Carbon emission calculation method and equipment based on hot mix plant regeneration of asphalt mixture

Abstract

The invention belongs to the technical field of carbon emission prediction, and provides a carbon emission calculation method and equipment based on hot mix plant regeneration of an asphalt mixture, and the method comprises the steps: firstly obtaining plan information of pavement construction based on hot mix plant regeneration in each stage of a life cycle; then according to the plan information of each stage, extracting a carbon emission characteristic value of each stage; and predicting the carbon emission according to the carbon emission characteristic value and a pre-established prediction model. According to the method, the carbon emission characteristics of each stage of the plant-mixed hot regeneration are innovatively extracted, the accurate prediction model is constructed, and the carbon emission condition of the whole process of the plant-mixed hot regeneration can be predicted in advance by virtue of the model, so that a key reference is provided for timely adjustment of a construction plan; and the hot mix plant regeneration construction is assisted to step towards a green and efficient new stage.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission prediction, and in particular relates to a carbon emission calculation method and equipment based on hot regeneration of asphalt mixture plant mix. Background Art

[0002] Factory-mixed hot recycling technology is an advanced method of repairing asphalt pavement. The specific operation is to first collect the recycled old asphalt pavement materials (RAP), transport them to a professional asphalt mixing station, and after pre-treatment such as crushing and screening, mix them with new asphalt, new aggregates, and regeneration agents in precise proportions under heating to finally produce a recycled asphalt mixture that meets high standards for new pavement paving.

[0003] In terms of carbon emissions, the whole process of plant-mix hot regeneration involves multiple emission sources. Energy consumption is the main factor in carbon emissions during the plant-mix hot regeneration process. Whether it is the combustion of fuels such as natural gas and heavy oil in the production process, or the fuel consumption of vehicles transporting raw materials, a large amount of carbon dioxide will be released. In the existing technology, the calculation of carbon emissions in the plant-mix hot regeneration process usually only calculates 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 completed, and it is impossible to provide energy-saving and emission reduction references for construction plans 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 regeneration of asphalt mixture, aiming to solve the problem of delayed carbon emission calculation in the plant-mixed hot regeneration process in the prior art.

[0005] A first aspect of an embodiment of the present invention provides a method for calculating carbon emissions based on hot regeneration of asphalt mixture in a plant, comprising: Obtain planning information for each stage of the life cycle of pavement construction based on plant-mix hot recycling; According to the planning information of each stage, the carbon emission characteristic values ​​of each stage are extracted; Carbon emissions are predicted based on carbon emission characteristic values ​​and a pre-established prediction model.

[0006] In a possible implementation, the life cycle of pavement construction based on plant-mixed hot recycling includes: original pavement milling stage, raw material production stage, raw material transportation stage, plant-mixed hot recycling stage and pavement construction stage; according to the planning information of each stage, the carbon emission characteristic values ​​of each stage are extracted, including: According to the planning information of the original road surface milling stage, the original road surface structural characteristics and milling time range are extracted; Extract production features based on planning information at the raw material production stage; Extract transportation characteristics and traffic delay characteristics based on the planning information of the raw material transportation stage; Extract the plant-mixing characteristics based on the planned information of the plant-mixing stage; According to the planning information of the pavement construction stage, the target pavement structure characteristics and construction time range are extracted.

[0007] In a possible implementation, the carbon emission amount is predicted according to the carbon emission characteristic value and a pre-established prediction model, including: The original pavement structure characteristics, milling time range, production characteristics, transportation characteristics, traffic delay characteristics, factory-mixed heat characteristics, target pavement structure characteristics and construction time range are input into the prediction model to predict carbon emissions.

[0008] In a possible implementation, the prediction model includes a preliminary prediction layer and a parameter adjustment layer; predicting carbon emissions according to the carbon emission characteristic value and the pre-established prediction model includes: Input the original pavement structure characteristics, production characteristics, transportation characteristics, factory-mixed hot characteristics and target pavement structure characteristics into the prediction model to predict the initial emissions; Initial emissions, milling duration range, traffic delay characteristics, and construction duration range are input into the parameter adjustment layer to obtain carbon emissions.

[0009] In a possible implementation, the method further includes: Obtain actual construction information of pavement construction based on plant-mix hot recycling at all stages of its life cycle; Based on the actual construction information, the actual milling time, actual traffic delay and actual construction time are extracted; Carbon emissions, actual milling time, actual traffic delays, and actual construction time are input into the parameter adjustment layer to obtain the actual carbon emissions.

[0010] In a possible implementation, the original pavement structure characteristics and milling time range are extracted based on the planning information of the original pavement milling stage, including: Extract the pavement type, pavement thickness, pavement composition and pavement disease information from the original pavement milling stage planning information to form the original pavement structure characteristics; Extract engineering planning information, margin setting information and operation condition prediction information from the original road surface milling stage planning information; The milling time range is determined based on the original pavement structure characteristics, engineering planning information, margin setting information and operating condition prediction information.

[0011] In a possible implementation, based on the planning information of the raw material transportation stage, the transportation characteristics and traffic delay characteristics are extracted, including: Extract the transport vehicle type and route characteristics from the planning information of the original road surface milling stage to form transport characteristics; Determine traffic delay characteristics based on transportation characteristics and traffic forecast information.

[0012] In a possible implementation, the target pavement structure characteristics and construction time range are extracted based on the planned information of the pavement construction stage, including: The number of pavement layers, materials of each layer, thickness of each layer, and compaction degree are extracted from the planned information of the original pavement milling stage to form the target pavement structure characteristics; The construction duration range is determined based on the target pavement structure characteristics, engineering planning information, margin setting information and operating condition prediction information.

[0013] A second aspect of an embodiment of the present invention provides an electronic device, comprising 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 hot regeneration of asphalt mixture plant as described in the first aspect above are implemented.

[0014] A third aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the carbon emission calculation method based on hot regeneration of asphalt mixture plant as described in the first aspect above.

[0015] The carbon emission calculation method and device based on hot regeneration of asphalt mixture in plant mix provided by the embodiment of the present invention first obtains the planning information of pavement construction based on hot regeneration in plant mix at each stage of the life cycle; then, based on the planning information of each stage, the carbon emission characteristic values ​​of each stage are extracted; then, based on the carbon emission characteristic values ​​and the pre-established prediction model, the carbon emission is predicted. The present invention innovatively extracts the carbon emission characteristics of each stage of hot regeneration in plant mix and constructs an accurate prediction model. With this model, the carbon emission status of the entire process of hot regeneration in plant mix can be predicted in advance, thereby providing a key reference for the timely adjustment of the construction plan, and helping the construction of hot regeneration in plant mix to move towards a new stage of greener and more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 It is a flow chart for realizing a carbon emission calculation method based on hot regeneration of asphalt mixture plant provided by an embodiment of the present invention; Figure 2It is a flow chart for implementing the hot regeneration process of asphalt mixture plant mix provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may 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 prevent unnecessary details from obstructing the description of the present invention.

[0019] Figure 1 It is a flow chart for implementing a carbon emission calculation method based on hot regeneration of asphalt mixture plant provided by an embodiment of the present invention. Figure 2 FIG. 1 is a flow chart of the hot regeneration process of asphalt mixture plant provided by the embodiment of the present invention. Figure 1 and 2 As shown, in some embodiments, a method for calculating carbon emissions based on hot regeneration of asphalt mixture plant includes: S110, obtaining planning information for pavement construction based on plant-mix hot recycling at each stage of its life cycle; S120, extracting carbon emission characteristic values ​​of each stage according to the planning information of each stage; S130, predicting carbon emissions based on the carbon emission characteristic value and a pre-established prediction model.

[0020] In the embodiment of the present invention, the pavement construction life cycle of factory-mixed hot regeneration covers multiple key stages, and obtaining planning information for each stage is the basis for accurately predicting carbon emissions. First, in the raw material preparation stage, it is necessary to clarify the recycling plan of recycled asphalt pavement materials (RAP), including the recycling section, milling depth and area, so as to estimate the recycling volume, and also to master the procurement plan of new aggregates, new asphalt and regeneration agents, such as supplier information, procurement volume, transportation distance and method, etc., which are related to transportation energy consumption and cost. In the factory mixing stage, it is necessary to collect the production plan of the mixing station, such as equipment model, daily production time, production batch arrangement, and raw material ratio of different batches, which will affect the energy consumption rate and total amount. In the transportation link, it is necessary to know the type of transportation vehicle, load, transportation route and daily transportation frequency to prepare for the subsequent calculation of transportation carbon emissions. In the final paving and compaction stage, the planning information covers the construction date, construction time, the number of equipment such as pavers and rollers, and operating parameters, because the construction time is closely related to the energy consumption of the equipment, and the carbon emissions of the equipment under different parameters vary. Only by integrating these detailed planning information can we fully outline the entire process of factory-mixed hot recycled pavement construction.

[0021] From the perspective of the RAP recycling plan, if the recycling area is large and the milling depth is deep, it means that the recycling equipment has a long operating time and high energy consumption. Its carbon emission characteristic value can be obtained by multiplying the equipment's energy consumption per unit time by the estimated operation time; new aggregate mining involves blasting and excavation equipment, and the larger the mining volume, the higher the characteristic value, which can be calculated in combination with the unit output energy consumption of the mining process. The carbon emission characteristic value of the transportation link is determined based on the transportation distance, vehicle load, and fuel type. For example, the fuel consumption of a diesel vehicle per kilometer per unit load is fixed, and the transportation carbon emission characteristics can be calculated by multiplying the transportation distance and cargo weight.

[0022] The model of mixing equipment determines the heating power and mixing efficiency. High-power equipment heats quickly but consumes high energy. The carbon emission characteristics of the heating link can be calculated based on the material heating time and temperature setting in the production plan and the equipment energy consumption parameters. The mixing time varies depending on the raw material ratio. The corresponding carbon emission characteristic values ​​can also be extracted based on the mixing time and equipment mixing power under different ratios.

[0023] The type of vehicle determines fuel efficiency. There is a big difference in fuel consumption between heavy-duty trucks and light-duty trucks. The road conditions of the transportation route, such as slope and congestion, will affect the vehicle speed and fuel consumption. Based on the vehicle selection, transportation frequency, and route conditions in the plan, the carbon emission characteristic values ​​for each trip or each day of the transportation process can be accurately calculated.

[0024] The power and operating time of pavers and rollers are key factors. The longer the high-power equipment operates, the higher the carbon emissions. By referring to the equipment input quantity and daily operating time in the construction plan, combined with the energy consumption parameters calibrated by the equipment before leaving the factory, the carbon emission characteristic value of this stage is obtained.

[0025] In some embodiments, the life cycle of pavement construction based on factory-mixed hot regeneration includes; original pavement milling stage, raw material production stage, raw material transportation stage, factory-mixed hot stage and pavement construction stage; according to the planning information of each stage, the carbon emission characteristic values ​​of each stage are extracted, including: according to the planning information of the original pavement milling stage, the original pavement structure characteristics and milling time range are extracted; according to the planning information of the raw material production stage, the production characteristics are extracted; according to the planning information of the raw material transportation stage, the transportation characteristics and traffic delay characteristics are extracted; according to the planning information of the raw material transportation stage, the factory-mixed hot characteristics are extracted; according to the planning information of the pavement construction stage, the target pavement structure characteristics and construction time range are extracted.

[0026] In the embodiment of the present invention, the planning information of the raw material production stage is rich and complex, and accurately extracting production characteristics from it is extremely critical to fully control the plant-mixed hot regeneration process. The production characteristics may specifically include the following characteristics: Mining-related characteristics include mining location and geological conditions, mining methods and scale. Specific planning information will indicate the location of the mine where the new aggregate will be mined. The geological conditions of different mines vary greatly. Some are sedimentary rock mines with low hardness, which are less difficult 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 initial equipment investment large, but the energy consumption and mechanical loss during mining are also considerable. This information constitutes the geological characteristics corresponding to the mining location. The mining method and scale need to be clear whether it is open-pit mining or underground mining. Open-pit mining has a wide field of vision and convenient construction. Large-scale machinery operations are not subject to too many space restrictions, which is conducive to large-scale and efficient mining, but it has a great impact on the surrounding environment and landforms. Although underground mining has little damage to the surface, the ventilation, lighting, and transportation systems are complex, the cost is high, and the output is limited. The scale of mining involves planned mining volume, daily output targets, etc., which determines the mining time and the continuous operation status of the equipment, and is a key production feature.

[0027] Processing technology characteristics include crushing link characteristics, screening and cleaning characteristics. Among them, the crushing link checks the type of crusher to be used. For example, the jaw crusher is suitable for coarse crushing, the cone crusher focuses on medium crushing, and the impact crusher can produce finished products with better particle shape. Different crushers have different energy consumption, processing capacity, and product particle size distribution. For example, the jaw crusher has a simple structure and low energy consumption, but the product particle size is not very uniform; the impact crusher product has a regular particle shape, but the equipment price and energy consumption are relatively higher. This series of characteristics constitutes the crushing process characteristics. When screening and cleaning, it is necessary to understand the specifications of the screening screen. The fine screen can screen out more specifications of aggregates to meet the precise grading requirements, but the number of screenings increases, and the energy consumption and time costs increase. If there is a cleaning process, it is necessary to pay attention to the cleaning method (water washing, dry cleaning) and wastewater treatment arrangements. Water washing can effectively remove soil impurities, but it will produce a lot of wastewater. Treating wastewater involves additional costs and environmental considerations. These details constitute the characteristics of the screening and cleaning process.

[0028] The characteristics of transportation planning include transportation distance and tools, transportation frequency and batch. The plan for the raw material production stage must include the distance data of the new aggregate transportation to the plant mixing station. For short-distance transportation, vehicles with smaller load and flexibility can be selected, which are low-cost; for long-distance transportation, although railway transportation has a large single-time transportation volume and low unit energy consumption, loading and unloading are complicated, and road transportation is flexible but energy-intensive. The selection of transportation tools and transportation distance jointly shape the transportation cost and energy consumption characteristics. Master the planned transportation frequency. High-frequency small-batch transportation can flexibly respond to the immediate material needs of the plant mixing station, but the transportation cost increases due to the increase in the empty driving rate; low-frequency large-batch transportation reduces transportation costs, but requires the plant mixing station to have a larger raw material storage space. The transportation frequency and batch information reflect the economy of transportation planning.

[0029] Crude oil raw material characteristics include the origin and quality of crude oil. The chemical composition and properties of crude oil vary significantly depending on its origin. Light crude oil in the Middle East has low sulfur content and low viscosity, and the process of processing it into asphalt is relatively streamlined, with less energy consumption in heating and distillation. Heavy crude oil from Venezuela and other places has many impurities and high viscosity, and needs to go through complex pretreatment processes such as desalting, dewaxing, and hydrogenation. The characteristics of the raw materials determine the energy consumption and process complexity of the starting point of asphalt production.

[0030] Refining process characteristics include refining process type and process parameters. There are many refining processes for new asphalt production, such as straight distillation, solvent deasphalting, oxidation, etc. The straight distillation method is based on the difference in boiling points of crude oil components for separation. The process is simple and direct, and the energy consumption is relatively low; the solvent deasphalting method uses the difference in the solubility of solvents in asphaltene to separate asphalt. Although it can obtain high-quality asphalt, the solvent recovery link consumes a lot of energy; the oxidation method changes the properties of asphalt by blowing air into the vacuum residue, but the process strictly controls the 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 speed is accelerated, but the equipment is under great pressure and the maintenance cost is high; although the mild process parameters have small equipment loss, the production cycle is long and the plant occupancy time is long. These parameters shape the refining process characteristics from the level of operational precision.

[0031] The characteristics of the formula composition include the main components and proportions. The regeneration agent formula is a mixture of multiple chemical substances in a specific proportion. The main components are determined from the planning information. If the proportion of components with high aromatic content is large, the difficulty of obtaining their sources, the cost and the complexity of the synthesis process must be considered. The proportion of each component is different, and the recovery effect of the regeneration agent on the old asphalt and the applicable scenarios are different, which constitute the core characteristics of the formula.

[0032] In an embodiment of the present invention, the factory-mixed heat stage is the core link in the factory-mixed heat regeneration process. Based on the planning information of this stage, the factory-mixed heat characteristics can be extracted from the following key dimensions: mixing equipment model, heating device type and power, raw material ratio, mixing process parameters and production plan arrangement.

[0033] The specific model of the mixing equipment to be used should be clarified from the planning information. Different models of mixing equipment have different mixing pot capacity, heating system power, mixing blade design, etc. For example, a large-capacity mixing pot can process more recycled asphalt pavement materials (RAP), new aggregates, new asphalt and regeneration agents at one time, which will affect the output and duration of a single mixing; a high-power heating system can quickly increase the material temperature, but energy consumption will also increase. These characteristics constitute the basic characteristics of mixing equipment.

[0034] Determine whether the heating device uses gas heating, oil heating or electric heating. Gas heating has relatively low cost, but requires high stability of gas supply; oil heating is highly mobile and suitable for temporary or remote construction sites; electric heating is more environmentally friendly and clean, but the electricity cost is greatly affected by local electricity prices. The power of the heating device determines the heating speed. High-power heating can shorten the preheating time of the material, but may increase the instantaneous energy consumption, which is a key plant mixing heat characteristic factor.

[0035] Accurately grasp the proportion of RAP in the mixture. A high proportion of RAP means more old materials are recycled, which not only affects the cost, but also affects the difficulty of subsequent heating and mixing. The degree of aging of old asphalt varies. A high-content RAP mixture may require stronger heating and mixing to promote better integration of old asphalt. This ratio is an important basis for measuring the degree of hot recycling and performance regulation of factory-mixed asphalt.

[0036] The mixing temperature in the planning information is a critical parameter. Different mixture compositions require different mixing temperatures. High temperature helps materials to mix quickly and evenly, activating the modification effect of the regeneration agent on the old asphalt, but too high a temperature will accelerate the aging of the new asphalt. A reasonable temperature range must ensure the workability of the mixture while taking into account the stability of the performance of each component, reflecting the accuracy and scientificity of the hot mixing operation in the plant.

[0037] The total mixing time and the time allocation for each stage, such as dry mixing time and wet mixing time. The dry mixing stage allows the aggregate to be initially mixed and the moisture to be removed; the wet mixing stage allows the asphalt and regeneration agent to evenly coat the aggregate. Appropriate time settings can ensure the uniformity of the mixture. Too long or too short mixing time will cause quality problems such as segregation and uneven coating of the mixture, which constitutes the key characteristics of the mixing process.

[0038] Determine the output of each batch of recycled asphalt mixture, as well as the daily and weekly production batch frequency. High-volume batches are conducive to the rapid advancement of large-scale projects, but they have high requirements for equipment stability and the consistency of raw material supply; frequent small-batch production is highly flexible and can respond to temporary changes in construction in a timely manner. Both reflect the organizational characteristics of the hot mix stage in the production rhythm.

[0039] In some embodiments, based on the planned information of the original pavement milling stage, the original pavement structure characteristics and the milling time range are extracted, including: extracting 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; extracting the engineering planning information, margin setting information and operating condition prediction information from the planned information of the original pavement milling stage; determining the milling time range according to the original pavement structure characteristics, engineering planning information, margin setting information and operating condition prediction information.

[0040] In the embodiment of the present invention, in the factory-mixed hot recycling project, the planning information of the original road surface milling stage contains a large amount of key data. First, we focus on extracting the structural characteristics of the original road surface, which requires accurately capturing multiple elements from the planning information: Pavement type: Whether the pavement belongs to a highway, a city trunk road, or an ordinary rural road, the design standards of different types of pavements vary greatly. Highways usually use high-grade asphalt concrete pavements to withstand heavy traffic and have thicker and more complex structural layers; while rural roads may be relatively simple, with relatively basic pavement thickness and material composition. Clarifying the pavement type provides a basic framework for subsequent analysis.

[0041] Pavement thickness: Accurate pavement thickness data is directly related to the amount of milling work. Thicker pavement means that the milling equipment needs to cut deeper, consume more energy, and the operation time will increase accordingly. The design drawings and preliminary inspection reports in the planning information can give the accurate thickness of each structural layer, and the total thickness is the overall pavement thickness.

[0042] Pavement composition: It is important to understand what materials each structural layer of the pavement is made of, such as whether it is a pure asphalt layer or a combination of multiple layers of asphalt mixtures and aggregates with different gradations. Different materials have different hardness and adhesion, and the difficulty of milling is different. For example, the asphalt stabilized base containing a lot of crushed stone will wear the tool faster and require more power when milling than the pure fine-grained asphalt surface layer.

[0043] Pavement damage information: Records damage conditions such as cracks, potholes, and rutting. In areas with large areas of damage, milling operations may require more sophisticated processing, or adjustments to milling depth and speed, which will affect the overall milling strategy and subsequent carbon emission calculations. Integrating these four key information forms the original pavement structure characteristics, laying the foundation for in-depth analysis of subsequent milling work.

[0044] Project planning information: The original road milling stage plan information is used to sort out the overall project schedule, milling area division, daily construction volume target, etc. The schedule determines the compactness of the milling operation, the area division is related to the frequency of equipment transfer, and the daily construction volume target clarifies the workload benchmark, all of which indirectly affect the milling time.

[0045] Margin setting information: Margin settings in construction include time margin and material margin, and here we focus on time margin. Time margin is the extra time reserved for dealing with emergencies, such as bad weather and sudden equipment failure. Reasonable time margin setting can ensure that the milling operation can be completed within a controllable time even if it encounters an accident, avoiding a significant extension of the construction period.

[0046] Operation condition prediction information: Based on weather forecasts and past construction environment records, the temperature, humidity, wind speed and other conditions during milling operations are predicted. In high temperature environments, the asphalt pavement is relatively soft, and the milling resistance is small, but the equipment has high heat dissipation requirements; in low temperature environments, the asphalt becomes hard, and the difficulty of milling increases dramatically. These factors affect the efficiency and duration of milling.

[0047] The milling time range is calculated based on the above-mentioned original pavement structure characteristics, engineering planning information, margin setting information and operating condition prediction information. In the original pavement structure characteristics, thick and complex pavement, hard and difficult to mill material composition, and large area of ​​diseased areas will increase the milling time of a single unit area; otherwise, it will decrease. The compact schedule and large-scale daily construction volume targets in the engineering planning information will compress the ideal milling time, prompting the construction team to speed up the milling speed, but are limited by the upper limit of equipment performance. The time margin in the margin setting information directly extends the upper limit of the milling time to ensure construction flexibility. In the operating condition prediction information, adverse weather or environmental conditions, such as heavy rain and extreme cold, will suspend operations and push the milling time to the boundary set by the time margin; a suitable environment will help shorten the milling time and approach the lower limit of the efficient operation time set by the engineering plan. By integrating multiple factors, the reasonable range of milling time can be accurately defined.

[0048] In some embodiments, transportation characteristics and traffic delay characteristics are extracted based on the planning information of the raw material transportation stage, including: extracting the type of transportation vehicle and route characteristics from the planning information of the original road milling stage to form the transportation characteristics; and determining the traffic delay characteristics based on the transportation characteristics and traffic forecast information.

[0049] In an embodiment of the present invention, Extracting transport features Type of transport vehicle: The planning information for the raw material transportation stage will clearly indicate the type of transport vehicle selected, which is a key element in constructing transport characteristics. For example, if it is a dump truck, you need to pay attention to its load capacity. Common specifications include 10 tons, 15 tons, 20 tons, etc. The larger the load, the higher the single transport volume, but the road conditions and vehicle performance requirements are also more stringent; tank trucks are used to transport liquid asphalt or regeneration agents. The tank capacity, material and sealing performance are very important. Tank trucks of different capacities are adapted to transportation needs of different scales. High-quality tank materials and sealing designs can prevent liquid leakage and volatilization, and ensure transportation safety and material quality. In addition, the vehicle's power form, whether it is diesel, natural gas or electric, also significantly affects transportation costs and environmental performance. Diesel vehicles are powerful but have high carbon emissions, and electric vehicles have zero emissions but limited endurance. These vehicle characteristics collectively constitute the basic part of transportation characteristics.

[0050] Route characteristics: Detailed information on the planned route. The length of the route directly determines the transportation distance. Long-distance transportation is not only time-consuming, but also increases costs due to fuel consumption and labor hours. The road grade cannot be ignored. Highways have fast speeds, good road conditions, and high transportation efficiency, but may involve tolls. Although low-grade roads are free, they are bumpy and narrow, which can easily cause vehicle damage and slow down the speed. The topography of the passing area is also critical. Mountain roads have large slopes and many bends, and vehicles need to frequently shift gears and slow down, which greatly increases energy consumption. Plain areas are conducive to maintaining a stable speed and reducing transportation difficulties. In addition, the traffic control situation around the route, such as whether there are limited driving time periods and weight-restricted areas, will also constrain transportation arrangements. The above route-related attributes together constitute the route characteristics, and together with the type of transportation vehicle, the transportation characteristic system is improved.

[0051] Characterizing Traffic Delays Based on the analysis of transportation characteristics: The extracted transportation characteristics play a key role in estimating traffic delays. Heavy-duty transport vehicles, such as large dump trucks loaded with new aggregates, are more likely to encounter traffic obstructions when driving on congested urban roads or narrow roads due to their poor maneuverability, slow start, and large turning radius, thus causing delays. For safety reasons, the speed of liquid materials transported by tank trucks is limited. During peak traffic hours, the length of slow driving will increase accordingly, accumulating potential delays. Long-distance transportation routes have more variables along the way, and the probability of emergencies such as vehicle failures and driver fatigue is higher, which also poses a hidden danger to traffic delays. When passing through complex terrain routes such as mountainous areas, sudden landslides and landslides on the road, or snow and ice accumulation in bad weather, will seriously hinder traffic and cause transportation to stagnate.

[0052] Combined with traffic forecast information: Traffic forecast information covers many aspects, such as the traffic volume estimation for specific time periods and sections. Based on historical data and real-time traffic monitoring, the congestion degree of transportation routes during the morning and evening rush hours on weekdays and the holiday travel peak is predicted. The greater the traffic volume, the higher the possibility that the transportation vehicles will be stuck in congestion, and the more uncontrollable the delay time will be. Weather forecast is also an important part. Severe weather such as heavy rain, fog, and strong winds will reduce road visibility, affect vehicle driving stability, and force vehicles to slow down or even close the road. If the planned route encounters such weather, the delay time must be fully estimated. Road construction notices are also critical. Knowing the location and duration of the construction section in advance can allow the transportation plan to be avoided or adjusted in advance. Otherwise, once entering the construction area, waiting for release, detours, and other operations will cause traffic delays. Combining transportation characteristics with these traffic forecast information can accurately determine the characteristics of possible traffic delays and reserve a reasonable margin for subsequent raw material transportation arrangements.

[0053] In some embodiments, target pavement structure characteristics and construction time range are extracted based on planned information of the pavement construction stage, including: extracting the number of 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; determining the construction time range based on the target pavement structure characteristics, engineering planning information, margin setting information, and operating condition prediction information.

[0054] In an embodiment of the present invention, the number of layers of the newly designed pavement will be clearly given in the planning information of the original pavement milling stage. The number of pavement layers is directly related to the bearing capacity and durability of the entire pavement structure. For example, heavy-load traffic roads such as highways often adopt a multi-layer structure, with the base layer, subbase layer and surface layer cooperating with each other to disperse vehicle loads; while some low-grade rural roads may only have a single-layer or double-layer structure, which is relatively simple. Accurately determining the number of pavement layers is the first step in outlining the target pavement structure.

[0055] The materials used in each layer are carefully sorted out, which has a great impact on the performance of the road surface. 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 provides better anti-skid and wear resistance, and is often used in the middle and lower layers. Common base materials include semi-rigid materials such as cement-stabilized gravel and lime-fly ash-stabilized gravel, which have high strength and stability; the subbase may also use flexible materials such as graded gravel to buffer and diffuse stress. Mastering the materials of each layer can provide a deep understanding of the functional characteristics of the road surface.

[0056] Accurate thickness information of each structural layer is indispensable. The thickness of the surface layer determines the smoothness and wear resistance of the road surface. If it is too thin, it is easy to break early, and if it is too thick, the cost will increase. The thickness of the base layer affects the overall bearing structure of the road surface. A thick base layer can bear a larger vertical load and avoid road deformation. The thickness of the subbase layer is also related to the stress dispersion effect. Obtaining the thickness of each layer from the planning information is the key data for quantifying the pavement structure.

[0057] Compactness reflects the degree to which the materials of each structural layer are compacted, and is an important indicator for ensuring the strength and stability of the road surface. A higher compaction degree means that the gaps between material particles are smaller, the structure is denser, and it can effectively resist water damage and vehicle loads. The compaction standards in the planning information have different requirements for different layers of materials and different road grades. These compaction values ​​are collected to improve the target road surface structure characteristics.

[0058] Complex pavement structures, multi-layer designs, and different special materials for each layer will undoubtedly prolong the construction time. For example, if the surface layer uses a high-performance but complex modified asphalt mixture, the paving temperature and compaction process must be precisely controlled, which takes a long time; the base layer uses cement-stabilized materials, which also involves a maintenance period and waits for the material strength to form, which increases the construction time according to the target pavement structure characteristics. Thicker layers require a lot of paving and compaction work, which will also extend the construction period; high compaction requirements mean that the roller needs to go back and forth multiple times, which takes more time to meet the quality standards.

[0059] The project plan arranges the overall construction progress, phase division and milestone nodes. A compact schedule will shorten the construction time and promote the rapid connection of each process, but it requires sufficient manpower and equipment resources. In the phased construction, the time for each stage of preliminary preparation, main construction and final acceptance is allocated. If the preliminary preparation is hasty, the subsequent rework may delay time. Reasonable project planning not only gives the ideal construction period target, but also implies the flexibility of the construction period.

[0060] Margin settings include 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 bad weather, equipment failure, delayed raw material supply, and other unexpected events. Reasonable time margin can prevent these emergencies from disrupting the construction rhythm, expand the upper limit of the construction time as a whole, and ensure that the project proceeds as planned.

[0061] Weather conditions are a key factor. High temperatures are good for asphalt paving, but they may cause heat stroke to workers, so the working hours need to be adjusted. Bad weather such as low temperatures, rain, and strong winds seriously hinder construction, causing the construction period to stagnate. Geological condition prediction is also very important. If the construction site is soft, it may need to be reinforced first, increasing the preparatory time. Traffic control around the site and restrictions on the entry and exit of construction vehicles will also affect construction efficiency. These operating condition prediction information are combined to determine the boundary conditions of the construction time range.

[0062] In some embodiments, carbon emissions are predicted based on carbon emission characteristic values ​​and a pre-established prediction model, including: inputting original pavement structure characteristics, milling time range, production characteristics, transportation characteristics, traffic delay characteristics, factory-mixed heat characteristics, target pavement structure characteristics and construction time range into the prediction model to predict carbon emissions.

[0063] In an embodiment of the present invention, the original pavement structure characteristics include key information such as the number of pavement layers, the materials of each layer, the thickness of each layer, and the degree of compaction. The more pavement layers there are, the more times the equipment needs to adjust the operating depth during milling, and the energy consumption increases accordingly. The differences in hardness and adhesion of materials in different layers determine the wear rate of the milling tool and the degree of power consumption. For example, a granite gravel base is more difficult to mill than an ordinary sand and gravel base. The thickness of each layer affects the milling workload. Milling thick pavement takes a long time, the equipment runs longer, and carbon emissions increase accordingly. The pavement structure with a high degree of compaction is denser, and the difficulty of milling increases, which also drives the growth of energy consumption and carbon emissions. These data provide basic working condition parameters for the prediction model and lay the foundation for carbon emissions estimation.

[0064] The milling time range defines the time span of equipment operation. A shorter milling time range means efficient operation, lower overall energy consumption of the equipment, and correspondingly less carbon emissions; on the contrary, if the time range is too large, due to the limited performance of the equipment, long-term operation will not only consume more electricity or fuel, but may also generate additional energy consumption due to equipment aging and fault maintenance. For example, the engine that runs for a long time will not burn completely, and emit more exhaust gas. This range of data gives the model an estimate of the time dimension of energy consumption in the milling process.

[0065] The production characteristics are subdivided into the production of new aggregates, new asphalt, and regeneration agents. The geological conditions and mining methods of new aggregate mining determine the energy consumption of mining equipment. The energy consumption of hard rock mines far exceeds that of soft mines. In the processing technology, the complex crushing and screening processes involve multiple steps and multiple equipment, which consume electricity continuously. From crude oil procurement and the complexity of the refining process to storage and transportation conditions, the energy consumption of new asphalt varies in each link. The refining process of heavy crude oil is cumbersome and has high carbon emissions. In the production of regeneration agents, the reaction conditions of the synthesis process and the difficulty of obtaining the raw material formula affect the energy consumption of the synthesis equipment and plant. The harsh high temperature and high pressure reaction consumes significant energy. These comprehensive characteristics allow the model to accurately grasp the overall carbon emissions of raw material production.

[0066] Transportation characteristics cover the types of transport vehicles and routes. When a heavy dump truck transports new aggregates, the greater the load, the greater the driving resistance and the higher the fuel consumption; when a tank truck transports liquid materials, the weight of the tank and the sealing requirements change the vehicle's driving energy consumption characteristics. In terms of route characteristics, long-distance transportation and passing through complex terrain such as mountainous areas require frequent speed changes and climbing, which leads to soaring fuel consumption; low-grade roads and many congested sections, stop-and-go driving patterns waste fuel and increase vehicle exhaust emissions. Transportation characteristic data helps the model quantify carbon emissions during transportation.

[0067] Traffic delay characteristics are based on transportation characteristics and traffic forecast information. When encountering delays such as traffic congestion, bad weather, and road construction, vehicles idle and frequently start and stop, with extremely low fuel combustion efficiency, emitting a large amount of incompletely burned pollutants. Compared with smooth traffic, the additional delay time will increase carbon emissions exponentially. This feature supplements the model's consideration of incremental carbon emissions in unexpected transportation scenarios.

[0068] The hot characteristics of factory mixing focus on mixing equipment, raw material ratio, mixing process parameters, etc. Although large-capacity mixing equipment heats up quickly, high-power operation consumes a lot of energy; a high proportion of recycled asphalt pavement material (RAP) in the mixture makes it difficult to integrate the old asphalt, requiring stronger heating and mixing, which increases energy consumption. Accurate mixing temperature and duration settings, any deviation will cause energy waste, or uneven material mixing will result in rework and remixing. These characteristics of the hot links of factory mixing are input into the model to improve the carbon emission calculation link.

[0069] The characteristics of the target pavement structure determine the subsequent construction process and energy consumption trend. When constructing a multi-layer pavement structure, the paving and compaction requirements of each layer are different, and it is necessary to frequently switch construction equipment and adjust parameters, increase the number of equipment starts and stops, and idling times, and increase energy consumption. The selection of special materials, such as high-performance but demanding asphalt mixtures, has high requirements for paving temperature and compaction times, and the construction time is extended. Energy consumption and carbon emissions grow simultaneously, providing the model with a key energy consumption reference for construction.

[0070] The construction duration range sets the boundaries of the entire road construction period. Under the requirement of a shorter construction period, construction equipment operates at full capacity, and some energy-saving operations may be ignored due to rushing to complete the work, resulting in a sharp increase in energy consumption in a short period of time; although a generous construction period can refine the construction, the long-term standby and idling of equipment will also generate energy consumption. This range of data combined with other features allows the prediction model to accurately calculate carbon emissions from the overall construction period dimension and output a carbon emission prediction value that fits the actual project.

[0071] The factory-mixed hot recycling technology collects recycled old asphalt pavement materials (RAP) and transports them to professional asphalt mixing plants. After pre-treatment such as crushing and screening, it is fully mixed with new asphalt, new aggregates, and regeneration agents in proportion under heating to make a recycled asphalt mixture for new pavement paving. In the carbon emission calculation and prediction model, the characteristic values ​​of each stage related to the asphalt mixture are important inputs. The original pavement structure characteristics, milling time range, production characteristics, transportation characteristics, traffic delay characteristics, factory-mixed hot characteristics, target pavement structure characteristics, and construction time range are input into the prediction model, which jointly affect the prediction results of carbon emissions. For example, the original pavement asphalt mixture has a dense structure, which makes milling difficult, and energy consumption and carbon emissions increase; in the factory-mixed hot stage, unreasonable asphalt mixture mixing parameters lead to increased energy consumption and increased carbon emissions.

[0072] In some embodiments, the prediction model includes a preliminary prediction layer and a parameter adjustment layer; based on the carbon emission characteristic values ​​and a pre-established prediction model, carbon emissions are predicted, including: inputting the original pavement structure characteristics, production characteristics, transportation characteristics, factory-mixed hot characteristics and target pavement structure characteristics into the prediction model to predict the initial emissions; inputting the initial emissions, milling time range, traffic delay characteristics and construction time range into the parameter adjustment layer to obtain carbon emissions.

[0073] In an embodiment of the present invention, the structural information of the original road surface is the basic starting point for the entire carbon emission prediction. The number of road surface layers, the thickness of each layer, the material composition and the degree of compaction together outline the difficulty and workload of the milling operation. If the number of road surface layers is large and the thickness of each layer is large, the milling equipment needs to continue to run for a longer time and consume more electricity or fuel. For example, a highway with a three-layer structure and a base layer thickness of more than 30 cm, compared with a single-layer thin road surface, has a large tool cutting depth and fast wear during milling. The power output of the equipment must continue to maintain a high level, and the energy consumption rises sharply. The carbon emissions corresponding to this part of the energy consumption are first captured and calculated by the preliminary prediction model. The characteristics of each layer of material are also critical. The hard rock aggregate base layer is more difficult to mill than the soft sand and gravel base layer, which makes the equipment consume more energy and increase carbon emissions.

[0074] From the perspective of production characteristics, in the production of new aggregates, mining geological conditions, mining methods and processing technology all affect carbon emissions. Mining new aggregates from hard mountains requires huge energy consumption for blasting and excavation equipment; the complex crushing and screening process consumes energy in each process, and the carbon emissions from these links are accumulated. For new asphalt production, the source and quality of crude oil determine the difficulty of refining. Heavy crude oil requires multiple complex processes for impurity removal and conversion, and the energy consumption far exceeds that of light crude oil. In preliminary forecasts, the carbon emissions caused by this part of energy consumption cannot be underestimated. In the production of regeneration agents, the difficulty of obtaining raw material formulas and the complexity of the synthesis process, such as the high temperature and high pressure synthesis process, are energy-intensive, and related carbon emission data are also included in the calculation.

[0075] The type of transport vehicles and route information shape the carbon emission trend of the transport link. Heavy transport vehicles, due to their heavy weight and load, have to do more work to overcome resistance when driving, consume more fuel, and emit a large amount of carbon dioxide from diesel exhaust. If the transport route is long and passes through mountainous areas, the vehicle frequently climbs and turns, the engine load increases, fuel consumption rises, and the additional fuel combustion brings more carbon emissions. The preliminary prediction layer calculates the approximate carbon emissions of the transportation process based on these factors.

[0076] The equipment parameters, raw material ratios, and mixing process parameters of the hot mix phase are crucial. High-power mixing equipment heats up quickly, but consumes a lot of energy. A high proportion of recycled asphalt pavement material (RAP) in the mixture makes it difficult to blend the old asphalt. Heating and mixing require stronger power and longer time, which increases energy consumption. Deviations in the setting of mixing temperature and duration will cause energy waste. The preliminary prediction model calculates the carbon emissions of this phase based on these hot mix characteristics, and adds them to the previous phases to derive the initial emissions.

[0077] The target pavement structure affects the carbon emissions of subsequent construction. For pavements with complex multi-layer structures, the paving and compaction equipment need to frequently switch parameters and operating modes during construction, and the number of equipment starts, stops, and idling increases, which increases energy consumption. The selection of special materials, such as high-adhesion asphalt mixtures, requires stringent construction conditions, precise paving temperature control, multiple compaction passes, and increased construction time. Energy consumption and carbon emissions grow simultaneously. After additional calculations in this link, the initial emissions of the layer output are preliminarily predicted.

[0078] The initial emissions enter the parameter adjustment layer as basic data. It covers the total estimated carbon emissions of each process under ideal conditions, from raw material acquisition to factory hot mixing. However, the actual project will be affected by factors such as time and unexpected conditions, so further correction is required.

[0079] The milling time range gives the fluctuation range of the actual milling operation time. If the actual milling time is close to the upper limit of the range, it means that the equipment operation time is far longer than expected, and additional energy consumption may be generated due to equipment aging and fault repair, which will reduce energy utilization efficiency and increase carbon emissions; on the contrary, if the time is close to the lower limit, energy consumption and carbon emissions will be reduced accordingly, and the parameter adjustment layer will make adjustments to the initial emissions accordingly.

[0080] Traffic delay characteristics reflect unexpected time consumption during transportation. When encountering traffic congestion, bad weather, road construction, transport vehicles idle for a long time, start and stop frequently, fuel combustion is incomplete, and tail gas pollutants increase dramatically. Compared with smooth transportation, this part of the additional carbon emissions should be added to the initial emissions to accurately calibrate carbon emissions.

[0081] 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 runs at full capacity, ignoring some energy-saving operations, and 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 to weigh the energy consumption changes of the entire construction cycle and ultimately output accurate carbon emissions.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] When the actual milling time deviates from the milling time range used in the prediction, it will cause changes in carbon emissions. If the actual milling time far exceeds the predicted upper limit, the equipment will run 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 increased carbon emissions; conversely, the shorter the time, the lower the carbon emissions, and the parameter adjustment layer adjusts the initial carbon emissions accordingly.

[0088] Actual traffic delays mean additional energy consumption in the transportation process. During the delay period, vehicles idle, start and stop frequently, and the fuel combustion is extremely incomplete, emitting a large amount of incompletely burned pollutants. Compared with smooth transportation, this extra carbon emission is superimposed on the original emission in the parameter adjustment layer to accurately correct the predicted value.

[0089] The actual construction duration affects the energy consumption of the entire construction cycle. When the construction period is extended, the standby and idling time of construction equipment increases, and even if it is not working at full capacity, basic energy consumption is generated; when the construction period is shortened, the equipment runs at high load under rush conditions, and 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 fit the reality.

[0090] In the embodiment of the present invention, the preliminary prediction layer includes the following structure: Data input interface module: This module is the channel for the preliminary prediction layer to interact with external data. It is responsible for receiving various data such as original pavement structure characteristics, production characteristics, transportation characteristics, factory-mixed hot 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 must match the corresponding accuracy requirements, and text data cannot be garbled, so as to ensure the accuracy and stability of subsequent operations and lay the foundation for the entire preliminary prediction process.

[0091] Feature quantification submodule: After receiving input data, this submodule 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 values ​​based on pre-set quantitative tables such as material hardness and adhesion; in terms of production characteristics, mining methods and processing technologies correspond to different scores from low to high according to energy consumption levels; in transportation characteristics, vehicle types and route conditions are quantified into values ​​based on energy consumption and emission coefficient tables to facilitate subsequent calculations.

[0092] Carbon emission calculation submodule: Based on the quantified characteristic parameters, this submodule uses the built-in algorithm and carbon emission coefficient library to perform calculations. For example, for new aggregate production, the carbon emissions of this link are calculated based on the mining quantification value and processing technology quantification value, combined with the corresponding mining equipment and processing equipment carbon emission coefficient; the transportation link calculates the transportation carbon emissions according to the vehicle driving distance quantification value, route difficulty quantification value and vehicle carbon emission coefficient. The carbon emissions of each link are accumulated in this module to obtain the initial emissions.

[0093] 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 debugging and verification. The output data is accompanied by a timestamp and data source identifier to ensure data traceability. If an abnormality is found in the subsequent parameter adjustment layer, it can quickly trace back to the preliminary prediction layer to troubleshoot the problem.

[0094] In the embodiment of the present invention, the parameter adjustment layer includes the following structure: Data secondary input interface module: Receives the initial emissions from the preliminary forecast layer, as well as newly input data such as actual milling time, actual traffic delays, and actual construction time. It also has a data verification function. In addition to the regular format check, it also verifies the logical correlation between the new data and the relevant data in the preliminary forecast layer to prevent unreasonable data from entering the subsequent adjustment process and ensure the continuity and accuracy of the data link.

[0095] Deviation analysis submodule: Compare the ideal working condition assumption corresponding to the initial emission with the actual working condition differences reflected by the actual milling time, actual traffic delays, and actual construction time. For example, calculate the deviation ratio of the actual milling time compared to the predicted milling time range, the theoretical increase in energy consumption caused by actual traffic delays, and the impact of the actual construction time deviation from the planned time on the equipment energy consumption, and accurately locate the direction and size of the deviation caused by various factors.

[0096] Adjustment calculation submodule: Based on the deviation analysis results, the preset adjustment algorithm and correction coefficient are called. If the actual milling time is too long, the carbon emissions are increased according to the correction coefficient corresponding to equipment aging and rising energy consumption; the additional emissions caused by traffic delays are multiplied by the vehicle idling emission coefficient based on the delay time and added to the original emissions; according to the actual construction time, the extension or shortening of the construction period is divided into different energy consumption adjustment rules, and the emissions are refined and corrected.

[0097] Actual carbon emissions output module: After adjustment and calculation, this module outputs the final actual carbon emissions. The output format is standardized and clear, with annotations of data comparison before and after adjustment and key adjustment basis, which is convenient for users to directly interpret the results. It also provides detailed and reliable data support for subsequent project carbon emissions review and environmental protection strategy optimization.

[0098] Some examples are given below to illustrate the above feature quantization and calculation, but they are not intended to be limiting.

[0099] For example, in the milling stage of the original pavement, the original pavement structure characteristics are quantified as follows: pavement type, thickness, composition and disease information together constitute the original pavement structure characteristics. Let the pavement type coefficient be (Different types of road surfaces are assigned different values), the road surface thickness is , the material composition complexity coefficient is (The coefficient is large if there are many types of materials and large hardness differences), the disease severity coefficient is (The larger the damage area and the deeper the degree, the larger the coefficient), then the quantitative value of the original pavement structure characteristic , , , , is the weight of each factor.

[0100] Determine the range of milling time: Assume that the daily construction volume of the project is A The original road surface area to be milled is Q , the margin coefficient considering the margin setting information is , the efficiency coefficient of the operating condition prediction information is (under severe conditions, the coefficient is less than 1), then the milling time range is .

[0101] In the raw material production stage, the new aggregate production characteristics are quantified as follows: Assume that the geological condition coefficient of the mining site is (Hard rock mines have a large coefficient), the mining method coefficient is (Underground mining has a larger coefficient than open-pit mining), and the mining scale coefficient is (large scale means large coefficient), the energy consumption coefficient of crushing process is , the energy consumption coefficient of the screening and cleaning process is , the transport distance coefficient is , the transport frequency coefficient is , then the quantitative value of the new aggregate production characteristic , , , , , , , is the weight of each factor.

[0102] Quantification of new asphalt production characteristics: Assume that the quality coefficient of the crude oil production area is (The light crude oil origin coefficient is small), the refining process coefficient is (The coefficient of the straight distillation method is small, and the coefficient of the solvent deasphalting method is large), the influence coefficient of the refining process parameters is (High temperature and high pressure parameter coefficient is large), then the quantitative value of the new asphalt production characteristic , , , is the weight of each factor.

[0103] Quantification of regeneration agent production characteristics: Assume that the difficulty coefficient of obtaining the main components is , the influence coefficient of component ratio is , the complexity coefficient of the synthesis process is , then the quantitative value of the regeneration agent production characteristic , , , is the weight of each factor.

[0104] Quantification of transportation characteristics at the raw material transportation stage: Assume that the transportation vehicle type coefficient is (heavy trucks have a large coefficient), and the route length coefficient is , the road grade coefficient is (The coefficient of expressway is small, and the coefficient of low-grade highway is large), the topography coefficient is (the coefficient in mountainous areas is large and that in plains is small), then the quantitative value of the transport characteristic , , , , is the weight of each factor.

[0105] Determination of traffic delay characteristics: Assume that the basic delay coefficient based on transportation characteristics is , the traffic flow impact coefficient is , the meteorological influence coefficient is , the road construction impact coefficient is , then the traffic delay characteristics .

[0106] In the factory mixing heat stage, the factory mixing heat characteristics are quantified as follows: Assume that the mixing equipment model coefficient is (large capacity, high power equipment coefficient is large), heating device type coefficient is (The fuel heating coefficient is large, the electric heating coefficient is small), the power coefficient of the heating device is , the RAP ratio is , the stirring temperature coefficient is (The coefficient of deviation from the reasonable temperature range is large), and the stirring time coefficient is , the production batch yield coefficient is , the production batch frequency coefficient is , then the quantitative value of the heat characteristic of the plant mix is , , , , , , , , is the weight of each factor.

[0107] In the pavement construction stage, the target pavement structure characteristics are quantified as follows: Assume the number of pavement layers is , the material complexity coefficient of each layer is , the thickness coefficient of each layer is The compaction coefficient is , then the target pavement structure characteristic quantitative value , , , , is the weight of each factor.

[0108] Determination of construction duration: Assume that the daily construction volume of the project is The target road construction area is , the margin coefficient considering the margin setting information is , the efficiency coefficient of the operating condition prediction information is (under severe conditions, the coefficient is less than 1), then the construction time range is .

[0109] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0110] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for calculating carbon emissions based on hot regeneration of asphalt mixture in a plant, characterized in that: include: Obtain planning information for each stage of the life cycle of pavement construction based on plant-mix hot recycling; According to the planning information of each stage, the carbon emission characteristic values ​​of each stage are extracted; The carbon emission amount is predicted based on the carbon emission characteristic value and a pre-established prediction model.

2. The carbon emission calculation method based on hot regeneration of asphalt mixture plant according to claim 1 is characterized in that: The life cycle of pavement construction based on hot-mixed regeneration includes the original pavement milling stage, raw material production stage, raw material transportation stage, hot-mixed stage and pavement construction stage. Based on the planning information of each stage, the carbon emission characteristic values ​​of each stage are extracted, including: According to the planning information of the original road surface milling stage, the original road surface structural characteristics and milling time range are extracted; Extract production features based on planning information at the raw material production stage; Extract transportation characteristics and traffic delay characteristics based on the planning information of the raw material transportation stage; Extract the plant-mixing characteristics based on the planned information of the plant-mixing stage; According to the planning information of the pavement construction stage, the target pavement structure characteristics and construction time range are extracted.

3. The carbon emission calculation method based on hot regeneration of asphalt mixture plant according to claim 2 is characterized in that: Predicting carbon emissions based on the carbon emission characteristic value and a pre-established prediction model, including: The original pavement structure characteristics, the milling time range, the production characteristics, the transportation characteristics, the traffic delay characteristics, the factory-mixed heat characteristics, the target pavement structure characteristics and the construction time range are input into the prediction model to predict the carbon emissions.

4. The carbon emission calculation method based on hot regeneration of asphalt mixture plant according to claim 2 is characterized in that: The prediction model includes a preliminary prediction layer and a parameter adjustment layer; predicting carbon emissions according to the carbon emission characteristic value and the pre-established prediction model, including: Inputting the original pavement structure characteristics, the production characteristics, the transportation characteristics, the factory-mixed heat characteristics and the target pavement structure characteristics into the prediction model to predict the initial emission; The initial emission, the milling time range, the traffic delay characteristics and the construction time range are input into the parameter adjustment layer to obtain the carbon emission.

5. The carbon emission calculation method based on hot regeneration of asphalt mixture plant according to claim 4 is characterized in that: The method further comprises: Obtain actual construction information of pavement construction based on plant-mix hot recycling at all stages of its life cycle; Extracting actual milling time, actual traffic delay and actual construction time based on the actual construction information; The carbon emissions, the actual milling time, the actual traffic delay and the actual construction time are input into the parameter adjustment layer to obtain the actual carbon emissions.

6. The carbon emission calculation method based on hot regeneration of asphalt mixture plant according to claim 5 is characterized in that: According to the planned information of the original road milling stage, the original road structure characteristics and milling time range are extracted, including: Extract the pavement type, pavement thickness, pavement composition and pavement disease information from the original pavement milling stage planning information to form the original pavement structure characteristics; Extract engineering planning information, margin setting information and operation condition prediction information from the original road surface milling stage planning information; The milling time range is determined according to the original pavement structure characteristics, the engineering planning information, the margin setting information and the operation condition prediction information.

7. The carbon emission calculation method based on hot regeneration of asphalt mixture plant according to claim 6 is characterized in that: According to the planning information of the raw material transportation stage, the transportation characteristics and traffic delay characteristics are extracted, including: Extract the transport vehicle type and route characteristics from the planning information of the original road surface milling stage to form transport characteristics; Traffic delay characteristics are determined based on the transportation characteristics and traffic forecast information.

8. The carbon emission calculation method based on hot regeneration of asphalt mixture plant according to claim 7 is characterized in that: According to the planned information of the pavement construction stage, the target pavement structure characteristics and construction time range are extracted, including: The number of pavement layers, materials of each layer, thickness of each layer, and compaction degree are extracted from the planned information of the original pavement milling stage to form the target pavement structure characteristics; The construction duration range is determined based on the target pavement structure characteristics, the engineering planning information, the margin setting information and the operating condition prediction information.

9. 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, the steps of the carbon emission calculation method based on hot regeneration of asphalt mixture plant as described in any one of claims 1 to 8 are implemented.

10. 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, the steps of the carbon emission calculation method based on hot regeneration of asphalt mixture plant as described in any one of claims 1 to 8 are implemented.

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