1409 results about "System optimization" patented technology

Disclosed is a microgrid optimal scheduling method taking into consideration a system operation risk and user satisfaction, comprising: generating a power prediction curve of wind power and photovoltaic new energy output in a microgrid, using a Monte Carlo simulation method to obtain a prediction error range to generate a typical scene set, and by means of a data fitting method, acquiring a wind-photovoltaic prediction error probability density distribution function; using value-at-risk and conditional value-at-risk theories to quantify a microgrid operation risk which is increased by slightly high or low output caused by wind power and photovoltaics; obtaining the scheduling cost of an adjustable load in a microgrid system; taking the minimum sum of conditional value-at-risk cost and microgrid system operation cost as an objective function and a user satisfaction level under a demand-side response as a constraint, establishing a microgrid optimal scheduling model taking into consideration the operation risk and the user satisfaction; and using a genetic algorithm to solve the model, and obtaining an optimal result of grid-connected microgrid system optimal scheduling. The safety, economic efficiency, stability and reliability of the microgrid are enhanced.

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

The invention discloses a hybrid energy storage system optimization scheduling method based on AI intelligent regulation and control, and relates to the technical field of hybrid energy storage, and the method comprises the following steps: collecting real-time data, and carrying out the cross verification of data consistency through a multi-source data fusion technology; and identifying data abnormity caused by sensor faults, communication delay and environmental interference by combining adaptive threshold detection with a statistical analysis method, and eliminating abnormal data. According to the method, energy storage scheduling is optimized through data cleaning, time sequence prediction and reinforcement learning, and the intelligent management and dynamic adaptive capacity is improved. Multi-source data fusion and anomaly detection are adopted to ensure data accuracy, energy consumption is predicted by means of LSTM and Transform, and an energy storage strategy is optimized in advance. By combining reinforcement learning and system dynamic adjustment charging and discharging, the photovoltaic consumption rate is improved, the electricity purchasing cost is reduced, the SOC is intelligently controlled to be 40%-80%, and the service life of the battery is prolonged. Meanwhile, the system stability is improved through anomaly detection and correction, and the maintenance cost is reduced.

The invention provides an operation and maintenance log event association analysis method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining an original operation and maintenance log set of a target system, the original operation and maintenance log set comprising a plurality of operation and maintenance log fragments; secondly, performing feature extraction processing on the original operation and maintenance log set to obtain context semantic features and time sequence behavior features of each operation and maintenance log fragment, and then calling a pre-trained association analysis model to perform association rule matching processing on the features to generate an association rule set among the operation and maintenance log fragments; the association rule set comprises a causal relationship definition and an event propagation path description, generating an operation and maintenance event optimization strategy based on the association rule set, and finally feeding back the operation and maintenance event optimization strategy to an operation and maintenance management interface of a target system to trigger system optimization operation. And the system operation and maintenance efficiency and stability are improved.

The invention discloses an intelligent scheduling and optimization method for an industrial electrical automation system, and the method comprises the following steps: deploying a distributed sensor network to collect electrical parameters, an equipment vibration spectrum and production work order data in real time, and constructing a unified feature vector based on time-space alignment and confidence weighting; a production system-energy management-external power grid three-layer interaction model is established, and a dynamic carbon emission calculation engine and process deadlock detection module is embedded; an improved NSGA-III algorithm is adopted to solve a multi-target Pareto leading edge, and energy consumption, productivity and carbon emission target priorities are adjusted in real time in combination with a dynamic weight mechanism; distributed optimization is executed through an edge-cloud federated architecture, cross-system instruction synchronization is achieved, and closed-loop dynamic feedback is formed. According to the method, the limitation of traditional single system optimization is broken through, the energy consumption is reduced by 15%-30%, the carbon emission intensity is reduced by 12%-18%, the abnormal response speed is increased to 3 seconds, and intelligent decision making and green transformation in a complex industrial scene are supported.

The invention provides a knowledge base question-answering system optimization method and device based on hybrid fine tuning and multi-dimensional evaluation and a readable storage medium thereof, and provides the following scheme: constructing a hybrid progressive fine tuning framework, fusing low rank adaptation (LoRA) and direct preference optimization (DPO), and realizing domain knowledge migration through hierarchical dynamic parameter configuration; establishing a logic-semantic-knowledge three-dimensional quantitative evaluation system, and forming closed-loop optimization by using dynamic weight fusion and a visual decision system; and designing a multi-domain prompt template library with layered parameter freezing, sparse constraint and attention driving, and realizing model lightweight and cross-domain logic constraint. According to the method, the adaptive bottleneck of a general model and domain characteristics is broken through, the small sample training efficiency and the generated content compliance are improved, the computing resource consumption is reduced, an efficient and reliable knowledge service base is provided for professional scenes such as laws, medical treatment and finance, and the technical advantages of specialization, light weight and interpretability are achieved.

The invention relates to the technical field of slope protection, in particular to a slope protection intelligent detection system based on deep learning. According to the technical scheme, the system comprises a multi-source heterogeneous data sensing module, a data fusion and feature extraction module, a slope state intelligent diagnosis and early warning module, an edge-cloud collaborative computing architecture and a system optimization module. Registration and feature complementation of multi-source heterogeneous data are realized through a multi-modal detection network, an overfitting phenomenon is effectively inhibited through a physical information neural network architecture, risk quantitative evaluation is realized through construction of a dynamic risk evaluation model, early warning response time is shortened in cooperation with a four-level early warning strategy, the false alarm rate is reduced, and the early warning efficiency is improved. Besides, the detection precision of the system in an extreme scene is improved through a physical constraint adversarial training method, so that the environmental adaptability of the system is improved, continuous updating and evolution of the model are realized through an online incremental learning module, and the problem of performance degradation of a traditional system caused by change of geological conditions is solved.

The invention relates to the technical field of instrument multi-task optimization, in particular to an intelligent instrument multi-task real-time optimization method and system based on dynamic resource scheduling. The optimization method comprises the following steps: acquiring a target item of each task in real time through a sensor array, constructing a multi-dimensional feature vector, dividing each task into task categories by using a fuzzy clustering algorithm, and presetting an initial priority for the task categories for multi-task feature parameter acquisition and classification modeling. According to the method, the multi-dimensional feature vectors including the task urgency degree, the calculation complexity and the data interaction frequency are constructed, and the fuzzy clustering algorithm of the task dependency constraint is introduced, so that the task categories are accurately divided, the cross-category interaction overhead of the dependency task is effectively reduced, and the compatibility of a scheduling strategy is improved from the source.

The invention relates to an intelligent agent system optimization method and device based on intelligent fault analysis and cross-generation knowledge inheritance, and belongs to the technical field of artificial intelligence. According to the method, interaction abnormal signals are captured in real time by deploying a lightweight log probe, and a tool benefit prediction model based on reinforcement learning is constructed to automatically generate an improvement proposal when the failure rate exceeds a threshold value; an agent genealogy map is established to realize automatic inheritance of a new agent on core memory and abandonment of failure knowledge, and a disastrous forgetting blocker is deployed to dynamically extract a functional module from a genealogy to deal with key capability degradation. Aiming at the problems of fault response lag, knowledge inheritance fracture, key capability degradation and the like in an intelligent agent system iteration process, the invention creatively provides a cooperation mechanism of an intelligent fault analysis layer and a cross-generation knowledge inheritance network, and the fault self-healing capability, version stability and service continuity guarantee level of the system are remarkably improved.

The present disclosure discloses an optimization control method for an integrated energy system based on a physical-informed neural network, which comprises the following steps: S1, constructing an a solar-electricity-heat-gas integrated energy system optimization control model; S2, generating a node connection relation matrix based on the network topology structure of the integrated energy system; S3, constructing a deep graph neural network model with physical-informed fusion; S4, constructing a loss function of the deep graph neural network model with physical-informed fusion; and S5, training a physical-informed neural network model according to the historical operation data to be used for system optimization control. The present disclosure can effectively deal with the influence of uncertainty of renewable energy and unexpected situations on the energy system, thereby ensuring the safe and stable operation of the integrated energy system.

The invention relates to a deep integrated electric drive assembly design method and system, and the method comprises the steps: obtaining the parametric modeling data of an electric drive assembly, and carrying out the digital twin synchronous motor modeling, and obtaining a drive three-dimensional model; topological structure data of the electric drive assembly are obtained, and mixed algorithm optimization calculation is carried out on the topological structure data and the drive three-dimensional model to obtain a system optimization solution set; performing model prediction control construction based on the system optimization solution set, and performing gear contour line modification processing on the electric drive assembly to obtain a speed reduction transmission strategy; performing heat dissipation management extraction optimization on the system optimization solution set according to a preset variable cross-section micro-channel heat dissipation algorithm to obtain a dynamic heat dissipation strategy; preliminarily integrating the speed reduction transmission strategy and the dynamic heat dissipation strategy to obtain an initial design scheme; eMI filtering optimization is carried out on the initial design scheme, multi-stage electromagnetic shielding construction is carried out, and a system performance optimization scheme is obtained. The comprehensive performance of the system can be improved through integral design, regulation and control.

The invention discloses a dynamic sensitive information filtering system and method based on context semantic understanding, and relates to the technical field of information security and natural language processing. Comprising the steps of 1, creating a dynamic sensitive information filtering system, 2, carrying out cleaning, structuring and standardization processing on an input text through a text preprocessing module, 3, capturing deep semantic features of preprocessed text data through a semantic feature extraction module by utilizing a deep learning model, constructing a context-associated semantic representation space, and carrying out dynamic sensitive information filtering on the context-associated semantic representation space. 4, performing multi-level sensitive information detection based on the semantic features through a sensitive information identification module, and identifying the type, the position and the risk level of the sensitive content; 5, on-line iteration of knowledge base and model ability is carried out through a dynamic updating module to cope with dynamic changes of sensitive information types, and 6, safety disposal is carried out on detected sensitive information through a result output module, a filtering result is output, auditing tracing ability is provided, and the auditing tracing ability is provided. And 7, forming a system optimization closed loop through a feedback mechanism module according to user feedback and manual auditing, wherein the system optimization closed loop is used for continuously improving the detection accuracy and adaptability.

The invention belongs to the technical field of industrial waste heat recovery, and discloses a flue gas waste heat recovery system optimization design method considering external parameter changes. The method comprises the steps of system monitoring data acquisition and feature extraction, thermodynamic modeling and parameter sensitivity analysis, external environment parameter and system performance coupling mechanism modeling, equipment failure prediction and reliability constraint determination, multi-objective optimization and self-adaptive control strategy generation and the like. Optimized operation of the flue gas waste heat recovery system under the condition of dynamic change of external environment parameters is achieved. According to the method, a dynamic influence matrix of external environment parameters and system performance is initiatively established, the reliability of the system is evaluated in combination with a stress-life analysis technology, efficiency and cost targets are balanced through a multi-target optimization algorithm, and a self-adaptive control strategy library for different working conditions is generated. The adaptability and stability of the system under complex working conditions are improved, the service life of equipment is prolonged, and the operation cost is reduced.

The invention relates to an ERP non-core service migration method based on K8s dynamic resource scheduling, and belongs to the technical field of cloud computing containerization and ERP system optimization. The method comprises the following steps: identifying a non-core module based on business criticality, and generating a migration list; an internal interface is transformed into a standard API gateway route, and strong dependency communication is asynchronously decoupled through a message queue. Historical resource load data is collected, a periodic rule is extracted by using a time sequence decomposition model, a reference value is set, and a K8s scheduling parameter is generated. Adopting multi-stage construction to separate compilation and operation environments, and generating a lightweight mirror image; and implementing security reinforcement, and dynamically updating the health examination probe and configuration. Selecting a controller according to stateless and persistent requirements; the locality of NUMA domain resources is optimized through topology awareness scheduling; dynamic storage binds a persistent volume according to QoS; the network policy implements container-level traffic isolation. And dynamically adjusting the resource quota and the number of copies. The efficient and safe migration of the ERP non-core business in the K8s environment is realized.

The invention discloses a heat supply system multi-working-condition scheduling control method based on a large model and multiple agents. The method comprises the steps that a heat supply system digital twinborn model is established; the method comprises the following steps: setting a scheduling control center management agent, predicting a thermal load demand by adopting a large language model and combining a thermal load prediction model, dividing a system into a plurality of operation conditions, and setting a plurality of condition sub-agents corresponding to the operation conditions; the dispatching control center management agent judges whether the working condition sub-agents under the corresponding operation working conditions are triggered to start working or not, and meanwhile heat load demand values, predicted through a large language model, of all time periods in the future and the heat production amount of the new energy heat supply unit are transmitted to the corresponding working condition sub-agents; and the working condition sub-agent adopts a large language model to establish a system optimization scheduling control knowledge graph based on the large language model, obtains a scheduling control strategy of each device on the source network load side of the heat supply system, and feeds back the scheduling control strategy to the digital twinborn model for guiding the actual operation management of the heat supply system.

The invention relates to the technical field of power system optimization, and discloses a regional building group source network load storage demand response optimization method. Comprising the following steps of multi-source heterogeneous data fusion collection and intelligent preprocessing, power utilization behavior spatial-temporal characteristic deep mining, multi-dimensional response potential dynamic evaluation modeling, multi-target layered optimization decision generation, personalized excitation strategy self-adaptive generation and closed-loop cooperative regulation execution and feedback. According to the method, user strategy updating is simulated through a replication dynamic equation of an evolutionary game, efficient search of excitation parameters is realized by combining a Bayesian optimization Gaussian process and an expectation improvement function, a user group strategy evolution rule can be dynamically captured, parameters such as electricity price discount and subsidy gradient are accurately optimized in a limited sampling range, and the method is suitable for large-scale popularization and application. A'behavior modeling-data optimization 'closed loop is formed, users are stimulated to participate in demand response, optimal configuration of power resources is realized, and the flexibility and economy of the system are improved.

The invention belongs to the technical field of wind power prediction, and discloses an ultra-short-term wind power prediction model construction method based on signal decomposition and parameter optimization, and the method specifically comprises the following steps: S1, original wind power data processing: employing a self-adaptive noise complete set empirical mode decomposition algorithm (CEEMDAN) to decompose the original wind power data; according to the method, a hybrid model fusing a bidirectional gating cycle unit (BiGRU), a bidirectional time convolution network (BiTCN) and a multi-head attention mechanism (MHA) is constructed, an improved parameter optimization algorithm is designed, the capacity of the model for capturing wind power short-term fluctuation characteristics is enhanced, the parameter optimization efficiency is improved, the local optimum problem is effectively avoided, and the method is suitable for the wind power short-term fluctuation characteristic capturing capability. According to the method, the limitation in traditional feature extraction is effectively improved, high-precision and high-efficiency ultra-short-term wind power prediction is realized, a reliable basis is provided for optimizing a power generation scheduling strategy for a power system, and the method can be popularized and applied to multivariate time sequence prediction scenes such as wind speed prediction and photovoltaic power generation prediction.

The invention relates to the technical field of energy storage scheduling, and discloses a distributed energy storage power system optimization scheduling method and system. The method comprises the following steps: collecting system operation data, performing normalization processing and anomaly detection, and predicting 24-hour load demand and new energy output in combination with standardized data; performing cost calculation and constraint analysis according to the prediction data, and generating a day-ahead scheduling plan; the operation state is updated according to the 15-minute cycle, and the output data is adjusted; performing multi-time-scale hierarchical processing on the power fluctuation; based on this, the energy storage capacity is optimally configured and the life characteristic is evaluated; and parameter optimization and updating are realized through operation index monitoring and analysis. According to the invention, through multi-dimensional performance evaluation and parameter optimization, efficient utilization and service life prolonging of the energy storage system are realized.

The invention belongs to the intelligent information technology, and discloses an e-commerce platform operation system and method based on intelligent decision and dynamic optimization. The system comprises a plurality of modules of multi-source data acquisition and preprocessing, user portrait and demand prediction, intelligent commodity recommendation and the like. A recommendation algorithm fusing a knowledge graph and reinforcement learning and an inventory prediction and optimization algorithm based on dynamic time sequence analysis are innovatively adopted, and a reward function formula and an inventory cost function formula are arranged respectively. According to the method, data acquisition, user portrait construction, business operation and system optimization are realized according to collaborative operation of the system and the modules. The system can accurately recommend commodities, intelligently manage inventory, efficiently plan promotion activities, collaboratively optimize a supply chain, and improve the operation efficiency and commercial competitiveness of an e-commerce platform.

The invention discloses a knowledge graph-based large-model intelligent question-answering system optimization method and apparatus, and an electronic device. The method comprises the steps of obtaining newly-added multi-modal data of a target domain; performing consistency check on the newly added multi-modal data and a historical knowledge graph, and updating the historical knowledge graph by adopting a mixed resolution strategy according to a consistency check result to obtain an updated knowledge graph; performing model parameter fine adjustment on the large model based on the updated knowledge graph; and generating a result corresponding to the problem data through the large model after fine adjustment of the model parameters, constructing a reverse optimization link based on the result corresponding to the problem data, and optimizing the large model after fine adjustment of the model parameters.

The invention relates to the field of air conditioning unit control, in particular to an industrial park elevator system optimization control system and a method thereof.The hardware part comprises a sensor unit, an executing mechanism, a field control unit, communication equipment and a server unit; the software part comprises a real-time operating system, a sensor data acquisition and fusion preprocessing module, a digital twinborn simulation platform, a model prediction control module and a cloud deep reinforcement learning module; the method comprises the steps of S1, data acquisition and primary processing, S2, data transmission and caching, S3, digital twin modeling and system simulation, S4, optimization control strategy generation and issuing, S5, online scheduling and deep reinforcement learning optimization, S6, safety monitoring and exception handling, and S7, closed-loop feedback and continuous optimization. Efficient integration of elevator modules is achieved through digital twinning and model prediction control cooperation; intelligent data processing is realized through deep reinforcement learning and adaptive data fusion; and safe and energy-saving operation is realized through fault tolerance and safety monitoring.

The invention relates to the technical field of power system optimization scheduling, in particular to a virtual power plant resource optimization scheduling method based on multi-head self-calibration tensor factors. Comprising the following steps: S1, collecting virtual power plant data, preprocessing the virtual power plant data, and carrying out standardization, anomaly detection and interpolation on various heterogeneous resource data in a virtual power plant; s2, constructing a hierarchical gradient tensor network, executing multi-head self-calibration decomposition, and establishing a virtual power plant decision optimization model, and S3, designing a multi-head self-calibration decomposition mechanism according to the virtual power plant decision optimization model, and generating a virtual power plant resource optimization scheduling scheme. The technical problems that in existing virtual power plant large-scale heterogeneous resource scheduling, calculation complexity is high, memory occupation is large, and control precision and efficiency are difficult to balance can be solved.

The invention relates to the technical field of cloud computing and data center energy efficiency optimization, and discloses a carbon neutralization-oriented cloud data center energy efficiency optimization method, which comprises the following steps of: 1, acquiring server computing load, power consumption, cooling state and carbon emission data of a data center based on a distributed sensor network; step 2, constructing an energy efficiency mathematical model of the data center based on a deep learning model of AI training; 3, training a task scheduling and cooling system optimization strategy based on a reinforcement learning agent; 4, analyzing the real-time carbon emission condition of the data center by using an AI prediction algorithm; 5, based on the carbon emission trend predicted by AI, triggering an intelligent carbon compensation strategy in combination with the real-time energy consumption condition of the data center; and step 6, dynamically adjusting task scheduling, a cooling control strategy and a carbon compensation scheme by adopting an AI adaptive learning method based on data feedback. And dynamic scheduling of calculation tasks of the data center is realized through a reinforcement learning agent trained by AI.

The invention relates to the technical field of section design, and discloses a shipyard steel bent frame column and beam section design simulation method and system. The method comprises the following steps: classifying and storing the historical data of the shipyard steel bent, establishing an experience library and extracting initial section parameters; inputting a parametric modeling system to generate a geometric model and a load condition; obtaining an internal force envelope value based on finite element analysis; obtaining a stress ratio and a displacement ratio according to standard combination and checking calculation; using an NSGA-II algorithm to optimize section parameters; and modeling and marking based on an optimization result, and generating a structure optimization scheme and a design drawing. The problems that in the prior art, preliminary section type selection of a steel bent frame structure lacks system optimization, a large amount of manual adjustment is needed in the design process, the overall design efficiency is low, and the material utilization rate is low are effectively solved, and automatic, refined and multi-target comprehensive optimization of steel bent frame column and beam section design is achieved.

The invention discloses an error self-calibration method of a hemispherical resonator gyroscope. The error self-calibration method comprises the following steps: realizing mapping and transition between modals; identifying and eliminating transient errors generated in the mode switching process; the error compensation process is optimized, and the adaptive capacity of the system to complex dynamic changes is enhanced; the control parameters of the gyroscope in different modes are automatically adjusted; the correction of an error mode is optimized, and the output precision is improved; transient errors are recognized and eliminated in real time, and system output signals are smoothed; constructing a mode switching decision support and prediction algorithm, judging a mode switching opportunity, predicting an error after switching and compensating the error in advance; system control and calibration parameters are adjusted in real time; layered optimization is carried out on errors of different modal hierarchies; and error self-learning and system optimization are realized by combining adaptive learning and a global optimization strategy. According to the method, the transient error problem caused in the mode switching process can be effectively solved, and global optimization and error correction can be carried out in real time.

The invention belongs to the technical field of power system optimization scheduling, and provides a source network load storage intelligent collaborative optimization method, which comprises the following steps of: deploying sensors at four ends of a source network load storage respectively, collecting in real time by utilizing a cloud data center, enabling data of the four ends to be consistent in time sequence through a PTP protocol, constructing a topological graph according to parameters and data, and establishing a source network load storage intelligent collaborative optimization system. Selecting a model in a digital twinning environment for simulation; dividing independent agents at four ends of a source network load storage, setting observation data, an execution space and excitation feedback, forming an excitation item by economy, stability and environmental protection, interactively circulating actual data, a prediction instruction and an excitation value, recording into a sequence, inputting the sequence into a strategy network, and calculating and outputting logarithmic probability gradient to update the parameters of the strategy network; and the intelligent agent completes interactive circulation according to the strategy network, generates a local scheduling instruction, aggregates the instruction to perform weighted calculation, generates a global scheduling scheme, issues the global scheduling scheme to execution equipment, updates parameters by using an average deviation calculated by a deviation vector, resolves the global scheduling scheme and issues the global scheduling scheme to form a closed-loop mechanism.

The invention provides a pneumatic conveying system optimization method and system based on a multi-target particle swarm, and relates to the technical field of pneumatic transportation, and the method comprises the steps: collecting key transportation data affecting the conveying efficiency, the energy consumption and the system stability, employing a particle swarm algorithm, regarding each particle as a group of optimizable parameter combinations, and obtaining a particle swarm optimization parameter combination; the method comprises the following steps: calculating a pneumatic transport performance index based on an engineering formula, generating a fitness value by adopting a weighted summation method, comparing the fitness value with a preset threshold value, determining whether optimization is needed or not, carrying out iterative optimization by updating particle positions and speeds by taking minimization of pipeline wear, energy consumption and pressure fluctuation as targets, and when the fitness value meets a threshold value condition, carrying out iterative optimization on the particle positions and speeds. And selecting an optimal solution based on a minimum deviation method. The pneumatic conveying system is optimized based on the multi-target particle swarm algorithm, and the conveying efficiency and the system stability are improved under the target of minimizing pipeline abrasion, energy consumption and pressure fluctuation.

The invention discloses a reservoir group joint scheduling optimization method based on multi-agent deep reinforcement learning, and relates to the technical field of hydroelectric energy system optimization scheduling and control, and the method comprises the steps: dividing X reservoirs in the same drainage basin into J agent subsystems, each intelligent agent only senses the local water level-inflow state and outputs the target water level / discharge amount in the next time period; in the training stage, a centralized evaluation-distributed execution (CTDE) framework is adopted, a value function is constructed by combining a central Critic network with global state-action information, and iterative updating is performed on each Actor policy network by utilizing a multi-agent depth deterministic policy gradient (MADDPG); and the reward function integrates power generation benefits, ecological discharge and final water level penalty to realize global collaborative optimization. After the offline training convergence, the autonomous and complementary scheduling instruction of each reservoir can be obtained only by executing millisecond-level forward reasoning based on real-time monitoring data in the online deployment stage.

The invention discloses a data index classification and intelligent recommendation method and device based on deep learning and a medium, belongs to the technical field of deep learning and big data, and aims to solve the technical problem of how to improve data management efficiency and precision and improve user experience. According to the technical scheme, the method comprises the following steps: data collection and preprocessing: determining a data source and a data type, collecting data corresponding to the data source and the data type, and preprocessing the collected data to obtain metadata information; constructing a deep learning model: selecting a corresponding deep learning model according to the property of the task and the data type, and carrying out training, parameter adjustment, optimization and deployment on the deep learning model; data index classification and label distribution; modeling according to user requirements and performing intelligent recommendation; designing a user interface and displaying an application; and optimizing and iterating the system.

The invention discloses an integrated energy load prediction method and system based on modal decomposition and TCN-Transform fusion, and aims to solve the key problems of low prediction precision, insufficient utilization of meteorological factor and load correlation, insufficient optimization of a model structure and the like in integrated energy system load prediction. The method comprises the following steps: comprehensively acquiring electric load, cold load, thermal load and various meteorological data, acquiring different types of data by adopting a special device, and then preprocessing the data; using a maximum information coefficient correlation analysis method to screen remarkably related meteorological features; determining an optimal decomposition parameter in combination with variational mode decomposition and a crown porcupine optimization algorithm; a prediction model fusing TCN and Transform advantages is constructed, and the structure is optimized according to load prediction characteristics; the precision and stability of load prediction of the integrated energy system are remarkably improved, the relation between the integrated energy load and external factors is reflected more comprehensively, and a reliable load prediction basis is provided for optimized operation and management of the integrated energy system.