28 results about "Response model" patented technology

The invention discloses a lightweight member marketing method and device for real-time conversation, a terminal and a storage medium, and the method comprises the steps: responding to a received user voice stream, continuously generating an incomplete intermediate text stream through streaming voice recognition, and inputting the intermediate text stream into a lightweight marketing response model; the lightweight marketing response model predicts the potential intention of the user in real time based on the intermediate text stream, and pre-assembles one or more candidate response frameworks from the fragmented marketing corpus; when it is detected that the utterance of the user ends, the real intention of the user is determined based on a complete final text stream generated by streaming speech recognition; and arbitrating the candidate reply framework according to the weighted decision factor set to obtain an arbitration result, determining final reply content according to the arbitration result, converting the final reply content into an instant response call, and outputting the instant response call. According to the method, the time delay of the real-time call marketing process can be reduced, and the concurrent processing capability is improved.

The invention discloses an intelligent fatigue prediction method and system based on recreation facilities, and belongs to the technical field of intelligent operation and maintaining.The method comprises the steps that stress-strain data, environmental parameters and acoustic emission signals of a target recreation facility in multiple operation modes are collected, and an original multi-source data set is constructed; identifying microcrack initiation time nodes and fatigue early-stage features thereof based on change point detection and spectral analysis; a fatigue response sensitivity matrix is generated by constructing a microscopic response model fused with environmental factors; predicting a microcrack propagation path by using a time recursion model, identifying a fatigue risk point, and automatically generating a maintenance suggestion; if a frequency domain abnormal signal is found, carrying out residual life reevaluation and dynamically optimizing model parameters; according to the method, early fatigue recognition, trend prediction and adaptive optimization can be realized, and the safety and maintenance efficiency of recreation facilities are improved.

The invention discloses a vehicle load identification method based on a multi-modal large model, and the method comprises the following steps: selecting a reference bridge in a region for testing, and obtaining a reference bridge case library; analyzing the new bridge through a multi-modal large model, and matching a plurality of reference bridges most similar to the core characteristics of the new bridge from a reference bridge case library; the multi-modal large model migrates a load response model and model parameters of the reference bridge case to a new bridge through a case reasoning mechanism; and according to the structural difference between the new bridge and the matched reference bridge and the actual working condition of the new bridge, the multi-modal large model carries out architecture reconstruction on the migrated load response model. The method has the beneficial effects that the new bridge can directly reuse the model parameters and the load response rule of the reference bridge through the constructed reference bridge case library in combination with the retrieval enhancement generation and bridge-crossing knowledge migration mechanism of the multi-modal large model, so that the time cost and the economic cost of bridge-crossing deployment can be greatly reduced.

The application discloses a kind of based on master-slave game integrated energy system optimization scheduling method, equipment and product, it is related to electric power system control technical field, the method includes obtaining the load prediction data of current scheduling period, renewable energy output prediction data and environmental parameter;Load the integrated energy system equipment model, integrated demand response model and ladder carbon transaction model constructed in advance, based on the model and the data obtained, the distribution robust master-slave game optimization scheduling model of current period is constructed, and the game equilibrium solution under the most unfavorable uncertainty scenario of energy operator is solved, and the optimization scheduling result is obtained.The application realizes the fair sharing of carbon responsibility between game subjects and emission reduction incentive, and still can obtain robust scheduling scheme under extremely unfavorable scenario, guarantee the economy and safety of system.

The application discloses a kind of multi-element load linkage regulation method, system, equipment and medium, comprising: constructing the two-stage operation transaction model of multi-element load aggregation main body user, forms lower user response model;Establish two-stage robust regulation model dominated by distribution network operator;Introduce power grid topology adjustable constraint;Coupling user response model and enhanced operator regulation model, build double-layer interactive optimization framework, form two-stage robust double-layer optimization problem containing integer variable;Optimization problem is solved using nested column constraint generation algorithm, output optimal electricity price guide strategy, direct load control amount and topology adjustment scheme, as the total scheme of multi-element load linkage regulation for weak point uncertainty.

The invention discloses a virtual power plant optimal load response method, system and device based on Copilot and a medium. The method comprises the steps that a target virtual power plant Copilot response model is acquired; decomposing the demand response task by using a decomposition module to obtain a fine-grained task of each participating node, and inputting the fine-grained tasks into a fine-grained task processing module for processing; a solver module is used for solving each fine-grained result, and a recommended first response strategy is obtained; evaluating the first response strategy according to the performance influence data and the empirical parameters to obtain an evaluation result, and adjusting the first response strategy according to the evaluation result to obtain a second response strategy; and executing the second response strategy by using the energy controller module, obtaining execution result data, and feeding back the execution result data and performing optimization training. The response strategy is corrected and adjusted by using the monitored environment change and system performance change, so that the stability and flexibility of the model are improved, and the technical problem is solved.

The invention discloses a garlic whole-process monitoring decision-making platform based on AI and big data, relates to the technical field of artificial intelligence, and solves the problems of data aperture heterogeneity and sample deviation in garlic price prediction in the prior art and the problem that a result deviates from the actual market due to the lack of inventory-behavior feedback modeling in the prediction process. According to the scheme, a multi-level linkage architecture comprising a data protocol layer, a causal deduction layer, an interactive analysis layer, a feedback simulation layer and a parameter calibration layer is constructed, and a consistent data stream is formed by performing time sequence protocol and causal correction on multi-source price data; performing interactive analysis on the price chain and the inventory sequence, introducing a multi-subject response model to simulate market feedback, and finally forming a closed-loop prediction sequence through a dynamic calibration mechanism; according to the method, the prediction stability of garlic whole-process monitoring decision and the reliability of risk research and judgment are remarkably improved.

This specification provides a method, apparatus, and storage medium for distribution transformer (DTR) scheduling based on differentiated edge demand. The method includes: acquiring load demand information of electricity users and a differentiated price-based response model of the electricity users; inputting the load demand information and the differentiated price-based response model into a pre-trained price-demand prediction model to obtain price-demand relationship data; and determining a DTR scheduling strategy based on the price-demand relationship data, preset constraints, and preset DTR costs. The technical solution provided in this application addresses the problem that existing technologies, which set a linear relationship between electricity consumption changes and electricity price changes, still struggle to achieve the desired DTR scheduling results.

The application discloses a vehicle load identification method based on a multi-modal large model, which comprises the following steps: selecting a reference bridge in a region for testing to obtain a reference bridge case library; analyzing a new bridge through a multi-modal large model to match a plurality of reference bridges with the closest core characteristics of the new bridge from the reference bridge case library; the multi-modal large model migrates the load response model and model parameters of the reference bridge case to the new bridge through a case reasoning mechanism; and the multi-modal large model reconstructs the migrated load response model according to the structural differences between the new bridge and the matched reference bridge and the actual working conditions of the new bridge. The application has the beneficial effect that, through the constructed reference bridge case library, the generation of the multi-modal large model retrieval enhancement and the cross-bridge knowledge migration mechanism, the new bridge can directly reuse the model parameters and load response law of the reference bridge, thereby greatly reducing the time cost and economic cost of cross-bridge deployment.

The application belongs to the field of intelligent conversation, and provides a conversation intelligent access system and method, which comprises the following steps: hosting different types of multiple conversation accounts to the cloud to realize centralized management and unified scheduling of the accounts; receiving conversation messages from the multiple conversation accounts in real time, and performing effectiveness screening and preprocessing on the accessed messages; classifying and distributing the accessed effective conversations based on a shunting model; identifying customer intention based on a response model, and automatically responding to conversations meeting preset rules; manually responding to conversations not shunted to the AI response module; performing feature extraction and labeling processing on accessed customer information to establish a customer portrait; and dynamically updating the shunting model and the response model based on the customer portrait.

The invention discloses an independent energy storage day-ahead market transaction decision optimization method and system, and relates to the technical field of power grid regulation and control, and the method comprises the steps: constructing an electricity price change response model, constructing an excitation compensation response model, and constructing a hybrid response model; constructing a multi-objective decision optimization function according to the maximum independent energy storage profit, the minimum demand response cost and the highest user satisfaction degree, and constructing a transaction decision optimization model through the multi-objective decision optimization function, an electricity price change response model, an excitation compensation response model and a mixed response model; and inputting current day-ahead market transaction information and current load data to the transaction decision optimization model, solving the transaction decision optimization model according to the multi-objective decision optimization function, and obtaining an independent energy storage day-ahead market transaction decision. The method has the beneficial effects that the cost and profit of the user side and the independent energy storage side are balanced, and the suitability and rationality of decision making are improved.

The application discloses a multi-aggregator demand response incentive method for overlapping users, which comprises the following steps: establishing a demand response model of users, a dissatisfaction model of users participating in demand response, and an optimal incentive model of aggregators implementing demand response; establishing a multi-aggregator cooperation game model considering overlapping users, and formulating a negotiation set in the cooperation game; regarding the overlapping users as users participating in the demand response plan of each aggregator, obtaining an incentive strategy S1 of each aggregator according to the response model of the users; regarding the overlapping users as users not participating in the demand response plan of each aggregator, obtaining an incentive strategy S2 of each aggregator according to the response model of the users; judging the optimal result of the negotiation set according to S1 and S2, and finally obtaining the best incentive of each aggregator under the condition of the overlapping users.

This invention relates to the field of electric vehicle charging technology, and discloses a method and system for optimizing charging fees for charging piles based on dynamic rates. The method includes: collecting multi-dimensional feature data and user charging data, performing preprocessing and feature extraction; constructing and training a charging demand prediction model to predict charging demand in real time; constructing a price response model and combining it with a charging load prediction curve for dynamic rate optimization; performing user cluster analysis and designing personalized incentive strategies; setting rate information release rules and dynamically scheduling charging resources; collecting operational data and user response data in real time, and optimizing the charging demand prediction model and price response model based on the charging load prediction curve; and performing data encryption and privacy protection. This invention, through a personalized incentive mechanism, formulates differentiated incentive strategies for three types of users: price-sensitive, time-sensitive, and convenience-first.

The invention discloses an optimal configuration method, system and device for exciting an electric vehicle to support power grid toughness under an extreme event and a storage medium, and relates to the technical field of power system operation and planning, and the method comprises the steps: constructing a typhoon wind field model, and calculating the local toughness reserve demand; constructing a market clearing model, and cooperatively determining the configuration of wide-area energy and local toughness reserve; constructing a capacity excitation-response model, and analyzing the electric vehicle capacity which can be actually dispatched by the aggregator under the influence of the typhoon based on a typhoon wind field model; a double-layer optimization bidding model is constructed, and quotation and incentive decisions of the aggregator are generated; and converting the double-layer optimization bidding model into a single-layer mathematical planning model, solving the single-layer mathematical planning model, and outputting an aggregator quotation strategy and a scheduling plan. According to the method, an electric vehicle aggregator is successfully stimulated to provide key flexible support for a power grid in an extreme event, and considerable economic returns are obtained.

The invention provides an industrial adjustable resource flexibility modeling and calling method for a power distribution network, and relates to the technical field of power systems, and the method comprises the steps: constructing a time sequence response model according to the power boundary and state switching constraint of industrial equipment in different process states; solving a power regulation capability upper limit value along a plurality of detection directions, constructing a single-device adjustable range boundary, and executing superposition aggregation operation to obtain an aggregation adjustable range boundary; solving a calling track based on the supply and demand mismatch loss average numerical constraint and the life degradation cost under a preset confidence level; executing a calling track in the disturbance scene set, and executing a correction operation on the aggregation adjustable range boundary according to the supply and demand difference; and issuing the corrected aggregated adjustable range boundary and the calling track to a power distribution network dispatching terminal, and sending a power adjusting instruction to industrial equipment. According to the method, the problems of rough industrial resource modeling, single aggregation and lack of robustness in calling of the existing power distribution network are solved, and accurate mining of flexible resources is realized.

The invention discloses an electric power supply and demand optimization method and device for park multi-element main body cooperation, and belongs to the field of electric power system operation scheduling. The method comprises the following steps: S1, respectively establishing power response models of a power supply party and a power user according to the income of the power supply party and the satisfaction degree of the power user; s2, establishing a power tracking model according to the power response models of the power supplier and the power consumer; s3, respectively inputting a preset initial time-of-use electricity price into the power response models of the power supply party and the power consumer, and solving the power response models to obtain power responses of the power supply party and the power consumer; s4, sequentially inputting the power responses of the power supply parties and the power consumers corresponding to each group of initial time-of-use electricity prices into the power tracking model, and optimizing the time-of-use electricity prices to obtain a time-of-use electricity price to be evaluated; and S5, repeating the steps S3-S5 on the time-of-use electricity price to be evaluated until a preset convergence condition is met, and outputting a global optimal solution. According to the invention, user satisfaction can be improved and low-carbon economic operation of the park can be realized.

A system is provided.SOLUTION: A system comprising: means for receiving input data from a user; means for analyzing the input data; means for providing feedback to the user based on the analysis; means for improving how the user interacts based on the feedback; means for performing learning using the input data and historical interaction data to update a response model; and means for generating a refined response based on the updated response model.SELECTED DRAWING: Figure 1

We provide the system. [Solution] An information processing device that collects training data and constructs a customer response model based on said training data, A speech recognition device that interacts with customers using a customer interaction model provided by the information processing device, A data analysis device that collects customer feedback and updates the customer response model based on that feedback, A conversion device that uses speech recognition means to convert voice input from customers into text data, A response generation device that generates a response based on text data obtained by a speech recognition device using a generative AI model, A communication system that includes this.

The application discloses a kind of power intelligent customer service response method and system based on knowledge base multi-condition constraint, the method includes: constructing power customer service generative response model and sample library, synonym library;Power customer service knowledge base is constructed based on sample library and synonym library;Formulating the constraint strategy of power customer service knowledge base from the angle of question content constraint and response content constraint respectively;Under the constraint strategy of power customer service knowledge base, response content is generated according to question content.The application introduces the concept of contact degree based on knowledge base on the basis of generative response model, and formulates the constraint strategy by the size relation between the numerical value of contact degree and experience threshold value, thereby effectively limits generative content, realizes the constraint of generative response content by industry data, can effectively shield the response content irrelevant to industry, improves the accuracy of response content.

Methods, systems, and computer storage media for providing domain-integrated contextual response management using a domain-integrated contextual response engine in an artificial intelligence (Al) system are described. Domain-integrated contextual response management is a systematic approach that combines specific industry knowledge with contextual understanding to generate accurate, relevant, and specific industry-tailored responses to user queries. Domain-integrated contextual response management further includes fine-tuning models for Retrieval-Augmented Generation (RAG) tasks using customer-specific data based on a two-fold approach involving skill distillation and knowledge distillation (i.e., skill distillation from a more powerful model like Large Language Model 'LLM" and knowledge distillation from domain-specific data). Domain-integrated contextual response management also includes creating a synthetic dataset that enables smaller models (e.g., domain-integrated contextual response models) to effectively manage RAG tasks while incorporating domain-specific knowledge. Domain-integrated contextual response management further ensures that the domain-integrated contextual response models can retrieve relevant information, support citations, and decline out-of-domain (OOD) questions.

This application provides a method for modeling and utilizing the flexibility of industrial adjustable resources in distribution networks, relating to the field of power system technology. The method includes: constructing a time-series response model based on the power boundaries and state-switching constraints of industrial equipment under different process states; solving for the upper limit of power regulation capability along multiple detection directions to construct the adjustable range boundary of a single device and performing superposition and aggregation operations to obtain the aggregated adjustable range boundary; solving for the utilization trajectory based on the average numerical constraint of supply-demand mismatch loss and lifetime degradation cost under a pre-set confidence level; executing the utilization trajectory in a set of disturbance scenarios, and performing a correction operation on the aggregated adjustable range boundary based on the supply-demand difference; and sending the corrected aggregated adjustable range boundary and utilization trajectory to the distribution network dispatch terminal to send power regulation commands to the industrial equipment. This application solves the problems of coarse modeling, singular aggregation, and lack of robustness in utilization of existing industrial resource models in distribution networks, achieving accurate mining of flexible resources.

This invention discloses a method for constructing a frequency constraint surrogate model for power systems considering operational margins, belonging to the field of power system security and stability analysis and dispatch optimization technology. The method includes: constructing a multi-machine frequency response model containing multi-machine oscillation equations and a reheat turbine speed control system model; performing time-domain simulations under active power disturbances based on this model to obtain the system frequency minimum point as a training label; constructing a neural network surrogate model with unit start-up and shutdown states as input features; introducing relaxed frequency constraints and using the augmented Lagrange multiplier method for hard constraint iterative training of the model; and introducing auxiliary binary variables through the Big M method to losslessly transform the model into a mixed-integer linear constraint set. This invention solves the problems of insufficient conservatism and difficulty in engineering application of traditional models, balancing prediction accuracy and security reliability. It can be directly embedded into dispatch optimization models and is suitable for frequency security analysis of onshore-offshore interconnected power systems with a high proportion of renewable energy grid connection.

This application discloses a method for obtaining a response model, a method for obtaining a response statement, an apparatus, and a device, belonging to the field of artificial intelligence technology. The method for obtaining the response model includes: obtaining a first training sample, which is generated based on a reference speech statement, a reference response statement, and first knowledge statements corresponding to each first word in the reference speech statement. A response model is trained based on the first training sample. The response model includes an encoding sub-model and a decoding sub-model. The encoding sub-model encodes the input target speech statement into a target vector, and the decoding sub-model outputs the target response statement based on the target vector. When using the response model trained by this application to output a response statement, there is no need to retrieve knowledge and input it into the response model, resulting in high efficiency in obtaining the response statement. The obtained response statement considers fine-grained knowledge and has high quality. This application can be applied to the mapping field.

The application discloses an analytical method for a full frequency response model of an equivalent three-area interconnected power system, and belongs to the field of power system frequency stability analysis and control. First, a single-machine frequency full response model is established, and dynamic equations and parameter expressions of the single-machine frequency full response model are obtained. Second, considering the frequency space-time distribution characteristics and the influence of power exchange of a tie line, the network structure of an interconnected system is simplified based on DC power flow, and equivalent aggregation and order reduction are performed based on three-area division of a disturbance area and a non-disturbance area, so as to establish an equivalent frequency response model of a three-area system. Finally, a three-area system frequency full response model is constructed, and analytical expressions of frequency differences of each area are obtained. The application can accurately depict the full frequency response process of the system, and simultaneously reflect the frequency space-time distribution characteristics and the influence of power exchange of the tie line between areas, and is suitable for large-disturbance frequency safety analysis of a three-area interconnected power system, and has the advantages of fast calculation speed, clear physical meaning and convenience for online fast evaluation.

The application provides a power grid accommodation calculation method and device, and relates to the technical field of energy power generation. The new energy accommodation calculation method comprises the following steps: establishing a power system new energy accommodation target function aiming at promoting wind power and photovoltaic power accommodation; establishing constraint conditions of power grid operation based on actual operation conditions of the power system; classifying demand side resources participating in interaction into reducible load, translatable load and transferable load according to user response characteristics; respectively establishing load response models for the reducible load, the translatable load and the transferable load; optimizing the load response models based on the target function; and running the optimized load response models under the constraint conditions and calculating new energy accommodation improvement efficiency. The application provides a new energy accommodation calculation method and device, which models multiple types of demand side flexible resources to improve or promote new energy accommodation.

The invention relates to the technical field of mine intelligent filling, in particular to a mine filling operation full-process intelligent optimization system based on digital twinning driving, which comprises the steps of collecting multi-source heterogeneous data to construct a four-dimensional spatio-temporal data cube, generating a correction vector by combining numerical deduction and real-time residual error, judging a conflict index according to a multi-target gradient different sign relationship, and calculating the full-process intelligent optimization system based on digital twinning driving. And dividing an execution path, extracting key control parameters and an adjustment scheme, and generating an optimization decision instruction packet. The method comprises the following steps: acquiring multi-source data to construct a multi-dimensional data cube, deducing a flow trend and generating a correction vector by combining fluidity, resistance and a response model, guiding dynamic adjustment of a path, identifying target conflicts and classifying the path according to a different sign relation of an index change trend, and extracting key control parameters and an adjustment scheme. And the equipment linkage coordination and resource allocation matching performance are enhanced, and the operation response capability under the complex working condition is improved.

The invention relates to a direct-current micro-grid dispatching method, device and system. The method comprises the following steps: acquiring a cost parameter, electricity price information and an initial demand load capacity of the DC micro-grid; inputting the cost parameter, the electricity price information and the initial demand load into a pre-constructed scheduling model; the scheduling model comprises a cost model, a load demand response model and an electricity price optimization model; the cost model is used for optimizing the total cost, the load demand response model is used for constraining the incidence relation between the scheduling load rate and the electricity price, and the load demand response model is used for optimizing the load scheduling rate; based on a preset constraint condition, solving the scheduling model to obtain a load scheduling scheme; the load scheduling scheme comprises a target load scheduling rate and a target scheduling time period; the target load scheduling rate is used for indicating that part of the demand load capacity in the initial demand load capacity is scheduled to the target scheduling period. According to the scheme, the power utilization cost of a user is reduced.

A method and system for determining the influence of flexible resource response on the quantity and price of a power spot market. The method includes quantitatively analyzing the adjustable capacity of a flexible resource based on the device response characteristics of the flexible resource, constructing a multi-time-scale dynamic response model of the flexible resource, and evaluating the adjustable performance of the flexible resource, and introducing an available coefficient to aggregate the flexible resources; based on the aggregation result of the flexible resources and combined with the trading rules of the power spot market, a market game model considering the participation of the flexible resources is constructed, and the model is solved by using the backward induction method; a coupling model of the power spot market and the response of the flexible resources is established by combining the dynamic response model of the flexible resources and the market game model, and the quantitative relationship between the response of the flexible resources and the electricity price of the power spot market is obtained. The scheme of the present application enables the flexible resource to quickly respond to the change of the electricity price and quickly feedback the state of the flexible resource, and realizes the dynamic quantity and price of the power spot market.