Carbon Emission Calculation Method and Equipment Based on Plant Mix Hot Recycling of Asphalt Mixture

Abstract

The present invention belongs to the technical field of carbon emission prediction. The present invention provides a carbon emission calculation method and device based on hot in-plant recycling of asphalt mixtures. First, obtain the planned information of road surface construction based on hot in-plant recycling in each stage of the life cycle; then, according to the planned information of each stage, extract the carbon emission characteristic values of each stage; next, according to the carbon emission characteristic values and a pre-established prediction model, predict the carbon emissions. The present invention innovatively extracts the carbon emission characteristics of each stage of hot in-plant recycling, constructs an accurate prediction model. With this model, it is possible to predict in advance the carbon emission status of the entire process of hot in-plant recycling, and thus provide key references for timely adjustment of the construction plan, helping the hot in-plant recycling construction to move towards a greener and more efficient new stage.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission prediction, and particularly relates to a carbon emission calculation method and device based on plant-mixed hot recycling of asphalt mixture. Background Art

[0002] The plant-mixed hot recycling technology is an advanced means for repairing asphalt pavements. The specific operation is to first collect the recycled reclaimed asphalt pavement (RAP) materials, transport them to a professional asphalt mixing plant, and after pretreatment such as crushing and screening, mix them with new asphalt, new aggregates, and recycling agents in precise proportions and fully stir them under a heated state to finally produce recycled asphalt mixtures that meet high standards for new pavement paving.

[0003] In terms of carbon emissions, the whole process of plant-mixed hot recycling involves multiple emission sources. Energy consumption is the main factor in carbon emissions during the plant-mixed hot recycling process. Whether it is the combustion of fuels such as natural gas and heavy oil during the production process or the fuel consumption of vehicles transporting raw materials, a large amount of carbon dioxide will be released. In the prior art, the calculation of carbon emissions during the plant-mixed hot recycling process usually only calculates the energy consumption and then calculates the carbon emissions caused by each type of energy consumption. Therefore, the calculation can only be completed after the construction is finished and cannot provide references for energy conservation and emission reduction for the construction plan in advance. Summary of the Invention

[0004] In view of this, the present invention provides a carbon emission calculation method and device based on plant-mixed hot recycling of asphalt mixture, aiming to solve the problem of lag in carbon emission calculation during the plant-mixed hot recycling process in the prior art.

[0005] The first aspect of the embodiment of the present invention provides a carbon emission calculation method based on plant-mixed hot recycling of asphalt mixture, including:

[0006] Obtaining the planned information of the pavement construction based on plant-mixed hot recycling in each stage of the life cycle;

[0007] Extracting the carbon emission characteristic values of each stage according to the planned information of each stage;

[0008] Predicting the carbon emissions according to the carbon emission characteristic values and a pre-established prediction model.

[0009] In a possible implementation manner, the life cycle of the pavement construction based on plant-mixed hot recycling includes: the original pavement milling stage, the raw material production stage, the raw material transportation stage, the plant-mixed hot stage, and the pavement construction stage; extracting the carbon emission characteristic values of each stage according to the planned information of each stage, including:

[0010] Extracting the original pavement structure characteristics and the milling duration range according to the planned information of the original pavement milling stage;

[0011] Extract production characteristics according to the planned information in the raw material production stage;

[0012] Extract transportation characteristics and traffic delay characteristics according to the planned information in the raw material transportation stage;

[0013] Extract plant mixing heat characteristics according to the planned information in the plant mixing heat stage;

[0014] Extract the target pavement structure characteristics and the construction duration range according to the planned information in the pavement construction stage.

[0015] In a possible implementation manner, predict the carbon emissions according to the carbon emission characteristic value and a pre-established prediction model, including:

[0016] Input the original pavement structure characteristics, milling duration range, production characteristics, transportation characteristics, traffic delay characteristics, plant mixing heat characteristics, target pavement structure characteristics and construction duration range into the prediction model to predict the carbon emissions.

[0017] In a possible implementation manner, the prediction model includes a preliminary prediction layer and a parameter adjustment layer; predict the carbon emissions according to the carbon emission characteristic value and a pre-established prediction model, including:

[0018] Input the original pavement structure characteristics, production characteristics, transportation characteristics, plant mixing heat characteristics and target pavement structure characteristics into the prediction model to predict the initial emissions;

[0019] Input the initial emissions, milling duration range, traffic delay characteristics and construction duration range into the parameter adjustment layer to obtain the carbon emissions.

[0020] In a possible implementation manner, the method further includes:

[0021] Obtain the actual construction information of the pavement construction based on plant mixing heat regeneration in each stage of the life cycle;

[0022] Extract the actual milling duration, actual traffic delay and actual construction duration according to the actual construction information;

[0023] Input the carbon emissions, actual milling duration, actual traffic delay and actual construction duration into the parameter adjustment layer to obtain the actual carbon emissions.

[0024] In a possible implementation manner, extract the original pavement structure characteristics and the milling duration range according to the planned information in the original pavement milling stage, including:

[0025] Extract the pavement type, pavement thickness, pavement composition and pavement disease information from the planned information in the original pavement milling stage to form the original pavement structure characteristics;

[0026] Extract engineering planning information, margin setting information, and operation condition prediction information from the planning information in the original pavement milling stage;

[0027] Determine the milling duration range according to the original pavement structure characteristics, engineering planning information, margin setting information, and operation condition prediction information.

[0028] In a possible implementation, extract transportation characteristics and traffic delay characteristics according to the planning information in the raw material transportation stage, including:

[0029] Extract the transportation vehicle type and route characteristics from the planning information in the original pavement milling stage to form transportation characteristics;

[0030] Determine the traffic delay characteristics according to the transportation characteristics and traffic prediction information.

[0031] In a possible implementation, extract the target pavement structure characteristics and construction duration range according to the planning information in the pavement construction stage, including:

[0032] Extract the number of pavement layers, materials of each layer, thickness of each layer, and degree of compaction from the planning information in the original pavement milling stage to form the target pavement structure characteristics;

[0033] Determine the construction duration range according to the target pavement structure characteristics, engineering planning information, margin setting information, and operation condition prediction information.

[0034] A second aspect of the embodiments of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the carbon emission calculation method based on asphalt mixture plant-mixed hot recycling in the first aspect above are implemented.

[0035] A third aspect of the embodiments of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the carbon emission calculation method based on asphalt mixture plant-mixed hot recycling in the first aspect above are implemented.

[0036] The carbon emission calculation method and device based on asphalt mixture plant-mixed hot recycling provided by the embodiments of the present invention first obtain the planning information of the pavement construction based on plant-mixed hot recycling in each stage of the life cycle; then extract the carbon emission characteristic values of each stage according to the planning information of each stage; then predict the carbon emissions according to the carbon emission characteristic values and a pre-established prediction model. The present invention innovatively extracts the carbon emission characteristics of each stage of plant-mixed hot recycling, constructs an accurate prediction model, and with this model, it is possible to predict the carbon emission status of the entire process of plant-mixed hot recycling in advance, thereby providing a key reference for the timely adjustment of the construction plan and helping the plant-mixed hot recycling construction to move towards a greener and more efficient new stage. Brief Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1 is the implementation flowchart of the carbon emission calculation method based on in-plant hot recycling of asphalt mixtures provided by the embodiments of the present invention;

[0039] Figure 2 is the implementation flowchart of the in-plant hot recycling process of asphalt mixtures provided by the embodiments of the present invention. Detailed Embodiments

[0040] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0041] Figure 1 is the implementation flowchart of the carbon emission calculation method based on in-plant hot recycling of asphalt mixtures provided by the embodiments of the present invention. Figure 2 is the implementation flowchart of the in-plant hot recycling process of asphalt mixtures provided by the embodiments of the present invention. As Figure 1 and 2 shown, in some embodiments, the carbon emission calculation method based on in-plant hot recycling of asphalt mixtures includes:

[0042] S110, obtaining the planned information of the pavement construction based on in-plant hot recycling in each stage of the life cycle;

[0043] S120, extracting the carbon emission characteristic values of each stage according to the planned information of each stage;

[0044] S130, predicting the carbon emissions according to the carbon emission characteristic values and the pre-established prediction model.

[0045] In the embodiments of the present invention, the life cycle of plant-mixed hot recycling pavement construction covers multiple key stages. Obtaining the planned information of each stage is the basis for accurately predicting carbon emissions. First is the raw material preparation stage. It is necessary to clarify the recycling plan of reclaimed asphalt pavement materials (RAP), including the recycling sections, milling depth and area, so as to estimate the recycling volume. It is also necessary to master the procurement plans of new aggregates, new asphalt and recycling agents, such as supplier information, procurement volume, transportation distance and methods, etc. These are related to transportation energy consumption and costs. In the plant-mixed stage, it is necessary to collect the production plan of the mixing plant, such as equipment models, daily production duration, production batch arrangements, and raw material ratios of different batches, which will affect the energy consumption rate and total amount. In the transportation link, it is necessary to know the types of transportation vehicles, load capacity, transportation routes and daily transportation frequencies to prepare for calculating transportation carbon emissions. In the final paving and compaction stage, the planned information includes construction dates, construction durations, the number of inputs of equipment such as pavers and rollers, and operating parameters. Because the construction duration is closely related to equipment energy consumption, the carbon emission performance of equipment under different parameters is different. Only by integrating these detailed planned information can the whole process of plant-mixed hot recycling pavement construction be completely outlined.

[0046] From the perspective of the RAP recycling plan, if the recycling area is large and the milling depth is deep, it means that the operating time of the recycling operation equipment is long and the energy consumption is high. The carbon emission characteristic value can be obtained by multiplying the unit time energy consumption of the equipment by the estimated operating duration; because new aggregate mining involves blasting and excavation equipment, the larger the mining volume, the higher the characteristic value, and it can be calculated by combining the unit output energy consumption of the mining process. The carbon emission characteristic value in the transportation link is determined according to the transportation distance, vehicle load capacity and fuel type. For example, the fuel consumption per unit load per kilometer of a diesel vehicle is fixed. Multiplying the transportation distance and the cargo weight can calculate the transportation carbon emission characteristics.

[0047] The type of mixing equipment determines the heating power and mixing efficiency. High-power equipment heats up quickly but has high energy consumption. According to the material heating duration and temperature setting in the production plan, combined with the equipment energy consumption parameters, the carbon emission characteristics of the heating link can be calculated; different raw material ratios result in different mixing durations. According to the mixing duration and equipment mixing power under different ratios, the corresponding carbon emission characteristic values can also be extracted.

[0048] The vehicle type determines the fuel efficiency. The fuel consumption of heavy-duty trucks and light-duty trucks varies greatly. The road conditions of the transportation route, such as slopes and congestion, will affect the vehicle speed and fuel consumption. According to the vehicle selection, transportation frequency and route conditions in the plan, the carbon emission characteristic values per trip or per day during transportation can be accurately calculated.

[0049] The power and operating duration of pavers and rollers are key factors. The longer the operating time of high-power equipment, the higher the carbon emissions. Referring to the number of equipment inputs and daily operating durations in the construction plan, combined with the energy consumption parameters calibrated by the equipment factory, the carbon emission characteristic values of this stage can be obtained.

[0050] In some embodiments, the life cycle of plant-mixed hot recycling-based pavement construction includes: the original pavement milling stage, the raw material production stage, the raw material transportation stage, the plant-mixed hot stage, and the pavement construction stage; according to the planned information of each stage, carbon emission characteristic values of each stage are extracted, including: according to the planned information of the original pavement milling stage, the original pavement structure characteristics and the milling duration range are extracted; according to the planned information of the raw material production stage, the production characteristics are extracted; according to the planned information of the raw material transportation stage, the transportation characteristics and the traffic delay characteristics are extracted; according to the planned information of the plant-mixed hot stage, the plant-mixed hot characteristics are extracted; according to the planned information of the pavement construction stage, the target pavement structure characteristics and the construction duration range are extracted.

[0051] In the embodiments of the present invention, the planned information in the raw material production stage is rich and complex. Precisely extracting the production characteristics from it is extremely crucial for comprehensively controlling the plant-mixed hot recycling process. The production characteristics may specifically include the following characteristics:

[0052] The mining-related characteristics include the mining location and geological conditions, the mining method and scale. Specific planned information will indicate the location of the new aggregate mining mine. The geological conditions of different mines vary greatly. Some are sedimentary rock mines with relatively low hardness, which are easier to mine, with slow equipment wear and low energy consumption; while igneous rock mines such as granite have high rock hardness and often require large-scale drilling and blasting equipment. Not only is the upfront equipment investment large, but the energy consumption and mechanical losses during mining are also quite significant. These information constitute the geological characteristics corresponding to the mining location. The mining method and scale need to clarify whether it is an open-pit mining or an underground mining method. Open-pit mining has a broad view and convenient construction. Large-scale machinery operations are not restricted by too much space, which is conducive to large-scale and high-efficiency mining, but it has a great impact on the surrounding environmental landscape; underground mining has little impact on the surface, but the ventilation, lighting, and transportation systems are complex, with high costs and limited production. The mining scale involves the planned mining volume, daily production target, etc., which determine the mining duration and the continuous operation state of the equipment and belong to the key production characteristics.

[0053] The processing technology features include the characteristics of the crushing process, screening and cleaning. Among them, for the crushing process, the type of crusher used in the inspection plan is considered. For example, jaw crushers are suitable for coarse crushing, cone crushers focus on medium crushing, and impact crushers can produce finished products with better particle shapes. Different crushers have different energy consumption, processing capacity, and product particle size distributions. For instance, jaw crushers have a simple structure and low energy consumption, but the product particle size is not very uniform; impact crushers have regular product particle shapes, but the equipment price and energy consumption are relatively higher. This series of characteristics constitutes the crushing process features. When screening and cleaning, the mesh specifications of the sieve need to be understood. Fine sieves can screen out more specifications of aggregates, meeting the precise grading requirements, but with an increase in the number of screenings, the energy consumption and time cost increase. If there is a cleaning process, attention should be paid to the cleaning method (water washing, dry cleaning) and the arrangement of wastewater treatment. Water washing can effectively remove soil impurities, but it will generate a large amount of wastewater, and the treatment of wastewater involves additional costs and environmental considerations. These details form the screening and cleaning process features.

[0054] The transportation planning features include transportation distance and tools, transportation frequency and batch. The production stage plan of raw materials must cover the distance data for transporting new aggregates to the plant mixing station. For short-distance transportation, vehicles with a smaller load capacity and greater flexibility can be selected, with low costs; for long-distance transportation, although railway transportation has a large single shipment volume and low unit energy consumption, the loading, unloading, and transshipment are complex, while road transportation is flexible but has high energy consumption. The selection of transportation tools and the transportation distance jointly shape the transportation cost and energy consumption characteristics. Understanding the planned transportation frequency, high-frequency small-batch transportation can flexibly meet the immediate material requirements of the plant mixing station, but the transportation cost increases due to the increase in the empty running rate; low-frequency large-batch transportation reduces the transportation cost, but requires the plant mixing station to have a large raw material storage space. The transportation frequency and batch information reflect the economy of transportation planning.

[0055] The crude oil raw material features include the origin and quality of the crude oil. Different sources of crude oil have significant differences in their chemical compositions and properties. Light crude oil from the Middle East has a low sulfur content and low viscosity, and the process of processing it into asphalt is relatively streamlined, with less energy consumption in processes such as heating and distillation; heavy crude oil from places like Venezuela has more impurities and high viscosity, and requires complex pretreatment processes such as desalting, dewaxing, and hydrogenation. The raw material characteristics determine the energy consumption and process complexity at the starting point of asphalt production.

[0056] The refining process characteristics include the type of refining process and process parameters. There are various refining processes for new asphalt production, such as straight-run method, solvent deasphalting method, oxidation method, etc. The straight-run method separates based on the boiling point differences of the components in crude oil, with a simple and direct process and relatively low energy consumption; the solvent deasphalting method separates asphalt using the solubility differences of solvents for asphaltenes. Although high-quality asphalt can be obtained, the solvent recovery process consumes a large amount of energy; the oxidation method changes the properties of asphalt by injecting air into vacuum residue, but this process requires strict control of reaction conditions. Different refining methods have different energy consumption and product quality, which constitute the core refining process characteristics. Pay attention to parameters such as temperature, pressure, and reaction time during the refining process. Under high temperature and high pressure conditions, the reaction rate increases, but the equipment is under high pressure and the maintenance cost is high; mild process parameters result in less equipment wear, but the production cycle is long and the plant occupancy time is long. These parameters shape the refining process characteristics from the aspect of operation fineness.

[0057] The formula component characteristics include the main components and their proportions. The regenerant formula is a mixture of various chemical substances in specific proportions. Determine the main components from the planned information. If the components with high aromatic hydrocarbon content account for a large proportion, the difficulty, cost, and complexity of the synthesis process for obtaining their sources should be considered key points. Different proportions of each component result in different recovery effects and applicable scenarios of the regenerant for old asphalt, which constitute the core characteristics of the formula.

[0058] In the embodiments of the present invention, the plant-mixed hot stage is the core link in the plant-mixed hot recycling process. Based on the planned information of this stage, the plant-mixed hot characteristics can be extracted from the following multiple key dimensions: the type of mixing equipment, the type and power of the heating device, the raw material ratio, the mixing process parameters, and the production plan arrangement.

[0059] Specify the specific model of the mixing equipment used from the planned information. Different models of mixing equipment have differences in the capacity of the mixing pot, the power of the heating system, the design of the mixing paddles, etc. For example, a large-capacity mixing pot can process more reclaimed asphalt pavement materials (RAP), new aggregates, new asphalt, and regenerant at one time, which will affect the output and duration of a single mixing; a high-power heating system can quickly increase the temperature of the materials, but the energy consumption also increases accordingly. These characteristics constitute the basic characteristics of the mixing equipment.

[0060] Determine whether the heating device uses gas heating, oil heating, or electric heating. Gas heating has relatively low costs, but has high requirements for the stability of gas supply; oil heating has strong mobility and is suitable for temporary or remote construction sites; electric heating is more environmentally friendly and clean, but the electricity cost is greatly affected by the local electricity price. The power of the heating device determines the heating rate. High-power heating can shorten the preheating time of the materials, but may increase the instantaneous energy consumption, which is a key factor in the plant-mixed hot characteristics.

[0061] Precisely grasp the proportion of RAP in the mixture. A high proportion of RAP means more old materials are involved in regeneration, which not only affects costs but also influences the difficulty of subsequent heating and mixing. The degree of aging of the old asphalt varies. Mixtures with a high content of RAP may require stronger heating and mixing forces to promote better fusion of the old asphalt. This proportion is an important basis for measuring the degree of plant-mixed hot recycling and performance regulation.

[0062] The mixing temperature in the planned information is a crucial parameter. Different mixture compositions require different mixing temperatures. High temperatures help the materials mix quickly and evenly, activating the modification effect of the regenerant on the old asphalt. However, excessively high temperatures will accelerate the aging of the new asphalt. A reasonable temperature range should not only ensure the workability of the mixture but also take into account the stability of the properties of each component, reflecting the accuracy and scientific nature of the plant-mixed hot operation.

[0063] The total mixing duration and the duration distribution of each stage, such as the dry mixing time and the wet mixing time. The dry mixing stage initially mixes the aggregates and removes moisture; the wet mixing stage evenly coats the aggregates with asphalt and the regenerant. Appropriate time settings can ensure the uniformity of the mixture. Excessively long or short mixing times will cause quality problems such as segregation and uneven coating of the mixture, which constitute the key characteristics of the mixing process.

[0064] Determine the output of the recycled asphalt mixture produced in each batch, as well as the production batch frequencies on a daily and weekly basis. High-output batches are beneficial for the rapid progress of large-scale projects, but they require high equipment stability and continuous raw material supply; frequent small-batch production has strong flexibility and can respond promptly to temporary changes during construction. Both reflect the organizational characteristics of the plant-mixed hot stage from the production rhythm.

[0065] In some embodiments, according to the planned information in the original pavement milling stage, extract the original pavement structure characteristics and the milling duration range, including: extract the pavement type, pavement thickness, pavement composition, and pavement disease information from the planned information in the original pavement milling stage to form the original pavement structure characteristics; extract the project planning information, margin setting information, and operation condition prediction information from the planned information in the original pavement milling stage; determine the milling duration range based on the original pavement structure characteristics, project planning information, margin setting information, and operation condition prediction information.

[0066] In the embodiments of the present invention, in the plant-mixed hot recycling project, the planned information in the original pavement milling stage contains a large amount of key data. First, focus on extracting the original pavement structure characteristics, which requires accurately capturing multiple elements from the planned information:

[0067] Pavement type: Whether the pavement belongs to expressways, urban arterial roads, or ordinary rural roads, the design standards for different types of pavements vary greatly. Expressways usually adopt high-grade asphalt concrete pavements to bear heavy traffic, and the structural layers are thicker and more complex; while rural roads may be relatively simple, with the pavement thickness and material composition being more basic. Clearly defining the pavement type provides a basic framework for subsequent analysis.

[0068] Pavement thickness: Accurate pavement thickness data is directly related to the milling workload. A thicker pavement means that the milling equipment needs to cut deeper, consuming more energy and increasing the operation duration accordingly. The design drawings and previous inspection reports in the project information can give the accurate thickness of each structural layer, and the sum is the overall pavement thickness.

[0069] Pavement composition: It is crucial to understand what materials each structural layer of the pavement is composed of. For example, is it a simple asphalt layer, or a combination of multiple layers of asphalt mixtures with different gradations and aggregates? The hardness and adhesiveness of different materials vary, and the milling difficulty is different. For example, an asphalt stabilized base containing a large amount of crushed stones has faster tool wear and requires more power during milling compared to a pure fine-grained asphalt surface layer.

[0070] Pavement disease information: Record disease conditions such as cracks, potholes, and ruts. In areas with large-scale diseases, the milling operation may require more delicate treatment, or adjusting the milling depth and speed, which will all affect the overall milling strategy and subsequent carbon emission calculations. Integrating these four key pieces of information forms the original pavement structure characteristics, laying a foundation for in-depth analysis of subsequent milling work.

[0071] Engineering planning information: Sort out the progress arrangement of the overall project, milling area division, daily construction volume target, etc. from the project information in the original pavement milling stage. The progress arrangement determines the compactness of the milling operation, the area division is related to the frequency of equipment relocation, and the daily construction volume target clarifies the workload benchmark, all of which indirectly affect the milling duration.

[0072] Margin setting information: The margin setting in construction includes time margin and material margin. Here, we focus on the time margin. The time margin is the extra time reserved to deal with unexpected situations, such as bad weather and sudden equipment failures. A reasonable time margin setting can ensure that the milling operation can be completed within a controllable duration even in case of accidents, avoiding a significant extension of the construction period.

[0073] Operating condition prediction information: Based on weather forecasts and past construction environment records during the same period, predict the temperature, humidity, wind force, etc. during the milling operation. In a high-temperature environment, the asphalt pavement is relatively soft, with less milling resistance, but the equipment has a high heat dissipation requirement; at low temperatures, the asphalt hardens, and the milling difficulty increases sharply. These factors all affect the milling efficiency and duration.

[0074] Based on the above-mentioned original road surface structure characteristics, engineering planning information, margin setting information, and operation condition prediction information, the milling duration range is deduced. In the original road surface structure characteristics, a thick and complex road surface, hard and difficult-to-mill material composition, and large areas of damaged areas will all increase the duration of milling a single unit area; conversely, it will decrease. In the engineering planning information, a tight schedule and a large daily construction volume target will compress the ideal milling duration, prompting the construction team to speed up the milling speed, but limited by the upper limit of equipment performance. In the margin setting information, the time margin directly extends the upper limit of the milling duration, ensuring construction flexibility. In the operation condition prediction information, adverse weather or environmental conditions, such as heavy rain and extreme cold, will suspend the operation and push the milling duration to the boundary set by the time margin; a suitable environment will help shorten the milling duration and approach the lower limit of the efficient operation time set by the engineering planning. By integrating multiple factors, the reasonable range of the milling duration can be accurately defined.

[0075] In some embodiments, according to the planned information in the raw material transportation stage, transportation characteristics and traffic delay characteristics are extracted, including: extracting the transportation vehicle type and route characteristics from the planned information in the original road surface milling stage to form transportation characteristics; determining the traffic delay characteristics according to the transportation characteristics and traffic prediction information.

[0076] In the embodiments of the present invention,

[0077] Extract transportation characteristics

[0078] Transportation vehicle type: The planned information in the raw material transportation stage will clearly indicate the selected transportation vehicle type, which is a key element in constructing transportation characteristics. For example, if it is a dump truck, attention needs to be paid to its load capacity, which commonly has different specifications such as 10 tons, 15 tons, 20 tons, etc. The greater the load, the higher the single transportation volume, but the requirements for road conditions and vehicle performance are also more demanding; tank trucks are used to transport liquid asphalt or regenerant, and their tank capacity, material, and sealing performance are very important. Tank trucks with different capacities are suitable for different scales of transportation needs, and high-quality tank materials and sealing designs can prevent liquid leakage and volatilization, ensuring transportation safety and material quality. In addition, the power form of the vehicle, whether it is diesel, natural gas, or electric, also significantly affects transportation costs and environmental protection efficiency. Diesel vehicles have strong power but high carbon emissions, while electric vehicles have zero emissions but limited endurance. These vehicle characteristics comprehensively constitute the basic part of transportation characteristics.

[0079] Route characteristics: Comprehensively sort out various information of the planned route. The route length directly determines the transportation distance. Long-distance transportation not only takes a long time, but also increases costs due to fuel consumption and personnel working hours. The road grade cannot be ignored. High-speed roads have fast speeds and good road conditions, with high transportation efficiency, but may involve tolls. Low-grade roads are free, but the road surface is bumpy and narrow, which is likely to cause vehicle wear and tear and slow down the vehicle speed. The topography and geomorphology of the areas along the route are also crucial. Mountain roads have large slopes and many curves, and vehicles need to shift gears and decelerate frequently, resulting in a significant increase in energy consumption. In the plain area, it is conducive to maintaining a stable vehicle speed and reducing the transportation difficulty. In addition, the traffic control situation around the route, such as whether there are restricted driving hours or weight-limited areas, will also restrict the transportation arrangement. All these route-related attributes together constitute the route characteristics, and together with the type of transportation vehicle, they improve the transportation characteristics system.

[0080] Determine traffic delay characteristics

[0081] Based on transportation characteristics analysis: The extracted transportation characteristics play a key role in predicting traffic delays. Heavy transportation vehicles, such as large dump trucks fully loaded with new aggregates, are more likely to encounter traffic obstacles when driving in urban congested sections or narrow roads due to poor mobility, slow start, and large turning radius, resulting in delays. The liquid materials transported by tank trucks have restricted driving speeds for safety considerations. During peak traffic hours, the slow driving time will increase correspondingly, accumulating potential delay time. For long-distance transportation routes, there are more variables on the way, and the probability of unexpected situations such as vehicle breakdowns and driver fatigue is higher, which also poses a hidden danger to traffic delays. For routes passing through complex terrains such as mountains, sudden road collapses, landslides, or snow and ice in bad weather will seriously impede traffic and cause transportation to stagnate.

[0082] Combined with traffic prediction information: Traffic prediction information covers many aspects, such as the estimated traffic flow at specific times and sections. Based on historical data and real-time road condition monitoring, predict the congestion degree of the transportation route during the morning and evening rush hours on weekdays and the travel peak during holidays. The greater the traffic flow, the higher the possibility that the transportation vehicle will get stuck in congestion, and the more uncontrollable the delay time will be. Meteorological forecasts are also an important part. Bad weather such as heavy rain, fog, and strong winds will reduce the road visibility and affect the driving stability of vehicles, forcing the vehicle speed to slow down or even close the road. If the planned route encounters such weather, it is necessary to fully estimate the delay time. Road construction notices are also crucial. Knowing the location and construction period of the construction section in advance can allow the transportation plan to avoid or adjust in advance. Otherwise, once driving into the construction area, operations such as waiting for release and detouring will cause traffic delays. By combining transportation characteristics with this traffic prediction information, the possible traffic delay characteristics can be accurately determined, leaving a reasonable margin for subsequent raw material transportation arrangements.

[0083] In some embodiments, according to the planned information of the road surface construction stage, the target road surface structure features and the construction duration range are extracted, including: extracting the number of road surface layers, the materials of each layer, the thickness of each layer, and the compaction degree from the planned information of the original road surface milling stage to form the target road surface structure features; determining the construction duration range according to the target road surface structure features, the engineering planning information, the margin setting information, and the operation condition prediction information.

[0084] In the embodiments of the present invention, in the planned information of the original road surface milling stage, the number of layers of the newly built road surface design will be clearly given. The number of road surface layers is directly related to the bearing capacity and durability of the entire road surface structure. For example, multi-layer structures are often used for heavy traffic roads such as highways, and the base course, sub-base course, and surface course cooperate with each other to disperse vehicle loads; while some low-grade rural roads may only have single-layer or double-layer structures, which are relatively simple. Accurately determining the number of road surface layers is the primary step in outlining the target road surface structure.

[0085] Carefully sort out the materials selected for each layer, which has a great impact on the road surface performance. The surface layer materials are mostly various asphalt mixtures. For example, fine-grained asphalt concrete has high flatness and good driving comfort and is suitable as the surface layer; medium-grained and coarse-grained asphalt concrete provide better anti-skid and wear-resistant performance and are commonly used in the middle and lower surface layers. The base course materials are commonly semi-rigid materials such as cement-stabilized macadam and lime-fly ash stabilized macadam, which have high strength and stability; the sub-base course may also use flexible materials such as graded crushed stone to play a role in buffering and spreading stress. Mastering the materials of each layer can deeply understand the functional characteristics of the road surface.

[0086] The accurate thickness information of each structural layer is indispensable. The thickness of the surface layer determines the flatness and anti-wear ability of the road surface. If it is too thin, early damage is likely to occur; if it is too thick, the cost will increase; the thickness of the base course affects the overall bearing structure of the road surface. A thick base course can bear greater vertical loads and avoid road surface deformation; the thickness of the sub-base course is also related to the stress dispersion effect. Obtaining the thickness of each layer from the planned information is the key data for quantifying the road surface structure.

[0087] The compaction degree reflects the degree to which the materials of each structural layer are compacted, and it is an important indicator to ensure the strength and stability of the road surface. A higher compaction degree means that the voids between material particles are smaller and the structure is denser, which can effectively resist water damage and vehicle load effects. The compaction degree standards in the planned information have different requirements for different layer materials and different road grades. Collecting these compaction degree values improves the target road surface structure features.

[0088] Complex pavement structures, with multi-layer designs and different special materials used in each layer, will undoubtedly lengthen the construction time. For example, if a high-performance but complex-construction-process modified asphalt mixture is selected for the surface layer, precise control of the paving temperature and compaction process is required, which takes a long time; when cement-stabilized materials are used for the base layer, a curing period is also involved, and waiting for the material strength to form, all of which increase the construction duration according to the characteristics of the target pavement structure. Thicker layer structures with large paving and compaction workloads will also extend the construction period; high-standard compaction requirements mean that the roller needs to make multiple round trips for compaction, consuming more time to meet the quality standards.

[0089] The project plan arranges the overall construction schedule, stage division, and milestone nodes. A tight schedule will compress the construction duration and prompt the rapid connection of each process, but sufficient human and equipment resources are required for guarantee; in phased construction, for the duration allocation of each stage of preliminary preparation, main construction, and final acceptance, if the preliminary preparation is hasty, it may delay time due to subsequent rework. A reasonable project plan not only gives the ideal construction period target but also implies the flexibility of the construction period.

[0090] Margin setting includes time margin and resource margin, with a focus on time margin. Time margin is the extra time reserved to deal with unexpected situations, such as unexpected events like bad weather, sudden equipment failures, and delays in raw material supply. A reasonable time margin can prevent these unexpected situations from disrupting the construction rhythm, expand the upper limit of the construction duration as a whole, and ensure the project progresses according to the plan.

[0091] Weather conditions are a key factor. High-temperature weather is conducive to asphalt paving but may cause heatstroke among workers, and the working hours need to be adjusted; severe weather such as low temperature, rainfall, and strong winds will seriously hinder construction and lead to construction delays. Geological condition prediction is also important. If the geological conditions of the construction site are soft, reinforcement treatment may be required first, increasing the preliminary preparation time; the traffic control situation around the site, which restricts the access time of construction vehicles, will also affect construction efficiency. Combining this predicted information on operating conditions determines the boundary conditions for the construction duration range.

[0092] In some embodiments, according to the carbon emission characteristic values and a pre-established prediction model, the carbon emissions are predicted, including: inputting the original pavement structure characteristics, milling duration range, production characteristics, transportation characteristics, traffic delay characteristics, plant mixing heat characteristics, target pavement structure characteristics, and construction duration range into the prediction model to predict the carbon emissions.

[0093] In the embodiments of the present invention, the original pavement structure features include key information such as the number of pavement layers, the materials of each layer, the thickness of each layer, and the compaction degree. The more pavement layers there are, the more times the equipment needs to adjust the operation depth during milling, and the corresponding energy consumption increases. The differences in the hardness and adhesiveness of different layer materials determine the wear speed of the milling cutter and the degree of power consumption. For example, it is more difficult to mill a granite gravel base layer than an ordinary sand and gravel base layer. The thickness of each layer affects the milling workload. Milling a thick pavement takes a long time, the running time of the equipment becomes longer, and the carbon emissions increase accordingly. A pavement structure with a high compaction degree is more dense, and the milling difficulty increases, which also promotes the growth of energy consumption and carbon emissions. These data provide basic working condition parameters for the prediction model and lay the foundation for estimating carbon emissions.

[0094] The defined range of milling duration represents the time span of the equipment operation. A shorter defined range of milling duration means efficient operation, with the overall energy consumption of the equipment at a relatively low level and corresponding less carbon emissions. On the contrary, if the duration range is relatively large, limited by the equipment performance, long-term operation not only consumes more electric energy or fuel, but may also generate additional energy consumption due to equipment aging and fault repair. For example, an engine running for a long time may have incomplete combustion, emitting more waste gas. This range of data gives the model an estimate of the energy consumption in the milling process from the time dimension.

[0095] The production features are broken down into the production of new aggregates, new asphalt, and regenerants. The geological conditions and mining methods for new aggregate mining determine the energy consumption of the mining equipment. Mining in hard rock quarries consumes far more energy than in soft rock quarries. In the processing technology, complex crushing and screening processes involve multiple procedures and multiple pieces of equipment, consuming electricity continuously. For new asphalt, the energy consumption varies in each link from crude oil procurement, the complexity of the refining process to storage and transportation conditions. The refining process of heavy crude oil is cumbersome and has high carbon emissions. In the production of regenerants, the reaction conditions of the synthesis process and the difficulty of obtaining raw material formulas affect the energy consumption of the synthesis equipment and the plant. The energy consumption of severe high-temperature and high-pressure reactions is significant. These comprehensive features enable the model to accurately grasp the overall picture of carbon emissions in raw material production.

[0096] The transportation features cover the types of transportation vehicles and routes. When using heavy dump trucks to transport new aggregates, the greater the load, the greater the driving resistance and the higher the fuel consumption. When using tank trucks to transport liquid materials, the self-weight of the tank body and the sealing requirements change the energy consumption characteristics of the vehicle during driving. In terms of route features, for long-distance transportation and passing through complex terrains such as mountainous areas, the vehicle frequently changes speed and climbs slopes, resulting in a sharp increase in fuel consumption. For roads with a low grade and many congested sections, the stop-and-go driving mode wastes fuel and exacerbates vehicle exhaust emissions. The transportation feature data helps the model quantify the carbon emissions during transportation.

[0097] Traffic delay characteristics are based on transportation characteristics and traffic prediction information. In case of traffic congestion, bad weather, road construction and other delay situations, vehicles idle and start / stop frequently, with extremely low fuel combustion efficiency, emitting a large amount of unburned pollutants. Compared with smooth traffic, the additional delay time will cause carbon emissions to increase exponentially. This characteristic supplements the model's consideration of carbon emission increments in transportation accident scenarios.

[0098] The characteristics of hot plant mixing focus on mixing equipment, raw material ratio, mixing process parameters, etc. Although large-capacity mixing equipment heats up quickly, it has high energy consumption during high-power operation; with a high proportion of reclaimed asphalt pavement materials (RAP) in the mixture, it is difficult to fuse the old asphalt, and stronger heating and mixing efforts are required, resulting in increased energy consumption. Precise setting of mixing temperature and duration, any deviation will cause energy waste, or uneven mixing of materials and rework, and these characteristics of the hot plant mixing link are input into the model to improve the carbon emission calculation link.

[0099] The characteristics of the target pavement structure determine the subsequent construction technology and energy consumption trend. During the construction of multi-layer pavement structures, the paving and compaction requirements of each layer are different, and it is necessary to frequently switch construction equipment and adjust parameters, increasing the number of starts / stops and idling of equipment, and the energy consumption rises. The selection of special materials, such as asphalt mixtures with high performance but demanding construction conditions, has high requirements for paving temperature and compaction times, resulting in an extended construction time, and the energy consumption and carbon emissions increase simultaneously, providing key energy consumption references for construction in the model.

[0100] The construction duration range sets the boundary of the entire pavement construction period. Under the requirement of a shorter construction period, construction equipment operates at full load, and some energy-saving operations may be ignored due to rushing the work, resulting in a sharp increase in energy consumption in a short time; although a generous construction period allows for refined construction, the long-term standby and idling of equipment also generate energy consumption. The data in this range, combined with other characteristics, enables the prediction model to accurately calculate the carbon emissions from the overall construction period dimension and output a carbon emission prediction value that fits the actual project.

[0101] The hot plant mixing recycling technology collects the reclaimed asphalt pavement materials (RAP), transports them to a professional asphalt mixing plant. After pretreatment such as crushing and screening, they are fully mixed with new asphalt, new aggregates, and recycling agents in a heated state in proportion to make recycled asphalt mixtures for new pavement paving. In the carbon emission calculation and prediction model, the characteristic values of each stage related to asphalt mixtures are important inputs. The original pavement structure characteristics, milling duration range, production characteristics, transportation characteristics, traffic delay characteristics, hot plant mixing characteristics, target pavement structure characteristics, and construction duration range are input into the prediction model, jointly affecting the prediction result of carbon emissions. For example, the structure of the asphalt mixture on the original pavement is dense, and the milling is difficult, resulting in an increase in energy consumption and carbon emissions; in the hot plant mixing stage, unreasonable mixing parameters of the asphalt mixture lead to increased energy consumption and more carbon emissions.

[0102] In some embodiments, the prediction model includes a preliminary prediction layer and a parameter adjustment layer; according to the carbon emission characteristic values and the pre-established prediction model, predicting the carbon emissions, including: inputting the original pavement structure characteristics, production characteristics, transportation characteristics, plant mixing heat characteristics, and target pavement structure characteristics into the prediction model to predict the initial emissions; inputting the initial emissions, milling duration range, traffic delay characteristics, and construction duration range into the parameter adjustment layer to obtain the carbon emissions.

[0103] In the embodiments of the present invention, the structural information of the original pavement is the basic starting point for the entire carbon emission prediction. The number of pavement layers, the thickness of each layer, the material composition, and the compaction degree together outline the difficulty and workload profile of the milling operation. If the number of pavement layers is large and the thickness of each layer is large, the milling equipment needs to run continuously for a longer time, consuming more electric energy or fuel. For example, a highway with a three-layer structure and a base layer thickness exceeding 30 cm requires a larger cutting depth and faster tool wear during milling compared to a single-layer thin pavement. The power output of the equipment needs to be continuously maintained at a high level, and the energy consumption increases linearly. The carbon emission rate corresponding to this part of the energy consumption is first captured and calculated by the preliminary prediction model. The characteristics of each layer of material are also crucial. A hard rock aggregate base layer is more difficult to mill than a soft sand and gravel base layer, resulting in more energy consumption by the equipment and an increase in carbon emissions.

[0104] Looking at the production characteristics in detail, in terms of new aggregate production, the mining geological conditions, mining methods, and processing technologies all affect carbon emissions. Mining new aggregates from hard mountainous areas requires huge energy consumption for blasting and excavation equipment; in complex crushing and screening processes, the operation of equipment in each process generates energy consumption, and the carbon emissions in these links are accumulated. For new asphalt production, the source and quality of crude oil determine the refining difficulty. Heavy crude oil requires multiple complex processes for impurity removal and conversion, consuming far more energy than light crude oil. In the preliminary prediction, the carbon emissions caused by this part of the energy consumption cannot be underestimated. For regenerant production, the difficulty of obtaining raw material formulas and the complexity of synthesis processes, such as high-temperature and high-pressure synthesis processes with high energy consumption, and the relevant carbon emission data are also included in the calculation.

[0105] The type of transport vehicle and route information shape the carbon emissions in the transportation link. Heavy transport vehicles, due to their large self-weight and load, do more work to overcome resistance during driving, consume a large amount of fuel, and diesel vehicles emit a large amount of carbon dioxide in their exhaust. If the transport route is long and passes through mountainous areas, the vehicle frequently climbs slopes and turns, the engine load increases, the fuel consumption rises, and the additional fuel combustion brings more carbon emissions. The preliminary prediction layer calculates the approximate carbon emissions during the transportation process based on these factors.

[0106] The equipment parameters, raw material ratios, mixing process parameters, etc. in the plant mixing and heating process are crucial. High-power mixing equipment heats up quickly but has high energy consumption; when a high proportion of reclaimed asphalt pavement materials (RAP) is in the mixture, it is difficult to fuse the old asphalt, and stronger power and longer time are required for heating and mixing, increasing energy consumption. Deviations in the setting of mixing temperature and duration will cause energy waste. The preliminary prediction model calculates the carbon emissions in this process based on these plant mixing and heating characteristics, and adds them to the previous processes to obtain the initial emissions.

[0107] The target pavement structure affects the carbon emissions in subsequent construction. For pavements with multi-layer complex structures, during construction, the paving and compaction equipment need to frequently switch parameters and operating modes, increasing the number of starts, stops, and idling of the equipment, and rising energy consumption. The selection of special materials, such as asphalt mixtures with high adhesiveness, has strict construction conditions, precise paving temperature control, and a large number of compaction passes, increasing the construction duration, and the energy consumption and carbon emissions increase synchronously. After supplementary calculation in this process, the preliminary prediction layer outputs the initial emissions.

[0108] The initial emissions enter the parameter adjustment layer as basic data. It covers the total estimated carbon emissions of each process based on ideal conditions from raw material acquisition to before the plant mixing and heating process. However, the actual project will be affected by factors such as time and unexpected situations, so further correction is needed.

[0109] The milling duration range gives the fluctuation range of the actual milling operation time. If the actual milling duration is close to the upper limit of the range, it means that the equipment operation time far exceeds the expectation, and additional energy consumption may be generated due to equipment aging and fault repair, resulting in a decline in energy utilization efficiency and an increase in carbon emissions; conversely, when the duration is close to the lower limit, the energy consumption and carbon emissions will decrease accordingly. The parameter adjustment layer makes adjustments to increase or decrease the initial emissions accordingly.

[0110] The traffic delay characteristics reflect the unexpected time-consuming situation during transportation. In case of traffic congestion, bad weather, and road construction, the transport vehicles idle for a long time and start and stop frequently, the fuel combustion is incomplete, and the pollutants in the exhaust gas increase sharply. Compared with smooth transportation, the additional carbon emissions generated in this part need to be superimposed on the initial emissions to accurately calibrate the carbon emissions.

[0111] The construction duration range adjusts the carbon emission prediction value from the perspective of the overall project duration. When the construction period is tight, the construction equipment operates at full load, ignoring some energy-saving operations, and the energy consumption surges in the short term; when the construction period is loose, the energy consumption of equipment standby and idling also needs to be considered. The parameter adjustment layer combines this range, weighs the energy consumption changes in the entire construction cycle, and finally outputs the accurate carbon emissions.

[0112] In some embodiments, the method also includes: obtaining actual construction information of pavement construction based on factory-mixed hot regeneration at various stages of its life cycle; extracting actual milling time, actual traffic delays and actual construction time based on the actual construction information; inputting carbon emissions, actual milling time, actual traffic delays and actual construction time into a parameter adjustment layer to obtain actual carbon emissions.

[0113] In the embodiment of the present invention, in the pavement construction project of factory-mixed hot regeneration, the actual construction information is scattered in various construction links and record documents. Starting from the preparatory materials for construction, it contains the equipment procurement and leasing list, the details of the construction team formation, and the specific models and quantities of the milling, transportation, and mixing equipment actually invested, as well as the scale and division of labor of the construction personnel. These basic configurations will affect the construction efficiency and energy consumption. The construction log records the site conditions day by day, such as the impact of weather on the daily construction progress, the details of the repair of sudden equipment failures, the arrival time and quality feedback of raw materials, and other key events. The project progress report presents the completion time nodes and quality acceptance results of each part of the project in the form of a phased summary, such as the production time and quality inspection report of different batches of mixed materials in the factory-mixed hot link, and the start and end time of the construction of each layer of pavement paving and compaction, which are all important sources of actual construction information. There are also financial statements, from which the actual expenses of fuel, electricity, and raw material procurement can be mined, which indirectly reflects the resource consumption during the construction process and provides data support for subsequent carbon emission accounting.

[0114] Sort through the construction logs and project progress reports to find the exact timestamps of the start and end of the milling operation. The difference between the two is the actual milling time. Note that non-milling operation time due to equipment debugging, temporary shutdowns, etc. should be excluded to ensure that the duration data accurately reflects the actual operating time of the milling equipment.

[0115] It is determined by combining construction logs, transportation records and announcements from the transportation department. The construction log records the situations where the transportation vehicles fail to arrive on time. The transportation records can track the planned and actual travel time of each trip. In case of delays such as traffic congestion, road control, and bad weather, the planned itinerary is compared with the actual itinerary, and the extra time consumed is accumulated to obtain the actual traffic delay. Based on the overall groundbreaking ceremony records and completion delivery documents of the project, the start and end times are accurately located to calculate the actual construction duration. However, some force majeure downtime, such as long periods of extreme weather and major social activity control, must be deducted to ensure that the duration reflects the effective construction period.

[0116] The carbon emissions obtained in the early prediction are basic data, which are calculated based on ideal models and theoretical parameters. Although they are of certain reference value, they do not fit the actual working conditions. This data enters the parameter adjustment layer as the starting value for subsequent calibration.

[0117] When there is a deviation between the actual milling duration and the milling duration range used in the prediction, it will trigger changes in carbon emissions. If the actual milling duration far exceeds the predicted upper limit and the equipment runs for a long time, not only will the energy consumption increase due to continuous work, but the aging and wear of the equipment will also reduce the energy utilization efficiency, resulting in an increase in carbon emissions; conversely, if the duration is shortened, the carbon emissions will decrease accordingly, and the parameter adjustment layer will adjust the initial carbon emissions based on this.

[0118] Actual traffic delays mean additional energy consumption in the transportation link. During the delay period, the vehicle idles and starts and stops frequently, and the fuel combustion is extremely inefficient, emitting a large amount of unburned pollutants. This additional part of the carbon emissions compared to smooth transportation is superimposed on the original emissions in the parameter adjustment layer to accurately correct the predicted value.

[0119] The actual construction duration affects the energy consumption of the entire construction period. If the construction period is extended, the standby and idling time of the construction equipment will increase, and there will be basic energy consumption even if the equipment is not operating at full capacity; if the construction period is shortened, the equipment will operate at a high load during the rush construction state, and the energy consumption may soar due to non-energy-saving operations. The parameter adjustment layer combines this actual duration, weighs the energy consumption of the entire cycle, and outputs the actual carbon emissions that conform to reality.

[0120] In the embodiment of the present invention, the preliminary prediction layer includes the following structure:

[0121] Data input interface module: This module is the channel for the preliminary prediction layer to interact with external data and is responsible for receiving various types of data such as the original pavement structure characteristics, production characteristics, transportation characteristics, plant mixing heat characteristics, and target pavement structure characteristics. It has a data format verification function to ensure that the input data follows the established standard format. For example, numerical data needs to match the corresponding precision requirements, and text data cannot appear as garbled characters, so as to ensure the accuracy and stability of subsequent operations and lay a foundation for the entire preliminary prediction process.

[0122] Feature quantization sub-module: After receiving the input data, this sub-module will convert various features into quantifiable parameters. For the original pavement structure characteristics, intuitive data such as the number of pavement layers and the thickness of each layer are directly extracted, while qualitative descriptions such as material properties are assigned according to the pre-set quantization tables of material hardness, adhesion, etc.; in terms of production characteristics, the mining method and processing technology correspond to different scores according to the energy consumption level from low to high; in the transportation characteristics, the vehicle type and route conditions are quantified into numerical values according to the energy consumption and emission coefficient table for convenient subsequent calculation.

[0123] Carbon Emission Calculation Sub-module: Based on the quantified characteristic parameters, this sub-module conducts calculations using built-in algorithms and a carbon emission coefficient library. For example, for new aggregate production, according to the quantification values of mining and processing technology, combined with the carbon emission coefficients of corresponding mining equipment and processing equipment, the carbon emissions of this link are calculated; in the transportation link, according to the quantification values of vehicle driving distance, route difficulty, and vehicle carbon emission coefficient, the transportation carbon emissions are calculated, and the carbon emissions of each link are accumulated in this module to obtain the initial emissions.

[0124] Result Output Interface Module: Responsible for transmitting the calculated initial emissions to the parameter adjustment layer, and can also output intermediate calculation results and log information for convenient debugging and verification. The output data is attached with a timestamp and a data source identifier to ensure the traceability of the data. If subsequent problems are found in the parameter adjustment layer, it can quickly trace back to the preliminary prediction layer to troubleshoot problems.

[0125] In the embodiment of the present invention, the parameter adjustment layer includes the following structures:

[0126] Data Secondary Input Interface Module: Receives the initial emissions from the preliminary prediction layer, as well as newly input data such as actual milling duration, actual traffic delay, and actual construction duration. It also has a data verification function. In addition to regular format checks, it will also check the logical relevance of the new data and the relevant data in the preliminary prediction layer to prevent unreasonable data from entering the subsequent adjustment process and ensure the coherence and accuracy of the data link.

[0127] Deviation Analysis Sub-module: Compares the ideal working condition assumptions corresponding to the initial emissions with the differences in the actual working conditions reflected by the actual milling duration, actual traffic delay, and actual construction duration. For example, calculates the deviation ratio of the actual milling duration compared to the predicted milling duration range, the theoretical increased value of additional energy consumption caused by actual traffic delay, and the impact amplitude of the deviation of the actual construction duration from the planned duration on equipment energy consumption, and accurately locates the deviation direction and magnitude caused by various factors.

[0128] Adjustment Calculation Sub-module: According to the deviation analysis results, calls preset adjustment algorithms and correction factors. If the actual milling duration is too long, the carbon emissions are increased according to the correction factors corresponding to equipment aging and increasing energy consumption; for the additional emissions caused by traffic delay, multiply the delay duration by the vehicle idling emission coefficient and accumulate it to the original emissions; for the actual construction duration, in the scenarios of extended or shortened construction periods, different energy consumption adjustment rules are corresponding to refine the correction of emissions.

[0129] Actual Carbon Emission Output Module: After adjustment calculations, this module outputs the final actual carbon emissions. The output format is standardized and clear, marking the data comparison before and after adjustment and the key adjustment basis, which is convenient for users to directly interpret the results and also provides detailed and reliable data support for subsequent project carbon emission review and environmental protection strategy optimization.

[0130] Some examples are given below to illustrate the above-mentioned feature quantification and calculation, but they are not intended as limitations.

[0131] For example, in the stage of milling the original road surface, the quantification of the original road surface structure features is as follows: the road surface type, thickness, composition, and disease information together constitute the original road surface structure features. Let the road surface type coefficient be (different road surface types are assigned different values), the road surface thickness be , the complexity coefficient of the material composition be (the coefficient is large when there are many material types and large hardness differences), the disease severity coefficient be (the coefficient is large when the disease area is large and the degree is deep), then the quantification value of the original road surface structure features , , , , are the weights of each factor.

[0132] Determination of the milling duration range: Let the daily construction volume planned for the project be A , the area to be milled of the original road surface be Q , the margin coefficient considering the margin setting information be , the efficiency coefficient affected by the prediction information of the operation conditions be (the coefficient is less than 1 under harsh conditions), then the milling duration range .

[0133] In the stage of raw material production, the quantification of the new aggregate production features is as follows: Let the geological condition coefficient of the mining location be (the coefficient is large for hard rock mines), the mining method coefficient be (the coefficient is larger for underground mining than for open-pit mining), the mining scale coefficient be (the coefficient is large for large scales), the energy consumption coefficient of the crushing process be , the energy consumption coefficient of the screening and washing process be , the transportation distance coefficient be , the transportation frequency coefficient be , then the quantification value of the new aggregate production features , , , , , , , are the weights of each factor.

[0134] Quantification of new asphalt production: Let the quality coefficient of the crude oil production area be (the coefficient is small for light crude oil production areas), the refining process coefficient be (The coefficient of the straight-run method is small, and the coefficient of the solvent deasphalting method is large). The influence coefficient of the refining process parameters is (The coefficient of high-temperature and high-pressure parameters is large), then the quantitative value of the new asphalt production characteristics , , , are the weights of each factor.

[0135] Quantification of the production characteristics of the regenerant: Let the difficulty coefficient of obtaining the main components be , the influence coefficient of the component ratio be , and the complexity coefficient of the synthesis process be , then the quantitative value of the production characteristics of the regenerant , , , are the weights of each factor.

[0136] Quantification of transportation characteristics in the raw material transportation stage: Let the coefficient of the transportation vehicle type be (The coefficient of heavy trucks is large), the coefficient of the route length be , the coefficient of the road grade be (The coefficient of highways is small, and the coefficient of low-grade roads is large), and the coefficient of the terrain and landform be (The coefficient of mountainous areas is large, and the coefficient of plains is small), then the quantitative value of the transportation characteristics , , , , are the weights of each factor.

[0137] Determination of traffic delay characteristics: Let the basic delay coefficient based on transportation characteristics be , the influence coefficient of traffic flow be , the influence coefficient of meteorology be , and the influence coefficient of road construction be , then the traffic delay characteristics .

[0138] In the plant mixing and heating stage, the plant mixing and heating characteristics are quantified as: Let the coefficient of the mixing equipment model be (The coefficient of large-capacity and high-power equipment is large), the coefficient of the heating device type be (The coefficient of fuel heating is large, and the coefficient of electric heating is small), the coefficient of the heating device power be , the RAP ratio coefficient be , the mixing temperature coefficient be (The coefficient of deviating from the reasonable temperature range is large), the mixing duration coefficient be , the production batch output coefficient be , and the production batch frequency coefficient be , then the quantitative value of the plant mixing and heating characteristics , , , , , , , , are the weights of each factor.

[0139] In the pavement construction stage, the target pavement structure characteristics are quantified as follows: Let the number of pavement layers be , the complexity coefficient of each layer material be , the thickness coefficient of each layer be , the compaction degree coefficient be , then the quantified value of the target pavement structure characteristics , , , , are the weights of each factor.

[0140] The construction duration range is determined as follows: Let the daily construction volume planned for the project be , the target pavement construction area be , the margin coefficient considering the margin setting information be , and the efficiency coefficient affected by the operation condition prediction information be (the coefficient is less than 1 under harsh conditions), then the construction duration range .

[0141] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or posterior. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0142] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A carbon emission calculation method based on plant-mixed hot recycling of asphalt mixture, characterized in that Including: Obtain the planned information of plant-mixed hot recycling-based pavement construction in each stage of the life cycle; The life cycle of plant-mixed hot recycling-based pavement construction includes: the original pavement milling stage, the raw material production stage, the raw material transportation stage, the plant mixing and heating stage, and the pavement construction stage; According to the planned information of the original pavement milling stage, extract the original pavement structure characteristics and the milling duration range; According to the planned information of the raw material production stage, extract the production characteristics; According to the planned information of the raw material transportation stage, extract the transportation characteristics and traffic delay characteristics; According to the planned information of the plant mixing and heating stage, extract the plant mixing and heating characteristics; According to the planned information of the pavement construction stage, extract the target pavement structure characteristics and the construction duration range; The prediction model includes a preliminary prediction layer and a parameter adjustment layer; Input the original pavement structure characteristics, the production characteristics, the transportation characteristics, the plant mixing and heating characteristics, and the target pavement structure characteristics into the prediction model to predict the initial emissions; Input the initial emissions, the milling duration range, the traffic delay characteristics, and the construction duration range into the parameter adjustment layer to obtain the carbon emissions; The method further includes: Obtain the actual construction information of plant-mixed hot recycling-based pavement construction in each stage of the life cycle; According to the actual construction information, extract the actual milling duration, the actual traffic delay, and the actual construction duration; Input the carbon emissions, the actual milling duration, the actual traffic delay, and the actual construction duration into the parameter adjustment layer to obtain the actual carbon emissions; The parameter adjustment layer includes a data secondary input interface module, a deviation analysis sub-module, an adjustment calculation sub-module, and an actual carbon emissions output module; the deviation analysis sub-module is used to compare the ideal working condition assumptions corresponding to the initial emissions with the differences in the actual working conditions reflected by the actual milling duration, the actual traffic delay, and the actual construction duration; the adjustment calculation sub-module is used to call the preset adjustment algorithm and correction coefficient according to the deviation analysis result to correct the carbon emissions to obtain the actual carbon emissions.

2. The carbon emission calculation method based on hot recycling of asphalt mixture in plant mixing according to claim 1, characterized in that, According to the planned information of the original pavement milling stage, extracting the original pavement structure characteristics and the milling duration range includes: Extract the pavement type, pavement thickness, pavement composition, and pavement disease information from the planned information of the original pavement milling stage to form the original pavement structure characteristics; Extract the project planning information, margin setting information, and operation condition prediction information from the planned information of the original pavement milling stage; Determine the milling duration range according to the original pavement structure characteristics, the project planning information, the margin setting information, and the operation condition prediction information.

3. The carbon emission calculation method based on hot recycling of asphalt mixture in plant mixing according to claim 2, characterized in that, According to the planned information of the raw material transportation stage, extracting the transportation characteristics and traffic delay characteristics includes: Extract the transportation vehicle type and route characteristics from the planned information of the original pavement milling stage to form the transportation characteristics; Determine the traffic delay characteristics according to the transportation characteristics and traffic prediction information.

4. The carbon emission calculation method based on hot recycling of asphalt mixture in plant mixing according to claim 3, characterized in that, According to the planned information of the pavement construction stage, extracting the target pavement structure characteristics and the construction duration range includes: Extract the pavement layers, materials of each layer, thickness of each layer, and compaction degree from the planned information of the original pavement milling stage to form the target pavement structure characteristics; Determine the construction duration range according to the target pavement structure characteristics, the project planning information, the margin setting information, and the operation condition prediction information.

5. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the carbon emission calculation method based on hot in-plant recycling of asphalt mixture according to any one of claims 1 to 4 as described above.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the carbon emission calculation method based on hot in-plant recycling of asphalt mixture according to any one of claims 1 to 4 as described above.

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