1084 results about "Energy consumption optimization" patented technology

The invention discloses a distributed intelligent warehouse scheduling system based on artificial intelligence, and belongs to the technical field of warehouse scheduling. Comprising a multi-source environment sensing module, a dynamic inventory management module, a distributed task scheduling module, an intelligent path planning module, a resource dynamic allocation module, an anomaly detection and emergency response module, an energy consumption optimization module, a supply chain collaboration module and a man-machine interaction and visualization module. A warehouse digital twinborn model is constructed, immersive display of a storage state and a scheduling strategy is realized, an AR scene is superposed through a color coding path, a thermodynamic diagram and a particle flow form, a manager can intuitively master inventory distribution, task progress and an abnormal region, eye movement tracking and a gesture recognition technology support an interactive decision, and the workload of the manager is reduced. The AR marking function can mark an abnormal area and synchronize the abnormal area to a decision making system, and through combination of AR and AI, a brand new interaction normal form is provided for intelligence and humanization of warehouse management.

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention relates to the technical field of granary ventilation and energy consumption optimization, in particular to an intelligent granary ventilation and energy consumption optimization decision-making method based on reinforcement learning, and the method comprises the steps: obtaining the internal and external multi-dimensional state data of a granary, respectively constructing a basic state vector, a space correlation feature vector and a grain condition change trend vector based on the multi-dimensional state data inside and outside the granary; combining the basic state vector, the space correlation feature vector and the grain condition change trend vector into a space-time feature tensor, inputting the space-time feature tensor into a space-time topology perception decision model based on a graph attention network, and outputting an optimal composite action vector for controlling ventilation equipment; and analyzing and converting the optimal composite action vector to drive ventilation equipment of the granary to execute control. According to the method, through fusion of space-time topology perception and deep reinforcement learning, accurate prediction and active regulation and control of the granary state are realized, and energy consumption of a ventilation system can be reduced to the greatest extent while grain storage safety is ensured.

The invention relates to the technical field of smart energy management, and discloses an energy consumption optimization method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting energy consumption data, environment data and equipment operation state data of a target region in real time, carrying out the data preprocessing, and generating a sample data set; based on space-time correlation analysis, integrating the sample data set into an energy consumption feature matrix by adopting a multi-dimensional data fusion strategy combining feature-level fusion and decision-level fusion; inputting into an energy consumption prediction model constructed based on a deep learning algorithm, and outputting an energy consumption prediction result of the target area in a future time period; and according to an energy consumption prediction result, generating an optimal energy consumption control strategy through a dynamic programming algorithm. The dynamic optimal energy consumption control strategy is generated through linkage of the deep learning algorithm and the dynamic programming algorithm, and the problems that in the prior art, energy consumption optimization precision is insufficient, and adaptability is poor are solved.

The invention discloses a smelting waste gas recycling optimization system based on fuzzy control, and relates to the technical field of automatic control. The problems of low control precision and response lag caused by the fact that an existing static membership function cannot adapt to multi-scale working condition changes in real time are solved. Comprising a sensing acquisition module, a membership reconstruction module, a feature decoupling module, a coupling compensation module, a rule optimization module and an execution control module. Standardized data are acquired through sensing acquisition and filtering, an adaptive membership function is constructed by adopting incremental fuzzy clustering, and closed-loop self-correction of a multi-modal control instruction is realized by combining wavelet packet decoupling, feed-forward compensation and double-depth Q network reinforcement learning online optimization; the waste gas desulfurization efficiency, energy consumption optimization and treatment system stability of waste gas treatment can be effectively improved.

The invention provides a sewage treatment carbon source intelligent adding method, which comprises the following steps: collecting multi-parameter feed-forward and feedback signals of water inlet and an anoxic tank, constructing a dynamic model containing feed-forward compensation, model prediction control and feedback compensation, calculating the theoretical adding amount of a carbon source, inputting a predicted value and feedback parameters into an LSTM network for correction, and optimizing a network structure by a genetic algorithm. The adding amount is controlled in a closed-loop mode through a variable frequency pump, the LSTM weight is updated on the basis that the error is larger than 5%, and finally a control strategy is optimized by using an NSGA-II algorithm and integrating carbon source consumption, effluent total nitrogen and energy consumption. The dynamic self-adaptive carbon source adding method is constructed by fusing feedforward perception, LSTM prediction, feedback regulation and multi-objective optimization, so that quick response and accurate control on water quality fluctuation are realized, the denitrification efficiency and the carbon source utilization rate are improved, and the method has excellent engineering adaptability and popularization value.

The invention discloses a task length prediction-based large language model reasoning calculation service energy consumption optimization scheduling method, which comprises the following steps of: firstly, reasoning by taking an Alpaca-52k instruction data set as an input source and a large language model (such as Llama3-8B), counting the number of output tokens of the large language model, and labeling each piece of input data; then, a Qwen2-1. 5B large language model is finely tuned by using an Alpaca-52k instruction data set and the response length of Llama3-8B reasoning, and computing resources of the prediction method are reduced on the premise that the prediction performance is guaranteed; then, the response length of the Llama3-8B reasoning task is predicted through the fine-tuned Qwen2-1. 5B model, task balanced sorting scheduling is carried out according to the response length so as to improve the large language model reasoning speed, and finally, a deep reinforcement learning power selection algorithm is used to reduce the calculation power as much as possible on the premise that the large language model reasoning task time delay is met so as to improve the large language model reasoning efficiency. Therefore, the energy consumption of large language model reasoning calculation is reduced.

The invention provides a customized energy-saving air conditioner control method and device for industrial process requirements, and is applied to the technical field of data processing. According to the method, electromagnetic interference and vibration noise are eliminated through Kalman filtering, and a standardized process-environment-energy consumption correlation sequence is generated; and converting the three-dimensional process load map into a three-dimensional process load map, and establishing an individualized air conditioner dynamic load prediction model in combination with heat and humidity characteristics of a scene through fluid dynamic simulation and self-adaptive grid division. Extracting process priority factors to construct an adjustment matrix, calculating an air conditioner parameter combination under target energy consumption, extracting energy consumption characteristics, dividing standard exceeding risk levels, and constructing a multi-dimensional characteristic matrix; and comparing real-time data with historical data to identify abnormity, and generating an energy consumption optimization correction factor. Users are grouped according to enterprise conditions, key factors are screened by using a gradient boosting tree, a personalized energy-saving control model is constructed by fusing multiple information, target parameters are output, and a real-time adjustment instruction and a time-phased energy-saving strategy are generated in combination with a time sequence.

The invention relates to the technical field of public transportation systems, and particularly discloses a bus departure scheduling method, which comprises the following steps of S1, integrating multi-dimensional data; s2, a dynamic prediction model containing machine learning parameters is adopted to calculate the passenger demand in the future period; s3, calculating the number of required vehicles according to the predicted demand, the vehicle capacity and the dynamic load coefficient; s4, constructing a multi-objective function including energy consumption optimization, and solving the optimal departure interval and route; and S5, according to the real-time data, correcting a scheduling scheme, collecting real-time feedback data through a passenger mobile application, analyzing the emotion and demand of the passenger by using a natural language processing technology, based on feedback intention recognition of an emotion analysis model, constructing a passenger demand knowledge base in combination with historical complaint data, and optimizing a dynamic prediction model and a scheduling strategy. Through technology integration and system innovation, the static and single bottleneck of traditional scheduling is broken through, and an intelligent solution considering efficiency, low carbon and user experience is provided for urban buses.

The invention relates to the technical field of industrial control, and discloses an energy-saving air conditioner intelligent control system which comprises an edge computing node, a dynamic load prediction module, a self-adaptive control module, an energy consumption optimization evaluation module and a fault diagnosis module. A data acquisition module, a real-time control module and a communication interface are integrated; based on the historical environment data and the air conditioner operation log, a long-short-term memory network model is adopted to predict the expected load demand in 2-4 hours in the future; dynamically adjusting the operation number of chilled water units, the frequency conversion frequency of water pumps and the opening degree of fresh air valves according to the expected load requirement; the unit cold energy consumption of a single air conditioner is calculated in real time, and a global optimization instruction is generated in combination with a time-of-use electricity price strategy; motor current harmonic characteristics are collected through the edge calculation nodes, the abnormity of the air conditioner is recognized, graded response is conducted, the intelligent control efficiency of the energy-saving air conditioner is improved, and the overall energy consumption cost of the air conditioner is reduced.

The invention relates to the technical field of cold-chain commodity transportation, in particular to a temperature monitoring and control method in a cold-chain commodity transportation process, which comprises the following steps: acquiring real-time temperature data, and generating a thermal response digital file of a cold-chain commodity according to the real-time temperature data and a cold-chain commodity category database; real-time positions and external air temperature data of the cold chain commodities are collected, and a temperature threshold value of the next time period is predicted according to the heat capacity characteristic parameters reflected by the thermal response digital archive; a three-dimensional temperature field distribution diagram is constructed based on the temperature threshold value of the next time period and the vibration data of the compartment where the cold-chain commodities are located; generating a control instruction according to the coordinates of the temperature anomaly region in the three-dimensional temperature field distribution diagram; after the control weight coefficient is calculated according to the control instruction and the preset energy consumption optimization objective function, the temperature control operation is executed according to the control weight coefficient and the control instruction, and balance temperature monitoring and control and energy consumption optimization are effectively achieved.

The invention relates to the technical field of remote monitoring, in particular to a remote monitoring system for power equipment, which comprises a load lag mapping module, a voltage drop rate identification module, a temperature control abnormity judgment module, a task energy consumption optimization module and a scheduling priority ordering module. According to the method, trend deviation is identified by extracting key sections of load and temperature change, an abnormal propagation path is constructed by combining continuity and directivity of a voltage change rate, an association relationship between a diffusion chain and nodes is determined, and a fault path with a time sequence recursion characteristic is extracted based on load change difference in a temperature rise process. According to the method and the system, the task type, the energy consumption trend and the communication requirement are combined to screen nodes with scheduling value, a scheduling list is dynamically generated, the response priority is synchronously adjusted, and efficient overall planning of remote monitoring node management and self-adaptive optimization of a scheduling strategy are realized.

The invention relates to the technical field of underground pipe gallery distributed intelligent control, and discloses an underground pipe gallery intelligent illumination and energy consumption optimization system, which comprises a dynamic credibility evaluation module for analyzing multi-modal environment sensing data in real time and generating a dynamic credibility score; the edge-cloud collaborative optimization module is used for realizing distributed collaborative learning through local model fine tuning and global knowledge distillation; and the event-driven resource allocation module dynamically optimizes a lighting strategy and a communication priority based on credibility scores and emergency information, and through multi-modal data cross validation and adaptive weight adjustment, the robustness of anomaly detection is significantly improved, and misjudgment caused by sensor failure or environmental interference is effectively suppressed; the dynamic planning algorithm is used for synchronously optimizing energy consumption and equipment service life, efficient balance of safety and energy efficiency is achieved, the system is particularly suitable for complex underground environments and can automatically adapt to sudden anomalies, and the intelligent level and the energy utilization efficiency of pipe gallery illumination are remarkably improved.

The invention discloses a network card data local preprocessing system fused with edge computing, and relates to the technical field of edge computing and artificial intelligence collaborative optimization. Comprising an edge computing unit, a hierarchical collaborative architecture, a model hot switching and generative fragmentation module, an intention recognition and adaptive scheduling module, a delay energy consumption optimization scheduling module, a CXL zero-copy sharing module, an edge computing unit integrated processor, an FPGA or ASIC and a neuromorphic computing unit. According to the method, an FPGA, an ASIC and a neuromorphic computing unit are integrated in an intelligent network card, microsecond-level dynamic connection reconfiguration and adaptive generative model fragmentation execution are realized through a reconfigurable Mesh interconnection matrix, an attention layer and a feed-forward layer of a Transform class model are fragmented and allocated to different computing units for parallel execution, and cross-card streamlined processing is realized in cooperation with a zero-copy shared memory. And the intention recognition module is deeply coupled with the model hot switching module, so that dynamic model switching and fragmentation strategy optimization based on service priorities and system loads are realized.

The invention discloses an information processing method and system based on the Internet of Things, and relates to the technical field of Internet of Things services, and the method comprises the steps: dynamically obtaining the computing power, energy consumption and network state of each node through an environment perception module, and constructing a node capability vector and a link performance vector; the task analysis module is used for carrying out structured modeling on tasks and identifying candidate segmentation points and unloading targets; the cost modeling module establishes a joint cost function of time delay and energy consumption; the strategy generation module generates an optimal unloading strategy based on multi-agent reinforcement learning; and the scheduling communication module executes task unloading and result integration according to the strategy, and feeds back execution data to update the system state. According to the method, intelligent, low-delay and energy-consumption optimization processing of task segmentation unloading is realized, and the method is suitable for a dynamic heterogeneous Internet of Things computing environment.

The invention relates to an intelligent regulation and control method for a fan coil of a central air conditioner, relates to an internet of things technology, and aims to solve the problems that multi-source environment parameter data is large in noise, working condition modeling is inaccurate, and equipment regulation response and energy consumption optimization are not coordinated. A regulation and control system integrating multi-parameter distributed sensing, data preprocessing, feature extraction and cross-domain knowledge migration is provided. A high-quality structured data set is formed through multi-dimensional real-time acquisition of room temperature and humidity, fan current, valve opening, human traffic, external weather and building information and in combination with denoising, normalization, alignment and missing completion. And a multi-target neural network and digital twinborn simulation are utilized to realize load trend prediction, equipment abnormity early warning and virtual deduction of an adjustment strategy, an optimal adjustment time sequence window and an activation threshold are output, and dynamic adaptive control is realized in combination with reinforcement learning.

The invention relates to the technical field of intelligent power grid communication, and discloses an HPLC communication-oriented power topology adaptive reconstruction and energy consumption optimization control method, which comprises the following steps of: acquiring HPLC communication characteristic data of power network nodes in real time; constructing a dynamic spatio-temporal topological graph based on the communication feature data and predicting a future topological state; generating a communication power distribution and routing path joint optimization strategy by using a multi-target reinforcement learning algorithm in combination with the predicted future topology state and the real-time energy consumption parameter; verifying the electromagnetic compatibility and protocol compliance of the optimization strategy through a digital twin model, and generating a control instruction which can be executed by equipment; and the transmitting power and the communication route of the HPLC node are dynamically adjusted through the control instruction. According to the method, the communication quality stability and the energy efficiency level are improved, topology dynamic perception, multi-target collaborative optimization and strategy security verification are realized, and efficient and reliable operation of HPLC communication in a complex power grid environment is guaranteed.

The invention provides an energy consumption optimization method and system based on dynamic carbon constraint. The method comprises the following steps: constructing a carbon emission fluctuation map of multiple time points according to a carbon emission time sequence data flow of a target region so as to construct a multi-region minimum carbon emission demand distribution map; performing multi-cycle output change analysis and short-term productivity filtering prediction based on the real-time operation state parameters of the energy power generation equipment in the target area to obtain an energy productivity prediction curve; carrying out nonlinear implicit feature analysis and modeling on the carbon emission time series data flow of the target area, and constructing a carbon emission nested energy structure collaboration graph; according to a carbon emission constraint condition, carrying out supply and demand matching coupling on the multi-region minimum carbon emission demand distribution diagram and the energy productivity prediction curve, constructing an energy intelligent distribution strategy based on a carbon emission nested energy structure collaborative diagram, and carrying out iterative distribution optimization to obtain an energy consumption optimization result; and dynamic evolution and high-dimensional complex characteristics of the system can be coped with so as to improve the energy consumption optimization effect.

The invention provides a visual guidance type automatic disassembly method and system for a photovoltaic module, and relates to the technical field of module disassembly, and the method comprises the steps: obtaining real-time production data, constructing a multi-objective optimization decision model, and solving through an adaptive inertia weight particle swarm algorithm; verifying and dynamically optimizing the optimization scheme by adopting a deep reinforcement learning method in combination with a digital twinning environment; production line optimization control is realized and operation data is fed back to form a closed loop. According to the invention, the disassembly efficiency is improved, the energy consumption is reduced, the production quality is optimized, and intelligent automatic disassembly is realized.

The invention relates to the technical field of smart home, and discloses a smart home real-time energy consumption monitoring and dynamic distribution system, which comprises a data acquisition module, a spatial-temporal feature fusion module, an energy distribution module and a dynamic control module. The data acquisition module acquires household energy supply and demand real-time data, the spatial-temporal feature fusion module processes the data to generate an energy state feature matrix, the energy distribution module constructs a model to optimize an energy distribution sequence according to the matrix, and the dynamic control module constructs a hierarchical control framework to complete energy dynamic distribution. According to the system, energy can be accurately and efficiently distributed, and the power utilization cost is reduced according to a real-time electricity price and equipment energy consumption optimization strategy. The service life of the energy storage battery is effectively prolonged by considering the charge-discharge cycle times of the energy storage battery. Meanwhile, the decision-making layer, the scheduling layer and the execution layer cooperate to improve the intelligent level of equipment management. The execution layer introduces a fault-tolerant mechanism to enhance the reliability and stability of the system, ensure stable supply of household energy and improve the user experience.

The invention discloses a method for adjusting carbon dioxide in a mushroom house based on multi-modal growth stage recognition. The method comprises the following steps: S1, obtaining pre-processed mushroom house environment data and pre-processed mushroom house multi-modal image data; s2, outputting a mushroom growth stage classification label and a mushroom growth stage confidence vector; s3, constructing a multi-target fitness function according to a stage sensing result and generating a stage weight group; s4, obtaining an optimal gas regulation control strategy; s5, adjusting the carbon dioxide concentration of the mushroom house according to the optimal gas adjusting control strategy; and S6, calculating an environmental stability index, triggering the whale optimization algorithm to re-execute the steps S4 to S6 when the environmental stability index exceeds a limit or the confidence vector in the mushroom growth stage is lower than a threshold value, and circularly executing the steps S1 to S6 in the whole mushroom house production period. According to the method, the problems of premature convergence and local optimization are effectively avoided, and the environmental adaptability and the energy consumption optimization effect of a gas regulation strategy are improved.

The invention discloses a full-equipment energy consumption optimization management system in a building, and relates to the technical field of intelligent control, and the system is provided with a multi-modal data collection module which collects building environment parameters and equipment operation data through a distributed sensor network, and generates a multi-modal sensing matrix; an environment state prediction module is set to construct a dynamic knowledge graph based on a multi-modal sensing matrix, a graph attention network is used, an energy flow matrix of equipment topology is output, micro-environment state prediction data is generated through prediction of a finite element solver, a scheduling scheme solving module is set, a mixed integer programming model is constructed based on the micro-environment state prediction data, and a scheduling scheme is set. And solving the mixed integer programming model to obtain a scheduling scheme of the energy consumption equipment, setting a control instruction optimization module, and driving the edge execution equipment to operate based on the scheduling scheme. The global scheduling optimization of the whole building energy consumption equipment is realized, the energy consumption is effectively reduced, and the environmental comfort is improved.

The invention discloses a mine semi-autogenous grinding process analog simulation method and system based on discrete elements, and relates to the field of analog simulation, and the method comprises the steps: according to a crushing characteristic label and a parameter mapping matrix, fusing Internet of Things sensor data, constructing a particle size distribution prediction model and an energy consumption optimization scheduling strategy, and obtaining an energy-saving operation plan; comparing the simulation data set with field production data, adjusting multi-phase coupling particle model parameters, generating a calibration parameter set, researching the influence law of semi-autogenous mill operation parameters by adopting a control variable method based on the calibration parameter set, obtaining a performance index database and an analysis report, and fusing multi-sensor data and laser radar data to obtain a semi-autogenous mill operation parameter analysis result. An ore grinding process sensing mechanism and optimization control logic are constructed, and a visual interface is generated; through multiphase modeling and data driving optimization, the simulation precision and the process suitability are comprehensively improved, and an efficient and economical solution is provided for the mine semi-autogenous grinding process.

The invention relates to a central air conditioner energy consumption optimization control method and system, electronic equipment and a storage medium, and the method comprises the steps that characteristic parameters of load calculation are obtained, and according to the characteristic parameters, the cooling load requirements of a central air conditioner under different working conditions are calculated through a dynamic load calculation model; acquiring real-time operation data of the central air conditioner; establishing a digital simulation model of the central air conditioner based on the cooling load demand and the operation data, and calculating the energy consumption distribution of each device of the central air conditioner under different working conditions through the digital simulation model; with the overall energy efficiency optimization of the central air conditioner as the target, the optimal operation parameter combination of the central air conditioner is obtained through an intelligent optimization algorithm; and synchronously displaying the operation data of the central air conditioner, the energy consumption distribution of each device and the optimal operation parameter combination through a visual interface, and executing an optimized operation strategy according to a user instruction based on the optimal operation parameter combination. According to the method, the accuracy of energy consumption calculation is improved, and the overall energy consumption of the central air conditioner is reduced.

The invention relates to the technical field of computing power-energy consumption optimization, in particular to an edge AI reasoning computing power-power consumption cooperative system, which comprises a task decomposition module for decomposing a complete reasoning task into n independent sub-tasks, n being greater than or equal to 1; the task analysis module is used for extracting feature parameters of the subtasks and generating task feature vectors; and the computing power prediction module inputs the obtained sub-task feature vectors and different computing unit type labels into a pre-constructed task computing power demand prediction model, and predicts the computing power demands of the sub-tasks on different computing units. In the scheme construction and task allocation stage, the system carries out candidate calculation unit screening and combination generation based on a multi-scheme calculation power energy consumption matrix, calculates the total energy consumption and time delay matching degree of each combination, and selects an execution scheme with the lowest energy consumption under the real-time condition from the total energy consumption and time delay matching degree. The mechanism enables each subtask to be flexibly migrated between different types of computing units.

The invention discloses an underwater LED fish gathering lamp control method based on a light field scattering model, and the method comprises the following steps: obtaining water optical characteristic parameters and environmental parameters of a target water area in real time, and building an underwater light field scattering model through employing a radiation transfer equation in combination with a Monte Carlo simulation method; with improvement of target fish school gathering efficiency and reduction of energy consumption as optimization objectives, environment parameters are taken as constraint conditions, illumination effects under different LED parameter combinations are simulated through a light field scattering model, and LED control parameters with optimal comprehensive performance are determined; a control instruction is generated according to the LED control parameters, and the working state of each LED unit is adjusted; meanwhile, fish school gathering data and current change values of water temperature, salinity and turbidity are monitored in real time and fed back to the light field scattering model for parameter correction, and closed-loop control is formed. The underwater LED fish gathering lamp control method solves the technical problems of low light field distribution prediction precision, insufficient fish school visual matching performance, poor environmental adaptability and unbalanced energy consumption optimization in the existing underwater LED fish gathering lamp control method.

The invention relates to the technical field of engineering machinery control, and discloses a carry-scraper electrical control system and method based on intelligent control, and the system comprises a sensor module, a man-machine interaction module, a control module, an execution module, and a communication module. The method comprises the following steps: collecting real-time operation data and an operator instruction, generating a predictive deterministic map by a control module, and outputting a final control instruction for energy consumption optimization according to the predictive deterministic map to drive the carry-scraper. And meanwhile, the system continuously monitors the state of the equipment to realize fault early warning, calculates an energy consumption optimization index, and finally graphically displays the operation data, the fault early warning and the energy consumption index through the man-machine interaction module. According to the invention, an intelligent algorithm is integrated, so that accurate control and early warning of faults can be realized, energy consumption is optimized, and safety and economy are improved; and through simplified man-machine interaction, the defects of poor adaptability, complex operation and high energy consumption of a traditional system are overcome.

The invention discloses a mobile phone energy consumption optimization method, and relates to the technical field of information, and the method comprises the steps: obtaining a current game frame of a mobile phone game application, and extracting a key rendering parameter of the current game frame; according to the current game frame and the historical game frame, calculating a static scene element space consistency score and a dynamic rendering object state change rate; according to the static scene element space consistency score and the dynamic rendering object state change rate, generating a computing resource allocation scheme; according to the computing resource allocation scheme, GPU rendering pipelines and CPU computing resources are configured, corresponding game computing execution paths are selected, and the computing execution paths comprise a complete rendering pipeline path, an incremental rendering path or an inter-frame interpolation path; aiming at high energy consumption of 3D scene rendering in a mobile game, a three-level rendering strategy selection mechanism based on scene change degree and the like are established, and the energy consumption of a mobile phone is reduced while the game quality is kept.

The invention discloses a building equipment energy consumption statistics and optimization method, and relates to the technical field of building energy consumption optimization, and the method comprises the steps: collecting the energy consumption data of building equipment, and carrying out the preprocessing, and the energy consumption data comprises the equipment power consumption, the operation time, and environment parameters; inputting the preprocessed energy consumption data into the constructed energy consumption reference model to obtain an energy efficiency deviation index; calculating an equipment energy efficiency score based on the energy efficiency deviation index in combination with the environmental parameters; and performing hierarchical management on the building equipment according to the equipment energy efficiency score, and when the equipment energy efficiency score is lower than a preset threshold value, triggering equipment maintenance early warning and generating an equipment optimization scheme. According to the invention, by establishing a dynamic grading threshold and a differentiated management and control mechanism, intelligent grading management of equipment is realized, and management standards can be adaptively adjusted according to historical data distribution characteristics.

The invention relates to the technical field of automobile energy supply, in particular to a solid hydrogen storage-fuel cell integrated power assembly system based on dual-mode hydrogen supply, which comprises a hydrogen supply path identification module, a hydrogen storage unit state monitoring module, a linkage path switching module, a fuel cell load matching module and a safe hydrogen discharge regulation and control module. According to the method, the hydrogen supply pressure difference value is obtained by dynamically comparing the pressure of the cavity of the hydrogen storage tank with the pressure of the hydrogen inlet of the fuel cell, the current hydrogen supply path state can be accurately recognized in the early stage, rapid judgment of the hydrogen supply mode and accurate control of path switching are achieved, whether the risk of hydrogen supply interruption exists or not can be effectively screened, and the reliability of hydrogen supply is improved. The switching reliability of a hydrogen supply channel is improved through cross comparison of key state signals of a main channel and a standby channel, and instant response and energy consumption optimization of hydrogen discharge safety regulation and control are guaranteed through a linkage feedback mechanism between the hydrogen concentration change rate in unit time and catalytic converter control power.