1184 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 relates to the technical field of multi-virtual power plant collaborative optimization, in particular to a multi-virtual power plant master-slave game collaborative optimization method considering carbon transactions, and the main body of the master-slave game comprises an operator as a leader and a plurality of virtual power plants as followers. Comprising the following steps: constructing a multi-virtual power plant master-slave game collaborative optimization architecture considering carbon transaction and dual compensation; constructing a stepped carbon transaction model containing a reward and punishment mechanism; constructing a virtual power plant dual compensation mechanism model; defining scheduling objective functions and constraint conditions of the operators and the virtual power plants, and constructing a multi-energy coupled master-slave game collaborative optimization model of the operators and the multiple virtual power plants; and inputting predicted values of power, heat load and wind and light output data of the user side into the master-slave game collaborative optimization model to obtain an optimal pricing strategy of an operator and a scheduling result of collaborative optimization operation of the multiple virtual power plants. Compared with the prior art, the method has the advantages that the economical efficiency is improved, and low-carbon emission is realized.

The invention discloses an energy equipment carbon emission scheduling optimization method in an integrated energy system, and the method comprises the steps: determining an energy equipment carbon emission factor, constructing a carbon emission cost function, employing Monte Carlo simulation to generate a carbon transaction price fluctuation scene, and carrying out the carbon transaction price fluctuation scene. The method comprises the following steps: calculating a carbon transaction risk index of equipment in each scene according to a cost function, determining start-stop time by economic benefit optimization, constructing a comprehensive energy system topology network structure, updating a carbon flow path set according to the start-stop time, calculating a comprehensive carbon emission coefficient, establishing an energy flow and carbon flow coupling model between a high emission path and a low emission path, and constructing a comprehensive energy system topology network structure. A scheduling strategy feasible space is obtained through a cooperation strategy, and an optimal cooperation scheduling operation strategy is obtained through optimization of a particle swarm algorithm by taking the minimum carbon emission as a target. The method can effectively reduce the carbon emission / optimize the start and stop of the equipment, improves the economical efficiency and environmental protection property of system operation, achieves the dual goals of energy conservation and emission reduction and benefit improvement, and has good interpretability at the same time.

The invention discloses a carbon emission monitoring method and system for a data center, and the method comprises the steps: collecting the power generation proportion data of different energy types in real time, predicting the power supply change trend of renewable energy in combination with meteorological information, calculating the power supply proportion of each energy type, and constructing a dynamic weight matrix; constructing a time sequence prediction model by using historical and real-time renewable energy data, and predicting the future renewable energy power supply capability; carbon emission factors and priorities of different data centers are calculated, a task migration feasibility matrix is constructed, calculation tasks are dynamically migrated to a low-carbon emission data center according to a scheduling revenue function, a task scheduling path is optimized, cross-regional data transmission time delay and energy consumption are reduced, and cross-regional optimization scheduling of computing power is achieved; a multi-dimensional time sequence prediction model is constructed, the carbon emission trend is predicted, and carbon emission abnormity is monitored in real time. According to the invention, the problem of matching of computing power load and renewable energy of a data center can be solved.

The invention discloses a modular power supply efficiency intelligent management method, and aims to record the working state, power output and conversion efficiency of a power supply module in real time through a sensor and monitoring software, carry out system energy efficiency analysis regularly, and can significantly improve the overall performance of a power supply system. Firstly, according to the scheme, by optimizing power distribution and fault early warning, the energy efficiency can be effectively improved, energy waste is reduced, and the operation cost is reduced. Meanwhile, the system can dynamically adjust the load of each module, prolong the service life of equipment, reduce the equipment fault risk through preventive maintenance, and improve the stability and reliability of the system. In addition, an energy efficiency analysis report of the system provides a data-driven decision basis for a manager, helps to discover the potential of efficiency improvement, and guides the future optimization direction. And by reducing energy consumption, the power supply system can also reduce carbon emission, promote sustainable development of enterprises and improve environmental protection benefits.

The invention relates to the technical field of boiler engineering and artificial intelligence modeling, in particular to a coal-fired boiler biomass blending combustion working condition intelligent optimization method, which comprises the following steps: constructing a three-dimensional mathematical model of coal-fired boiler biomass blending combustion based on experimental test data; performing multi-working-condition numerical simulation according to boiler combustion working condition fluctuation in the three-dimensional mathematical model; according to the numerical simulation data, constructing a time-space coupling model for co-combustion of biomass in the coal-fired boiler; the modeling accuracy is improved through a numerical simulation technology, the boiler operation condition is dynamically optimized in combination with space-time modeling, and theoretical support is provided for energy conservation and emission reduction and blending combustion proportion optimization; the method is suitable for environment-friendly transformation and operation optimization of the coal-fired boiler, and carbon emission and pollutant emission can be effectively reduced.

The invention provides an intelligent zero-carbon-emission park building energy-saving control method and system and a medium, which are used for optimal control of window light transmittance, illumination intensity and air conditioner operation power of a building. According to the system, future environment parameters (such as temperature and illumination intensity) are predicted through an LSTM model, and current environment data and historical control signals are combined and input into an MLP model to generate a real-time control strategy, so that the running states of windows, illumination and air conditioners are dynamically adjusted. The system can significantly improve the energy efficiency of a building, reduce carbon emission and optimize the indoor environment comfort, and has adaptive learning and automatic fault detection functions. The system is suitable for the fields of green buildings, intelligent buildings and the like, intelligent and automatic environment control can be achieved, and energy conservation, emission reduction and sustainable development of the buildings are promoted.

The invention relates to the technical field of building design, and discloses a GAN and GA-based low-carbon building design decision-making method in a hot-summer and warm-winter region, and the method comprises the steps: setting a generator in a GAN model to generate external design parameters and internal design parameters of a building in a hierarchical manner, introducing a reinforcement learning algorithm, and setting an adaptive discriminator, thereby achieving the design of a low-carbon building in a hot-summer and warm-winter region. And the genetic algorithm GA is combined with particle swarm optimization PSO, so that the balance between the energy efficiency and the building material cost can be achieved while the low-carbon emission requirement is met, and the intelligence and dynamic response capability of the building design are fully realized.

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 belongs to the technical field of island integrated energy systems, and particularly discloses a short-time scale low-carbon optimal scheduling method and system for an island integrated energy system, and the method comprises the steps: constructing an electricity-hydrogen-heat multi-energy coupled island integrated energy system model; constructing a real-time stepped carbon transaction model based on the interval to which the carbon emission quota participating in the carbon emission transaction belongs by using an island integrated energy system model; on the basis of a real-time stepped carbon transaction model, deviation penalty of new energy prediction is considered, one day is divided into n scheduling cycles, n is an integer larger than 1, a target function is constructed with the highest total income of a system as an optimization target, the optimization target is solved, and an optimized scheduling result is obtained. According to the invention, wind energy, solar energy, wave energy, stored energy, a photo-thermal power station and other energy sources are co-scheduled with electricity, hydrogen and water loads to form a dynamically balanced energy system; and high consumption and low carbon emission are kept as far as possible.

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 relates to the technical field of carbon emission data processing, in particular to a carbon emission data processing method, device and equipment and a readable storage medium. According to the technical scheme, the method comprises the steps of data acquisition and preprocessing, abnormal data identification and correction based on a hybrid machine learning model, energy optimization configuration assistance and real-time monitoring and feedback adjustment, and data acquisition and preprocessing: constructing a multivariate data acquisition network and preprocessing the acquired data; therefore, standard carbon emission data for subsequent analysis can be obtained. Through a multi-element acquisition network and fine preprocessing, the comprehensiveness, accuracy and timeliness of data are greatly improved, a foundation is built for accurate analysis, abnormity is accurately recognized and corrected through a hybrid machine learning model, the accuracy is improved, data reliability is guaranteed, association between carbon emission and energy is mined, enterprises are assisted in optimizing energy configuration, and the energy utilization efficiency is improved. The carbon emission is reduced, and the system stability is enhanced by real-time monitoring feedback.

The invention provides a data center and pumped storage power station combined operation system and method. The system comprises an upper reservoir, a lower reservoir, a data center and a pumped storage power station. Wherein an air conditioning system is mounted in the data center; the air conditioning system comprises a heat exchange station, a hybrid heat exchanger and an air conditioning terminal. The system has the following advantages: 1, energy conservation and consumption reduction: a natural low-temperature water source is utilized, and the power consumption of a data center cooling system is greatly reduced; 2, the efficiency is improved: the cooling efficiency of the data center is obviously improved through an efficient heat exchange system; 3, the cost is reduced: the power use cost and the maintenance cost of air conditioning equipment are reduced, and the operation cost of the data center is reduced. And 4, environmental friendliness: the power consumption is reduced, the carbon emission is reduced, and the method is more environment-friendly. 5, initial investment is reduced: a natural cold source is utilized, and a refrigeration air-conditioning system of the data center is omitted, so that the initial investment is reduced; the pumped storage power station provides a UPS function for the data center, and initial investment is further reduced.

The invention relates to the technical field of computing power distribution, in particular to a computing network energy integrated collaborative task scheduling platform and scheduling method. The scheduling platform comprises an infrastructure layer, a management and control layer and an application service layer. The infrastructure layer is composed of a computing power supplier, a network supplier and an energy supplier; the application service layer at least comprises a user platform, and the user platform is at least used for receiving and analyzing task requests initiated by all users and reporting the task requests to the management and control layer; the management and control layer at least comprises a sensing module, a measurement module and a computing network energy task scheduling module, the sensing module is used for sensing computing power resources, transmitting network resources and energy resources, and the measurement module is used for measuring the computing power resources, the network resources and the energy resources required for processing each task; and the calculation network energy task scheduling module is used for selecting an optimal calculation power supplier and an optimal network supplier of each task based on the total time delay, the total energy consumption and the total carbon emission. According to the platform, the computing resource utilization rate and the task processing speed can be improved, and carbon emission can be effectively reduced.

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 provides a port energy dynamic optimization method and system based on digital twinning, and relates to the technical field of port energy optimization. According to the method, the full-link closed-loop control of the port energy data is realized by constructing a physical-edge-virtual three-layer digital twin architecture, and the utilization rate of renewable energy sources is improved and the carbon emission is reduced through real-time data synchronization and multi-time scale optimization strategies. Through combination of edge layer preprocessing and a virtual layer physical constraint model, the problem that dynamic response is delayed and a physical rule is disjointed in a traditional method is solved.

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.

According to the carbon emission factor dynamic matching method based on the large language model, efficient and accurate matching is achieved through multi-step optimization. The method comprises the following steps: firstly, collecting carbon emission factors and activity data from multiple sources, and cleaning and normalizing the data; then, performing deep semantic analysis on the data by utilizing a pre-trained large language model subjected to field fine adjustment to generate a high-quality semantic vector; the system unifies data formats and completes physical quantity conversion through a unit identification and conversion mechanism; in the matching process, the system performs intelligent matching by calculating cosine similarity of semantic vectors, and under the condition that the similarity is close, a weighted average mechanism is applied to optimize a result, so that the matching precision is further improved; meanwhile, the system analyzes the relevance of newly submitted data by utilizing a factor library dynamic updating mechanism, dynamically expands the factor library and continuously optimizes the matching performance; the whole method improves the matching precision and efficiency, reduces manual intervention and errors, and reduces the calculation cost of carbon emission.

The invention discloses a deep reinforcement learning-based multimodal transport path optimization method, which belongs to the field of logistics management, and comprises the following steps of S1, forming a plurality of feasible paths from a starting point to an end point as training samples, calculating transportation time, freight cost, carbon emission, accident rate and maximum load index of each sub-path as quality evaluation indexes of the corresponding feasible path; s2, constructing a deep reinforcement learning agent based on the state space, the action space and the reward function; s3, selecting a sample by the intelligent agent by adopting a Sum Tree priority sampling mechanism, and generating an optimal path; and S4, in the intermodal process, monitoring the quality evaluation index of the path, and dynamically generating an optimal path according to the real-time path state data. The method can adapt to dynamic factors such as real-time traffic changes, manual intervention is reduced, the emergency response efficiency is high, the occurrence probability of accidents can be reduced, and carbon emission and emission of other pollutants are 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 embodiment of the invention provides a park energy carbon supervision method and system, and belongs to the technical field of energy carbon supervision. The method is executed by an energy-carbon management platform, the energy-carbon management platform is in communication connection with area control modules arranged on all energy sides based on an energy internet-of-things base, and the method comprises the steps that energy consumption data and equipment operation data which are collected in real time and correspond to all enterprises are recycled; updating a sub-energy grid of each enterprise based on the equipment operation data, and updating a main energy grid of each enterprise based on the energy consumption data of each enterprise; wherein the lowest carbon emission is taken as an optimization target, and real-time regulation and control of the energy scheduling scheme of each enterprise in the park are executed based on the updated main energy grid and the updated sub-energy grid; and monitoring the regulation and control process of each enterprise energy scheduling scheme, executing park data operation and maintenance based on a monitoring result, and generating a regulation and control report in real time. The scheme of the invention not only improves the accuracy of energy management, but also effectively promotes the realization of the carbon emission reduction target of the park.

The invention discloses a power distribution network transformer carbon footprint analysis method based on life cycle evaluation, which is based on a life cycle evaluation method, takes a transformer with the capacity of 200kVA as an example, calculates the carbon emission and the total energy demand of the whole life cycle, and further analyzes the carbon emission in each stage. Research finds that the carbon emission with the highest proportion is the operation stage and accounts for 99.03% of the whole life cycle, the waste recovery stage effectively reduces the influence of the carbon emission on the environment, the technology in the production and manufacturing process is improved, more energy-saving and efficient devices are adopted, and the carbon emission can be effectively reduced. Sensitivity analysis shows that the silicon steel sheets have obvious influence on carbon emission in the life cycle of the power distribution network transformer, and the higher the silicon steel sheets are, the lower the carbon emission is.

Disclosed is a marine fuel cell-based integrated heat, electricity, and cooling supply system comprising a power supply system and a waste heat recovery system; the power supply system comprises wind turbine generator sets, solar generator sets, and a fuel cell power module; the waste heat recovery system encompasses a turbine power generation module and a lithium bromide refrigeration module; the fuel cell power module is connected to both the turbine power generation module and the lithium bromide refrigeration module; the turbine power generation module is used to generate electricity using waste heat. This approach fully exploits the waste heat from the exhaust gas generated by the fuel cell power module, resulting in a high overall energy utilization rate. The self-consumption electricity and pure hydrogen fuel for the integrated energy supply system can be obtained from solar and wind energy, ensuring low carbon emissions for the entire system.

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 relates to the technical field of building construction materials, in particular to low-carbon high-performance concrete and a preparation method thereof. The low-carbon high-performance concrete is prepared from the following preparation raw materials in parts by mass: 150 to 180 parts of Portland cement, 24 to 27 parts of sulphoaluminate cement, 120 to 150 parts of activated coal gangue powder, 60 to 80 parts of fly ash, 600 to 700 parts of gravel, 60 to 65 parts of modified basalt fiber, 20 to 25 parts of limestone powder, 10 to 12 parts of light calcined magnesia, 2 to 3 parts of accelerant, 7 to 10 parts of modified polycarboxylate superplasticizer and 180 to 220 parts of water. According to the low-carbon high-performance concrete disclosed by the invention, excellent mechanical properties and durability are ensured, the environmental protection targets of low cost and low carbon emission are achieved, and a new solution is provided for sustainable development of concrete materials.

The invention is suitable for the technical field of virtual power plant control, and provides a virtual power plant low-carbon interactive operation optimization method and device, and the method comprises the steps: obtaining the data of a production side and a user side of a virtual power plant, determining a dynamic emission factor and an energy attribute cost, establishing a low-carbon demand response mechanism and a double-layer low-carbon scheduling model of the virtual power plant; the upper layer of the model determines the carbon emission quota of the virtual power plant under the optimization target of the highest income, the lower layer introduces a load aggregator, and the excitation response amount of each energy is determined according to the dynamic emission factor and the energy attribute cost; and solving the double-layer low-carbon scheduling model of the virtual power plant, and determining a load demand after response excitation and an updated dynamic carbon emission factor to obtain an optimal low-carbon scheduling scheme. According to the application, the guiding effect of the dynamic carbon emission factor can be maximized, the energy demand of the user side is met while the carbon emission is reduced, the user is guided to adjust the energy consumption behavior, and the purpose of reducing the carbon emission of the system is achieved.

According to the method, a set of parameterized simulation optimization design method integrating passive low carbon emission and high comfort is constructed for the design stage of a building scheme, and the method is a building low-carbon optimization design method based on an energy consumption-thermal comfort correlation model and comprises the steps that based on a parameterized building performance optimization model, an energy consumption-thermal comfort correlation model is established; establishing a parameterized model, performing microenvironment simulation by using a plug-in, and collecting data; sensitivity analysis of the parameterized model: carrying out sensitivity analysis by adopting random forest regression, gradient enhanced regression and decision tree regression, and discussing a relationship between a design variable and an environment target; and performing multi-target optimization based on performance simulation to obtain a compromise optimal result under multiple targets. According to the method, corresponding geometric and enclosure structure design parameter strategies can be provided from the aspects of building greenhouse gas emission, indoor and outdoor thermal environment comfort and the like, efficient design iteration and decision support is provided for designers, and sustainable development of green communities is promoted.

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 data center energy system scheduling method, device and equipment, a medium and a product in the technical field of intelligent management systems. The method comprises the following steps: constructing an economical objective function for minimizing the operation cost and an environmental objective function for minimizing the carbon emission; constructing a computing power side model and an energy supply side model; according to the supply data information and the demand data information in the data center energy system and constraint conditions, solving a supply and demand matching optimal solution of the data center energy system; and configuring the data center energy system according to a supply and demand matching optimal configuration scheme corresponding to the supply and demand matching optimal solution so as to realize an optimal operation target. According to the method, under the condition that the minimum operation cost and the minimum carbon emission serve as target functions, the operation models of the computing power side and the energy supply side and the constraint conditions are combined, and the targets of optimal energy system operation, optimal computing power resource allocation, the minimum carbon emission and the minimum energy operation cost of the data center are achieved.