265 results about "Carbon management" patented technology

The invention provides an AI-based carbon footprint and supply chain carbon emission collaborative management method and platform, and the method comprises the steps: collecting various types of carbon-related data from each link of a supply chain in real time, and carrying out the standardization processing to form a unified data flow. An intelligent sensing technology is combined with a dynamic reference model, and sudden carbon emission fluctuation generated in production operation is automatically recognized. The propagation influence and potential risk of these carbon emission fluctuations in the entire supply chain are analyzed. The risk propagation condition and the processing capacity of each node are comprehensively considered, and an optimal carbon emission distribution processing scheme is formulated through an optimization algorithm. A distributed account book technology and an automatic execution mechanism are utilized, and an excitation system is combined to ensure that each participant completes a corresponding carbon processing task according to an optimal scheme. According to the method, the intelligent level and the overall effect of supply chain carbon management can be effectively improved, and a technical support is provided for an enterprise to realize a carbon emission reduction target.

The invention provides a carbon emission digital management system based on a cloud platform. The carbon emission digital management system comprises a data acquisition layer, a hardware resource layer, a data management layer, a platform algorithm layer, an application layer and a display layer, according to the system, through multi-source data acquisition and data analysis based on an AI large model, artificial filling errors are avoided, meanwhile, carbon emission factors are dynamically adjusted through introduction of machine learning and automatic calculation, and the carbon emission accounting precision is comprehensively improved; secondly, the system is combined with a digital twinning technology to help enterprises to realize multi-level carbon emission trend analysis of groups, enterprises, processes, equipment and the like, comprehensive strategy optimization is carried out through multi-level data arrangement, and wider application services are provided; besides, the system is in butt joint with international standards such as the international carbon market and CBAM, can help enterprises to monitor carbon price fluctuation and predict in real time, intelligently adjust carbon tax and provide a compliant carbon transaction scheme, and helps the enterprises to reduce cost, thereby realizing comprehensive upgrading of carbon management from static statistics to intelligent prediction and optimization.

The invention discloses an energy-carbon integrated management method based on digital twinning, and relates to the technical field of energy-carbon management, and the method comprises the steps: obtaining clean energy data, thermoelectric equipment data, outsourcing energy data, planned load data and production process data; predicting by using a preset first carbon emission propagation model to obtain a supply carbon emission propagation network; obtaining a required carbon emission propagation network by using a preset second carbon emission propagation prediction model; respectively calculating carbon emission factors according to the clean energy data, the thermoelectric equipment data and the outsourcing energy data; generating a preliminary regulation and control instruction according to the carbon emission factor, the supply carbon emission propagation network and the demand carbon emission propagation network; performing simulation in combination with the equipment execution instruction and the load regulation and control instruction to obtain a simulation result, and determining a carbon deviation result; and adjusting the equipment execution instruction and the load regulation instruction according to the carbon deviation result and a preset carbon integral mechanism to obtain an energy-carbon coordinated regulation instruction. According to the invention, the accuracy of carbon management can be effectively improved.

The invention provides an intelligent management method and system for reducing pollution and carbon of rural domestic sewage. According to the method, sewage flow velocity, COD concentration, ammonia nitrogen content data, user water use time and detergent use amount are collected, and a database is constructed. And weighting the flow velocity and COD to generate a pollution load evaluation value, and calculating an organic matter pollution influence value in combination with ammonia nitrogen and detergent data. A porous ceramic medium is arranged in the biological treatment device, and the dissolved oxygen concentration is adjusted according to the pollution load. A correlation curve of a COD concentration peak value and water consumption time is established, a prediction model is constructed in combination with dissolved oxygen parameters, the gas injection and reflux ratio is adjusted through graded pressure instructions, and the critical value of dissolved oxygen is maintained. The day and night temperature difference is fused to correct the degradation rate, and the optical detection data is combined to optimize the critical range and the instruction priority, so that the cooperative control of sewage treatment and carbon emission reduction is realized. According to the invention, self-adaptive adjustment of dissolved oxygen in sewage treatment is realized, and the sewage reduction and carbon reduction management efficiency of domestic sewage is improved.

The invention relates to the technical field of intelligent carbon management, in particular to a building carbon emission monitoring system based on BIM (Building Information Modeling), which comprises a model construction module, a thermal inertia analysis module, a carbon list generation module, a carbon emission monitoring module and an energy consumption parameter optimization module. According to the method, the carbon emission parameter set is generated in combination with the IFC data structure through standardized analysis of the material type, the size and the engineering quantity of the component, so that the carbon emission factor has the targeted adjustment capability. An actual measurement environment temperature change sequence is introduced into thermal inertia calculation, response difference correction is carried out on the thermal capacity and the heat conduction behavior of the component, and the dynamic thermal performance of the material in different environments is accurately reflected. In stage carbon emission measurement and calculation, component thermal inertia response and functional time periods are fused for refined staging, and functional frequency and time occupancy ratio are superposed in carbon emission calculation, so that dual sensitive adjustment of carbon emission on time and component function dimensions is realized.

A main line traffic flow management and control optimization method based on a high-performance traffic simulation agent comprises the steps that a target area road network is selected, and a main line road section and intercommunication road section structure is constructed; a multi-scene sample set is generated through microscopic traffic simulation, and traffic operation states under different traffic requirements, speed limiting and hard shoulder open conditions are simulated; training a neural network model based on a deep learning method, and constructing an efficient traffic simulation agent by taking the target road section and upstream and downstream multilayer associated topological information as input features and taking travel time and average speed as labels; and in combination with a multi-objective optimization algorithm, taking total travel time and carbon emission as optimization objectives, and dynamically iterating to generate an optimal management and control scheme of road section speed limitation and hard road shoulder opening. According to the method, traditional low-efficiency microscopic simulation is replaced with the simulation agent, the calculation performance is remarkably improved, and collaborative optimization of the complex road network and scientific formulation of a low-carbon management and control strategy are achieved.

The invention relates to an organic carbon multi-source classification and dynamic list construction method for reservoir methane accounting, and belongs to the technical field of reservoir ecology and greenhouse gas accounting. Aiming at the technical problem that the dynamic change and degradation influence of the organic carbon source cannot be accurately quantified by a traditional method, the invention proposes that the organic carbon source is refined into four types of submerged soil, sediment sediment, algae autogenesis and in-river pollution, calculation models are respectively established, and a dynamic degradation list is constructed by combining a time attenuation factor and reservoir age-month weighted calculation. According to the method, dynamic quantification of the organic carbon input-degradation process is achieved, the CH4 emission prediction error is reduced by 30% or above, and high-precision data support is provided for reservoir carbon management and emission reduction.

The invention discloses a sewage treatment plant carbon management and control method and system based on artificial intelligence. The method comprises the steps that a carbon emission prediction model is constructed according to collected real-time monitoring data, historical data and a mechanism model; generating a parameter optimization trigger instruction based on the processing load data and the water condition change rate output by the carbon emission prediction model, and further generating a process unit operation parameter configuration scheme based on the water condition; obtaining a first carbon emission predicted value corresponding to the process unit operation parameter configuration scheme, and establishing a dynamic parameter regulation and control strategy library; and matching the real-time water condition fluctuation data with the dynamic parameter regulation and control strategy library and the mechanism model, determining a real-time parameter regulation and control strategy, and executing an equipment parameter adjustment instruction according to the real-time parameter regulation and control strategy. The problem that a current sewage treatment plant cannot predict the carbon emission conditions under different water conditions in real time is solved, and the effects of predicting the carbon emission conditions under different water conditions in real time and reducing energy loss are achieved.

The invention provides a method for determining the influence of a straw returning mode on tobacco-rice rotation soil carbon emission, and belongs to the technical field of soil carbon emission. The method comprises the steps that test plots are divided, and different straw returning treatment groups are arranged; installing a static gas tank measuring system and regularly collecting a gas sample; measuring the emission of CO2 and N2O and the concentration of CH4 by using a gas chromatographic method, and calculating the carbon emission flux of the soil; analyzing a straw decomposition process by applying a soil microorganism kinetic equation; analyzing the dynamic change of a soil carbon library based on a carbon recurrent neural network model; constructing a carbon flow network topological structure by using a minimum spanning tree algorithm and identifying key nodes; according to the method, comprehensive influences of environmental factors, crop growth parameters and microbial community structures on carbon emission are integrated through a multi-head cross attention mechanism, accurate quantification and prediction of straw returning carbon emission are achieved, and a scientific basis is provided for optimizing a farmland carbon management strategy and promoting conversion from a farmland carbon source to a carbon sink.

The invention belongs to the technical field of environmental protection control, and relates to a cost reduction method and device based on carbon asset full life cycle management, and the method comprises the steps: obtaining the carbon emission data converted from the cost input of each stage of an enterprise; the carbon emission data is analyzed; formulating a carbon emission list based on the analysis result of the carbon emission data, the carbon emission thermodynamic diagram and the carbon quota of the enterprise; predicting the carbon emission of the enterprise based on the carbon emission list; establishing an enterprise carbon management personal incentive mechanism based on the result of predicting the enterprise carbon emission; and preposed carbon sink design is carried out, so that the full-life-cycle income of carbon assets is improved. The preposed carbon sink design is emphasized, and carbon sink projects such as afforestation and energy conservation and emission reduction technology application are reasonably planned, so that the carbon emission of enterprises is effectively counteracted, the full-life-cycle income of carbon assets can be improved, and the win-win situation of economic benefits and environmental benefits is realized.

The invention provides a power grid dispatching operation optimization method considering carbon quota granularity difference in a multi-system environment, and belongs to the technical field of power grid operation. The problems of unmatched carbon quota time scales, unsmooth information transmission, insufficient equipment coordination and the like in a multi-system environment in the prior art are solved; the method comprises the following steps that a multi-time-scale optimization scheduling model used for a green certificate-carbon transaction power transmission and distribution project carbon management system is constructed, the multi-time-scale optimization scheduling model is an annual-monthly double-layer planning model, and an annual planning layer is used for realizing planning of a long-time-scale emission reduction strategy of a power transmission and distribution project acceptance enterprise; the monthly planning layer is used for realizing planning of annual to monthly time scale transaction strategies of the power transmission and distribution project acceptance enterprises; performing rolling correction on the two-stage decision of the power transmission and distribution project acceptance enterprise based on the multi-time scale optimization scheduling model; the method is used for power grid operation.

The invention relates to the technical field of environment monitoring, in particular to an urban carbon emission intelligent prediction method based on multi-modal data and machine learning integration, which comprises the following steps: feature acquisition and preprocessing: acquiring multi-source heterogeneous data and processing the multi-source heterogeneous data into a standardized data set; constructing an LSTM (Long Short Term Memory) and XGBoost hybrid prediction model; predicting future data on the basis of existing data, and predicting input features of future time steps of the weight integration prediction model on the basis of historical data in a standardized data set; and predicting carbon emission in the future. Industrial structure indexes, environment variables and social and economic substitution indexes are integrated through time characteristic engineering, and emission driving factors in different urban environments can be comprehensively captured; a recursive prediction mechanism is established, development trajectories of different cities are considered, reliable emission prediction can be carried out, meanwhile, time consistency is kept, and prediction uncertainty is quantified; interference influences under different policies are quantified, and a quantitative basis is provided for carbon management decisions.

The invention provides a carbon emission accounting method, system, equipment and medium in a port and aviation container transportation process, and belongs to the technical field of logistics carbon management and intelligent algorithm crossing. The method comprises the following steps: collecting transportation and energy consumption data of roads, waterways, railways and logistics storage links in a classified manner, and preprocessing the transportation and energy consumption data to form a standardized data set; a carbon emission factor parameter library is created in combination with industrial standards, different transportation modes and cargo types, and dynamic updating is performed through a machine learning algorithm. And special accounting sub-models are constructed for different transportation modes, and carbon emission accounting is carried out by using corresponding formulas and parameters. And inputting the standardized data into the nuclear operator model, calculating the carbon emission and screening an optimal path. And model parameters are calibrated by using historical data to ensure accuracy. And finally, collecting data in real time, inputting the data into the accounting sub-model, generating a decision support report comprising implementation steps, resource demands and expected effects, and assisting in realizing low-carbon and efficient management of the transportation process.

The invention relates to the technical field of power system carbon metering, in particular to a source-side power carbon space-time coupling power carbon emission tracing method and device and a medium, and the method comprises the steps: constructing a multi-dimensional reference model through the fusion of power grid topology, generator fuel characteristics and real-time output data; decoupling electric energy transmission and carbon propagation paths based on an admittance matrix, and calculating the spatial distribution of apportioned carbon emission through load flow; carbon emission factors are corrected in real time to capture transient characteristics, and limitation of static factors is broken through; fusing the power flow distribution and the dynamic factor, and calculating direct / indirect carbon emission contribution of a load node by adopting a power flow proportion recursive mechanism; real-time data are connected to generate a dynamic carbon flow distribution map and a traceability decision report, a key carbon conduction path and a responsibility subject are accurately identified, a verifiable decision basis is provided for low-carbon transformation, carbon transaction and scheduling optimization, and the timeliness and operability of carbon management are improved.

The invention discloses a zero-carbon park-oriented AI pollution reduction and carbon reduction evaluation method and system and a medium, and relates to the technical field of carbon reduction management. The method comprises the following steps: executing multi-source data acquisition of a park, and establishing a multi-source data set; establishing a fusion data set; predicting park carbon footprint and pollutant distribution through a multi-mode machine learning model, and establishing a prediction result; executing causal analysis and anti-factual reasoning of a prediction result by utilizing a pollution backtracking channel, and after identifying a key pollution source and a hidden collaborative emission reduction point, generating an intervention priority map according to a fusion data set in combination with causal relationship strength and a potential collaborative emission reduction effect; and constructing a multi-scale park digital twinborn model, and executing simulation evaluation of a pollution reduction and carbon reduction strategy. The technical problem that in the prior art, park pollution reduction and carbon reduction evaluation depends on static statistics and lacks dynamic data support, and consequently the reliability of the evaluation result is insufficient is solved, and the technical effect of improving the reliability of the evaluation result through multi-source data fusion and causal reasoning is achieved.

The invention discloses a new energy truck carbon emission monitoring, verification and correction method and system, belongs to the technical field of new energy automobile carbon management, and aims to solve the problems that the dimension of existing new energy truck carbon emission monitoring data is incomplete, the accounting deviation is large, charging carbon emission attribution is distorted, and full-process dynamic management and control are lacked. The method comprises the steps that in the loading stage, basic information of vehicle-mounted goods and new energy trucks is obtained, a loading initial vector and a parameter incidence matrix are generated, alternative paths are split into carrying sub-paths, a feature table is constructed, and an initial carbon emission reference value is generated in combination with historical data matching; in the carrying stage, parameters and actual carbon emission are corrected based on real-time data, carbon emission values of alternative paths are updated, and recommended paths are screened; when the charging pile is connected, the charging carbon emission is accurately calculated and is split and corrected according to the transportation progress; and finally identifying an abnormal path, generating a standardized report and updating a historical database. According to the invention, accurate monitoring and dynamic correction of the whole process are realized, and reliable data support is provided for low-carbon transportation management and control.

The invention discloses a building energy scheduling method and platform based on regional energy carbon management, and relates to the technical field of building energy scheduling. The method comprises the steps of multi-dimensional data acquisition, energy-carbon collaborative optimization, dynamic scheduling execution, block chain energy transaction and community-based energy collaboration. The platform comprises a multi-dimensional data acquisition module, an energy-carbon collaborative optimization module, a dynamic scheduling execution module, a block chain energy transaction module and a community collaborative module, and adopts a cloud edge collaborative architecture and a digital twin system. According to the method, double optimization of economy and carbon targets is realized through a cooperation mechanism of a genetic whale algorithm and an NSGA-II algorithm, an energy and carbon credit closed-loop system is constructed by using a block chain, idle energy storage resources are activated in combination with community cooperation, dynamic mapping and early warning are realized by means of digital twinning, and the method is high in practicability. The problems that in the prior art, economy and carbon targets are difficult to balance, energy transaction trust is lost, the user participation degree is low, and carbon management and control lack perspectiveness are solved.

The invention relates to the technical field of industrial enterprise carbon data management, and discloses an industrial enterprise supply chain carbon footprint economic accounting and management method for a dual-carbon target, and the method comprises the steps: selecting and obtaining a public high-frequency economic agent signal based on a technical premise that core factors for driving industrial product price and carbon emission intensity have homology, dynamically correcting the static reference emission factor through a relevance conduction model; according to the method, a public economic signal is utilized to avoid a traditional mode of relying on static lagging data, the technical problem that the real dynamic state of the carbon intensity of the supply chain cannot be sensed is solved, and the real dynamic state of the carbon intensity of the supply chain can be sensed. The almost real-time carbon risk situation sensing ability of an enterprise is improved, and passive bookkeeping after carbon management is engaged is converted into active intervention fused into daily business.

The invention relates to the technical field of environmental science, discloses a wetland carbon cycle model construction and optimization method and system, and aims to solve the problems of complex wetland carbon cycle process, insufficient model precision generalization, difficulty in coordination of multi-carbon component management decisions and the like. The method comprises the following steps: acquiring preprocessed multi-source wetland data, and constructing multi-scale spatial-temporal characteristic representation; synergistically simulating the dynamic state of the carbon components of the wetland through a mechanism fusion network; and based on a simulation result, a management strategy is generated through multi-objective optimization, scene prediction is performed, and a response is evaluated. The system comprises a data acquisition and preprocessing module, a multi-scale spatial-temporal feature coding module, a mechanism fusion network module, a multi-target optimization decision module and a scene prediction visualization module. According to the technical scheme, a high-precision and high-generalization model can be constructed, deep learning and a physical mechanism are fused, multi-target optimization decision and scene prediction capabilities are provided, multi-carbon components are effectively simulated and optimized, and the wetland carbon management precision and efficiency are improved.

The invention belongs to the technical field of carbon accounting methods, and particularly relates to a carbon accounting method based on a green supply chain carbon management platform, and the method comprises the following steps: S1, constructing a whole industry chain product carbon footprint accounting model; s2, upstream product carbon footprint calculation; s3, performing factorization on the upstream product carbon footprint value; and S4, calculating the carbon footprint of the industrial product in each industry. According to the overall structure provided by the embodiment of the invention, in order to solve the problems that the current data islanding phenomenon is serious, and carbon emission data in each stage is lack of effective integration, an innovative mechanism of'upstream carbon footprint value factorization 'is put forward for the first time: actually measured emission in each stage of a supply chain is converted into a standardized carbon footprint factor and the standardized carbon footprint factor is incorporated into a shared library; downstream enterprises can call in real time according to batches and processes, and complete full-life-cycle accounting automatically after being coupled with production data of the enterprises; the factor supports version tracing, third-party verification and dynamic updating, data islands are thoroughly broken, and the carbon footprint precision, transparency and supply chain cooperation efficiency are remarkably improved.

The invention discloses an industrial park enterprise carbon performance evaluation method, system, equipment and medium based on a multi-dimensional carbon index, and belongs to the technical field of industrial carbon management, and the method comprises the steps: collecting real-time power consumption data of an enterprise in a period and power carbon data of a corresponding time period, clearly defining an evaluation range, determining an evaluation object, and carrying out the evaluation of the enterprise carbon performance. The method comprises the following steps: calculating a core index to obtain a core index original value, carrying out standardization processing on the core index original value, endowing the standardized core index with a corresponding weight according to a preset standard, carrying out calculation through a primary algorithm to obtain an enterprise carbon performance comprehensive score, and dividing enterprise rating according to a comprehensive score interval. According to the method, three core indexes are constructed, and the problems of limitation of a single efficiency index, evaluation time lag and lack of power grid collaboration are solved; through standardization processing and weight distribution, quantitative evaluation of enterprise carbon emission efficiency, load regulation capability and clean energy consumption level is realized, and the defect that a traditional method only pays attention to total amount and static efficiency is overcome.

The invention provides an energy-carbon management system, a construction method of the energy-carbon management system, a calculation method of energy-carbon management data and a computer readable storage medium. The energy-carbon management system comprises a data entry port, at least one virtual node, a data processing module and a stream calculation engine. The data entry port can receive virtual data, and the virtual data cannot be directly obtained through measurement equipment. One virtual node corresponds to one piece of virtual data, and the corresponding virtual data can be obtained through the data entry port. And the data processing module communicates with the virtual nodes and can execute a preset calculation process to obtain secondary data. The stream calculation engine can obtain the secondary data from the data processing module, and can execute a preset stream calculation based on calculation data, and output energy carbon management data, the calculation data including the secondary data. According to the embodiment of the invention, the energy carbon management data can be quickly and accurately output in real time, and the manual labor is reduced.

The invention discloses an electric power and carbon management collaborative optimization method and system for a multi-industry symbiotic zero-carbon park, and the method comprises the steps: constructing a mixed integer programming model which comprises an electric power dispatching sub-model, a carbon collection sub-model and a source-sink matching sub-model, and defining a coupling relation between an electric power dispatching variable and a carbon management variable; designing a multi-objective optimization function, and bringing coal consumption cost, start-stop cost, carbon transaction cost, wind curtailment penalty cost, deep peak regulation cost, peak regulation compensation benefit and transportation cost into a unified optimization framework; and setting time-space coordination constraint conditions, including power balance constraint, output upper and lower limit constraint, climbing rate constraint, capture-transport volume balance constraint and carbon sink capacity limitation constraint, to realize coordination optimization of power production and carbon logistics in time and space dimensions. According to the method, the total cost of the system is effectively reduced, the utilization efficiency of carbon sink resources is improved, real-time controllability of carbon emission intensity is ensured, and the problem of global optimum deficiency caused by a traditional two-stage optimization method is solved.

The invention discloses an urban rail station full life cycle carbon accounting method, and relates to the technical field of urban rail transit engineering low carbon. The method comprises the following steps: constructing and regularly updating a carbon emission factor library, collecting and storing full-life-cycle activity level data and time information, determining an accounting dimension according to a scene, screening an accounting cardinal number of unit time from the data, accounting carbon emission increment of each dimension of the unit time in combination with the factor library, and finally accumulating to obtain the total amount of full-life-cycle carbon emission. According to the method, complete-cycle carbon emission accurate accounting of the urban rail station is realized, data support is provided for low-carbon decision, and the efficiency and scientificity of low-carbon management of the urban rail station are improved.

The invention discloses a building carbon emission credible authentication method and system fusing the Internet of Things and an alliance chain, and belongs to the technical field of block chains, the Internet of Things and carbon emission management, and the method comprises the following steps: S1, introducing a multi-channel mechanism, and constructing an alliance chain network; s2, building energy consumption original data and basic building information are collected to form uplink data, and signature authentication is carried out; s3, each energy supply enterprise submits the data subjected to signature authentication to an alliance chain network through an on-chain API interface for integrity verification, and after verification is passed, the data subjected to signature authentication form a solidified storage evidence through a multi-node consensus mechanism; s4, on the basis of an intelligent contract authentication rule, performing authentication on the solidified and stored uplink building data; and S5, the building carbon emission intensity of the energy supply enterprise is calculated, and a calculation result and corresponding original data are bound and written into the alliance chain. According to the invention, the whole-process credible acquisition, authentication and supervision of the building carbon emission data are realized, and the carbon management efficiency and the data transparency are improved.

The invention discloses a cement enterprise carbon management system and method, and particularly relates to the technical field of cement carbon management, and the system comprises an intelligent carbon closed-loop management module which is used for achieving the whole-process monitoring, management and closed-loop control of enterprise carbon emission through the advanced data tracking and optimization technology. According to the invention, end-to-end carbon footprint tracing is carried out by using the block chain and the Internet of Things equipment, production parameters are dynamically adjusted through real-time data acquisition and an AI algorithm, dynamic balance of carbon emission is maintained, meanwhile, a digital twinborn model and multi-dimensional carbon accounting analysis help an enterprise to accurately make a carbon treatment decision, and the carbon treatment efficiency is improved. The intelligent carbon compensation module ensures the realization of a zero carbon emission target, the leading edge low-carbon technology integration module promotes green transformation of a production process through carbon capture conversion, green material research and development and renewable energy source integration, and the intelligent operation optimization module promotes the green transformation of the production process through optimization of production scheduling, predictive maintenance and supply chain carbon management. And the energy consumption and the carbon emission are obviously reduced.

The invention discloses a carbon emission dynamic accounting method and system. The method comprises the following steps: collecting production equipment direct emission, logistics carbon emission, social media / satellite remote sensing and other abnormal event data; extracting unit yield carbon intensity and supply chain path sensitivity through principal component analysis; constructing a QUBO model and solving an optimal transportation path by using quantum annealing; training a prediction model in combination with the sudden emission risk index to output a carbon emission value; dynamically updating an emission factor library according to the cleanliness of the power grid, and accelerating generation of a confidence interval by adopting a quantum random number generator; and when the predicted value exceeds the threshold value and the lower limit of the confidence interval is higher than the threshold value, triggering carbon capture, carbon credit purchase or production regulation compensation according to the priority. The system correspondingly comprises a multi-mode acquisition module, a calculation optimization module, a dynamic accounting module and a carbon compensation module. Full-chain real-time carbon tracking and intelligent compensation are achieved, the problems of traditional accounting data isolation and compensation lag are solved, and the industrial carbon management accuracy is improved.

The invention provides an end-cloud collaborative equipment energy-carbon efficiency real-time prediction method and system, and relates to the technical field of energy-carbon efficiency prediction.The method comprises the steps that an equipment end side collects current data, vibration data and temperature data of equipment in real time, and electrical energy efficiency characteristic parameters, mechanical energy efficiency characteristic parameters and thermodynamic energy efficiency characteristic parameters are extracted; the equipment end side calculates a cross-domain coupling coefficient, an efficiency health index and a characteristic parameter fluctuation rate, generates an energy efficiency diagnosis report and uploads the energy efficiency diagnosis report to the cloud platform; the cloud platform dynamically dispatches the basic prediction model and outputs an equipment power utilization efficiency prediction value; the cloud platform performs energy efficiency attenuation correction on the equipment power utilization efficiency predicted value to obtain a corrected equipment power utilization efficiency predicted value; and the cloud platform outputs an energy-carbon efficiency prediction value of the equipment through carbon energy efficiency conversion calculation based on the real-time carbon emission factor of the power grid and the corrected power utilization efficiency prediction value of the equipment. The technical problems that in the prior art, energy-carbon efficiency prediction is insufficient in accuracy and poor in real-time performance, and energy-carbon cooperative management and control are not considered are solved.

The invention discloses a park dynamic carbon portrait generation method and system, and the method comprises the steps: collecting building, equipment and traffic multi-source energy consumption data in real time through Internet of Things equipment, and calculating the carbon emission intensity in combination with a dynamic carbon emission factor library; constructing a three-dimensional park model, mapping the carbon emission data into a dynamic thermodynamic diagram layer according to a spatial hierarchical structure (park-building-floor-equipment), and synchronously generating a vehicle carbon emission intensity diagram; positioning a key emission source based on the energy flow-carbon flow mulberry-based graph, setting a grading early warning threshold value, and triggering a three-dimensional highlight alarm and carbon reduction strategy suggestion; an optimization algorithm is adopted to generate a multi-energy cooperative regulation and control instruction, and the instruction is pushed to the energy management system for execution; and closed-loop verification and iterative optimization are realized by monitoring emission reduction, economic benefits and carbon potential indexes. The space-time dynamic visualization of carbon emission, key source intelligent positioning and adaptive optimization closed loop are realized, the problems of lack of real-time performance and low traceability precision of park carbon management are solved, and the carbon emission intensity is reduced through actual measurement.

The invention provides an ecological pasture carbon reserve dynamic prediction method and system based on meteorological data, and relates to the technical field of carbon reserve prediction, and the method comprises the steps: collecting a variety of data, constructing a carbon reserve calculation model, and calculating the basic carbon reserve of a pasture according to pasture parameter data. Analyzing the influence of the pasture parameter data on the target ecological pasture area to obtain a carbon cycle parameter, and calculating a carbon reserve influence index according to the influence of the cow breeding factor on the pasture basic carbon reserve. And constructing a multitask learning model optimized based on a secretary bird algorithm, inputting carbon cycle parameters and meteorological data, and outputting to obtain meteorological influence carbon data. And generating a carbon reserve change rule according to the carbon reserve influence index and the meteorological influence carbon data, and making a carbon management strategy according to the carbon reserve change rule. According to the method, the accuracy and scientificity of dynamic prediction of the carbon reserves of the ecological pasture are improved, and powerful support is provided for sustainable management and carbon emission reduction of the ecological pasture.