1260 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 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 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 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 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 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 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 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 discloses a base station air conditioner energy consumption management energy storage method and system, and the method comprises the steps: collecting multi-source data in real time according to the load data of a temperature sensor, a humidity sensor, an air conditioner operation state monitoring device and a communication device in a base station room, and generating a multi-source fusion data set; for the multi-source fusion data set, dynamic refrigeration requirements in a future time window are predicted, and a dynamic optimization model of air conditioner energy consumption is constructed through an energy consumption efficiency curve; according to the prediction result of the dynamic refrigeration demand and the energy consumption optimization model, the operation mode, the wind speed and the temperature set value of the air conditioner are dynamically adjusted, the energy storage scheduling strategy is optimized, and a combined optimization scheme of air conditioner operation and energy storage scheduling is generated; and monitoring the operation state of the air conditioner in real time, dynamically adjusting the joint optimization scheme, and generating a final base station air conditioner energy consumption management scheme. By utilizing the embodiment of the invention, the energy consumption management of the air conditioner has real-time performance and accuracy, and more efficient energy consumption control and energy storage scheduling optimization can be realized.

The invention relates to the technical field of air conditioning energy consumption optimization, and discloses an HVAC multi-unit air conditioning system energy consumption optimization matching system and method, a polynomial function model is constructed based on historical data and real-time operation parameters, and a nonlinear relation between energy consumption and power distribution of each unit is accurately described; secondly, the adaptability of the model is enhanced by means of principal component analysis, segmented modeling and the like, global optimization of model parameters is realized in combination with a genetic algorithm, and the prediction precision and robustness of the model are improved; in the optimization control level, a multi-unit collaborative weight factor is introduced, a weighted particle swarm optimization algorithm is adopted, a power distribution strategy is dynamically adjusted, and collaborative optimization control among multiple units is achieved. Through a complete acquisition-modeling-optimization-execution closed-loop control structure, intelligent sensing of the running state of the multi-unit air conditioning system and optimal energy consumption scheduling are achieved.

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 discloses a dough humidity and temperature dynamic regulation and control system based on multi-modal sensor fusion, and relates to the technical field of operation parameter regulation and control, and the method comprises the steps: carrying out the non-blind area monitoring of a three-dimensional temperature and humidity field in a fermentation box, and eliminating the local failure risk of a point-type sensor; capturing a space-time coupling relationship of temperature and humidity-carbon dioxide concentration-dough volume through a self-attention mechanism; constructing a differential equation set comprising at least one state variable; establishing a fermentation equipment thermodynamic model, optimizing refrigeration and heating time sequences through reinforcement learning, and designing an energy consumption optimization strategy based on digital twinning; performing cross-device cooperative control; constructing a hierarchical diagnosis tree containing fault features; performing microbial activity enhanced humidity regulation and control, and forming a protective layer on the surface of the dough based on a humidity gradient control algorithm of a water molecule diffusion coefficient. And by arranging the environment regulation and control module, the time compression module and the time sequence optimization module, the product quality, the production efficiency and the energy utilization rate are remarkably improved.

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 discloses an electric actuator energy consumption optimization method and system based on big data analysis, and particularly relates to the technical field of energy consumption optimization. Multi-source operation data are collected, a sliding window is utilized to extract time sequence features to generate a working condition modal feature vector, and an energy efficiency influence factor set is constructed in combination with an energy efficiency influence factor weight matrix. Performing working condition state clustering on the energy efficiency influence factor set by using a dynamic density clustering algorithm to generate an operation mode classification tree; and performing energy consumption traceability analysis on the unsteady operation mode to generate an energy efficiency degradation characteristic spectrum. Inputting the energy efficiency degradation characteristic spectrum into a pre-trained energy consumption optimization decision model, outputting a dynamic regulation strategy set, and outputting a final optimization control parameter packet by comparing the deviation degree of an energy efficiency transition track and a theoretical optimal curve and iteratively updating the weight of the model after the strategy takes effect. And intelligent optimization and energy-saving control of the operation mode of the electric actuator are realized.

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 spinning production, and discloses a multi-process spinning energy consumption optimization decision system, which comprises a data acquisition module, an energy consumption analysis module, a strategy generation module, a scheduling optimization module, a reference comparison module and a decision output module. The data acquisition module acquires equipment parameters and energy consumption data and sets a monitoring interval; the energy consumption analysis module divides process nodes and generates energy consumption feature vectors; the strategy generation module extracts key indexes to establish optimization rules; the scheduling optimization module identifies the operation mode and calculates the energy consumption fluctuation amplitude; the reference comparison module deduces an optimal energy consumption threshold value and generates a deviation sequence; and the decision output module integrates the deviation sequence to form an optimized decision scheme. According to the system, fine management and dynamic optimization of energy consumption of the whole spinning process are achieved, the energy utilization efficiency is improved, the production cost is reduced, and the system is suitable for energy consumption optimization control in the spinning production process.

The invention discloses an intelligent scheduling method for video equipment based on solar power supply, and relates to the technical field of energy management and video. Through multi-dimensional data acquisition, intelligent prediction and dynamic scheduling technologies, the energy management challenge faced by solar power supply video equipment in remote areas is effectively solved, the power generation amount, equipment power consumption and environmental change data are acquired in real time by using an Internet of Things sensor, and the energy management efficiency is improved by combining time sequence analysis and a machine learning algorithm. The power generation amount and power consumption fluctuation are accurately predicted, an energy supply and demand matching model is established based on a dynamic planning algorithm, a power distribution strategy is optimized, operation of high-priority equipment is guaranteed preferentially when the power generation amount is insufficient, meanwhile, the working mode of the equipment is dynamically adjusted through a reinforcement learning algorithm, and energy consumption optimization and self-adaptive matching are achieved. The energy utilization efficiency and the system stability are improved, the adaptive capacity to dynamic environment changes is enhanced, and continuous and stable operation of video equipment under complex conditions is ensured.

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 present invention is an auxiliary hospital space design method and device based on a building energy consumption model, including: obtaining a natural language description of the initial hospital space layout diagram and functional requirements; extracting features from the space layout diagram and performing semantic analysis on the functional requirements description; inputting the spatial structure feature matrix into the energy consumption analysis model, combining the demand semantic vector, predicting the energy consumption performance of each functional area, and generating an energy consumption assessment diagram; obtaining an energy-optimized space design scheme based on the energy consumption assessment diagram and functional requirements; and simulating and verifying the energy-optimized space design scheme. The present invention realizes the intelligentization, optimization, and dynamic adjustment of hospital space design by integrating advanced technologies such as multimodal deep learning, generative adversarial networks, building energy consumption simulation, adaptive optimization algorithms, and multi-objective decision support systems.