135 results about "Global energy" patented technology

The invention discloses a mining area methane emission inversion method based on satellite observation and a Gaussian plume model. The method comprises the following steps: S1, acquiring a satellite inversion XCH4 data set, meteorological data, emission list data and global energy monitoring data; s2, determining a methane emission source and a research area in a geographic map by using the emission list data and the global energy monitoring data; s3, obtaining methane background concentration and XCH4 enhancement in the geographic map by using an atmospheric background concentration calculation method; s4, combining XCH4 enhanced spatial distribution and meteorological data in a geographic map to identify a methane enhanced plume of a methane emission source in the research area; and S5, performing Gaussian plume flow field simulation inversion of the methane emission source through the Gaussian plume model, and outputting the methane emission rate of the methane emission source at the current time through inversion of the Gaussian plume model. According to the method, the methane emission and the time sequence data of the methane emission can be obtained through accurate inversion, the frequency of obtaining the methane emission is improved, and the methane emission can be obtained according to time scale accumulation.

The invention discloses a micro-grid energy coordination control method considering communication topology dynamic change, which belongs to the technical field of electric energy control and management, and comprises the following steps: monitoring a communication connection state between control nodes, and generating a topology change event; evaluating energy supply capability and communication reliability and adjusting influence in energy coordination to generate adaptive weights; in combination with real-time sensor information and historical operation data, short-term energy prediction data is generated, in combination with adaptive weight, a global energy distribution consensus is achieved, and then an energy distribution decision is generated; in the execution process, the topology change event and the deviation between the short-term energy prediction data and the real-time energy data are monitored, and if a deviation threshold value is exceeded, an event signal is triggered to drive the control strategy to be updated. According to the method, the adaptive weight is generated by monitoring the communication topology and the energy state, and distributed negotiation and event triggering updating are performed in combination with short-term prediction, so that the energy coordination robustness and the operation stability of the micro-grid in a dynamic environment are improved.

The invention relates to the technical field of heat pump drying control, and discloses a dynamic identification control method of a closed heat pump drying system based on waste heat recovery. The method comprises the following steps: establishing a multi-level parameter monitoring network by arranging a sensor array, and continuously collecting system operation parameters to form time sequence data; constructing a waste heat distribution characteristic spectrum, and mapping the collected parameters to a thermodynamic state space to identify a coupling relationship among the parameters; analyzing a heat transfer path to determine a key control variable, and establishing a dynamic weight adjustment mechanism to generate a parameter priority sequence; and a multi-target coordination control strategy is made based on the sequence and the real-time operation state, and an actuator control instruction is output. According to the method, through global energy state recognition and dynamic priority adjustment, adaptive optimization operation of the system under different working conditions is realized, and the energy utilization efficiency and the control quality are improved.

The invention discloses a dual-scale resource optimization method for a multi-unmanned aerial vehicle auxiliary edge computing system. The method comprises the following steps: establishing a dynamic mobile edge computing system model comprising multiple unmanned aerial vehicles and ground mobile users; establishing a joint optimization problem of task unloading, computing resource allocation and unmanned aerial vehicle trajectory planning by taking minimization of total energy consumption of an unmanned aerial vehicle system as an optimization target; based on a designed double-time-scale layered optimization framework, aiming at a joint optimization problem, on a small time scale, an improved clustering algorithm and a closed solution method are adopted, and a real-time optimal task unloading decision and a computing resource allocation strategy are efficiently obtained; and on a large time scale, a near-end strategy optimization algorithm in deep reinforcement learning is used to carry out autonomous learning optimization, and an optimized unmanned aerial vehicle flight path is obtained. The invention provides a resource optimization method which can give consideration to dynamic adaptability, multi-time scale collaboration, global energy efficiency optimality and low calculation complexity.

The invention relates to the technical field of transformer substation operation environment regulation and control, and discloses a transformer substation operation environment low-carbon energy-saving intelligent regulation and control method, system and device and a medium. The method comprises the steps that transformer substation operation environment parameters and device state signals are collected, and a first environment device coupling dynamic representation model is constructed; and in combination with the time sequence prediction and condensation risk assessment sub-model, the nonlinear response relationship of various factors to the heat and humidity distribution and condensation risk of the cabinet is described, and a foundation is laid for accurate regulation and control. And in collaborative optimization, the second multi-target collaborative optimization regulation and control model comprises an upper-layer global energy consumption optimization and lower-layer local environment precise control target function, so that the overall energy consumption is reduced. And low-carbon operation targets and equipment safety constraints are considered, so that low-carbon energy conservation is realized during safe operation of the equipment. The solving strategy is a first improved solving strategy based on model predictive control and particle swarm optimization improvement, the efficiency and precision of processing the complex multi-target collaborative optimization regulation and control problem are improved, and the optimal regulation and control scheme is found quickly and accurately.

The invention provides a GRPO-RL driven data center multi-area cooling system group control method, which comprises the following steps of S1, implementing environment arrangement at inlets of machine room air conditioning units, a hot channel and a cold channel in each area of a controlled data center, and collecting multi-dimensional operation data required by air conditioning system group control and reinforcement learning; s2, data center cold source system strategy optimization modeling is carried out, and the control problem of each air conditioning system of a data center is approximately modeled as a Markov decision process; and S3, performing reinforcement learning control strategy online learning in combination with a group relative strategy optimization algorithm to obtain optimal energy-saving and environment control effects. According to the invention, the generalization ability and stability of the control strategy in the multi-region data center control can be improved, the cooperative control of the global energy conservation of the large data center and the comfort of each region can be realized, and the memory and computing resource occupation in the RL training process can be obviously reduced through the optimization of the multi-region sharing strategy.

The invention discloses an intelligent energy management control method, device and equipment for an electric tractor and a medium thereof, and relates to the technical field of electric drive control in agricultural machinery and vehicle engineering. Planning a global energy demand based on a predicted load; dynamically optimizing control and distribution in real time; executing and coordinating control; by constructing a dynamic closed-loop control framework integrating feedforward prediction and real-time feedback, energy management of the electric tractor can be passively responded in real time, and through multi-source information fusion and intelligent prediction, the prediction result is creatively used as feedforward input in a real-time control layer and is combined with current running state feedback, so that the real-time control of the electric tractor is realized. The control system can smooth instantaneous power impact caused by soil mutation and topographic relief, and the service life of a core power component is remarkably prolonged; in addition, through the combination of pre-matching and dynamic fine adjustment, the operation endurance mileage of the whole vehicle and the economy of the whole life cycle are improved.

The invention relates to an intelligent construction system and method for an energy consumption model based on artificial intelligence, in particular to the field of artificial intelligence. Expert operation experience is extracted from historical smelting data to form a safety benchmark, a dynamic virtual environment capable of simulating complex working conditions is constructed, and on the basis, a multi-agent cooperative training mechanism is utilized to construct an energy consumption model. According to the method, a matching strategy among control units is explored and optimized in a virtual environment to seek an optimal solution of global energy consumption and cost, finally, a maturely trained control strategy is applied to an actual smelting process, and through online monitoring and an active learning mechanism, the method continuously adapts to changes of a production environment and absorbs new excellent operation practices, so that the actual smelting process is optimized. Therefore, continuous, safe and intelligent control and reduction of energy consumption in the titanium alloy smelting process are achieved.

The invention relates to the technical field of power supply, in particular to a temperature difference self-powered system and a control method thereof, and the method comprises the steps: collecting the real-time operation data of the system, carrying out the feature extraction, obtaining a feature tensor, inputting the feature tensor into a trained classification model, recognizing the current energy state, and carrying out the dynamic adjustment of the energy operation strategy of the system. The control method of the temperature difference self-energized system is based on a technical path of'perception-analysis-decision-optimization ', fully considers cooperation of multi-dimensional parameters such as thermal management and energy storage state of the system in an extreme environment, and can accurately judge the energy state stage of the system; and corresponding energy operation strategies are adaptively executed according to the energy state stages, so that autonomous diagnosis and optimization of global energy are realized, the energy utilization efficiency is remarkably improved, and the problems that an existing temperature difference self-energy-supply system lacks environment self-adaption capability, the operation strategies cannot be automatically adjusted according to dynamic changes of an extreme environment, and the energy utilization efficiency is low are solved. And the energy utilization efficiency is low.

Along with global energy transformation, a grid-connected micro-grid serves as a key carrier for distributed energy access, and operation faces complex working conditions. A system is often interfered by colored noise caused by an intermittent power supply and an electromagnetic environment, and meanwhile, information loss is caused by communication aging, acquisition faults and the like, so that challenges are brought to modeling and parameter identification. According to the method, a grid-connected micro-grid Volterra-Wiener model of a static nonlinear block based on a Volterra sequence is taken as an object, and a two-stage expansion-minimization iterative estimation algorithm based on filtering is provided. Firstly, data filtering is carried out on collected input and output data to suppress colored noise, then a hierarchical principle is utilized to decompose a filtered system into two subsystems, and efficient and high-precision identification of model parameters under missing output data is realized through a proposed algorithm. The invention aims to improve the parameter identification precision of the micro-grid system with noise and incomplete data.

This application relates to the field of energy dispatching, and in particular to a method and system for distributed energy storage dispatching based on multi-energy collaboration. The method includes: acquiring a set of factory order information; based on this, extracting and fusing multi-dimensional production value features to generate a dynamic production value assessment information set; based on this, combined with preset energy cost constraints, calculating dynamic electricity value thresholds and making global energy dispatching strategy decisions to generate an energy dispatching strategy information set; acquiring a set of energy supply influencing factors; based on the energy dispatching strategy information set and the energy supply influencing factors set, performing multi-timescale rolling optimization and global preset energy allocation to generate an energy allocation execution instruction set; based on this, issuing and executing dispatching instructions and dynamic production compensation for production lines and energy storage layers, generating and outputting a multi-energy collaborative dispatching report. This application can improve the economic efficiency of factories during energy dispatching.

This invention relates to a multi-constraint trajectory planning method for bouncing movement on the surface of complex small celestial bodies, belonging to the field of deep space exploration. The implementation method is as follows: Establishing a closed-form relationship between bouncing movement distance, velocity, takeoff angle, and energy consumption, and a two-layer expansion radius obstacle representation model; establishing escape velocity constraints, no-fall constraints, fly-through feasibility constraints, and convex obstacle fly-through constraints; using the two-layer expansion radius obstacle representation model and multiple constraints, establishing a fly-through feasibility determination mechanism for obstacles; using Poisson-disk sampling to establish the bouncing reachability domain and an analytical solution for the energy-optimal trajectory satisfying multiple constraints; establishing a reachability domain filtering method based on the bouncing reachability domain and the analytical solution for the energy-optimal trajectory; using energy consumption as the weight, employing the A* algorithm to search for a global energy-optimal bouncing path point sequence and the corresponding velocity and takeoff angle of each path point, generating a global energy-optimal trajectory, thus realizing multi-constraint trajectory planning for bouncing movement on the surface of complex small celestial bodies.

The invention belongs to the technical field of robot intelligent control and energy management crossing, and particularly relates to a robot servo system self-adaptive energy-saving control method based on a task power spectrum, which comprises the following steps: establishing a task power spectrum model based on multi-modal sensor data fusion, and quantifying robot task dynamic characteristics; establishing a multi-physical field coupling loss model comprising a motor loss model and a power device loss model, cooperatively optimizing the bus voltage and the PWM switching frequency based on the power spectrum model and the loss model, and determining an optimal working point parameter combination; smooth parameter switching is realized by adopting a variable parameter self-adaptive predictive control algorithm; system global energy efficiency optimization and control performance guarantee are realized through a three-layer control architecture comprising a cognitive layer, a decision layer and an execution layer and a four-domain collaborative optimization mechanism in a time domain, a frequency domain, an energy domain and a heat domain. According to the method, the prediction precision under extreme working conditions is improved by adopting multi-physical coupling modeling, the performance degradation of a traditional method is avoided, and the robust performance of the system is improved.

The application discloses a kind of multi-node server network topology and energy consumption perception's task scheduling method, belong to data center and high performance computing technical field.It includes: constructing the weighted directed graph topology model including server node and switch node, the dynamic energy consumption weight function of link is defined;Establish the three-dimensional energy consumption and thermal interference evaluation model of node, introduce space thermal interference factor;Analysis the characteristics of task to be scheduled;Based on energy consumption potential field screening candidate node;Build the global energy consumption cost function including computing energy consumption, network transmission energy consumption and thermal coupling penalty energy consumption;According to the principle of minimum total energy consumption, select target node to execute scheduling and update topology state.The application solves the problem of splitting computing and network energy consumption, ignoring the space coupling effect of heat dissipation in the prior art by the fusion of network topology perception and multi-dimensional energy consumption model, and realizes the optimization of global energy consumption of data center.

A method and system for AI-based collaborative energy-saving valve cluster control, belonging to the interdisciplinary field of industrial process control and artificial intelligence, comprises the following steps: First, modeling and defining the optimization problem of the valve cluster system; second, constructing an intelligent optimization model; third, training the intelligent optimization model based on federated learning; and fourth, online collaborative control and continuous learning. This is achieved through an energy-saving valve cluster control system, including a valve cluster system modeling module, an intelligent optimization model construction module, an intelligent optimization model federated learning training module, and a collaborative control and continuous learning module. This invention enables direct and dynamic mapping between the valve cluster system state and the optimal valve opening vector, dynamically and selectively integrating key local information of valves, edges, and nodes to achieve precise and adaptive collaboration; significantly reducing the total energy consumption of the system and achieving optimal global energy efficiency and dynamic continuous optimization of the valve cluster system.

This application discloses an optimization control method, apparatus, equipment, and medium for an iron and steel smelting system, relating to the technical field of iron and steel smelting systems. The method includes: determining target control parameters for each subsystem based on material parameters and target optimization parameters; adjusting the target control parameters based on the global energy parameters of the iron and steel smelting system and the weight information of each subsystem in the smelting process to generate actual control parameters for each subsystem; and controlling the operation of each subsystem based on the actual control parameters. This optimization control method can control the operation of each subsystem of the iron and steel smelting system from the perspective of global energy parameters and the importance of each subsystem, avoiding the impact of optimizing a single link on the overall optimization effect of the smelting process, and improving the overall optimization effect of the iron and steel smelting system operation.

The invention discloses an automatic control system of an electric agricultural machine, and relates to the technical field of control systems. The system comprises an atomic task decomposition and energy consumption modeling module, which is used for decomposing a macroscopic job into three types of atomic tasks of movement, job and action and establishing a refined energy consumption model; the global sequence optimization module is used for optimizing an atomic task sequence and distributing an energy budget by taking total energy as a constraint and taking comprehensive benefit maximization as a target; the real-time energy consumption monitoring and budget cancel-after-verification module is used for tracking execution and accounting energy consumption deviation; and the dynamic re-planning decision module triggers the re-optimization of the residual atomic task parameters or sequences when the deviation threatens the total atomic task. Through atomic task level energy budget management, global energy optimal planning before operation and dynamic adaptive adjustment in operation are realized, the problems of low operation reliability and low energy utilization efficiency caused by limited electric quantity of the electric agricultural machinery are solved, and the effective operation quantity of single charging is improved.

The application discloses a preset time control multi-electron gun cooperative scanning optimization method and system, relates to the technical field of industrial process control and optimization, and comprises the following steps: S1, a dynamic coupling model is constructed, and multi-electron gun system parameters and a communication topology are initialized; S2, a leadership comprehensive score is dynamically calculated, and a main leader and an auxiliary leader are elected; S3, the main leader solves a local constraint optimization problem, and a global energy reference trajectory is generated; S4, each electron gun independently generates a control instruction through a respective preset time distributed optimal controller based on the global energy reference trajectory, and converges a tracking error; S5, the control instruction of each electron gun is subjected to constraint processing, and is executed; and S6, system performance is monitored, and adaptive adjustment is performed; the preset time control multi-electron gun cooperative scanning optimization method and system provided by the application solve the problems of serious interference between electron guns and poor anti-disturbance robustness in the prior art.

The application discloses a kind of based on adaptive boundary perception's spare part point cloud hierarchical super voxel segmentation method, it is related to three-dimensional point cloud super voxel segmentation technical field, this method calculates the multidimensional geometry feature of each point of point cloud first, constructs point cloud boundary weight model by weighted fusion and nonlinear transformation;Again, complete point cloud voxelization based on octree space division mechanism, according to boundary weight value, voxel is divided into boundary voxel and seed voxel, and the initial super voxel is generated by merging the point set in voxel through union-find set data structure;Then, relying on global energy optimization framework, combined with distance measurement function containing boundary weight difference item, hierarchical iterative fusion is carried out on the representative point of initial super voxel;Finally, the super voxel after fusion is corrected and the spatial connectivity is corrected, and the multi-resolution super voxel segmentation result is output.The application can solve the problem that traditional method is easy to over-segmentation, under-segmentation, and enhance the adaptive level of complex equipment spare part structure.

The invention relates to the field of digital energy management and control, in particular to a multi-level energy abnormity early warning method and system and a storage medium, and the method comprises the steps: obtaining multi-source data of an energy system; performing multi-dimensional feature extraction on the multi-source data and obtaining fusion features; based on the fusion features, constructing a multi-scale sliding window to calculate a dynamic threshold; performing multi-dimensional anomaly judgment on the current energy consumption data according to the dynamic threshold value to obtain a comprehensive anomaly score and a persistent score; and performing multi-level early warning aggregation based on the comprehensive abnormal score and the persistence score to obtain an early warning scheme. According to the embodiment of the invention, by constructing the dynamic adaptive threshold system, the multi-dimensional anomaly judgment mechanism and the multi-level early warning aggregation framework, accurate anomaly detection, timely early warning response and global energy efficiency management of the industrial energy system are realized.

This invention relates to the field of manufacturing large-scale hydraulic engineering irregular-shaped gradient steel pipes, and discloses a method and system for automatically generating the unfolded blanking contour curve of large-scale hydraulic engineering irregular-shaped gradient steel pipes. The method includes: acquiring and discretizing an initial design model; extracting shape operator tensors and binding them to a topological feature set; performing curvature-wall-thickness coupling analytical calculation of the neutral layer offset; translating to generate a feature compensation mesh and extracting the unfolding constraint set; constructing a two-dimensional coordinate system and performing dimensionality reduction and tiling iteration based on the initial coordinate set of the generatrix; determining the two-dimensional coordinates by minimizing the global energy penalty function; and generating the blanking contour curve through smooth curve fitting. This invention solves the problems of geometric interference and accuracy distortion in thick plate forming, achieves accurate restoration of the physical neutral layer and eliminates the need for secondary fitting, and improves the manufacturing accuracy and efficiency of large-scale hydraulic engineering components.

The application discloses a green clean energy comprehensive energy-saving management platform based on an Internet of Things intelligent terminal and particularly relates to the technical field of intelligent monitoring and optimal control, and is used for solving the problem that the prior art has no solution or outputs a high-risk operation strategy due to neglecting the coupling relationship between subsystems when dealing with multiple security constraints; through the Internet of Things terminal, operation parameters and security constraint boundaries are acquired, when multiple constraints are close to being reached, the key coupling path of the connected constraint is identified; then, the change of the conflict correlation strength between the constraints under different relaxation schemes is simulated, a target scheme that can reduce the network structure tightness to the maximum extent is selected by constructing and analyzing the conflict relationship network, a group of coordinated constraint boundaries is generated; finally, the global energy efficiency optimization calculation is performed and the equipment set value is output based on the boundaries, so that the coordinated control of the safe and efficient operation of the complex energy system is realized.

The invention relates to the technical field of industrial ovens, in particular to an energy-saving industrial oven control system. A central control module receives and fuses multi-source input information such as real-time temperature data of each temperature zone fed back by a partition temperature control module, heat recovery efficiency parameters fed back by a waste heat recovery module and real-time energy cost and availability information fed back by a multi-energy scheduling module, and target collaborative decision is optimized based on system global energy efficiency and cost. And generating three types of corresponding control instructions. The three kinds of instructions respectively regulate and control the partition temperature control module to achieve accurate temperature control of all temperature zones, the waste heat recovery module adjusts the working state of the device to recover high-temperature waste gas heat energy, the multi-energy dispatching module completes dynamic distribution and switching of multiple kinds of energy, feedback dynamic optimization collaborative decision logic is executed by combining all the modules, and a closed-loop control system is formed. The whole process of temperature zone control, waste heat recovery and energy allocation is comprehensively planned through collaborative decision making of the central control module, and the operation energy efficiency utilization rate, the temperature control accuracy and the energy cost management and control reasonability of the industrial oven are improved.

The invention discloses a global tonnage modular electro-hydraulic decoupling hybrid power platform and a control method. The global tonnage modular electro-hydraulic decoupling hybrid power platform is suitable for 30-99 ton large hydraulic excavators. The system is composed of a power and transmission subsystem, an energy storage unit subsystem, a high-voltage electric drive subsystem and a low-voltage system subsystem. The flywheel end of the diesel engine is connected with the transfer case, the two-way driving main hydraulic pump and the ISG starting and generating all-in-one machine. Engine deep decoupling, independent electric driving walking and rotation, braking energy recovery, full-electric heat dissipation independent driving, DC-DC independent power supply, intelligent range extending energy complementing and electric transmission starting and stopping are achieved, a traditional hydraulic walking and rotation motor, a multi-way valve, a hydraulic heat dissipation pump and a motor are omitted, and a full-tonnage machine type is modularly adapted. The whole vehicle controller achieves multi-mode control and global energy scheduling, the comprehensive working condition fuel saving rate ranges from 25% to 35%, the high idle speed and transition fuel saving rate ranges from 35% to 45%, and the problems that existing hybrid power is large in coupling degree, heat dissipation depends on an engine and hydraulic pressure, energy is wasted, and global expansion cannot be achieved are solved.

The invention discloses a global energy complementation and solid waste cooperative treatment system based on day and night time difference satellite microwaves, and belongs to the technical field of new energy scheduling and environment-friendly solid waste treatment. According to the system, on the basis of a satellite microwave transnational energy complementation scheduling architecture based on day and night time difference, photovoltaic surplus abandoned electricity is converted into microwave signals, transnational directional transmission is realized through a low-orbit relay satellite cluster, and meanwhile, surplus microwave energy is transmitted to realize transnational energy complementation. And solid wastes such as household garbage, excrement in a septic tank and sludge in a sewage treatment plant are subjected to closed anaerobic microwave cracking treatment. According to the system, five core modules of new energy waste electricity collection, microwave conversion emission, low-orbit relay satellite cluster, ground microwave receiving rectification and intelligent time difference scheduling control are reserved, a microwave solid waste cracking treatment module and a product separation and recovery module are additionally arranged, solid waste can be automatically separated into condensate water, combustible gas, fuel oil and sterile carbon residues, and full resource utilization is achieved. The two problems of photovoltaic abandoned electricity waste and solid waste pollution are synchronously solved, fossil energy consumption is zero, secondary pollution is avoided, the new energy utilization rate is greatly increased, the traditional solid waste treatment bottleneck is thoroughly broken through, and an integrated innovative solution is provided for global low-carbon energy development and ecological environment protection.

The invention relates to a refrigerator control method, device and system and a storage medium. The method comprises the steps of analyzing operation data packets of a plurality of refrigerators in combination with a preset cooperative control strategy, allocating different energy efficiency optimization configuration instructions for different refrigerators to cooperatively control the plurality of refrigerators, and enabling each refrigerator to adjust operation parameters in combination with a local operation state according to the corresponding energy efficiency optimization configuration instruction, therefore, global energy efficiency optimization corresponding to the multiple refrigerators is achieved, the overall energy-saving rate of the multiple refrigerators is increased, and the problem that cross-device energy-saving cooperative control cannot be achieved for the multiple refrigerators in the prior art is solved.

The invention relates to the technical field of large models, and discloses an extreme environment energy storage regulation and control method and system based on an interconnected large model, and the method comprises the steps: simulating a federated learning environment of energy storage equipment in an extreme environment, and constructing a global energy storage regulation and control large model of the energy storage equipment; collecting a multi-modal time sequence data set of the energy storage equipment in an extreme environment to construct an environment perception vector of the energy storage equipment; the extreme environment event probability and the equipment operation risk of the energy storage equipment are analyzed; the method comprises the steps of generating a regulation and control instruction of the energy storage equipment, simulating the regulation and control instruction to obtain a simulation result, and analyzing the performability and safety of the regulation and control instruction based on the simulation result to finally generate a decision instruction of the energy storage equipment; and adjusting operation parameters of the energy storage equipment to execute regulation and control of the energy storage equipment in the extreme environment. Accuracy and adaptability of energy storage regulation and control in the extreme environment can be improved.

The invention relates to the technical field of energy efficiency data acquisition and analysis oriented to factory management, in particular to an intelligent factory standardized production line energy efficiency data acquisition and analysis method and system, and the method comprises the following steps: a multi-source data perception interaction module captures a component pose and thermal parameters to construct a data set; the thermal energy efficiency dynamic analysis module calculates thermal demand and energy supply deviation based on a process stage; the logistics transmission power consumption evaluation module constructs a damping loss model and distinguishes effective work and abnormal loss, and the global energy efficiency optimization decision-making module executes an adaptive weighted fusion algorithm, solves an optimal working point and generates a closed-loop feedback energy-saving regulation and control strategy. The energy flow relation of production nodes is analyzed in real time through a high-frequency sensor and a dynamic thermodynamic model, hidden energy waste points are accurately positioned, process-level standardized energy efficiency indexes are formed, and energy consumption optimization, abnormity tracing and production line energy utilization rate improvement are achieved.

This invention discloses a method and system for coordinated management of oil and gas pipeline transportation at a joint station, relating to the field of oil and gas field gathering and transportation technology. The system consists of several functional modules, including: a status identification module, which collects multi-dimensional data from joint station equipment via edge computing nodes, identifies real-time equipment efficiency curves online, and generates real-time equipment status sequences; a pipeline network coupling construction module, which constructs a nonlinear hydrothermal coupling mechanism model of the pipeline network based on the real-time equipment status sequences through a cloud-based digital twin platform, and performs online self-correction to obtain a global pipeline network simulation model of real-time field conditions; and a two-layer hierarchical optimization algorithm, which, based on the global pipeline network simulation model and combined with historical inflow data, outputs the optimal pressure and temperature setpoints for each joint station in the future period; wherein the two-layer hierarchical optimization algorithm includes an upper-layer global energy consumption minimization optimization layer and a lower-layer local predictive control tracking layer.

A computer-implemented method is executed using a global energy management system computer in an energy and load resource network, the energy and load resource network comprising a plurality of energy generating resources, a plurality of load resources, a plurality of energy storage resources, and a plurality of virtual energy resources, the computer-implemented method comprising receiving a plurality of energy market data and a plurality of capacity market data items; executing an inference stage of a trained machine learning model over the plurality of energy market data and the plurality of capacity market data items to output predictions of generating capacity and load requirements; executing an optimization algorithm over the predictions of generating capacity and load requirements to output a plurality of optimized generating capacity and load requirements; forming a plurality of operational instructions for a plurality of the resources in the energy and load resource network, the plurality of operational instructions being formatted to cause the plurality of the resources in the energy and load resource network to conform to the plurality of optimized generating capacity and load requirements; transmitting the plurality of operational instructions to the plurality of the resources in the energy and load resource network to instruct the plurality of the resources in the energy and load resource network to conform to the plurality of optimized generating capacity and load requirements.