6474 results about "Economic benefits" patented technology

A power grid planning method and system adapted to multiple uncertainties and multi-objective requirements, which relate to the technical field of power grid planning. The method comprises: constructing a deterministic multi-objective coordinated planning model with the objectives of minimizing economic cost, reliability risks and environmental impact; on the basis of an IGDT, constructing a multi-uncertainty model considering wind power, photovoltaic power and load; converting a model into a second-order cone programming model by using variable substitution, absolute value linearization, second-order cone relaxation and McCormick envelopes relaxation methods; and solving the second-order cone programming model, in order to obtain a power grid planning scheme that adapts to uncertainties in wind and photovoltaic power output and load demand growth and meets the requirements for the economy, reliability and environmental friendliness of power grid operation. Thus, economic benefits of power grid planning are improved, carbon emissions from power grid operation are reduced, the reliability of a power grid is enhanced, and an obtained planning scheme has high robustness within expected objectives of planners.

The invention provides an intelligent optimization method and system for a revenue mode of a commercial energy storage power station, and relates to the technical field of energy storage power stations. Establishing a multi-dimensional life loss coefficient matrix by analyzing the health state data of the energy storage equipment; a candidate charging and discharging scheduling strategy is generated based on a deep reinforcement learning model, and a double-constraint optimization charging and discharging strategy with predicted income and life loss as a reward function is adopted; and finally generating a control instruction to realize optimized operation of the energy storage power station. According to the method, equipment life loss and economic benefits are comprehensively considered, and long-term benefit maximization of the energy storage power station is realized.

The invention discloses a multi-objective optimization decision control method for shrimp and silkworm polyculture environment parameters, and relates to the technical field of polyculture control. The method comprises the following steps: deploying a water quality sensor network, and combining prawn and nereis growth monitoring equipment; extracting criticality of environmental parameters and biological growth indexes by using historical breeding data and experimental data; constructing a dynamic coupling model, and establishing a dynamic model of the polyculture system based on a differential equation; developing a hybrid optimization algorithm to balance the competition and symbiotic relationship between the prawns and the nereis; and generating a control instruction set based on an optimization result, and adjusting the control equipment through a fuzzy PID controller. According to the invention, intelligent regulation and control of shrimp and silkworm polyculture environment parameters are realized, and economic benefits of shrimp and silkworm polyculture are improved.

The invention discloses an intelligent fishery comprehensive evaluation system based on multi-dimensional indexes, and belongs to the field of intelligent fishery. The intelligent fishery comprehensive evaluation system based on the multi-dimensional indexes comprises a water quality monitoring unit, a prediction and early warning unit, a breeding regulation and control unit, an ecological evaluation unit and a decision output unit. The method solves the problem of hysteresis of the detection result in the prior art, can advance the water quality change trend and the red tide occurrence probability in the future by monitoring the water temperature, the dissolved oxygen, the pH value and other key water quality parameters in real time and by means of the prediction model, provides a key basis for breeding decision making, and improves the breeding quality. Execution equipment is flexibly adjusted according to real-time data and early warning information to improve the breeding environment and improve breeding benefits, meanwhile, multi-source data is integrated to comprehensively evaluate the influence of breeding activities on the ecological environment, ecological balance maintenance is assisted, comprehensive evaluation results are integrated to generate ecological improvement suggestions, resource allocation is optimized, and the economic benefit is improved. And the intelligent and ecological level of fishery is integrally improved.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention discloses a deep and large reservoir ecological scheduling method, system and equipment based on a data-driven model of a coupling physical mechanism, and belongs to the technical field of water resource management and environmental protection. Firstly, a physical water temperature model is constructed based on measured data, diversified water temperature change scenes are generated, and a deep learning model constrained by a physical mechanism is constructed. Secondly, carrying out sensitivity analysis to identify key influence factors for driving water temperature change, constructing a reservoir optimization scheduling model, coupling a deep learning model constrained by a physical mechanism, and deducing a scheduling rule set on the premise of meeting a water temperature target; and finally, carrying out multi-index optimization analysis. The invention further provides a deep and large reservoir ecological scheduling system and electronic equipment, and the deep and large reservoir ecological scheduling method is realized. According to the method, the power generation scheduling rule set of the deep and large reservoir can be scientifically deduced, the optimal scheduling scheme with both ecological benefits and economic benefits is screened out by introducing the multi-index optimization method, and overall balance of ecological requirements and power generation benefits is achieved.

The invention relates to the technical field of power system regulation and control, in particular to a mutual aid regulation and control method and system for multiple power distribution transformer areas considering capacity margin, and the multiple power distribution transformer areas comprise N low-load power distribution transformer areas and M high-load power distribution transformer areas which are connected through N + M interconnection devices; the load rate of the low-load power distribution area is less than or equal to a first load rate threshold, and the load rate of the high-load power distribution area is greater than a second load rate threshold; the regulation and control method comprises the following steps: constructing a comprehensive constraint condition based on an economic operation constraint condition and a mutual aid capacity margin constraint condition of a power distribution area; constructing a multi-objective function based on the power distribution area loss objective function and the economic benefit objective function; building a target optimization model through a comprehensive constraint condition and a multi-target function; and obtaining an optimal power distribution scheme based on the target optimization model, and performing hierarchical cooperative control execution. Through the regulation and control method and system, the problem of insufficient load sensitivity can be effectively solved.

The invention discloses a furniture production informatization management system based on data analysis, and relates to the technical field of production management, and the system comprises a production plan optimization module, a raw material cutting optimization module and a quality prediction center module. The production plan optimization module collects data by deploying an RFID sensing network, establishes a multi-dimensional evaluation matrix to determine order priority, optimizes a production plan by using a mixed integer programming model and an improved algorithm, and generates a production scheduling scheme after digital twinborn verification; the raw material cutting optimization module analyzes order data to construct a raw material model, optimizes component layout by applying a self-adaptive algorithm, and generates a cutting scheme with process constraint and an excess material map in combination with stress analysis; through an intelligent production management means, the production efficiency, the raw material utilization rate and the product quality stability are remarkably improved, and remarkable economic benefits are brought to enterprises.

The invention discloses a complex geological foundation pit construction method and system based on intelligent monitoring and self-adaptive supporting, and the method comprises the steps: carrying out the investigation of a construction region, and obtaining the investigation data; constructing a three-dimensional geologic model based on the survey data; designing a supporting structure based on the three-dimensional geologic model; based on the designed supporting structure, construction is conducted, and the construction process is monitored in real time; and dynamically adjusting an excavation sequence and a supporting scheme in combination with the three-dimensional geologic model and monitoring data to finish construction. The method has remarkable innovativeness and practicability in the field of foundation pit construction under the complex geological condition, the construction risk can be effectively reduced, the construction period is shortened, the construction quality is improved, and the method has wide application prospects and remarkable economic benefits.

The invention relates to the technical field of photovoltaic power generation. The invention provides a photovoltaic module cooling method, system and device and a storage medium, and the method comprises the steps: collecting the working condition parameter data of a photovoltaic module and the environment parameter data in a corresponding range of the photovoltaic module in real time, and obtaining a basic data set; based on the basic data set, a relation model between the working condition parameters and the environmental parameters of the photovoltaic module is established, the relation model is utilized to predict the temperature change trend and the output power change condition of the photovoltaic module under different environmental conditions, and a prediction result is obtained; and starting a liquid cooling, air cooling and phase change material collaborative cooling mechanism according to the prediction result, continuously monitoring the working condition parameters of the photovoltaic module in the cooling process, and dynamically adjusting and optimizing the cooling mode and the corresponding parameters according to the monitoring result. The problems that an existing photovoltaic module lacks an effective temperature adjusting means, the output power is obviously reduced when the working temperature rises, and the overall efficiency and economic benefits of a photovoltaic power generation system are reduced are solved.

The invention relates to the technical field of soilless culture nutrient solution dynamic optimization, and particularly discloses a soilless culture nutrient solution dynamic optimization method which comprises the steps of multi-source fusion monitoring data set generation, main nutrient element demand prediction, nutrient solution adjustment instruction set generation and nutrient solution adjustment effect evaluation. According to the invention, environment, plant physiology and nutrient solution state data are synchronously acquired, a short-term demand prediction model is utilized to predict nutritional demands, and an adjustment instruction set containing the supplement amount and supplement rate of the current nutrient solution is generated in combination with a plant growth stage, a current nutrient solution state and an environment factor; the adjustment effect of the nutrient solution adjustment instruction set is obtained by comprehensively evaluating the improvement degree of the plant physiological status and the stability of the nutrient solution after adjustment is finished; dynamic matching of nutrient solution supply and plant requirements is achieved, the nutrient utilization efficiency is improved, the adaptability of the system to environment changes is enhanced, and remarkable economic benefits and social benefits are achieved.

A forging process optimization system and optimization method based on digital twin, which system and method belong to the technical field of forging. The forging process optimization system comprises a physical entity collection module and a digital twin system, wherein the physical entity collection module comprises a data collection module, and the data collection module is used for collecting information data of the physical entity collection module during operation. A digital twin technique is integrated into a forging process, so as to facilitate the formation of intelligent and networked production, facilitate manufacturing resource configuration and production technique sharing, and facilitate offline operation training and online production guidance. Thus, the yield of parts is increased, resource waste is reduced, and a relatively high economic benefit is achieved; and the blindness and the subjectivity of when a conventional optimization method is used are reduced, and the time for searching for an optimal process parameter combination is shortened, thereby effectively improving the process design efficiency and reducing the sample development cost.

The invention belongs to the technical field of enzyme engineering, and discloses an MHETase-EstD9 mutant and an application of the MHETase-EstD9 mutant. The invention provides an MHETase-EstD9 mutant, the amino acid sequence of the MHETase-EstD9 mutant is shown as SEQ ID No.2, and on the basis of the mutant, the amino acid of the MHETase-EstD9 mutant is further mutated to design five MHETase-EstD9 mutants. At 70 DEG C, compared with wild type MHETase-EstD9, the BHET degradation activity of the MHETase-EstD9 mutant provided by the invention is improved by 15% or more, and the highest BHET degradation activity can be improved by 24 times; meanwhile, the thermal stability of the MHETase-EstD9 mutant is also improved to a certain extent, and the Tm value is maximally improved by 18.0 DEG C. The method can be applied to the fields of degradation of PET, MHET or BHET, preparation of degradation agents of PET and degradation products thereof, TPA recovery and the like, and has important environmental benefits, economic benefits and social benefits.

The invention relates to the technical field of environmental protection and climate change, in particular to an enterprise carbon emission reduction data intelligent monitoring and analysis system, which comprises a data acquisition and preprocessing module, a mechanism model library module, a carbon emission accounting and monitoring module, an abnormality diagnosis and traceability module, an emission reduction potential analysis module and a visualization and report module. Through multi-source data access, cleaning and standardization processing, high-quality and uniform-format basic data are provided for the system, the accuracy and reliability of subsequent carbon accounting and analysis are guaranteed, accurate positioning and source tracing of carbon emission abnormity are achieved by means of causal reasoning and a path optimization algorithm, and the carbon emission abnormity analysis method has the advantages of being high in practicability and easy to popularize. A scientific basis is provided for an enterprise to quickly solve the problem that emission exceeds the standard, and through reinforcement learning simulation, multi-target optimization and knowledge graph matching, an optimal strategy considering the emission reduction effect, cost and productivity is dynamically recommended, and the enterprise is assisted to achieve collaborative improvement of carbon emission reduction and economic benefits.

The invention belongs to the technical field of petrochemical process simulation and intelligent optimization control, and particularly relates to a method for constructing and optimizing a full-fraction oil catalytic cracking reaction kinetic model. The construction method comprises the steps of raw material matrix construction, reaction network construction, product property calculation, model parameter optimization and process prediction calculation. The optimization method comprises the following steps: constructing a process optimization index system comprehensively reflecting environmental and economic targets; systematic optimization of reaction conditions and product distribution is realized; intelligent regulation and efficient prediction of the catalytic cracking process are realized; and optimal reaction operation conditions meeting multiple requirements are obtained. The method is high in adaptability, process parameters can be adjusted according to the properties of the raw materials, and efficient conversion is achieved. By optimizing the catalytic cracking process, the yield of aromatic hydrocarbon can be remarkably improved. By predicting and optimizing reaction conditions through the kinetic model, the energy consumption is reduced, the reaction efficiency is improved, meanwhile, the equipment investment and operation cost can be reduced, and the production efficiency and economic benefits are improved.

The invention relates to the technical field of green intelligent digital construction, and discloses a green building digital collaborative design and optimization method based on BIM and cloud computing, comprising the following steps: S1, project initialization and data collection, S2, preliminary design and simulation based on BIM, S3, sensor equipment layout and optimization, S4, fault detection and early warning processing, and S5, cloud computing. According to the method, a green intelligent digital construction key process technology is adopted, energy conservation and emission reduction benefits and environment quality improvement benefits generated in the whole life period of a green building technology can be improved, and digital intelligent technologies such as BIM, 5G, IoT, BMS and EMS are adopted, so that the construction period is shortened, and the construction efficiency is improved. Visualization and perceptibility of energy conservation and emission reduction benefits and environment quality improvement benefits generated in the whole life period of the green building technology can be achieved, and economic benefits are generated from the energy conservation and emission reduction benefits generated by the green building technology in a carbon trading mode.

The invention discloses a tuna pond culture strategy optimization method and system based on meteorological influence analysis, and the method comprises the steps: collecting regional meteorological data and pond culture monitoring data, and constructing a multi-modal pond ecological feature vector; establishing a weather-water body dynamic coupling model by using a space-time diagram neural network, and generating water body situation response prediction information; a breeding regulation and control strategy is made based on the prediction information, and strategy self-adaptive optimization is achieved through real-time monitoring data; evaluating the anti-disaster capability of the tuna after the meteorological influence is finished, screening high-quality germplasm resources by combining genetic analysis, and generating an anti-disaster breeding scheme. According to the method, full-chain optimization from meteorological early warning to culture regulation and control to genetic breeding is achieved, and the climate toughness and economic benefits of tuna pond culture are comprehensively improved.

The invention discloses a traditional Chinese medicinal material standard base pest and disease damage intelligent monitoring and precise control method and control system, and relates to the technical field of agricultural intelligence. The Internet of Things technology, the block chain and the artificial intelligence algorithm are integrated to improve monitoring accuracy and prevention timeliness, environment and plant states are acquired through the sensor and image data, safe storage of data is ensured by using the block chain, early recognition of diseases and pests is performed by using the convolutional neural network, and the accuracy of disease and pest control is improved. A disease risk index is calculated in combination with environmental parameters, a reinforcement learning algorithm dynamically optimizes a control strategy, control measures are automatically executed through Internet of Things equipment, an execution result is recorded on a block chain, in addition, the strategy is continuously adjusted according to a control effect evaluation result, intelligent and precise disease and pest management is realized, resource consumption is effectively reduced, and the economic benefit is improved. The traditional Chinese medicinal material production safety and efficiency are improved.

The invention relates to the technical field of intelligent control systems, and discloses a multifunctional embedded intelligent cleaning control system which comprises a control execution layer, an internet-of-things transmission layer and a decision analysis layer. The control execution layer comprises an intelligent variable-frequency scraping device, a wind-water sweeping device and the like and executes sweeping and dust removal operation; the Internet of Things transmission layer realizes data interconnection and transmission through an edge computing node, a protocol conversion gateway and the like; and the decision analysis layer adopts a time sequence data analysis and convolutional neural network algorithm, dynamically optimizes the cleaning operation model, constructs a multi-dimensional linkage cleaning control strategy model, and realizes cleaning intensity adjustment, air and water parameter cooperative control and dust removal efficiency dynamic optimization. According to the system, through multi-technology fusion and collaborative optimization, the intelligent, precise and efficient level of sweeping operation is improved, the system is suitable for a conveying belt sweeping scene, the sweeping efficiency can be improved, equipment loss and dust emission can be reduced, and remarkable economic benefits and environmental protection benefits are achieved.

The invention discloses a hydrogen energy park planning method and system based on a heuristic algorithm, and relates to the technical field of smart energy management, and the method comprises the steps: constructing a mathematical model of a park comprehensive energy system which is composed of a renewable energy power generation unit, an energy conversion device, a carbon capture and gas power device and a multi-type energy storage unit; based on the mathematical model, with the minimum annual comprehensive cost of the park comprehensive energy system as a target, establishing a cost target function, setting constraint conditions, and formulating an energy management strategy; and according to an energy management strategy, solving according to an improved taboo horse racing optimization algorithm THRO, and planning the hydrogen energy park according to a solving result. According to the invention, not only is the interior of the exterior of the environment realized, but also the maximization of economic benefits is realized.

The invention discloses a multi-energy complementary cooperative scheduling method based on an improved step-by-step optimization algorithm, and belongs to the technical field of water, wind and light storage multi-energy complementary scheduling. The method comprises the following steps: acquiring hydrological, engineering and load data, and generating a preliminary feasible scheduling solution; carrying out iterative search by adopting an improved step-by-step optimization algorithm (POA), wherein the improvement comprises adopting a variable discrete step size, a dynamic feasible region and combining a heuristic candidate update strategy; combining the output sequence of each subsystem, and introducing a dynamic credit value based on nonlinear updating to carry out weighted redistribution on the load deviation; and finally, evaluating and selecting a scheme with the maximum peak regulation benefit through a scheduling objective function coupled with a dynamic penalty function. According to the method, a credit mechanism, a gradient dead zone solution and the like are introduced, so that the intelligent level of scheduling, the calculation stability and the economic benefit of a final scheme are remarkably improved.

The present application relates to the technical field related to greenhouse planting, and particularly relates to a crop management and control method and apparatus for a smart greenhouse, and a device and a medium. The method comprises: acquiring environmental data and crop data; inputting the environmental data and the crop data into a preset regulation and control model to obtain an optimal planting scheme and / or an optimal environment control strategy; and on the basis of the environment control strategy, regulating and controlling preset smart remote control devices in a smart greenhouse. By means of the configuration in the solution provided in the present application, accurate control of a planting environment can be performed by collecting environmental data and crop data in a timely manner, thereby increasing the crop growth rate and improving the production efficiency. Furthermore, a preset regulation and control model is used in the present application, so as to automatically optimize a planting scheme and an environment control strategy on the basis of different environmental conditions and crop requirements, thereby reducing manual intervention and management costs, and thus improving the production efficiency and economic benefits.

The invention discloses a low-temperature pump for a fusion device and the fusion device, and relates to the field of fusion devices.The low-temperature pump comprises a low-temperature pump body, an adsorption space is defined by the low-temperature pump body, and a first medium inlet communicating with the adsorption space is formed in the low-temperature pump body; the valve assembly is arranged on the cryogenic pump main body, and the valve assembly is used for opening or closing the first medium inlet; and at least part of the adsorption assembly is arranged in the adsorption space, the adsorption assembly is used for adsorbing gas in the adsorption space, and the adsorption assembly is detachably arranged on the cryogenic pump body. Therefore, the adsorption assembly is detachably arranged on the cryopump main body, the adsorption assembly can be independently replaced, the maintenance cost of the cryopump can be reduced, the production cost of fusion energy can be reduced, the economic benefit is further improved, and the adsorption assembly can be replaced without shutdown under the condition that a certain adsorption assembly is replaced.

The invention provides an irrigation district water resource scheduling optimization method based on cloud computing, and the method comprises the following steps: collecting irrigation district multi-source data in real time through a space-air-ground integrated sensing network, and uploading the data to a cloud platform after preprocessing; the method comprises the following steps: constructing digital twin bodies of an irrigation area in a cloud platform, dividing the irrigation area into a plurality of sub-areas according to water consumption attributes, and integrating hydraulics and hydrological agriculture models; real-time data and future weather information are fused, and a water demand prediction value is generated through a water demand prediction model by taking the sub-region as a unit; a scheduling scheme is generated by adopting a multi-objective optimization algorithm based on the predicted value, and the minimum economic benefit, water resource utilization rate and energy consumption are taken as optimization objectives; and performing simulation evaluation on the scheme by using the digital twinborn body, outputting a performance index aided decision, and finally determining an optimal scheduling scheme. According to the method, accurate prediction, dynamic optimization and intelligent scheduling of the water resources in the irrigation area are realized, and the utilization efficiency of the water resources and the irrigation management level are improved.

The invention belongs to the technical field of medical material management, and particularly relates to a medical material intelligent supply chain management scheme and system based on multi-modal AI. According to the invention, through multi-modal data fusion, full-life-cycle and full-link tracking of materials is realized, information islands are eliminated, management transparency is improved, unexpected consumption and physical damage risks are identified in an early stage, a large amount of overstock or clinical supply interruption is avoided, operation cost is reduced, and based on a dynamic loss threshold, replenishment and cross-warehouse allocation can be realized just in need. The inventory turnover rate and the supply guarantee rate are considered, environment monitoring and validity period information are processed, it is ensured that temperature and humidity sensitive materials are stored under the optimal condition, losses caused by expiration or environment abnormity are reduced, the waste rate and the excessive stockpiling risk are comprehensively reduced, meanwhile, timely availability of key materials is clinically guaranteed, and the service life of the materials is prolonged. And the economic benefit and safety of the medical material supply chain are improved.

The invention relates to the technical field of new energy grid connection, and discloses a wind power plant power generation performance evaluation method, device and equipment and a storage medium, and the method comprises the steps: calculating the power generation capability parameters of a target wind power plant according to the operation parameters and configuration parameters of the target wind power plant; calculating an economic effect evaluation parameter according to the availability rate of the target wind power plant, the power grid compatibility comprehensive score, the power generation capacity parameter and the health coefficient; and determining a power generation performance evaluation result of the target wind power plant according to the power generation capacity parameter and the economic effect evaluation parameter. According to the power generation performance evaluation method for the wind power plant provided by the embodiment of the invention, the limitation that evaluation is carried out only from a single index of short-term power generation capacity traditionally is avoided in a mode of combining the power generation capacity parameters and the economic effect evaluation parameters, and comprehensive evaluation of the power generation capacity and the economic benefits is realized.

The present application provides a reverse-type integrated miniature linear servo electric cylinder. An open end of a rotor housing in a power transmission mechanism is fixedly connected to a planetary carrier by means of a coupling, the planetary carrier is directly driven by means of integration of the motor rotor housing and the planetary carrier, and after power transmission via the planetary carrier, an output end is directly driven to achieve linear motion without adding a gear reduction mechanism to a power transmission system of the electric cylinder, thereby significantly reducing the internal space of the electric cylinder occupied by the power transmission mechanism, allowing the electric cylinder to develop to have a more miniaturized and compact structure, saving costs; moreover, the energy utilization rate of the electric cylinder is improved, the electric energy consumption of the electric cylinder is reduced, and relatively good economic benefits can be yielded.

The invention belongs to the technical field of bioinformatics, particularly relates to a computer simulation and regulation method and system for beef cattle fat metabolism, and aims to solve the problems of excessive fat deposition, low feed conversion efficiency, difficulty in multi-objective optimization and the like in existing beef cattle breeding. A multi-scale biological network model is constructed, a computer simulation and prediction engine is developed, an intelligent intervention strategy optimization engine is designed, and implementation and feedback optimization are carried out. By reducing unnecessary fat deposition, improving the feed conversion efficiency and improving the beef quality, the economic benefit of beef cattle breeding is remarkably improved, the requirements of consumers for high-quality and safe beef products are met, and the method has important value for promoting intelligent upgrading and sustainable development of the beef cattle industry.

The invention discloses an energy storage system multi-scene optimization scheduling method and device, equipment, a medium and a product, and relates to the field of model optimization, and the method comprises the steps: obtaining an initial decision variable and a constraint term which are used for training a scheduling model; obtaining a prediction data set; based on the predicted photovoltaic output in the prediction data set, the first predicted electricity price set, the second predicted electricity price set, the initial decision variable and the constraint term, generating the predicted total income of the electric energy market and the auxiliary service market; on the basis of the predicted total income, updating the initial decision variable so as to optimize the scheduling model, and obtaining an optimized target scheduling model; based on the optimal decision variable of the target scheduling model, adjusting a charging and discharging control strategy of the energy storage system; according to the predicted electricity price and total income, the charging and discharging strategy of the energy storage system is dynamically adjusted, so that the energy storage system can participate in an electric energy market and an auxiliary service market at the same time, and the economic benefit of the whole electric power market is maximized.

The invention provides a rail transit engineering low-carbon evaluation method, and relates to the technical field of green low carbon, and the method comprises the steps: obtaining multi-source data of rail transit engineering, and determining a dynamic carbon emission factor of the rail transit engineering based on the multi-source data; based on the dynamic carbon emission factor, determining the total carbon emission of the full life cycle of the rail transit project, wherein the total carbon emission comprises a first carbon emission in a construction stage, a second carbon emission in an operation stage and a third carbon emission caused by the influence of the rail transit project on the social environment; and constructing a correlation model of the total carbon emission and the economic benefit, and determining the comprehensive benefit index of the rail transit project based on the correlation model. According to the method, the multi-source operation data of the rail transit project can be integrated to calculate the dynamic carbon emission factor, accurate quantification of the full-life-cycle carbon emission of the rail transit project is achieved, and therefore a decision basis is provided for low-carbon planning, operation and policy making of the rail transit project.