Micro-rut asphalt mixture production temperature control method and system

Abstract

The invention discloses a micro-rut asphalt mixture production temperature control method and system, and relates to the technical field of material production.The method comprises the steps that a signal reference system of an infrared probe under the normal working condition is established, and the signal reference system comprises an average intensity reference value and a fluctuation range reference value; acquiring average intensity and fluctuation characteristics of a current infrared probe signal; according to the average intensity reference value and the fluctuation range reference value, performing threshold judgment on the average intensity and the fluctuation characteristics to obtain a judgment result; if the judgment result is that the water vapor interference exists, temperature correction is calculated according to the fluctuation characteristics; and correcting the current production temperature according to the temperature correction amount. The water vapor interference can be identified and the production temperature can be corrected by combining the average intensity and fluctuation characteristics of the signal, so that the production temperature control is realized, and the accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of material production, and in particular to a temperature control method and system for producing micro-rutting asphalt mixture. Background Art

[0002] In modern asphalt mixing plants, when mixing micro-rutting asphalt mixtures, existing methods use infrared sensors installed inside the mixing drum to capture thermal images. These images are then converted into images that accurately reflect the spatial temperature distribution of the material within the drum, enabling monitoring of temperature changes during the production of the micro-rutting mixture. However, asphalt mixing plants are subject to dynamic production demands. When producing batches of asphalt concrete for pavement, the aggregates for the asphalt mixture are typically stored in open-air storage areas. If heavy rainfall occurs the night before or during production, these exposed aggregates absorb significant amounts of water, causing their overall moisture content to significantly exceed normal dry weather standards. The high temperatures generated by the burners within the mixing drum rapidly heat and evaporate the excess water in the aggregates, forming a high-temperature water vapor cloud within the drum that is far more concentrated than normal production conditions. This cloud absorbs the infrared energy emitted by the high-temperature aggregate curtain, significantly attenuating the signal strength received by the sensors. This makes precise control of production temperatures difficult, 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 purpose of the embodiments of the present invention is to propose a method and system for controlling the production temperature of micro-rutting asphalt mixture, which can identify water vapor interference and correct the production temperature by combining the average intensity and fluctuation characteristics of the signal to achieve production temperature control and improve accuracy.

[0005] In one aspect, an embodiment of the present invention provides a method for controlling temperature in the production of micro-rutting asphalt mixture, comprising the following steps: Establishing a signal reference system for the infrared probe under normal working 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; Performing threshold judgment on the average intensity and the fluctuation characteristics according to the average intensity reference value and the fluctuation range reference value to obtain a judgment result; If the judgment result is that water vapor interference exists, calculating a temperature correction amount according to the fluctuation characteristics; The current production temperature is corrected according to the temperature correction amount.

[0006] In some embodiments, establishing a signal reference system of the infrared probe under normal operating conditions includes: At the start of asphalt mixture production, determine the initial steady state window; In the initial stable state window, collecting raw signal strength data through the infrared probe; Calculating an average value based on the original signal intensity data as the average intensity reference value; According to the original signal intensity data, a standard deviation is calculated as a reference value of the fluctuation range.

[0007] In some embodiments, performing threshold judgment on the average intensity and the fluctuation characteristics according to the average intensity reference value and the fluctuation range reference value to obtain a judgment result includes: If the average intensity is less than the average intensity reference value, determining whether the fluctuation characteristic is less than the fluctuation range reference value; If the fluctuation characteristic is smaller than the fluctuation range reference value, determining that the judgment result is that mist medium interference exists; If the fluctuation characteristic is greater than the fluctuation range reference value, the moisture content of the material in the mixing drum is obtained; If the moisture content of the material is greater than a preset moisture content threshold, determining that the judgment result is that water vapor interference exists; If the moisture content of the material is less than the preset moisture content threshold, it is determined that the judgment result is interfered by other factors.

[0008] In some embodiments, if the moisture content of the material is greater than a preset moisture content threshold, determining that the judgment result is that water vapor interference exists includes: Obtain material type information, internal ambient temperature information of the mixing drum and aggregate particle size distribution information; Adjusting the preset moisture content threshold according to the material type information, the internal ambient temperature information of the mixing drum, and the aggregate particle size distribution information; Comparing the moisture content of the material with the adjusted preset moisture content threshold; If the moisture content of the material is greater than the adjusted preset moisture content threshold, it is determined that the judgment result is that water vapor interference exists.

[0009] In some embodiments, if the moisture content of the material is greater than the adjusted preset moisture content threshold, determining that the judgment result is that water vapor interference exists includes: If the moisture content of the material is greater than the adjusted preset moisture content threshold, monitoring the change trend of the moisture content of the material; If the change trend of the moisture content of the material is an increase, the change range of the average strength and the fluctuation characteristics is judged; If the variation range of the average intensity increases and the variation range of the fluctuation characteristic increases, it is determined that the judgment result is that water vapor interference exists.

[0010] In some embodiments, if the variation range of the average intensity increases and the variation range of the fluctuation characteristic increases, determining that the judgment result is that water vapor interference exists includes: Obtain the infrared probe's self-diagnosis status information and historical operation data; Analyzing the operating status of the infrared probe according to the self-diagnosis status information and the historical operating data; If the operating state is aging or failure, calibrating the current infrared probe signal; updating the variation of the average intensity and the variation of the fluctuation characteristic according to the corrected current infrared probe signal; If the change amplitude of the updated average intensity increases, and the change amplitude of the updated fluctuation characteristic increases, then the judgment result is determined to be the presence of water vapor interference.

[0011] In some embodiments, if the variation range of the average intensity increases and the variation range of the fluctuation characteristic increases, determining that the judgment result is that water vapor interference exists includes: Obtaining production load information, asphalt viscosity information and aggregate gradation information of asphalt mixture; Determining a target change threshold according to the production load information, the asphalt viscosity information, and the aggregate gradation information, wherein the target change threshold includes a material moisture content change threshold, an average strength change threshold, and a fluctuation characteristic change threshold; Calculating the instantaneous rate of change of the moisture content of the material according to the moisture content of the material; Calculating the average intensity instantaneous change rate based on the average intensity; Calculating the instantaneous rate of change of the fluctuation characteristics according to the fluctuation characteristics; If the instantaneous change rate of the material moisture content is greater than the material moisture content change threshold, the instantaneous change rate of the average strength is greater than the average strength change threshold, and the instantaneous change rate of the fluctuation characteristic is greater than the fluctuation characteristic change threshold, then the judgment result is determined to be that water vapor interference exists.

[0012] In some embodiments, the method further comprises: If the judgment result is that there is a mist medium interference, obtaining reference fuel supply rate and reference temperature data of the burner under normal operating conditions; Calculating a reference energy input power based on the reference fuel supply rate and the dye calorific value; Calculating a reference temperature rising rate of change according to a preset sliding window and the reference temperature data; constructing a power-temperature rise relationship curve according to the plurality of reference energy input powers and the plurality of reference temperature rise change rates; The current production temperature is controlled according to the power-temperature rise relationship curve.

[0013] In some embodiments, controlling the current production temperature according to the power-temperature rise relationship curve includes: Get current fuel supply rate and current temperature data; Calculating current energy input power based on the current fuel supply rate and the dye calorific value; Calculating the current temperature rise rate according to the preset sliding window and the current temperature data; Extracting the temperature rise rate corresponding to the current energy input power from the power-temperature rise relationship curve as the target temperature rise rate; If the current temperature rising rate of change is less than the target temperature rising rate of change, the burner power is reduced and a temperature abnormality alarm is issued.

[0014] On the other hand, an embodiment of the present invention provides a temperature control system for producing micro-rutting asphalt mixture, comprising: A reference system establishment module is used to establish a signal reference system of the infrared probe under normal working conditions, wherein the signal reference system includes an average intensity reference value and a fluctuation range reference value; The data acquisition module is used to obtain the average intensity and fluctuation characteristics of the current infrared probe signal; a judgment module, configured to perform threshold judgment on the average intensity and the fluctuation characteristics according to the average intensity reference value and the fluctuation range reference value, and obtain a judgment result; a correction value calculation module, configured to calculate a temperature correction value based on the fluctuation characteristics if the judgment result is that water vapor interference exists; The temperature correction module is used to correct the current production temperature according to the temperature correction amount.

[0015] The embodiments of the present application include at least the following beneficial effects: the embodiments of the present application first establish a signal reference system of the infrared probe under normal working conditions, then obtain the average intensity and fluctuation characteristics of the current infrared probe signal, and then perform threshold judgment on the average intensity and fluctuation characteristics based on the average intensity reference value and the fluctuation range reference value to obtain a judgment result. If the judgment result is that there is water vapor interference, the temperature correction amount is calculated based on the fluctuation characteristics. Finally, the current production temperature is corrected based on the temperature correction amount, so that water vapor interference can be identified and the production temperature can be corrected in combination with the average intensity and fluctuation characteristics of the signal to achieve production temperature control, thereby improving accuracy.

[0016] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the structures particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 This is a flow chart of a method for controlling temperature in the production of micro-rutting asphalt mixture according to an embodiment of the present invention; Figure 2 This is a schematic structural diagram of a temperature control system for producing micro-rutting asphalt mixture according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of this application more clear, this application is further described in detail below with reference to the accompanying drawings and embodiments.

[0020] In modern asphalt mixing plants, particularly those focused on producing high-performance pavement materials, production control systems typically possess a high degree of sophistication. For example, when manufacturing micro-rutting asphalt mixtures, which require extremely stringent temperature control, the core temperature control strategy often relies on an advanced computational framework. This framework processes in real time thermal images captured by infrared sensors installed within the mixing drum. As aggregate and asphalt tumble and fall within the drum, they form a continuous, dynamic "curtain." The infrared sensors are precisely calibrated to capture the heat radiated by this curtain. This sophisticated computational framework then transforms these raw thermal image data into an image accurately representing the spatial temperature distribution of the material within the drum through a series of precise calculation steps and pre-established calibration relationships. This temperature distribution is crucial because it not only provides information on the average material temperature but also reveals temperature variations within local areas of the material flow, which is essential for ensuring the uniform heating required for micro-rutting mixtures. The entire system, including its sensors, data processing logic, and control methods, has been carefully optimized and calibrated for the unique thermal conductivity and mixing behavior of micro-rutting asphalt mixtures.

[0021] However, the actual operating environment of an asphalt mixing plant is fraught with dynamic production demands. Depending on the specific requirements of the project and material availability, the production line frequently needs to switch between different types of asphalt mixes. This is a common and completely normal production scheduling behavior, not a sudden, abnormal decision. For example, when the production task shifts from a high-precision micro-rutting mix to the production of larger batches of more conventional asphalt concrete for pavement, despite the plant's advanced control system, it still operates based on parameters and assumptions set for the micro-rutting mix, which may not be optimal for the conventional mix.

[0022] A common challenge during this production switchover process is closely related to the characteristics of the raw materials used in conventional mixes. Aggregates (stones) used in ordinary road asphalt mixtures are usually piled in open-air material yards. If heavy rainfall occurs the night before or during production, these exposed aggregate piles will absorb a large amount of water, causing their overall moisture content to be significantly higher than the normal standard 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 the pursuit of high efficiency is high. Operators may not be able to perform real-time and precise measurements of the exact moisture content of all incoming aggregates. Although some mixing plants may be equipped with preliminary moisture content detection devices at the aggregate feed inlet, their accuracy and feedback speed may not be enough to capture subtle changes in the moisture distribution of large batches of materials, and it is difficult to fully quantify their potential impact on the subsequent heating process.

[0023] When these aggregates, with moisture contents far exceeding normal standards, are fed into the hot mixing drum, a critical physical phenomenon occurs. The high temperatures generated by the burners within the mixing drum rapidly heat the excess water in the aggregate, causing it to evaporate violently. This instantaneous and massive evaporation process forms a "hot water vapor cloud" within the mixing drum, with a concentration far exceeding that found in normal production conditions. This dense water vapor cloud sits perfectly between the incandescent aggregate "curtain" and the infrared probe used to measure temperature. Because infrared radiation is essentially an electromagnetic wave, it experiences significant absorption and scattering when passing through dense water vapor. This means that a significant portion of the infrared energy emitted from the hot aggregate curtain is absorbed by water vapor molecules or scattered in other directions before reaching the infrared probe, significantly attenuating the signal strength received by the probe.

[0024] The advanced computing framework at the heart of the temperature control system was designed primarily to account for common disturbances 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 the extreme concentrations of "water vapor clouds" caused by high-moisture aggregates. Therefore, when the infrared probe receives a severely attenuated and distorted infrared signal, the computing framework faithfully executes its pre-set logic. It mistakenly interprets the reduced signal strength as insufficient heat radiated by the material curtain itself, concluding that the material temperature is too low and generating an overall "cold" temperature distribution map.

[0025] After receiving this "cold" temperature profile, the control program, based on its pre-set control logic, determines that the material is being severely underheated. In response, the system automatically commands the burner's fuel control valve to significantly increase the gas or oil supply. This action aims to increase flame intensity, rapidly heating the material, which the system "perceives" as being underheated, to the set temperature required by the process. From a control logic perspective, this is a completely reasonable and expected corrective action, but it is based on fundamentally erroneous temperature information.

[0026] However, in reality, the aggregate in the mixing drum has already reached or even exceeded the ideal heating temperature. The burner, under erroneous instructions, continues to overheat, causing the aggregate to severely burn. When these scalding aggregates, whose temperatures far exceed the upper limit of the specification, are mixed with liquid asphalt, the high temperature instantly destroys the asphalt's molecular structure, a process known as "thermal aging." Thermal aging significantly reduces key asphalt properties such as viscosity, elasticity, and adhesion, significantly reducing the service life of the resulting paved road surface and even causing premature failure. During this overheated mixing process, a clearly visible "blue smoke" will emerge from the mixing drum or discharge port, a clear sign that the asphalt is cracking or burning due to the instantaneous high temperature. The appearance of blue smoke means that the entire batch of asphalt mixture is completely scrapped, resulting in not only direct economic losses in raw materials and energy, but also environmental pollution.

[0027] In the asphalt mixing drum, when high-moisture aggregate produces a high-concentration water vapor cloud under high temperature, it seriously interferes with the infrared probe's accurate perception of the spatial temperature distribution of the material "material curtain". Existing technologies find it difficult to effectively identify and compensate for this interference, causing the system to overheat the burner based on erroneous temperature information, resulting in thermal aging and scrapping of the asphalt mixture, and low production temperature control accuracy.

[0028] In light of this, embodiments of the present application utilize in-depth analysis of infrared probe signals to intelligently identify and quantify water vapor interference, thereby enabling precise correction of production temperature. When high-moisture aggregate enters the mixing drum, the generation of large amounts of water vapor significantly impacts 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 determining signal anomalies. After obtaining the average intensity and fluctuation characteristics of the current infrared probe signal, a threshold determination is performed against the reference system. If the determination indicates the presence of water vapor interference (for example, a significant decrease in average intensity and an abnormal increase in the fluctuation characteristics), a precise temperature correction is calculated based on the intensity of the fluctuation characteristics. This correction compensates for the absorption and scattering effects of water vapor on the infrared signal, allowing the apparent temperature measured by the infrared probe to be corrected to a value closer to the actual material temperature. 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, and adaptively addressing water vapor interference in the production environment, ensuring the stable production of micro-rutting asphalt mixtures.

[0029] The following is a detailed explanation of the embodiments of the present application with reference to the accompanying drawings: Figure 1 This is an optional flow chart of a method for controlling temperature in the production of micro-rutting asphalt mixture provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S105.

[0030] Step S101: establishing a signal reference system of the infrared probe under normal working conditions, wherein the signal reference system includes an average intensity reference value and a fluctuation range reference value; Step S102: obtaining the average intensity and fluctuation characteristics of the current infrared probe signal; Step S103: performing threshold judgment on the average intensity and the fluctuation characteristics according to the average intensity reference value and the fluctuation range reference value to obtain a judgment result; Step S104: If the result of the judgment is that water vapor interference exists, the temperature correction amount is calculated according to the fluctuation characteristics; Step S105: Correct the current production temperature according to the temperature correction amount.

[0031] Steps S101 to S105 shown in the embodiment of the present application can identify water vapor interference and correct the production temperature by combining the average intensity and fluctuation characteristics of the signal to achieve production temperature control and improve accuracy.

[0032] 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, raw signal intensity data from the infrared probe can be continuously collected during the initial stages of asphalt mixture production, when the material moisture content is normal and production is stable. This data can be recorded and stored in a database. Subsequently, statistical analysis can be performed on this raw signal intensity data to calculate its average value as the average intensity reference value, and its standard deviation as the fluctuation range reference value. Alternatively, a lookup table containing multiple average intensity reference values ​​and fluctuation range reference values ​​can be established in advance through extensive experiments and historical data analysis under normal operating conditions, such as for different material types and production loads. During actual production, the system selects the most suitable reference system based on the current operating conditions. It should be 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 it. It is used in asphalt mixing drums to monitor 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.

[0033] The average strength and fluctuation characteristics of the current infrared probe signal are then 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 most recently collected signal strength data to determine the average strength and fluctuation characteristics of the current signal. For example, algorithms such as sliding average or exponential smoothing can be used to calculate the average strength to reflect the short-term trend of the signal. For the fluctuation characteristics, the standard deviation or coefficient of variation of the signal within a certain time window can be calculated to quantify the degree of signal dispersion. It can be understood that the average strength refers to the arithmetic mean of the infrared probe signal strength within a certain time window, reflecting the overall energy level of the signal. The 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.

[0034] Then, based on the average intensity reference value and the fluctuation range reference value, a threshold judgment is performed on the average intensity and the fluctuation characteristics to obtain a judgment result. 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, there may be signal attenuation. 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, there may be severe interference. Specifically, a series of thresholds can be set. For example, if the current average intensity is lower than a certain percentage of the average intensity reference value, and the current fluctuation characteristics are higher than a certain percentage of the fluctuation range reference value, it is preliminarily judged that there may be water vapor interference.

[0035] If water vapor interference is detected, a temperature correction is calculated based on the fluctuation characteristics. For example, the temperature correction can be calculated based on the intensity of the fluctuation characteristics using a pre-established mathematical model or lookup table. More intense fluctuation characteristics generally indicate more severe water vapor interference, thus requiring a larger temperature correction. This model can be fitted by experimental data. For example, the fluctuation characteristics of the infrared probe signal and the deviation between the actual and measured temperatures are recorded at different water vapor concentrations, thereby establishing a mapping between the fluctuation characteristics and the temperature correction.

[0036] Finally, the current production temperature is corrected according to the temperature correction value. The calculated temperature correction value will be applied to the original temperature data measured by the infrared probe. For example, if the temperature measured by the infrared probe is , the calculated temperature correction is , then the corrected true temperature This corrected temperature is closer to the actual temperature of the material and will be fed back to the temperature control system to guide the adjustment of the burner power to avoid overheating or underheating.

[0037] Through the above-mentioned technical solution, this embodiment introduces the concepts of signal reference frame and fluctuation characteristics and applies them to the identification and temperature correction of water vapor interference. By establishing a reference value for average intensity and fluctuation range under normal operating conditions, this embodiment can accurately determine whether the current infrared probe signal is subject to abnormal water vapor interference. When water vapor interference is identified, a precise temperature correction factor is 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 true 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 interpret the material temperature as low, thereby increasing the burner power. However, this embodiment can identify water vapor interference and calculate the corresponding temperature correction factor, correcting the measured temperature to the actual value, thus avoiding unnecessary heating and significantly reducing the risk of asphalt thermal aging and product rejection. Therefore, this embodiment not only improves the production quality and stability of micro-rutting asphalt mixtures, but also saves energy and raw materials, with significant economic and environmental benefits.

[0038] In some embodiments, in step S101, establishing a signal reference system of the infrared probe under normal working conditions may include but is not limited to the following steps: At the start of asphalt mixture production, determine the initial steady state window; In the initial stable state window, the original signal intensity data is collected by the infrared probe; Based on the original signal intensity data, the average value was calculated as the average intensity reference value; Based on the original signal intensity data, the standard deviation is calculated as the reference value of the fluctuation range.

[0039] In some embodiments, an initial stable state window can be determined at the start of asphalt mixture production. This initial stable state window refers to a specific period of time during the initial stages of asphalt mixture production, when equipment operation is stable, material supply is continuous, and there are no significant external interference. This window is determined to ensure that the collected raw signal strength data truly reflects the infrared probe's operating status under normal production conditions. For example, the stability of key production line parameters (such as aggregate feed rate, asphalt pumping pressure, and mixing drum speed) can be monitored to determine whether the initial stable state window has been reached.

[0040] Then, within the initial stable state window, the infrared probe collects raw signal strength data. This data can be collected continuously by the infrared probe. Raw signal strength data is a series of values ​​obtained by measuring the infrared radiation intensity emitted by the asphalt mixture or its surroundings under normal operating conditions. This data forms the basis for subsequent calculations of the average intensity reference value and the fluctuation range reference value.

[0041] Based on the raw signal strength data, an average value is calculated as the average strength reference value, and a standard deviation is calculated based on the raw signal strength data as the fluctuation range reference value. The average strength reference value is obtained by taking the arithmetic mean of the raw signal strength data collected within the initial stable state window. This average value represents the typical intensity level of the infrared sensor signal under normal operating conditions. The fluctuation range reference value is obtained by calculating the standard deviation of the raw signal strength data. As a statistical quantity, the standard deviation effectively reflects the degree of dispersion of data points relative to the average value, that is, the volatility of the signal. The fluctuation range reference value represents the natural fluctuation range of the infrared sensor signal strength under normal production conditions.

[0042] This embodiment ensures representative and reliable data by determining an initial stable state window at the start of production and collecting raw signal strength data within this window. Consequently, by calculating the mean and standard deviation of this raw signal strength data, a precise reference system for the infrared sensor's signal under normal operating conditions can be established, including a reference value for the average intensity and a reference value for the fluctuation range. This method provides a solid foundation for subsequent assessment of the current infrared sensor signal and effectively distinguishes normal fluctuations from abnormal interference.

[0043] 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 associated with fixed thresholds, thereby improving the sensitivity and accuracy of detecting 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.

[0044] In some embodiments, in step S103, threshold judgment is performed on the average intensity and the fluctuation characteristics according to the average intensity reference value and the fluctuation range reference value to obtain a judgment result, which may include but is not limited to the following steps: Step S201: If the average intensity is less than the average intensity reference value, determine whether the fluctuation characteristic is less than the fluctuation range reference value; Step S202: If the fluctuation characteristic is smaller than the fluctuation range reference value, it is determined that the mist medium interference exists; Step S203: If the fluctuation characteristic is greater than the fluctuation range reference value, the moisture content of the material in the mixing drum is obtained; Step S204: If the moisture content of the material is greater than the preset moisture content threshold, it is determined that water vapor interference exists; Step S205: If the moisture content of the material is less than the preset moisture content threshold, it is determined that the judgment result is that there are other factors interfering.

[0045] In some embodiments, a threshold judgment can be made on the average intensity first. If the average intensity is less than the average intensity reference value, it indicates that the infrared signal may have been attenuated. This is usually due to the medium blocking or absorbing infrared radiation. It can be further judged whether the fluctuation characteristic is less than the fluctuation range reference value. If the fluctuation characteristic is less than the fluctuation range reference value, the judgment result is determined to be the presence of mist medium interference. It is understandable that mist media, such as water mist or fine dust, will uniformly attenuate the infrared signal, resulting in a decrease in the average intensity, but because of its relatively uniform distribution, it has little effect on the volatility of the signal, so the fluctuation characteristic remains at a low level. If the fluctuation characteristic is greater than the fluctuation range reference value, it indicates that the signal is not only attenuated, but also its volatility has increased significantly. The moisture content of the material in the mixing drum can be obtained, and a threshold judgment of the moisture content of the material can be further made.

[0046] If the moisture content of the material is greater than the preset moisture content threshold, it is determined that there is water vapor interference. The generation of water vapor is usually accompanied by the drying process of the material, which will form uneven steam clusters in the field of view of the infrared probe, resulting in a decrease in the average intensity of the infrared signal. At the same time, due to the dynamic changes in the steam clusters, it will cause violent fluctuations in the signal. More importantly, the preset moisture content threshold can be a critical value determined based on experience or experiments to distinguish between normal water evaporation and abnormal water vapor interference. If the moisture content of the material is less than the preset moisture content 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 interference from other factors. These other factors may include but are not limited to failure of the infrared probe itself, scaling on the inner wall of the mixing drum, abnormal material accumulation, etc. These factors may also cause abnormal changes in the infrared signal.

[0047] 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, and incorporating the material moisture content for auxiliary judgment. Specifically, the system first compares the average intensity with a reference value to preliminarily determine whether signal attenuation exists. Second, by comparing the fluctuation characteristics with the reference value, it distinguishes whether the signal attenuation is caused by a uniform medium (such as a mist medium) or an inhomogeneous medium (such as water vapor). When the signal exhibits attenuation and increased volatility, 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 interference type causing infrared temperature measurement errors, thereby providing a reliable basis for subsequent temperature correction or control.

[0048] Through the above-mentioned technical solution, this embodiment can effectively distinguish different types of interference to infrared probe signals during asphalt mixture production, including interference from mist media, water vapor, and other factors. This refined interference type identification capability avoids the misjudgments or omissions that may occur with traditional methods, thereby providing more accurate input for subsequent temperature correction or control. This significantly improves the accuracy and reliability of temperature control during asphalt mixture production, reduces temperature measurement deviations caused by interference, and thus ensures the quality and stability of asphalt mixture production.

[0049] In some embodiments, in step S204, if the moisture content of the material is greater than a preset moisture content threshold, it is determined that water vapor interference exists, which may include but is not limited to the following steps: Step S301: obtaining material type information, internal ambient temperature information of the mixing drum, and aggregate particle size distribution information; Step S302: adjusting the preset moisture content threshold according to the material type information, the internal ambient temperature information of the mixing drum, and the aggregate particle size distribution information; Step S303: comparing the moisture content of the material with the adjusted preset moisture content threshold; Step S304: If the moisture content of the material is greater than the adjusted preset moisture content threshold, it is determined that the judgment result is that water vapor interference exists.

[0050] In some embodiments, various factors, such as material type, ambient temperature inside the mixing drum, and aggregate particle size distribution, can significantly influence the actual moisture content of the material and its appearance in the infrared signal. Using a single, fixed, preset moisture content threshold for determination can lead to misjudgment or omission of water vapor interference, thus affecting the accuracy and production efficiency of subsequent temperature corrections. To this end, information on material type, ambient temperature inside the mixing drum, and aggregate particle size distribution can be obtained. Material type information refers to whether the aggregate used in the asphalt mixture is basalt, limestone, or another type, and whether it contains recycled asphalt aggregate (RAP). Ambient temperature inside the mixing drum refers to the real-time temperature inside the mixing drum, which can be obtained using a temperature sensor installed inside the mixing drum. Aggregate particle size distribution refers to the ratio of different aggregate sizes within the aggregate, for example, obtained through screening test data. These parameters are key parameters influencing the moisture content of the material and its drying characteristics.

[0051] Then, based on the material type information, the internal ambient temperature information of 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 relationship between different material types, ambient temperatures, and aggregate particle size distributions and the corresponding moisture content thresholds can be pre-established. For example, for material types with strong water absorption or at lower internal ambient temperatures of the mixing drum, the preset moisture content threshold can be appropriately adjusted higher; while for aggregates with smaller particle sizes, their surface area is relatively large, and a lower moisture content threshold may be required to accurately determine the presence of water vapor. This adjustment process is intended to make the threshold more in line with 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.

[0052] This embodiment introduces material type information, internal ambient temperature information of the mixing drum, and aggregate particle size distribution information, and dynamically adjusts the preset moisture content threshold based on this information, thereby solving the problem of inaccurate judgment of fixed thresholds in complex and changing production environments. Specifically, different types of materials have different water absorption and dehydration characteristics; the internal ambient temperature of the mixing drum directly affects the evaporation rate of water in the material; and the particle size distribution of the aggregate affects the overall porosity of the material and its water retention capacity. 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 moisture content of the material exceeds this dynamically adjusted threshold, it can more reliably indicate the actual presence of water vapor, avoiding misjudgment due to environmental changes.

[0053] To more clearly illustrate this technical solution, a specific example is provided below. Assume that during asphalt mixture production, the average intensity and fluctuation characteristics of the infrared sensor signal indicate possible water vapor interference, and the moisture content of the material within the mixing drum is initially detected as 0.8%. In a traditional solution, if the preset moisture content threshold is fixed at 0.7%, water vapor interference would be directly determined. However, in this embodiment, information about the current material type (for example, assuming that highly water-absorbent basalt aggregate is currently being used), the ambient temperature within the mixing drum (for example, the current ambient temperature is 150°C, lower than the normal operating temperature of 170°C), and the aggregate particle size distribution (for example, a high content of fine aggregate) can be obtained. Based on this information, an adjusted preset moisture content threshold is calculated using a preset model or table lookup. For example, due to the high water absorption of basalt and the low ambient temperature, the system may adjust the preset moisture content threshold from 0.7% to 0.9%. In this case, the current material moisture content of 0.8% is compared with the adjusted preset moisture content threshold of 0.9%. Because 0.8% is less than 0.9%, the system determines that there is no water vapor interference in the current situation, 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, thereby ensuring the accuracy of subsequent temperature corrections and avoiding unnecessary production adjustments.

[0054] Through the above-mentioned technical solution, this embodiment can significantly improve the accuracy and robustness of water vapor interference judgment in the asphalt mixture production process. Compared with the traditional method of using a fixed threshold, this embodiment can adaptively adjust the judgment criteria according to the actual material properties and environmental conditions, thereby effectively reducing the false alarm or missed alarm phenomenon caused by threshold mismatch. This more accurate interference judgment makes subsequent temperature correction measures more timely and effective, avoiding unnecessary energy waste or product quality issues, and ultimately helping to achieve refined temperature control in the micro-rutting asphalt mixture production process, improving product quality and production efficiency.

[0055] In some embodiments, in step S304, if the moisture content of the material is greater than the adjusted preset moisture content threshold, then determining that water vapor interference exists may include but is not limited to the following steps: Step S401: If the moisture content of the material is greater than the adjusted preset moisture content threshold, monitor the change trend of the moisture content of the material; Step S402: If the change trend of the moisture content of the material is increasing, the change range of the average strength and the fluctuation characteristics is determined; Step S403: If the variation range of the average intensity increases and the variation range of the fluctuation characteristic increases, it is determined that the judgment result is that water vapor interference exists.

[0056] In some embodiments, because the moisture content of a material may be affected instantaneously by a variety of factors, a judgment method based solely on threshold comparison may be at risk of misjudgment. For example, when the moisture content of a material briefly exceeds a threshold but does not continue to rise, or when the degree of water vapor interference is relatively mild but has a subtle effect on the infrared signal, accurate identification or differentiation may not be possible. To this end, the moisture content of the material can be judged. If the moisture content of the material is greater than the adjusted preset moisture content threshold, the trend of the moisture content of the material is monitored. For example, continuous measurement data of the moisture content of the material over a period of time can be analyzed to determine whether it is rising, falling, or relatively stable. For example, the trend of the moisture content of the material can be determined by calculating the instantaneous rate of change of the moisture content of the material or the average rate of change within a preset time window. Among them, an upward trend in the moisture content of the material usually means that there is continuous water evaporation or the addition of new wet material, which is closely related to the generation of water vapor.

[0057] If the trend of the change in the moisture content of the material is upward, the change amplitude of the average intensity and the fluctuation characteristics is judged. If the change amplitude of the average intensity increases and the change amplitude of the fluctuation characteristics increases, the judgment result is determined to be the presence of water vapor interference. For example, the degree of change of the average intensity and fluctuation characteristics of the infrared probe signal relative to its normal state or the previous moment can be evaluated. The increase in the change amplitude of the average intensity is usually manifested as a significant decrease in the intensity of the infrared signal, which is due to the enhanced absorption and scattering of infrared light by water vapor. The increase in the change amplitude of the fluctuation characteristics indicates that the stability of the infrared signal has deteriorated and the volatility has increased. This is usually due to the formation of uneven clumps or flows of water vapor in the mixing drum, resulting in drastic changes in the density and distribution of the medium on the infrared light path.

[0058] To more clearly illustrate this technical solution, a specific example is provided below. Assume that during the asphalt mixture production process, the average intensity and fluctuation characteristics of the infrared sensor signal have been acquired, and the moisture content of the material has also been measured. The material moisture content can first be compared with an adjusted preset moisture content threshold. If the material moisture content exceeds the threshold, the moisture content trend is further monitored. For example, the system continuously records the material moisture content data and calculates its average rate of change over the past 30 seconds. A positive rate of change indicates that the material moisture content is increasing. Based on this, the magnitude of the change in the average intensity and fluctuation characteristics can be further analyzed. For example, the difference between the current average intensity and the average intensity of the previous minute, as well as the difference between the current fluctuation characteristics and the fluctuation characteristics of the previous minute, can be compared. If the average intensity decreases by more than a preset threshold (e.g., a decrease of more than 5%) and the fluctuation characteristics increase by more than a preset threshold (e.g., an increase of more than 10%), a comprehensive judgment indicates the presence of water vapor interference. Through this multiple confirmation mechanism, even if the moisture content of the material is slightly higher than the threshold, if its changing 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 only slightly exceeds 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.

[0059] Through the above-mentioned technical solution, this embodiment can significantly improve the accuracy and reliability of water vapor interference judgment during asphalt mixture production. By introducing a multi-dimensional, dynamic judgment standard, namely combining the changing trend of the material moisture content and the average intensity and fluctuation amplitude of the infrared probe signal, it can effectively filter out transient interference or atypical situations, avoiding misjudgments or missed judgments. This ensures that subsequent temperature corrections are triggered only when water vapor interference is actually present, making production temperature control more precise and stable, reducing unnecessary temperature adjustments, improving the production quality and efficiency of asphalt mixtures, and reducing energy consumption.

[0060] In some embodiments, in step S403, if the variation range of the average intensity increases and the variation range of the fluctuation characteristic increases, then determining that water vapor interference exists may include but is not limited to the following steps: Obtain the infrared probe's self-diagnosis status information and historical operation data; Analyze the operating status of the infrared probe based on self-diagnosis status information and historical operating data; If the operating status is aging or failure, the current infrared probe signal is calibrated; According to the corrected current infrared probe signal, the change amplitude of the average intensity and the change amplitude of the fluctuation characteristic are updated; If the change amplitude of the updated average intensity increases, and the change amplitude of the updated fluctuation characteristic increases, it is determined that the judgment result is that water vapor interference exists.

[0061] In some embodiments, the infrared probe's operating status (such as aging or malfunction) may affect its signal accuracy, leading to misjudgment or omission of water vapor interference. To this end, the infrared probe's self-diagnostic status information and historical operating data can be obtained. For example, various internal parameters of the infrared probe, such as internal temperature, power supply voltage, and sensor health status, can be monitored in real time. This constitutes the self-diagnostic status information. Furthermore, the probe's cumulative operating hours, maintenance records, calibration history, and past signal anomalies can be recorded. This constitutes historical operating data. This information is intended to comprehensively assess the infrared probe's current health and potential performance degradation.

[0062] The infrared probe's operating status is then analyzed based on self-diagnostic status information and historical operating data. Pre-set algorithms or rules are used to comprehensively analyze these acquired self-diagnostic status information and historical operating data. For example, if the probe's internal temperature consistently exceeds the normal range, or if historical data shows frequent signal drift under specific operating conditions, the probe may be aging or faulty. This allows for timely detection of probe anomalies to prevent them from interfering with measurement results. If the operating status indicates aging or fault, the current infrared probe signal is corrected. Once aging or fault is determined, the currently acquired infrared probe signal can be corrected. For example, a compensation algorithm based on historical calibration curves or advanced signal processing techniques (such as filtering, denoising, and drift compensation) can be used to eliminate or mitigate signal deviations caused by probe issues. This ensures that the signal data used for subsequent analysis is accurate and reliable.

[0063] Based on the corrected current infrared probe signal, the average intensity variation and fluctuation characteristic variation are updated to ensure that the input data used to determine water vapor interference is corrected for the probe's own state and more accurately reflects the material's condition. If the updated average intensity variation increases, and the updated fluctuation characteristic variation increases, the judgment result indicates the presence of water vapor interference. This means that after eliminating interference from the infrared probe itself, if the signal variation trend still meets the characteristics of water vapor interference, water vapor interference can be confirmed.

[0064] This embodiment effectively solves the limitation of traditional methods that may lead to misjudgment due to probe aging or failure by introducing an evaluation and correction mechanism for the infrared probe's own operating status when judging water vapor interference. By obtaining the infrared probe's self-diagnostic status information and historical operating data, it is possible to promptly detect whether the probe is aging or failing. Once an abnormal operating state of the probe is found, its current signal is corrected, thereby eliminating the impact of the probe's own problems on signal accuracy. Subsequently, the average intensity and the amplitude of the fluctuation characteristics are recalculated based on the corrected signal, ensuring that the data used to judge water vapor interference truly reflects the material state rather than the probe state. This consideration of the probe's own state and the preprocessing of the signal make the judgment of water vapor interference more accurate and reliable.

[0065] To better illustrate this technical solution, a specific example is provided below. Suppose, during asphalt mixture production, the system detects an increasing trend in both the average intensity and fluctuation characteristics of an infrared probe's output, initially indicating possible water vapor interference. However, before making a final judgment, the system first obtains self-diagnostic status information from the infrared probe, such as the reading of its internal temperature sensor, power supply voltage stability, and the time of its most recent calibration. The system also queries the probe's historical operating data, including its cumulative operating hours, past maintenance records, and any abnormal signal drift. By analyzing this information, if the infrared probe has been operating continuously for an extended period, and its self-diagnostic information indicates that a key internal parameter is approaching a warning threshold, or if historical data indicates that this probe model is susceptible to signal attenuation after long-term operation, the system will determine that the probe's operating condition may be aging. In this case, the system does not directly use the raw infrared probe signal for judgment, but instead applies corrections to the current infrared probe signal. For example, the system can compensate the current signal based on the typical aging curve for that probe model, or filter and correct the signal using a pre-set calibration algorithm. After calibration is complete, the system recalculates the change in average intensity and the change in the fluctuation characteristics based on the corrected infrared sensor signal. If the updated change still significantly increases, the system will determine that water vapor interference is present and trigger the subsequent temperature correction process. Conversely, if the change no longer significantly increases after calibration, it indicates that the previous signal anomaly was caused by a problem with the sensor itself, not water vapor interference, thus avoiding incorrect temperature corrections.

[0066] Through the above-mentioned technical solution, this embodiment significantly improves the accuracy and reliability of water vapor interference determination in the temperature control method for micro-rutting asphalt mixture production. By real-time monitoring of the infrared probe's operating status and signal correction, the accuracy of the signal data used to determine water vapor interference is ensured, effectively avoiding misjudgments caused by probe problems. This makes production temperature correction more precise, avoiding unnecessary or untimely temperature adjustments, thereby improving the production quality and stability of asphalt mixtures, while also helping to reduce energy consumption and production costs.

[0067] In some embodiments, in step S403, if the variation range of the average intensity increases and the variation range of the fluctuation characteristic increases, then determining that water vapor interference exists may include but is not limited to the following steps: Obtaining production load information, asphalt viscosity information and aggregate gradation information of asphalt mixture; Determine target change thresholds based on production load information, asphalt viscosity information, and aggregate gradation information. Target change thresholds include material moisture content change thresholds, average strength change thresholds, and fluctuation characteristic change thresholds. According to the moisture content of the material, calculate the instantaneous change rate of the moisture content of the material; According to the average intensity, calculate the average intensity instantaneous change rate; According to the fluctuation characteristics, calculate the instantaneous change rate of the fluctuation characteristics; If the instantaneous change rate of the material moisture content is greater than the material moisture content change threshold, the average strength instantaneous change rate is greater than the average strength change threshold, and the fluctuation characteristic instantaneous change rate is greater than the fluctuation characteristic change threshold, then the judgment result is determined to be that water vapor interference exists.

[0068] In some embodiments, factors such as production load, asphalt viscosity, and aggregate gradation can dynamically change. These changes can cause fluctuations in the average intensity and fluctuation characteristics of the infrared sensor signal. Determining water vapor interference based solely on whether the amplitude of these fluctuations increases can lead to misjudgments or insufficient sensitivity, thus affecting temperature control accuracy. To this end, information on the production load, asphalt viscosity, and aggregate gradation of the asphalt mixture can be obtained. Production load information reflects the current production line's processing capacity and material flow rate, asphalt viscosity information is related to asphalt fluidity and heating requirements, and aggregate gradation information influences the material's drying characteristics and thermal conductivity. These factors, as they significantly influence the infrared sensor signal characteristics, are used to more accurately determine water vapor interference. Target change thresholds are then determined based on these information. These target change thresholds are not fixed but are adjusted based on current production conditions to better adapt to actual operating conditions. These target change thresholds include material moisture content change thresholds, average strength change thresholds, and fluctuation characteristic change thresholds. These thresholds serve as key references for determining whether the instantaneous rate of change of the corresponding parameters is abnormal.

[0069] Based on the material moisture content, the instantaneous rate of change of the material moisture content is calculated. Based on the average intensity, the instantaneous rate of change of the average intensity is calculated. Based on the fluctuation characteristics, the instantaneous rate of change of the fluctuation characteristics is calculated. The instantaneous rate of change of the material moisture content reflects the real-time rate of change of the material's dryness; the instantaneous rate of change of the average intensity reflects the rapid fluctuations in infrared signal intensity; and the instantaneous rate of change of the fluctuation characteristics reflects the real-time trend of changes in 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 moisture content is greater than the material 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 characteristics change threshold, the judgment result is determined to be water vapor interference. This multi-dimensional, dynamic threshold judgment method can effectively avoid misjudgments caused by single indicators or fixed thresholds.

[0070] In order to more clearly illustrate the technical solution, a specific example is used for explanation below. Suppose that on an asphalt mixture production line, an infrared probe continuously monitors the signal in the mixing drum. When the system detects that the average strength is less than the average strength reference value, and the fluctuation characteristic is greater than the fluctuation range reference value, and further obtains that the moisture content of the material is greater than the preset moisture content threshold, the system will enter a further judgment process for water vapor interference. At this time, the system will obtain the current production load information, such as medium load; obtain asphalt viscosity information, such as medium viscosity; obtain aggregate grading information, such as conventional grading. Based on these real-time production parameters, the system will dynamically determine the target change threshold under the current working 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.

[0071] The system then calculates the instantaneous rate of change of the material's moisture content in real time. For example, if the material's moisture content rose from 2.0% to 2.3% over the past 5 seconds, its instantaneous rate of change is (2.3% - 2.0%) / 5 seconds = 0.06% / second. Simultaneously, the instantaneous rate of change of the average intensity is calculated, for example, as 12 units / second; and the instantaneous rate of change of the fluctuation characteristic is calculated, for example, as 6 units / second. The system then compares these instantaneous rates of change with the dynamically adjusted target change thresholds: the instantaneous rate of change of the material moisture content of 0.06% / second is greater than the material moisture content change threshold of 0.05% / second; the instantaneous rate of change of the average intensity of 12 units / second is greater than the average intensity change threshold of 10 units / second; and the instantaneous rate of change of the fluctuation characteristic of 6 units / second is greater than the fluctuation characteristic change threshold of 5 units / second. Because 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, effectively addressing water vapor interference and ensuring the quality of asphalt mixture production. This dynamic, multi-dimensional judgment method enables the system to accurately identify water vapor interference under different production conditions, avoiding misjudgments that can occur with fixed thresholds or single indicators.

[0072] Through the above technical solution, this embodiment can significantly improve the accuracy and robustness of water vapor interference judgment in the production process of micro-rutting asphalt mixture. This embodiment introduces key production parameters such as production load, asphalt viscosity, aggregate grading, and dynamically adjusts the judgment threshold accordingly, so that the system can better adapt to complex and dynamically changing production environments. In addition, 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 misjudgment or missed judgment, thereby ensuring the accuracy of production temperature control, and ultimately helping to improve the production quality and stability of asphalt mixtures.

[0073] In some embodiments, the method further comprises: Step S501: If the result of the judgment is that there is a mist medium interference, obtain reference fuel supply rate and reference temperature data of the burner under normal operating conditions; Step S502: Calculate reference energy input power based on reference fuel supply rate and dye calorific value; Step S503: Calculate the reference temperature rising rate of change according to the preset sliding window and the reference temperature data; Step S504: constructing a power-temperature rise relationship curve according to a plurality of reference energy input powers and a plurality of reference temperature rise change rates; Step S505: Control the current production temperature according to the power-temperature rise relationship curve.

[0074] In some embodiments, temperature anomaly determination may involve not only water vapor interference but also mist interference, such as that formed by fine dust or incompletely burned fuel particles. Failure to effectively address this mist interference can distort infrared sensor signals, affecting temperature measurement accuracy and leading to deviations in production temperature control, ultimately impacting asphalt mixture quality and production efficiency. Therefore, if mist interference is determined, reference fuel supply rate and reference temperature data for the burner under normal operating conditions can be obtained. This data is typically historical data from a period of stable production line operation, precise temperature control, and the absence of 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, expressed, for example, in terms of fuel volume or mass per unit time. The reference temperature data refers to the actual production temperature measured at these reference fuel supply rates using a reliable temperature sensor (such as a thermocouple or a calibrated infrared thermometer).

[0075] Then, the reference energy input power is calculated based on the reference fuel supply rate and the calorific value of the fuel. The calorific value of fuel refers to the energy released when the unit mass or unit volume of fuel is completely burned. It is a known physical parameter. By multiplying the fuel supply rate with the calorific value of the fuel, the energy power input by the burner to the inside of the mixing drum can be obtained. At the same time, the reference temperature rise rate is calculated based on the preset sliding window and the reference temperature data. The preset sliding window refers to a time period, such as 5 seconds, 10 seconds or longer, which is used to average or trend the 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 under the action of a specific energy input power. For example, it can be expressed as the number of degrees Celsius rising per second. The rate of change can be obtained by performing linear regression or differential calculation on the reference temperature data within the sliding window.

[0076] Then, based on multiple reference energy input powers and multiple reference temperature rise rates, a power-temperature rise relationship curve is constructed. The power-temperature rise relationship curve reflects the intrinsic correlation between the burner input energy power and the temperature rise rate 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 medium interference is detected, temperature correction is no longer solely dependent on the potentially distorted signal of the infrared probe. Instead, the temperature is adjusted using the stable relationship between the burner's energy input and the actual temperature response, thereby avoiding the impact of measurement errors caused by mist medium interference on temperature control.

[0077] This embodiment can effectively solve the problem of temperature control under the interference of mist medium. When the judgment result is that there is interference from mist medium, the signal collected by the infrared probe may be distorted due to the scattering and absorption of the medium, resulting in an inability to accurately reflect the actual temperature. This embodiment obtains the 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 energy input of the burner and the temperature rise rate of the material in the mixing drum. The model is not affected by the interference of the mist medium 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.

[0078] Through the above-mentioned technical solution, this embodiment can effectively deal with the problem of mist medium interference in the asphalt mixture production process. Compared with the solution that only deals with water vapor interference, this solution expands the scope of application of temperature control and ensures 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 by the mist medium, the system can perform temperature control based on the inherent physical relationship between the energy input and temperature response of the burner, thereby avoiding temperature fluctuations and control deviations caused by measurement errors, significantly improving the robustness and accuracy of temperature control in asphalt mixture production, and thus ensuring the quality and production efficiency of the final product.

[0079] In some embodiments, in step S505, controlling the current production temperature according to the power-temperature rise relationship curve may include but is not limited to the following steps: Get 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 current temperature rise rate based on the preset sliding window and current temperature data; Extract the temperature rise rate corresponding to the current energy input power from the power-temperature rise relationship curve as the target temperature rise rate; If the current temperature rise rate is less than the target temperature rise rate, the burner power will be reduced and a temperature abnormality alarm will be issued.

[0080] In some embodiments, the current fuel supply rate and current temperature data can be first obtained. Based on the current fuel supply rate and the calorific value of the dye, the current energy input power is calculated. Furthermore, the current temperature rise rate is calculated based on a preset sliding window and the current temperature data. The temperature rise rate corresponding to the current energy input power is then extracted from the power-temperature rise curve as the target temperature rise rate. This curve can be used for table lookup to obtain the target temperature rise rate expected to be achieved 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 curve, this may indicate heat loss or insufficient heating efficiency, such as reduced 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, prompting the operator to inspect or intervene.

[0081] This embodiment monitors the fuel supply rate and temperature data under the current production status in real time, and converts them into energy input power and temperature rise rate, so that it can be compared with the pre-established power-temperature rise relationship curve. When it is detected that the current temperature rise rate is lower than the target temperature rise rate corresponding to the current energy input power, this indicates that under the current energy input, the actual heating effect of the material has not met expectations. This deviation may be due to the interference of the mist medium, which leads to a decrease in heat transfer efficiency. By reducing the burner power in time, energy waste or potential overheating risks of materials caused by continuous high power input can be avoided. At the same time, by issuing a temperature abnormality alarm, the operator can be quickly prompted to pay attention to and deal with potential production abnormalities, ensuring the stability of the production process and product quality.

[0082] Through the above technical solution, this embodiment can achieve refined control of the production temperature of asphalt mixture, especially in the presence of mist medium interference. This embodiment can promptly detect and respond to heating efficiency anomalies by comparing the actual temperature rise rate with the target temperature rise rate based on the power-temperature rise relationship curve in real time. In this way, the temperature control lag or deviation caused by the decrease in heat transfer efficiency due to the interference of the mist medium can be effectively avoided, thereby ensuring that the asphalt mixture is produced within the optimal temperature range, improving product quality, and reducing energy consumption. In addition, the mechanism of issuing temperature anomaly alarms further enhances the system's early warning capabilities, helps operators quickly locate problems and take corrective measures, and improves the reliability and safety of the production process.

[0083] The beneficial effects of implementing the embodiments of the present invention include: the embodiments of the present application first establish a signal reference system of the infrared probe under normal working conditions, then obtain the average intensity and fluctuation characteristics of the current infrared probe signal, and then perform threshold judgment on the average intensity and fluctuation characteristics based on the average intensity reference value and the fluctuation range reference value to obtain a judgment result. If the judgment result is that there is water vapor interference, the temperature correction amount is calculated based on the fluctuation characteristics. Finally, the current production temperature is corrected based on the temperature correction amount, so that water vapor interference can be identified and the production temperature can be corrected in combination with the average intensity and fluctuation characteristics of the signal to achieve production temperature control, thereby improving accuracy.

[0084] like Figure 2 As shown, the embodiment of the present invention also provides a temperature control system for producing micro-rutting asphalt mixture, comprising: A reference system establishment module 601 is used to establish a signal reference system of the infrared probe under normal working conditions, wherein the signal reference system includes an average intensity reference value and a fluctuation range reference value; The data acquisition module 602 is used to obtain the average intensity and fluctuation characteristics of the current infrared probe signal; A judgment module 603 is used to perform threshold judgment on the average intensity and the fluctuation characteristics according to the average intensity reference value and the fluctuation range reference value to obtain a judgment result; The correction amount calculation module 604 is used to calculate the temperature correction amount according to the fluctuation characteristics if the result of the judgment is that water vapor interference exists; The temperature correction module 605 is used to correct the current production temperature according to the temperature correction amount.

[0085] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0086] The embodiments described in the embodiments of this application are intended to more clearly illustrate 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. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

Claims

1. A method for controlling temperature in the production of micro-rutting asphalt mixture, characterized in that: The following steps are involved: Establishing a signal reference system for the infrared probe under normal working 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; Performing threshold judgment on the average intensity and the fluctuation characteristics according to the average intensity reference value and the fluctuation range reference value to obtain a judgment result; If the judgment result is that water vapor interference exists, calculating a temperature correction amount according to the fluctuation characteristics; The current production temperature is corrected according to the temperature correction amount.

2. The method according to claim 1, characterized in that The establishment of a signal reference system of the infrared probe under normal working conditions includes: At the start of asphalt mixture production, determine the initial steady state window; In the initial stable state window, collecting raw signal strength data through the infrared probe; Calculating an average value based on the original signal intensity data as the average intensity reference value; According to the original signal intensity data, a standard deviation is calculated as a reference value of the fluctuation range.

3. The method according to claim 1, characterized in that The step of performing threshold judgment on the average intensity and the fluctuation characteristics according to the average intensity reference value and the fluctuation range reference value to obtain a judgment result includes: If the average intensity is less than the average intensity reference value, determining whether the fluctuation characteristic is less than the fluctuation range reference value; If the fluctuation characteristic is smaller than the fluctuation range reference value, determining that the judgment result is that mist medium interference exists; If the fluctuation characteristic is greater than the fluctuation range reference value, the moisture content of the material in the mixing drum is obtained; If the moisture content of the material is greater than a preset moisture content threshold, determining that the judgment result is that water vapor interference exists; If the moisture content of the material is less than the preset moisture content threshold, it is determined that the judgment result is interfered by other factors.

4. The method according to claim 3, characterized in that If the moisture content of the material is greater than a preset moisture content threshold, determining that the judgment result is that water vapor interference exists includes: Obtain material type information, internal ambient temperature information of the mixing drum and aggregate particle size distribution information; Adjusting the preset moisture content threshold according to the material type information, the internal ambient temperature information of the mixing drum, and the aggregate particle size distribution information; Comparing the moisture content of the material with the adjusted preset moisture content threshold; If the moisture content of the material is greater than the adjusted preset moisture content threshold, it is determined that the judgment result is that water vapor interference exists.

5. The method according to claim 4, characterized in that If the moisture content of the material is greater than the adjusted preset moisture content threshold, determining that the judgment result is that water vapor interference exists includes: If the moisture content of the material is greater than the adjusted preset moisture content threshold, monitoring the change trend of the moisture content of the material; If the change trend of the moisture content of the material is an increase, the change range of the average strength and the fluctuation characteristics is judged; If the variation range of the average intensity increases and the variation range of the fluctuation characteristic increases, it is determined that the judgment result is that water vapor interference exists.

6. The method according to claim 5, characterized in that If the variation range of the average intensity increases and the variation range of the fluctuation characteristic increases, determining that the judgment result is that water vapor interference exists includes: Obtain the infrared probe's self-diagnosis status information and historical operation data; Analyzing the operating status of the infrared probe according to the self-diagnosis status information and the historical operating data; If the operating state is aging or failure, calibrating the current infrared probe signal; updating the variation of the average intensity and the variation of the fluctuation characteristic according to the corrected current infrared probe signal; If the change amplitude of the updated average intensity increases, and the change amplitude of the updated fluctuation characteristic increases, then the judgment result is determined to be the presence of water vapor interference.

7. The method according to claim 5, characterized in that If the variation range of the average intensity increases and the variation range of the fluctuation characteristic increases, determining that the judgment result is that water vapor interference exists includes: Obtaining production load information, asphalt viscosity information and aggregate gradation information of asphalt mixture; Determining a target change threshold according to the production load information, the asphalt viscosity information, and the aggregate gradation information, wherein the target change threshold includes a material moisture content change threshold, an average strength change threshold, and a fluctuation characteristic change threshold; Calculating the instantaneous rate of change of the moisture content of the material according to the moisture content of the material; Calculating the average intensity instantaneous change rate based on the average intensity; Calculating the instantaneous rate of change of the fluctuation characteristics according to the fluctuation characteristics; If the instantaneous change rate of the material moisture content is greater than the material moisture content change threshold, the instantaneous change rate of the average strength is greater than the average strength change threshold, and the instantaneous change rate of the fluctuation characteristic is greater than the fluctuation characteristic change threshold, then the judgment result is determined to be that water vapor interference exists.

8. The method according to claim 1, characterized in that The method further comprises: If the judgment result is that there is a mist medium interference, obtaining reference fuel supply rate and reference temperature data of the burner under normal operating conditions; Calculating a reference energy input power based on the reference fuel supply rate and the dye calorific value; Calculating a reference temperature rising rate of change according to a preset sliding window and the reference temperature data; constructing a power-temperature rise relationship curve according to the plurality of reference energy input powers and the plurality of reference temperature rise change rates; The current production temperature is controlled according to the power-temperature rise relationship curve.

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

10. A temperature control system for micro-rutting asphalt mixture production, characterized in that: include: A reference system establishment module is used to establish a signal reference system of the infrared probe under normal working conditions, wherein the signal reference system includes an average intensity reference value and a fluctuation range reference value; The data acquisition module is used to obtain the average intensity and fluctuation characteristics of the current infrared probe signal; a judgment module, configured to perform threshold judgment on the average intensity and the fluctuation characteristics according to the average intensity reference value and the fluctuation range reference value, and obtain a judgment result; a correction value calculation module, configured to calculate a temperature correction value based on the fluctuation characteristics if the judgment result is that water vapor interference exists; The temperature correction module is used to correct the current production temperature according to the temperature correction amount.

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