945 results about "Total energy" patented technology

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

This invention advances power grid operational planning by introducing a mid-term scheduling framework for integrated hydro-wind-solar systems that accounts for heterogeneous daily load profiles across multiple receiving-end power grids. The proposed approach utilizes an adaptive variable-step search algorithm to segment loads into peak, flat, and valley intervals. By synthesizing five key metrics, including mean daily load, daily load factor, peak-valley differential ratio, load rates during peak / valley periods, and timing of peak / valley occurrences, the method accurately captures region-specific load patterns and peak-shaving demands. This enables a refined reconstruction of load profiles of receiving-end power grids. A nested multi-temporal scheduling model that couples medium- and short-term horizons to simultaneously maximize total energy production and minimize transmission imbalances among power grids. The model is addressed by using the mixed-integer linear programming (MILP) to obtain medium- and short-term generation schedules and power transmission schedules.

Provided in the embodiments of the present application are an energy storage apparatus, an energy storage system and a charging network. The energy storage apparatus comprises a container, a control module and a thermal management module, wherein the container comprises a container body and a plurality of battery cells, the plurality of battery cells are accommodated in the container body, and at least one of the dimensions of the container in the length direction, the width direction and the height direction is not equal to a corresponding dimension of a standard container; the control module is used for electrically connecting to the plurality of battery cells to perform electrical control over the battery cells; and the thermal management module is connected to the container, the thermal management module is used for managing the temperature of the battery cells, and at least part of the thermal management module is located at the top of the container. When the total energy of a container is fixed, the container can have a smaller volume, such that an energy storage apparatus occupies a smaller floor area, thereby increasing energy per unit floor area of the energy storage apparatus, and thus increasing the areal energy density of the energy storage apparatus.

The invention relates to a clustering and entropy-guided reentrant hybrid flow shop scheduling method. The method comprises the following steps: step 1, establishing a problem model; step 2, setting algorithm operation parameters; 3, adopting an initialization strategy to generate an exploration population and a development population; 4, judging whether a first-stage termination condition is met or not, if not, executing a first-stage evolutionary strategy and an updating strategy on the exploration population and the development population, and otherwise, executing the step 5; 5, constructing an elite population; 6, judging whether a second-stage termination condition is met or not, and if not, executing a second-stage evolutionary strategy on the elite population; otherwise, outputting a Pareto solution set; and 7, updating the elite population. According to the method, dynamic balance of global exploration and local development is realized, and the solution distribution can be improved while the solution set convergence is ensured, so that the completion time and the total energy consumption are reduced, the production cost is reduced, and the workshop scheduling efficiency is improved.

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

The invention provides an anti-surge control method. The anti-surge control method comprises the following steps: acquiring historical data of a surge point; inputting the historical data of the surge points into the to-be-trained model to obtain an anti-surge model; based on the anti-surge model, determining an anti-surge line of the fan; the anti-surge line is generated by translating the abscissa of the surge line of the fan in the anti-surge coordinate system rightwards by a first preset unit and translating the ordinate downwards by the first preset unit; the real-time fan inlet flow and the real-time fan total energy head of the fan are obtained, and the real-time working condition point of the fan is determined; whether the real-time working condition point is located below an anti-surge line or not is judged, and if not, the opening degree of an anti-surge valve is adjusted to 100%; if yes, the anti-surge opening degree and the anti-surge valve opening time are determined based on the real-time working condition point, the anti-surge valve is opened according to the anti-surge valve opening time, the anti-surge valve is controlled to operate at the anti-surge opening degree, and the problem that the position area where the working condition point of the draught fan is located cannot be accurately and automatically adjusted at present is solved.

The invention discloses a building group energy-saving management and control method and system based on big data analysis, and relates to the field of big data analysis, and the method comprises the steps: carrying out the standardization processing of the real-time visitor flow data of a commercial district, and the subentry energy consumption data, time information and environmental parameters of the commercial district and a residential district, and constructing a current scene feature vector; screening matched similar historical scene samples, and generating a commercial district people flow prediction curve in a future target time period through weighted fitting calculation; screening matched historical regulation and control records, and performing weighted calculation to obtain a mean value as a historical reference regulation and control parameter; generating a candidate parameter combination according to a preset step length, calling the corresponding total energy consumption and the indoor temperature of the residential area, and selecting an energy-saving regulation and control instruction; and adjusting operation parameters of the commercial district energy system according to the energy-saving regulation and control instruction. According to the building group energy-saving management and control method and system based on big data analysis, the problem that the use comfort of a residential area is poor due to the fact that the energy consumption fluctuation of a commercial area is too large is solved.

The invention discloses a multi-rotating-speed flexible job shop scheduling method applied to double-resource constraint, and the method comprises the steps: taking the maximum completion time and minimum total energy consumption of a minimum machine as target functions, and constructing a flexible job shop scheduling model considering the rotating speed energy consumption of the machine and the production demands of a fine process; a machine speed gear constraint, a fine process constraint, a process sequence constraint, a completion time constraint, a machine processing constraint and a worker operation constraint are established as constraint conditions of the model; the flexible job shop scheduling problem is solved by adopting an improved artificial bee colony algorithm, bee colony search guided by excellent genes is adopted in bee learning operation in the improved artificial bee colony algorithm, and nectar source optimization is carried out based on the searched excellent genes; the following bee operation adopts a neighborhood structure which considers machine speed change and balances the working time of workers to carry out dynamic neighborhood search so as to optimize a nectar source. The effectiveness of the improved strategy is verified through experiments, and the superiority is verified through comparison of different algorithms on expansion standard examples.

The invention discloses an unmanned aerial vehicle-unmanned agricultural machine collaborative operation staged scheduling method under energy consumption constraint, belongs to the technical field of intelligent scheduling, and innovatively designs a three-stage dynamic scheduling mechanism based on operation urgency, and realizes stage adaptive switching through real-time energy consumption monitoring. And an unmanned aerial vehicle-agricultural machinery energy consumption prediction model considering flight conditions and operation parameters is established, and accurate prediction of the residual endurance time is realized by using an LSTM network. An improved NSGA-II multi-objective optimization algorithm is provided, and under a three-objective Pareto optimization framework of total energy consumption minimization, task completion rate maximization and time cost minimization, an energy consumption perception auction mechanism is combined to realize intelligent distribution of emergency tasks to high-electric-quantity equipment. A distributed real-time scheduling system based on edge computing is developed, an edge server is deployed to run a lightweight algorithm, and a real-time scheduling algorithm and a dynamic task conflict resolution mechanism are combined, so that the task redistribution delay is controlled within 500ms, and the conflict resolution success rate is increased to 92.3%.

The invention discloses a task scheduling and energy consumption optimization system and method based on distributed satellite calculation, and the method comprises the following steps: obtaining the floating point calculation amount and data size of each subtask, obtaining the calculation power and link rate of each satellite, and calculating the estimated execution time of the subtasks on the satellites according to an execution time formula; further obtaining the calculation power, the communication power and the corresponding duration of each satellite, and estimating the total energy consumption through an energy consumption formula; and optimizing a task allocation scheme based on the energy consumption evaluation, thereby reducing the overall energy consumption of the satellite cluster.

The invention discloses a gas purification real-time monitoring method, system and device and a medium, and belongs to the technical field of gas purification. The method comprises the following steps: a data acquisition stage: acquiring inlet gas flow and outlet component data; in the adsorbent state monitoring stage, the adsorbent state is monitored in real time through the monitoring point position in the adsorption barrel; in the adsorption capacity calculation stage, the residual adsorption capacity is predicted through an impurity penetration curve model on the basis of inlet flow and outlet component data, and bidirectional verification and correction are carried out on the residual adsorption capacity and an internal monitoring state; in the regeneration time prediction stage, the optimal regeneration time is dynamically predicted according to discharged gas component analysis and residual adsorption capacity; in the real-time regulation and control stage, parameters of the purifier and the regenerative heater are adjusted in real time according to the prediction result. Accurate evaluation of the state of the adsorption cylinder and intelligent optimization of the regeneration process are achieved, the utilization rate of the adsorption cylinder is remarkably increased, and the total energy consumption of regeneration is reduced.

The invention relates to a power transmission line unmanned aerial vehicle airport optimal configuration method. The method comprises the steps of determining an inspection area and unmanned aerial vehicle inspection task requirements; constructing an unmanned aerial vehicle energy consumption model; based on the unmanned aerial vehicle energy consumption model, combining a neural network and a Kalman filtering algorithm to predict the energy consumption of the unmanned aerial vehicle arriving at the tower; an improved ant colony algorithm is adopted, and optimal energy consumption path planning of unmanned aerial vehicle inspection is carried out; based on the planned optimal energy consumption path, the tower distance and the energy consumption prediction of the unmanned aerial vehicle reaching the towers, determining the position and the number of optimal supply points with the goal that the remaining electric quantity of the unmanned aerial vehicle reaching the supply points is not lower than a safety threshold value; in the inspection process, according to real-time energy consumption prediction, a flight path and a supply strategy are dynamically adjusted, and it is ensured that the unmanned aerial vehicle arrives at the nearest supply point for supply before the remaining electric quantity is lower than a safety threshold value. The method can achieve the minimization of the total energy consumption of the inspection and the maximization of the inspection efficiency, and improves the cruising ability in the inspection of the unmanned aerial vehicle through the reasonable configuration of the supply point of the unmanned aerial vehicle airport.

The energy management method based on the automatic cleaning system of the buoy ship for the river comprises the steps that the surface dirt area and dirt thickness of the buoy ship are collected and input into a pollution degree evaluation model, and the cleanliness level is judged; constructing a relation model of the cleanliness grade, the water pump power and the nozzle spraying angle; based on the output power and the operation time of the electric cleaning machine of the buoy ship under different cleaning degree grades, the corresponding relation between the actual cleaning degree grade and the output power and the operation time of the electric cleaning machine of the buoy ship is obtained, and a relation model between the cleaning degree grade and the output power and the operation time of the electric cleaning machine of the buoy ship is constructed; taking the minimum total energy consumption of the water pump and the electric cleaning machine as a target, solving a water pump output power model by using a differential evolution algorithm, updating the output power of the electric cleaning machine in real time, and obtaining an optimal energy-saving scheme of the automatic cleaning system of the buoy ship for the river. On the premise of guaranteeing the cleaning effect, the method reduces the overall power consumption of the system to the maximum extent, improves the energy utilization efficiency and prolongs the service life of equipment.

The invention relates to the technical field of sewage treatment process control, in particular to a sewage treatment process control system and device based on a multi-scale time sequence diagram neural network, and the system comprises an energy consumption digital twin modeling module which constructs a whole-process equipment energy consumption and process state twin mapping model; the energy consumption trend prediction module captures three-level energy consumption association by using a multi-scale time sequence diagram neural network based on the model data to obtain a prediction result; the multi-objective optimization module constructs an optimization function containing total energy consumption and the like according to the total energy consumption and the like to obtain a balance strategy; the process self-adaptive regulation and control module adjusts process parameters and data updating network according to the inlet water quality and the optimization target; and the energy scheduling optimization module constructs a model containing peak and valley electricity price perception and outputs a scheduling result. By designing a multi-objective optimization framework and combining water quality adaptive adjustment process parameters and a peak-valley electricity price sensing mechanism, global energy consumption collaborative optimization of the sewage treatment plant can be realized, the energy utilization efficiency can be improved, the operation cost can be reduced, carbon emission can be reduced, and efficient green treatment can be realized.

The invention relates to the technical field of refrigeration energy efficiency monitoring of refrigeration houses, in particular to a dynamic monitoring system and method for refrigeration energy efficiency of a refrigeration house. A multi-objective optimization scheme analysis module in the system is used for analyzing the refrigeration energy efficiency of the refrigeration house based on the running state of each temperature zone of the current refrigeration house, the heat state of stored goods and an external electricity price signal; and by taking the minimization of the total operation energy consumption of the refrigeration house and the minimization of the temperature fluctuation of each temperature zone as optimization objectives, constructing a multi-objective optimization function, and generating at least one candidate energy-saving operation scheme in a future time window of a preset duration. According to the method, through multi-dimensional data perception, a digital twinborn model for specific characteristics of goods and refrigeration house scenes is established; a multi-objective optimization function with the lowest total energy consumption, the lowest temperature difference fluctuation of the temperature zone and the highest energy efficiency ratio of the system as the core is constructed; and a plurality of candidate energy-saving schemes are dynamically generated by using the function, and an optimal scheme is screened out through a normalized comprehensive evaluation system, so that clear energy efficiency data and optimization suggestions are provided for managers, and invalid energy consumption is avoided.

The invention provides a numerical control multi-objective optimization energy-saving method and system based on an improved wolf pack algorithm, and the method comprises the steps: constructing a mixed integer linear programming model of multi-objective flexible job shop scheduling, taking machine tool distribution, a machining sequence, tool distribution and a machining speed as decision variables, and taking total weighted tardiness and total energy consumption as optimization targets; the method comprises the following steps: constructing an MOGWO-ALNS algorithm for solving a mixed integer linear programming model based on an MOGWO algorithm and an adaptive large neighborhood search mechanism, constructing a three-stage collaborative optimization architecture by the MOGWO-ALNS algorithm through a dual-threshold trigger mechanism, solving the mixed integer linear programming model based on the MOGWO-ALNS algorithm, obtaining a Pareto optimal solution set of the mixed integer linear programming model, decoding the Pareto optimal solution set, and finally obtaining the mixed integer linear programming model. And generating a multi-target balanced energy-saving scheduling scheme. According to the method, collaborative optimization of the delivery date and the energy consumption in numerical control machining is achieved, the solving efficiency and the optimization effect are improved, and the method has good engineering application value.

The invention discloses a power control method, system, equipment and medium for a modular multilevel converter, and belongs to the technical field of power electronics and power system application, and the method comprises the steps: collecting the bridge arm current, the sub-module capacitor voltage, the DC bus voltage and the AC side power of the modular multilevel converter; calculating a power error according to the AC side power and a power reference value, and calculating a sub-module capacitor voltage error according to the sub-module capacitor voltage and a voltage reference value; constructing a Lyapunov function comprising a plurality of control targets, wherein the Lyapunov function comprises a power error term, a sub-module capacitor voltage error term, a circulating current suppression term and a total energy balance term; and determining a control law on the basis that the Lyapunov function meets a stability condition, and controlling the modular multilevel converter according to the control law. According to the method, the coupling control problem of multi-terminal direct current system power distribution and sub-module energy balance is considered and improved.

The invention discloses an energy-saving type airflow cooperative control system of an impact type tunnel instant freezer and an optimization method. The system obtains multi-source data of materials, environment and equipment in real time through a data sensing and collecting module; the collaborative optimization decision module generates a global control instruction for coordinating the air supply subsystem, the refrigeration subsystem and the conveying subsystem based on an optimization model with the lowest total energy consumption of the system as a target; the airflow dynamic execution module and the refrigeration conveying cooperative execution module respectively drive the adjustable airflow generation unit, the refrigerating unit and the conveying chain to generate a dynamic airflow field matched with the freezing load and adjust the cooling capacity and the speed; the intelligent defrosting scheduling module optimally triggers defrosting based on the prediction model; according to the method, the steps of data collection, decision optimization, airflow dynamic adjustment, refrigeration conveying cooperation and intelligent defrosting are synchronously executed. According to the invention, deep collaboration and dynamic optimization of multiple subsystems are realized, the energy consumption of the instant freezer can be obviously reduced, and the freezing uniformity and the continuous production efficiency are improved.

The invention relates to the technical field of power monitoring, in particular to an online monitoring method and system for a power control cabinet. The method comprises the following steps: acquiring a time domain signal, tracking a fundamental wave frequency, adjusting a sampling window, and performing Fourier transform to obtain a harmonic amplitude and a phase; harmonic waves are divided into harmonic wave correlation groups according to association rules, harmonic wave phase consistency coefficients are calculated to weight the total energy in the groups, and weighted harmonic wave energy is obtained; total inter-harmonic energy is calculated, and an inter-harmonic structure factor is obtained based on the spectral entropy. And acquiring a real-time load rate and a change rate, and calculating a synchronous change coefficient and a load factor. And summing the weighted harmonic energy and the corrected inter-harmonic energy to obtain total distortion energy, and obtaining a comprehensive distortion index according to the total distortion energy. According to the scheme, the structured inter-harmonics possibly caused by the early failure of the equipment can be found earlier, and the requirements of early warning and predictive maintenance are met.

The invention relates to the technical field of intelligent breeding and logistics optimization control, and solves the technical problems of high energy consumption, unstable efficiency, unbalanced distribution, lack of an intelligent scheduling mechanism and the like in feed conveying of a multi-storey pig farm. The method comprises the following steps: acquiring static parameters (physical characteristics of feed, physical attributes of a conveying system and a pig house structure) and dynamic parameters (real-time feeding requirements, equipment and material states and external environment factors); establishing a multi-objective optimization model of a collaborative optimization energy consumption model E (x), a time model T (x) and a conveying balance degree model U (x); solving by adopting a genetic algorithm with a special design crossover and mutation operator to obtain an optimal scheduling scheme; in the execution process, a closed-loop self-learning calibration mechanism is started, actual power is measured through a current sensor, and when the deviation between predicted energy consumption and actual energy consumption exceeds a preset threshold value, efficiency parameters in the energy consumption model are reversely corrected through a gradient descent method; the weight coefficients of the three models are dynamically adjusted according to the real-time electricity price and the inventory state. The system adopts a three-layer architecture of a perception and data acquisition layer, a decision and control core layer and an execution layer. According to the method, multi-target collaborative optimization and intelligent adaptive scheduling are realized, the total energy consumption is effectively reduced, the transmission time is shortened, the distribution balance degree is improved, and the energy consumption prediction accuracy is remarkably improved.

The present invention relates to an optimization method for integrated joint scheduling of production and logistics in a flexible job shop (FJSP) having a plurality of different types of automated guided vehicles (AGVs). The invention belongs to the field of assembly workshop production scheduling. Comprising the following steps: 1) according to a special assembly workshop machine and AGV combined scheduling process, Tent chaotic mapping is adopted to initialize a scheduling scheme and encode the scheduling scheme; 2) performing iterative optimization adjustment on the scheduling scheme through an improved multi-target artificial bee colony algorithm; and 3) carrying out production scheduling by using the optimized scheduling scheme. According to the method, the maximum completion time and the total energy consumption are optimized at the same time, the production efficiency is concerned, the requirements of green manufacturing and sustainable development are also considered, and enterprises are helped to achieve cost reduction and efficiency improvement, especially in the production process sensitive to energy consumption.

The invention belongs to the technical field related to operation control of a power system, and provides a transient instability judgment method and system of a network-building type converter in order to solve the problem that an existing transient instability judgment method of the network-building type converter is prone to causing maloperation or missed judgment of a control strategy, and a nonlinear equivalent circuit model of the network-building type converter is established. Calculating transient total energy and critical instability energy of the system in real time, and adaptively switching a control mode according to the magnitude relationship of the transient total energy and the critical instability energy; during normal operation, an original control strategy of the virtual synchronous machine is maintained, and oscillation is suppressed by using the damping characteristic of the virtual synchronous machine, so that the voltage and frequency supporting capacity of a power grid is maximized; and when an instability risk is detected, entering a fault ride-through mode to realize rapid and accurate identification and control of the instability risk.

The invention discloses a low-energy-consumption resource allocation method based on multi-agent deep reinforcement learning in a cellular-free large-scale MIMO system, and the method comprises the steps: taking the minimization of the total energy consumption of all users as a target, and building a joint optimization problem of a user task unloading strategy, an uplink power allocation strategy and a computing resource allocation strategy; the optimization problem is converted into a deep reinforcement learning model, the user, the access point and the cloud server are respectively converted into intelligent agents, the state, the action and the total reward function of each intelligent agent are set, the actions of the intelligent agents of the user are set to be a user task unloading strategy and an uplink power distribution strategy, and the total reward function of the intelligent agents of the user is set to be a total reward function. Actions of agents of the access point and the cloud server are set as a computing resource allocation strategy, and a total reward function is set as an inverse number of total energy consumption; and solving the deep reinforcement learning model to obtain an optimal computing resource allocation strategy. According to the invention, joint optimization of unloading, power allocation and resource allocation is realized.

The invention provides a rolling pass parameter intelligent inversion and dynamic correction method based on machine learning, and the method comprises the steps: outputting a deformation resistance Kf value according to the obtained current rolling temperature and strain rate; based on the deformation resistance Kf value, predicted values of the total rolling force and the rolling moment are obtained; establishing a transient heat balance equation, and outputting a final rolling temperature predicted value; by taking the minimization of the pass number and production time and the minimization of the total energy consumption as targets, an optimal rolling schedule is generated and decided; and the corresponding model parameters are optimized in batches by regularly utilizing historical data, so that short-term self-adaption and long-term optimization control of the rolling process is realized. According to the method, various feasible rolling schemes are automatically generated by utilizing an optimization algorithm, so that an operator can select the optimal scheme according to actual conditions; by comparing a predicted value with an actual value in real time, current model parameters are quickly adjusted, and accumulated production data are regularly utilized to comprehensively optimize the model, so that long-term stable operation and accurate prediction of the system are ensured.

The invention relates to the technical field of flight safety and flight data analysis, in particular to a last approach artificial flight stage evaluation method based on random forest and K-Means clustering. The method comprises the following steps: intercepting original time series data of a final approach stage from an aircraft QAR record, and calculating kinetic energy and potential energy changes to obtain total energy change data; establishing an evaluation system taking trajectory control and energy management as indexes, calculating importance scores of features of time sequences corresponding to the indexes by using a random forest model, and selecting two features with the highest scores as evaluation sub-indexes; after the sub-index values are standardized, scoring intervals are divided by adopting a K-Means clustering algorithm, and scoring is carried out; according to feature importance score normalization, a weight is obtained, and final scores of glide track control, course track control and energy management indexes are calculated through weighted summation. According to the method, comprehensive and objective quantitative evaluation is realized, and the method is suitable for pilot ability evaluation and flight safety monitoring.

The invention provides an energy consumption prediction method and related equipment, and the method comprises the steps: carrying out the recognition of a scene where a vehicle is located based on scene feature information, and the scene feature information comprises environment information; if the scene where the vehicle is located is the preset scene, the value of the first parameter is corrected; the corrected value of the first parameter is input into an energy consumption prediction model to predict the total energy consumption of the vehicle in the target path, and an energy consumption prediction value is obtained; and if the travel is not finished, acquiring an actual energy consumption value, and correcting the value of the second parameter based on the predicted energy consumption value and the actual energy consumption value. According to the method provided by the invention, the prediction error can be reduced when the energy consumption of the vehicle is predicted.

The invention discloses a distributed heterogeneous flexible flow shop batch processing scheduling method and system, relates to the technical field of distributed production scheduling in the manufacturing industry, and aims to solve the problems that an existing scheduling method is not comprehensive in constraint consideration, poor in energy consumption optimization and low in algorithm efficiency. According to the method, a mixed integer linear programming model containing multiple constraints such as release time and sequence-related preparation time is constructed, a learning-assisted dual-objective co-evolution framework is established, and the maximum completion time and the total energy consumption are synchronously optimized by combining mixed initialization, global-local search collaboration, decision reinforcement learning operator selection and a collaborative energy-saving strategy. The release time, the sequence-related preparation time, the inter-stage transportation time and the batch processing scheduling are simultaneously considered in the distributed heterogeneous flexible flow shop scheduling for the first time, the established mixed integer linear programming model better fits the actual production scene, and the method fits the actual production scene, is good in energy consumption optimization effect and can be adapted to the non-ferrous metal metallurgy aluminum production process.

The invention relates to the technical field of distributed power supplies, in particular to a source network load storage park integrated energy storage collaborative optimization control method and system, and the method comprises the steps: obtaining photovoltaic real-time output power, fan real-time output power, park real-time load and energy storage real-time charge state; calculating energy storage total target power and grid-connected point power based on the acquired data; and adjusting the output power of each energy storage converter based on the energy storage total target power. According to the method, multi-source real-time data are synchronously collected and aggregated, the total energy storage target power and the grid-connected point power are calculated according to scenes, the power is converted into all adjusting instructions, the on-site consumption rate and the energy storage utilization rate of new energy are finally improved, grid-connected power fluctuation is restrained, and economic, safe and stable operation of a multi-source park is guaranteed.

The invention provides a crack propagation prediction method, system and device based on a fracture phase field method and a medium. The method comprises the steps that a crack structure of a single pile foundation is obtained; based on the crack structure, performing regularization processing by using a fracture phase field method to obtain a total energy functional of the crack structure; carrying out fatigue damage treatment on the basis of the crack structure by utilizing a fatigue propagation model to obtain a critical energy release rate of the crack structure; calculating based on the total energy functional of the crack structure and the critical energy release rate of the crack structure to obtain a phase field of the crack structure; and performing fatigue extension strength prediction based on the phase field of the crack structure to obtain the fatigue extension strength of the crack structure. According to the method, the complex three-dimensional fatigue crack propagation behavior under coexistence and interaction of the surface crack and the internal buried crack can be effectively and accurately predicted by combining the phase field fracture mechanics, the fatigue damage model and the cycle jump algorithm.

The invention discloses an air compression station flow supply and demand gap calculation method based on multi-source data and a dynamic game, and belongs to the technical field of industrial air compression station intelligent control. Constructing a pipeline pressure drop dynamic model based on the multi-source real-time data; establishing a workshop demand dynamic response function based on the multi-source real-time data, and further calculating an air compression station flow supply and demand gap; a non-cooperative game model of multiple air compressor units and multiple workshops is constructed, and the minimum total energy consumption of air compression stations serves as a target function; based on the air compression station flow supply and demand gap, an improved Shapley value method is adopted to distribute game benefits, a Q-learning strategy updating mechanism is combined to realize dynamic game parameter adaptive adjustment, and a gas production distribution scheme of multiple units is optimized, so that the method can reduce pressure drop calculation errors, improve supply and demand gap prediction accuracy, solve the problem of large matching errors of a traditional method, and improve the air compression station flow supply and demand gap prediction efficiency. And the comprehensive energy consumption of the air compression station is high.