Intelligent processing method for highway engineering reconstruction and expansion multi-source solid waste comprehensive road

Abstract

The invention relates to the technical field of solid waste treatment, and provides an intelligent treatment method for a comprehensive road of multi-source solid waste for reconstruction and expansion of highway engineering, the method comprises the following steps: automatically adjusting the temperature and time of a dryer based on initial physical and chemical properties, and controlling the moisture content within a predetermined range; meanwhile, the intelligent vibrating screen conducts screening according to the preset particle size requirement; the solid waste is converted into pretreated solid waste with the moisture content and the particle size distribution meeting standards and uniform characters; the pretreated solid waste is subjected to physical modification, a quantitative chemical modifier is automatically added according to the real-time chemical component monitoring result of the solid waste, mixing is conducted through intelligent stirring equipment, and regulated and controlled modified solid waste is obtained; according to real-time monitoring data of key performance indexes of the modified solid waste, the mix proportion of the modified solid waste to the road material is automatically calculated and adjusted. According to the invention, resource utilization of solid wastes and environmental protection are realized.

Description

Technical Field

[0001] This invention relates to the field of solid waste management technology, and in particular to an intelligent treatment method for the comprehensive road utilization of multi-source solid waste generated during highway engineering reconstruction and expansion. Background Technology

[0002] During the reconstruction and expansion of highways, a large amount of solid waste materials are often required. If these solid waste materials are used directly without treatment, they will fail to meet the performance requirements of road construction and will pollute the environment. Therefore, developing a technology that can effectively treat these solid waste materials is of great significance for improving resource utilization and reducing environmental pollution.

[0003] Existing technology one, publication number CN120243602A, discloses an intelligent and environmentally friendly method and system for the resource utilization of organic solid waste. Before the organic solid waste enters the crushing mechanism, it uses computer vision technology to identify and classify the waste to determine if there are any unsuitable impurities. When impurities are detected, a robotic arm removes them before the waste is sent to the crushing mechanism for further crushing. A dissolving solution is then used to dissolve the crushed powder, separating the liquid containing dissolved organic matter from the solid powder containing the organic matter. While this effectively optimizes the overall process of organic solid waste resource utilization and helps achieve efficient and pure utilization, improving waste treatment efficiency and resource utilization quality, it does not handle inorganic solid waste, despite its visual sorting and liquid-phase separation capabilities.

[0004] Prior art two, publication number CN120325663A, discloses a harmless treatment device and method for organic solid waste, including: a crushing tank, which is equipped with a crushing component for chopping solids; a feeding tank, which is fixedly connected above the crushing tank, and has a top cover fixedly installed at the top port of the feeding tank, and is equipped with a draining component inside the feeding tank, which reduces the water content in the solids; and a decomposition tank, which is located on the side of the crushing tank, and is connected between the crushing tank and the decomposition tank for conveying the chopped solids to the decomposition tank for decomposition. Although adding a draining component above the crushing component allows the organic solid waste to squeeze out excess water before crushing, reducing the impact of water on the crushing process and the degree of damage to the crushing component, thereby helping to improve the working efficiency of the crushing process and the protection of the crushing component; however, controlling the water content through mechanical draining lacks precise intervention in the material modification process.

[0005] Existing technology three, publication number CN119910016A, discloses a comprehensive solid waste treatment device, mainly including key components such as a tunnel drying box, a sorting system, a material crushing table, and a pyrolysis tower. The tunnel drying box uses hot air pipes and negative pressure suction strips to dry the input solid waste and purify the exhaust gas, ensuring environmental protection requirements during the treatment process. The sorting system consists of a light material sorting conveyor, a heavy material sorting conveyor, and a blowing sorting strip, accurately classifying the waste. The material crushing table is equipped with multiple extrusion conveying rollers and pressure cutting strips for crushing the sorted waste, providing better conditions for the subsequent pyrolysis process. Although the pyrolysis tower ultimately pyrolyzes solid waste into valuable liquid fuels, combustible gases, and solid carbon materials, and the combustible gases generated by pyrolysis are burned in a sealed combustion chamber to produce high-temperature flue gas, providing the heat required for pyrolysis, thus greatly reducing the emission of harmful substances and reducing carbon emissions through solid carbon fixation, the use of separate equipment in stages results in material transfer losses and excessive energy consumption.

[0006] Current technologies 1, 2, and 3 fail to address the challenge of transforming multi-source industrial solid waste into high-value-added road materials that meet road engineering specifications through an intelligent closed-loop treatment system. Therefore, this invention provides an intelligent treatment method for the comprehensive road utilization of multi-source solid waste generated during highway reconstruction and expansion projects. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides an intelligent treatment method for the comprehensive road utilization of multi-source solid waste from highway engineering reconstruction and expansion, comprising the following steps: The pretreated solid waste is physically modified. Based on the real-time chemical composition monitoring results of the solid waste, a certain amount of chemical modifier is automatically added and mixed through intelligent stirring equipment to obtain regulated modified solid waste. Modified solid waste is transported to an intelligent mixing plant, where it is automatically calculated and adjusted to match road materials based on real-time monitoring data of key performance indicators. In the intelligent mixing equipment, each component is mixed to produce a finished product whose performance indicators meet the requirements of road base material specifications.

[0008] Optionally, the process of automatically adding a measured amount of chemical modifier includes the following steps: The system continuously reads real-time concentration data of chemical components from online component analysis sensors to form a real-time chemical component set; compares the real-time chemical component set with the reference components pre-stored in the database; based on the comparison results, it calculates the types and amounts of chemical modifiers required to adjust the chemical composition of the current solid waste to the target chemical composition range, and outputs a modifier dosing instruction set. According to the type and precise amount specified in the modifier dosing instruction set, the chemical modifier is quantitatively taken from the corresponding storage bin, and the weighed chemical modifier is combined with the pre-treated solid waste running in the intelligent mixing equipment; the intelligent mixing equipment starts the preliminary mixing program and outputs the preliminary mixture of chemical modifier and waste. By using a monitoring probe integrated into the intelligent mixing equipment, the uniformity index of the initial mixture is acquired in real time. The uniformity index is compared with the set uniformity threshold. When the uniformity index meets the standard, it is determined that the chemical modifier is dispersed, and the initial mixture is transformed into regulated modified solid waste that meets the quality requirements.

[0009] Optionally, the process of outputting a modifier dosing instruction set includes the following steps: Based on each chemical component defined in the baseline composition, the algebraic difference between the target concentration data of the chemical component and the measured concentration data of the corresponding item in the real-time chemical component set is obtained, and a set of component concentration deviation values ​​are generated. Based on the concentration deviation value of each component, a pre-set modifier efficacy relation database is invoked. By querying the modifier efficacy relation database, each component concentration deviation value is mapped to the theoretical required dose of one or more chemical modifiers. The required dose of each chemical modifier mapped from the component concentration deviation values ​​of all chemical components is summarized, and summation and conflict verification are performed to generate a preliminary demand vector containing the types of chemical modifiers and their corresponding theoretical doses. The system reads the real-time operating parameters of the intelligent mixing equipment, including the real-time processing flow rate of the pretreated solid waste and the effective volume of the mixing equipment. It then couples the theoretical dosage in the initial demand vector with the real-time processing flow rate to calculate the theoretical demand per unit of material into a continuous dosing rate per unit time. Simultaneously, it dynamically calibrates the dosing rate based on the effective volume of the intelligent mixing equipment, generating a modifier dosing instruction set that includes the type of chemical modifier and the dosing amount expressed in instantaneous flow rate.

[0010] Optionally, the process of dynamically calibrating the acceleration rate includes the following steps: Read the preliminary demand vector and obtain the theoretical dosage for each chemical modifier defined therein; simultaneously read the real-time processing flow rate of the pretreated solid waste, multiply the theoretical dosage per unit mass of each chemical modifier by the real-time processing flow rate, and calculate the preliminary mass dosing rate of each chemical modifier to match the current material flow rate; The effective volume of the intelligent mixing equipment and the average time required for the material to be effectively mixed within the intelligent mixing equipment are obtained. Using the effective volume and average time, the inherent dynamic buffering effect when processing continuous materials is calculated, and the initial mass feed rate is calibrated. The calibration is to match the addition of chemical modifiers with the residence distribution of materials in the mixing chamber of the intelligent mixing equipment in the time dimension, and generate the calibrated mass feed rate. The calibrated mass dosing acceleration rate is converted into the corresponding drive actuator action signal based on the pre-stored characteristic curve of the feeding equipment; the action signals corresponding to all chemical modifiers are packaged with the chemical modifier type information to generate a modifier dosing instruction set.

[0011] Optionally, the process of converting the signal into an action signal for the corresponding driving actuator includes the following steps: Obtain the pre-stored characteristic curve of the feeding device and define the correspondence between the mass feeding acceleration rate and the execution parameters of the drive actuator; for each value in the calibrated mass feeding acceleration rate, query the corresponding characteristic curve of the feeding device to map out the unique theoretical value of the execution parameter of the feeding device. The theoretical values ​​of the parameters for each feeding device are converted into standard industrial control signals based on the electrical characteristics of the actuator; the industrial control signals are the action signals that drive the actuator. The action signals of the driving execution structure are associated and packaged with the information on the types of chemical modifiers to generate a set of modifier dosing instructions, which includes action commands for each type of chemical modifier.

[0012] Optionally, the process of converting to standard industrial control signals includes the following steps: Call the physical quantity-electrical quantity conversion coefficient that defines the fixed conversion relationship between the target physical parameter unit quantity and the standard electrical control quantity unit quantity; multiply the theoretical value of the feeder's execution parameter with the corresponding physical quantity-electrical quantity conversion coefficient to calculate the basic electrical quantity required to achieve the theoretical value of the feeder's execution parameter, i.e., the electrical control reference value; The electrical control reference value is normalized according to the entire range of electrical drive quantities, and then linearly mapped to the specified industrial standard signal range to obtain the standard signal interval value. Based on the communication protocol rules adopted by current industrial fieldbuses, standard signal range values ​​are encapsulated to generate a complete signal output that conforms to the communication protocol and contains effective control information. This signal is the standard industrial control signal that is recognized and executed by the driven actuator.

[0013] Optionally, the process of setting the conversion factor between physical and electrical quantities includes the following steps: A set of calibration dosing rate instructions covering its working range is sent to the feeding equipment, corresponding to the mass of the target chemical modifier added per unit time; the actuator acts according to the instructions, while measuring the actual calibration control signal value required by the current actuator control terminal; Each set of calibration acceleration command is correlated with its corresponding actual calibration control signal value; through linear regression analysis, the relationship from acceleration to control signal is established, and the slope of the relationship is determined as the physical quantity to electrical quantity conversion coefficient. The conversion coefficients between physical and electrical quantities are stored in a fixed format and used to convert real-time mass input acceleration rates into electrical control reference values.

[0014] Optionally, the process of establishing the relationship between the acceleration rate and the control signal includes the following steps: Each calibration acceleration command is associated with the actual calibration control signal value measured at the same time point to form a set of dynamic calibration data pairs with a one-to-one correspondence; each dynamic calibration data pair contains an independent acceleration value and a corresponding control signal value. Fit all dynamic calibration data pairs to find a straight line that is closest to all data points. The equation of the straight line is y=kx+b, where x represents the acceleration rate and y represents the control signal value. Establish a linear relationship model from the acceleration rate to the control signal. From the linear equation of the linear relationship model, the slope parameter k is extracted. The slope k represents the unit rate of change of the control signal with the acceleration rate, that is, the amount that the control signal needs to change for every unit increase in the acceleration rate. The value of the slope k is determined as the physical quantity to electrical quantity conversion factor.

[0015] Optionally, the process of automatically calculating and adjusting its mix ratio with road materials includes the following steps: The measured values ​​of each key performance indicator in the real-time monitoring data are compared with the pre-stored road base material specification requirements to obtain the real-time performance deviation value of each performance indicator, that is, the algebraic difference between the measured value of the key performance indicator and the road base material specification requirements; based on the algebraic difference, the theoretical compensation amount of various road materials required to compensate for the real-time performance deviation value is obtained. Read the preset road material reference mix ratio, and algebraically add the theoretical compensation amount of various road materials to the corresponding road base material amount in the road material reference mix ratio to obtain the corrected target mix ratio amount of various road materials. By combining the real-time processing flow rate of modified solid waste, the target proportion dosage is converted from mass ratio to material conveying flow rate per unit time, thereby generating the road material proportion execution parameters for controlling the feeding equipment.

[0016] Optionally, it also includes collecting solid waste from different industrial sources, automatically sorting to remove impurities and foreign objects, and using online sensors to monitor the initial physical and chemical properties of the solid waste in real time; automatically adjusting the temperature and time of the dryer based on the initial physical and chemical properties to control the moisture content within a predetermined range; at the same time, an intelligent vibrating screen performs screening according to preset particle size requirements; the solid waste is transformed into pre-treated solid waste with uniform properties and moisture content and particle size distribution that meet the standards.

[0017] This invention targets five types of solid waste: fly ash, slag powder, steel slag, coal gangue, and tailings. Through intelligent equipment and control, it achieves automated, precise, and efficient solid waste treatment compared to previous methods. The treatment method for each type of solid waste is customized based on its unique physical and chemical properties, ensuring that the treated solid waste meets the requirements for road base materials, achieving resource utilization and environmental protection. Specific treatment steps include solid waste collection and pretreatment, drying and screening, solid waste modification, mixture design and preparation, and quality control and monitoring. Intelligent sensors and monitoring equipment monitor various performance indicators in real time during the treatment process, automatically adjusting process parameters to ensure stable quality of the treated solid waste. This achieves resource utilization of solid waste materials, reduces environmental pollution, and lowers construction costs. By providing targeted intelligent treatment for different types of solid waste, it ensures that they meet the performance requirements of road base materials, achieving resource utilization and environmental protection while improving treatment efficiency and quality stability.

[0018] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the intelligent treatment method for the comprehensive road use of multi-source solid waste in highway engineering reconstruction and expansion in Embodiment 1 of the present invention; Figure 2 This is a block diagram of an intelligent treatment method for the comprehensive road use of multi-source solid waste in highway engineering reconstruction and expansion in Embodiment 1 of the present invention; Figure 3This is a process diagram of how solid waste is transformed into pretreated solid waste with uniform properties and moisture content and particle size distribution in Embodiment 2 of the present invention. Figure 4 This is a process diagram illustrating the automatic addition of a quantitative amount of chemical modifier in Example 4 of the present invention; Figure 5 This is a flowchart illustrating the process of automatically calculating and adjusting the mix ratio of the material with road materials in Embodiment 11 of the present invention. Figure 6 This is a flowchart illustrating the application process of the intelligent treatment method for the comprehensive road use of multi-source solid waste in highway engineering reconstruction and expansion in Embodiment 13 of the present invention. Figure 7 This is a schematic diagram of the physical modification in Embodiment 13 of the present invention; Figure 8 This is a schematic diagram illustrating the road performance of the solid waste material in Embodiment 13 of the present invention; Figure 9 This is a schematic diagram of the environmental impact assessment of an embodiment of the present invention. Detailed Implementation

[0021] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0022] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0023] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0024] Example 1: As Figure 1 As shown in the figure, this embodiment of the invention provides an intelligent treatment method for the comprehensive road use of multi-source solid waste from highway engineering reconstruction and expansion, comprising the following steps: S100: Collects solid waste from various industrial sources, automatically sorts and removes impurities and foreign objects, and uses online sensors to monitor the initial physical and chemical properties of the solid waste in real time; based on the initial physical and chemical properties, it automatically adjusts the temperature and time of the dryer to control the moisture content within a predetermined range; at the same time, an intelligent vibrating screen performs screening according to preset particle size requirements; the solid waste is transformed into pre-treated solid waste with uniform properties and moisture content and particle size distribution that meet the standards. S200: Physically modify the pretreated solid waste, automatically add a quantitative amount of chemical modifier based on the real-time chemical composition monitoring results of the solid waste, and mix it through intelligent stirring equipment to obtain regulated modified solid waste; S300: Modified solid waste is transported to an intelligent mixing plant. Based on real-time monitoring data of key performance indicators of the modified solid waste, the mixing ratio with road materials is automatically calculated and adjusted. In the intelligent mixing equipment, each component is mixed to prepare a finished product whose performance indicators meet the requirements of road base material specifications.

[0025] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first collects solid waste from different industrial sources, removes impurities and foreign objects through automatic sorting, and uses online sensors to monitor the initial physical and chemical properties of the solid waste in real time. Based on the initial physical and chemical properties, the temperature and time of the dryer are automatically adjusted to control the moisture content within a predetermined range. Simultaneously, an intelligent vibrating screen performs sieving according to preset particle size requirements. The solid waste is transformed into pre-treated solid waste with uniform properties and moisture content and particle size distribution meeting standards. Secondly, the pre-treated solid waste undergoes physical modification. Based on the real-time chemical composition monitoring results of the solid waste, a quantitative amount of chemical modifier is automatically added, and the mixture is obtained through intelligent mixing equipment to obtain regulated modified solid waste. Finally, the modified solid waste is transported to an intelligent mixing station. Based on real-time monitoring data of the key performance indicators of the modified solid waste, its mixing ratio with road materials is automatically calculated and adjusted. In the intelligent mixing equipment, each component is stirred to prepare a finished product whose performance indicators meet the requirements of road base material specifications (the specific principle is as follows). Figure 2(As shown). The above scheme, through a closed-loop control system of automatic sorting, online monitoring, drying regulation, and vibrating screening, ensures that solid waste from different sources is transformed into pre-treated products with moisture content and particle size distribution that meet road material standards. An online sensor network monitors physicochemical parameters in real time, providing a basis for subsequent process control. Based on real-time monitoring of chemical composition, the amount of chemical modifier added is automatically adjusted to optimize the gelling properties of solid waste. Intelligent mixing equipment dynamically adjusts mixing parameters to ensure uniform distribution of modifiers and improve material stability. Based on feedback from the performance indicators of modified solid waste, the material ratio of the mixing station is dynamically adjusted. The combination of multi-component synergistic metering and efficient mixing ensures that the mechanical properties of the finished material meet the specifications. Sensor data from each stage are linked across processes through the Industrial Internet of Things, forming a closed-loop control chain to ensure that even with a high solid waste blending rate, the finished product still meets the mechanical and durability standards for road base materials.

[0026] This embodiment addresses five types of solid waste—fly ash, slag powder, steel slag, coal gangue, and tailings—by utilizing intelligent equipment and control. Compared to previous solid waste treatment methods, it achieves automated, precise, and efficient processing. The treatment method for each type of solid waste is customized based on its unique physical and chemical properties, ensuring that the treated solid waste meets the requirements for road base materials, achieving resource utilization and environmental protection. Specific processing steps include solid waste collection and pretreatment, drying and screening, solid waste modification, mixture design and preparation, and quality control and monitoring. Intelligent sensors and monitoring equipment monitor various performance indicators in real time during the processing, automatically adjusting process parameters to ensure stable quality of the treated solid waste. This achieves resource utilization of solid waste materials, reduces environmental pollution, and lowers construction costs. By providing targeted intelligent treatment for different types of solid waste, it ensures that they meet the performance requirements of road base materials, achieving resource utilization and environmental protection while improving processing efficiency and quality stability.

[0027] This embodiment achieves efficient treatment and resource utilization of solid waste, while ensuring its excellent performance in road construction.

[0028] Example 2: As Figure 3 As shown, based on Example 1, the process by which solid waste is converted into pretreated solid waste with uniform properties and meeting the standards for moisture content and particle size distribution, provided in this embodiment of the invention, includes the following steps: S101: Real-time monitoring to obtain initial physical and chemical property datasets; adjustment of operating parameters for the coordinated control stage of drying and sieving based on the initial property datasets; S102: Based on the real-time moisture content data in the initial property dataset, the thermal action is dynamically calculated and set to control the moisture content of the output material within a predetermined range; the configuration specifications and vibration mode of the intelligent vibrating screen are determined by the original particle size distribution information contained in the initial property dataset, and are matched with the moisture content reached after drying. S103: After coordinated regulation, the physical properties of solid waste, such as moisture content and particle size, are simultaneously stabilized within a preset range; at this point, multi-source solid waste is transformed into pre-treated solid waste that achieves uniformity in moisture content and physical particle size.

[0029] The working principle and beneficial effects of the above technical solution are as follows: First, this embodiment obtains an initial physical and chemical property dataset through real-time monitoring. Then, based on the initial property dataset, the operating parameters for the coordinated control stage of drying and screening are adjusted. Second, based on the real-time moisture content data in the initial property dataset, the thermal effect is dynamically calculated and set to control the moisture content of the output material within a predetermined range. The configuration specifications and vibration mode of the intelligent vibrating screen are determined by the original particle size distribution information contained in the initial property dataset, matching the moisture content achieved after drying. Finally, after coordinated control, the physical property indicators of the solid waste's moisture content and particle size are synchronously stabilized within a preset range. At this point, multi-source solid waste is transformed into pre-treated solid waste that achieves uniformity in moisture content and physical particle size. The initial physical and chemical property monitoring data of the above solution constitute the basic parameter set for closed-loop control, ensuring that the dynamic adjustments of the subsequent drying and screening processes are based on the same data source, avoiding parameter drift in multi-stage control. The adjustment of thermal parameters during the drying stage not only controls moisture content, but also serves as input for the operating parameters of the screening equipment. This allows the vibration mode and screen configuration of the vibrating screen to adapt to the actual physical state of the dried material, rather than relying on preset fixed values. The coordinated operation of moisture content control and the screening process brings the dynamic fluctuation range of moisture content and particle size closer together, ultimately achieving a uniform and stable physical state for solid wastes from different sources. This embodiment achieves mutual compensation between moisture content and particle size control through data-driven dynamic feedback, enabling multi-source heterogeneous solid wastes to form predictable and consistent pre-treated products in terms of physical properties.

[0030] Example 3: Based on Example 2, the process provided in this embodiment of the invention for achieving a matching moisture content state after drying includes the following steps: S1021: Reads the continuous monitoring value of real-time moisture content in the initial dataset, calls the built-in moisture content control program, and outputs a specific target moisture content setpoint and the heat energy supply strategy required to achieve this target based on the current and historical moisture content; Simultaneously reads the original particle size distribution data in the initial dataset, uses the obtained target moisture content setpoint as a key input parameter, and outputs a specific target particle size distribution range in combination with the original particle size distribution data. S1022: Based on the performance parameter library of the drying equipment, the heat supply strategy is calculated into a set of directly executable thermodynamic control parameters; the target particle size distribution range and the heat supply strategy are calculated into a set of directly executable screening mechanical parameters based on the performance parameter library of the intelligent vibrating screen; two sets of low-level control commands that directly drive the physical equipment are output. S1023: Two sets of bottom-level control commands are simultaneously sent to the drying equipment and the intelligent vibrating screen. The material passes through the drying zone and the screening zone, which operate according to the commands, in sequence. In the drying zone, the moisture content of the material is processed to the target moisture content set value under the regulation of the thermodynamic control parameter set. The material that has reached the target moisture content enters the screening zone, which operates according to the screening mechanical parameter set.

[0031] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first reads the continuous monitoring value of real-time moisture content in the initial dataset, calls the built-in moisture content control program, and outputs a specific target moisture content setpoint and the heat energy supply strategy required to achieve this target based on the current and historical moisture content; simultaneously, it reads the original particle size distribution data in the initial dataset, uses the obtained target moisture content setpoint as a key input parameter, and outputs a specific target particle size distribution range based on the original particle size distribution data; secondly, it calculates the heat energy supply strategy according to the performance parameter library of the drying equipment. The system employs a set of directly executable thermodynamic control parameters. Based on the performance parameter library of the intelligent vibrating screen, the target particle size distribution range and heat supply strategy are calculated into a set of directly executable screening mechanical parameters. Two sets of low-level control commands directly drive the physical equipment. These commands are then synchronously sent to the drying equipment and the intelligent vibrating screen. The material sequentially passes through the drying zone and screening zone, which operate according to the commands. In the drying zone, under the control of the thermodynamic control parameter set, the moisture content of the material is reduced to the target moisture content set value. The material that has reached the target moisture content enters the screening zone, which operates according to the screening mechanical parameter set. This scheme is based on real-time monitoring values ​​of the initial morphology dataset. The moisture content control program and the particle size distribution prediction module form a parallel computing architecture, ensuring data consistency between the target moisture content and the target particle size distribution set values, and avoiding parameter conflicts caused by step-by-step decision-making. The heat supply strategy and the generation process of screening mechanical parameters share the same target moisture content input. Physical parameters are calculated through the performance parameter library of the drying equipment and vibrating screen, enabling dynamic matching between the thermodynamic control parameter set and the screening mechanical parameter set at the equipment execution level. The synchronous issuance of two sets of low-level control commands ensures that the drying and screening processes form a closed loop. The moisture content output of the drying zone directly matches the mechanical operating conditions of the screening zone, ensuring the progressive stability of the physical properties of the material during continuous processing. This achieves dynamic coupling between moisture content control and particle size distribution at the equipment execution stage, enabling the two key indicators (moisture content and particle size) of the pretreated product to achieve the synergistic convergence effect required by the process.

[0032] Example 4: Figure 4 As shown, based on Example 1, the process for automatically adding a quantitative amount of chemical modifier provided in this embodiment of the invention includes the following steps: S201: Continuously reads real-time concentration data of chemical components from online component analysis sensors to form a real-time chemical component set; compares the real-time chemical component set with the reference components pre-stored in the database, the reference components defining the target chemical component range required to prepare qualified road materials; based on the comparison results, calculates the types and amounts of chemical modifiers required to adjust the chemical composition of the current solid waste to the target chemical component range, and outputs a modifier dosing instruction set; S202: According to the type and precise amount specified in the modifier dosing instruction set, the chemical modifier is quantitatively taken from the corresponding storage bin, and the weighed chemical modifier is combined with the pre-treated solid waste running in the intelligent mixing equipment; the intelligent mixing equipment starts the preliminary mixing program, so that the solid waste and the chemical modifier begin to make physical contact and dispersion, and outputs the preliminary mixture of chemical modifier and waste; S203: Using a monitoring probe integrated into the intelligent mixing equipment, the uniformity index of the preliminary mixture is acquired in real time and compared with the set uniformity threshold. If the uniformity index does not reach the threshold, the operating parameters of the intelligent mixing equipment are dynamically adjusted until the uniformity index meets the set uniformity threshold. When the uniformity index meets the standard, it is determined that the chemical modifier is dispersed, and the preliminary mixture is converted into controlled modified solid waste that meets the quality requirements.

[0033] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first continuously reads the real-time concentration data of chemical components transmitted by the online component analysis sensor to form a real-time chemical component set; the real-time chemical component set is compared with the reference components pre-stored in the database, and the reference components define the target chemical component range required to prepare qualified road materials; based on the comparison results, the types and amounts of chemical modifiers required to adjust the chemical components of the current solid waste to the target chemical component range are calculated, and the result is output as a modifier dosing instruction set; secondly, according to the type and precise amount specified in the modifier dosing instruction set, the chemical modifier is quantitatively taken from the corresponding storage bin, and the weighed chemical modifier is mixed with the intelligent stirring agent. The pre-treated solid waste running in the mixing equipment is combined; the intelligent mixing equipment starts the preliminary mixing program, allowing the solid waste and chemical modifier to begin physical contact and dispersion, outputting a preliminary mixture of chemical modifier and waste; finally, the monitoring probe integrated in the intelligent mixing equipment acquires the uniformity index of the preliminary mixture in real time, and compares the uniformity index with the set uniformity threshold; if the uniformity index does not reach the threshold, the operating parameters of the intelligent mixing equipment are dynamically fine-tuned until the uniformity index meets the set uniformity threshold; when the uniformity index meets the standard, it is determined that the chemical modifier is dispersed, and the preliminary mixture is transformed into regulated modified solid waste that meets quality requirements. The above scheme forms a dynamic feedback mechanism based on chemical composition deviation through real-time comparison between online component analysis sensors and database benchmark components, realizing adaptive calculation of the type and amount of modifier added, establishing a closed-loop control link of detection-analysis-decision, and ensuring that the material composition always converges to the target range. The precise matching of the quantitative dispensing mechanism and instruction set in the storage silo solves the measurement error problem inherent in traditional manual dosing. Mechatronics execution transforms calculated values ​​into physical dosing actions, enabling digital traceability of modifier quality control. Real-time coupling of monitoring probes and stirring equipment parameters forms a mixing feedback loop, overcoming passive mixing by dynamically adjusting parameters such as stirring intensity and time. This improves contact efficiency in heterogeneous systems, avoiding insufficient modification caused by excessive local concentration gradients. The cascaded operation of component analysis, reagent dosing, and mixing control eliminates the intermittent pauses of traditional batch processing. Data bus synchronization creates a continuous flow modification system, significantly increasing throughput and reducing energy costs. A dual constraint mechanism of real-time chemical composition deviation correction and mixing uniformity threshold control ensures that key performance indicators of the output material meet preset standard deviation ranges, achieving stable output of modified product quality parameters.

[0034] Example 5: Based on Example 4, the process of providing a modifier dosing instruction set as the output of this embodiment of the invention includes the following steps: S2011: Based on each chemical component defined in the reference composition, obtain the algebraic difference between the target concentration data of the chemical component and the measured concentration data of the corresponding item in the real-time chemical component set, and generate a set of component concentration deviation values. S2012: Based on the concentration deviation value of each component, call the preset modifier efficacy relationship database. By querying the modifier efficacy relationship database, map each component concentration deviation value to the theoretical required dose of one or more chemical modifiers. Summarize the required dose of each chemical modifier mapped from the component concentration deviation values ​​of all chemical components, perform summation and conflict verification, and generate a preliminary demand vector containing the types of chemical modifiers and their corresponding theoretical doses. S2013: Read the real-time operating parameters of the current intelligent mixing equipment, including the real-time processing flow rate of the pretreated solid waste and the effective volume of the mixing equipment; couple the theoretical dosage in the preliminary demand vector with the real-time processing flow rate to calculate the theoretical demand per unit of material into the continuous dosing rate per unit time; at the same time, combine the effective volume of the intelligent mixing equipment to dynamically calibrate the dosing rate and generate a modifier dosing instruction set, including the type of chemical modifier and the dosing amount expressed in instantaneous flow rate.

[0035] The working principle and beneficial effects of the above technical solution are as follows: First, based on each chemical component defined in the reference composition, the algebraic difference between the target concentration data of the chemical component and the measured concentration data of the corresponding item in the real-time chemical component set is obtained, generating a set of component concentration deviation values. Second, based on each component concentration deviation value, a preset modifier efficacy relationship database is called. By querying the modifier efficacy relationship database, each component concentration deviation value is mapped to the theoretical required dose of one or more chemical modifiers. The required dose of each chemical modifier mapped from the component concentration deviation values ​​of all chemical components is summed and combined. Conflict verification generates a preliminary demand vector containing the types of chemical modifiers and their corresponding theoretical dosages. Finally, the real-time operating parameters of the intelligent mixing equipment are read, including the real-time processing flow rate of the pretreated solid waste and the effective volume of the mixing equipment. The theoretical dosage and real-time processing flow rate in the preliminary demand vector are coupled and calculated to convert the theoretical demand per unit of material into a continuous dosing rate per unit time. Simultaneously, the dosing rate is dynamically calibrated based on the effective volume of the intelligent mixing equipment, generating a modifier dosing instruction set containing the types of chemical modifiers and the dosage expressed in instantaneous flow rate. This scheme quantifies the deviation between the target concentration and the measured concentration into a calculable numerical index through algebraic difference calculations, establishing a direct mathematical correlation between component differences and modifier demand; ensuring that the data basis for subsequent modifier calculations has a clear chemometric basis. A pre-built database is used to achieve a nonlinear mapping from chemical component deviations to modifier demand, solving the quantification problem of multi-component synergistic / antagonistic effects; through addition and conflict verification mechanisms, the coupling relationship between different chemical component demands is effectively handled, avoiding systematic errors caused by single-component optimization. By performing dual-parameter calibration of flow rate and equipment volume, the engineering conversion from theoretical dosage to operating parameters was achieved. Flow rate coupling calculation ensures the spatiotemporal matching of dosage and material throughput, while volume calibration ensures that the hybrid dynamic parameters meet the equipment's operating boundary conditions. A closed-loop control system was established, capable of generating modifier dosing schemes that meet process requirements and equipment operating constraints in real time based on component detection results. Automatic conversion from chemical analysis data to engineering control commands was achieved, ensuring the accuracy, coordination, and operability of modifier dosing.

[0036] Example 6: Based on Example 5, the process for dynamically calibrating the acceleration rate provided in this embodiment of the invention includes the following steps: S20131: Read the preliminary demand vector and obtain the theoretical dosage for each chemical modifier defined therein; synchronously read the real-time processing flow rate of the pretreated solid waste, multiply the theoretical dosage per unit mass of each chemical modifier by the real-time processing flow rate, and calculate the preliminary mass dosing rate of each chemical modifier to match the current material flow rate. Specifically, the theoretical dosage per unit mass for each chemical modifier is read, i.e., the number of grams of modifier to be added per kilogram of solid waste, and multiplied with the solid waste treatment flow rate collected in real time by the sensor, i.e. the number of kilograms of solid waste processed per unit time; the static addition ratio for a fixed material is converted into the instantaneous addition requirement to match the continuously changing material flow, and the calculation result is directly output as the initial mass addition acceleration rate, i.e. the mass of modifier to be added per unit time; S20132: Obtain the effective volume of the intelligent mixing equipment and the average time required for the material to complete effective mixing within the intelligent mixing equipment; using the effective volume and average time, calculate the inherent dynamic buffering effect when processing continuous materials, and calibrate the initial mass feed rate. The calibration involves matching the addition of chemical modifiers with the residence distribution of materials within the mixing chamber of the intelligent mixing equipment in the time dimension, and generating the calibrated mass feed rate. The calculation process of dynamic buffering effect involves reading the effective volume of the intelligent mixing equipment and the preset average mixing time of the material, dividing the effective volume by the average mixing time to calculate the theoretical average flow rate of the material in the equipment; then, based on the theoretical average flow rate and the volume characteristics of the intelligent mixing equipment, the time distribution law describing the material from feeding to discharging is obtained, the time correlation between the chemical modifier addition point and the optimal mixing point of the material is identified, and the initial addition rate is calibrated by time offset to synchronize the chemical modifier addition timeline with the material residence distribution, ensuring that the chemical modifier is introduced in the most critical mixing stage of the material flow. This calibration process is the quantitative application of dynamic buffering effect. S20133: Convert the calibrated mass feed rate into the corresponding motor speed and valve opening and other drive actuator action signals based on the pre-stored characteristic curves of feeding equipment such as screw feeders or metering pumps; package the action signals corresponding to all chemical modifiers with the chemical modifier type information to generate a modifier dosing instruction set.

[0037] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first reads the preliminary demand vector to obtain the theoretical dosage defined for each chemical modifier; simultaneously reads the real-time processing flow rate of the pretreated solid waste, multiplies the theoretical dosage per unit mass of each chemical modifier by the real-time processing flow rate, and calculates the preliminary mass addition acceleration rate for each chemical modifier to match the current material flow rate; secondly, obtains the effective volume of the intelligent mixing equipment and the average time required for the material to complete effective mixing in the intelligent mixing equipment; using the effective volume and average time, calculates the inherent dynamic buffering effect when processing continuous materials, and calibrates the preliminary mass addition acceleration rate. The calibration content is to match the addition of chemical modifiers with the residence distribution of materials in the mixing chamber of the intelligent mixing equipment in the time dimension, and generates the calibrated mass addition acceleration rate; finally, the calibrated mass addition acceleration rate is converted into the corresponding motor speed and valve opening and other drive actuator action signals based on the pre-stored characteristic curves of feeding equipment such as screw feeders or metering pumps; all the action signals corresponding to chemical modifiers are packaged with the chemical modifier type information to generate a modifier addition instruction set. The above scheme achieves precise dosing control of chemical modifiers in solid waste treatment through multi-step collaborative processes: by real-time processing of the product of flow rate and theoretical dosage, a linear mapping relationship between material throughput and modifier demand is established to ensure that the baseline value of dosing acceleration rate matches the current material processing scale; a dynamic buffer is established based on the volume parameters and mixing time parameters of the mixing equipment, and the time phase difference of the initial dosing acceleration rate is corrected by time distribution to achieve synchronous optimization of dosing sequence and material residence time distribution; the mass flow rate parameter is mapped to the control parameters of specific actuators, and the precise conversion from theoretical dosing acceleration rate to physical execution signal is achieved through equipment characteristic curves.

[0038] In summary, this embodiment eliminates the time delay effect of the mixing process through the buffering effect, and synchronously processes multi-dimensional variables such as material flow rate, equipment parameters, and actuator characteristics; it encapsulates complex control logic into an executable set of equipment instructions; and ultimately achieves precise matching between the chemical modifier addition process and the solid waste treatment process in the time and space dimensions.

[0039] Example 7: Based on Example 6, the process of converting the action signals of the drive actuator, such as the corresponding motor speed and valve opening, provided in this embodiment of the invention includes the following steps: S201331: Obtain the pre-stored characteristic curve of the feeding equipment and define the correspondence between the mass feeding acceleration rate and the execution parameters of the drive actuator; for each value in the calibrated mass feeding acceleration rate, query the corresponding characteristic curve of the feeding equipment to map out the unique theoretical value of the execution parameter of the feeding equipment. S201332: Convert the theoretical values ​​of the execution parameters of each feeding device into standard industrial control signals, such as analog voltage values ​​or pulse width modulation signals, based on the electrical characteristics of the actuator, such as the speed-voltage relationship of a servo motor or the opening-current relationship of a proportional valve; the industrial control signals are the action signals that drive the actuator. S201333: The action signals of the driving execution structure are associated and packaged with the chemical modifier type information to generate a modifier dosing instruction set, which includes action commands for each chemical modifier.

[0040] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first obtains the pre-stored characteristic curve of the feeding equipment and defines the correspondence between the mass feeding acceleration rate and the execution parameters of the drive actuator; for each value in the calibrated mass feeding acceleration rate, a query is performed in the corresponding characteristic curve of the feeding equipment to map out the unique theoretical value of the execution parameter of the feeding equipment; secondly, each theoretical value of the execution parameter of the feeding equipment is converted into a standard industrial control signal, such as an analog voltage value or a pulse width modulation signal, according to the electrical characteristics of the actuator, such as the speed-voltage relationship of a servo motor or the opening degree-current relationship of a proportional valve; the industrial control signal is the action signal of the drive actuator; finally, the action signal of the drive actuator is associated and packaged with the chemical modifier type information to generate a modifier dosing instruction set, which contains action commands for each chemical modifier. The above scheme establishes a mapping relationship between the characteristic curves of the feeding equipment, converts the calibrated mass dosing rate into theoretical parameter values ​​of the actuator, and then converts it into standard industrial control signals based on the electrical characteristics of the actuator. Finally, the action signals are associated and packaged with the modifier type information to form a complete modifier dosing instruction set. This achieves a closed-loop conversion from process parameters to equipment drive signals, ensuring the accuracy and traceability of mass dosing control. The combination of each step forms a standardized conversion chain of process parameters-equipment characteristics-electrical signals-operation instructions, providing a core control logic implementation path for automated dosing.

[0041] Example 8: Based on Example 7, the process of converting signals into standard industrial control signals provided in this embodiment of the invention includes the following steps: S2013321: Call the physical quantity-electrical quantity conversion coefficient that defines the fixed conversion relationship between the target physical parameter unit quantity and the standard electrical control quantity unit quantity; multiply the theoretical value of the feeder's execution parameter with the corresponding physical quantity-electrical quantity conversion coefficient to calculate the basic electrical quantity required to achieve the theoretical value of the feeder's execution parameter, i.e., the electrical control reference value; S2013322: Normalize the electrical control reference value according to the entire range of electrical drive quantities in which it is located, and linearly map it to the specified industrial standard signal range to obtain the standard signal interval value. S2013323: Based on the communication protocol rules adopted by the current industrial fieldbus, the standard signal range values ​​are encapsulated to generate a complete signal output that conforms to the communication protocol and contains effective control information. The signal is the standard industrial control signal that is recognized and executed by the driven actuator.

[0042] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first calls the physical quantity-electrical quantity conversion coefficient, which defines a fixed conversion relationship between the target physical parameter unit quantity and the standard electrical control quantity unit quantity; it then multiplies the theoretical value of the feeding equipment execution parameter with the corresponding physical quantity-electrical quantity conversion coefficient to calculate the basic electrical quantity required to achieve the theoretical value of the feeding equipment execution parameter, i.e., the electrical control reference value; secondly, it normalizes the electrical control reference value according to the entire range of electrical drive quantities in which it is located, and linearly maps it to a specified industrial standard signal range to obtain the standard signal interval value; finally, according to the communication protocol rules adopted by the current industrial fieldbus, it encapsulates the standard signal interval value to generate a complete signal output that conforms to the communication protocol and contains effective control information. This signal is the standard industrial control signal that is recognized and executed by the driven actuator. The physical quantity-electrical quantity conversion of the above solution establishes a deterministic relationship between industrial field physical parameters and electrical control quantities, and achieves accurate conversion from physical units to electrical units through fixed conversion coefficients, providing a quantifiable electrical reference value for the control system. Signal standardization eliminates the impact of equipment range differences through normalization processing and uses a linear mapping algorithm to adapt electrical reference values ​​to the industrial standard signal range, ensuring signal compatibility between equipment from different manufacturers. The protocol encapsulation layer reconstructs the standardized signals into data frames according to the fieldbus protocol specification, adding necessary protocol headers, check bits, and other control fields to form digital instructions that conform to industrial communication protocols.

[0043] Example 9: Based on Example 8, the process for setting the physical quantity-electrical quantity conversion factor provided in this embodiment of the invention includes the following steps: S20133211: Send a set of calibration dosing rate instructions covering its working range to the feeding equipment, corresponding to the mass of the target chemical modifier added per unit time; the actuator acts according to the instructions, and at the same time measures the actual calibration control signal value required by the current actuator control terminal; S20133212: Correlate each set of calibration acceleration command with its corresponding actual calibration control signal value; establish the relationship from acceleration to control signal through linear regression analysis, and determine the slope of the relationship as the physical quantity to electrical quantity conversion coefficient. S20133213: Solidify and store the conversion coefficient between physical and electrical quantities, which is used to convert real-time mass input acceleration rate into electrical control reference value.

[0044] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, a set of calibration acceleration rate instructions covering its working range is first sent to the feeding equipment, corresponding to the mass of the target chemical modifier added per unit time; the actuator acts according to the instructions, and at the same time measures the actual calibration control signal value required by the current actuator control terminal; secondly, each set of calibration acceleration rate instructions is correlated with its corresponding actual calibration control signal value; through linear regression analysis, the relationship from acceleration rate to control signal is established, and the slope of the relationship is determined as the physical quantity-electrical quantity conversion coefficient; finally, the physical quantity-electrical quantity conversion coefficient is solidified and stored, which is used to convert the real-time mass acceleration rate into an electrical control reference value. The above scheme establishes a dataset of correspondences between acceleration rates and control signals by using a calibration instruction set covering the working range, ensuring that the conversion coefficients are applicable to the entire operating range of the equipment; it uses linear regression analysis to quantify the functional relationship between acceleration rates and control signals, ensuring the mathematical validity of the conversion coefficients and enabling a deterministic mapping relationship between physical and electrical quantities; and it writes the determined conversion coefficients into non-volatile storage media to ensure their stability during the equipment's operating cycle, providing persistent and callable reference parameters for real-time control.

[0045] In summary, this embodiment realizes a closed-loop setting process from experimental calibration to coefficient solidification. The calibration method based on measured data ensures the accuracy of the conversion coefficients, linear regression ensures the mathematical reliability of the conversion relationship, and the storage solidification mechanism maintains the long-term availability of parameters. It provides a verifiable, traceable, and stable calculation benchmark for the conversion of physical quantities to electrical quantities, supporting the accurate generation of subsequent control signals.

[0046] Example 10: Based on Example 9, the process for establishing the relationship between the acceleration rate and the control signal provided in this embodiment of the invention includes the following steps: S201332121: Correspond each calibration acceleration command to the actual calibration control signal value measured at the same time point to form a set of dynamic calibration data pairs with a one-to-one correspondence; each dynamic calibration data pair contains an independent acceleration value and a corresponding control signal value. S201332122: Fit all dynamic calibration data pairs to find a straight line that is closest to all data points. The equation of the straight line is y=kx+b, where x represents the acceleration rate and y represents the control signal value. Establish a linear relationship model from the acceleration rate to the control signal. S201332123: Extract the slope parameter k from the linear equation of the linear relationship model. The slope k represents the unit rate of change of the control signal with the acceleration rate, that is, the amount that the control signal needs to change for every unit increase in the acceleration rate. The value of the slope k is determined as the physical quantity to electrical quantity conversion factor.

[0047] The working principle and beneficial effects of the above technical solution are as follows: First, each calibration acceleration command is associated with the actual calibration control signal value measured at the same time point to form a set of dynamic calibration data pairs with a one-to-one correspondence. Each dynamic calibration data pair contains an independent acceleration value and a corresponding control signal value. Second, all dynamic calibration data pairs are fitted to find a straight line that is closest to all data points. The equation of the straight line is y=kx+b, where x represents the acceleration and y represents the control signal value, establishing a linear relationship model from the acceleration to the control signal. Finally, the slope parameter k is extracted from the linear equation of the linear relationship model. The slope k represents the unit rate of change of the control signal with the acceleration, that is, the amount by which the control signal needs to change for every unit increase in the acceleration. The value of the slope k is determined as the physical quantity to electrical quantity conversion factor. The above scheme establishes a one-to-one dynamic calibration data pair by precisely synchronizing the acceleration command and control signal measurements, ensuring the data foundation has temporal consistency and numerical matching. Using the least squares method or other linear fitting algorithms, the optimal linear equation y=kx+b is solved from the dynamic calibration data pair, minimizing the overall deviation of this line from all data points and ensuring adaptability to actual operating conditions. The slope parameter k is extracted from the fitted linear equation as the unit rate of change between the acceleration and control signals, quantifying the conversion ratio between them, and ultimately defining it as a physical quantity-electrical quantity conversion coefficient. The final output is a mathematically deterministic physical quantity-electrical quantity conversion coefficient, providing a standardized basis for control signal generation.

[0048] Example 11: As Figure 5 As shown, based on Example 1, the process of automatically calculating and adjusting the mix ratio with road materials provided in this embodiment of the invention includes the following steps: S301: Compare the measured values ​​of each key performance indicator in the real-time monitoring data with the pre-stored road base material specification requirements to obtain the real-time performance deviation value of each performance indicator, that is, the algebraic difference between the measured value of the key performance indicator and the road base material specification requirements; and obtain the theoretical compensation amount of various road materials required to compensate for the real-time performance deviation value based on the algebraic difference. The process of obtaining the theoretical compensation amount based on algebraic difference establishes a quantitative conversion relationship from performance deviation to material compensation amount; an internal performance-material compensation coefficient database is pre-stored, and through extensive experimental calibration in the early stage, the precise mass of various road materials that need to be supplemented or reduced for a specific performance index deviation per unit value is determined; during processing, the real-time performance deviation value is multiplied with the corresponding compensation coefficient to directly calculate the theoretical compensation amount of various road materials required to compensate for the deviation; S302: Read the preset road material reference mix ratio, and algebraically add the theoretical compensation amount of various road materials to the corresponding road base material amount in the road material reference mix ratio to obtain the corrected target mix ratio amount of various road materials. S303: Combining the real-time processing flow rate of modified solid waste, the target proportion dosage is converted from mass ratio to material conveying flow rate per unit time, generating the road material proportion execution parameters for controlling the feeding equipment.

[0049] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first compares the measured value of each key performance indicator in the real-time monitoring data with the pre-stored road base material specification requirements to obtain the real-time performance deviation value of each performance indicator, that is, the algebraic difference between the measured value of the key performance indicator and the road base material specification requirements; based on the algebraic difference, the theoretical compensation amount of various road materials required to compensate for the real-time performance deviation value is obtained; secondly, the preset road material benchmark mix ratio is read, and the theoretical compensation amount of various road materials is algebraically superimposed with the corresponding road base material amount in the road material benchmark mix ratio to obtain the corrected target mix ratio amount of various road materials; finally, combined with the real-time processing flow rate of modified solid waste, the target mix ratio amount is converted from mass ratio to material conveying flow rate per unit time, generating the road material mix ratio execution parameters for controlling the feeding equipment. The above scheme calculates the theoretical compensation amount by the algebraic difference between the measured values ​​and the standard values ​​of key performance indicators, thereby realizing the quantitative compensation of material performance gaps; the theoretical compensation amount and the benchmark mix ratio are algebraically superimposed to establish a mathematical mapping relationship between material usage and performance deviation; and the static mass ratio is converted into time-series flow parameters to realize the coupling of mix ratio parameters with continuous production process.

[0050] In summary, this embodiment forms a closed-loop control system of monitoring-calculation-compensation, which realizes adaptive matching between the mix proportion parameters and real-time operating conditions, ensures that the material performance indicators continuously meet the specifications, and completes the seamless connection from discrete mix proportion parameters to continuous production process.

[0051] Example 12: Based on Example 11, the process for obtaining the modified target mix proportions of various road materials provided in this embodiment of the invention includes the following steps: S3021: Perform sign verification on the theoretical compensation amount for each type of road material, that is, determine the sign of its value. A positive value indicates that the base amount of road material needs to be increased, while a negative value indicates that it needs to be reduced. At the same time, the theoretical compensation amount that passes the verification is marked as the effective compensation amount. S3022: Add the baseline quantity of each material in the road material reference mix proportion to the compensation value of the corresponding material in the effective compensation amount algebraically; for example, if the cement quantity in the reference mix proportion is 100 units and the calculated effective compensation amount is +5 units, then the corrected target quantity is 105 units; generate the target mix proportion quantity.

[0052] The working principle and beneficial effects of the above technical solution are as follows: First, this embodiment performs sign verification on the theoretical compensation amount of each road material, that is, determines whether its value is positive or negative. A positive value indicates that the benchmark amount of road material needs to be increased, while a negative value indicates that it needs to be decreased. Simultaneously, the verified theoretical compensation amount is marked as the effective compensation amount. Second, the benchmark amount of each material in the benchmark mix proportion is algebraically added to the corresponding compensation value in the effective compensation amount. For example, if the cement amount in the benchmark mix proportion is 100 units, and the calculated effective compensation amount is +5 units, then the corrected target amount is 105 units; thus, the target mix proportion amount is generated. The above solution distinguishes the adjustment direction of the theoretical compensation amount through sign verification—positive values ​​indicate increase, negative values ​​indicate decrease—and filters the effective compensation amount to ensure the rationality of the correction operation. The algebraic addition of the material amount in the benchmark mix proportion with the effective compensation amount achieves quantitative adjustment of the material amount, ensuring that the correction process conforms to mathematical logic. By correcting each material and outputting the target amount, directly executable mix proportion parameters are formed.

[0053] In summary, this embodiment eliminates invalid compensation amounts through symbol verification, reducing the risk of error propagation. Based on the algebraic operation of the benchmark value and the compensation amount, it achieves precision in dosage adjustment, ensuring that the corrected target mix ratio meets the performance deviation compensation requirements. Symbol verification provides valid input for algebraic superposition, and algebraic superposition completes the precise correction. Together, the two constitute a complete calculation chain for generating the target mix ratio.

[0054] Example 13: As Figures 6-9 As shown, based on Examples 1-12, the application process of the intelligent treatment method for comprehensive road use of multi-source solid waste in highway engineering reconstruction and expansion provided by the embodiments of the present invention is as follows: Figure 6 As shown, it includes the following steps: The first step, solid waste material treatment: This involves the necessary processing of five types of solid waste materials, including fly ash, slag powder, steel slag, coal gangue, and tailings. 1.1 Solid waste collection and pretreatment: Fly ash: Fly ash is automatically collected from the dust collection equipment of coal-fired power plants and stored in closed warehouses or ash storage tanks through an intelligent storage system, with real-time monitoring of moisture content and particle distribution; Slag powder: Granulated blast furnace slag is automatically collected from the smelter, and impurities and foreign objects are removed through intelligent pretreatment equipment, while chemical composition and particle size distribution are monitored in real time; Steel slag: Steel slag is automatically collected from steel plants and removed from impurities such as iron filings and furnace linings through intelligent sorting equipment. Its strength and wear resistance are monitored in real time. Coal gangue: Coal gangue is automatically collected from coal mines and initially crushed by intelligent crushing equipment, with real-time monitoring of water absorption and internal moisture content; Tailings: Tailings are automatically collected from the concentrator and preliminarily classified using intelligent screening equipment, with real-time monitoring of the content of harmful components.

[0055] 1.2 Drying and Sieving Based on the real-time monitored moisture content, the temperature and time of the dryer are adjusted to control the moisture content. At the same time, the particle content in each particle size range is controlled by screening through an intelligent vibrating screen. Fly ash: Moisture content controlled below 10%; ensure that the content of particles smaller than 0.075mm is not less than 70%; Slag powder: Moisture content controlled below 10%; ground using a grinding mill, with real-time monitoring of specific surface area to ensure a specific surface area greater than 450 m² / kg, and a particle size of less than 0.045 mm content of 95%; Steel slag: The moisture content is controlled below 10%; at the same time, it is crushed by a crusher, and the particle size distribution is monitored in real time to ensure that the content of particles smaller than 2.36mm is 35% and the content of particles smaller than 0.075mm is 12%. Coal gangue: The moisture content is controlled below 12%; at the same time, it is crushed by a crusher, and the particle size distribution is monitored in real time to ensure that the content of particles smaller than 5cm is 35% and the content of particles smaller than 0.075mm is 12%. Tailings: Moisture content controlled below 10%. Simultaneously, crushing is performed using a crusher, with real-time monitoring of particle size distribution to ensure that particles smaller than 5cm account for 35% and particles smaller than 0.075mm account for 12%.

[0056] 1.3 Physical modification, such as Figure 7 As shown: Fly ash: Mechanical grinding is used to reduce the average particle size of fly ash to 10-30 μm, thereby improving its reactivity; Slag powder: Ultrasonic treatment is used to break down the glassy network structure of slag powder, increase its specific surface area, and improve its reactivity. Steel slag: Microwave treatment is used to improve the surface activity of steel slag and enhance its bonding ability with cement and other cementitious materials; Coal gangue: Mechanical grinding is used to further refine the particle size of coal gangue, thereby improving its uniformity and stability in the road base layer; Tailings: Ultrasonic treatment is used to break down the particle structure of the tailings and increase their reactivity with cementitious materials such as cement.

[0057] 1.4 Chemical Modification Fly ash: Based on real-time monitoring of chemical composition, 5%-10% lime is automatically added for activation, and the mixture is thoroughly mixed using intelligent mixing equipment; Slag powder: Based on real-time monitoring of chemical composition, 0.8% water-reducing agent and 0.2% retarder are automatically added, and the mixture is thoroughly mixed using intelligent mixing equipment; Steel slag: Based on real-time monitoring of chemical composition, 8% lime and 4% cement are automatically added, and the mixture is thoroughly mixed using intelligent mixing equipment for stabilization treatment. Coal gangue: The aging process is automatically controlled for 6 months through an aging system, and the release of moisture and easily weatherable substances is monitored regularly. Tailings: Based on the real-time monitoring of the content of harmful components, 8% lime is automatically added to adjust the pH value to 8.5. The mixture is then thoroughly stirred by intelligent stirring equipment to cause heavy metal ions to form hydroxide precipitates.

[0058] 1.5 Design and preparation of the mixture: Fly ash: Based on real-time monitoring of performance indicators, the fly ash content is automatically adjusted to 20%, the cement content to 5%, and the crushed stone content to 75%. The mixture is then thoroughly mixed using intelligent mixing equipment to prepare road base materials. Slag powder: Based on real-time monitoring of performance indicators, the slag powder content is automatically adjusted to 15%, the cement content to 4%, and the crushed stone content to 81%. The mixture is then thoroughly mixed using intelligent mixing equipment to prepare road base material. Steel slag: Based on real-time monitoring of performance indicators, the steel slag content is automatically adjusted to 30% and the crushed stone content to 70%. The mixture is thoroughly mixed using intelligent mixing equipment to prepare road base material. Coal gangue: Based on real-time monitoring performance indicators, the coal gangue content is automatically adjusted to 35%, the lime content to 7.5%, and the fly ash content to 12.5%. The mixture is then thoroughly mixed using intelligent mixing equipment to prepare road base material. Tailings: Based on real-time monitoring performance indicators, the tailings content is automatically adjusted to 25%, the lime content to 7.5%, the cement content to 3.75%, and the crushed stone content to 63.75%. The materials are then thoroughly mixed using intelligent mixing equipment to prepare road base materials.

[0059] The second step is intelligent quality control and monitoring. Real-time monitoring, quality feedback, data recording and analysis make the process of solid waste resource utilization more efficient and intelligent than previous solid waste treatment methods; Real-time monitoring: Throughout the entire treatment process, intelligent sensors and monitoring equipment are used to monitor the performance indicators of solid waste in real time, such as moisture content, particle distribution, chemical composition, strength, and abrasion resistance, to ensure that the treated solid waste meets the requirements for road base materials. Quality feedback: Based on real-time monitoring data, the intelligent control system automatically adjusts the processing parameters, such as temperature, time, and dosage, to ensure the stable quality of the treated solid waste. Data recording and analysis: Through an intelligent data management system, all data during the processing is recorded, and data analysis and quality assessment are conducted to provide a basis for optimizing the processing technology.

[0060] This embodiment also includes effect evaluation and performance monitoring, including at least one of the following indicators: the strength and durability of solid waste materials; the road performance of solid waste materials (such as...). Figure 8 As shown), such as anti-slip properties, resistance to deformation; environmental impact assessment (such as... Figure 9 (As shown), such as carbon emissions and pollutant leaching concentrations.

[0061] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of equivalents of this invention, this invention also intends to include these modifications and variations.

Claims

1. An intelligent treatment method for the comprehensive road utilization of multi-source solid waste from highway engineering reconstruction and expansion, characterized in that, Includes the following steps: The pretreated solid waste is physically modified. Based on the real-time chemical composition monitoring results of the solid waste, a certain amount of chemical modifier is automatically added and mixed through intelligent stirring equipment to obtain regulated modified solid waste. Modified solid waste is transported to an intelligent mixing plant, where it is automatically calculated and adjusted to match road materials based on real-time monitoring data of key performance indicators. In the intelligent mixing equipment, each component is mixed to produce a finished product whose performance indicators meet the requirements of road base material specifications.

2. The intelligent treatment method for comprehensive road utilization of multi-source solid waste in highway engineering reconstruction and expansion as described in claim 1, characterized in that, The process of automatically adding a quantitative amount of chemical modifier includes the following steps: The system continuously reads real-time concentration data of chemical components from online component analysis sensors to form a real-time chemical component set; compares the real-time chemical component set with the reference components pre-stored in the database; based on the comparison results, it calculates the types and amounts of chemical modifiers required to adjust the chemical composition of the current solid waste to the target chemical composition range, and outputs a modifier dosing instruction set. According to the type and precise amount specified in the modifier dosing instruction set, the chemical modifier is quantitatively taken from the corresponding storage bin, and the weighed chemical modifier is combined with the pre-treated solid waste running in the intelligent mixing equipment; the intelligent mixing equipment starts the preliminary mixing program and outputs the preliminary mixture of chemical modifier and waste. By using a monitoring probe integrated into the intelligent mixing equipment, the uniformity index of the initial mixture is acquired in real time. The uniformity index is compared with the set uniformity threshold. When the uniformity index meets the standard, it is determined that the chemical modifier is dispersed, and the initial mixture is transformed into regulated modified solid waste that meets the quality requirements.

3. The intelligent treatment method for comprehensive road utilization of multi-source solid waste in highway engineering reconstruction and expansion as described in claim 2, characterized in that, The process of outputting a modifier dosing instruction set includes the following steps: Based on each chemical component defined in the baseline composition, the algebraic difference between the target concentration data of the chemical component and the measured concentration data of the corresponding item in the real-time chemical component set is obtained, and a set of component concentration deviation values ​​are generated. Based on the concentration deviation value of each component, a pre-set modifier efficacy relation database is invoked. By querying the modifier efficacy relation database, each component concentration deviation value is mapped to the theoretical required dose of one or more chemical modifiers. The required dose of each chemical modifier mapped from the component concentration deviation values ​​of all chemical components is summarized, and summation and conflict verification are performed to generate a preliminary demand vector containing the types of chemical modifiers and their corresponding theoretical doses. Read the real-time operating parameters of the current intelligent mixing equipment. The real-time operating parameters include the real-time processing flow rate of the pre-treated solid waste and the effective volume of the mixing equipment. The theoretical dosage in the initial demand vector is coupled with the real-time processing flow rate to calculate the theoretical demand per unit of material into the continuous dosing rate per unit time. At the same time, the dosing rate is dynamically calibrated by combining the effective volume of the intelligent mixing equipment to generate a modifier dosing instruction set, which includes the type of chemical modifier and the dosing amount expressed in instantaneous flow rate.

4. The intelligent treatment method for comprehensive road utilization of multi-source solid waste in highway engineering reconstruction and expansion as described in claim 3, characterized in that, The process of dynamically calibrating the acceleration rate includes the following steps: Read the preliminary demand vector and obtain the theoretical dosage for each chemical modifier defined therein; simultaneously read the real-time processing flow rate of the pretreated solid waste, multiply the theoretical dosage per unit mass of each chemical modifier by the real-time processing flow rate, and calculate the preliminary mass dosing rate of each chemical modifier to match the current material flow rate; To obtain the effective volume of the intelligent mixing equipment and the average time required for the materials to be effectively mixed within the intelligent mixing equipment; Using effective volume and average time, the inherent dynamic buffering effect in processing continuous materials is calculated, and the initial mass feed rate is calibrated. The calibration is to match the addition of chemical modifiers with the residence distribution of materials in the mixing chamber of the intelligent mixing equipment in the time dimension, and generate the calibrated mass feed rate. The calibrated mass dosing acceleration rate is converted into the corresponding drive actuator action signal based on the pre-stored characteristic curve of the feeding equipment; the action signals corresponding to all chemical modifiers are packaged with the chemical modifier type information to generate a modifier dosing instruction set.

5. The intelligent treatment method for comprehensive road use of multi-source solid waste in highway engineering reconstruction and expansion as described in claim 4, characterized in that, The process of converting signals into action signals for the corresponding actuators includes the following steps: Obtain the pre-stored characteristic curve of the feeding device and define the correspondence between the mass feeding acceleration rate and the execution parameters of the drive actuator; for each value in the calibrated mass feeding acceleration rate, query the corresponding characteristic curve of the feeding device to map out the unique theoretical value of the execution parameter of the feeding device. The theoretical values ​​of the parameters for each feeding device are converted into standard industrial control signals based on the electrical characteristics of the actuator; the industrial control signals are the action signals that drive the actuator. The action signals of the driving execution structure are associated and packaged with the information on the types of chemical modifiers to generate a set of modifier dosing instructions, which includes action commands for each type of chemical modifier.

6. The intelligent treatment method for comprehensive road use of multi-source solid waste in highway engineering reconstruction and expansion as described in claim 5, characterized in that, The process of converting signals into standard industrial control signals includes the following steps: Call the physical quantity-electrical quantity conversion coefficient that defines the fixed conversion relationship between the target physical parameter unit quantity and the standard electrical control quantity unit quantity; multiply the theoretical value of the feeder's execution parameter with the corresponding physical quantity-electrical quantity conversion coefficient to calculate the basic electrical quantity required to achieve the theoretical value of the feeder's execution parameter, i.e., the electrical control reference value; The electrical control reference value is normalized according to the entire range of electrical drive quantities, and then linearly mapped to the specified industrial standard signal range to obtain the standard signal interval value. Based on the communication protocol rules adopted by current industrial fieldbuses, standard signal range values ​​are encapsulated to generate a complete signal output that conforms to the communication protocol and contains effective control information. This signal is the standard industrial control signal that is recognized and executed by the driven actuator.

7. The intelligent treatment method for comprehensive road utilization of multi-source solid waste in highway engineering reconstruction and expansion as described in claim 6, characterized in that, The process of setting the conversion factor between physical quantities and electrical quantities includes the following steps: A set of calibration dosing rate instructions covering its working range is sent to the feeding equipment, corresponding to the mass of the target chemical modifier added per unit time; the actuator acts according to the instructions, while measuring the actual calibration control signal value required by the current actuator control terminal; Each set of calibration acceleration command is correlated with its corresponding actual calibration control signal value; through linear regression analysis, the relationship from acceleration to control signal is established, and the slope of the relationship is determined as the physical quantity to electrical quantity conversion coefficient. The conversion coefficients between physical and electrical quantities are stored in a fixed format and used to convert real-time mass input acceleration rates into electrical control reference values.

8. The intelligent treatment method for comprehensive road use of multi-source solid waste in highway engineering reconstruction and expansion as described in claim 7, characterized in that, The process of establishing the relationship between the acceleration rate and the control signal includes the following steps: Each calibration acceleration command is associated with the actual calibration control signal value measured at the same time point to form a set of dynamic calibration data pairs with a one-to-one correspondence; each dynamic calibration data pair contains an independent acceleration value and a corresponding control signal value. Fit all dynamic calibration data pairs to find a straight line that is closest to all data points. The equation of the straight line is y=kx+b, where x represents the acceleration rate and y represents the control signal value. Establish a linear relationship model from the acceleration rate to the control signal. From the linear equation of the linear relationship model, the slope parameter k is extracted. The slope k represents the unit rate of change of the control signal with the acceleration rate, that is, the amount that the control signal needs to change for every unit increase in the acceleration rate. The value of the slope k is determined as the physical quantity to electrical quantity conversion factor.

9. The intelligent treatment method for comprehensive road utilization of multi-source solid waste in highway engineering reconstruction and expansion as described in claim 1, characterized in that, The process of automatically calculating and adjusting its mix ratio with road materials includes the following steps: The measured values ​​of each key performance indicator in the real-time monitoring data are compared with the pre-stored road base material specification requirements to obtain the real-time performance deviation value of each performance indicator, that is, the algebraic difference between the measured value of the key performance indicator and the road base material specification requirements; based on the algebraic difference, the theoretical compensation amount of various road materials required to compensate for the real-time performance deviation value is obtained. Read the preset road material reference mix ratio, and algebraically add the theoretical compensation amount of various road materials to the corresponding road base material amount in the road material reference mix ratio to obtain the corrected target mix ratio amount of various road materials. By combining the real-time processing flow rate of modified solid waste, the target proportion dosage is converted from mass ratio to material conveying flow rate per unit time, thereby generating the road material proportion execution parameters for controlling the feeding equipment.

10. The intelligent treatment method for comprehensive road utilization of multi-source solid waste in highway engineering reconstruction and expansion as described in claim 1, characterized in that, It also includes solid waste collected from different industrial sources, which is automatically sorted to remove impurities and foreign objects, and online sensors are used to monitor the initial physical and chemical properties of the solid waste in real time. Based on the initial physical and chemical properties, the temperature and time of the dryer are automatically adjusted to control the moisture content within a predetermined range. At the same time, an intelligent vibrating screen is used to screen according to the preset particle size requirements. The solid waste is transformed into pre-treated solid waste with uniform properties and moisture content and particle size distribution that meet the standards.

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