1518 results about "Energy consumption optimization" patented technology

The invention discloses an intelligent management system for energy consumption optimization and fault self-diagnosis of cleaning equipment, and relates to the field of intelligent maintenance of the cleaning equipment, and the system comprises the steps: obtaining three groups of core parameters, i.e., a historical vibration spectrum, a motor current harmonic component and a bearing temperature gradient, constructing a dynamic failure mode knowledge graph, performing time sequence correlation analysis on the historical fault data to obtain failure mode analysis data; establishing a multi-dimensional analysis platform, identifying a high-risk component, and updating a fault threshold value; introducing a service time attenuation factor and a working condition correction coefficient, establishing an aging degree quantitative model, and calculating an aging coefficient; and constructing and developing an energy consumption-reliability joint optimization module, and adjusting equipment operation parameters. The method has the advantages that the dynamic knowledge graph and the time sequence analysis model are constructed by integrating multi-source sensor data, precise diagnosis and self-adaptive threshold adjustment of the coupling fault of the cleaning equipment are achieved, aging evaluation and task scheduling optimization are combined, the energy consumption efficiency is improved, and the maintenance cost is reduced.

The invention provides a machine room energy consumption optimization method and system based on temperature monitoring, and relates to the technical field of energy consumption optimization, and the method comprises the steps: collecting the temperature data of a machine room in real time, continuously monitoring the temperature gradient of each region, carrying out the fusion processing, and constructing a three-dimensional dynamic temperature distribution model; a hot spot area, an airflow dead angle and a cooling capacity waste area are identified through a deep learning algorithm, a flowing track and a mixing process of cold and hot air in a machine room are predicted through a track regression neural network in combination with machine room layout and equipment distribution, and a machine room temperature control strategy set is generated through adaptive neural fuzzy reasoning, correction and updating of a prediction result; and by taking minimum energy consumption and maximum refrigeration efficiency as optimization objectives and taking a safe operation temperature range of equipment in the machine room as a constraint condition, a plurality of parameters are jointly adjusted through a temperature control optimization algorithm to obtain an optimal temperature control curve, and the optimal temperature control curve is converted into a refrigeration system control instruction, so that collaborative optimization of the temperature and the energy consumption of the machine room is realized.

The invention provides a BIM-based building energy consumption simulation analysis optimization method and system, and relates to the technical field of building energy consumption, and the method comprises the steps: building a building energy consumption digital twinborn model through integrating a building information model (BIM) and real-time operation data, such as indoor and outdoor temperature, equipment operation, electrical load and personnel density, collected by an Internet of Things sensor. An LSTM and GNN mixed deep learning algorithm is used to predict building energy consumption, and clustering analysis is used to identify an energy consumption abnormal mode and a peak period. And finally, based on a reinforcement learning algorithm, constructing an energy consumption optimization model taking total energy consumption minimization as a target and taking indoor thermal comfort and air quality as constraints, outputting equipment optimization operation parameters such as air conditioner temperature, fresh air volume, illumination brightness and the like, and realizing intelligent optimization control of building energy consumption, so that the building energy consumption is reduced, and the building operation efficiency is improved.

The invention discloses a building energy consumption dynamic optimization method and system based on BIM and reinforcement learning, and belongs to the technical field of building energy management and intelligent control, and the method comprises the steps: building a BIM containing building component physical attribute parameters, and generating a building digital twinborn body with dynamic thermal attribute evolution; extracting the spatial topological relation and the physical property parameters of the components, and constructing a multi-dimensional state space of a preset reinforcement learning model; embedding physical constraint conditions, and training the reinforcement learning model to generate a multi-objective optimization strategy of the energy equipment; and analyzing the multi-objective optimization strategy into an equipment control instruction set, and feeding back the equipment control instruction set to the building digital twin for real-time physical attribute simulation. According to the method, the physical accuracy of the BIM and the self-adaptive decision-making ability of reinforcement learning are combined, adversarial training under physical constraints is introduced, an energy consumption optimization strategy which conforms to actual operation limitation and dynamically adapts to environmental changes can be generated, and the energy utilization efficiency and the system response speed are remarkably improved.

The invention discloses a central air-conditioning system optimization control method oriented to building load prediction, which comprises the following steps: building a building load prediction model based on a long short-term memory network, combining real-time acquired building electrical load, indoor and outdoor temperature, humidity and equipment operation power data to accurately predict building cooling load requirements in a future time period, and on this basis, optimizing a central air-conditioning system. According to the method, time-of-use electricity price information is used for dynamically adjusting a cooling capacity distribution strategy of the central air conditioning system, so that the energy consumption and the operation cost of the system are reduced to the maximum extent while the load requirement is met. Compared with the prior art, the method has the advantages that the problems of insufficient load fluctuation response and insufficient energy consumption optimization are effectively solved, and the operation efficiency and economical efficiency of the central air-conditioning system of the building can be remarkably improved.

The invention relates to the technical field of power system optimization scheduling, and discloses a multi-load equipment energy consumption management method and device and computer equipment, and the method comprises the steps: obtaining electricity price data, and the energy consumption data, the operation state and the load demand of electric equipment, based on the electricity price data, the energy consumption data, the operation state and the load demand, the priorities of the electric equipment are classified; adopting a preset multi-task learning model to predict the load demand of the electric equipment in a future preset time period; generating a multi-target optimization model based on the priority, the load demand and the electricity price data of the electric equipment, and generating a dynamic power distribution scheme of the electric equipment based on the multi-target optimization model; and obtaining the load demand and the external environment of the electric equipment in real time, and adjusting the dynamic power distribution scheme of the electric equipment based on the load demand and the external environment. According to the method, the energy consumption optimization and the operation efficiency are always kept balanced, and the problem that the energy consumption optimization and the operation efficiency are not balanced is solved.

The invention discloses a building intelligent operation and maintenance management system and method based on big data analysis, and relates to the field of building intelligent operation and maintenance, and the system comprises a data collection layer including high-precision multi-type sensors and adaptive collection and transmission, a data storage layer including distributed storage and high-redundancy cache, and a big data analysis layer including data cleaning and data storage. The operation and maintenance decision-making layer is used for generating a strategy according to analysis and performing dynamic adjustment; the user interaction layer is used for performing visual interface and convenient feedback; the intelligent early warning module is used for performing dynamic threshold and accurate early warning; the analog simulation optimization module is used for performing model simulation and VR / AR auxiliary decision making; and the operation and maintenance management level and the energy utilization efficiency are improved. The system integrates building data, monitors equipment in real time, accurately predicts faults and energy consumption, optimizes operation and maintenance decisions, improves the management efficiency and the energy utilization rate, reduces the cost, and improves the user experience.

The invention relates to a path planning method for a mobile charging robot in an unstructured environment, which combines global path planning and local dynamic obstacle avoidance, performs global path planning through an improved A * algorithm, and performs real-time local obstacle avoidance by using a dynamic window method. The global path and the local path are fused through dynamic weight adjustment, and it is ensured that the robot keeps the continuity of the global path while avoiding obstacles. In the path planning process, factors such as terrain complexity and robot advancing speed are considered, and the energy consumption of the path is minimized by adopting an energy consumption optimization algorithm, so that the working time of the robot is prolonged, and the execution efficiency is improved. The path planning method is suitable for storage, industry, field inspection and other complex dynamic environments, has high adaptability, real-time performance and robustness, and provides an effective solution for efficient application of the mobile charging robot in an unstructured environment.

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 present application relates to the field of energy, and specifically to an energy consumption grading optimization method, a computer device, and a storage medium. The method comprises: acquiring target operation data related to energy consumption optimization; performing data grading on the target operation data to obtain data sets corresponding to optimization levels, wherein the data sets of different optimization levels correspond to different optimization indexes; determining an initial optimization level on the basis of a device operation mechanism and a current state indicated by the target operation data; and using the initial optimization level as an optimization starting point, and performing grading optimization calculation on the basis of optimization models of the optimization levels and the data sets corresponding to the optimization levels to obtain a target energy consumption optimization strategy, so as to optimize the energy consumption of a heating and ventilation device on the basis of the target energy consumption optimization strategy.

The invention relates to the technical field of resource scheduling control, and discloses a network resource hierarchical coordination method and system based on multiple agents. The method comprises the following steps: monitoring a network node load to obtain a resource state parameter; virtually constructing a resource pool and dividing a regulation domain; deploying an intelligent agent to construct a self-adaptive control system; extracting load characteristics to generate an analysis report; executing a double-layer adjustment strategy recording execution process; and analyzing the energy consumption data to optimize control strategy parameters. According to the application, the efficient utilization of network resources and energy consumption optimization can be realized while the performance of a computer network system is guaranteed.

The invention discloses an exhibition hall intelligent control system and method based on the Internet of Things technology, and the method comprises the following steps: collecting environment data in an exhibition hall in real time through a self-adaptive multi-mode sensor network, and dynamically adjusting the sampling frequency of a sensor through a multi-dimensional data sudden change monitoring algorithm by the system, the central processing module is combined with historical and real-time data, time sequence multi-dimensional regression analysis is adopted, an environment change prediction model is generated, then equipment control parameters are generated, the system dynamically adjusts the operation states of temperature equipment, illumination equipment and the like in the exhibition hall according to behavior data of visitors and the environment parameters, the experience of the visitors is optimized, and the experience of the visitors is improved. The intelligent anomaly detection module monitors the operation state of equipment in real time and identifies and isolates fault equipment in time, and the system preferentially guarantees equipment operation and reduces energy consumption when visitors are dense through a long-term energy consumption optimization learning algorithm, so that energy-saving management and long-term self-optimization of the exhibition hall are realized.

The invention discloses an industrial environment information wireless monitoring system based on the Internet of Things and a control method thereof, and relates to the technical field of industrial management, and the system comprises an intelligent edge calculation module, a data visualization module, a self-healing network management module, a precise energy consumption management module, an environment anomaly detection module and a wireless network optimization module. According to the industrial environment information wireless monitoring system based on the Internet of Things and the control method thereof, real-time analysis and decision are realized at a data source by using an intelligent edge computing module, data transmission delay is reduced, response can be quickly made in an emergency, potential faults are predicted through a time sequence abnormity self-learning network model, and the monitoring accuracy of the industrial environment information wireless monitoring system based on the Internet of Things is improved. And network self-healing is actively carried out before a problem occurs, so that the network downtime is effectively reduced. In addition, due to the introduction of a low-power-consumption ad hoc network protocol, the energy consumption optimization management of the sensor nodes is realized, and the service life of equipment is prolonged.

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 provides an intelligent building elevator traffic scheduling method and system for energy-saving optimization. The method comprises the steps that real-time data of building sensing equipment and an elevator monitoring system are collected; constructing a prediction model to obtain the flow thermal distribution of future floors; designing a scheduling strategy based on multi-target reinforcement learning, wherein the scheduling strategy comprises target floor distribution and a dynamic operation path of the elevator; and formulating an execution control strategy for energy consumption optimization to realize global energy consumption minimization, and generating an optimized control instruction. Through deep fusion of flow prediction, scheduling optimization and energy consumption management, the passenger experience is remarkably improved while the adaptability, flexibility and energy efficiency of the system are improved, and an intelligent and efficient elevator scheduling solution is provided for modern high-rise buildings.

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 discloses a flight path planning method for unmanned aerial vehicle energy consumption optimization, and relates to the technical field of unmanned aerial vehicle energy consumption optimization, and the method comprises the following steps: 1, a multi-sensor fusion system is arranged on an unmanned aerial vehicle, and the multi-sensor fusion system comprises a visual sensor, an IMU inertial measurement unit and a GPS standby module; according to the flight path planning method for optimizing the energy consumption of the unmanned aerial vehicle, through a flight path optimization algorithm based on machine learning in combination with current position information, an original energy consumption optimization flight path and surrounding environment information, a connection path with the shortest connection path from a current offset position to the original flight path and the highest energy efficiency is calculated in real time; an improved path is obtained through optimization according to the current remaining electric quantity of the unmanned aerial vehicle and the target point distance, and it is ensured that the unmanned aerial vehicle can smoothly transition back to the original energy consumption optimized track and continue to fly in an energy-saving mode; the continuity and reliability of task execution of the unmanned aerial vehicle in a complex environment are improved, and the risk of task failure or great energy consumption increase caused by signal interruption is reduced.

The invention discloses a satellite multi-hop task offload optimization method and system based on edge computing, and the method comprises the steps: obtaining a task offload optimization problem through employing an on-demand routing protocol under a constraint condition based on environment exploration information with the minimum energy consumption of a system model as a target when the access satellite computing resources of a ground user are insufficient; and constructing a task unloading optimization problem into a Markov decision process, and solving by combining a deep reinforcement learning algorithm to obtain a task unloading and routing optimization strategy. In the invention, under the condition of insufficient computing resources of an access satellite, an on-demand routing protocol is combined to support efficient multi-hop task transmission unloading, so that energy consumption is comprehensively considered and selection of an intermediate satellite node on a routing path from a source satellite node to a target satellite node is optimized on the basis of the minimum hop count of the task; and the processing nodes are dynamically reached through multiple hops, and meanwhile, the control overhead of the whole network topology in the task unloading process is reduced to the greatest extent.

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 provides an HVAC system temperature control and energy consumption optimization method based on deep reinforcement learning. Through an entropy-based data screening method, a strategy evaluation module and a design reward function, HVAC system temperature control and energy consumption optimization are realized under external condition interference. According to the entropy-based data screening method, the value of data for agent training is calculated through an error formula, high-value data is repeatedly used for agent training, and the training effect is enhanced; the strategy evaluation module is used for further solving the problem of overestimation of a Q value on a double-Q structure, correcting the output of the Q value, reducing the influence of a random Q value, and adding regularization of a strategy and an actual action into a loss function updating process, so that the influence of random noise on the Q value is eliminated, and the accuracy of strategy output is ensured; a reward function for controlling the temperature and optimizing the energy consumption is designed, so that the intelligent agent can balance the contradiction among multiple tasks, and the optimal solution of multi-target task intersection is searched, so that the HVAC system can control the temperature to be always kept in the temperature range, and meanwhile, unnecessary energy consumption is reduced.

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 discloses an intelligent online learning system and method for multi-device energy consumption optimization, particularly relates to the field of multi-device energy consumption optimization, and is used for solving the problems of energy consumption fluctuation and low operation efficiency caused by non-optimization of a feedback path. Extracting feedback path weights and action chain features, and analyzing a dynamic interaction relationship between devices; clustering analysis and reinforcement learning are combined to optimize classification boundaries, a high-risk feedback mode is marked, an optimization template is generated, and a basis is provided for path adjustment and strategy optimization; by dynamically adjusting the feedback path weight, generating an equipment adjustment instruction and combining with a feedback optimization template, realizing efficient control of equipment cooperative operation; therefore, the negative influence of a high-risk feedback path on operation is effectively inhibited, the energy consumption cumulative effect caused by feedback enhancement between equipment is reduced, the equipment cooperation relation is optimized, and the energy consumption distribution efficiency and the operation stability of the system in a dynamic load environment are improved.

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 discloses an energy consumption optimization management system and method, and particularly relates to the field of energy management, and the system comprises an intelligent information collection terminal collection module, an energy data preprocessing module, an intelligent prediction module, a multi-target optimization scheduling module, a real-time feedback module, an abnormity alarm module, and a scheduling scheme output module. According to the energy consumption optimization management system and the method thereof, data are acquired through the intelligent information acquisition terminal acquisition module by means of various intelligent information acquisition terminals, so that efficient management of energy consumption is realized; dynamic prediction, optimal scheduling and efficient management of energy consumption are realized through the intelligent prediction module, and the accuracy of energy consumption trend prediction is improved. And a multi-target optimization scheduling scheme is obtained by applying a multi-target optimization algorithm through the multi-target optimization scheduling module, so that the scientificity and rationality of energy scheduling are improved, and energy waste caused by unreasonable scheduling is reduced.

The invention relates to an energy consumption optimization method and device for a server, equipment and a storage medium, and aims to reduce the energy consumption of the server in operation and improve the energy efficiency management level. According to the method, energy consumption of a server in different load states is monitored through a super-resolution data acquisition technology, and load feature embedded representation is generated in combination with a contrast learning network. And based on the embedded representations, performing energy consumption prediction by using a generative adversarial network, and generating a hardware operation parameter adjustment scheme through a Markov decision process and a particle swarm optimization algorithm. And finally, iterative optimization is performed on the scheduling strategy through distributed reinforcement learning, adaptive regulation and control are realized, a target energy consumption optimization scheme is generated, and the accuracy and dynamic response capability of server energy consumption management are remarkably improved.

The invention provides a fertilizer production energy consumption real-time optimization method, and particularly relates to the technical field of industrial process optimization control based on artificial intelligence, and the method comprises the steps: constructing a mechanism sub-model based on a chemical reaction kinetic equation, and outputting a theoretical energy consumption constraint value; and meanwhile, a data driving model is used for receiving temperature, pressure and raw material ratio data collected by a real-time sensor and outputting a real-time energy consumption predicted value. And inputting the theoretical energy consumption constraint value and the real-time energy consumption predicted value into a dynamic weight distribution module to generate a fused energy consumption predicted value. And generating a production parameter adjustment instruction through a rolling optimization algorithm according to the fused energy consumption predicted value, and sending the production parameter adjustment instruction to a production execution system. And if the deviation between the data acquired after the adjustment instruction is executed and the fused energy consumption predicted value exceeds a preset threshold value, performing online fine adjustment on the data-driven model parameters. The technical problems that real-time energy consumption optimization lags behind due to multivariable dynamic coupling in the fertilizer production process, and an optimal production parameter combination cannot be rapidly generated are solved, and the fertilizer production energy consumption is remarkably reduced.

The invention relates to the technical field of robot control, in particular to an energy consumption optimization and automatic charging control system for an intelligent pet robot. The system comprises a robot data acquisition module, an energy consumption state evaluation module, an energy efficiency optimization module, an automatic charging scheduling module and a battery health management module which are in communication connection in sequence. The system is driven by multi-dimensional sensing data, energy consumption characteristics are extracted in combination with potential energy field mapping and sparse modeling, and an energy consumption influence map is constructed; joint optimization of the path and the servo strategy is realized by using a quantum behavior particle swarm algorithm; scheduling a charging path through a graph attention network; and the battery health state is dynamically predicted based on an incremental learning algorithm, and the operation efficiency, the cruising ability and the system intelligence of the robot are improved. According to the method, a closed-loop control process is constructed from energy consumption evaluation to energy efficiency optimization, automatic charging and battery health management, good system integration and intelligent response capability are achieved, and the cruising ability and operation stability of the pet robot in a complex application scene are remarkably improved.

The invention relates to the field of household kitchens, and discloses a power plant pneumatic ash conveying pipeline abrasion reduction and energy consumption optimization method based on real-time monitoring, which comprises the following steps: S1, arranging a sensor group at a node of an ash conveying pipeline; s2, preprocessing the sensor data through a data processing system; s3, constructing a wear prediction model based on the preprocessed data; s4, based on the pipeline abrasion trend, the airflow speed and pressure are dynamically adjusted; s5, adjusting the equipment power according to the influence of the speed and pressure of the adjusted airflow on the sensor data; s6, feeding back the data of each step to a control system; and S7, establishing a pipeline three-dimensional model. Through real-time collection of pipeline operation state data, data processing and a wear prediction model, the airflow speed and pressure are dynamically adjusted, and the equipment power is adaptively adjusted according to the change of the airflow, so that the wear of the pipeline can be obviously slowed down while the conveying efficiency is kept, and the service life of the pipeline is prolonged.