Mixing uniformity monitoring method adaptive to recycled asphalt mixture

Abstract

The invention relates to the technical field of recycled asphalt mixture recovery, in particular to a mixing uniformity monitoring method adaptive to a recycled asphalt mixture, which comprises the following steps: S101, performing time-space fusion on infrared thermal radiation field dynamic capture and microwave dielectric property synchronous monitoring bimodal sensing to jointly perform mixing defect alarm; s102, based on the dynamic sequence of the infrared thermal radiation field, judging the particle motion consistency through the standard deviation of the motion direction angle, integrating the infrared temperature field and microwave dielectric data, constructing a three-dimensional feature vector, comprehensively evaluating the uniformity through a weighting function, judging defects according to the maximum contribution term, and generating an optimization instruction; and S103, setting a grading standard based on the multi-modal features, and feeding back and adjusting the process according to a grading result. According to the invention, infrared thermal radiation and microwave dielectric characteristics are combined, temperature uniformity and material component distribution are evaluated synchronously, and the problem of monitoring that the thermal uniformity of the recycled asphalt is not equal to the component uniformity is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of recycled asphalt mixture recovery, and in particular to a mixing uniformity monitoring method suitable for recycled asphalt mixture. Background Art

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

[0003] Due to long-term aging, old asphalt mortar contains a large amount of non-volatile components, and its viscosity is very different from that of new asphalt. When the two are mixed, they easily form an "island-like" distribution. Traditional indicators can only reflect the overall miscibility effect and cannot quantify the degree of local enrichment. The old asphalt film attached to the surface of the old aggregate in RAP will change its adhesion properties with the new aggregate, causing the old aggregate 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 has failed to establish a clear correlation with mechanical indicators such as the 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 aggregate and asphalt mortar in three-dimensional space. For example, the angular effect of coarse aggregate may aggravate the local enrichment of mortar in three-dimensional space, but two-dimensional evaluation is difficult to capture such characteristics. Summary of the Invention

[0004] The present invention aims to solve the technical problems existing in the prior art and provides a mixing uniformity monitoring method suitable for recycled asphalt mixture.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: A method for monitoring the mixing uniformity of recycled asphalt mixture, comprising the following steps: S101, using dual-modal sensing that dynamically captures infrared thermal radiation fields and simultaneously monitors microwave dielectric properties, integrating the two in time and space to jointly generate mixed defect alarms; S102: Based on the dynamic sequence of the infrared thermal radiation field, the particle motion consistency is determined 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 using a weighted function. Defects are determined based on the maximum contribution term and optimization instructions are generated. S103. Establish grading standards based on multimodal features and adjust the process based on the grading result feedback.

[0006] In a preferred embodiment, in said S101, a medium-wave infrared thermal imager is fixedly installed at the center of the top of the mixing bin, and a polarization filter array is synchronously covered on the observation window. When the mixture is exposed to the observation window during the mixing process, a thermal radiation signal with a high signal-to-noise ratio is obtained by filtering out non-polarized metal reflected light and retaining the polarization component of the mixture's spontaneous radiation. Since the thermal emissivity of the old material is significantly higher than that of the new asphalt component due to the aged asphalt coating, an observable emissivity difference feature is formed. This physical property produces a detectable temperature difference during the heat conduction process. This temperature difference feature presents a temperature gradient field distribution in a dynamic mixing environment, wherein the area where the old material is united appears as an abnormal temperature area due to the difference in thermophysical properties. To optimize data acquisition efficiency, a time-series adaptive dynamic sampling strategy is adopted: in the early stage of mixing, a first high-frequency sampling frequency is used to capture the temperature mutation event when the old material is crushed. When the temperature gradient dispersion is detected to drop to a first preset level, the second sampling frequency is automatically switched to balance the processing load. The micro microwave sensor embedded in the stirring shaft blade works according to the dielectric resonance principle: when the blade sweeps the mixture, the difference in dielectric constants of new asphalt, old RAP material and regeneration agent causes the resonance frequency to shift. and the dielectric constant satisfies The functional relationship between the dielectric constant and the measured point is determined by the stirring shaft encoder, and a three-dimensional spatial distribution model of the dielectric constant can be constructed. By sharing the pulses of the stirring shaft encoder to achieve time synchronization, a timing benchmark for bimodal data association is formed. A unified three-dimensional grid is established with the stirring blade as the reference coordinate system. The infrared image pixels and microwave measurement points are mapped to the same spatial coordinate system. The non-uniformity index is established based on the spatiotemporal synchronization framework. The specific calculation formula is as follows: in, represents the unevenness index, 、 represents the weight coefficient, represents the standard deviation of infrared temperature gradient, represents the standard deviation of microwave dielectric, 、 The reference standard deviation is respectively Standard deviation of microwave dielectric When the comprehensive evaluation value exceeds the second preset threshold, a mixed defect alarm is triggered.

[0007] In a preferred embodiment, in said S102, based on the dynamic sequence of the infrared thermal radiation field, the temperature gradient mutation point is identified by a preset temperature difference threshold, and the boundary of the old material particles is calibrated. Since the friction coefficient of the old material surface is significantly higher than that of the new material due to the aging asphalt coating, its movement process presents unique dynamic characteristics: when the optical flow algorithm is used to calculate the motion trajectory of the particle group, the instantaneous velocity direction change rate of the old material particles is always lower than that of the new material particles. By statistically analyzing the standard deviation of the motion direction angle per unit time, the old material particles are significantly different from the new material particles. , the specific calculation formula is as follows: Among them, N represents the number of sampling points per unit time, represents the particle motion direction angle at the i-th sampling point, Represents the arithmetic mean of the Nth direction angle, when When the value is continuously lower than the second preset threshold, it is determined that the movement direction of the particle group tends to be consistent. This state indicates that the stirring and shearing effect is sufficient and the mixing uniformity is improved. The specific calculation formula of the second preset threshold is as follows: in, represents the mean value of a standard homogeneous mixture, represents the standard deviation of the standard uniform mixture, and k represents the safety factor; By fusing the infrared temperature field and microwave dielectric data within a spatiotemporally registered 3D grid, the following 3D eigenvectors are constructed, including the temperature gradient stability factor, the dielectric constant consistency factor, and the particle motion synergy factor: Temperature gradient stability factor: Calculate the spatial dispersion of the distribution of high and low temperature areas within the grid unit. The value is the ratio of the standard deviation of the infrared temperature gradient to the reference value. The lower the ratio, the more uniform the heat exchange between the new and old materials. The specific calculation formula of the temperature gradient stability factor is as follows: in, represents the temperature gradient stability factor; Dielectric constant consistency factor: The dielectric constant data set measured by the microwave sensor is extracted. The ratio of the microwave dielectric standard deviation of the data in the three-dimensional grid to the reference value is used as a quantitative indicator. This directly correlates to the distribution uniformity of the regeneration agent on the surface of the old material. The specific calculation formula of the dielectric constant consistency factor is as follows: in, represents the dielectric constant consistency factor; Particle motion synergy factor: standard deviation of motion direction angle Based on the directional consistency index, the specific calculation formula of the particle motion synergy factor is as follows: in, represents the particle motion synergy factor, represents the standard deviation of the maximum allowed direction angle, When it approaches 1, it indicates that the stirring shear force is most effective in mixing materials; Construct a weighted evaluation function of the three-dimensional feature vector to calculate the uniformity index. The specific calculation formula is as follows: in, Represents the uniformity index, 、 、 Represents the preset weight coefficient, satisfying , when the function value U exceeds the third preset threshold, the defect type is determined by feature contribution. If is the largest contribution item, and it is determined that the heat exchange between new and old materials is insufficient. If is the largest contribution item, and it is determined that the regeneration agent is unevenly distributed. If is the largest contribution term, and the stirring shear force is judged to be insufficient; In a preferred embodiment, in S103, an objective classification basis is constructed based on the multimodal characteristics of infrared temperature field, microwave dielectric distribution and particle motion synergy. The specific classification rules are as follows: Excellent judgment condition: temperature gradient stability factor Dielectric constant consistency factor Particle Motion Synergistic Factor , it is judged as excellent. This state indicates that the temperature field is evenly distributed without high and low temperature difference areas, the distribution of regenerated agent and old material is consistent, and the movement trajectory of old material particles is highly coordinated. and Any one of the above exceeds the corresponding threshold but ≤ ,and It is judged as qualified. This state allows local temperature gradient and slight dielectric fluctuation. The agglomeration of old materials is in the dispersible range. 、 When any of the conditions are met, it is judged as inferior. This state corresponds to significant temperature anomaly areas, severe segregation of regenerated agents or large-scale agglomeration of old materials. The process optimization instruction is triggered according to the classification results. The superior grade maintains the current parameters and records the characteristic data of the batch for threshold self-learning. The qualified grade needs to extend the stirring time to the preset extension time. , and monitor in real time during the extension period 、 The rate of change, when it is at a bad level, it will immediately suspend generation and generate an alarm code, and adjust the spatial coordinates or injection pressure of the regeneration agent nozzle based on the abnormal positioning results.

[0008] The beneficial effects of the present invention are: the present invention combines infrared thermal radiation and microwave dielectric properties to simultaneously evaluate temperature uniformity and material component distribution, solves the monitoring problem that thermal uniformity of recycled asphalt does not mean component uniformity, and targets the problems of large friction resistance and special motion characteristics of RAP particles. The present invention optimizes the particle motion tracking algorithm, improves the sensitivity of agglomeration identification, and directly links the uniformity evaluation results to the adjustment of stirring parameters to realize automated monitoring, analysis, and control, thereby improving the efficiency of traditional manual detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a flow chart of the present invention; Figure 2 This is the uniformity detection logic diagram of the present invention. DETAILED DESCRIPTION

[0010] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0011] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0012] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". 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 given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0013] like Figure 1 This embodiment provides: a method for monitoring the mixing uniformity of recycled asphalt mixture, comprising the following steps: S101, using dual-modal sensing that dynamically captures infrared thermal radiation fields and simultaneously monitors microwave dielectric properties, integrating the two in time and space to jointly generate mixed defect alarms; Furthermore, a medium-wave infrared thermal imager is fixedly installed at the center of the top of the mixing chamber, and a polarization filter array is synchronously 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 mixture's spontaneous radiation is retained to obtain a high signal-to-noise ratio thermal radiation signal. Since the thermal emissivity of the old material is significantly higher than that of the new asphalt component due to the aging asphalt coating, an observable emissivity difference characteristic is formed. This physical property produces a detectable temperature difference during the heat conduction process. This temperature difference characteristic presents a temperature gradient field distribution in a dynamic mixing environment, where the area where the old material is concentrated appears as an abnormal temperature area due to the difference in thermophysical properties. To optimize data acquisition efficiency, a time-series adaptive dynamic sampling strategy is adopted: in the early stage of mixing, a first high-frequency sampling frequency is used to capture the temperature mutation event when the old material is crushed. When the temperature gradient dispersion is detected to drop to a first preset level, the second sampling frequency is automatically switched to balance the processing load. The micro microwave sensor embedded in the stirring shaft blade works according to the dielectric resonance principle: when the blade sweeps the mixture, the difference in dielectric constants of new asphalt, old RAP material and regeneration agent causes the resonance frequency to shift. and the dielectric constant satisfies The functional relationship between the blade rotation angle encoder and the measuring point is used to locate the measurement point, and a three-dimensional spatial distribution model of the dielectric constant can be constructed; By sharing the pulses of the stirring shaft encoder to achieve time synchronization, a timing benchmark for bimodal data association is formed. A unified three-dimensional grid is established with the stirring blade as the reference coordinate system. The infrared image pixels and microwave measurement points are mapped to the same spatial coordinate system. The non-uniformity index is established based on the spatiotemporal synchronization framework. The specific calculation formula is as follows: in, represents the unevenness index, 、 represents the weight coefficient, represents the standard deviation of infrared temperature gradient, represents the standard deviation of microwave dielectric, 、 The reference standard deviation is respectively Standard deviation of microwave dielectric When the comprehensive evaluation value exceeds the second preset threshold, a mixed defect alarm is triggered.

[0014] It should be noted that the second preset threshold is a reference value for comparison with the UI calculated in real time. When the UI exceeds the threshold, an alarm is triggered to indicate a hybrid defect.

[0015] It should be noted that the spatiotemporal synchronization framework includes a temporal benchmark for bimodal data association and a unified three-dimensional grid; It should be noted that the temperature mutation time is defined as the instantaneous temperature change exceeding the preset temperature difference threshold. After the equipment is installed, the stirring chamber is preheated to 160°C, and a standard specimen with a known RAP content of 50% and a regeneration agent dosage of 3% is placed. The infrared thermal imager is calibrated to ensure that the temperature measurement error does not exceed 1.5°C. A micro microwave sensor is simultaneously embedded in the stirring shaft blade. The dielectric constant measurement error is verified to be ≤3% using standard specimens with different RAP contents, completing the cross-calibration of multiple sensors. When the recycled asphalt mixture is officially put into mixing, within 0-10 minutes after the mixing begins, the infrared thermal imager is activated in a high-frequency sampling mode of 5 frames / second to track the temperature mutation during the agglomeration and breakup of the old material in real time. During this stage, when the old material agglomerates are broken, the new asphalt inside will produce local temperature changes when it comes into contact with the air. High-frequency sampling can capture such transient characteristics. After 10 minutes of mixing, when the temperature field distribution is observed to be stable, the sampling frequency is reduced to 2 frames / second, ensuring real-time performance while reducing the amount of data storage. S102: Based on the dynamic sequence of the infrared thermal radiation field, the particle motion consistency is determined 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 using a weighted function. Defects are determined based on the maximum contribution term and optimization instructions are generated. Furthermore, based on the dynamic sequence of infrared thermal radiation field, the temperature gradient mutation point is identified by presetting the temperature difference threshold, and the boundary of the old material particles is calibrated. Due to the aging asphalt coating on the surface of the old material, the friction coefficient is significantly higher than that of the new material, and its movement process presents unique dynamic characteristics: when the optical flow algorithm is used to calculate the motion trajectory of the particle group, the instantaneous velocity direction change rate of the old material particles is always lower than that of the new material particles. By statistically analyzing the standard deviation of the motion direction angle per unit time, the old material particles are more sensitive to the change of the direction of the old material. , the specific calculation formula is as follows: Among them, N represents the number of sampling points per unit time, represents the particle motion direction angle at the i-th sampling point, Represents the arithmetic mean of the Nth direction angle, when When the value is continuously lower than the second preset threshold, it is determined that the movement direction of the particle group tends to be consistent. This state indicates that the stirring and shearing effect is sufficient and the mixing uniformity is improved. The specific calculation formula of the second preset threshold is as follows: in, represents the mean value of a standard homogeneous mixture, represents the standard deviation of the standard uniform mixture, and k represents the safety factor; By fusing the infrared temperature field and microwave dielectric data within a spatiotemporally registered 3D grid, the following 3D eigenvectors are constructed, including the temperature gradient stability factor, the dielectric constant consistency factor, and the particle motion synergy factor: Temperature gradient stability factor: Calculate the spatial dispersion of the distribution of high and low temperature areas within the grid unit. The value is the ratio of the standard deviation of the infrared temperature gradient to the reference value. The lower the ratio, the more uniform the heat exchange between the new and old materials. The specific calculation formula of the temperature gradient stability factor is as follows: in, represents the temperature gradient stability factor; Dielectric constant consistency factor: The dielectric constant data set measured by the microwave sensor is extracted. The ratio of the microwave dielectric standard deviation of the data in the three-dimensional grid to the reference value is used as a quantitative indicator. This directly correlates to the distribution uniformity of the regeneration agent on the surface of the old material. The specific calculation formula of the dielectric constant consistency factor is as follows: in, represents the dielectric constant consistency factor; Particle motion synergy factor: standard deviation of motion direction angle Based on the directional consistency index, the specific calculation formula of the particle motion synergy factor is as follows: in, represents the particle motion synergy factor, represents the standard deviation of the maximum allowed direction angle, When it approaches 1, it indicates that the stirring shear force is most effective in mixing materials; Construct a weighted evaluation function of the three-dimensional feature vector to calculate the uniformity index. The specific calculation formula is as follows: in, Represents the uniformity index, 、 、 Represents the preset weight coefficient, satisfying , when the function value U exceeds the third preset threshold, the defect type is determined by feature contribution. If is the largest contribution item, and it is determined that the heat exchange between new and old materials is insufficient. If is the largest contribution item, and it is determined that the regeneration agent is unevenly distributed. If The maximum contribution item is used to determine that the stirring shear force is insufficient, and process optimization instructions are generated accordingly, such as extending the stirring time, adjusting the regeneration agent injection position, or increasing the stirring blade speed; It should be noted that the third preset threshold is set by preparing multiple batches of recycled asphalt mixture samples that meet the uniformity standards under laboratory conditions and by strictly controlling the mixing process, namely standard uniform samples. For each standard uniform sample, the uniformity index U is calculated according to the aforementioned weighted evaluation function to form a data set. The U values ​​of all standard uniform samples are sorted from small to large, and the 95% quantile is taken as the threshold candidate. For example, if there are 100 samples, the U value of the 95th sample after sorting is the 95% quantile. The 95% quantile means that among the standard uniform samples, only 5% of the sample U values ​​may exceed the threshold, thereby ensuring that the threshold The representativeness of the value to the uniform state is avoided to avoid misjudgment. The threshold candidate value determined in the laboratory is applied to the on-site production and compared with the actual mixing effect. If frequent misjudgments or missed judgments occur, the quantile ratio needs to be adjusted, such as to 90% or 98%. As production data accumulates, the historical data set is updated regularly and the quantile is recalculated to ensure that the threshold adapts to raw material changes and process improvements and maintains the timeliness of the judgment standard. When the third preset threshold value = 0.7, its essence is to define the boundary between uniform and non-uniform through statistical methods. When the U value of the on-site sample exceeds 0.7, it indicates that its uniformity is lower than the standard sample level of 95%, and process optimization needs to be triggered.

[0016] 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 bin. After the samples are pre-processed, a high-precision CT scanner is used to perform a three-dimensional scan of the mixture. A three-dimensional model of the spatial distribution of aggregates is generated through image reconstruction technology. During the scanning process, metal artifacts are eliminated through parameter optimization to ensure that the model can clearly distinguish between old materials, new asphalt, regeneration agent voids and other components. In the three-dimensional model, all old material aggregation areas are identified and marked through image segmentation technology. The system automatically calculates the volume of each aggregation area and counts the total volume proportion of old materials in the sample. In order to quantify the uniformity of the old material distribution, the volume proportion of old materials at each slice level is further analyzed, and the degree of discreteness is evaluated by statistical methods. The degree of discreteness reflects the uniformity of the distribution of old materials in the mixture: the lower the degree of discreteness, the more uniform the old material mixture, and vice versa, there is aggregation.

[0017] Based on the statistical data of multiple batches of samples, a linear correlation model is established between the uniformity evaluation function value and the discrete degree of the old material distribution. The model is trained through historical data to reflect the evaluation function's ability to characterize 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 prediction value and the actual detection value, the model's adaptability to the current working conditions is evaluated.

[0018] When the model prediction deviation continues to exceed the preset threshold, it is determined that the current uniformity evaluation model deviates from the actual operating conditions, triggering the automatic optimization procedure of the weight coefficient. The optimization algorithm uses the current detection data as a constraint. While keeping the sum of the weight coefficients at 1, it adjusts the weight ratio of each characteristic factor through iterative calculation so that the evaluation function can more accurately reflect the actual mixing state. This mechanism can adapt to changes in operating conditions such as the degree of RAP aging and the type of regenerant on site to ensure the long-term reliability of the monitoring system.

[0019] S103. Establishing grading standards based on multimodal features and adjusting the process based on grading result feedback; Furthermore, based on the multimodal characteristics of infrared temperature field, microwave dielectric distribution and particle motion coordination, an objective classification basis is constructed. The specific classification rules are as follows: Excellent judgment condition: temperature gradient stability factor Dielectric constant consistency factor Particle Motion Synergistic Factor , it is judged as excellent. This state indicates that the temperature field is evenly distributed without high and low temperature difference areas, the distribution of regenerated agent and old material is consistent, and the movement trajectory of old material particles is highly coordinated. and Any one of the above exceeds the corresponding threshold but ≤ ,and It is judged as qualified. This state allows local temperature gradient and slight dielectric fluctuation. The agglomeration of old materials is in the dispersible range. 、 When any of the conditions are met, it is judged as inferior. This state corresponds to significant temperature anomaly areas, severe segregation of regenerated agents or large-scale agglomeration of old materials. The process optimization instruction is triggered according to the classification results. The superior grade maintains the current parameters and records the characteristic data of the batch for threshold self-learning. The qualified grade needs to extend the stirring time to the preset extension time. , and monitor in real time during the extension period 、 The rate of change, when it is at a bad level, it will immediately suspend generation and generate an alarm code, and adjust the spatial coordinates or injection pressure of the regeneration agent nozzle based on the abnormal positioning results.

[0020] It should be noted that The calibration is carried out by selecting 100 groups of uniform recycled material samples, collecting infrared temperature field data, calculating the ratio of the temperature gradient standard deviation to the mean of each group of samples, 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 excellent grade judgment, and verifying it through critical experiments. =0.8, the performance of the recycled material began to decline significantly, so it was determined as the inferior trigger threshold. Microwave dielectric detection equipment was used to scan samples with different regeneration agent spraying amounts, and the dielectric constant fluctuation range was recorded. The dielectric fluctuation variation coefficient corresponding to the uniform distribution of the regeneration agent was ≤5%. The best matching point between this value and the dispersion of the regeneration agent was determined through orthogonal experiments. When the dielectric constant variation coefficient was greater than 15%, the segregation of the regeneration agent caused the dielectric signal to be abnormal, so it was used as the inferior judgment standard. The movement trajectory of the old material particles was tracked by high-speed video, the particle movement synergy factor was calculated, and the performance indicators of the recycled material after compaction were simultaneously detected. When the particle movement synergy factor was ≥0.85( ≥0.85), the particles are fully stirred and sheared, and the performance compliance rate exceeds 90%, which is determined as the lower limit of the excellent grade. If the particle movement synergy factor is less than 0.7 ( <0.7), the probability of particle agglomeration increases significantly and forced interference is required, so it is used as the dividing line between qualified and inferior grades.

[0021] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0022] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0023] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0024] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0025] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0026] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0027] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for monitoring the mixing uniformity of recycled asphalt mixture, characterized in that: The following steps are involved: S101, using dual-modal sensing that dynamically captures infrared thermal radiation fields and simultaneously monitors microwave dielectric properties, integrating the two in time and space to jointly generate mixed defect alarms; S102: Based on the dynamic sequence of the infrared thermal radiation field, the particle motion consistency is determined 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 using a weighted function. Defects are determined based on the maximum contribution term and optimization instructions are generated. S103. Establish grading standards based on multimodal features and adjust the process based on the grading result feedback.

2. A method for monitoring mixing uniformity of recycled asphalt mixture according to claim 1, characterized in that: In the S101, a medium-wave infrared thermal imager is fixedly installed at the center of the top of the mixing bin, and a polarization filter array is synchronously 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 mixture's spontaneous radiation. Since the thermal emissivity of the old material is significantly higher than that of the new asphalt component due to the aging asphalt coating, an observable emissivity difference feature is formed. This physical property produces a detectable temperature difference during the heat conduction process. This temperature difference feature presents a temperature gradient field distribution in a dynamic mixing environment, wherein the area where the old material is united appears as an abnormal temperature area due to the difference in thermophysical properties.

3. The method for monitoring mixing uniformity of recycled asphalt mixture according to claim 1, characterized in that: The micro microwave sensor embedded in the stirring shaft blade works according to the dielectric resonance principle: when the blade sweeps the mixture, the difference in dielectric constants of new asphalt, old RAP material and regeneration agent causes the resonance frequency to shift. and the dielectric constant satisfies The functional relationship between the dielectric constant and the measured point is determined by the stirring shaft encoder, and a three-dimensional spatial distribution model of the dielectric constant can be constructed.

4. A method for monitoring mixing uniformity of recycled asphalt mixture according to claim 3, characterized in that: By sharing the pulses of the stirring shaft encoder to achieve time synchronization, a timing benchmark for bimodal data association is formed. A unified three-dimensional grid is established with the stirring blade as the reference coordinate system. The infrared image pixels and microwave measurement points are mapped to the same spatial coordinate system. The non-uniformity index is established based on the spatiotemporal synchronization framework. The specific calculation formula is as follows: in, represents the unevenness index, 、 represents the weight coefficient, represents the standard deviation of infrared temperature gradient, represents the standard deviation of microwave dielectric, 、 The reference standard deviation is respectively Standard deviation of microwave dielectric When the comprehensive evaluation value exceeds the second preset threshold, a mixed defect alarm is triggered.

5. The method for monitoring mixing uniformity of recycled asphalt mixture according to claim 1, characterized in that: In the above S102, based on the dynamic sequence of infrared thermal radiation field, the temperature gradient mutation point is identified by the preset temperature difference threshold, and the boundary of the old material particles is calibrated. Since the friction coefficient of the old material surface is significantly higher than that of the new material due to the aging asphalt coating, its movement process presents unique dynamic characteristics: when the optical flow algorithm is used to calculate the movement trajectory of the particle group, the instantaneous velocity direction change rate of the old material particles is always lower than that of the new material particles. By statistically analyzing the standard deviation of the movement direction angle per unit time, the old material particles are significantly different from the new material particles. , the specific calculation formula is as follows: Among them, N represents the number of sampling points per unit time, represents the particle motion direction angle at the i-th sampling point, Represents the arithmetic mean of the Nth direction angle, when When the value is continuously lower than the second preset threshold, it is determined that the movement direction of the particle group tends to be consistent, which indicates that the stirring and shearing effect is sufficient and the mixing uniformity is improved.

6. The method for monitoring mixing uniformity of recycled asphalt mixture according to claim 1, characterized in that: The infrared temperature field and microwave dielectric data are fused within a spatiotemporally registered 3D grid to establish the following 3D eigenvectors, including the temperature gradient stability factor, the dielectric constant consistency factor, and the particle motion synergy factor.

7. A method for monitoring mixing uniformity of recycled asphalt mixture according to claim 6, characterized in that: Temperature gradient stability factor: Calculates the spatial dispersion of the distribution of high and low temperature areas within the grid unit. Its value is the ratio of the standard deviation of the infrared temperature gradient to the reference value. The lower the ratio, the more uniform the heat exchange between the new and old materials. Dielectric constant consistency factor: Extract the dielectric constant data set measured by the microwave sensor, and use the ratio of the microwave dielectric standard deviation of the data in the three-dimensional grid to the reference value as a quantitative indicator, which is directly related to the distribution uniformity of the regeneration agent on the old material surface; Particle motion synergy factor: standard deviation of motion direction angle Based on the directional consistency index, the specific calculation formula of the particle motion synergy factor is as follows: in, represents the particle motion synergy factor, represents the standard deviation of the maximum allowed direction angle, When it approaches 1, it indicates that the stirring shear force is most effective in mixing materials.

8. A method for monitoring mixing uniformity of recycled asphalt mixture according to claim 7, characterized in that: Construct a weighted evaluation function of the three-dimensional feature vector to calculate the uniformity index. The specific calculation formula is as follows: in, Represents the uniformity index, 、 、 Represents the preset weight coefficient, satisfying , when the function value U exceeds the third preset threshold, the defect type is determined by feature contribution. If is the largest contribution item, and it is determined that the heat exchange between new and old materials is insufficient. If is the largest contribution item, and it is determined that the regeneration agent is unevenly distributed. If is the largest contribution item, and it is determined that the stirring shear force is insufficient.

9. The method for monitoring mixing uniformity of recycled asphalt mixture 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 basis is established. The specific classification rules are as follows: Excellent judgment condition: temperature gradient stability factor Dielectric constant consistency factor Particle Motion Synergistic Factor , it is judged as excellent. This state indicates that the temperature field is evenly distributed without high and low temperature difference areas, the distribution of regenerated agent and old material is consistent, and the movement trajectory of old material particles is highly coordinated. and Any one of the above exceeds the corresponding threshold but ≤ ,and It is judged as qualified. This state allows local temperature gradient and slight dielectric fluctuation. The agglomeration of old materials is in the dispersible range. 、 When any of the conditions are met, it is judged as inferior. This state corresponds to significant temperature anomaly areas, severe segregation of regenerated agents or large-scale agglomeration of old materials. The process optimization instruction is triggered according to the classification results. The superior grade maintains the current parameters and records the characteristic data of the batch for threshold self-learning. The qualified grade needs to extend the stirring time to the preset extension time. , and monitor in real time during the extension period 、 The rate of change, when it is at a bad level, it will immediately suspend generation and generate an alarm code, and adjust the spatial coordinates or injection pressure of the regeneration agent nozzle based on the abnormal positioning results.

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