395 results about "Interaction model" patented technology

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

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 relates to a DVS vibration signal semantic representation method based on prior knowledge enhancement, and relates to the field of signal processing and artificial intelligence. The method comprises the following steps: (1) constructing a distributed vibration signal text enhancement data set; (2) cross-diffusing the standardized DVS vibration signal; (3) multi-band input alignment is carried out; (4) residual signal enhancement; (5) DVS vibration signal feature extraction; (6) aligning and mapping DVS vibration signal semantic features; and (7) fine adjustment of the DVSLLM model. According to the method, the technical problems that in the field of signal processing, sampling frequencies are not uniform, spatiotemporal features are difficult to extract, semantic information is difficult to represent and the like are solved, and a text enhancement data set is constructed by utilizing Deepseek-V3; a fine-grained interaction model from DVS vibration signals to natural language questions and answers is constructed through cross diffusion standardization, two-dimensional discrete transformation, multi-scale convolution, a CBAM attention mechanism, bilingual sense mapping alignment and DVSLLM model fine tuning, and an intelligent method is provided for road roadbed vibration event health monitoring.

The invention provides a micro-grid group-containing AI active distribution network scheduling optimization method, a medium and a system, and belongs to the technical field of power grid scheduling. The method comprises the following steps: firstly, constructing a micro-grid group and main and distribution network interaction model, and determining boundary constraints; predicting key parameters of the micro-grid group by using deep reinforcement learning; constructing an active distribution network power balance equation and topology constraint conditions; solving by adopting mixed integer programming to obtain power flow distribution of the distribution network; constructing a power grid dispatching optimization objective function based on a quantum evolutionary algorithm; processing multi-source data by using an MGPN deep neural network to output an optimal scheduling strategy; monitoring a running state verification effect in real time through a state estimation technology; updating the strategy in real time by applying a rolling optimization mechanism; and establishing an evaluation system to dynamically optimize neural network model parameters, realizing efficient collaborative scheduling of the micro-grid group and the active power distribution network, and solving the technical problem of low distributed energy consumption rate in the collaborative scheduling optimization process of the micro-grid group and the active power distribution network.

The invention discloses a time-space interaction vehicle trajectory prediction method based on speed perception, and the method comprises the steps: obtaining driving trajectory data of a target vehicle and surrounding vehicles in a perception range of the target vehicle, inputting the driving trajectory data into a pre-trained trajectory prediction model, and obtaining the trajectory data of the target vehicle in a prediction time period; the trajectory prediction model comprises an encoder module, a speed sensing interaction modeling module and a decoder module; in the encoder module, a spatial-temporal feature encoder is used for obtaining spatial-temporal feature codes based on the driving tracks of the target vehicle and other vehicles; the scene perception encoder is used for extracting a global spatial dependency relationship between vehicles to obtain a scene perception spatial code; the speed perception interaction modeling module is used for extracting space-time interaction features and scene interaction features of the target vehicle and surrounding vehicles by using a multi-head attention mechanism based on the space-time features and scene perception space codes, and further obtaining global interaction features; and the decoder module is used for obtaining a driving track of the target vehicle in the prediction time period based on the space-time interaction characteristics and the global interaction characteristics. According to the method, the vehicle-scene spatial dependency can be accurately captured, and the trajectory prediction accuracy is improved.

The invention provides a digital human generation method based on a multi-modal large model. The method comprises the following steps: constructing a digital human basic model; generating a structured training set; generating a question and answer model supporting multi-channel interaction; semantic answers of the user questions are output, text emotional tendencies of the semantic answers are extracted, and emotional intensity parameters are output; generating facial muscle movement track data, and performing real-time rendering on the digital human basic model according to the facial muscle movement track data to output a digital human three-dimensional image with emotion expression. According to the embodiment of the invention, cross-modal alignment is carried out on text, image and audio data, and a multi-modal large model containing visual, voice and knowledge models is optimized by using a joint training method, so that more natural and smoother multi-channel interaction experience is realized; in addition, by introducing an emotion recognition model and a face interaction model, the emotion tendency contained in the semantic answer can be captured and reflected more accurately, so that a digital human three-dimensional image with real emotion expression is output.

The invention relates to the technical field of action recognition, in particular to a somatosensory action interaction recognition method and system based on skeleton coordinate points. The method comprises the following steps of collecting real-time skeleton coordinate data of a human body and performing multi-modal feature extraction to obtain a real-time skeleton coordinate sequence; obtaining a standard skeleton posture corresponding to the target interaction action, performing pre-recording and feature coding, and generating a target posture skeleton feature template library; performing skeleton time sequence filtering and joint mapping and joint included angle calculation on the real-time skeleton coordinate sequence, performing similarity measurement and dynamic binding tracking at the same time, and starting a binding recovery mechanism when binding loss is detected so as to guide the user to execute a preset binding posture and re-establish a binding relationship; and mapping the joint included angle time sequence data to a corresponding joint of the virtual human shape interaction model in real time, outputting a somatosensory interaction instruction, and driving to repeat a human body action so as to trigger a somatosensory action interaction event. According to the invention, the stability of somatosensory action interaction recognition can be improved.

The embodiment of the invention provides a memory processing method based on an intelligent agent, a storage medium and an electronic device, a memory architecture of the intelligent agent comprises a first memory layer, a second memory layer and a third memory layer, the first memory layer is used for storing historical dialogue information of interactive dialogue between at least one interactive object and the intelligent agent, and the second memory layer is used for storing historical dialogue information of interactive dialogue between at least one interactive object and the intelligent agent. The second memory layer is used for storing structured events extracted from historical dialogue information, and the third memory layer is used for storing pattern induction memory obtained by performing pattern induction on the structured dialogue events; the method comprises the steps that in response to current round dialogue input information of a target interaction object, target storage information associated with the current round dialogue input information is retrieved from at least one memory layer in a memory architecture, and the current round dialogue input information and the target storage information are assembled into a current cue word; and submitting the current prompt word to a specified interaction model through the intelligent agent, and outputting a response result of the specified interaction model to the interaction object.

The invention discloses an Alzheimer's disease long-term prediction and dynamic intervention method based on deep learning. Accurate dynamic intervention from group statistics to individual dynamics is realized through time sequence alignment, multi-scale time sequence feature extraction, dynamic risk assessment and personalized intervention. Through time sequence alignment and multi-scale time sequence feature extraction, a time-sensitive personalized intervention scheme can be generated. And carrying out continuous time modeling by adopting a cubic Hermite interpolation method and a neural control differential equation, and predicting the long-term risk. By establishing a double-track interaction model, a pathological track and a functional track of a user are analyzed, so that time-varying association among multi-modal data can be dynamically captured. A personalized intervention strategy is provided through reinforcement learning, the intervention strategy is dynamically adjusted in combination with risk reduction amplitude, intervention measure compliance and physiological index change, the effect of short-term behavior change and long-term prediction is balanced, and the Alzheimer's disease is further promoted to be converted from passive treatment to active intervention.

The invention provides an urban space intelligent processing method based on multi-modal fusion, and the method comprises the steps: taking multi-modal data as input, and constructing a unified data stream processing and feature alignment mechanism; a physical space is used as a core framework, and the multi-modal data is converted into a space behavior graph with space-time position semantics; constructing an entity attribute-relation type-influence weight ternary interaction model on the basis of an interaction layer of the spatial behavior map, and analyzing a human, object and environment ternary interaction relation in the city and the park based on the ternary interaction model; designing a space intelligent engine with time sequence modeling and dynamic prediction capabilities; and constructing a task processing system. According to the invention, through deep combination of multi-modal fusion and a space intelligent technology, full-link upgrading of urban space from data perception to intelligent decision is realized, and powerful technical support is provided for fine management, efficient operation and safety guarantee of complex space scenes.

The invention discloses a VR teaching experience enhancement system and method, and belongs to the technical field of virtual teaching, and the method specifically comprises the steps: collecting the position data and posture data of a trainee in a VR environment, building a multi-user coordination interaction model based on the position data and posture data of the trainee in the VR environment, and carrying out the interaction of the multi-user coordination interaction model. The method comprises the following steps: acquiring training participants, identifying an interaction relationship and an interaction event among the training participants, generating tactile feedback data corresponding to the interaction event based on the interaction event, performing calibration processing on the tactile feedback data, and synchronously sending the calibrated tactile feedback data to VR equipment of the training participants, the calibration processing comprises adjusting the tactile feedback data based on the spatial propagation difference quantity and the tactile feedback time offset value of the trainees; according to the invention, when multiple trainees cooperatively operate the same virtual object or scene, consistent and real-time tactile response and spatial perception can be obtained, and the teaching experience in a multi-person cooperation scene is improved.

The invention relates to a rigid-flexible trans-mass self-adaptive robot force position and rigidity collaborative optimization method and system, and relates to the technical field of robot control, and the method comprises the steps: firstly, carrying out the linearization processing of a robot dynamic model through a semi-implicit integral method, and obtaining a robot dynamic model; and in combination with an extended Kalman filter, stiffness parameters in the contact operation process are estimated in real time, a dynamic stiffness sensing model is constructed, and the system response capability is effectively improved. Then, a rigidity parameter is embedded into a Hertz contact model, the dependence of a traditional model on prior information of a contact surface is broken through, a dynamic interaction model between the robot and an operation object is established, and high-precision real-time sensing of normal force and friction force is achieved. And finally, under a nonlinear model predictive control framework, multi-dimensional physical constraints are constructed based on the contact force, the position and the contact rigidity, an optimal control model is obtained, control input is dynamically and adaptively updated through rolling optimization, and the stability and the control precision of the robot in rigid-flexible heterogeneous contact operation are improved.

A system for serving personalized advertisements based on biometric and identifying data captured by sensors. The system employs pre-processing, feature extraction, and recognition modules to identify individuals. A context-aware processing module adapts recognition strategies based on environmental data and analyzes personal information to confirm the identity of the targeted individual and send targeted advertisements. The system includes display and speaker modules for visual and audio advertisements, a predictive interaction modeling module for generating personalized advertisements, and a cross-platform behavior analysis module for analyzing user behavior across offline and online platforms.

The invention discloses a dietary pattern evaluation method and system based on digital intelligent analysis, and relates to the technical field of dietary pattern evaluation.The method comprises the steps that a mobile terminal is used for synchronously collecting multi-modal data, and the multi-modal data are preprocessed; food pictures are retrieved based on keywords to form a data set, an image recognition model is constructed, the volume and area of food materials are calculated in combination with a 3D reconstruction technology, a food material density database is matched, and automatic estimation of the quality of the food materials is achieved; building a voice interaction model fusing characters and regional voice features, and recognizing food material types and food material quality by using voice data; a dynamic allocation weight is calculated through multi-modal confidence, a graph attention mechanism is utilized to detect and correct data conflicts, and a space-time constraint rule base is combined to align dispersed intake records; and constructing a data visualization chart library. According to the method, the rise of dietary structure analysis from extensive statistics to refined intelligent evaluation is realized, and the problem of error accumulation caused by dependence on a single data source in a traditional method is effectively solved.

The invention relates to the technical field of medicine and artificial intelligence, and discloses an elderly care-oriented medical and nursing combined knowledge service system construction method, which comprises the following steps of collecting sentiment expression data of the elderly, applying a nonlinear time sequence analysis and deep learning model, extracting sentiment dynamic characteristics, and obtaining sentiment expression data of the elderly; recognizing an early signal with reduced emotion regulation ability; on the basis of the obtained emotion dynamic features, a memory enhanced attention network is combined with a psychological knowledge graph, an individualized emotion cognitive interaction model of the old people is constructed, a dynamic interaction mechanism between an emotion state and a cognitive mode is revealed, and key intervention nodes are recognized; based on an emotion cognition interaction model and a key node recognition algorithm, the system can accurately position key links of mutual influence of emotion and cognition, and targeted intervention is achieved; and a dual-channel collaborative intervention strategy adjusts an emotional state and reconstructs a cognitive mode through multi-agent reinforcement learning, and breaks through an emotional cognitive vicious circle.

The invention discloses a power electronic networking equipment stability analysis method, system, equipment and medium, and the method comprises the following steps: building a full-system state space model, carrying out the cross validation of the full-system state space model through employing a modal analysis method, an impedance analysis method and a complex torque analysis method, and obtaining the stability of the full-system state space model; obtaining a multi-mode coupling stability analysis result; according to the multi-mode coupling stability analysis result, constructing a power grid s domain node admittance matrix; establishing a virtual power angle dynamic equation of the network-forming converter, and optimizing control parameters according to an evaluation result; an inertia center reference system is adopted, a dynamic interaction model of the synchronous machine and the network-forming converter is established, and dynamic coupling characteristics between devices are analyzed through a relative motion equation. According to the method, the comprehensiveness, the precision and the engineering applicability of dynamic stability analysis of the novel power system are improved through fusion of a multi-dimensional collaborative evaluation framework and an innovative technical system.

Methods, systems, and computer readable media for rendering context-aware and interactive artificial intelligence-generated videos in real time. A talking face (“TF”) model may traverse from a first node to a second node of a state graph via an edge based on a TF instruction generated by an interaction model. The TF model may retrieve a transition video associated with the edge and a pre-computed video template associated with the second node from one or more TF databases. The pre-computed video template may include a plurality of masked video frames and a plurality of pre-computed mouth positions for each masked video frame. The TF model may inpaint a pre-computed mouth position into a masked region of each masked video frame to form a video frame stream. The interaction model may generate a video from the transition video and the video frame stream and present the video on a user device.

The invention discloses a simulation method, device and equipment for predicting flow and deformation of a porous medium and the medium. The method comprises the steps that a computational domain is selected, a corresponding computational grid is generated, and porosity initialization is conducted on the computational domain; determining a time step length and a discrete format, and setting a non-uniform factor to solve a control equation set which comprises a continuity equation, a fluid dynamics equation, a skeleton mechanics equation and a porosity updating equation; and analyzing and displaying a solving result. According to the porous medium flow and deformation simulation method independent of hypothesis and empirical parameters, the method is based on the basic principle of the microcosmic fluid-solid coupling effect, a porous medium fluid-solid coupling body kinetic equation is adopted, the situation that a traditional modeling method depends on volume averaging hypothesis and depends on a multi-phase interaction model is avoided, and the modeling efficiency is improved. The method has cross-scale applicability, and provides a new theoretical basis and prediction means for the development of technologies related to porous medium multiphase dynamics and multi-field coupling problems.

The invention relates to the technical field of user behavior data processing, and discloses a user behavior analysis and personalized recommendation method and system, and the method comprises the steps: collecting multi-dimensional behavior data of a user, and carrying out the feature extraction of the multi-dimensional behavior data; key features are defined based on behavior data, redundant features are reduced through a feature selection algorithm, the dimension of numerical features is unified through normalization processing, and an input feature vector used for a deep learning model is generated; processing the input feature vector by adopting a hybrid deep learning architecture, and generating a user-commodity interaction model in combination with the user behavior sequence and the commodity features; performing real-time prediction on the new user behavior data based on a deep learning model, and dynamically adjusting a recommendation strategy according to a prediction result; and displaying a user behavior analysis result through a visual interface, and continuously optimizing a recommendation strategy in combination with an A / B test framework. According to the method, the defects of a traditional recommendation system in the aspects of multi-source data processing, recommendation individuation and the like are effectively overcome.

The invention relates to the technical field of ecological remote sensing and image processing, and particularly discloses a method and system for monitoring the ecological restoration state of thinopyrum intermediary grassland in an alpine region. The method comprises the following steps: acquiring a multi-temporal remote sensing image and ground ecological parameters, and constructing a remote sensing-ground combined observation data set; utilizing a semantic segmentation model of a multi-scale attention mechanism to extract a thinopyrum intermedium block mask; vegetation indexes, texture features and ecological factors are extracted from the mask region to construct a multi-dimensional feature sequence; inputting the feature sequence and manual intervention information into a training factor interaction model in a graph neural network and causal modeling architecture, and outputting a repair level and evolution data according to the training factor interaction model; and generating a visual layer in combination with historical data, and updating a remote sensing feature extraction strategy based on model feedback. The alpine grassland ecological restoration state monitoring system realizes intelligent monitoring and dynamic feedback of the alpine grassland ecological restoration state, has strong timeliness, clear causality and adaptive optimization capability, and is suitable for continuous monitoring and management of a complex ecological system.

The invention provides a Jinhua pork quality improvement method and system based on data mining, and relates to the technical field of intelligent breeding, and the method comprises the steps: obtaining an original data set; performing data fusion according to the original data set, and obtaining a multi-modal association graph by constructing an entity-relation network topology structure; performing potential influence path extraction according to the multi-modal association map to obtain an influence path set; performing interaction effect modeling according to the influence path set to obtain a dynamic effect model; performing meat defect tracing according to the dynamic action model to obtain a defect driving factor set; and generating an optimization strategy according to the defect driving factor set to obtain a meat quality improvement decision scheme. According to the method, the multi-factor relation network is constructed by integrating Jinhua pig full-period multi-source data, the key conduction path and the dynamic interaction model influencing meat quality formation are accurately extracted, and the nonlinear antagonism relation between the leaf fat deposition tendency and intramuscular fat accumulation under the specific physiological mechanism of the Jinhua pigs is systematically quantified.

The invention belongs to the field of robots, and particularly discloses a mechanical arm man-machine cooperation self-adaptive admittance control method and device, a terminal and a medium, a man-machine interaction model omitting virtual stiffness is constructed, and admittance parameters in the man-machine interaction model comprise virtual inertia and virtual damping; designing a stability observer of the physical man-machine interaction system, monitoring an external force applied by an operator by the stability observer, and performing spectral analysis on a force signal to obtain a system instability index; constructing an admittance parameter adaptive controller through a system instability index, wherein the admittance parameter adaptive controller comprises a virtual inertia adaptive controller and a virtual damping adaptive controller; and the virtual inertia and the virtual damping are adjusted based on the admittance parameter self-adaptive controller, so that the physical man-machine interaction system returns to a stable state. The method realizes self-adaptive adjustment of controller parameters, adapts to a complex environment, and keeps stability of physical man-machine interaction.

Provided in the embodiments of the present application are a human-computer interaction method, a human-computer interaction model training method and an electronic device. The human-computer interaction method comprises: determining a knowledge graph corresponding to input question data, and by means of a graph model, obtaining, from the knowledge graph, node data corresponding to a node matching the question data; on the basis of the node data, generating input data which can be accepted by a first interactive language model, wherein the first interactive language model is a language model used for executing an open-ended task; and on the basis of the input data and the question data, performing answer generation by means of the first interactive language model, so as to generate answer data corresponding to the question data. By means of the embodiments of the present application, not only can the accuracy of task processing be ensured, but the capability of an interactive language model processing an open-ended task can also be fully utilized, such that the solution has better openness and can be flexibly applicable to various scenarios and environments.

The invention discloses a privacy protection recommendation method based on graph federal learning, and belongs to the technical field of intelligent recommendation. The method mainly comprises the following steps: a client receives an initial weight, and constructs an initial user-article interaction graph based on user local data; modeling interaction between nodes on the initial user-article interaction graph based on the graph neural network and the initial weight; training the local user-article interaction model based on user local data; the server side aggregates the article embedding matrix gradient uploaded by each client side; the server side clusters the updated article embedding matrix to generate a sampling article set; and the client performs multi-task joint training based on self-supervised learning on the local user-article interaction model based on the user local interaction subgraph, and performs article recommendation based on the trained local user-article interaction model. The problem of unbalanced data distribution can be relieved, and meanwhile the risk of user privacy disclosure is reduced.

The invention relates to an instruction-level man-machine collaborative driving method and device, and the method comprises the steps: designing an uncertainty quantitative model for the problem of hybrid uncertainty in a man-machine fusion process, and proposing a human instruction optimal approximation strategy based on safety constraints, and guaranteeing that when man-machine decisions are inconsistent, the human instruction optimal approximation strategy is not consistent with the human instruction optimal approximation strategy. Safety and human intention are considered; a human intention prediction model is constructed based on a learning and interaction model, so that the efficiency and effectiveness of man-machine fusion intelligent decision making are improved. Therefore, the problems that control right switching of a traditional cooperation mode lacks smooth transition, and the movement freedom degree of a driver is limited are solved.

The invention discloses a physical man-machine interaction contact safety protection method and system based on a Maxwell model, and the method comprises the steps: building a human body physical model, and expressing the effective mass, elastic coefficient and maximum allowable impact force physical attributes of each part of a human body; kinematics, statics and dynamics models of the robot are established, and the relation between the joint space displacement, speed, torque and mass of the robot and the tail end speed, tail end rigidity and effective mass is expressed; a physical man-machine interaction model is established, and the relation between the joint displacement, speed and mass of the robot and the physical man-machine interaction impact force is expressed; physical man-machine interaction contact characteristics are calculated, and impact duration, the maximum impact force, a contact damping coefficient and a recovery coefficient are calculated; and 5, robot safety protection measures are formulated, and the safety of physical man-machine interaction is guaranteed. According to the method, the solution of a Hertz contact stiffness parameter is avoided, the safety of physical man-machine interaction can be evaluated through the impact duration and the maximum impact force, and a theoretical basis is provided for the design of a robot man-machine interaction system.

The invention relates to the technical field of intelligent network connection automobiles, in particular to an intelligent network connection automobile information interaction method and device based on category personnel judgment, and the method comprises the steps: obtaining a multi-modal data set, designing an improved intelligent interaction model, training the model, carrying out feature analysis, carrying out classification decision making, and generating a personalized interaction scheme. Personnel categories are accurately identified through a multi-modal sensing unit, personalized contents are generated in combination with a dynamic classification module and a context adaptation sub-module, and data security and response speed are guaranteed through a privacy protection module and a real-time enhancement mechanism. According to the invention, the pertinence of information pushing can be improved, the real-time performance of interaction is enhanced, the privacy of the user is effectively protected, and an efficient and safe information interaction solution is provided for the intelligent networked automobile.

The invention discloses a retrieval enhancement system and method based on a multi-modal interaction agent, and relates to the field of artificial intelligence, and the retrieval enhancement system comprises the following steps: obtaining an initial prompt; the initial prompt comprises first data of at least one mode; when a retrieval operation is determined to be performed on the basis of the initial prompt through a multi-modal interaction agent, retrieval is performed on the basis of the initial prompt through a retrieval model to obtain a retrieval result, and the retrieval result comprises second data of at least one modal; determining a target interaction model from a plurality of candidate interaction models through the multi-modal interaction agent based on the modal of the first data and the modal of the second data; and interacting with a user based on the initial prompt and the retrieval result through the target interaction model.

The invention discloses an electric vehicle-agent-power distribution network day-ahead interaction optimization method and device considering time-space characteristics, and a storage medium, and the method comprises the following steps: 1, considering the safety constraint of a power distribution network, and building a power distribution network operator regional time-of-use electricity price optimization model and an electric vehicle agent scheduling model; step 2, writing the optimality strategy of the electric vehicle agent scheduling model into the power distribution network operator regional time-of-use electricity price optimization model in a constraint condition form, thereby obtaining a double-layer interaction model; then solving the double-layer interaction model based on a feasible region iterative algorithm to obtain an optimal operation scheme of day-ahead interaction between a power distribution network operator and an electric vehicle and an agent; the equipment and the storage medium are used for implementing the method. According to the invention, space-time ordered guidance of multi-area electric vehicle charging is realized, the advantage of mobile energy storage of the electric vehicle is exerted, and multi-space-time balance of network supply load is guaranteed.

The invention discloses a new energy power station network source coordination performance detection method and system, and relates to the technical field of new energy power grids, and the method comprises the steps: building an interaction model between a new energy power station and a power grid based on operation data, and analyzing the interaction behavior of the energy power station and the power grid through the interaction model; identifying a coordination contradiction point between the random gene of the new energy power station and the deterministic demand of the power grid, and optimizing the coordination contradiction point into a coordination balance point by using a distributed balance control model; and simulating a real-time operation state when the new energy power station accesses the power grid, comparing the real-time operation state with the initial state, and identifying the abnormal coordination performance when the new energy power station accesses the power grid. Through the distributed balance control model driven by the homotopy algorithm, the abstract coordination contradiction point is converted into the specific optimal adjustment set value, a dynamic self-adaptive coordination mechanism between the new energy and the power grid is formed, and the power grid stability and scheduling efficiency after the new energy power station is connected are improved.