A method of monitoring the mixing uniformity of recycled asphalt mixtures

Abstract

The present application relates to the technical field of recycling of asphalt mixture, and particularly relates to a mixing uniformity monitoring method suitable for recycled asphalt mixture, comprising the following steps: S101, a dual-mode sensor of infrared thermal radiation field dynamic capture and microwave dielectric characteristic synchronous monitoring is used, and the dual-mode sensor is spatiotemporally fused to jointly perform mixing defect alarm; S102, based on the infrared thermal radiation field dynamic sequence, the particle motion consistency is judged through the standard deviation of the motion direction angle, the infrared temperature field and the microwave dielectric data are integrated, a three-dimensional feature vector is constructed, the uniformity is comprehensively evaluated through a weighted function, the defect is judged according to the maximum contribution item, and an optimization instruction is generated; S103, a grading standard is set up based on the multi-modal features, and the process is adjusted according to the grading result feedback. The present application combines infrared thermal radiation and microwave dielectric characteristics, synchronously evaluates temperature uniformity and material component distribution, and solves the monitoring problem that the uniformity of recycled asphalt heat is not equal to the uniformity of components.

Description

Technical Field

[0001] This invention relates to the field of recycled asphalt mixture technology, and more specifically to a method for monitoring the mixing uniformity of recycled asphalt mixtures. Background Technology

[0002] With the development of road material recycling, plant-mixed hot recycling technology achieves resource conservation and low-carbon environmental protection by incorporating waste asphalt mixtures. However, the high viscosity of old asphalt mastic in RAP and its poor miscibility with new asphalt mastic, coupled with the uneven particle size distribution of old aggregates, lead to phenomena such as asphalt stratification and aggregate agglomeration in recycled mixtures. This multi-scale inhomogeneity significantly affects the mechanical properties and durability of recycled asphalt pavements, becoming a technical bottleneck restricting the high-proportion application of RAP.

[0003] Due to long-term aging, old asphalt mastic contains a large number of non-volatile components and has a large viscosity difference from new asphalt. When the two are mixed, they are prone to forming "island-like" distributions. Traditional indicators can only reflect the overall mixing effect and cannot quantify the degree of local enrichment. The old asphalt film attached to the surface of old aggregates in RAP will change its adhesion characteristics with new aggregates, causing the old aggregates to tend to agglomerate after the mixture is formed. Traditional uniformity evaluation is often based on geometric parameters such as volume or area ratio, but it has not established a clear correlation with mechanical indicators such as compressive strength and fracture energy of the mixture. Although CT scanning technology can construct three-dimensional models, most studies still calculate uniformity indicators through two-dimensional slices or projection data, ignoring the interaction between aggregates and asphalt mastic in three-dimensional space. For example, the angular effect of coarse aggregates may aggravate the local enrichment of mastic in three-dimensional space, but two-dimensional evaluation is difficult to capture such characteristics. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a method for monitoring the mixing uniformity of recycled asphalt mixtures.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for monitoring the mixing uniformity of recycled asphalt mixtures, comprising the following steps:

[0006] S101. A dual-mode sensor is used to dynamically capture infrared thermal radiation field and synchronously monitor microwave dielectric properties. The two are spatiotemporally fused to jointly provide mixed defect alarms.

[0007] S102. Based on the dynamic sequence of infrared thermal radiation field, the uniformity of particle motion is judged by the standard deviation of the motion direction angle. The infrared temperature field and microwave dielectric data are integrated to construct a three-dimensional feature vector. The uniformity is comprehensively evaluated by the weighting function. Defects are judged based on the maximum contribution item and optimization instructions are generated.

[0008] S103. Establish grading standards based on multimodal characteristics, and adjust the process based on the grading results.

[0009] In a preferred embodiment, in S101, a mid-wave infrared thermal imager is fixedly installed at the center of the top of the mixing chamber, and a polarization filter array is simultaneously covered on the observation window. When the mixture is exposed to the observation window during the mixing process, a high signal-to-noise ratio thermal radiation signal is obtained by filtering out non-polarized metal reflected light and retaining the polarization component of the spontaneous radiation of the mixture. Since the thermal emissivity of the old material is significantly higher than that of the new asphalt component due to the aging asphalt coating layer, an observable difference in emissivity is formed. This physical characteristic produces a detectable temperature difference during the heat conduction process. This temperature difference feature presents a temperature gradient field distribution pattern under dynamic mixing environment. The old material solidification area appears as a temperature anomaly area due to the difference in thermophysical properties.

[0010] To optimize data acquisition efficiency, a time-adaptive dynamic sampling strategy is adopted: in the initial stage of mixing, the temperature change event during the crushing of old materials is captured at the first high-frequency sampling frequency; when the temperature gradient dispersion is detected to drop to the first preset level, the sampling frequency is automatically switched to the second sampling frequency to balance the processing load.

[0011] A miniature microwave sensor is embedded in the mixing shaft blades and operates based on the principle of dielectric resonance: when the blades sweep across the mixture, the difference in dielectric constants between the new asphalt, the old RAP material, and the recycling agent causes a shift in the resonant frequency, the amount of which is... With dielectric constant The functional relationship can be used to locate the measurement point by the encoder of the stirring shaft, and a three-dimensional spatial distribution model of the dielectric constant can be constructed.

[0012] Time synchronization is achieved by sharing the encoder pulses of the stirring shaft, forming a temporal reference for dual-modal data association. A unified three-dimensional mesh is established with the stirring blade as the reference coordinate system, and infrared image pixels and microwave measurement points are mapped to the same spatial coordinate system. Based on the spatiotemporal synchronization framework, a non-uniformity index is established, and the specific calculation formula is as follows:

[0013]

[0014] in, Indicates the unevenness index. , Indicates the weighting coefficient. Indicates the standard deviation of the infrared temperature gradient. Indicates the standard deviation of microwave dielectric. , These are the reference standard deviations and the standard deviations of the infrared temperature gradient. microwave dielectric standard deviation When the comprehensive evaluation value exceeds the second preset threshold, a mixed defect alarm is triggered.

[0015] In a preferred embodiment, in S102, based on the dynamic sequence of the infrared thermal radiation field, the temperature gradient abrupt change point is identified by a preset temperature difference threshold, and the boundary of the old material particles is calibrated. Due to the aging asphalt coating layer on the surface of the old material, the friction coefficient is significantly higher than that of the new material, and its motion process exhibits unique dynamic characteristics: when the particle group motion trajectory is calculated using the optical flow algorithm, the instantaneous velocity direction change rate of the old material particles is always lower than that of the new material particles. This is achieved by statistically analyzing the standard deviation of the motion direction angle per unit time. The specific calculation formula is as follows:

[0016]

[0017] Where N represents the number of sampling points per unit time. This represents the particle motion direction angle at the i-th sampling point. This represents the arithmetic mean of the Nth direction angle, when When the particle group remains below the second preset threshold, it is determined that the particle group's movement direction tends to be consistent. This state indicates that the stirring and shearing action is sufficient and the mixing uniformity is improved. The specific calculation formula for the second preset threshold is as follows:

[0018]

[0019] in, This represents the mean of a standard homogeneous mixture. represents the standard deviation of a standard homogeneous mixture, and k represents the safety factor;

[0020] Infrared temperature field and microwave dielectric data are fused within a spatiotemporally registered 3D grid to establish the following 3D feature vectors, including temperature gradient stability factor, dielectric constant consistency factor, and particle motion coordination factor:

[0021] Temperature gradient stability factor: This factor calculates the spatial dispersion of high and low temperature regions within a grid cell. Its value is the ratio of the standard deviation of the infrared temperature gradient to the baseline value. A lower ratio indicates more uniform heat exchange between new and old materials. The specific formula for calculating the temperature gradient stability factor is as follows:

[0022]

[0023] in, This represents the temperature gradient stability factor.

[0024] Dielectric constant uniformity factor: The dielectric constant dataset measured by the microwave sensor is extracted, and the ratio of the standard deviation of the microwave dielectric constant in the three-dimensional grid to the benchmark value is used as a quantitative index, which is directly related to the uniformity of the recycler distribution on the surface of the recycled material. The specific calculation formula of the dielectric constant uniformity factor is as follows:

[0025]

[0026] in, Indicates the dielectric constant uniformity factor;

[0027] Particle motion coordination factor: expressed as the standard deviation of the motion direction angle Based on this, the directional consistency index is calculated, and the specific calculation formula for the particle motion coordination factor is as follows:

[0028]

[0029] in, Indicates the particle motion coordination factor. This represents the standard deviation of the maximum permissible orientation angle. When the value approaches 1, it indicates that the stirring shear force is optimal for material mixing.

[0030] A weighted evaluation function for the three-dimensional feature vectors is constructed to calculate the uniformity index. The specific calculation formula is as follows:

[0031]

[0032] in, Indicator of uniformity , , This represents the preset weighting coefficients, which satisfy... When the function value U exceeds the third preset threshold, the defect type is determined by the feature contribution. As the largest contributor, insufficient heat exchange between new and old materials is determined. If the largest contribution is determined to be uneven distribution of regenerant, then... The component contributing the most is deemed to have insufficient stirring shear force;

[0033] In a preferred embodiment, in step S103, an objective grading basis is constructed based on the multimodal characteristics of infrared temperature field, microwave dielectric distribution, and particle motion coordination. The specific grading rules are as follows:

[0034] Criterion for excellence: Temperature gradient stability factor Dielectric constant uniformity factor And particle motion coordination factor The condition is rated as excellent. This state indicates a uniform temperature field distribution with no high or low temperature differences, consistent distribution of the regenerator and the recycled material, and highly coordinated movement trajectories of the recycled material particles. and Any one of them exceeds the corresponding threshold but is ≤ ,and Determined to be at the qualified level, this state allows for local temperature gradients and slight dielectric fluctuations, and the agglomeration of old materials is within the dispersible range. , If any condition is met, the grade is determined to be inferior. This state corresponds to a significant temperature anomaly, severe segregation of the regenerator, or large-scale agglomeration of the old material. Based on the grading results, a process optimization instruction is triggered. For superior grades, the current parameters are maintained and the characteristic data of this batch is recorded for threshold self-learning. For qualified grades, the stirring time needs to be extended to the preset extension time. And monitor in real time during the extension period. , If the rate of change is poor, generation should be immediately paused and an alarm code generated. The spatial coordinates of the regenerator nozzle or the injection pressure should be adjusted based on the abnormal positioning results.

[0035] The beneficial effects of this invention are as follows: This invention combines infrared thermal radiation and microwave dielectric properties to simultaneously evaluate temperature uniformity and material composition distribution, solving the monitoring problem that thermal uniformity of recycled asphalt does not equal component uniformity. In response to the problems of high frictional resistance and special motion characteristics of RAP particles, the particle motion tracking algorithm is optimized to improve the sensitivity of agglomeration identification. The uniformity evaluation results are directly correlated with the adjustment of stirring parameters, realizing the automation of monitoring, analysis and control, and improving the efficiency of traditional manual detection. Attached Figure Description

[0036] Figure 1 This is a flowchart of the present invention;

[0037] Figure 2 This is a logic diagram for uniformity detection in this invention. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0040] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0041] like Figure 1 This embodiment provides a method for monitoring the mixing uniformity of recycled asphalt mixtures, comprising the following steps:

[0042] S101. A dual-mode sensor is used to dynamically capture infrared thermal radiation field and synchronously monitor microwave dielectric properties. The two are spatiotemporally fused to jointly provide mixed defect alarms.

[0043] Furthermore, a mid-wave infrared thermal imager is fixedly installed at the center of the top of the mixing chamber, and a polarization filter array is simultaneously covered on the observation window. When the mixture is exposed to the observation window during the mixing process, the non-polarized metallic reflected light is filtered out while retaining the polarization component of the spontaneous radiation of the mixture, and a high signal-to-noise ratio thermal radiation signal is obtained. Since the thermal emissivity of the old material is significantly higher than that of the new asphalt component due to the aging asphalt coating layer, an observable difference in emissivity is formed. This physical characteristic produces a detectable temperature difference during the heat conduction process. This temperature difference feature presents a temperature gradient field distribution pattern under dynamic mixing environment, in which the old material cohesion area appears as a temperature anomaly area due to the difference in thermophysical properties.

[0044] To optimize data acquisition efficiency, a time-adaptive dynamic sampling strategy is adopted: in the initial stage of mixing, the temperature change event during the crushing of old materials is captured at the first high-frequency sampling frequency; when the temperature gradient dispersion is detected to drop to the first preset level, the sampling frequency is automatically switched to the second sampling frequency to balance the processing load.

[0045] A miniature microwave sensor is embedded in the mixing shaft blades and operates based on the principle of dielectric resonance: when the blades sweep across the mixture, the difference in dielectric constants between the new asphalt, the old RAP material, and the recycling agent causes a shift in the resonant frequency, the amount of which is... With dielectric constant The functional relationship can be used to locate the measurement point by the blade rotation angle encoder, and a three-dimensional spatial distribution model of the dielectric constant can be constructed.

[0046] Time synchronization is achieved by sharing the encoder pulses of the stirring shaft, forming a temporal reference for dual-modal data association. A unified three-dimensional mesh is established with the stirring blade as the reference coordinate system, and infrared image pixels and microwave measurement points are mapped to the same spatial coordinate system. Based on the spatiotemporal synchronization framework, a non-uniformity index is established, and the specific calculation formula is as follows:

[0047]

[0048] in, Indicates the unevenness index. , Indicates the weighting coefficient. Indicates the standard deviation of the infrared temperature gradient. Indicates the standard deviation of microwave dielectric. , These are the reference standard deviations and the standard deviations of the infrared temperature gradient. microwave dielectric standard deviation When the comprehensive evaluation value exceeds the second preset threshold, a mixed defect alarm is triggered.

[0049] It should be noted that the second preset threshold is a baseline value used to compare with the UI calculated in real time. When the UI exceeds this threshold, an alarm is triggered, indicating a mixed defect.

[0050] It should be noted that the spatiotemporal synchronization framework includes a temporal reference for bimodal data association and a unified three-dimensional mesh;

[0051] It should be noted that the temperature change time is defined as the instantaneous temperature change exceeding the preset temperature difference threshold. After the equipment is installed, the mixing chamber is preheated to 160°C. A standard specimen with a known RAP content of 50% and a regenerant dosage of 3% is placed in the chamber to calibrate the infrared thermal imager and ensure that the temperature measurement error does not exceed 1.5°C. At the same time, a micro microwave sensor is embedded in the mixing shaft blades. The dielectric constant measurement error is verified to be ≤3% using standard specimens with different RAP contents, and the cross-calibration of multiple sensors is completed.

[0052] When the recycled asphalt mixture is put into the mixing process, the infrared thermal imager is activated in a high-frequency sampling mode of 5 frames / second within 0-10 minutes after the start of mixing to track the temperature change during the crushing process of the old material agglomerates in real time. During this stage, when the old material agglomerates are crushed, the new asphalt inside will produce local temperature changes when it comes into contact with air. High-frequency sampling can capture these instantaneous features. After mixing for 10 minutes, when the temperature field distribution tends to stabilize, the sampling frequency is reduced to 2 frames / second to reduce the amount of data storage while ensuring real-time performance.

[0053] S102. Based on the dynamic sequence of infrared thermal radiation field, the uniformity of particle motion is judged by the standard deviation of the motion direction angle. The infrared temperature field and microwave dielectric data are integrated to construct a three-dimensional feature vector. The uniformity is comprehensively evaluated by the weighting function. Defects are judged based on the maximum contribution item and optimization instructions are generated.

[0054] Furthermore, based on the dynamic sequence of infrared thermal radiation field, temperature gradient abrupt change points are identified by pre-set temperature difference thresholds, and the boundaries of old material particles are calibrated. Due to the significantly higher friction coefficient caused by the aged asphalt coating layer on the surface of the old material compared to the new material, its motion process exhibits unique dynamic characteristics: when calculating the particle group trajectory using optical flow algorithm, the instantaneous velocity direction change rate of old material particles is always lower than that of new material particles. This is achieved by statistically analyzing the standard deviation of the motion direction angle per unit time. The specific calculation formula is as follows:

[0055]

[0056] Where N represents the number of sampling points per unit time. This represents the particle motion direction angle at the i-th sampling point. This represents the arithmetic mean of the Nth direction angle, when When the particle group remains below the second preset threshold, it is determined that the particle group's movement direction tends to be consistent. This state indicates that the stirring and shearing action is sufficient and the mixing uniformity is improved. The specific calculation formula for the second preset threshold is as follows:

[0057]

[0058] in, This represents the mean of a standard homogeneous mixture. represents the standard deviation of a standard homogeneous mixture, and k represents the safety factor;

[0059] Infrared temperature field and microwave dielectric data are fused within a spatiotemporally registered 3D grid to establish the following 3D feature vectors, including temperature gradient stability factor, dielectric constant consistency factor, and particle motion coordination factor:

[0060] Temperature gradient stability factor: This factor calculates the spatial dispersion of high and low temperature regions within a grid cell. Its value is the ratio of the standard deviation of the infrared temperature gradient to the baseline value. A lower ratio indicates more uniform heat exchange between new and old materials. The specific formula for calculating the temperature gradient stability factor is as follows:

[0061]

[0062] in, This represents the temperature gradient stability factor.

[0063] Dielectric constant uniformity factor: The dielectric constant dataset measured by the microwave sensor is extracted, and the ratio of the standard deviation of the microwave dielectric constant in the three-dimensional grid to the benchmark value is used as a quantitative index, which is directly related to the uniformity of the recycler distribution on the surface of the recycled material. The specific calculation formula of the dielectric constant uniformity factor is as follows:

[0064]

[0065] in, Indicates the dielectric constant uniformity factor;

[0066] Particle motion coordination factor: expressed as the standard deviation of the motion direction angle Based on this, the directional consistency index is calculated, and the specific calculation formula for the particle motion coordination factor is as follows:

[0067]

[0068] in, Indicates the particle motion coordination factor. This represents the standard deviation of the maximum permissible orientation angle. When the value approaches 1, it indicates that the stirring shear force is optimal for material mixing.

[0069] A weighted evaluation function for the three-dimensional feature vectors is constructed to calculate the uniformity index. The specific calculation formula is as follows:

[0070]

[0071] in, Indicator of uniformity , , This represents the preset weighting coefficients, which satisfy... When the function value U exceeds the third preset threshold, the defect type is determined by the feature contribution. As the largest contributor, insufficient heat exchange between new and old materials is determined. If the largest contribution is determined to be uneven distribution of regenerant, then... As the largest contributor, the stirring shear force is determined to be insufficient, and process optimization instructions are generated accordingly, such as extending the stirring time, adjusting the regenerator injection position, or increasing the stirring paddle speed.

[0072] It should be noted that the third preset threshold is set by preparing multiple batches of recycled asphalt mixture samples that meet the homogeneity standard under laboratory conditions and through strict control of the mixing process. These are called standard homogeneous samples. For each standard homogeneous sample, the homogeneity index U is calculated according to the aforementioned weighted evaluation function, forming a dataset. The U values ​​of all standard homogeneous samples are sorted from smallest to largest, and the 95th percentile is taken as the candidate threshold. For example, if there are 100 samples, the U value of the 95th sample after sorting is the 95th percentile. The 95th percentile means that in the standard homogeneous samples, only 5% of the samples may have a U value exceeding this threshold, thus ensuring that the threshold is met. To ensure the representativeness of the uniform state and avoid misjudgment, candidate threshold values ​​determined in the laboratory are applied to on-site production and compared with the actual mixing effect. If frequent misjudgments or omissions occur, the quantile ratio needs to be adjusted, such as changing it to 90% or 98%. As production data accumulates, the historical dataset is updated regularly, and the quantiles are recalculated to ensure that the threshold adapts to changes in raw materials and process improvements, and to maintain the timeliness of the judgment standard. When the third preset threshold is 0.7, its essence is to define the boundary between uniformity and non-uniformity through statistical methods. When the U value of the on-site sample exceeds 0.7, it indicates that its uniformity is lower than the 95% standard sample level, and process optimization needs to be triggered.

[0073] It should be noted that when the uniformity evaluation function U ≤ the third preset threshold, representative samples are automatically collected from the discharge port of the mixing silo. After preprocessing, the samples are scanned in three dimensions using a high-precision CT scanner. Image reconstruction technology is used to generate a three-dimensional model of the aggregate spatial distribution. During the scanning process, parameter optimization is used to eliminate metal artifacts, ensuring that the model can clearly distinguish components such as recycled aggregate, new asphalt, and recycling agent voids. In the three-dimensional model, image segmentation technology is used to identify and mark all recycled aggregate aggregation areas. The system automatically calculates the volume of each aggregation area and statistically analyzes the total volume ratio of recycled aggregate in the sample. To quantify the uniformity of recycled aggregate distribution, the volume ratio of recycled aggregate in each slice layer is further analyzed, and its dispersion is evaluated using statistical methods. This dispersion reflects the uniformity of recycled aggregate distribution in the mixture: the lower the dispersion, the more uniform the recycled aggregate mixture; conversely, agglomeration exists.

[0074] Based on statistical data from multiple batches of samples, a linear correlation model between the uniformity evaluation function value and the dispersion of old material distribution is established. This model is trained with historical data to reflect the evaluation function's ability to represent the actual mixing state. After the model is established, its accuracy needs to be continuously verified with new samples: by comparing the deviation between the model's predicted values ​​and the actual detected values, the model's adaptability to the current working conditions is evaluated.

[0075] When the model prediction deviation continues to exceed the preset threshold, it is determined that the current uniformity evaluation model deviates from the actual working conditions, triggering an automatic optimization program for the weight coefficients. The optimization algorithm uses the current detection data as a constraint, and adjusts the weight ratio of each feature factor through iterative calculation while keeping the sum of the weight coefficients at 1, so that the evaluation function can more accurately reflect the actual mixing state. This mechanism can adapt to changes in working conditions such as the degree of RAP aging and the type of regenerant on site, ensuring the long-term reliability of the monitoring system.

[0076] S103. Establish grading standards based on multimodal characteristics, and adjust the process based on the grading results.

[0077] Furthermore, based on the multimodal characteristics of infrared temperature field, microwave dielectric distribution, and particle motion coordination, an objective classification basis is constructed, and the specific classification rules are as follows:

[0078] Criterion for excellence: Temperature gradient stability factor Dielectric constant uniformity factor And particle motion coordination factor The condition is rated as excellent. This state indicates a uniform temperature field distribution with no high or low temperature differences, consistent distribution of the regenerator and the recycled material, and highly coordinated movement trajectories of the recycled material particles. and Any one of them exceeds the corresponding threshold but is ≤ ,and Determined to be at the qualified level, this state allows for local temperature gradients and slight dielectric fluctuations, and the agglomeration of old materials is within the dispersible range. , If any condition is met, the grade is determined to be inferior. This state corresponds to a significant temperature anomaly, severe segregation of the regenerator, or large-scale agglomeration of the old material. Based on the grading results, a process optimization instruction is triggered. For superior grades, the current parameters are maintained and the characteristic data of this batch is recorded for threshold self-learning. For qualified grades, the stirring time needs to be extended to the preset extension time. And monitor in real time during the extension period. , If the rate of change is poor, generation should be immediately paused and an alarm code generated. The spatial coordinates of the regenerator nozzle or the injection pressure should be adjusted based on the abnormal positioning results.

[0079] It should be noted that, The calibration was performed by selecting 100 sets of uniform recycled material samples, collecting infrared temperature field data, calculating the ratio of the standard deviation of the temperature gradient to the mean of each sample, and taking the upper limit of the sample statistical value under the 95% confidence interval, that is, 95% of the uniform samples ≤0.6, as the benchmark for determining the quality level. This was verified through critical experiments. When the dielectric constant reaches 0.8, the performance of the recycled material begins to decline significantly, thus setting it as the threshold for inferior performance. A microwave dielectric constant testing device is used to scan samples with different amounts of recyclant sprayed, recording the dielectric constant fluctuation range. The dielectric constant variation coefficient corresponding to uniform recyclant distribution is ≤5%. An orthogonal experiment is used to determine the optimal matching point between this value and the recyclant dispersion. When the dielectric constant variation coefficient is >15%, recyclant segregation leads to abnormal dielectric signals, thus serving as an inferior performance criterion. High-speed cameras are used to track the movement trajectory of recycled material particles, calculating the particle motion synergy factor. Simultaneously, the performance indicators of the compacted recycled material are detected. When the particle motion synergy factor is ≥0.85, the performance is considered inferior. When the particle mixing and shearing coefficient is ≥0.85, the performance compliance rate exceeds 90%, which is set as the lower limit of the superior grade. If the particle motion coordination factor is <0.7, ( <0.7), the probability of particle agglomeration increases significantly, requiring forced interference, and is therefore used as the dividing line between qualified and inferior grades.

[0080] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0081] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0083] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0084] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0085] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0086] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for monitoring the mixing uniformity of recycled asphalt mixtures, characterized in that, Includes the following steps: S101. A dual-mode sensor is used to dynamically capture infrared thermal radiation field and synchronously monitor microwave dielectric properties. The two are spatiotemporally fused to jointly provide mixed defect alarms. S102. Based on the dynamic sequence of infrared thermal radiation field, the uniformity of particle motion is judged by the standard deviation of the motion direction angle. The infrared temperature field and microwave dielectric data are integrated to construct a three-dimensional feature vector including temperature gradient stability factor, dielectric constant uniformity factor and particle motion coordination factor. The uniformity is comprehensively evaluated by weighting function, and defects are judged based on the maximum contribution item and optimization instructions are generated. S103. Establish grading standards based on multimodal characteristics, and adjust the process based on the grading results.

2. The method for monitoring the mixing uniformity of recycled asphalt mixtures according to claim 1, characterized in that, In S101, a mid-wave infrared thermal imager is fixedly installed at the center of the top of the mixing chamber, and a polarization filter array is simultaneously covered on the observation window. When the mixture is exposed to the observation window during the mixing process, the non-polarized metal reflected light is filtered out and the polarization component of the spontaneous emission of the mixture is retained to obtain a thermal radiation signal with a high signal-to-noise ratio. Since the thermal emissivity of the old material is significantly higher than that of the new asphalt component due to the aging asphalt coating layer, an observable difference in emissivity is formed. This physical characteristic produces a detectable temperature difference during the heat conduction process. This temperature difference feature presents a temperature gradient field distribution pattern under dynamic mixing environment. The old material solidification area appears as a temperature anomaly area due to the difference in thermophysical properties.

3. The method for monitoring the mixing uniformity of recycled asphalt mixtures according to claim 1, characterized in that, A miniature microwave sensor is embedded in the impeller blades. This sensor operates based on the principle of dielectric resonance: when the blades sweep across the mixture, the difference in dielectric constants between the new asphalt, the old RAP material, and the recycling agent causes a shift in the resonant frequency. The amount of this shift... With dielectric constant By determining the functional relationship between the dielectric constant and the measurement points through the encoder on the stirring shaft, a three-dimensional spatial distribution model of the dielectric constant can be constructed.

4. The method for monitoring the mixing uniformity of recycled asphalt mixtures according to claim 3, characterized in that, Time synchronization is achieved by sharing the encoder pulses of the stirring shaft, forming a temporal reference for dual-modal data association. A unified three-dimensional mesh is established with the stirring blade as the reference coordinate system, and infrared image pixels and microwave measurement points are mapped to the same spatial coordinate system. Based on the spatiotemporal synchronization framework, a non-uniformity index is established, and the specific calculation formula is as follows: in, Indicates the unevenness index. , Indicates the weighting coefficient. Indicates the standard deviation of the infrared temperature gradient. Indicates the standard deviation of microwave dielectric. , These are the reference standard deviations and the standard deviations of the infrared temperature gradient. microwave dielectric standard deviation When the comprehensive evaluation value exceeds the second preset threshold, a mixed defect alarm is triggered.

5. The method for monitoring the mixing uniformity of recycled asphalt mixtures according to claim 1, characterized in that, In step S102, based on the dynamic sequence of the infrared thermal radiation field, the temperature gradient abrupt change point is identified by a preset temperature difference threshold, and the boundary of the old material particles is calibrated. Due to the aging asphalt coating layer on the surface of the old material, the friction coefficient is significantly higher than that of the new material, and its motion process exhibits unique dynamic characteristics: when the particle group motion trajectory is calculated using the optical flow algorithm, the instantaneous velocity direction change rate of the old material particles is always lower than that of the new material particles. This is achieved by statistically analyzing the standard deviation of the motion direction angle per unit time. The specific calculation formula is as follows: Where N represents the number of sampling points per unit time. This represents the particle motion direction angle at the i-th sampling point. Represents the arithmetic mean of N direction angles, when When the particle group moves in a direction that is consistent with the second preset threshold, it is determined that the stirring and shearing action is sufficient and the mixing uniformity is improved.

6. The method for monitoring the mixing uniformity of recycled asphalt mixtures according to claim 1, characterized in that, Infrared temperature field and microwave dielectric data are fused within a spatiotemporally registered three-dimensional grid to establish the following three-dimensional feature vectors, including temperature gradient stability factor, dielectric constant consistency factor, and particle motion coordination factor.

7. The method for monitoring the mixing uniformity of recycled asphalt mixtures according to claim 6, characterized in that, Temperature gradient stability factor: This factor calculates the spatial dispersion of high and low temperature regions within a grid cell. Its value is the ratio of the standard deviation of the infrared temperature gradient to the baseline value. A lower ratio indicates more uniform heat exchange between the old and new materials. The specific calculation formula is as follows: ,in, The temperature gradient stability factor and dielectric constant uniformity factor are defined as follows: The dielectric constant dataset measured by the microwave sensor is extracted, and the ratio of the microwave dielectric standard deviation of this data in the three-dimensional grid to the baseline value is used as a quantitative indicator, directly related to the uniformity of the recycler's distribution on the recycled material surface. The specific calculation formula is as follows: ,in, Dielectric constant uniformity factor; Particle motion coordination factor: expressed as the standard deviation of the motion direction angle. Based on this, the directional consistency index is calculated using the following formula: ,in, This represents the standard deviation of the maximum permissible orientation angle. When the value approaches 1, it indicates that the stirring shear force is optimal for material mixing.

8. The method for monitoring the mixing uniformity of recycled asphalt mixtures according to claim 7, characterized in that, A weighted evaluation function for the three-dimensional feature vectors is constructed to calculate the uniformity index. The specific calculation formula is as follows: in, Indicator of uniformity , , This represents the preset weighting coefficients, which satisfy... , This is the temperature gradient stability factor. The dielectric constant uniformity factor; when the function value When the threshold is exceeded, the defect type is determined by feature contribution. As the largest contributor, insufficient heat exchange between new and old materials is determined. If the largest contribution is determined to be uneven distribution of regenerant, then... The factor contributing the most is considered insufficient stirring shear force.

9. The method for monitoring the mixing uniformity of recycled asphalt mixtures according to claim 1, characterized in that, Based on the multimodal characteristics of infrared temperature field, microwave dielectric distribution, and particle motion coordination, an objective classification criterion is constructed. The specific classification rules are as follows: Superiority level determination criterion: Temperature gradient stability factor. Dielectric constant uniformity factor And particle motion coordination factor At that time, among them, ≤0.6、 ≤0.05、 ≥0.85, the above threshold was calibrated using 100 sets of standard homogeneous mixture samples and determined based on a 95% confidence interval, and was judged as excellent. This state indicates a uniform temperature field distribution with no high or low temperature difference zones, consistent distribution of regenerator and recycled material, and highly coordinated movement trajectories of recycled material particles; qualified level judgment condition: when and Any one of them exceeds the corresponding threshold but is ≤ ≤0.8, and When the dielectric constant is ≥0.7, it is judged as qualified. This state allows for local temperature gradients and slight dielectric fluctuations, and the agglomeration of old materials is within the dispersible range; the condition for judging inferior grade is: when > , > >0.15、 If any condition is met, the grade is determined to be inferior. This state corresponds to a significant temperature anomaly, severe segregation of the regenerator, or large-scale agglomeration of the old material. Based on the grading results, a process optimization instruction is triggered. For the superior grade, the current parameters are maintained and the characteristic data of this batch is recorded for threshold self-learning. For the qualified grade, the stirring time needs to be extended to the preset extension time. And monitor in real time during the extension period. , If the rate of change is deemed poor, production should be immediately suspended and an alarm code generated. Based on the abnormal positioning results, the spatial coordinates of the regenerator nozzle or the injection pressure should be adjusted.

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