A method and system for controlling production temperature of a micro rut asphalt mixture

Abstract

The application discloses a kind of micro rut asphalt mixture production temperature control method and system, it is related to material production technical field, method includes: establishing the signal reference system of infrared probe under normal condition, the signal reference system includes average intensity reference value and fluctuation range reference value;The average intensity and fluctuation characteristics of current infrared probe signal are obtained;According to the average intensity reference value and the fluctuation range reference value, the average intensity and the fluctuation characteristics are threshold judged, and a judgment result is obtained;If the judgment result is water vapor interference, then according to the fluctuation characteristics, calculate temperature correction amount;According to the temperature correction amount, the current production temperature is corrected.The application can identify water vapor interference and correct production temperature by combining the average intensity and fluctuation characteristics of signal, to realize production temperature control, improve accuracy.

Description

Technical Field

[0001] This invention relates to the field of material production technology, and in particular to a method and system for controlling the temperature of micro-rutting asphalt mixture production. Background Technology

[0002] In modern asphalt mixing plants, existing methods for mixing asphalt mixtures with minor rutting involve using infrared sensors installed inside the mixing drum to capture thermal images. These raw thermal images are then converted into images that accurately reflect the temperature distribution within the material space, allowing for monitoring of temperature changes during the production process. However, asphalt mixing plants face dynamic production demands in actual operation. When producing large batches of asphalt concrete for road surfaces, the aggregates are typically stockpiled in open yards. If heavy rainfall occurs the night before or during production, these exposed aggregates absorb significant amounts of moisture, resulting in an overall moisture content significantly higher than normal in dry weather. The high temperatures generated by the burners inside the mixing drum rapidly heat and evaporate the excess moisture in the aggregates, creating a high-temperature water vapor cloud with a concentration far exceeding normal production levels. This vapor absorbs the infrared energy emitted by the high-temperature aggregate cloud, causing a significant attenuation of the signal received by the sensors, making precise temperature control difficult and resulting in low temperature control accuracy.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this invention is to propose a method and system for controlling the production temperature of micro-rutting asphalt mixtures. This method can identify water vapor interference and correct the production temperature by combining the average intensity and fluctuation characteristics of the signal, thereby achieving production temperature control and improving accuracy.

[0005] On one hand, embodiments of the present invention provide a method for controlling the production temperature of micro-rutting asphalt mixtures, comprising the following steps:

[0006] Establish a signal reference system for the infrared probe under normal operating conditions, wherein the signal reference system includes an average intensity reference value and a fluctuation range reference value;

[0007] Obtain the average intensity and fluctuation characteristics of the current infrared probe signal;

[0008] Based on the average intensity reference value and the fluctuation range reference value, threshold judgments are made on the average intensity and the fluctuation characteristics to obtain the judgment result;

[0009] If the judgment result indicates the presence of water vapor interference, then the temperature correction amount is calculated based on the fluctuation characteristics.

[0010] The current production temperature is corrected based on the stated temperature correction amount.

[0011] In some embodiments, establishing a signal reference system for the infrared probe under normal operating conditions includes:

[0012] Determine the initial steady-state window at the start of asphalt mixture production;

[0013] Within the initial stable state window, raw signal strength data is acquired using the infrared probe;

[0014] Based on the original signal strength data, the average value is calculated as the average strength reference value;

[0015] Based on the original signal strength data, the standard deviation is calculated as a reference value for the fluctuation range.

[0016] In some embodiments, the step of performing a threshold judgment on the average intensity and the fluctuation characteristic based on the average intensity reference value and the fluctuation range reference value to obtain a judgment result includes:

[0017] If the average intensity is less than the average intensity reference value, then it is determined whether the fluctuation characteristic is less than the fluctuation range reference value;

[0018] If the fluctuation characteristic is less than the fluctuation range reference value, then the judgment result is determined to be the presence of fog-like medium interference;

[0019] If the fluctuation characteristic is greater than the fluctuation range reference value, then the moisture content of the material in the mixing drum is obtained;

[0020] If the moisture content of the material is greater than the preset moisture content threshold, then the judgment result is determined to be that water vapor interference exists;

[0021] If the moisture content of the material is less than the preset moisture content threshold, then the judgment result is determined to be due to interference from other factors.

[0022] In some embodiments, determining that water vapor interference exists if the moisture content of the material is greater than a preset moisture content threshold includes:

[0023] Obtain information on material type, internal ambient temperature of the mixing drum, and aggregate particle size distribution;

[0024] The preset moisture content threshold is adjusted based on the material type information, the internal ambient temperature information of the mixing drum, and the aggregate particle size distribution information.

[0025] The moisture content of the material is compared with the adjusted preset moisture content threshold.

[0026] If the moisture content of the material is greater than the adjusted preset moisture content threshold, then the judgment result is determined to be the presence of water vapor interference.

[0027] In some embodiments, determining that water vapor interference exists if the moisture content of the material is greater than the adjusted preset moisture content threshold includes:

[0028] If the moisture content of the material is greater than the adjusted preset moisture content threshold, then the trend of the change in the moisture content of the material is monitored.

[0029] If the moisture content of the material changes in an upward trend, then the magnitude of the change in the average strength and fluctuation characteristics is determined.

[0030] If the change in the average intensity increases and the change in the fluctuation characteristics increases, then the judgment result is determined to be the presence of water vapor interference.

[0031] In some embodiments, determining that water vapor interference exists if both the average intensity variation amplitude and the fluctuation characteristic variation amplitude increase include:

[0032] Acquire the self-diagnostic status information and historical operating data of the infrared probe;

[0033] The operating status of the infrared probe is analyzed based on the self-diagnostic status information and the historical operating data.

[0034] If the operating status is aging or faulty, then the current infrared probe signal is corrected;

[0035] Based on the corrected current infrared probe signal, update the change range of the average intensity and the change range of the fluctuation characteristics;

[0036] If the change in the updated average intensity increases and the change in the updated fluctuation characteristics increases, then the judgment result is determined to be the presence of water vapor interference.

[0037] In some embodiments, determining that water vapor interference exists if both the average intensity variation amplitude and the fluctuation characteristic variation amplitude increase include:

[0038] Obtain production load information, asphalt viscosity information, and aggregate gradation information of asphalt mixtures;

[0039] Based on the production load information, the asphalt viscosity information, and the aggregate gradation information, a target change threshold is determined, which includes a material moisture content change threshold, an average strength change threshold, and a fluctuation characteristic change threshold.

[0040] Calculate the instantaneous rate of change of the material's moisture content based on the material's moisture content;

[0041] Calculate the instantaneous rate of change of the average intensity based on the average intensity;

[0042] Calculate the instantaneous rate of change of the fluctuation characteristics based on the fluctuation characteristics;

[0043] If the instantaneous rate of change of the material's moisture content is greater than the material's moisture content change threshold, the instantaneous rate of change of the average strength is greater than the average strength change threshold, and the instantaneous rate of change of the fluctuation characteristic is greater than the fluctuation characteristic change threshold, then the judgment result is determined to be the presence of water vapor interference.

[0044] In some embodiments, the method further includes:

[0045] If the judgment result indicates the presence of mist-like medium interference, then obtain the reference fuel supply rate and reference temperature data of the burner under normal operating conditions;

[0046] Calculate the reference energy input power based on the reference fuel supply rate and the calorific value of the dye;

[0047] Calculate the rate of change of the reference temperature based on the preset sliding window and the reference temperature data;

[0048] A power-temperature rise relationship curve is constructed based on the multiple reference energy input powers and the multiple reference temperature rise rates.

[0049] The current production temperature is controlled based on the power-temperature rise relationship curve.

[0050] In some embodiments, controlling the current production temperature based on the power-temperature rise relationship curve includes:

[0051] Obtain current fuel supply rate and current temperature data;

[0052] Calculate the current energy input power based on the current fuel supply rate and the calorific value of the dye;

[0053] Calculate the rate of change of the current temperature rise based on the preset sliding window and the current temperature data;

[0054] Extract the rate of temperature rise corresponding to the current energy input power from the power-temperature rise relationship curve as the target rate of temperature rise.

[0055] If the current temperature rise rate is less than the target temperature rise rate, the burner power is reduced and a temperature anomaly alarm is issued.

[0056] On the other hand, embodiments of the present invention provide a temperature control system for the production of micro-rutting asphalt mixtures, comprising:

[0057] The reference system establishment module is used to establish a signal reference system for the infrared probe under normal operating conditions. The signal reference system includes an average intensity reference value and a fluctuation range reference value.

[0058] The data acquisition module is used to acquire the average intensity and fluctuation characteristics of the current infrared probe signal;

[0059] The judgment module is used to perform threshold judgment on the average intensity and the fluctuation characteristics based on the average intensity reference value and the fluctuation range reference value, and obtain the judgment result;

[0060] The correction calculation module is used to calculate the temperature correction amount based on the fluctuation characteristics if the judgment result indicates the presence of water vapor interference.

[0061] The temperature correction module is used to correct the current production temperature according to the temperature correction amount.

[0062] The embodiments of this application include at least the following beneficial effects: First, the embodiment of this application establishes a signal reference system for the infrared probe under normal operating conditions. Then, it obtains the average intensity and fluctuation characteristics of the current infrared probe signal. Next, based on the average intensity reference value and the fluctuation range reference value, it performs threshold judgment on the average intensity and fluctuation characteristics to obtain the judgment result. If the judgment result indicates the presence of water vapor interference, it calculates the temperature correction amount based on the fluctuation characteristics. Finally, it corrects the current production temperature based on the temperature correction amount. This enables the identification of water vapor interference and correction of the production temperature by combining the average intensity and fluctuation characteristics of the signal, thereby achieving production temperature control and improving accuracy.

[0063] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a flowchart of a method for controlling the production temperature of micro-rutting asphalt mixture according to an embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of a temperature control system for producing micro-rutted asphalt mixture according to an embodiment of the present invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0068] In related technologies, modern asphalt mixing plants, especially those focused on producing high-performance pavement materials, typically employ highly precise production control systems. For example, in manufacturing micro-rutting asphalt mixtures with extremely stringent temperature control requirements, the core temperature control strategy often relies on an advanced computational framework. This framework processes thermal images captured in real-time by infrared sensors installed inside the mixing drum. As aggregates and asphalt tumble and fall within the drum, they form a continuous, dynamic "material curtain." The infrared sensors are precisely calibrated to capture the heat radiated by this curtain. Subsequently, this complex computational framework, through a series of precise calculations and pre-established calibration relationships, transforms this raw thermal image data into an image that accurately reflects the temperature distribution within the material space of the mixing drum. This temperature distribution map is crucial because it not only provides information on the average temperature of the material but also reveals temperature differences in localized areas of the material flow, which is indispensable for ensuring the uniform heating effect required for micro-rutting mixtures. The entire system, including its sensors, data processing logic, and control methods, has been meticulously optimized and calibrated to address the unique thermal conductivity and mixing behavior of micro-rut asphalt mixtures.

[0069] However, the actual operating environment of an asphalt mixing plant is characterized by dynamically changing production demands. Depending on the specific requirements of the project and the availability of materials, the production line frequently needs to switch between different types of asphalt mixtures. This is a common and perfectly normal production scheduling behavior, not a sudden or unusual decision. For example, when the production task shifts from high-precision rutting mixtures to producing larger batches of more conventional pavement asphalt concrete, even though the mixing plant's control system is advanced, it will still operate based on the parameters and assumptions set for rutting mixtures, which may not be optimal for conventional mixtures.

[0070] During this production switchover, a common challenge is closely related to the characteristics of the raw materials used in conventional asphalt mixtures. Aggregates (stone) used in ordinary road asphalt mixtures are typically stockpiled in open yards. If heavy rainfall occurs the night before or during production, these exposed aggregate piles absorb significant amounts of moisture, resulting in an overall moisture content significantly higher than normal in dry weather. This high moisture content is a common but easily overlooked environmental variable in asphalt production, especially when production scheduling is tight and efficiency is paramount. Operators may not be able to accurately measure the moisture content of all incoming aggregates in real time. While some mixing plants may have preliminary moisture content detection devices at the aggregate inlet, their accuracy and feedback speed may be insufficient to capture subtle changes in moisture distribution within large quantities of material, and it is also difficult to fully quantify their potential impact on subsequent heating processes.

[0071] When aggregates with moisture content far exceeding normal standards are fed into a high-temperature mixing drum, a crucial physical phenomenon occurs. The high temperature generated by the burners inside the mixing drum rapidly heats the excess moisture in the aggregates, causing it to evaporate violently. This instantaneous and massive evaporation process creates a "high-temperature water vapor cloud" inside the mixing drum with a concentration far exceeding that of normal production conditions. This dense water vapor cloud happens to envelop the incandescent aggregate "curtain" between itself and the infrared probe used to measure the temperature. Since infrared radiation is essentially an electromagnetic wave, it undergoes significant absorption and scattering when passing through high-density water vapor. This means that a large portion of the infrared energy emitted from the high-temperature aggregate curtain is absorbed by water vapor molecules or scattered in other directions before reaching the infrared probe, resulting in a significant attenuation of the signal strength received by the probe.

[0072] At this point, the advanced computing framework, which forms the core of the temperature control system, was designed primarily to account for common interference factors within the mixing drum, such as normal levels of dust particles or small amounts of water vapor. However, its built-in calculation rules and compensation methods were not specifically calibrated or optimized for this extreme concentration of "water vapor cloud" caused by high-moisture aggregates. Therefore, when the infrared probe receives a severely attenuated and distorted infrared signal, the computing framework faithfully executes its preset logic. It incorrectly interprets the weakened signal strength as insufficient heat radiated by the material curtain itself, thus concluding that the material temperature is too low and generating an overall "cool" temperature distribution map.

[0073] Upon receiving this "too cold" temperature distribution map, the control program, based on its preset control logic, determines that the current material is being insufficiently heated. In response, the system automatically sends a command to the burner's fuel control valve, requesting a significant increase in the supply of natural gas or fuel oil. This operation aims to enhance flame intensity, hoping to quickly heat the material that the system "believes" is too cold to the set temperature required by the process. From the control logic's perspective, this is a perfectly reasonable and expected corrective action, but it is based on a fundamentally erroneous temperature information.

[0074] However, in reality, the aggregate in the mixing drum has already reached or even exceeded the ideal heating temperature. The burner, operating under incorrect instructions, continues to overheat, causing the aggregate to be severely charred. When this scalding hot aggregate, far exceeding the specification limits, is mixed with liquid asphalt, the high temperature instantly destroys the molecular structure of the asphalt—a process known as "thermal aging." Thermal aging significantly reduces the viscosity, elasticity, and adhesion of asphalt, resulting in a drastically reduced service life of the final paved road surface, and even premature damage. During this overheated mixing process, a clearly visible "blue smoke" will emerge from the mixing drum or discharge port, a clear indication that the asphalt has cracked or burned due to the instantaneous high temperature. The appearance of blue smoke means that the entire batch of asphalt mixture is completely unusable, causing not only direct economic losses of raw materials and energy but also environmental pollution problems.

[0075] Inside the asphalt mixing drum, when high-moisture aggregates generate high-concentration water vapor clouds under high temperature, which severely interferes with the infrared probe's accurate perception of the temperature distribution in the material "curtain" space, existing technologies struggle to effectively identify and compensate for this interference. This causes the system to overheat the burner based on erroneous temperature information, resulting in thermal aging and scrapping of the asphalt mixture, and low accuracy in production temperature control.

[0076] In view of this, this embodiment of the application achieves precise correction of production temperature by intelligently identifying and quantifying water vapor interference through in-depth analysis of infrared probe signals. When high-moisture aggregates enter the mixing drum, the generation of a large amount of water vapor will significantly affect the signal received by the infrared probe. This embodiment establishes a signal reference system under normal operating conditions, including an average intensity reference value and a fluctuation range reference value, providing a benchmark for judging signal anomalies. After obtaining the average intensity and fluctuation characteristics of the current infrared probe signal, a threshold judgment is made between it and the reference system. If the judgment result indicates the presence of water vapor interference (e.g., a significant decrease in average intensity and an abnormal increase in fluctuation characteristics), a precise temperature correction amount is calculated based on the intensity of the fluctuation characteristics. This correction amount can compensate for the absorption and scattering effects of water vapor on the infrared signal, so that the apparent temperature measured by the infrared probe can be corrected to a value closer to the true temperature of the material. Finally, based on the corrected temperature, the current production temperature is precisely adjusted, thereby avoiding overheating or underheating caused by water vapor interference, ensuring the production quality of asphalt mixtures, adaptively responding to water vapor interference in the production environment, and ensuring the stable production of micro-rutting asphalt mixtures.

[0077] The embodiments of this application will be explained in detail below with reference to the accompanying drawings:

[0078] Figure 1 This is an optional flowchart of a method for controlling the production temperature of micro-rutting asphalt mixtures provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0079] Step S101: Establish a signal reference system for the infrared probe under normal operating conditions. The signal reference system includes an average intensity reference value and a fluctuation range reference value.

[0080] Step S102: Obtain the average intensity and fluctuation characteristics of the current infrared probe signal;

[0081] Step S103: Based on the average intensity reference value and the fluctuation range reference value, perform threshold judgment on the average intensity and fluctuation characteristics to obtain the judgment result;

[0082] Step S104: If the judgment result indicates the presence of water vapor interference, calculate the temperature correction amount based on the fluctuation characteristics.

[0083] Step S105: Correct the current production temperature according to the temperature correction amount.

[0084] Steps S101 to S105 as shown in the embodiments of this application can identify water vapor interference and correct the production temperature by combining the average strength and fluctuation characteristics of the signal, so as to achieve production temperature control and improve accuracy.

[0085] In some embodiments, steps S101-S105 can first establish a signal reference system for the infrared probe under normal operating conditions. This signal reference system includes an average intensity reference value and a fluctuation range reference value. For example, this can be achieved by continuously collecting raw signal intensity data from the infrared probe during the initial stage of asphalt mixture production, when the material moisture content is at a normal level and production is stable. This data can be recorded and stored in a database. Subsequently, statistical analysis is performed on these raw signal intensity data to calculate their average value as the average intensity reference value and their standard deviation as the fluctuation range reference value. Another implementation method is to establish a lookup table containing multiple average intensity reference values ​​and fluctuation range reference values ​​under normal operating conditions such as different material types and production loads, through extensive experimental and historical data analysis. During actual production, the system selects the most suitable reference system based on the current operating conditions. It is understood that an infrared probe is a non-contact temperature measurement device that infers the temperature of an object by receiving infrared radiation emitted by the object. In asphalt mixing drums, it is used to monitor the material temperature in real time. The average intensity reference value represents the average level of the infrared signal under normal conditions, while the fluctuation range reference value reflects the amplitude of signal fluctuations under normal conditions.

[0086] Then, the average intensity and fluctuation characteristics of the current infrared probe signal are obtained. During the production process, the infrared probe continuously collects real-time signal strength data. This real-time data can be transmitted to a data processing unit. This processing unit periodically calculates the latest acquired signal strength data to obtain the current signal's average intensity and fluctuation characteristics. For example, algorithms such as moving average or exponential smoothing can be used to calculate the average intensity to reflect the short-term trend of the signal. For fluctuation characteristics, the standard deviation or coefficient of variation of the signal within a certain time window can be calculated to quantify the signal's dispersion. It can be understood that the average intensity refers to the arithmetic mean of the infrared probe signal intensity within a certain time window, reflecting the overall energy level of the signal. Fluctuation characteristics refer to the changing characteristics of the infrared probe signal over a certain period of time, such as the signal's standard deviation, variance, or fluctuation frequency, reflecting the signal's stability or degree of interference.

[0087] Then, based on the average intensity reference value and the fluctuation range reference value, threshold judgments are performed on the average intensity and fluctuation characteristics to obtain the judgment results. For example, the current average intensity can be compared with the average intensity reference value. If the current average intensity is significantly lower than the reference value, signal attenuation may exist. At the same time, the current fluctuation characteristics are compared with the fluctuation range reference value. If the current fluctuation characteristics are significantly higher than the reference value, severe interference may exist. Specifically, a series of thresholds can be set. For example, if the current average intensity is lower than the average intensity reference value by a certain percentage, and the current fluctuation characteristics are higher than the fluctuation range reference value by a certain percentage, it is preliminarily judged that water vapor interference may exist.

[0088] If the determination result indicates the presence of water vapor interference, the temperature correction is calculated based on the fluctuation characteristics. For example, the temperature correction can be calculated using a pre-established mathematical model or lookup table based on the intensity of the fluctuation characteristics. More severe fluctuations generally indicate more serious water vapor interference, thus requiring a larger temperature correction. This model can be obtained by fitting experimental data; for instance, by recording the fluctuation characteristics of the infrared probe signal and the deviation between the actual and measured temperatures at different water vapor concentrations, a mapping relationship between the fluctuation characteristics and the temperature correction can be established.

[0089] Finally, the current production temperature is corrected based on the temperature correction amount. The calculated temperature correction amount is applied to the raw temperature data measured by the infrared sensor. For example, if the temperature measured by the infrared sensor is... The calculated temperature correction is So, the corrected true temperature This corrected temperature is closer to the actual temperature of the material. It will be fed back to the temperature control system to guide the adjustment of the burner power, thereby avoiding overheating or underheating.

[0090] Through the above technical solution, this embodiment introduces the concepts of signal reference system and fluctuation characteristics, applying them to the identification and temperature correction of water vapor interference. By establishing an average intensity reference value and a fluctuation range reference value under normal operating conditions, this embodiment can accurately determine whether the current infrared probe signal is abnormally interfered with by water vapor. When water vapor interference is identified, a precise temperature correction amount can be calculated based on the signal fluctuation characteristics, thereby correcting the temperature measured by the infrared probe. This correction mechanism based on signal characteristic analysis can effectively distinguish between real temperature changes and water vapor interference, avoiding misjudgment and overheating. For example, in traditional methods, when water vapor causes infrared signal attenuation, the system may mistakenly assume that the material temperature is too low, thus increasing the burner power. However, this embodiment can identify this as water vapor interference and calculate the corresponding temperature correction amount, correcting the measured temperature to the true value, thereby avoiding unnecessary heating and significantly reducing the risk of asphalt thermal aging and product scrapping. Therefore, this embodiment not only improves the production quality and stability of micro-rutting asphalt mixtures but also saves energy and raw materials, resulting in significant economic and environmental benefits.

[0091] In some embodiments, establishing a signal reference system for the infrared probe under normal operating conditions in step S101 may include, but is not limited to, the following steps:

[0092] Determine the initial steady-state window at the start of asphalt mixture production;

[0093] Within the initial stable state window, raw signal strength data is acquired using an infrared probe;

[0094] Based on the original signal strength data, the average value is calculated as the average strength reference value;

[0095] Based on the original signal strength data, the standard deviation is calculated as a reference value for the fluctuation range.

[0096] In some embodiments, an initial steady-state window can be determined at the start of asphalt mixture production. This initial steady-state window refers to a specific period during the initial stages of asphalt mixture production when equipment operation is stable, material supply is continuous, and there are no significant external disturbances. Determining this window aims to ensure that the collected raw signal strength data accurately reflects the operating status of the infrared probe under normal production conditions. For example, the stability of key parameters of the production line (such as aggregate feed rate, asphalt pump pressure, and mixing drum speed) can be monitored to determine whether the initial steady-state window has been entered.

[0097] Then, within the initial stable state window, raw signal intensity data is acquired using an infrared probe. Raw signal intensity data can be acquired continuously using an infrared probe. The raw signal intensity data is a series of values ​​obtained by measuring the infrared radiation intensity emitted by the asphalt mixture or its surrounding environment under normal operating conditions. These data form the basis for subsequent calculations of the average intensity reference value and the fluctuation range reference value.

[0098] Next, based on the original signal strength data, an average value is calculated as the average strength reference value, and a standard deviation is calculated as the fluctuation range reference value. The average strength reference value is obtained by calculating the arithmetic mean of the original signal strength data collected within the initial stable state window. This average value represents the typical strength level of the infrared probe signal under normal operating conditions. The fluctuation range reference value is obtained by calculating the standard deviation of the original signal strength data. As a statistical quantity, the standard deviation can effectively reflect the degree of dispersion of data points relative to the average value, i.e., the volatility of the signal. The fluctuation range reference value characterizes the natural fluctuation range of the infrared probe signal strength under normal production conditions.

[0099] This embodiment ensures the representativeness and reliability of the acquired data by determining an initial stable state window at the start of production and collecting raw signal strength data within this window. Therefore, by calculating the average and standard deviation of these raw signal strength data, a precise signal reference system for the infrared probe under normal operating conditions can be established, including the average intensity reference value and the fluctuation range reference value. This method provides a solid foundation for subsequent judgments of the current infrared probe signal, effectively distinguishing between normal fluctuations and abnormal interference.

[0100] Through the above technical solution, this embodiment provides an accurate and reliable infrared probe signal reference for temperature control in asphalt mixture production. This method of establishing a reference system based on actual production data avoids the inaccuracies that may arise from using a fixed threshold, thereby improving the sensitivity and accuracy of judging interference from water vapor or other media. This provides a more precise basis for subsequent temperature correction and control, helping to improve the quality stability and energy efficiency of asphalt mixture production.

[0101] In some embodiments, in step S103, threshold judgment is performed on the average intensity and fluctuation characteristics based on the average intensity reference value and the fluctuation range reference value to obtain the judgment result, which may include, but is not limited to, the following steps:

[0102] Step S201: If the average intensity is less than the average intensity reference value, then determine whether the fluctuation characteristic is less than the fluctuation range reference value.

[0103] Step S202: If the fluctuation characteristic is less than the fluctuation range reference value, then the judgment result is determined to be that there is interference from the fog medium;

[0104] Step S203: If the fluctuation characteristic is greater than the fluctuation range reference value, then obtain the moisture content of the material in the mixing drum;

[0105] Step S204: If the moisture content of the material is greater than the preset moisture content threshold, the judgment result is determined to be that water vapor interference exists;

[0106] Step S205: If the moisture content of the material is less than the preset moisture content threshold, the judgment result is determined to be that there are other factors interfering.

[0107] In some embodiments, a threshold judgment can be first applied to the average intensity. If the average intensity is less than a reference value, it indicates that the infrared signal may be attenuated, usually due to medium blocking or absorption of infrared radiation. Further judgment can be made regarding whether the fluctuation characteristic is less than a reference value for the fluctuation range. If the fluctuation characteristic is less than the reference value for the fluctuation range, the judgment result is determined to be the presence of fog-like medium interference. It is understood that fog-like media, such as water mist or fine dust, will uniformly attenuate the infrared signal, leading to a decrease in average intensity. However, because their distribution is relatively uniform, their impact on signal volatility is small, and therefore the fluctuation characteristic remains at a low level. If the fluctuation characteristic is greater than the reference value for the fluctuation range, it indicates that the signal is not only attenuated but also that its volatility has significantly increased. The moisture content of the material in the mixing drum can be obtained, and a threshold judgment can be further applied to the material moisture content.

[0108] If the material's moisture content exceeds a preset moisture content threshold, the judgment result is determined to be the presence of water vapor interference. Water vapor generation typically accompanies the material drying process, forming uneven vapor clouds within the infrared probe's field of view. This leads to a decrease in the average intensity of the infrared signal, and the dynamic changes in these vapor clouds cause significant signal fluctuations. More often, the preset moisture content threshold is a critical value determined empirically or experimentally to distinguish between normal moisture evaporation and abnormal water vapor interference. If the material's moisture content is below the preset threshold, even if the average intensity decreases and the fluctuation characteristics increase, the possibility of water vapor interference is ruled out, and the judgment result can be determined to be the presence of other interfering factors. These other factors may include, but are not limited to, malfunctions of the infrared probe itself, scaling on the inner wall of the mixing drum, and abnormal material accumulation, all of which can also cause abnormal changes in the infrared signal.

[0109] This embodiment achieves accurate identification of different types of interference sources by performing multi-level threshold judgments on the average intensity and fluctuation characteristics of the infrared probe signal, combined with the material moisture content for auxiliary judgment. Specifically, firstly, by comparing the average intensity with a reference value, a preliminary judgment is made as to whether signal attenuation exists; secondly, by comparing the fluctuation characteristics with the reference value, it is distinguished whether the signal attenuation is caused by a homogeneous medium (such as a misty medium) or a non-homogeneous medium (such as water vapor). When the signal exhibits attenuation and increased fluctuation, the material moisture content is introduced as a key criterion to distinguish water vapor interference from other factors that may cause similar signal characteristics. This hierarchical and progressive judgment logic enables the system to more accurately identify the specific type of interference causing infrared temperature measurement errors, thereby providing a reliable basis for subsequent temperature correction or control.

[0110] Through the above technical solution, this embodiment can effectively distinguish different types of interference received by the infrared probe signal during asphalt mixture production, including interference from fogging media, water vapor, and other factors. This refined interference type identification capability avoids potential misjudgments or omissions in traditional methods, thus providing more accurate input for subsequent temperature correction or control. Therefore, it can significantly improve the accuracy and reliability of temperature control in asphalt mixture production, reduce temperature measurement deviations caused by interference, and ultimately ensure the production quality and stability of asphalt mixtures.

[0111] In some embodiments, in step S204, if the moisture content of the material is greater than a preset moisture content threshold, the determination result is that water vapor interference exists, which may include, but is not limited to, the following steps:

[0112] Step S301: Obtain material type information, internal ambient temperature information of the mixing drum, and aggregate particle size distribution information;

[0113] Step S302: Adjust the preset moisture content threshold according to the material type information, the ambient temperature inside the mixing drum, and the aggregate particle size distribution information;

[0114] Step S303: Compare the material moisture content with the adjusted preset moisture content threshold;

[0115] Step S304: If the moisture content of the material is greater than the adjusted preset moisture content threshold, the judgment result is determined to be that there is water vapor interference.

[0116] In some embodiments, various factors, such as the type of material, the ambient temperature inside the mixing drum, and the aggregate particle size distribution, can significantly affect the actual moisture content characteristics of the material and its performance in infrared signals. Using a single, fixed preset moisture content threshold for judgment may lead to misjudgments or omissions of water vapor interference, thus affecting the accuracy of subsequent temperature corrections and production efficiency. Therefore, information on the material type, the ambient temperature inside the mixing drum, and the aggregate particle size distribution can be obtained first. The material type information refers to whether the aggregate used in the asphalt mixture is basalt, limestone, or other types, and whether it contains recycled asphalt mixture (RAP), etc. The ambient temperature inside the mixing drum refers to the real-time temperature inside the mixing drum, which can be obtained through a temperature sensor installed inside the mixing drum. The aggregate particle size distribution information refers to the proportion of particles of different sizes in the aggregate, for example, obtained through sieve analysis data. This information is a key parameter affecting the moisture content and drying characteristics of the material.

[0117] Then, based on the material type information, the ambient temperature inside the mixing drum, and the aggregate particle size distribution information, the preset moisture content threshold is adjusted. For example, a database or model containing the relationships between different material types, ambient temperatures, aggregate particle size distributions, and corresponding moisture content thresholds can be pre-established. For instance, for materials with high water absorption or at lower ambient temperatures inside the mixing drum, the preset moisture content threshold can be appropriately increased; while for aggregates with smaller particle sizes and relatively larger surface areas, a lower moisture content threshold may be needed to accurately determine the presence of water vapor. This adjustment process aims to make the threshold more closely match the current actual production conditions. The material moisture content is then compared with the adjusted preset moisture content threshold. If the material moisture content is greater than the adjusted preset moisture content threshold, the judgment result is determined to be the presence of water vapor interference.

[0118] This embodiment solves the problem of inaccurate judgment under complex and changing production environments by introducing material type information, mixing drum internal temperature information, and aggregate particle size distribution information, and dynamically adjusting the preset moisture content threshold based on this information. Specifically, different types of materials have different water absorption and dehydration characteristics; the mixing drum internal temperature directly affects the evaporation rate of moisture in the material; and the aggregate particle size distribution affects the overall porosity and moisture retention capacity of the material. By comprehensively considering these factors, a more refined and accurate moisture content threshold model can be constructed, so that the judgment of water vapor interference is no longer based on a static empirical value, but on a real-time assessment of the current production conditions. Therefore, when the material moisture content exceeds this dynamically adjusted threshold, it can more reliably indicate the actual presence of water vapor and avoid misjudgments caused by environmental changes.

[0119] To illustrate this technical solution more clearly, a specific example is used below. Suppose that during the asphalt mixture production process, the average intensity and fluctuation characteristics of the infrared probe signal indicate potential water vapor interference, and the initial moisture content of the material in the mixing drum is detected as 0.8%. In a traditional solution, if the preset moisture content threshold is fixed at 0.7%, it would be directly determined that water vapor interference exists. However, in this embodiment, information about the type of material currently being produced (e.g., assuming that highly absorbent basalt aggregate is being used), the ambient temperature inside the mixing drum (e.g., the current ambient temperature is 150°C, lower than the normal operating temperature of 170°C), and the aggregate particle size distribution (e.g., a high content of fine aggregate) can be obtained first. Based on this information, an adjusted preset moisture content threshold is calculated using a preset model or lookup table. For example, due to the high absorbency of basalt and the low ambient temperature, the system may adjust the preset moisture content threshold from 0.7% to 0.9%. At this point, the current material moisture content of 0.8% is compared with the adjusted preset moisture content threshold of 0.9%. Since 0.8% is less than 0.9%, the system will determine that there is no water vapor interference under the current conditions, but rather that other factors may be interfering. This dynamic adjustment avoids misjudgments caused by fixed thresholds under specific operating conditions, making the judgment of water vapor interference more accurate. This ensures the accuracy of subsequent temperature corrections and avoids unnecessary production adjustments.

[0120] Through the above technical solution, this embodiment can significantly improve the accuracy and robustness of judging water vapor interference during asphalt mixture production. Compared with the traditional method using a fixed threshold, this embodiment can adaptively adjust the judgment criteria according to the actual material characteristics and environmental conditions, thereby effectively reducing false alarms or missed alarms caused by threshold mismatch. This more accurate interference judgment allows subsequent temperature correction measures to be more timely and effective, avoiding unnecessary energy waste or product quality problems, and ultimately helping to achieve refined temperature control in the production process of micro-rutting asphalt mixtures, improving product quality and production efficiency.

[0121] In some embodiments, in step S304, if the moisture content of the material is greater than the adjusted preset moisture content threshold, the determination result is that there is water vapor interference, which may include, but is not limited to, the following steps:

[0122] Step S401: If the moisture content of the material is greater than the adjusted preset moisture content threshold, monitor the trend of the material moisture content.

[0123] Step S402: If the moisture content of the material is trending upward, then the magnitude of the changes in average strength and fluctuation characteristics is determined.

[0124] Step S403: If the change range of the average intensity increases and the change range of the fluctuation characteristics increases, then the judgment result is determined to be that water vapor interference exists.

[0125] In some embodiments, since the moisture content of materials may be affected instantaneously by various factors, a judgment method based solely on threshold comparison may be prone to misjudgment. For example, when the moisture content of materials momentarily exceeds the threshold but does not rise continuously, or when the degree of water vapor interference is slight but has a minor impact on the infrared signal, it may not be accurately identified or distinguished. Therefore, the moisture content of materials can be judged. If the moisture content of materials is greater than an adjusted preset moisture content threshold, the trend of change in the moisture content is monitored. For example, continuous measurement data of the moisture content of materials over a period of time can be analyzed to determine whether it is rising, falling, or relatively stable. For example, the trend can be determined by calculating the instantaneous rate of change of the moisture content of materials or the average rate of change within a preset time window. An upward trend in the moisture content of materials usually indicates continuous evaporation of moisture or the addition of new wet materials, which is closely related to the generation of water vapor.

[0126] If the moisture content of the material shows an increasing trend, the changes in average intensity and fluctuation characteristics are assessed. If both the average intensity and fluctuation characteristics increase, the result indicates the presence of water vapor interference. For example, the degree of change in the average intensity and fluctuation characteristics of the infrared probe signal relative to its normal state or the previous moment can be evaluated. An increase in the average intensity typically manifests as a significant decrease in infrared signal intensity, due to enhanced absorption and scattering of infrared light by water vapor. An increase in the fluctuation characteristics indicates decreased stability and increased volatility of the infrared signal, usually due to the formation of uneven clumps or flow of water vapor within the mixing drum, causing drastic changes in the density and distribution of the medium along the infrared light path.

[0127] To illustrate this technical solution more clearly, a specific example is used below. Assume that during the asphalt mixture production process, the average intensity and fluctuation characteristics of the infrared probe signal have been acquired, and the material moisture content has also been measured. The material moisture content can first be compared with an adjusted preset moisture content threshold. If the material moisture content is greater than this threshold, the trend of change in material moisture content is further monitored. For example, the system continuously records material moisture content data and calculates its average rate of change over the past 30 seconds. If this rate of change is positive, it indicates that the material moisture content is increasing. Based on this, the magnitude of changes in average intensity and fluctuation characteristics can be further analyzed. For example, by comparing the difference between the current average intensity and the average intensity of the previous minute, and the difference between the current fluctuation characteristics and the fluctuation characteristics of the previous minute. If it is found that the decrease in average intensity exceeds a preset threshold (e.g., a decrease of more than 5%), and the increase in fluctuation characteristics also exceeds a preset threshold (e.g., an increase of more than 10%), it is comprehensively judged that water vapor interference exists. Through this multi-confirmation mechanism, even if the moisture content of the material is slightly higher than the threshold, if its change trend is stable and the infrared signal characteristics do not change significantly, it will not be misjudged as water vapor interference. Conversely, even if the moisture content of the material is only slightly higher than the threshold, if it rises rapidly and the infrared signal characteristics change drastically, water vapor interference can be identified in a timely and accurate manner, thus ensuring the accuracy of the judgment.

[0128] Through the above technical solution, this embodiment can significantly improve the accuracy and reliability of judging water vapor interference during asphalt mixture production. By introducing multi-dimensional and dynamic judgment criteria, that is, combining the changing trend of material moisture content with the average intensity and fluctuation characteristics of infrared probe signals, instantaneous interference or atypical situations can be effectively filtered out, avoiding misjudgment or missed judgment. Therefore, it can be ensured that subsequent temperature correction is triggered only when water vapor interference is actually present, resulting in more precise and stable production temperature control, reducing unnecessary temperature adjustments, improving the production quality and efficiency of asphalt mixtures, and reducing energy consumption.

[0129] In some embodiments, in step S403, if the change in average intensity increases and the change in fluctuation characteristics increases, then the determination result is that water vapor interference exists, which may include, but is not limited to, the following steps:

[0130] Acquire the self-diagnostic status information and historical operating data of the infrared probe;

[0131] Analyze the operating status of the infrared probe based on self-diagnostic status information and historical operating data;

[0132] If the operating status is aging or faulty, the current infrared probe signal will be corrected.

[0133] Based on the corrected current infrared probe signal, update the change range of average intensity and the change range of fluctuation characteristics;

[0134] If the change in the updated average intensity increases and the change in the updated fluctuation characteristics increases, then the judgment result is determined to be the presence of water vapor interference.

[0135] In some embodiments, the operating status of the infrared probe (such as aging or malfunction) may affect the accuracy of its signal, leading to misjudgment or missed detection of water vapor interference. To address this, the self-diagnostic status information and historical operating data of the infrared probe can be acquired first. For example, various parameters inside the infrared probe, such as its internal temperature, power supply voltage, and sensor health status, can be monitored in real time; these constitute the self-diagnostic status information. Simultaneously, the probe's cumulative operating time, maintenance records, calibration history, and past signal anomalies can also be recorded; these constitute the historical operating data. Acquiring this information aims to comprehensively assess the current health status and potential performance degradation of the infrared probe.

[0136] Then, based on the self-diagnostic status information and historical operating data, the operating status of the infrared probe is analyzed. This can be done through a comprehensive analysis of the acquired self-diagnostic status information and historical operating data using preset algorithms or rules. For example, if the probe's internal temperature consistently exceeds the normal range, or historical data shows frequent signal drift under specific operating conditions, it can be determined that the probe may be aging or faulty. The purpose is to promptly detect any abnormalities within the probe itself and prevent them from interfering with the measurement results. If the operating status is aging or faulty, the current infrared probe signal is corrected. Once it is determined that the infrared probe is aging or faulty, the currently acquired infrared probe signal can be corrected. For example, a compensation algorithm based on historical calibration curves can be used, or advanced signal processing techniques (such as filtering, noise reduction, drift compensation, etc.) can be employed to eliminate or reduce signal deviations caused by problems with the probe itself. The purpose is to ensure that the signal data used for subsequent judgment is accurate and reliable.

[0137] Then, based on the corrected current infrared probe signal, the changes in average intensity and fluctuation characteristics are updated to ensure that the input data used to determine water vapor interference is corrected for the probe's own condition and more accurately reflects the material's state. If the updated average intensity change rate increases, and the updated fluctuation characteristic change rate increases, then the determination result is that water vapor interference exists. This means that after excluding interference from factors within the infrared probe itself, if the signal trend still matches the characteristics of water vapor interference, its presence can be confirmed.

[0138] This embodiment effectively addresses the limitations of traditional methods that may lead to misjudgments due to probe aging or malfunction by introducing an evaluation and correction mechanism for the infrared probe's own operating status when judging water vapor interference. By acquiring the infrared probe's self-diagnostic status information and historical operating data, it is possible to promptly detect whether the probe is aging or malfunctioning. Once an abnormal probe operating status is detected, its current signal is corrected, thereby eliminating the impact of probe-related problems on signal accuracy. Subsequently, based on the corrected signal, the average intensity and fluctuation characteristic variation amplitude are recalculated, ensuring that the data used to judge water vapor interference accurately reflects the material state rather than the probe's state. This consideration of the probe's own state and signal preprocessing makes the judgment of water vapor interference more accurate and reliable.

[0139] To illustrate this technical solution more clearly, a specific example is used below. Suppose that during the asphalt mixture production process, the system detects an increasing trend in the average intensity and fluctuation characteristics of the infrared probe output, initially indicating potential water vapor interference. However, before making a final judgment, the system first obtains the self-diagnostic status information of the infrared probe, such as its internal temperature sensor readings, power supply voltage stability, and the date of the most recent calibration. Simultaneously, the system queries the probe's historical operating data, including its cumulative operating time, past maintenance records, and any abnormal signal drift records. By analyzing this information, if it is found that the infrared probe has been operating continuously for a long time, and its self-diagnostic information indicates that a key internal parameter is approaching a warning threshold, or historical data shows that this type of probe is prone to signal attenuation after long-term operation, the system will determine that the probe's operating state may be aging. In this case, the system will not directly use the original infrared probe signal for judgment, but will instead correct the current infrared probe signal. For example, it can compensate for the current signal based on the typical aging curve of this probe model, or use a preset calibration algorithm to filter and correct the signal for drift. After calibration, the system recalculates the changes in average intensity and fluctuation characteristics based on the calibrated current infrared probe signal. If the updated changes are still significantly larger, the system will ultimately determine that water vapor interference exists and trigger the subsequent temperature correction process. Conversely, if the changes no longer increase significantly after calibration, it indicates that the previous signal anomaly was caused by a problem with the probe itself rather than water vapor interference, thus avoiding erroneous temperature correction.

[0140] Through the above technical solution, this embodiment significantly improves the accuracy and reliability of water vapor interference judgment in the temperature control method for micro-rutting asphalt mixture production. Real-time monitoring and signal correction of the infrared probe's operating status ensures the accuracy of the signal data used to judge water vapor interference, effectively avoiding misjudgments caused by probe malfunctions. This makes the correction of production temperature more precise, avoiding unnecessary temperature adjustments or failures to adjust in a timely manner, thereby improving the production quality and stability of asphalt mixtures, while also helping to reduce energy consumption and production costs.

[0141] In some embodiments, in step S403, if the change in average intensity increases and the change in fluctuation characteristics increases, then the determination result is that water vapor interference exists, which may include, but is not limited to, the following steps:

[0142] Obtain production load information, asphalt viscosity information, and aggregate gradation information of asphalt mixtures;

[0143] Based on production load information, asphalt viscosity information, and aggregate gradation information, the target change threshold is determined. The target change threshold includes the material moisture content change threshold, the average strength change threshold, and the fluctuation characteristic change threshold.

[0144] Calculate the instantaneous rate of change of the material's moisture content based on the material's moisture content;

[0145] Calculate the instantaneous rate of change of average strength based on the average strength;

[0146] Calculate the instantaneous rate of change of the fluctuation characteristics based on the fluctuation characteristics;

[0147] If the instantaneous rate of change of material moisture content is greater than the threshold for material moisture content change, the instantaneous rate of change of average strength is greater than the threshold for average strength change, and the instantaneous rate of change of fluctuation characteristics is greater than the threshold for fluctuation characteristics change, then the judgment result is determined to be the presence of water vapor interference.

[0148] In some embodiments, factors such as production load, asphalt viscosity, and aggregate gradation can dynamically change, potentially causing fluctuations in the average intensity and wave characteristics of the infrared probe signal. Judging water vapor interference solely based on whether the amplitude of these changes increases may lead to misjudgments or insufficient sensitivity, thus affecting the accuracy of temperature control. Therefore, it is advisable to first obtain information on the production load, asphalt viscosity, and aggregate gradation of the asphalt mixture. Production load information reflects the current production line's processing capacity and material flow rate; asphalt viscosity information relates to asphalt flowability and heating requirements; and aggregate gradation information affects the material's drying characteristics and heat transfer efficiency. This information is crucial for influencing the characteristics of the infrared probe signal and is therefore used to more accurately determine water vapor interference. Then, based on the production load, asphalt viscosity, and aggregate gradation information, a target change threshold is determined. This target change threshold is not a fixed value but is adjusted according to current production conditions to better adapt to actual operating conditions. The target change threshold includes a threshold for material moisture content change, an average intensity change threshold, and a wave characteristic change threshold. These thresholds are key references for determining whether the instantaneous rate of change of the corresponding parameters is abnormal.

[0149] Next, based on the material's moisture content, the instantaneous rate of change of the moisture content is calculated; based on the average intensity, the instantaneous rate of change of the average intensity is calculated; and based on the fluctuation characteristics, the instantaneous rate of change of the fluctuation characteristics is calculated. The instantaneous rate of change of the material's moisture content reflects the real-time rate of change in the material's dryness; the instantaneous rate of change of the average intensity reflects the rapid fluctuations in the infrared signal intensity; and the instantaneous rate of change of the fluctuation characteristics reflects the immediate trend of signal stability. These instantaneous rates of change can more sensitively detect signal anomalies caused by water vapor interference. If the instantaneous rate of change of the material's moisture content is greater than the material's moisture content change threshold, the instantaneous rate of change of the average intensity is greater than the average intensity change threshold, and the instantaneous rate of change of the fluctuation characteristics is greater than the fluctuation characteristic change threshold, then the judgment result is determined to be the presence of water vapor interference. This multi-dimensional, dynamic threshold judgment method can effectively avoid misjudgments caused by single indicators or fixed threshold judgments.

[0150] To illustrate this technical solution more clearly, a specific example is used below. Assume an infrared sensor continuously monitors signals inside the mixing drum on an asphalt mixture production line. When the system detects that the average strength is lower than a reference value and the fluctuation characteristic is greater than a reference value for the fluctuation range, and further obtains that the material moisture content is greater than a preset moisture content threshold, the system will enter a further judgment process for water vapor interference. At this time, the system will obtain current production load information, such as medium load; asphalt viscosity information, such as medium viscosity; and aggregate gradation information, such as conventional gradation. Based on these real-time production parameters, the system will dynamically determine the target change threshold under the current operating conditions through a preset model or lookup table. For example, the material moisture content change threshold is adjusted to 0.05% / second, the average strength change threshold is adjusted to 10 units / second, and the fluctuation characteristic change threshold is adjusted to 5 units / second.

[0151] Subsequently, the system calculates the instantaneous rate of change of the material's moisture content in real time. For example, if the material's moisture content increases from 2.0% to 2.3% in the past 5 seconds, its instantaneous rate of change is (2.3% - 2.0%) / 5 seconds = 0.06% / second. Simultaneously, it calculates the instantaneous rate of change of the average strength, for example, 12 units / second; and the instantaneous rate of change of the fluctuation characteristics, for example, 6 units / second. Next, the system compares these instantaneous rates of change with dynamically adjusted target change thresholds: the instantaneous rate of change of material moisture content (0.06% / second) is greater than the material moisture content change threshold (0.05% / second); the instantaneous rate of change of average strength (12 units / second) is greater than the average strength change threshold (10 units / second); and the instantaneous rate of change of fluctuation characteristics (6 units / second) is greater than the fluctuation characteristic change threshold (5 units / second). Since all three instantaneous rates of change are greater than their respective target change thresholds, the system ultimately determines that water vapor interference exists. This triggers subsequent temperature correction calculations and production temperature correction steps, enabling timely and effective responses to water vapor interference and ensuring the production quality of asphalt mixtures. This dynamic, multi-dimensional judgment method allows the system to accurately identify water vapor interference under different production conditions, avoiding potential misjudgments caused by fixed thresholds or single-indicator assessments.

[0152] Through the above technical solution, this embodiment can significantly improve the accuracy and robustness of water vapor interference judgment during the production of micro-rutting asphalt mixtures. This embodiment introduces key production parameters such as production load, asphalt viscosity, and aggregate gradation, and dynamically adjusts the judgment threshold accordingly, enabling the system to better adapt to complex and dynamically changing production environments. Furthermore, by calculating the instantaneous change rate of material moisture content, average strength, and fluctuation characteristics, and performing multi-dimensional comprehensive judgment, it can more sensitively and accurately capture signal anomalies caused by water vapor interference, effectively avoiding misjudgments or omissions, thereby ensuring the accuracy of production temperature control and ultimately contributing to improving the production quality and stability of asphalt mixtures.

[0153] In some embodiments, the method further includes:

[0154] Step S501: If the judgment result is that there is interference from mist medium, then obtain the reference fuel supply rate and reference temperature data of the burner under normal operating conditions;

[0155] Step S502: Calculate the reference energy input power based on the reference fuel supply rate and the calorific value of the dye;

[0156] Step S503: Calculate the rate of change of the reference temperature based on the preset sliding window and the reference temperature data;

[0157] Step S504: Construct a power-temperature rise relationship curve based on multiple reference energy input power and multiple reference temperature rise rates;

[0158] Step S505: Control the current production temperature according to the power-temperature rise relationship curve.

[0159] In some embodiments, in temperature anomaly detection, in addition to water vapor interference, there may also be fogging interference, such as fog formed by fine dust or incompletely burned fuel particles. If such fogging interference is not effectively addressed, it may cause distortion of the infrared probe signal, thereby affecting the accuracy of temperature measurement, leading to deviations in production temperature control, and ultimately impacting the quality and production efficiency of asphalt mixtures. Therefore, when fogging interference is detected, reference fuel supply rate and reference temperature data of the burner under normal operating conditions can be obtained first. This data is typically historical data from a production line operating stably with precise temperature control and no significant interference. The reference fuel supply rate refers to the amount of fuel consumed by the burner to maintain a stable temperature under specific production conditions, which can be expressed as fuel volume or mass per unit time. The reference temperature data refers to the actual production temperature measured by a reliable temperature sensor (e.g., a thermocouple or a calibrated infrared thermometer) at these reference fuel supply rates.

[0160] Then, based on the reference fuel supply rate and the calorific value of the fuel, the reference energy input power is calculated. The calorific value of the fuel refers to the energy released when a unit mass or unit volume of fuel is completely burned; it is a known physical parameter. By multiplying the fuel supply rate by the calorific value, the energy input power from the burner to the mixing drum can be obtained. Simultaneously, based on a preset sliding window and reference temperature data, the reference temperature rise rate is calculated. The preset sliding window refers to a time period, such as 5 seconds, 10 seconds, or longer, used for averaging or trend analysis of temperature data over a period of time. The reference temperature rise rate refers to the rate at which the temperature inside the mixing drum changes with time under normal operating conditions and a specific energy input power; it can be expressed as a rise in degrees Celsius per second. This rate of change can be obtained by performing linear regression or difference calculations on the reference temperature data within the sliding window.

[0161] Then, based on multiple reference energy input powers and multiple reference temperature rise rates, a power-temperature rise relationship curve is constructed. This curve reflects the intrinsic correlation between the burner input energy power and the rate of temperature rise inside the mixing drum under normal operating conditions. This curve can be a linear model, a polynomial model, or a nonlinear model obtained through data fitting; its purpose is to establish a stable and predictable mapping relationship between energy input and temperature response. Finally, the current production temperature is controlled based on the power-temperature rise relationship curve. This means that when mist-like media interference is detected, temperature correction no longer relies solely on potentially distorted signals from the infrared probe, but instead utilizes the stable relationship between the burner's energy input and the actual temperature response for temperature regulation, thereby avoiding the impact of measurement errors caused by mist-like media interference on temperature control.

[0162] This embodiment effectively solves the temperature control problem under the interference of misty media. When the judgment result indicates the presence of misty media interference, the signal collected by the infrared probe may be distorted due to scattering and absorption by the medium, resulting in an inaccurate reflection of the actual temperature. This embodiment acquires reference fuel supply rate and reference temperature data of the burner under normal operating conditions, and calculates the reference energy input power and the reference temperature rise rate based on these data, thereby constructing a power-temperature rise relationship curve. This curve essentially establishes a physical model between the burner energy input and the temperature rise rate of the material in the mixing drum. This model is unaffected by the interference of misty media on the infrared probe signal. By utilizing this stable physical relationship, even if the infrared probe signal is interfered with, the system can predict or control the temperature rise rate based on the actual energy input of the burner, thereby achieving precise control of the current production temperature and avoiding control deviations caused by measurement errors.

[0163] Through the above technical solution, this embodiment can effectively address the interference problem of misting media in the asphalt mixture production process. Compared with solutions that only address water vapor interference, this solution expands the applicability of temperature control, ensuring the accuracy of temperature measurement and the stability of control under various interference conditions. Specifically, by constructing and utilizing the power-temperature rise relationship curve, even if the infrared probe signal is distorted due to misting media, the system can still perform temperature control based on the inherent physical relationship between the burner's energy input and temperature response. This avoids temperature fluctuations and control deviations caused by measurement errors, significantly improving the robustness and accuracy of temperature control in asphalt mixture production, thereby ensuring the quality of the final product and production efficiency.

[0164] In some embodiments, step S505, controlling the current production temperature based on the power-temperature rise relationship curve, may include, but is not limited to, the following steps:

[0165] Obtain current fuel supply rate and current temperature data;

[0166] Calculate the current energy input power based on the current fuel supply rate and the calorific value of the dye;

[0167] Calculate the rate of change of the current temperature rise based on the preset sliding window and the current temperature data;

[0168] Extract the rate of temperature rise corresponding to the current energy input power from the power-temperature rise relationship curve as the target rate of temperature rise.

[0169] If the current rate of temperature increase is less than the target rate of temperature increase, the burner power will be reduced and a temperature anomaly alarm will be issued.

[0170] In some embodiments, the current fuel supply rate and current temperature data can be acquired first. Based on the current fuel supply rate and dye calorific value, the current energy input power is calculated, and the current temperature rise rate is calculated based on a preset sliding window and the current temperature data. Then, the temperature rise rate corresponding to the current energy input power is extracted from the power-temperature rise relationship curve as the target temperature rise rate. This curve can be used to look up a table to obtain the expected target temperature rise rate at the current energy input power. If the current temperature rise rate is less than the target temperature rise rate, the burner power is reduced, and a temperature anomaly alarm is issued. If the actual measured current temperature rise rate is lower than the target temperature rise rate predicted based on the power-temperature rise relationship curve, this may indicate a problem of heat loss or insufficient heating efficiency, such as a decrease in heat absorption efficiency due to interference from the mist medium. In this case, to avoid overheating or energy waste, the system can proactively reduce the burner power and simultaneously issue a temperature anomaly alarm to remind the operator to check or intervene.

[0171] This embodiment monitors the fuel supply rate and temperature data in real time during the current production process, converting them into energy input power and temperature rise rate. This allows for comparison with a pre-established power-temperature rise relationship curve. When the current temperature rise rate is detected to be lower than the target temperature rise rate based on the current energy input power, it indicates that the actual temperature rise of the material under the current energy input has not met expectations. This deviation may be due to reduced heat transfer efficiency caused by interference from the mist medium. By promptly reducing the burner power, energy waste or potential overheating risks to the material caused by continuous high power input can be avoided. Simultaneously, issuing a temperature anomaly alarm can quickly alert operators to potential production abnormalities and ensure the stability of the production process and product quality.

[0172] Through the above technical solution, this embodiment enables precise control of the production temperature of asphalt mixtures, especially in the presence of interference from misting media. By comparing the actual temperature rise rate with the target temperature rise rate based on the power-temperature rise relationship curve in real time, this embodiment can promptly detect and respond to abnormal heating efficiency. This effectively avoids temperature control lag or deviation caused by decreased heat transfer efficiency due to misting media interference, thereby ensuring that asphalt mixtures are produced within the optimal temperature range, improving product quality, and reducing energy consumption. Furthermore, the temperature anomaly alarm mechanism further enhances the system's early warning capabilities, helping operators quickly locate problems and take corrective measures, thus improving the reliability and safety of the production process.

[0173] The beneficial effects of implementing the embodiments of the present invention include: First, the embodiment of this application establishes a signal reference system for the infrared probe under normal operating conditions. Then, it obtains the average intensity and fluctuation characteristics of the current infrared probe signal. Next, based on the average intensity reference value and the fluctuation range reference value, it performs threshold judgment on the average intensity and fluctuation characteristics to obtain the judgment result. If the judgment result indicates the presence of water vapor interference, it calculates the temperature correction amount based on the fluctuation characteristics. Finally, it corrects the current production temperature based on the temperature correction amount. This enables the identification of water vapor interference and correction of the production temperature by combining the average intensity and fluctuation characteristics of the signal, thereby achieving production temperature control and improving accuracy.

[0174] like Figure 2 As shown, this embodiment of the invention also provides a temperature control system for the production of micro-rutting asphalt mixtures, including:

[0175] The reference system establishment module 601 is used to establish the signal reference system of the infrared probe under normal operating conditions. The signal reference system includes the average intensity reference value and the fluctuation range reference value.

[0176] Data acquisition module 602 is used to acquire the average intensity and fluctuation characteristics of the current infrared probe signal;

[0177] The judgment module 603 is used to make threshold judgments on the average intensity and fluctuation characteristics based on the average intensity reference value and the fluctuation range reference value, and to obtain the judgment result;

[0178] The correction calculation module 604 is used to calculate the temperature correction amount based on the fluctuation characteristics if the judgment result is that there is water vapor interference.

[0179] The temperature correction module 605 is used to correct the current production temperature according to the temperature correction amount.

[0180] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0181] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A method for controlling the production temperature of micro-rutting asphalt mixture, characterized in that, Includes the following steps: Establish a signal reference system for the infrared probe under normal operating conditions, wherein the signal reference system includes an average intensity reference value and a fluctuation range reference value; Obtain the average intensity and fluctuation characteristics of the current infrared probe signal; Based on the average intensity reference value and the fluctuation range reference value, threshold judgments are made on the average intensity and the fluctuation characteristics to obtain the judgment result; If the judgment result indicates the presence of water vapor interference, then the temperature correction amount is calculated based on the fluctuation characteristics. The current production temperature is corrected based on the stated temperature correction amount; The step of performing threshold judgment on the average intensity and the fluctuation characteristics based on the average intensity reference value and the fluctuation range reference value to obtain the judgment result includes: If the average intensity is less than the average intensity reference value, then it is determined whether the fluctuation characteristic is less than the fluctuation range reference value; If the fluctuation characteristic is less than the fluctuation range reference value, then the judgment result is determined to be the presence of fog-like medium interference; If the fluctuation characteristic is greater than the fluctuation range reference value, then the moisture content of the material in the mixing drum is obtained; If the moisture content of the material is greater than the preset moisture content threshold, then the judgment result is determined to be that water vapor interference exists; If the moisture content of the material is less than the preset moisture content threshold, then the judgment result is determined to be due to interference from other factors.

2. The method according to claim 1, characterized in that, The establishment of the signal reference system for the infrared probe under normal operating conditions includes: Determine the initial steady-state window at the start of asphalt mixture production; Within the initial stable state window, raw signal strength data is acquired using the infrared probe; Based on the original signal strength data, the average value is calculated as the average strength reference value; Based on the original signal strength data, the standard deviation is calculated as a reference value for the fluctuation range.

3. The method according to claim 1, characterized in that, If the moisture content of the material is greater than a preset moisture content threshold, then determining that the judgment result indicates the presence of water vapor interference includes: Obtain information on material type, internal ambient temperature of the mixing drum, and aggregate particle size distribution; The preset moisture content threshold is adjusted based on the material type information, the internal ambient temperature information of the mixing drum, and the aggregate particle size distribution information. The moisture content of the material is compared with the adjusted preset moisture content threshold. If the moisture content of the material is greater than the adjusted preset moisture content threshold, then the judgment result is determined to be the presence of water vapor interference.

4. The method according to claim 3, characterized in that, If the moisture content of the material is greater than the adjusted preset moisture content threshold, then determining that the judgment result indicates the presence of water vapor interference includes: If the moisture content of the material is greater than the adjusted preset moisture content threshold, then the trend of the change in the moisture content of the material is monitored. If the moisture content of the material changes in an upward trend, then the magnitude of the change in the average strength and fluctuation characteristics is determined. If the change in the average intensity increases and the change in the fluctuation characteristics increases, then the judgment result is determined to be the presence of water vapor interference.

5. The method according to claim 4, characterized in that, If the change in the average intensity increases and the change in the fluctuation characteristics increases, then determining that the judgment result indicates the presence of water vapor interference includes: Acquire the self-diagnostic status information and historical operating data of the infrared probe; The operating status of the infrared probe is analyzed based on the self-diagnostic status information and the historical operating data. If the operating status is aging or faulty, then the current infrared probe signal is corrected; Based on the corrected current infrared probe signal, update the change range of the average intensity and the change range of the fluctuation characteristics; If the change in the updated average intensity increases and the change in the updated fluctuation characteristics increases, then the judgment result is determined to be the presence of water vapor interference.

6. The method according to claim 4, characterized in that, If the change in the average intensity increases and the change in the fluctuation characteristics increases, then determining that the judgment result indicates the presence of water vapor interference includes: Obtain production load information, asphalt viscosity information, and aggregate gradation information of asphalt mixtures; Based on the production load information, the asphalt viscosity information, and the aggregate gradation information, a target change threshold is determined, which includes a material moisture content change threshold, an average strength change threshold, and a fluctuation characteristic change threshold. Calculate the instantaneous rate of change of the material's moisture content based on the material's moisture content; Calculate the instantaneous rate of change of the average intensity based on the average intensity; Calculate the instantaneous rate of change of the fluctuation characteristics based on the fluctuation characteristics; If the instantaneous rate of change of the material's moisture content is greater than the material's moisture content change threshold, the instantaneous rate of change of the average strength is greater than the average strength change threshold, and the instantaneous rate of change of the fluctuation characteristic is greater than the fluctuation characteristic change threshold, then the judgment result is determined to be the presence of water vapor interference.

7. The method according to claim 1, characterized in that, The method further includes: If the judgment result indicates the presence of mist-like medium interference, then obtain the reference fuel supply rate and reference temperature data of the burner under normal operating conditions; Calculate the reference energy input power based on the reference fuel supply rate and the calorific value of the dye; Calculate the rate of change of the reference temperature based on the preset sliding window and the reference temperature data; A power-temperature rise relationship curve is constructed based on the multiple reference energy input powers and the multiple reference temperature rise rates. The current production temperature is controlled based on the power-temperature rise relationship curve.

8. The method according to claim 7, characterized in that, The step of controlling the current production temperature based on the power-temperature rise relationship curve includes: Obtain current fuel supply rate and current temperature data; Calculate the current energy input power based on the current fuel supply rate and the calorific value of the dye; Calculate the rate of change of the current temperature rise based on the preset sliding window and the current temperature data; Extract the rate of temperature rise corresponding to the current energy input power from the power-temperature rise relationship curve as the target rate of temperature rise. If the current temperature rise rate is less than the target temperature rise rate, the burner power is reduced and a temperature anomaly alarm is issued.

9. A temperature control system for producing asphalt mixtures with minor rutting, applied to the temperature control method for producing asphalt mixtures with minor rutting asphalt as described in claim 1, characterized in that, include: The reference system establishment module is used to establish a signal reference system for the infrared probe under normal operating conditions. The signal reference system includes an average intensity reference value and a fluctuation range reference value. The data acquisition module is used to acquire the average intensity and fluctuation characteristics of the current infrared probe signal; The judgment module is used to perform threshold judgment on the average intensity and the fluctuation characteristics based on the average intensity reference value and the fluctuation range reference value, and obtain the judgment result; The correction calculation module is used to calculate the temperature correction amount based on the fluctuation characteristics if the judgment result indicates the presence of water vapor interference. The temperature correction module is used to correct the current production temperature according to the temperature correction amount.

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