Intelligent control system for adding black asphalt and colored asphalt

Abstract

The invention relates to the technical field of asphalt material production control, and discloses an intelligent control system for adding black asphalt and colored asphalt, and the system comprises a raw material parameter collection module, a formula decision module and a dynamic execution module. The multi-sensor fusion technology is adopted to synchronously obtain the physical property parameters of the basic asphalt and the optical characteristics of the color pigment, so that the proportioning error caused by artificial experience is reduced; raw material batch fluctuation is corrected in real time in combination with dynamic normalization processing, the problems that in a traditional method, the performance of black asphalt does not reach the standard, and the color difference of colored asphalt exceeds the limit are solved, and the formula deviation degree is calculated in real time by establishing a color-performance collaborative decision-making unit and constructing a two-dimensional feature matrix; when parameter abnormity is detected, a dynamic compensation algorithm is automatically triggered, collaborative optimization of the modifier and pigment ratio is completed in a single production cycle, hysteresis of traditional repeated shutdown debugging is avoided, and the continuous operation efficiency of a production line is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of asphalt material production control, and in particular to an intelligent control system for adding black asphalt and colored asphalt. Background Art

[0002] Asphalt is a dark brown complex mixture composed of hydrocarbons of different molecular weights and their non-metallic derivatives. It is a high-viscosity organic liquid that mostly exists in liquid or semi-solid petroleum form. It has a black surface and is soluble in carbon disulfide and carbon tetrachloride. Asphalt is an organic cementitious material that is waterproof, moisture-proof and corrosion-resistant. As a key material in road construction, its performance indicators and color uniformity directly affect the quality of the project.

[0003] Currently, in the production of black and colored asphalt, defects exist in the intelligent control of additives due to fluctuations in raw material composition and complex coupling of process parameters. When collecting basic asphalt physical property parameters, traditional systems use a step-by-step detection method, with the infrared spectrometer and colorimeter working independently. This makes it impossible to synchronously obtain the coupled data of penetration and hue angle in real time. Differences in raw material batches lead to synergistic deviations in physical and optical properties, which in turn causes a lag in the feedback of composite parameters. Furthermore, during the formulation decision-making stage, existing technologies rely on single-dimensional threshold judgments. When penetration exceeds the limit, only viscosity-enhancing agent compensation is triggered, without considering the chain reaction effect of hue angle deviation on pigment dispersion. This results in a serious misalignment of additive strategies, causing asphalt products to exhibit both softening point anomalies and regional color deviations. During the execution control process, the mixing process uses constant speed and temperature parameters, making it impossible to dynamically adjust the modifier miscibility stage and the pigment dispersion stage in stages. This can easily lead to uneven regional distribution of additives, further expanding the error in the dual-objective control of performance and color, ultimately affecting the color uniformity and durability of the asphalt pavement.

[0004] Therefore, an intelligent control system for adding black asphalt and colored asphalt is proposed to solve the above problems. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides an intelligent control system for adding black asphalt and colored asphalt, which solves the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: an intelligent control system for adding black asphalt and colored asphalt, the system comprising a raw material parameter acquisition module, a formula decision module and a dynamic execution module; The raw material parameter acquisition module uses an infrared spectrometer and a spectrocolorimeter to synchronously collect the physical performance parameters of the base asphalt and the component parameters of the color pigments, normalizes multiple batches of raw material data to generate standardized feature data, and transmits it to the formula decision module in real time; The formula decision module constructs a color-performance dual-dimensional feature matrix based on the standardized feature data, calculates parameter deviations from the target asphalt standard in real time, and activates a dynamic compensation algorithm to generate optimized formula data when exceeding the needle penetration limit or hue angle deviation is detected; The dynamic execution module links the metering pump group and the stirring equipment through the PID controller, accurately adds the modifier, regeneration agent and compensation pigment according to the optimized formula data, and adjusts the stirring process parameters in stages through the online monitoring device; The central control module coordinates the data processing flow of each module and automatically triggers parameter calibration instructions when raw material anomalies or execution deviations are detected.

[0007] Preferably, the recipe decision module specifically includes: Two-dimensional matrix construction unit: physical parameters and optical parameters are combined into a characteristic matrix according to the weight ratio. The matrix structure satisfies: Where P is the needle penetration weight value, T is the softening point weight value, is the hue angle deviation coefficient, β is the saturation correlation coefficient; When the exception handling protocol library performs hue offset calibration: Obtain the pigment spectral reflectance curve and calculate the main wavelength offset ; Solve the amount of compensating pigment based on the complementary color model: in, is the dispersion correction coefficient, is the starting wavelength of the visible spectrum, is the end wavelength of the visible spectrum, is the current scanning wavelength, is the reflectivity of the target color sample at wavelength λ, is the reflectance of the actual sample at wavelength λ, Wavelength differential variable.

[0008] Preferably, the system comprises the following steps: S1. Collecting raw material parameters: Obtain the physical properties of base asphalt and the pigment component parameters of colored asphalt through testing equipment; S2. Data preprocessing: normalizing the physical property parameters and pigment component parameters to generate standardized raw material characteristic data; S3, formula matching judgment: Based on the color standard and performance index of the target asphalt, feature matching is performed with the standardized raw material feature data to generate a formula consistency judgment result; if the match is successful, execute S5; S4. Dynamic adjustment: If the matching fails, the amount of modifier added to the base asphalt and the ratio of color pigments are adjusted in real time according to the target performance indicators to generate optimized formula data; S5. Constructing a recipe matrix: combining the standardized raw material characteristic data, the optimized recipe data, and the color-performance parameters of the target asphalt to generate a recipe characteristic matrix; S6. Intelligent algorithm matching: performing intelligent matching based on the formula feature matrix and the pre-stored standard formula database to select the optimal additive control algorithm; S7. Execution control: calling the optimal additive control algorithm to control the production equipment to add modifiers, regeneration agents, and color pigments in proportion to generate the target asphalt product.

[0009] Preferably, the S1 includes: S11. Analyzing the component content of the base asphalt using an infrared spectrometer, wherein the physical performance parameters include needle penetration, softening point, and ductility; S12. Collect hue, brightness and saturation data of color pigments using a colorimeter and a spectrum analyzer, and detect compatibility parameters of the pigments and asphalt.

[0010] Preferably, the S2 includes: S21. Using a bidirectional search algorithm, matching historical performance data corresponding to the raw material batch in the database, and performing deviation correction on the real-time detected penetration and softening point; S22. Map the physical property parameters and pigment parameters to the interval [0, 1] by the minimum-maximum normalization method to generate the standardized raw material characteristic data. The normalization formula is: in, is the normalized parameter value is the original parameter value; The minimum value of the same type of parameter in the database; It is the maximum value of the same type of parameter in the database.

[0011] Preferably, the S3 includes: S31, comparing the standardized raw material characteristic data with the color tolerance range and performance threshold of the target asphalt; S32. If the penetration of the base asphalt is within the target range and the hue deviation of the color pigment is less than 5%, the match is determined to be successful; otherwise, dynamic adjustment is triggered.

[0012] Preferably, the S4 includes: S41. When the base asphalt penetration is lower than the target value, increase the amount of plasticizer added; when the color pigment hue is out of range, adjust the pigment ratio according to the principle of complementary colors; S42. Calculate the compensation amount of the modifier and pigment based on the fuzzy control rule to generate optimized formula data containing dynamic proportions.

[0013] Preferably, the S5 includes: S51, aligning the standardized raw material characteristic data and the optimized formula data according to the color parameter and performance parameter dimensions to construct the formula characteristic matrix; S52. The structure of the formula feature matrix is: the rows are the combination parameters of the base asphalt and the color pigment, and the columns are the color-performance index data pairs.

[0014] Preferably, the S6 includes: S61. Establish a standard formula database to store standard formula feature matrices corresponding to different additive control algorithms; S62, using a particle swarm optimization algorithm to match the formula feature matrix with the standard formula feature matrix: Initialize the particle swarm position: Iteratively update particle positions: in: is the position of the i-th particle in the d-dimensional space; 、 Index boundary values ​​for the recipe database; A random number in the interval [0,1]; is the particle moving speed; is the inertia weight factor; is the learning factor; is the historical optimal position of the particle; is the historical optimal position of the group; is the number of iterations.

[0015] Preferably, the S7 includes: S71, calling a control program according to the additive control algorithm identifier to generate instructions for adding proportions of a modifier, a regenerating agent, and a color pigment; S72. Use the PLC controller to drive the metering pump to add raw materials according to instructions, and monitor the color uniformity and performance parameters of the finished asphalt in real time.

[0016] Compared with the existing technology, the present invention provides an intelligent control system for adding black asphalt and colored asphalt, which has the following beneficial effects: 1. In the present invention, an intelligent raw material feature collection unit is set up, and multi-sensor fusion technology is used to synchronously obtain the basic asphalt physical performance parameters and color pigment optical properties, thereby reducing the ratio error caused by manual experience; combined with dynamic normalization processing, raw material batch fluctuations are corrected in real time, ensuring the formula accuracy from the source, and solving the problems of substandard black asphalt performance and excessive color difference of colored asphalt in traditional methods.

[0017] 2. In the present invention, a color-performance collaborative decision-making unit is established, and a two-dimensional feature matrix is ​​constructed to calculate the formula deviation in real time. When a parameter abnormality is detected, the dynamic compensation algorithm is automatically triggered to complete the collaborative optimization of the modifier and pigment ratio within a single production cycle, avoiding the lag of traditional repeated shutdown and debugging, and improving the continuous operation efficiency of the production line.

[0018] 3. In the present invention, by configuring a multi-level closed-loop execution unit, a linkage control mechanism is used to synchronously process performance optimization and color calibration; when switching between black and white and color production lines, the process parameters are adaptively corrected based on a hierarchical adjustment strategy to achieve seamless connection between the two modes, fundamentally overcoming the problem of increased scrap rate caused by the incompatibility of traditional systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the overall system architecture of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] See also Figure 1 , an intelligent control system for adding black asphalt and colored asphalt, the system includes a raw material parameter acquisition module, a formula decision module and a dynamic execution module; The raw material parameter acquisition module uses an infrared spectrometer and a spectrocolorimeter to synchronously collect the physical performance parameters of the base asphalt and the component parameters of the color pigments, normalizes multiple batches of raw material data to generate standardized feature data, and transmits it to the formula decision module in real time; The formula decision module constructs a color-performance dual-dimensional feature matrix based on the standardized feature data, calculates parameter deviations from the target asphalt standard in real time, and activates a dynamic compensation algorithm to generate optimized formula data when exceeding the needle penetration limit or hue angle deviation is detected; The dynamic execution module links the metering pump group and the stirring equipment through the PID controller, accurately adds the modifier, regeneration agent and compensation pigment according to the optimized formula data, and adjusts the stirring process parameters in stages through the online monitoring device; The central control module coordinates the data processing flow of each module and automatically triggers parameter calibration instructions when raw material anomalies or execution deviations are detected.

[0022] The recipe decision module specifically includes: Two-dimensional matrix construction unit: physical parameters and optical parameters are combined into a characteristic matrix according to the weight ratio. The matrix structure satisfies: Where P is the needle penetration weight value, T is the softening point weight value, is the hue angle deviation coefficient, β is the saturation correlation coefficient; When the exception handling protocol library performs hue offset calibration: Obtain the pigment spectral reflectance curve and calculate the main wavelength offset ; Solve the amount of compensating pigment based on the complementary color model: in, is the dispersion correction coefficient, is the starting wavelength of the visible spectrum, is the end wavelength of the visible spectrum, is the current scanning wavelength, is the reflectivity of the target color sample at wavelength λ, is the reflectance of the actual sample at wavelength λ, Wavelength differential variable.

[0023] The system includes the following steps: S1. Collecting raw material parameters: Obtain the physical properties of base asphalt and the pigment component parameters of colored asphalt through testing equipment; S2. Data preprocessing: normalizing the physical property parameters and pigment component parameters to generate standardized raw material characteristic data; S3, formula matching judgment: Based on the color standard and performance index of the target asphalt, feature matching is performed with the standardized raw material feature data to generate a formula consistency judgment result; if the match is successful, execute S5; S4. Dynamic adjustment: If the matching fails, the amount of modifier added to the base asphalt and the ratio of color pigments are adjusted in real time according to the target performance indicators to generate optimized formula data; S5. Constructing a recipe matrix: combining the standardized raw material characteristic data, the optimized recipe data, and the color-performance parameters of the target asphalt to generate a recipe characteristic matrix; S6. Intelligent algorithm matching: performing intelligent matching based on the formula feature matrix and the pre-stored standard formula database to select the optimal additive control algorithm; S7. Execution control: calling the optimal additive control algorithm to control the production equipment to add modifiers, regeneration agents, and color pigments in proportion to generate the target asphalt product.

[0024] S1 includes: S11. Analyzing the component content of the base asphalt using an infrared spectrometer, wherein the physical performance parameters include needle penetration, softening point, and ductility; S12. Collect hue, brightness and saturation data of color pigments using a colorimeter and a spectrum analyzer, and detect compatibility parameters of the pigments and asphalt.

[0025] S2 includes: S21. Using a bidirectional search algorithm, matching historical performance data corresponding to the raw material batch in the database, and performing deviation correction on the real-time detected penetration and softening point; S22. Map the physical property parameters and pigment parameters to the interval [0, 1] by the minimum-maximum normalization method to generate the standardized raw material characteristic data. The normalization formula is: in, is the normalized parameter value is the original parameter value; The minimum value of the same type of parameter in the database; It is the maximum value of the same type of parameter in the database.

[0026] S3 includes: S31, comparing the standardized raw material characteristic data with the color tolerance range and performance threshold of the target asphalt; S32. If the penetration of the base asphalt is within the target range and the hue deviation of the color pigment is less than 5%, the match is determined to be successful; otherwise, dynamic adjustment is triggered.

[0027] S4 includes: S41. When the base asphalt penetration is lower than the target value, increase the amount of plasticizer added; when the color pigment hue is out of range, adjust the pigment ratio according to the principle of complementary colors; S42. Calculate the compensation amount of the modifier and pigment based on the fuzzy control rule to generate optimized formula data containing dynamic proportions.

[0028] S5 includes: S51, aligning the standardized raw material characteristic data and the optimized formula data according to the color parameter and performance parameter dimensions to construct the formula characteristic matrix; S52. The structure of the formula feature matrix is: the rows are the combination parameters of the base asphalt and the color pigment, and the columns are the color-performance index data pairs.

[0029] S6 includes: S61. Establish a standard formula database to store standard formula feature matrices corresponding to different additive control algorithms; S62, using a particle swarm optimization algorithm to match the formula feature matrix with the standard formula feature matrix: Initialize the particle swarm position: Iteratively update particle positions: in: is the position of the i-th particle in the d-dimensional space; 、 Index boundary values ​​for the recipe database; A random number in the interval [0,1]; is the particle moving speed; is the inertia weight factor; is the learning factor; is the historical optimal position of the particle; is the historical optimal position of the group; is the number of iterations.

[0030] S7 includes: S71, calling a control program according to the additive control algorithm identifier to generate instructions for adding proportions of a modifier, a regenerating agent, and a color pigment; S72. Use the PLC controller to drive the metering pump to add raw materials according to instructions, and monitor the color uniformity and performance parameters of the finished asphalt in real time.

[0031] Black asphalt performance optimization When an asphalt production base launched SBS-modified black asphalt production, the intelligent raw material characteristic acquisition unit detected, through infrared spectroscopy analysis, that the base asphalt softening point was below the standard threshold. The collaborative decision-making unit immediately initiated a dual-target analysis: a performance characteristic matrix was constructed based on the real-time penetration and ductility parameters. This matrix was then matched against the target asphalt standard model, confirming insufficient rutting resistance.

[0032] The dynamic compensation mechanism is then activated: first, the required viscosity-enhancing agent ratio is calculated based on the modifier response curve, and at the same time, the particle swarm algorithm is called to screen out the co-addition scheme of the regeneration agent from the historical formula library; the multi-stage closed-loop execution unit synchronously drives the metering pump group to inject the liquid SBS modifier and regeneration agent into the molten asphalt with millisecond-level accuracy; the asphalt rheological parameters are tracked through the online monitoring system throughout the production process.

[0033] The results showed that after the modifier was injected, the asphalt softening point continued to rise and stabilized in the target range. The finished product sampling showed no segregated particles and improved ductility. The entire optimization process was completed within a single production cycle, and the production line maintained continuous operation throughout the entire process.

[0034] Color asphalt color difference calibration During the production of permeable red asphalt, the color pigment analysis unit, using a real-time spectrocolorimeter, discovered a shift in the dominant wavelength of the iron red pigment, causing the asphalt's hue angle to deviate from the standard. The system implemented a multi-stage process: the raw material acquisition module simultaneously fed back pigment saturation and base asphalt viscosity data to the decision-making unit. Analysis of the color-performance matrix confirmed that the anomaly stemmed from uneven pigment dispersion.

[0035] The collaborative decision-making unit initiates a dual-path correction program. On the one hand, it calls the complementary color compensation model to calculate the amount of phthalocyanine green pigment to be added to offset the color deviation; on the other hand, it dynamically optimizes the dispersion process parameters to generate a gradient increase plan for the stirring intensity; the execution unit links the pigment micro-injection system and the variable frequency stirring equipment through the PID controller: while accurately injecting complementary pigments, it increases the stirring blade speed in three stages, and monitors the mixing uniformity in real time through the online colorimeter.

[0036] The final product inspection showed that the hue angle of red asphalt was completely restored to the standard range, cross-sectional microscopic observations showed that the uniformity of pigment distribution was improved, and after switching from the black asphalt production line, the system automatically identified the color control mode, and no raw material waste was caused throughout the process.

[0037] Recycled asphalt compatibility control When a recycled asphalt production line uses 30% recycled old materials, the raw material collection unit detects that the fluctuation of the old material composition causes the needle penetration to increase abnormally. The decision-making unit immediately activates the recycling compatibility mode: after analyzing the proportion of aged asphalt in the recycled material, it constructs a synergistic response model of the regeneration agent and the new asphalt.

[0038] The dynamic compensation algorithm performs three-order adjustments. First, a softener is injected to compensate for the needle penetration deviation, then a polymer is added to restore the viscoelasticity, and finally the uniformity of the blend is optimized through temperature-controlled stirring. The execution unit links the temperature-controlled reactor and the precision injection system to complete the adjustment while maintaining the continuous operation of the production line. After testing, the final product shows that the needle penetration of the recycled asphalt has returned to the standard range, and there is no stratification at the interface between the new and old materials.

[0039] Temperature-sensitive colored asphalt production During the production of blue asphalt in low-temperature areas, the ambient temperature and humidity sensor detects that the sudden drop in environment causes the pigment to crystallize, and the system automatically switches to low-temperature mode: the acquisition unit simultaneously analyzes the low-temperature ductility of asphalt and the dispersion threshold of ultramarine pigment, and the decision-making unit generates an anti-crystallization plan based on the phase change prediction model. First, anticoagulant dispersant is injected to improve fluidity, and then the stirring rate is reduced in stages to prevent shear crystallization.

[0040] The execution unit synchronously adjusts the additive injection and the blade speed through a dual-channel metering valve, and uses infrared thermal imaging to monitor the mixing temperature field in real time. The finished product tested at -15°C shows that the blue pigment is evenly distributed without crystallization, and the low-temperature bending and tensile performance meets the standards.

[0041] Color stability control of high elastic asphalt During the production of highly elastic modified colored asphalt, an online colorimeter detected color shift in an azo pigment during the stirring and heating process. The decision-making unit activated a thermal stability compensation mechanism: after analyzing the pigment's thermal decomposition curve, it dynamically generated a two-stage temperature control strategy: low-temperature mixing in the early stages to prevent decomposition, and gradient heating in the later stages to ensure crosslinking of the elastomer.

[0042] The execution unit is implemented through a temperature-speed linkage module: the pigment is first dispersed by stirring at a low speed, and the heat stabilizer is injected before the temperature reaches the critical point; then the temperature is increased in steps and the stirring is accelerated simultaneously. The finished product has been verified by accelerated aging tests, and the color retention rate is improved, and the high elastic performance is not affected by the heat stabilizer.

[0043] Seamless switching of dual-mode production lines When the factory switches from black SBS modified asphalt to fluorescent green permeable asphalt, the system automatically performs a mode conversion: the raw material unit first detects the residual amount in the pipeline and feedbacks the cleaning requirements; the decision-making unit simultaneously builds a color coverage model and calculates the basic addition amount of the first batch of green pigment to cover the black base color.

[0044] The execution unit starts the three-stage cleaning-premixing program, first flushing the pipeline with regenerated base liquid, then injecting double-concentration base color covering agent, and finally switching to the standard ratio mode. The whole process takes only 40% of the time of traditional cleaning switching, and the color test of the first barrel of fluorescent green asphalt meets the standard at one time, with no black spots or variegated colors.

[0045] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0046] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control system for adding black asphalt and colored asphalt, characterized by: The system includes a raw material parameter acquisition module, a recipe decision module and a dynamic execution module; The raw material parameter acquisition module uses an infrared spectrometer and a spectrocolorimeter to synchronously collect the physical performance parameters of the base asphalt and the component parameters of the color pigments, normalizes multiple batches of raw material data to generate standardized feature data, and transmits it to the formula decision module in real time; The formula decision module constructs a color-performance dual-dimensional feature matrix based on the standardized feature data, calculates parameter deviations from the target asphalt standard in real time, and activates a dynamic compensation algorithm to generate optimized formula data when exceeding the needle penetration limit or hue angle deviation is detected; The dynamic execution module links the metering pump group and the stirring equipment through the PID controller, accurately adds the modifier, regeneration agent and compensation pigment according to the optimized formula data, and adjusts the stirring process parameters in stages through the online monitoring device; The central control module coordinates the data processing flow of each module and automatically triggers parameter calibration instructions when raw material anomalies or execution deviations are detected.

2. The intelligent control system for adding black asphalt and colored asphalt according to claim 1 is characterized by: The recipe decision module specifically includes: Two-dimensional matrix construction unit: physical parameters and optical parameters are combined into a characteristic matrix according to the weight ratio. The matrix structure satisfies: ; Where P is the needle penetration weight value, T is the softening point weight value, is the hue angle deviation coefficient, β is the saturation correlation coefficient; When the exception handling protocol library performs hue offset calibration: Obtain the pigment spectral reflectance curve and calculate the main wavelength offset ; Solve the amount of compensating pigment based on the complementary color model: ;in, is the dispersion correction coefficient, is the starting wavelength of the visible spectrum, is the end wavelength of the visible spectrum, is the current scanning wavelength, is the reflectivity of the target color sample at wavelength λ, is the reflectance of the actual sample at wavelength λ, Wavelength differential variable.

3. The intelligent control system for adding black asphalt and colored asphalt according to claim 1 is characterized by: The system comprises the following steps: S1. Collecting raw material parameters: Obtain the physical properties of base asphalt and the pigment component parameters of colored asphalt through testing equipment; S2. Data preprocessing: normalizing the physical property parameters and pigment component parameters to generate standardized raw material characteristic data; S3, formula matching judgment: Based on the color standard and performance index of the target asphalt, feature matching is performed with the standardized raw material feature data to generate a formula consistency judgment result; if the match is successful, execute S5; S4. Dynamic adjustment: If the matching fails, the amount of modifier added to the base asphalt and the ratio of color pigments are adjusted in real time according to the target performance indicators to generate optimized formula data; S5. Constructing a recipe matrix: combining the standardized raw material characteristic data, the optimized recipe data, and the color-performance parameters of the target asphalt to generate a recipe characteristic matrix; S6. Intelligent algorithm matching: performing intelligent matching based on the formula feature matrix and the pre-stored standard formula database to select the optimal additive control algorithm; S7. Execution control: calling the optimal additive control algorithm to control the production equipment to add modifiers, regeneration agents, and color pigments in proportion to generate the target asphalt product.

4. The intelligent control system for adding black asphalt and colored asphalt according to claim 3 is characterized by: Said S1 comprises: S11. Analyzing the component content of the base asphalt using an infrared spectrometer, wherein the physical performance parameters include needle penetration, softening point, and ductility; S12. Collect hue, brightness and saturation data of color pigments using a colorimeter and a spectrum analyzer, and detect compatibility parameters of the pigments and asphalt.

5. The intelligent control system for adding black asphalt and colored asphalt according to claim 3 is characterized by: The S2 includes: S21. Using a bidirectional search algorithm, matching historical performance data corresponding to the raw material batch in the database, and performing deviation correction on the real-time detected penetration and softening point; S22. Map the physical property parameters and pigment parameters to the interval [0, 1] by the minimum-maximum normalization method to generate the standardized raw material characteristic data. The normalization formula is: ;in, is the normalized parameter value is the original parameter value; The minimum value of the same type of parameter in the database; It is the maximum value of the same type of parameter in the database.

6. The intelligent control system for adding black asphalt and colored asphalt according to claim 3 is characterized by: The S3 includes: S31, comparing the standardized raw material characteristic data with the color tolerance range and performance threshold of the target asphalt; S32. If the penetration of the base asphalt is within the target range and the hue deviation of the color pigment is less than 5%, the match is determined to be successful; otherwise, dynamic adjustment is triggered.

7. The intelligent control system for adding black asphalt and colored asphalt according to claim 3 is characterized by: The S4 includes: S41. When the base asphalt penetration is lower than the target value, increase the amount of plasticizer added; when the color pigment hue is out of range, adjust the pigment ratio according to the principle of complementary colors; S42. Calculate the compensation amount of the modifier and pigment based on the fuzzy control rule to generate optimized formula data containing dynamic proportions.

8. The intelligent control system for adding black asphalt and colored asphalt according to claim 3 is characterized by: The S5 includes: S51, aligning the standardized raw material characteristic data and the optimized formula data according to the color parameter and performance parameter dimensions to construct the formula characteristic matrix; S52. The structure of the formula feature matrix is: the rows are the combination parameters of the base asphalt and the color pigment, and the columns are the color-performance index data pairs.

9. The intelligent control system for adding black asphalt and colored asphalt according to claim 3 is characterized by: The S6 includes: S61. Establish a standard formula database to store standard formula feature matrices corresponding to different additive control algorithms; S62, using a particle swarm optimization algorithm to match the formula feature matrix with the standard formula feature matrix: Initialize the particle swarm position: ; Iteratively update particle positions: ;in, is the position of the i-th particle in the d-dimensional space; 、 Index boundary values ​​for the recipe database; A random number in the interval [0,1]; is the particle moving speed; is the inertia weight factor; is the learning factor; is the historical optimal position of the particle; is the historical optimal position of the group; is the number of iterations.

10. The intelligent control system for adding black asphalt and colored asphalt according to claim 3 is characterized by: The S7 includes: S71, calling a control program according to the additive control algorithm identifier to generate instructions for adding proportions of a modifier, a regenerating agent, and a color pigment; S72. Use the PLC controller to drive the metering pump to add raw materials according to instructions, and monitor the color uniformity and performance parameters of the finished asphalt in real time.

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