743 results about "Low-carbon emission" patented technology

The invention discloses a power grid dispatching method and system adapting to power system requirements, and relates to the field of industrial big data, and the method comprises the following operation steps: S1, multi-source heterogeneous data collection and edge preprocessing; s2, knowledge graph construction and data fusion; s3, load prediction and renewable energy output prediction based on deep learning; s4, generating a dynamic optimization scheduling strategy; s5, carrying out security and credible execution on the data endowed by the block chain; and S6, real-time monitoring and closed-loop feedback optimization are carried out. According to the power grid scheduling method and system adapting to the power system demand, the scheduling method integrates edge calculation, block chain, deep learning and reinforcement learning, can realize multi-source data real-time processing, dynamic optimization strategy generation and data security and credibility, improves the power grid operation efficiency, stability and renewable energy consumption capability, and improves the power grid scheduling efficiency. And the dynamically optimized scheduling strategy can reduce the operation cost of the power grid, and can reduce carbon emission at the same time.

The invention discloses an energy efficiency management system for low-carbon and energy-saving operation of building electromechanical equipment, and belongs to the technical field of building energy saving and intelligent control. The system comprises a data acquisition module, an energy efficiency analysis module, an optimization decision module and an execution control module; the data acquisition module acquires information such as equipment operation parameters in real time through the heterogeneous sensor network and the LoRaWAN edge gateway; the energy efficiency analysis module performs multi-dimensional evaluation on the basis of a dynamic energy efficiency reference algorithm by fusing an equipment energy efficiency ratio, carbon emission per unit area and the like; the optimization decision module adopts a mixed integer nonlinear programming algorithm, simulates working conditions through a mixed digital twin model, and generates a control strategy for collaborative optimization of energy consumption, carbon emission and equipment life; the execution control module dynamically adjusts equipment parameters by using a fuzzy PID composite controller; the method breaks through the limitation of static evaluation and single-target optimization of a traditional system, the comprehensive energy efficiency can be improved by 15%-30%, carbon emission is reduced by 20% or above, the service life of equipment is prolonged, and the credibility requirement of carbon checking is met.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention discloses a cementing material for a solid waste-based road of a coal-fired power plant and a preparation method of the cementing material. The cementing material mainly comprises a cementing component, an alkali-activated component and a reinforcing component, the gelling component is a mixture of coal-fired power plant fly ash and furnace slag; the alkali-activated component is a mixture of alkali residues, red mud, blast furnace slag and coal-fired power plant desulfurized gypsum; the reinforcing component is rice hull ash or peanut hull ash. The mass ratio of the gelling component to the alkali-activated component is (4 / 1)-(10 / 1); and the addition amount of the reinforcing component is 1-3% of that of the main body material. The coal-fired power plant solid waste fly ash, the furnace slag and the desulfurized gypsum are used as main bodies, a small amount of other industrial solid waste is added, and the cementing material belongs to a full-solid-waste low-carbon cementing material and can be applied to a road base, a subbase or a base layer. The invention has the characteristics of low carbon emission, high solid waste consumption, simple production process and strong pavement performance, and in addition, the invention has a good stabilization effect on the problem that part of solid waste heavy metals and sulfate ions in a coal-fired power plant exceed the standard.

The invention discloses a high-content steel slag-based functionalized nano-composite cementing material and a preparation method thereof, and belongs to the technical field of solid waste resource comprehensive utilization. The nano composite cementing material is prepared from the following raw materials in parts by mass: 50 to 65 parts of steel slag, 10 to 20 parts of fly ash, 10 to 25 parts of carbide slag, 8 to 15 parts of desulfurized gypsum, 0.3 to 1.0 part of nano silicon dioxide and 0.003 to 0.03 part of silane coupling agent. On the basis of a solid waste synergism theory and an internal alkali excitation effect, quaternary solid wastes such as steel slag and fly ash are optimized and compounded by adopting methods of mechanical-thermal activation, addition of modified nano silicon dioxide, synergistic gelling between solid wastes and the like; the paint is prepared through gradient grinding and a medium-temperature wet curing process. The preparation method is simple, high-temperature calcination is not needed, cement clinker and exogenous excitant do not need to be added, the obtained cementing material can replace part of cement to be applied to the fields of road bases, mine filling, low-carbon building material products and the like, resource utilization and high-value utilization of regional bulk industrial solid waste can be achieved, and carbon emission and production cost are effectively reduced.

The invention discloses a method for low-carbon preparation of a high-reactivity blast furnace burden from magnetite concentrate and the blast furnace burden, and belongs to the technical field of ferrous metallurgy. According to the method, an iron-containing raw material, an organic-inorganic composite binder, a multifunctional additive and water are sequentially subjected to uniform mixing, high-pressure double-roller forming and consolidation, and the finished product cold pressing block is obtained. According to the method, the strength and reducibility of the cold pressing block can be remarkably improved by optimizing the particle size composition of the raw materials and adding the multifunctional additive, the technological process is simple, a high-temperature process is not needed, and carbon emission is low.

The invention belongs to the technical field of novel power system photovoltaic and energy storage system optimization configuration, and discloses an optical storage system optimization method based on carbon emission and line loss rate collaborative modeling, which comprises the following steps: constructing a double-layer planning structure of an upper layer planning model and a lower layer operation model, a carbon emission calculation model and an improved line loss rate calculation model are introduced into the lower layer, joint optimization of configuration and operation is realized through parameter coupling, and the charge and discharge efficiency loss power consumption of the energy storage device is introduced as an independent parameter in line loss rate calculation, so that the line loss calculation precision is improved; and solving by adopting an improved particle swarm optimization algorithm, and objectively sorting candidate schemes in combination with an information entropy method and a TOPSIS comprehensive evaluation method. A simulation result based on an IEEE33 node power distribution system shows that the model can effectively reduce carbon emission and line loss rate, improves node voltage level, and has good convergence and engineering applicability.

The invention provides an industrial manufacturing industry-oriented carbon asset full life cycle management method and system, and the method comprises the steps: carrying out the cross-link feature fusion processing through obtaining an original carbon activity data set of an industrial manufacturing whole process, and generating carbon footprint feature data; and calling a pre-trained deep learning model to perform carbon asset state evolution analysis on the carbon footprint feature data, predicting a carbon asset evolution path, generating a carbon asset optimization strategy set based on a prediction result, including equipment energy efficiency regulation and control and a production plan recombination strategy, and pushing the carbon asset optimization strategy set to an industrial manufacturing execution system. And triggering an automatic control instruction reconstruction operation to realize automation and intelligence of carbon asset management, thereby comprehensively and accurately mastering a carbon asset state and an evolution rule, formulating a scientific and effective optimization strategy, reducing carbon emission, improving energy utilization efficiency, and improving green production capacity of an enterprise.

The invention discloses a low-carbon path real-time planning method and system based on multi-source data fusion and reinforcement learning. According to the invention, through a dynamic weight self-adjustment mechanism, the real-time adaptation capability of low-carbon path planning is significantly improved. Meanwhile, scenes such as traffic flow abrupt change, carbon price abnormity and energy station high load are recognized in real time through high-frequency scanning of multi-source fusion data, weights are dynamically adjusted through a formula, and planning can quickly respond to emergencies. For example, energy weight is reduced during charging pile queuing, emission constraint is enhanced when carbon price rises, planning lag caused by weight fixing in a traditional method is avoided, path selection better fits current environment changes, the problems of congestion and energy supplement shortage can be solved more timely in actual use, emission, time and cost are more accurately balanced under low-carbon constraint, and the method is suitable for popularization and application. In practical application, carbon emission can be effectively reduced, and user time and energy expenditure are saved.

The invention relates to a power distribution network partition and transformer substation power-carbon collaborative planning method considering multi-terminal uncertainty. The method comprises the following steps: extracting a representative wind-light output curve and a load curve; constructing an electrical coupling degree index, a load carbon flux index and a load balance degree index, calculating a comprehensive division index, and performing dynamic partitioning; and planning the transformer substation capacity which meets the main transformer N-1 verification, considers the influence of the load coincidence rate and the load prediction coefficient, and can meet the maximum load demand in the network element. The node carbon flux clustering index is combined with the traditional electrical coupling degree and load balance in the partition index, the limitation that traditional partition only depends on the electrical topological relation is broken through, the real-time carbon emission intensity of the load nodes is accurately quantified, and balanced partition of the power distribution network elements is achieved; carbon loss can be obviously reduced, and joint optimization of low carbon emission and high safety in the planning process of the transformer substation is realized; a typical scene expected value curve is used as input, and the influence of uncertainty on power distribution network planning is remarkably reduced.

The invention relates to a digital intelligent regulation and control preparation method and system for a high-solid-waste low-carbon high-performance grouting material, and solves the problems that a traditional preparation technology lacks an autonomous and controllable digital intelligent regulation and control system and is weak in adaptability to fluctuation of raw materials, and the method comprises the following steps: obtaining XRF chemical components and XRD mineral composition data of industrial solid waste raw materials; constructing a material gene database; based on the database, screening a proportioning scheme by using a performance prediction model, and predicting workability, strength development and shrinkage performance; inputting a prediction result as a fitness function into a multi-objective optimization algorithm, and outputting an optimal material gene combination; a batching scheme is generated based on the optimal combination, and a stirring process is started after technological parameters are preset; collecting data through a real-time monitoring system, and comparing the data with the digital twin model; and based on a comparison result, automatically adjusting material proportioning parameters. The high-solid-waste, low-carbon and high-performance grouting material has the advantages that accurate design and regulation of the high-solid-waste, low-carbon and high-performance grouting material are achieved, material performance is improved, and carbon emission and cost are reduced.

The common rail oil injector with the static leakage structure comprises an oil injector body, a control valve seat, a control valve matching part, a needle valve assembly and a needle valve body, and is characterized in that a middle hole pressure storage cavity is formed in the oil injector body and used for containing the control valve matching part and the needle valve assembly; the control valve matching part serves as the only static leakage matching part and is composed of a control valve sleeve and a control valve rod in clearance fit with the control valve sleeve, a static oil return channel comprising a first inclined oil return hole, a first vertical oil return hole and an annular groove is constructed in the control valve sleeve, a second oil return through hole in the high-pressure sealing face of the control valve seat is matched, and a specific fuel leakage backflow path is formed. Compared with a traditional 2000 bar common rail oil injector, static leakage matching parts are reduced, the fuel oil leakage amount is greatly reduced, oil consumption is effectively reduced, carbon emission is reduced, meanwhile, the reliability of the oil injector is improved, and the service life of the oil injector is prolonged. The middle hole pressure storage cavity can reduce oil injection pressure fluctuation, and the average effective injection pressure and the multi-time oil injection precision are improved. The oil injector body and the needle valve body are simplified in structure, and machining difficulty and manufacturing cost are reduced.

The present application discloses a method, device, electronic device and storage medium for tracing the flow of equivalent carbon emissions from electricity consumption, wherein the method includes: obtaining real-time flow data of the power grid, establishing a power grid flow matrix, and calculating the flow process of equivalent carbon emissions of the power system in the power grid based on the node average distribution rule, and finally establishing a user-power plant carbon tracking matrix and a line-power plant carbon tracking matrix. These matrices can directly characterize which power plants in the power grid the indirect carbon emissions of the user's electricity consumption come from and the proportion of each power plant. By using this method to measure the carbon emissions of user electricity consumption, the relationship between the user's carbon emissions and the carbon emissions of a specific power plant can be traced in real time, thereby improving the carbon measurement efficiency of the power grid, providing decision-making reference and support for power grid operators, and providing a reference basis for users to reduce carbon emissions. In this way, problems such as accurate measurement of equivalent carbon emissions of the power system are solved.

The invention discloses a comprehensive control system and method for carbon emission of a methanol dual-fuel power ship. The method comprises the following steps: analyzing carbon emission characteristics at different moments by collecting and acquiring sensor data in real time; solving based on the constructed carbon emission optimization model in combination with a particle swarm algorithm to obtain the optimal ship navigational speed, adsorbent circulation rate and methanol fuel injection pressure corresponding to the minimum carbon emission of the methanol dual-fuel power ship; cO2 in tail gas is captured in real time through the ship carbon capture unit, the circulation rate of an adsorbent is adjusted in time according to optimization information of the ship navigation optimization control unit, the circulation efficiency of the adsorbent is calculated according to the carbon emission amount, the adsorbent is updated or replaced in time, and the comprehensive control process of the ship carbon emission is comprehensively evaluated. And comprehensive control of ship carbon emission is realized according to the comprehensive evaluation result. The problem of poor carbon emission control effect caused by insufficient cooperative control of a methanol dual-fuel power ship carbon capture device and a power system is solved.

The invention discloses a park zero carbon detection management planning method, which belongs to the technical field of carbon emission management and comprises the steps of park data implementation and acquisition, park total carbon emission analysis, carbon reduction measure formulation and implementation, carbon reduction measure adjustment and equipment maintenance, park carbon reduction measure evaluation, park carbon reduction efficiency scoring and the like. The real-time data acquisition unit covers key information of carbon emission, carbon reduction equipment use cost, renewable energy quantity and abnormal carbon emission condition processing duration, direct and indirect carbon emission source emission data are acquired, the carbon emission intensity of each source is calculated, and accurate total carbon emission analysis is realized; a constraint and scoring mechanism for the implementation effect of the carbon reduction measures is provided, whether the carbon reduction measures meet requirements can be accurately judged according to preset parameters, the optimal carbon reduction strategy is selected, and the efficiency and effect of carbon reduction measure selection are remarkably improved; by continuously optimizing and adjusting carbon reduction measures and regularly maintaining related equipment, the overall energy efficiency of the park is improved, and carbon emission is reduced.

The invention relates to the technical field of energy system optimization and low-carbon economy crossing, in particular to a low-carbon agricultural and pastoral park energy system operation evaluation method considering electricity-carbon transaction, which comprises the following steps: S1, constructing an agricultural and pastoral park energy system operation model based on the energy supply and demand characteristics and morphological structure of an agricultural and pastoral park; s2, calculating the carbon emission intensity of the main network based on a carbon emission flow theory, and establishing an actual carbon emission responsibility model of the agricultural and pastoral park; s3, based on the carbon emission responsibility model of the agricultural and pastoral park energy system and the operation model of the agricultural and pastoral park energy system, in combination with the influence of renewable energy power generation on the total carbon emission amount, establishing an agricultural and pastoral park energy system economic operation evaluation model considering the electricity-carbon transaction; and S4, solving the economic operation evaluation model of the agricultural and pastoral park energy system, obtaining an optimal operation strategy of the agricultural and pastoral park comprehensive energy system, and realizing operation of the agricultural and pastoral park comprehensive energy system. The total amount of carbon emission is effectively reduced, and the economic cost caused by carbon emission is reduced.

The invention discloses a load response type coal-fired boiler multi-coal-type-ammonia collaborative blending optimization method and system, and belongs to the technical field of coal-fired boiler combustion optimization and low-carbon power generation. The method comprises the following steps: establishing a coal quality database and a compatibility matrix, and generating an initial blending scheme by adopting a multi-objective optimization algorithm; combining real-time load rate data of the unit, and dynamically adjusting the blending proportion through a three-section type load interval division model; and calculating the ammonia doping amount based on the calorific value compensation model to realize calorific value notch compensation, and finally generating an ammonia-coal collaborative blending combustion scheme and forming a cost evaluation report. The system is correspondingly provided with a data acquisition module, a scheme generation and optimization module, an ammonia blending module and a cost evaluation module. According to the method, the technical aims of improving the utilization rate of inferior coal and reducing carbon emission under the variable-load working condition of the coal-fired unit are achieved.

The invention relates to a building engineering green low-carbon construction evaluation and decision support system, in particular to the field of building engineering, and aims to construct a space-time atlas to comprehensively characterize a construction process by integrating an Internet of Things sensor, a building information model and meteorological data, and accurately estimate a future carbon emission trend by using a dynamic prediction mechanism. And key influence factors and contribution degrees thereof are identified through factor analysis, and finally real-time optimization suggestions are provided by virtue of a visual interface, so that scientific decision support for green low-carbon construction is realized, carbon emission is effectively reduced, the resource utilization efficiency is improved, and the environmental sustainability of constructional engineering is promoted.

The invention belongs to the technical field of masonry cement, and particularly discloses all-solid-waste low-carbon-emission masonry cement and a preparation method thereof. Comprising the following steps: 1, finely grinding iron tailings, carbide slag, fly ash and silica fume, and proportioning according to a design to obtain a cement raw material; 2, agglomerating or pelletizing the cement raw material, and calcining according to a designed temperature to obtain a cement clinker; and 3, mixing the cement clinker with a certain proportion of gypsum, and grinding to the target fineness to obtain the masonry cement. The masonry cement is prepared from all solid wastes, meanwhile, a calcium source is mainly provided by the carbide slag with low carbon emission, compared with cement fired by calcium carbonate calcium sources, the carbon emission is greatly reduced, the goal of high-valued and green utilization of various solid wastes including iron tailings is achieved, and the method is suitable for industrial production. The compressive strength, the sulfur trioxide content and the screen residue of the prepared masonry cement meet the national standard requirements of masonry cement. In addition, the masonry cement is prepared from all solid waste raw materials, so that the preparation cost of the masonry cement is reduced.

The invention discloses a factory carbon emission monitoring method and system based on an electricity-carbon calculation model, relates to the technical field of carbon emission monitoring and energy management, and is used for solving the problem that the real-time performance and accuracy of carbon emission monitoring are reduced due to the fact that carbon emission results are mostly presented as coarse-grained indexes. An initial carbon flow distribution matrix is constructed by collecting carbon concentration and electric energy consumption data of distributed energy access points in a plant, a carbon flow model is generated based on a digital twinborn body, a carbon flow weight is dynamically corrected by combining a Bayesian inference algorithm, the total carbon emission amount of the plant is calculated, and a monitoring result is output. According to the method, the spatial precision and the real-time correction capability of carbon emission tracking can be improved, early warning of the carbon emission trend of the key node is realized, and data support and decision basis are provided for a low-carbon operation strategy of a factory.

The invention provides a water plant energy conservation and carbon reduction optimization method and system based on full-process carbon emission accounting, and belongs to the technical field of energy conservation and emission reduction. The method comprises the following steps: constructing a mountain city water plant whole-process carbon emission accounting model; obtaining energy consumption data and carbon emission factors of each link based on a mountain city water plant whole-process carbon emission accounting model; according to the energy consumption data and the carbon emission factor of each link, calculating the carbon emission of each link and the contribution degree of the carbon emission to the total carbon emission of the water plant; and on the basis of the carbon emission and contribution degree of each link, evaluating water plant whole-process energy-saving and carbon-reducing potential data, and determining an energy-saving and carbon-reducing optimization strategy according to the water plant whole-process energy-saving and carbon-reducing potential data. Therefore, by adopting a water plant energy-saving and carbon-reducing optimization scheme based on full-process carbon emission accounting, the carbon emission of each link of the water plant can be accurately accounting, and a targeted energy-saving and carbon-reducing optimization strategy is proposed in combination with special geographical conditions of a mountain city, so that high-efficiency energy conservation and low-carbon emission of water plant operation are realized.

The invention belongs to the technical field of ironmaking and steelmaking, and particularly discloses a direct reduction iron production process and system of variable feed gas. The production process comprises the following steps: under a basic working condition, a hydrogen-rich working condition or an all-hydrogen working condition, introducing high-temperature reducing gas into a shaft furnace to produce the direct reduced iron; the production system comprises a shaft furnace, a self-heating modification device, an upgrading device, a heating furnace and a preheating furnace, and is provided with three hydrogen supplementing points and two hydrogenation points. By changing the structure of the raw material gas, supplementing hydrogen at three positions or supplementing or adding hydrogen at at least one of two hydrogenation points, increasing the H2 content of the reducing gas at the inlet of the shaft furnace and the like, the carbon emission, the process energy consumption and the pellet reduction difficulty are reduced.

The invention provides an intelligent control algorithm for dust removal of a steel ore tank and an operation effect monitoring method thereof, and aims to improve the energy efficiency and emission control capability of a dust removal system of the steel ore tank. The system comprises a data acquisition module, a hierarchical control module and a multi-objective optimization module. The data acquisition module is used for acquiring dust concentration, fan rotating speed, energy consumption and equipment state signals in real time; the hierarchical control module adopts the combination of model predictive control (MPC) and fuzzy PID control to accurately adjust the rotating speed of a fan, the ash removal period and the opening degree of an air door; and the multi-objective optimization module dynamically adjusts the dust-energy consumption weight ratio according to the electricity price time period, and optimizes the balance between energy consumption and emission. The invention further comprises an operation effect monitoring method, the operation effect of the system is monitored in real time, the energy-saving effect is evaluated and optimization suggestions are provided by means of data comparison and analysis, comprehensive evaluation of the energy-saving rate and the performance, visualization of a monitoring report and the like. And green transformation and low-carbon emission targets of a dust removal system of the steel and iron ore tank can be realized.

The invention provides a water resource optimization scheduling method and system, computer equipment and a storage medium, and belongs to the field of water resource optimization scheduling, and the method comprises the steps: obtaining the local water supply amount, the department water demand amount and the water demand interval of each water receiving area; under the common constraint of the three parameters, the calculated economic output corresponding to the water resource input, the ecological service value of the water area, the water satisfaction quantitative result and the carbon emission in the water transfer process are used as optimization indexes, and the monthly scale is used as a time unit; according to an optimization result, a monthly water resource scheduling scheme is generated through iteration in sequence, and an annual water resource scheduling scheme is formed through integration; and determining a scheduling scheme with the optimal comprehensive benefit from the annual water resource scheduling schemes as a final water resource scheduling scheme. According to the method, the ecological benefit and social benefit of water transfer in the water transfer system can be coordinated, the ecological service value of a water area is remarkably improved and the carbon emission is reduced while the water demand of a water receiving area is ensured, and the comprehensive optimization of water resource scheduling is realized.

The invention relates to the technical field of building power system energy saving optimization, and discloses a central air conditioner green power consumption optimization control system based on carbon footprint tracing. The system comprises a carbon footprint traceability data acquisition unit, a green power consumption priority evaluation unit, an energy scheduling optimization unit, a real-time load matching unit and a carbon footprint verification unit. The carbon footprint traceability data acquisition unit acquires related data; the green power consumption priority evaluation unit generates a consumption strategy sequence based on a dynamic game theory model; the energy scheduling optimization unit adopts a specific algorithm to construct an optimization model; the real-time load matching unit adjusts a matching relationship by using deep reinforcement learning; the carbon footprint verification unit verifies the carbon footprint based on a zero-knowledge proof algorithm. According to the method, optimal control over green power consumption of the central air conditioner is achieved, the energy utilization efficiency is improved, carbon emission is reduced, and carbon footprints are accurately managed.

The invention relates to the technical field of power distribution network dispatching, in particular to a power distribution network dispatching optimization method and device. Constructing a dynamic digital twin of the power distribution network to be dispatched, and reversely correcting a line impedance parameter and an equipment efficiency coefficient in combination with a twin simulation result; multiple scheduling strategies are rehearsed in parallel, and the optimal scheduling strategy is selected through weighted scoring; generating a multi-energy flow collaborative scheduling instruction set according to the optimal scheduling strategy; dynamically distributing a control task by utilizing an edge node; and performing differentiated load response in combination with the power consumption behavior classification of the user. Space-time synchronous acquisition of electricity-heat-gas data is realized by deploying a multi-energy-flow sensor group, a multi-energy-flow collaborative scheduling instruction is generated by combining reverse parameter correction and scene rehearsal optimization of a dynamic digital twin, and dynamic resource allocation is realized based on an edge node dynamic allocation control task and load classification response. The new energy consumption rate capability is obviously improved, and the carbon emission intensity is reduced.

The invention relates to a carbon emission regulation and control method and a low-carbon concrete product production system adopting the method, and solves the problems that in the whole concrete production system, parameters of all links are lack of effective regulation and control means, the production process is difficult to optimize on the whole, and efficient carbon emission control is difficult to achieve. The method comprises the steps that a parameter combination obtained through genetic algorithm optimization is applied to actual production equipment, and the production process is regulated and controlled in real time; after equipment regulation and control, continuously collecting key parameter data of each link of a production system; according to the collected key parameter data, carbon emission equivalent is calculated according to a set carbon emission calculation formula, and the carbon emission equivalent comprises direct carbon emission and indirect carbon emission; and automatically generating a carbon emission accounting report according to a calculation result. The method has the following effects that intelligent regulation and control of carbon emission in the low-carbon concrete production process are achieved, and the total carbon emission amount is effectively reduced.

The invention belongs to the field of solid waste resource utilization, and particularly discloses a method for preparing flow-state solidified soil through cooperation of construction waste recycled aggregate and steel slag micro powder. The fluidized solidified soil is prepared from the following components in parts by weight: 40 to 60 parts of steel slag micro powder, 10 to 30 parts of recycled micro powder, 20 to 40 parts of recycled coarse aggregate, 15 to 30 parts of recycled fine aggregate, 10 to 20 parts of recycled fine aggregate, 25 to 40 parts of water and 0.5 to 1.5 parts of polycarboxylate superplasticizer. A steel slag micro powder and regenerated micro powder self-activated gelling system is adopted to replace a traditional gelling material and is matched with regenerated coarse, fine and fine aggregate three-stage grading to construct a close packing structure, and then staged CO2 mineralization treatment is performed, so that the solid waste mixing amount is increased, carbon emission is reduced, the drying shrinkage rate of the prepared flow-state solidified soil is greatly reduced, and the quality of the prepared flow-state solidified soil is improved. Meanwhile, the fluidity is regulated and controlled through the polycarboxylic acid water reducing agent, the constructability is guaranteed, and a low-carbon and high-efficiency path is provided for large-scale utilization of the construction waste and the steel slag.

The invention relates to a metro station equipment area decoration construction stage carbon emission calculation method, which comprises the following steps: S1, only performing carbon emission calculation on metro station equipment area decoration, dividing the equipment area decoration into three subsystems, namely a ceiling system, a wall surface system and a ground system, and each subsystem comprises a plurality of unit components; s2, establishing a comprehensive carbon emission factor of the equipment area decoration unit component based on the bill of quantity; s3, according to the engineering quantity of each unit component in the engineering quantity list and the corresponding comprehensive carbon emission factor, calculating the construction stage carbon emission of each unit component; and S4, accumulating the construction stage carbon emission of all unit components of the equipment area decoration to obtain the total carbon emission of the station equipment area decoration construction stage. According to the method, the carbon emission calculation difficulty is reduced by defining the calculation boundary of the carbon emission in the construction stage and creating the comprehensive carbon emission factors of equipment area decoration including production, transportation, construction and installation based on the bill of quantity.

The application discloses a kind of atmospheric environment ozone concentration prediction system based on chemical reaction mechanism, including obtaining weather forecast parameter, historical observation data cleaning analysis, analysis screening suitable model, model training and prediction, prediction result analysis, data visualization and so on functional module composition.This system is based on Lagrangian coordinate system, establishes the response relationship between meteorological parameter, pollutant concentration and ozone concentration, using thermodynamic equilibrium principle, chemical reaction equilibrium principle and photochemical reaction principle and other basic chemical reaction engineering theory, deduces the non-linear model of meteorological parameter, pollutant concentration and other parameters on ozone concentration influence, by the independent design of each software function module, with low energy consumption, low carbon emission, low operating cost, data automatic processing, easy to deploy, can be connected ecological cloud platform, system expansibility is strong, maintenance is easy, and computing efficiency is high.Therefore, it has broad market application prospect and good social and economic benefits.