490 results about "Green power" patented technology

The invention discloses a green data center scheduling optimization method under computing power and electric power coupling, data load types of a data center comprise a transferable load, a reducible load and a rigid load, and the method comprises the following steps: constructing a double-target scheduling optimization model by taking the minimum power consumption cost of the data center and the maximum green power fitting degree as targets; the power consumption cost of the data center is used for representing the sum of expenses generated by the data center for purchasing power from a power grid in order to meet the power required for processing the data load in each time period, and the green power fitting degree is used for evaluating the matching degree between the power consumption demand of the data center and the renewable energy source supply; solving the dual-target scheduling optimization model by using a multi-target dogvessel swarm algorithm to obtain a Pareto frontier; and determining an optimal scheduling scheme based on the Pareto frontier, wherein the optimal scheduling scheme comprises a data center power consumption curve, an energy storage power station charging and discharging curve and a transferable load scheduling strategy. According to the invention, the power consumption cost of the data center can be reduced, and the renewable energy consumption capability can be improved.

The invention provides a green power consumption classification accounting and right tracing method and system of a multi-dimensional account, and relates to the technical field of power, and the method comprises the steps: generating green power transaction matching data through collecting power utilization data and power generation plan data in real time; constructing a distributed account book node network, verifying the matched data, and generating a green power transaction block; and constructing a green power right traceability model based on the transaction block, calculating a green power consumption weight coefficient of the power consumption enterprise, and generating a green power consumption level and a right certificate. According to the invention, precise accounting and right traceability of green power consumption are realized, and the energy management efficiency is improved.

The invention relates to the technical field of sewage plant-oriented carbon neutralization regulation and control scheme design, in particular to a sewage plant-oriented carbon neutralization regulation and control method and a sewage plant-oriented carbon neutralization regulation and control system. The method comprises the steps of collecting water quality, energy consumption and gas emission data in real time through a multi-source sensor, inputting the data into a space-time diagram convolutional network to predict a carbon emission trend after edge computing node cleaning, and dynamically optimizing an aeration rate and a sludge reflux ratio based on a fuzzy PID controller. A whole-process carbon footprint is tracked by adopting a life cycle evaluation method, a multi-target compensation strategy is generated in combination with an improved NSGA-III algorithm, and photovoltaic, biogas and green power resources are scheduled preferentially. A digital twin platform and a block chain evidence storage module are innovatively integrated, the former calibrates model parameters in real time, and the latter generates a non-tampering carbon neutralization voucher. According to the method, the problems of one-sided data, regulation and control lag and insufficient credibility of a traditional method are solved, and minute-level sensing, prediction and offset closed-loop control of carbon emission in the sewage treatment process is realized.

The invention discloses a green power computing power fusion scheduling model construction method and a scheduling platform, and relates to the technical field of green computing power. The green power computing power fusion scheduling model construction method comprises the steps of determining an adjustable reference load, determining a peak clipping and valley filling optimization load, determining a clean energy output load, determining a computing power center computing power task execution optimal load curve, monitoring a computing power center fusion scheduling task execution process and the like. A green electric power computing power fusion scheduling platform is constructed, and fusion scheduling task execution responsivity monitoring, computing power center electricity charge economy analysis, computing power center carbon emission reduction analysis and the like of a computing power center are carried out. The beneficial effects of the invention are that the system employs the technologies of big data, cloud computing and the like, builds a green power calculation power scheduling platform, monitors and analyzes the clean energy supply and demand conditions of the calculation power centers of the whole province, provides calculation power task scheduling services for the calculation power centers of the whole province, integrates clean energy consumption and green power calculation power, and improves the efficiency of the whole province. And the calculation power load active matching clean energy supply is realized.

Disclosed in the present invention a green power tracing method for a high-proportion renewable energy supply park, comprising: building a park network architecture model, and, on the basis of power flow data at any moment t throughout the year, splitting the park network architecture model into a plurality of active radiation-type power distribution lines; calculating the green power component of each renewable energy in each power distribution line in a single power user; accumulating the green power components to obtain green power consumed by the single power user at any time t and green power generation capacity output by a single renewable energy at any moment t; and forming a green power consumption matrix of the single power user throughout the year and a green power generation capacity matrix of the single renewable energy throughout the year. According to the present invention, the green power consumption of power users can be accurately evaluated, thereby providing accurate data support for green power tracing for users within parks.

The invention relates to the technical field of power grids, and discloses a port green power virtual energy storage aggregation regulation and control method, which comprises the following steps: acquiring an aggregation scene and resource data; generating a power hard constraint mapping and pile division execution reference by calculating a multi-band energy index and identifying a system power transfer characteristic; a local rapid control configuration set is generated by solving a virtual impedance feasible region and combining power hard constraint allocation compensation budget; and fusing a sub-pile execution reference (slow channel) generated based on the power hard constraint with a fast channel execution track obtained after local online execution compensation, performing priority arbitration under the constraint of the power hard constraint, and outputting a final execution power track. According to the invention, a double-layer regulation and control framework of slow channel planning and fast channel compensation is constructed, and through a core link of power hard constraint mapping, coordinated operation of fast and slow channels and safety and stability of the whole system are ensured, and fast and reliable dynamic support capability can be provided for a power grid.

The invention provides a new energy green electricity transaction quantity reporting strategy algorithm based on deep learning. The new energy green electricity transaction quantity reporting strategy algorithm based on deep learning comprises the following steps: a, data collection: acquiring multi-source data from an electricity market, a weather forecast system, historical power generation data and a load prediction system, the data including historical electricity price, weather conditions, power generation power and power grid load data, and recording as X = {x1, x2,..., xn}, wherein xi is a time sequence data feature vector. According to the new energy green power transaction quantity reporting strategy algorithm based on deep learning, the problem of prediction errors caused by randomness and intermittency in new energy power generation is innovatively solved through a deep learning technology, and the prediction accuracy of the new energy green power transaction quantity reporting strategy algorithm based on deep learning is improved. Power generation fluctuation and weather change features are extracted through a multilayer convolutional neural network, and extreme weather events are automatically identified in combination with an attention mechanism, so that the algorithm can more accurately capture market fluctuation.

The invention relates to a multi-target operation optimization method and system for a green electricity hydrogen production grid-connected system considering multi-scale flexibility of a power grid, and the method comprises the steps: obtaining the power output of new energy at a grid side and the historical data of user load power, and obtaining typical operation days at the grid side and a load side through a clustering algorithm; establishing physical models of an electrolytic cell, a hydrogen storage tank, a fuel cell and the like in the system; considering the transferable characteristic of an electric load and the requirements of system peak regulation and new energy consumption, and formulating a day-ahead peak regulation optimal scheduling strategy according to typical operation days of a source side and a load side; an electricity-hydrogen operation optimization strategy with a primary frequency modulation capability is established based on self-adaptive droop control by considering factors such as an electrolytic bath rotation strategy, a wide-power operation characteristic and a transferable characteristic of a hydrogen load; power grid frequency deviation and electrolytic cell operation power are used as decision variables, multi-target operation optimization with the power grid frequency deviation rate and the new energy consumption rate as target functions is established, a non-dominated sorting genetic algorithm is adopted for solving, and an optimal operation scheme is obtained.

The invention relates to the technical field of green power data monitoring, in particular to a green power data monitoring optimization method and system based on a particle swarm algorithm. The method comprises the following steps: acquiring green power data, namely acquiring generating capacity, power, voltage, current and environmental parameters in a green power system, performing preprocessing based on the acquired green power data, performing feature extraction on the preprocessed green power data, constructing a monitoring model by using the green power data after feature extraction, and optimizing model parameters based on the monitoring model. Comprising the steps of performing model parameter optimization by using a particle swarm optimization algorithm; and setting an early warning threshold according to the characteristics of the green power system and the monitoring target, and comparing the output of the monitoring model with the early warning threshold. According to the method, the particle swarm is divided into a plurality of sub-groups through a multi-group collaborative optimization mechanism, and information exchange is carried out, so that the search diversity can be increased, and the probability of finding a globally optimal solution is further improved.

The invention relates to the technical field of power systems, in particular to an indirect carbon emission method considering new energy and green power transactions, which comprises the following steps: S1, load flow calculation and network loss consideration: determining load flow distribution and forming a matrix required by calculation; s2, unit carbon emission intensity calculation and node carbon potential determination: determining the carbon emission property and intensity of the connected unit; s3, carbon emission responsibility affiliation and green power transaction allocation: distinguishing carbon emission responsibilities of contract power and non-contract power; s4, carbon emission property right distribution and time weighted calculation: after obtaining the carbon emission flow rate allocated by the power supply and the load of each unit, multiplying the carbon emission flow rate by a preset time scale to obtain the carbon emission property right limit of each part of each power grid; according to the method, the accuracy of carbon emission accounting is improved, the definition of the carbon emission distribution characteristics of the power system is enhanced, and the method is suitable for analysis of a lossy network and carbon emission management.

The invention provides a comprehensive energy system optimization scheduling method and device considering diversified application of wind, light and green power, electronic equipment and a storage medium, and relates to the technical field of new energy technology and power system integration optimization. According to the method, a comprehensive energy system mathematical model integrating wind, light, green electricity, a hydrogen and ammonia production process, thermal power ammonia-doped combustion and thermal power oxygen-enriched combustion is constructed; combining the comprehensive energy system mathematical model to construct a comprehensive energy system scheduling model considering cost effectiveness, new energy utilization rate and environmental protection; and selecting a target solution algorithm from the plurality of solution algorithms, constructing an operation scene, and performing simulation solution on the integrated energy system scheduling model to obtain an integrated energy system optimization scheduling result. According to the embodiment of the invention, wind-solar-green-electricity hydrogen production ammonia synthesis and thermal-electricity ammonia-doped combustion and oxygen-enriched combustion technologies are effectively integrated, an ecological system for efficient cyclic utilization of energy and resources is created, and green transformation and industrial upgrading of an energy structure are promoted.

The invention relates to the field of electric power systems, and particularly discloses a combined dispatching method for charging and configuring a mobile robot, the mobile robot is suitable for charging electric vehicles in an expressway service area, and the method comprises the following steps: constructing a combined dispatching system for charging and configuring the mobile robot, the dispatching link of the combined dispatching system comprises a charging link and a distribution link; on the basis of a sequential relationship between a charging link and a distribution link, taking the optimal green power consumption capability as an upper-layer target and taking the lowest scheduling cost as a lower-layer target, constructing a combined scheduling double-layer model of charging and configuration of the mobile robot; and solving the joint scheduling double-layer model, and obtaining a scheduling scheme of the mobile robots, so that the distribution vehicle distributes the corresponding number of mobile robots to the service area with the energy supplement demand in advance according to the distribution path. The method not only can meet the urgent energy supplement demand of the vehicle owner in the peak period of the traffic flow, but also can assist the green power consumption and relieve the power grid pressure.

The invention belongs to the technical field of power systems, and particularly relates to a virtual power plant carbon-green transaction optimal scheduling method considering a green power and carbon emission coupling relationship, which comprises the following steps: S1, by taking the lowest total cost as an objective function, constructing an optimal scheduling model for a virtual power plant to participate in carbon transaction and green certificate transaction; the optimal scheduling model comprises a calculation model or a calculation formula of carbon transaction cost, green certificate transaction cost and operation cost of each device; s2, solving the optimal scheduling model according to actual parameters of a virtual power plant to be subjected to optimal scheduling to obtain an optimal scheduling scheme of the virtual power plant participating in carbon transaction and green certificate transaction; and S3, optimizing the operation scheduling of the virtual power plant by using the optimized scheduling scheme obtained in the S2. The method can ensure that the operation scheduling of the virtual power plant comprehensively complies with and optimizes the complex operation mechanism of the power market while considering the carbon transaction cost and the green certificate transaction cost, and realizes accurate regulation and control.

The invention discloses a collaborative scheduling method and system for a centralized energy storage power station to participate in a joint market, and the method comprises the steps: enabling an energy storage station and a new energy station to form a joint system, taking the maximization of the income of the joint system in a green power and spot market as an optimization target, and constructing a joint system optimization scheduling model considering a green power transaction electric quantity decomposition curve; carrying out optimization scheduling on the capacity of the shared part of the energy storage; independently participating in power market competition by using the residual capacity after energy storage sharing, constructing a double-layer optimization model of participating in spot goods-peak regulation-green power combined market by energy storage, and solving the double-layer optimization model to obtain an energy storage operation strategy; and calculating the contribution degree level of each subject in the joint system, calculating a profit distribution proportion according to the contribution degree level, and further performing profit distribution on each subject in the joint system. According to the invention, flexible diversity of participation of centralized energy storage in multiple types of markets and multiple operation modes can be brought into full play, so that the energy storage utilization rate and the operation income are improved.

The invention belongs to the technical field of high-energy-consumption industrial energy conservation and carbon reduction, and particularly relates to an electrolysis process waste heat gradient utilization and green electricity energy storage coupling system and a regulation and control method thereof. An electrolysis process waste heat gradient utilization and green electricity energy storage coupling system comprises a waste heat recovery module used for recovering and gradient utilization of electrolysis high-temperature and medium-low-temperature waste heat for power generation or heat supply; the green electricity direct supply module comprises a photovoltaic unit, a hybrid energy storage unit and an electricity-heat conversion unit and is used for preferential direct supply, fluctuation absorption and storage of green electricity; and the multi-energy coupling regulation and control module is in communication connection with each module and a power grid, and dynamically optimizes and coordinates energy distribution among waste heat power, green power, energy storage and the power grid based on a preset algorithm.

The invention provides a photovoltaic hybrid hydrogen production system and a capacity configuration optimization method. The system is formed by sequentially connecting a renewable energy power generation unit, an electrolytic hydrogen production unit and a hydrogen energy storage unit. Wherein the power generation unit outputs electric energy by means of a photovoltaic array; aLK and PEM are used in the water electrolysis hydrogen production unit, through cooperative coupling, the adaptability of the hydrogen production process to unstable solar power generation is enhanced, and efficient and stable preparation of green hydrogen under driving of fluctuating photovoltaic electric energy is enhanced. According to the invention, the two types of electrolytic cells are combined and researched, and the two types of electrolytic cells are combined for hydrogen production to form an electrolytic hydrogen production unit so as to give full play to respective advantages. And the economy of the whole hydrogen production system is improved by determining the optimal ratio of the two components in the system. And the prepared hydrogen can be conveyed to a user side or stored in a hydrogen storage tank through a high-pressure storage technology and the like, so that cross-regional transmission and utilization of renewable energy sources are realized.

The invention discloses an energy storage type virtual power plant energy distribution method considering green power transaction and new energy consumption, and the method comprises the steps: building an uncertainty model of each random variable under a green power transaction condition through a scene random differential method through generating a classification scene data set of frequency modulation demands and wind and light output original data under a green power transaction environment; generating a frequency modulation demand and a wind and light output scene with time sequence correlation; with the optimal benefit of the energy storage type virtual power plant as a target function, establishing an energy management two-stage stochastic optimization model of the energy storage type virtual power plant participating in the electric energy-frequency modulation combined market, and setting energy management model constraint conditions of the energy storage type virtual power plant participating in the electric energy-frequency modulation combined market; and solving the energy management model to obtain an optimization scheme that the energy storage type virtual power plant participates in power market profit. According to the method, the specific flexible adjustment characteristic of the energy storage type virtual power plant in a novel power system can be exerted, the scheduling plan performability is improved, the income mode of the virtual power plant is enriched, and the income level of the independent virtual power plant participating in the green electricity transaction market is effectively improved.

The invention discloses an intelligent electric energy meter data acquisition device, method and equipment for electric carbon metering and a storage medium, and relates to the technical field of electric power systems and carbon emission management, and the device comprises a carbon emission hardware calculation circuit module which verifies frozen data and regional-level electric carbon factors and line loss power of a superior node, and generates carbon emission data; a first non-volatile processor storing carbon emission data; and the second nonvolatile processor and the first nonvolatile processor are backups for each other and store electric quantity freezing data. The device expands a carbon emission metering function at low cost through an idle RS485 port of an existing intelligent electric energy meter, generates a dynamic electric carbon factor through local calculation, accurately calculates carbon emission data, and improves the accuracy of the carbon emission data. The data accuracy, real-time performance and reliability are improved, the centralized calculation burden is relieved, the system modification cost is reduced, large-scale popularization and application are supported, and low-carbon transformation and green power management are effectively promoted.

The invention relates to the technical field of electric energy management, in particular to a high-energy-consumption load side resource aggregation pricing calculation method for source-load collaborative optimization clearing. Comprising the following steps: proposing a cooperative operation model of renewable energy sources and industrial loads in a county power grid; and proposing an industrial load day-ahead dynamic time-sharing pricing model oriented to renewable energy consumption. The invention provides a renewable energy source and industrial load coordinated operation method, a local green electricity market clearing model is established, and green electricity transaction between renewable energy sources and industrial loads is realized. By constructing a clearing model of a local green electricity market, social welfare maximization can be realized through green electricity transaction between renewable energy sources and industrial loads; the designed dynamic green power pricing method can effectively maintain the balance of supply and demand of green power and guide the industrial load to optimize the green power demand.

The invention relates to a full space-time dynamic electric power carbon intensity measurement method, which belongs to the field of electric power system carbon emission, and comprises the following steps of: constructing a power grid carbon emission factor model based on a power generation direct carbon emission model of a constructed region in unit time; constructing a static electric power carbon emission intensity model based on the power grid carbon emission factor model of the region in unit time; constructing a dynamic carbon emission factor model of the region in unit time; constructing dynamic power carbon emission intensity models of different types of units based on the dynamic carbon emission factor model; based on the dynamic power carbon emission intensity model of different types of units in the region, considering the power generation process, inter-region power transmission and inter-region green power input and output power carbon intensity, and constructing a region full space-time dynamic power carbon intensity measurement model; and acquiring electric power carbon emission data of different types of units in the region, and performing hour-level dynamic measurement on the regional electric power carbon intensity. Accurate mastering and dynamic monitoring of regional electric power carbon emission conditions are realized.

The invention discloses an integrated device for coupling in-situ remediation and membrane filtration water purification of a reservoir water body, which realizes effective removal of pollutants such as ammonia nitrogen, iron, manganese and the like by fusing the advantages of a gravitational flow ultrafiltration (GDM) technology and a water-lifting aeration technology and utilizing the biochemical action of a biological filter cake layer of the GDM technology and an ultrafiltration membrane interception mechanism, so that the water body in-situ remediation and membrane filtration water purification of the reservoir can be realized. Meanwhile, by utilizing the in-situ remediation capability of a water lifting aeration technology, the ecological environment of a water body is improved, and the release of endogenous pollution is reduced; the device continuously runs for a long time under the designed flux, and the membrane pollution is controlled without adopting hydraulic backwashing and chemical agent cleaning, so that the use of chemicals is reduced from the source, pipelines, water pumps and valve accessories for backwashing and chemical cleaning are not needed, the cost can be effectively saved, and the workload of operation, operation and maintenance is greatly reduced; in addition, solar energy is adopted as a power source, green power supply is achieved, energy consumption is reduced, and negative effects on the environment are effectively relieved.

The invention discloses a rod-pumped well group control method based on a flexible load, and belongs to the technical field of rod-pumped well group control. The method is characterized by comprising the following steps that 1, modeling is conducted on an oil pumping system; step 2, establishing a flexible load model suitable for a rod-pumped well group; 3, taking the minimum difference value between the well group power and the input power as an objective function, considering the power constraint of each pumping unit and the frequency constraint of a motor, and solving the power constraint and the frequency constraint to obtain a pumping adjustment degree scheme between pumping unit well groups; and 4, forming a rod-pumped well group prediction control algorithm. According to the rod-pumped well group control method based on the flexible load, the flexible load characteristic of the rod-pumped well group is fully utilized, and green electric power resources can be efficiently absorbed and utilized. Dynamic matching of load and energy supply is realized by constructing an accurate flexible load model of the rod-pumped well group and performing scheduling optimization control on the system.

The embodiment of the invention provides a method and device for analyzing the influence of policies on economy based on a CGE model, and the method comprises the steps: setting model parameters of the CGE model and the policy scene of each region in a domestic region set through constructing the CGE model and a solving equation set of the CGE model; and according to the model parameters and the policy scenario of each region in the domestic region set, solving the equation set through the CGE model to obtain the economic condition of the target region. Quantitative evaluation of regional economic and environmental benefits by green power development of a target region is realized, and comprehensive benefits of power structure adjustment, carbon emission reduction target realization and industrial optimization and upgrading under different policy scenarios can be accurately quantified, so that an enterprise is assisted in accurately identifying a green power value; and the government is assisted to scientifically formulate policies for promoting industrial green development and economic structure transformation.

The invention provides a nutritive salt in-situ monitoring method and device based on an ocean buoy, the device comprises a buoy main body, a nutritive salt in-situ monitoring unit, a data transmission unit and a data processing unit, the buoy main body is used for supporting the whole device and floating on the sea surface; according to the nutritive salt in-situ monitoring method and device based on the ocean buoy provided by the embodiment of the invention, a plurality of functional modules such as green power generation, nutritive salt in-situ monitoring, data transmission and data processing are integrated, so that the concentration of nutritive salt in an ocean environment is accurately monitored in real time. The device is reasonable in design, compact in structure, stable in performance and capable of stably running in a severe marine environment for a long time. Meanwhile, due to the application of the green power generation mechanism, green and sustainable power supply of the device is realized, and the operation cost is reduced. On the whole, the device provides important technical support and data guarantee for marine environmental protection and scientific research.

The invention discloses an isolated network green power-green hydrogen-green ammonia three-flexible coupling integrated system and an operation method. The system comprises a wind-light storage flexible power generation system, a renewable energy flexible water electrolysis hydrogen production system and a flexible low-temperature low-pressure ammonia synthesis system. The wind and light storage flexible power generation system is used for carrying out green energy power generation through wind power generation equipment and photovoltaic power generation equipment, and carrying out flexible adjustment on the output power generation amount according to a wind power and photovoltaic output curve coupled by electric energy storage equipment; the renewable energy flexible water electrolysis hydrogen production system is used for flexibly adjusting the hydrogen output quantity by starting and stopping the number of hydrogen production electrolytic cells in the hydrogen electrolysis hydrogen production device and the hydrogen energy storage equipment; the flexible low-temperature and low-pressure ammonia synthesis system is used for automatically reducing or increasing the load of the ammonia reactor and generating green ammonia according to the hydrogen output quantity of the renewable energy flexible electrolytic water hydrogen production system and the nitrogen output quantity of the air separation equipment. The problems that a stable power supply is needed for traditional ammonia hydrogen production and flexible production cannot be achieved due to the fact that traditional ammonia synthesis is limited by the narrow load application range are solved.

The invention discloses a wind, light and green power coupled pumping unit well group operation collaborative optimization method. The method comprises the following steps: acquiring field data of a multi-energy micro-grid pumping unit well group; establishing a wind and light output uncertainty model, an energy storage battery model, a pumping unit power model and bidirectional AC / DC converter equipment; based on each model, establishing a multi-objective optimization scheduling model for the intermittent pumping operation of the multi-energy micro-grid pumping unit well group; an NSGA-II method is adopted to solve the established multi-objective optimization scheduling model, and a Pareto optimal scheduling scheme is obtained; and carrying out comprehensive evaluation and decision analysis based on an entropy weight TOPSIS method, and selecting an optimal scheme from a Pareto optimal scheduling scheme set obtained by NSGA-II. According to the multi-energy micro-grid rod-pumped well group off-peak interval pumping operation multi-objective optimization method based on NSGA-II and entropy weight TOPSIS, the power utilization requirement of the rod-pumped well group can be met, the working system of rod-pumped well group interval pumping operation is optimized, the economical efficiency and stability of the operation of a rod-pumped well interval pumping system under a wind-solar storage micro-grid are ensured, and the green power consumption capability of the system is enhanced.

The embodiment of the invention is suitable for the technical field of computing power, and provides a computing power and electric power cooperative scheduling method, computer equipment and a medium, and the method comprises the steps: determining the predicted green power generation amount within a preset time; current electricity consumption information, to-be-executed calculation task information and available calculation power information of the data center are obtained, the current electricity consumption information comprises the current electricity consumption demand, and the calculation task information comprises calculation time information and interaction relation information of a plurality of calculation tasks; on the basis of the calculation time information and the interaction relationship information, sorting the calculation tasks by using a queuing network model to obtain a calculation task sequence; and determining a plurality of calculation tasks from the calculation task sequence as target calculation tasks in the preset time based on the predicted green power generation amount, the current power consumption demand and the available calculation power information. Through the method, the operation cost can be reduced when the cooperative scheduling of the computing power and the electric power is realized.

The invention discloses a port equipment green power system risk early warning method and system, and belongs to the technical field of risk assessment. Comprising the steps of collecting operation data related to new energy in a port equipment green power system; the method comprises the following steps of: constructing a risk map by taking port equipment as nodes and energy transmission and operation coordination relationships among the port equipment as edges, and endowing each node and each edge with characteristics by utilizing acquired operation data; constructing a graph convolutional network model, processing the risk graph data, and marking potential risk nodes; and calculating a risk level for the potential risk node, and implementing a layered alarm system. According to the invention, the limitation that a traditional method is only used for monitoring single equipment or single energy is overcome. And the collaborative operation intensity coefficient and the energy transmission loss coefficient between equipment in the port scene are considered in learning and analyzing the risk map in the graph convolutional network, so that the potential risk nodes can be identified more accurately, and the accuracy and reliability of risk identification are improved.

The invention relates to the technical field of electricity markets, in particular to a green electricity transaction tracing method and system, and the method comprises the following steps: collecting original green electricity transaction information generated after the transaction of a green electricity producer is completed, and carrying out the preprocessing of the original green electricity transaction information, and obtaining the standardized green electricity transaction information; verifying the standardized green electricity transaction information by using an intelligent contract, and calculating a decision factor value of each node in the block chain network by using a node decision model after the verification is passed; based on the decision factor value, using a diffusion storage strategy to perform uplink storage on the verified standardized green electricity transaction information; and retrieving each block in the block chain according to the query information provided by the producer, thereby obtaining the corresponding traceability information and feeding back the traceability information to the producer. According to the method, the security of the consensus node is improved by using the node judgment model and the diffusion storage strategy, the transaction processing speed and throughput are improved, and the credibility and speed of transaction tracing are further improved.

The invention relates to the technical field of charging scheduling, in particular to an expressway unmanned heavy truck intelligent charging system which comprises a data acquisition and preprocessing system and a vehicle pile network cloud integrated cooperative operation and green electricity source tracing carbon emission reduction module. Driving and charging strategies are optimized in combination with algorithms such as reinforcement learning, the energy consumption of the unmanned heavy truck can be effectively reduced, the transportation resource utilization rate is improved, and it is guaranteed that transportation tasks are efficiently completed in a time window; green power full-link traceability is realized based on the block chain technology, and the charging time is dynamically adjusted in combination with peak and valley electricity prices, so that the authenticity of use of green power is ensured, low-cost green power can be preferentially selected, and the charging and overall operation cost is remarkably reduced; the carbon emission in the charging and operation process is accurately quantified, the emission reduction benefit of a traditional heavy truck is scientifically evaluated, meanwhile, the method is in butt joint with a green logistics authentication system, support is provided for obtaining authoritative green authentication for transportation services, and low-carbon transformation of the industry is assisted.