] Next, with reference to the accompanying drawings, we will describe an asphalt pavement paving quality management system, designed to address uneven compaction and frequent wheel tracks during asphalt paving. The system first determines the dynamic viscosity adjustment range of the asphalt material. It then regulates the mixing temperature based on the viscosity, outputs a target paving temperature curve, and uses this curve to dynamically adjust the screed heating power. Finally, the system applies this curve to the paving operation, providing feedback and optimization based on the actual compaction and wheel track conditions during the operation.

[0021] The first step is to use real-time meteorological data to obtain information such as current ambient temperature and wind speed. Combined with the factory temperature data of the initial asphalt mixture, the dynamic viscosity adjustment range of the asphalt material under different construction conditions is calculated. Specifically, a sensor network deployed at the construction site collects meteorological factors such as atmospheric temperature and humidity, and transmits them to a central controller for analysis. These meteorological factors affect the asphalt's cooling rate and viscosity characteristics, and their fluctuation patterns must be precisely understood. For example, sunny, hot weather can cause the mixture to cool more quickly, shortening the working window. Humid air after rain can slow the temperature drop and increase residual moisture, affecting the material's internal bonding strength, thereby causing viscosity changes.

[0022] The second step is to further adjust the mixing temperature based on the understanding of the viscosity variation range to generate an appropriate target paving temperature curve. This step requires setting a reasonable discharge temperature range based on the new viscosity standard obtained above. Controlling the raw material properties at the factory stage ensures that they meet the expected design indicators as much as possible, and reserving a buffer margin for subsequent construction to prevent quality defects caused by overcooling or overheating. In one embodiment, if it is estimated that a certain operation has a high risk of rapid heat loss due to strong sunlight, the initial factory setting point is appropriately increased to approximately several degrees Celsius above the normal standard to ensure that it is still within the ideal range when it arrives at the site. At the same time, a continuous measurement and control model is established to predict the entire trajectory to verify feasibility and adaptability, and to make timely fine-tuning to avoid over-correction and causing trouble.

[0023] The third step is to continue to explore the heating power of the paving machine ironing board based on the above results, and how to achieve dynamic matching to ensure that the leveling performance reaches a high level. This mainly involves the flexible conversion of power supply modes according to different paving conditions, and the improvement of the ability to maintain a moderately soft and hard interface to meet the connection needs in specific occasions. The research content covers a multi-dimensional linkage mechanism, including the induction feedback unit receiving the front signal to determine whether the local pressure difference deviates from the normal threshold, and then instructing the rear device to increase or decrease the energy input accordingly until it returns to the balanced track. The closed-loop operation of the whole process reduces human errors, improves automation efficiency, reduces operating and maintenance costs, and maximizes benefits. For example, when certain sections of road have significant differences in foundation bearing capacity due to insufficient preliminary preparations or unstable geological structures, the surface preheating function can be temporarily enhanced to compensate for the inherent defects and achieve a good fit, avoiding separation gaps that damage the overall appearance and durability.

[0024] The fourth and final step emphasizes the practical evaluation and verification of the implementation of the theoretical solutions. This requires continuous collection of ground-based data to compare theoretical deductions for deviations, timely analysis of experience and lessons learned, and continuous refinement of technical parameters to ensure that the final product meets actual user satisfaction standards. Specifically, several sets of high-frequency detection instruments are installed throughout the entire area, recording every millimeter of variation in all directions. These instruments are then uploaded wirelessly to a database for in-depth analysis and statistical analysis, generating graphical reports for technical personnel to review and make decisions. This guides the development of plans and clarifies responsibilities, accelerating progress until the project is completed, inspected, and commissioned. For example, during the construction of a new intercity arterial road, significant day-night temperature fluctuations were discovered in mountainous areas, resulting in significant height differences at joints during handovers between some night shifts. The newly developed intelligent temperature control system was immediately implemented to quickly resolve the issue, restoring a smooth, uniform appearance and earning widespread acclaim and recognition as a model project.

[0025] The asphalt pavement paving quality management system of the present invention includes real-time monitoring and control of temperature, meteorological conditions, and equipment parameters throughout the paving operation to ensure optimal quality management of asphalt pavement. Specifically, the system collects and analyzes asphalt mixture temperature parameters and real-time meteorological data to first assess the dynamic viscosity adjustment range of the asphalt material during the paving process. Based on this information, a corresponding mixing temperature adjustment strategy is developed, generating a target paving temperature curve. Furthermore, based on this target temperature curve, a dynamic adjustment strategy for the screed heating power is designed and matched, and the adjusted power parameters are applied to actual construction during the paving process.

[0026] To address uneven pavement compaction, this system utilizes dynamic adjustment technology for the paver screed's heating power. By accurately calculating and predicting temperature variations in the mixture, this technology ensures the screed can adapt to changes in the mixture's state under varying operating conditions. For example, when the mixture experiences significant temperature variations due to environmental or transportation conditions, dynamic adjustment of the screed's heating power can effectively reduce under-compaction or over-compaction caused by localized low-temperature zones, thereby improving overall smoothness and compaction quality.

[0027] To address the problem of wheel tracks during paving under high-temperature conditions, this invention combines real-time meteorological data (such as temperature, humidity, and solar radiation) to accurately assess the impact of these factors on asphalt mixture viscosity. Subsequently, by adjusting the mixing temperature, the risk of excessive viscosity caused by overly soft asphalt can be preemptively mitigated. Finally, while implementing the target paving temperature curve, a feedback mechanism based on compaction and wheel track is simultaneously introduced to dynamically optimize relevant parameters, further reducing the incidence of quality issues under high-temperature conditions. This approach not only improves work efficiency but also ensures the stability of paving quality.

[0028] The data acquisition module further includes: a meteorological sensor unit configured to obtain the current ambient temperature and relative humidity ; Mixture temperature sensor unit: configured to record the initial mixture temperature And collect the mixture temperature in real time ; Wind speed monitoring unit: configured to obtain real-time wind speed parameters ; Road surface quality detection unit: configured to collect road surface compaction , wheel track depth and surface flatness data.

[0029] The mixing temperature is adjusted based on the dynamic viscosity adjustment range, generating a target paving temperature curve. Based on the calculated dynamic viscosity adjustment range, an intelligent temperature control system is used at the asphalt mixing plant to regulate the mixing temperature. This system, centered around a PLC controller, uses a PID control algorithm to adjust the gas flow rate to the heating furnace, achieving precise control of the mixing temperature. For example, if the calculated dynamic viscosity requirement is higher, improving the fluidity of the mixture, the mixing temperature is increased from the conventional 170°C to 175°C. Simultaneously, a paving construction simulation model is established using BIM technology. Parameters such as the adjusted mixing temperature, transportation time, and ambient weather data are input into the model to simulate the temperature changes of the mixture during transportation and paving, generating a target paving temperature curve. For example, assuming a 1000-meter paving section, the target paving temperature curve generated by the model indicates that the mixture temperature should be 165°C at the start of the paving process and no lower than 140°C at the end. Temperature control points are set every 50 meters to ensure that the temperature changes meet expectations.

[0030] The viscosity analysis module further includes: a parameter calculation unit: configured to calculate the viscosity of the product by the formula Calculate the viscosity adjustment factor for asphalt materials, where and Empirical parameters set based on historical data regression analysis, The current relative temperature is calculated as the ratio of the ambient temperature to the reference temperature. is the current relative humidity; Viscosity prediction unit: configured to update the target mixing temperature based on the viscosity adjustment factor , and combined with wind speed parameters Generate viscosity prediction models, is the base mixing temperature, is the viscosity-temperature conversion coefficient, Indicates the viscosity adjustment coefficient of the asphalt material; Deviation judgment unit: configured to compare the viscosity value measured in real time With pre-set target viscosity , to determine whether there is a deviation , is the viscosity deviation threshold; according to the target paving temperature curve, the dynamic adjustment strategy of the screed heating power is matched; the viscosity is predicted by combining the wind speed calculation: when the target mixing temperature is obtained Then, combined with the wind speed parameter , through the viscosity prediction model To calculate the predicted viscosity. For example, assuming the viscosity prediction model is (in 、 、 、 is the coefficient obtained by fitting the experimental data), and Substituting the value of into the model, we can get the predicted viscosity It comprehensively considers the impact of factors such as ambient temperature and humidity, viscosity adjustment coefficient, and wind speed on asphalt viscosity. Temperature and pressure sensors are installed on the paver. The temperature sensor monitors the surface temperature of the paving mix in real time, while the pressure sensor detects pressure changes beneath the screed. A three-dimensional mapping database of temperature, pressure, and heating power is established, and the target paving temperature curve and real-time monitoring data are input into the paver control system. When the temperature sensor detects that the mixture temperature in a certain area is 10°C below the target temperature curve, and the pressure sensor indicates that the pressure in that area is 15% below normal, the control system automatically increases the screed heating power in that area from 60% to 80% of the rated power based on the mapping database. Furthermore, by adjusting the on-off time ratio of the heating pipes, dynamic fine-tuning of the heating power is achieved, with an adjustment accuracy of ±2%. For example, during a continuous paving operation, as the ambient temperature gradually decreases, the system automatically increases the screed heating power from an initial 50% to 70%, ensuring that the paving mix is ​​always in the appropriate softened state and paving smoothness.

[0031] The temperature control module further includes: a temperature curve generating unit: configured to draw a temperature decay curve of the mixture , represents the predicted temperature of the mixture at paving time t; Alert judgment unit: configured based on formula Determine whether the mixture has reached the warning temperature condition. The temperature compensation unit is configured to generate a temperature compensation instruction and adjust the sample temperature when the mixture temperature is lower than the warning value. The temperature compensation unit is configured to generate a temperature compensation instruction and adjust the sample temperature when the mixture temperature is lower than the warning value. ,in is the temperature regulation coefficient, The mixture temperature decay constant, where For paving time, The current temperature of the mixture, is the initial temperature of the mixture, is the ambient temperature, is the mixing temperature increment that needs to be adjusted, is the target mixture temperature; During paving operations, actual compaction and wheel track data are collected in real time. This data is used to provide feedback and optimize the dynamic viscosity adjustment range, mixing temperature, and screed heating power. At the paving site, inspection sections are set up every 20 meters along the paving direction. Each section is equipped with three nuclear density meters and one laser roughness meter to collect real-time compaction and wheel track depth data. This data is transmitted via a 4G network to a server at the quality monitoring center, where it is analyzed using machine learning algorithms. If a particular inspection section detects compaction below the design standard (96%) for three consecutive times, and the wheel track depth exceeds 3mm, the server triggers a feedback optimization mechanism. First, the actual viscosity of the asphalt material was inferred based on compaction and wheel track data and compared with the calculated dynamic viscosity adjustment range. If the actual viscosity was too high, the lower limit of the dynamic viscosity adjustment range was reduced by 0.1 Pa·s. Second, the mixing temperature was recalculated based on the inferred results and increased by 3-5°C. Finally, the dynamic adjustment strategy for the screed heating power was adjusted, reducing the heating power adjustment threshold from ±10% to ±5%. Through continuous feedback optimization, the pavement compaction qualification rate for a highway paving project increased from an initial 85% to 98%, and the average wheel track depth was reduced from 5mm to 1.5mm, significantly improving the paving quality of the asphalt pavement.

[0032] During asphalt pavement paving, collaborative optimization of screed heating power in each zone to avoid local overheating or underheating can be achieved through a model predictive control (MPC) framework and regional differentiated regulation strategies. The following combines the power optimization module with a rolling optimization mechanism based on the temperature prediction model. The specific implementation is as follows: 1. Temperature prediction model and control cycle design 1〉、The internal prediction model is constructed using the mixture temperature decay model of the temperature control module as the prediction basis: This model describes the decay law of mixture temperature with paving time t, where is the initial temperature, is the ambient temperature, is the temperature decay constant. By inputting real-time environmental data (such as wind speed, temperature and humidity) and mixture parameters (such as initial temperature and viscosity), the temperature evolution of different areas during the future control cycle can be predicted.

[0033] 2>, Control cycle and rolling optimization Cycle setting: define the control cycle as (e.g. once per second), perform the following steps in each cycle: ① Collect the current temperature distribution of each measuring point (k is the current cycle number); ② Generate the temperature prediction sequence for the next m cycles based on the prediction model ; ③ Solve the open-loop optimization problem and obtain the power adjustment instructions for the next m steps , only execute step 1 ; Rolling characteristics: Through the "prediction-optimization-execution-feedback" cycle, model errors are dynamically corrected to adapt to environmental changes (such as sudden changes in wind speed and mixture temperature fluctuations).

[0034] 2. Multi-region Power Collaborative Optimization Algorithm 1〉Objective Function and Constraints - Optimization Objective: Minimize temperature deviation and power fluctuation costs while avoiding local overheating / underheating: Where: n represents the number of measuring points of the temperature sensor, To predict the number of steps; It is the power smoothing weight coefficient to avoid frequent adjustments; is the target screed plate temperature; Constraints: ① Physical power limit: ;②Temperature safety threshold: (Core constraint of the global optimization unit); ③ Regional differentiation constraint: set higher priority for key areas (such as the heat dissipation area at the edge of the paver) to ensure their power adjustment Prioritize temperature requirements; 2> Distributed solution and regional compensation Measuring point grouping and coordination: Divide the screed into several areas (such as left, middle, and right), and the measuring points in each group share the power adjustment strategy to reduce the calculation complexity. For example, the left area is exposed to the outside and dissipates heat faster, so the power adjustment gain can be set. ; Based on the formula , for high temperature areas Reduce the power and increase the power in low temperature areas.

[0035] Local overheating / underheating processing: When the temperature deviation of a certain measuring point exceeds the threshold When , the independent compensation mechanism is triggered: in 、 , quickly correct the abnormality by increasing the adjustment gain.

[0036] 3>, Global power adjustment table generation Based on the optimization results, a global power adjustment table is generated under two-dimensional coordinates. ,in Corresponding to each position of the screed: Direction (paving width) is divided into equal parts, Direction (paving forward direction) is divided into Equally divided; each cell The power value is calculated by the deviation between the average temperature of the measuring points in the area and the target value, realizing fine-grained control in space.

[0037] 3. Implementation Process and Key Technologies 1〉Data acquisition and transmission: Real-time acquisition through temperature sensor array (such as infrared thermal imager + thermocouple) Temperature distribution of each measuring point with an accuracy of ; After the data is pre-processed by the edge computing node, it is transmitted to the central controller, and the delay is controlled within within; 2〉、Model prediction and optimization solution: Use recursive least squares method to update the temperature decay constant online , adapt to different asphalt mixture characteristics; the optimization problem is solved by the quadratic programming (QP) algorithm, using parallel computing acceleration to ensure Complete calculations within 3> Execution and feedback: Power adjustment instructions are sent to each heating unit through the distributed control system (DCS), driving the solid-state relay to adjust the heating power with a resolution of 1% of the rated power. In the next cycle, the predicted temperature is compared with the measured temperature, the model error is calculated, and the prediction parameters are corrected to form a closed-loop feedback.

[0038] 4. Application Effects and Advantages Improved temperature uniformity: Through collaborative optimization, the standard deviation of screed surface temperature has been reduced from ±8°C under traditional control to ±3°C, reducing the incidence of local overheating / underheating by 70%; Improved construction quality: Uneven road compaction was reduced by 50%, and the average wheel track depth was reduced from 5mm to 1.5mm. Intelligent upgrade: Combine machine learning to continuously optimize model parameters (such as ), the system's adaptive ability continues to increase with the accumulation of construction data, forming an intelligent closed loop of "prediction-control-learning".

[0039] The execution feedback module further includes: Power execution unit: configured to apply the adjusted heating power to the paver screed; quality monitoring unit: configured to collect the road surface compaction , wheel track depth and surface flatness Data; Feedback optimization unit: configured to calculate the compaction deviation based on the quality monitoring data and flatness deviation , is the target compaction degree, For the target flatness, parameter optimization suggestions are generated and fed back to the temperature control module and power optimization module; the central controller builds a data storage and sharing platform based on blockchain technology to realize the distributed storage and encrypted transmission of meteorological data, temperature data and calculation results, ensuring the integrity and traceability of the data. In actual implementation, the central controller is equipped with the Hyperledger Fabric blockchain framework to establish a consortium chain network between multiple nodes such as the construction company headquarters, supervision units, and material suppliers. The meteorological data, temperature data, and dynamic viscosity calculation results collected by the sensors are encrypted by the hash algorithm and stored in the form of blocks at each node. For example, when the temperature data of a batch of asphalt mixture leaving the factory is uploaded to the chain, the system automatically generates a unique hash value and forms a chain association with the previous and next blocks. If the data is tampered with, the hash value will change and the system will immediately trigger an alarm. In terms of data sharing, if the supervision unit needs to retrieve meteorological data for a certain period of time for quality verification, it can initiate a request to each node through a smart contract. After multi-party consensus verification, the encrypted data can be obtained and decrypted for viewing, thus achieving transparency and tamper-proofness of the entire construction data process, providing a reliable basis for quality traceability.

[0040] Among them, it also includes a special control module for high temperature environment, which is configured to: identify that the external temperature is higher than the critical high temperature value Entering a special regulation state; Using the modified power regulation model Calculate, among which For sensitivity tuning parameters, is the current ambient temperature, is the basic heating power, For real-time measurement of asphalt viscosity, is the base viscosity, High temperature reference temperature; Priority is given to ensuring a constant working state in key areas; factors affecting additional cost expenditures under special conditions are evaluated, and a cost-quality optimization report is generated. The screed heating system of the paver adopts zoned independent heating control, dividing the screed into multiple heating units. Each heating unit is equipped with an independent temperature sensor and heating power adjustment device to adapt to the differentiated heating requirements under different paving widths and thicknesses. In specific implementation, the paver screed is divided equally across its width into five heating units, each 1.2 meters long. K-type thermocouple temperature sensors are installed at the center of each unit's surface. Each heating unit is controlled on and off by an independent solid-state relay, with heating power adjusted using PWM (pulse-width modulation) technology. For example, when expanding the paving width from 3 meters to 6 meters, if the system detects a 5°C drop in temperature in an outer heating unit due to the increased heat dissipation area, it automatically increases the heating power of that unit from 70% to 85%, while the inner unit maintains its original power. This ensures temperature uniformity across the entire screed working surface within ±3°C, preventing paving quality defects caused by localized overheating or overcooling.

[0041] The invention also includes a wheel track correction control module configured to: when it is found that the continuous wheel track length exceeds the predetermined length, In the case of a fault, an alarm signal is triggered; the working angle of the ironing board is reset ,in is the initial angle, is the path sensitivity coefficient, sensitivity coefficient Reasonable determination and adjustment should be made based on the specific characteristics of the paver equipment, construction materials, construction environment and other factors to ensure that the screed angle adjustment can effectively deal with the problem of uneven road surface. Adjust the cycle for paving; Finite element simulation is used to predict the impact trend of the new solution on the overall flatness of the road surface, and adjustment suggestions are generated. During the feedback optimization process, digital twin technology is introduced to construct a virtual model of asphalt pavement paving. Real-time collected data is input into the virtual model for simulation and deduction to predict the impact of different parameter adjustment solutions on the final paving quality, assisting in determining the optimal optimization strategy. During the implementation phase, a digital twin model, 1:1 scale, was built using the Unity3D platform to model elements such as the paver, roller, and weather conditions. When the quality monitoring center server receives data on compaction and wheel track depth anomalies, it synchronizes the data to the digital twin model. Based on the pre-set physics engine and material property parameters, the model simulates 10 different adjustment scenarios for parameters such as dynamic viscosity, mixing temperature, and screed power. For example, during feedback optimization for insufficient compaction on a certain road section, model simulations showed that increasing the mixing temperature by 4°C and the screed heating power by 12% could increase compaction to 97%, while maintaining wheel track depth below 2mm. Technicians adjusted construction parameters accordingly, ensuring that actual paving quality met expectations, reducing trial-and-error costs and construction delays.

[0042] Among them, it also includes a microstructure detection module, which is configured to: collect microscopic particle shape distribution data on the asphalt surface and calculate the surface roughness ,in is the height of the surface profile deviating from the mean line, and n is the number of road surface roughness detection points; Using the formula Determine whether the particle irregularity exceeds the standard, To allow the maximum roughness, is the sampling length; When the limit is exceeded, the warning program is triggered and a local enhanced thermal compensation instruction is generated , is the local thermal compensation power for surface roughness deviation, Surface roughness adjustment factor, Target surface roughness. During the transportation of asphalt mixture, intelligent transport vehicles with temperature control devices are used. The on-board control system communicates with the central controller to adjust the insulation measures of the transport vehicles in real time to ensure that the temperature fluctuation range of the mixture is controlled within ±5°C when it arrives at the construction site. In actual application, transport vehicles are equipped with electric heating insulation blankets and circulating hot air systems, and the on-board control system is equipped with a 4G communication module to interact with the central controller in real time. When the vehicle is in transit, the central controller calculates the target temperature that the mixture should maintain based on real-time meteorological data and the remaining transportation time, and sends instructions to the on-board control system. For example, if there is a sudden drop in temperature and the remaining transportation time is 30 minutes, the central controller predicts that the mixture temperature will drop by 8°C, then it sends a command to the vehicle to start the electric heating insulation blanket and increase the circulating hot air temperature from 50°C to 65°C. At the same time, the on-board control system monitors the mixture temperature in real time and dynamically adjusts the heating power through the PID control algorithm so that the temperature of the mixture is stable within the target temperature range of ±5°C when it arrives at the site, ensuring the continuity and quality stability of the paving operation.

[0043] It also includes an intelligent decision support module, which is configured to: establish a comprehensive rating index system ,in is the weight coefficient, is each quality indicator, n represents the quality indicators in the comprehensive rating index system I Generate an intelligent recommendation strategy database based on historical data; continuously iterate and optimize key system parameters through machine learning algorithms , generate complete construction parameter documents and archive them, and establish an asphalt pavement paving quality early warning mechanism. When the deviation between the real-time collected data and the preset threshold exceeds a certain range, the system automatically generates early warning information and notifies relevant construction management personnel through SMS, APP push, etc., so that timely intervention measures can be taken. During implementation, early warning thresholds for key indicators such as compaction, temperature, and wheel track depth were set on the quality monitoring center server. For example, alerts were triggered when the compaction fell below 95%, the paving temperature fell below 135°C, and the wheel track depth exceeded 4mm. When sensor data triggered a threshold, the server sent a text message containing the abnormal data, location information, and preliminary treatment suggestions to the project manager, technical director, and others via Alibaba Cloud's SMS service. Simultaneously, an early warning pop-up window was pushed to the construction management app, displaying detailed quality anomaly analysis charts. For example, if the compaction of a certain test section showed 94% for two consecutive times, the system immediately issued an early warning. Construction management personnel could then quickly rush to the site based on the early warning information, invoke the digital twin model to analyze the cause, arrange for the roller to recompact, and adjust subsequent paving parameters, eliminating quality issues before they were even established.

[0044] 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. An asphalt pavement paving quality management system, characterized in that: include: Data acquisition module: configured to obtain real-time meteorological data, mixture temperature parameters and pavement quality monitoring data; Viscosity analysis module: determines the dynamic viscosity adjustment range of the asphalt material based on the real-time meteorological data and mixture temperature parameters; Temperature control module: regulates mixing temperature according to the viscosity change of asphalt material and generates target paving temperature curve; Power optimization module: Based on the target paving temperature curve, it matches the dynamic adjustment strategy of the screed heating power of the paver; The execution feedback module applies the adjusted heating power to the paving operation, monitors the road surface compaction and wheel track conditions, and feeds back to the temperature control module and the power optimization module to optimize the parameters.

2. The asphalt pavement paving quality management system according to claim 1, characterized in that: The data acquisition module includes: Weather sensor unit: configured to obtain the current ambient temperature and relative humidity ; Mixture Temperature Sensor Unit: Configured to record initial mixture temperature And collect the mixture temperature in real time ; Wind speed monitoring unit: configured to obtain real-time wind speed parameters ; Pavement quality detection unit: configured to collect pavement compaction , wheel track depth and surface flatness data.

3. The asphalt pavement paving quality management system according to claim 2, characterized in that: The viscosity analysis module includes: Parameter calculation unit: configured to use formula Calculate the viscosity adjustment factor for asphalt materials, where and Empirical parameters set based on historical data regression analysis, The current relative temperature is calculated as the ratio of the ambient temperature to the reference temperature. is the current relative humidity; Viscosity prediction unit: configured to update the target mixing temperature based on the viscosity adjustment factor , and combined with wind speed parameters Generate viscosity prediction models, is the base mixing temperature, is the viscosity-temperature conversion coefficient, Indicates the viscosity adjustment factor of asphalt material; Deviation judgment unit: configured to compare the viscosity value measured in real time With pre-set target viscosity , to determine whether there is a deviation , is the viscosity deviation threshold.

4. The asphalt pavement paving quality management system according to claim 3, characterized in that: The temperature control module includes: Temperature curve generation unit: configured to draw the mixture temperature decay curve , represents the predicted temperature of the mixture at paving time t, is the initial temperature of the mixture, For paving time, is the mixture temperature decay constant; Alert judgment unit: configured based on formula Determine whether the mixture has reached the warning temperature condition, Current temperature of the mixture; Temperature compensation unit: When the mixture temperature is lower than the warning value, it generates a temperature compensation instruction and adjusts the sample temperature. ,in is the temperature regulation coefficient, is the mixing temperature increment that needs to be adjusted, is the target mixture temperature.

5. The asphalt pavement paving quality management system according to claim 4, characterized in that: The power optimization module includes: Temperature distribution acquisition unit: configured to collect the actual temperature distribution of multiple measurement points in real time through a temperature sensor array , calculate the average value of the measurement , n represents the number of measuring points of the temperature sensor; and based on the mixture temperature attenuation curve of the temperature control module Generate a temperature prediction sequence for future control periods; Power adjustment unit: configured to adjust the power of the system based on the target temperature value in each control cycle. And predict the temperature series by optimizing the objective function: Solving the future Step power adjustment command, only execute the first step command ,in is the proportional gain factor, is the power smoothing weight coefficient; k is the current control cycle, m is the number of prediction steps; Global optimization unit: configured to detect temperature deviation exceeding the threshold The measuring points are compensated by independent Generate local compensation power and generate global paving power adjustment table based on 2D coordinates , to achieve regional differentiated power collaborative optimization.

6. The asphalt pavement paving quality management system according to claim 5, characterized in that: The execution feedback module includes: A power execution unit: configured to apply the adjusted heating power to the paver screed; Quality monitoring unit: configured to collect road surface compaction , wheel track depth and surface flatness data; Feedback Optimization Unit: Configured to calculate compaction deviation based on quality monitoring data and flatness deviation , is the target compaction degree, The target flatness is achieved, and parameter optimization suggestions are generated and fed back to the temperature control module and the power optimization module.

7. The asphalt pavement paving quality management system according to claim 6, characterized in that: It also includes a special control module for high temperature environments, which is configured as follows: Identify that the outside temperature is higher than the critical high temperature value Entering a special regulation state; Using the modified power regulation model Calculate, among which For sensitivity tuning parameters, is the current ambient temperature, is the basic heating power, For real-time measurement of asphalt viscosity, is the base viscosity, High temperature reference temperature.

8. The asphalt pavement paving quality management system according to claim 7, characterized in that: Also included is a wheel tracking correction control module configured to: If the continuous wheel track length exceeds the predetermined length In the case of, trigger the alarm signal; Resetting the screed's working angle ,in is the initial angle, is the path sensitivity coefficient, Adjust the cycle for paving.

9. The asphalt pavement paving quality management system according to claim 8, characterized in that: Also included is a microstructure detection module configured as: Collect microscopic particle shape distribution data on asphalt surface and calculate surface roughness ,in is the height of the surface profile deviating from the mean line, and n is the number of road surface roughness detection points; Using the formula Determine whether the particle irregularity exceeds the standard, To allow the maximum roughness, is the sampling length; When the limit is exceeded, the warning program is triggered and a local enhanced thermal compensation instruction is generated , is the local thermal compensation power for surface roughness deviation, Surface roughness adjustment factor, Target surface roughness.

10. The asphalt pavement paving quality management system according to claim 9, characterized in that: It also includes an intelligent decision support module, configured to: Establish a comprehensive rating index system ,in is the weight coefficient, is each quality indicator, n represents the quality indicators in the comprehensive rating index system I the number of Generate an intelligent recommendation strategy database based on historical data; Continuously iteratively optimize key system parameters through machine learning algorithms , generate complete construction parameter documents and archive them.

Images