1400 results about "Network construction" patented technology

The invention relates to the technical field of electric power monitoring analysis, and discloses an intelligent analysis system for electric power monitoring data based on a mutual inductor. The system comprises a mutual inductor data acquisition and structuring module which acquires a current waveform, a voltage waveform and a harmonic component in real time, and generates a standardized monitoring data unit through preprocessing, field mapping and association identification establishment; the theoretical monitoring value calculation module constructs a dynamic calculation model according to historical power data, and an input standard data unit outputs a theoretical value; the rule conformity verification module is used for matching the equipment type with the operation rule and generating single compliance judgment; a multi-dimensional difference analysis module compares a theoretical value with a measured value from time domain deviation, frequency domain deviation and waveform distortion, and generates an equipment-level difference coefficient matrix; the association network construction module is used for constructing a multi-monitoring-point association map according to the equipment identifier and the position information; and the anomaly positioning and strategy generation module combines the map, compliance judgment and a difference matrix, calculates a risk score, generates an anomaly probability distribution map, positions anomaly and matches a monitoring strategy.

The invention discloses an energy storage system real-time diagnosis and networking control method and system based on digital twinning and deep learning, and the method comprises the steps: collecting the electrical, thermal and aging state data of an energy storage battery cluster through a multi-mode sensor, constructing a multi-physics field coupled digital twinborn model by using a graph neural network and a long short-term memory network; performing synchronous mapping on battery cluster operation data acquired in real time and the digital twinborn model to generate a state evolution sequence in the battery cluster with advanced prediction capability; based on the state evolution sequence, predicting a dynamic stability boundary of the key node of the power grid and a possible instability risk time period in the future; and according to the dynamic stability boundary and the instability risk time period, generating a cooperative adjustment instruction of the output voltage amplitude, the phase and the virtual impedance of the network construction type energy storage equipment. According to the embodiment of the invention, the diagnosis reliability, the control foresight and the operation safety of the energy storage system in a complex power grid environment can be improved.

The invention discloses an urban power grid real-time load collaborative peak regulation method based on multi-energy complementation and AI scheduling. The method comprises the following steps of multi-source data access and high-dimensional feature space construction, embedded entropy calculation and interactive network construction, domain knowledge and data-driven model fusion, hierarchical scheduling and dual-stage optimization, and real-time decision and closed-loop feedback. According to the method, the real-time performance and hierarchical scheduling thought are emphasized, and an organic closed loop is formed on the three aspects of intra-day scheduling, hour-level rolling correction and minute-level or second-level emergency response. Meanwhile, by means of a multi-stage optimizer switching mechanism, the model can complete rapid convergence of high-dimensional parameters in a short time, finer strategy fine adjustment is carried out in the later period, and the accuracy and reliability of a peak regulation scheme are guaranteed; the method can be applied to advanced power grid systems such as intelligent power grid dispatching, a multi-energy collaborative optimization platform and demand side response management, and has the characteristics of high real-time performance, strong adaptability and good expandability.

The invention belongs to the field of computer vision and industrial defect detection, and discloses a steel coil end face defect detection method and system based on CBAM-BiFPN and multi-loss optimization, and the method comprises the steps: image preprocessing and region extraction: extracting a steel coil end face region through OpenCV and other image processing methods; dividing the high-resolution image into a plurality of small blocks and performing data enhancement; the method comprises the following steps: constructing a YOLOv11 network based on CBAM-BiFPN, introducing a CBAM attention mechanism and BiFPN feature fusion structure, and constructing an improved YOLOv11 detection network; multi-loss function joint optimization training is carried out, and model training is carried out in combination with loss functions such as Focal Loss and WIoU; and fusion of detection results and defect reconstruction output: reconstructing original image defects of all tile image detection results in a space coordinate mapping and redundant region fusion mode, and realizing high-precision overall detection output. Compared with a traditional method, the method still has the high recognition capability in a complex background and low-contrast scene, the detection precision and stability are obviously improved, and higher industrial adaptability and practical value are achieved.

The invention belongs to the field of combat management, and particularly relates to an air real-time combat management system based on hybrid intelligence, which is characterized in that a cross-domain thermodynamic perception situation map and a cross-domain fusion situation map are generated through sensing module multi-modal target data through situation fusion, threat analysis and target sorting; the hierarchical autonomous decision-making module analyzes combat requirements by using an enhanced hierarchical and hierarchical subsystem, constructs and generates a sub-target sequence and an interactive network through target decomposition and a constraint node network, and generates an optimal tactical control instruction set in combination with resource prediction allocation and tactical evaluation; the cross-layer intervention module dynamically adjusts an autonomous decision permission weight and an instruction according to a task risk, a communication state and a decision score, and realizes the control right of a person in a decision ring by using the instruction of a commander as the highest intervention decision instruction of each layer; the analogue simulation module integrates the subsystems and the man-machine constraint boundary, and performs collaborative decision simulation until the standard is reached; and integrated management of real-time sensing of the air combat situation, intelligent hierarchical decision under dynamic constraint, man-machine permission flexible adjustment and collaborative simulation verification is realized.

The invention provides a fault ride-through control method and a fault ride-through control system for a grid-forming converter, which are characterized in that the current of the grid-forming converter under an asymmetric fault is controlled by adopting a negative-sequence current reference generation strategy aiming at an inner ring of a controller, and negative-sequence reactive current is injected into a power grid; adopting a partial sequence current amplitude limiting strategy to control the maximum phase current amplitude of the injected asymmetric current not to exceed an amplitude limiting value; controlling the boundary of an active reference value by adopting an active reference value amplitude limiting strategy for an active synchronization ring; a synchronous damping link is adopted to enhance damping in power angle swing; for a reactive voltage loop, a reactive voltage variable coefficient control strategy is adopted to control the output of the maximum reactive current; and an internal potential locking strategy is adopted to suppress overvoltage. According to the invention, negative-sequence reactive power is provided, and overcurrent of each phase is avoided; the power angle characteristic is improved, and the transient stability is improved; stable transient voltage support is provided in a current output capability range; and after the fault is recovered, the voltage of the converter grid-connected point is smoothly recovered.

The invention discloses a network construction type energy storage capacity optimal matching method based on a frequency response model, and belongs to the technical field of gas turbine power plant financial supervision and artificial intelligence, and the method comprises the steps: S1, quantifying the power vacancy of a typical station under an extreme short-circuit fault, determining a virtual inertia constant and a virtual droop coefficient of network construction type energy storage, and carrying out the calculation of the virtual inertia constant and the virtual droop coefficient; setting an initial output coefficient and adjusting a step length; s2, constructing and integrating a system frequency response model based on the inertia and frequency modulation characteristics of the conventional unit, the wind turbine generator and the network construction type energy storage; s3, applying power disturbance to the system, gradually increasing an initial output coefficient and performing simulation, and recording the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation; and S4, when the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation are respectively smaller than or equal to set threshold values, the energy storage capacity proportion corresponding to the current initial output coefficient is the optimal proportion. According to the method, the problem of accurately measuring the optimal configuration proportion of the network construction type energy storage in the new energy station is solved.

The invention discloses a network construction type inverter for a photovoltaic energy storage system and a virtual synchronous machine control method. The network construction type inverter comprises an inverter power module and a central controller. The central controller is used for collecting power grid operation data in real time and generating a power grid impedance state vector through online impedance identification; and dynamically adjusting virtual inertia and damping parameters of the inverter power module according to the impedance state vector by using an adaptive mapping function so as to realize cooperative control and circulating current suppression between the modules. Performing stability boundary evaluation based on the adjusted parameters, then performing intelligent early warning of the photovoltaic energy storage system, and smoothly switching the operation mode of the inverter power module; according to the invention, through deep integration of a modular hardware architecture and intelligent adaptive control, the inverter has strong autonomous network construction capability, the problems of weak power grid adaptability and transient stability are effectively solved, and the dynamic response and reliability of a photovoltaic energy storage system are improved.

The invention discloses a stability evaluation and optimization method for a multi-infeed series-parallel new energy station based on equivalent external characteristics of a network construction type converter, and belongs to the technical field of electric power automation. Comprising the following steps: constructing a new energy multi-infeed hybrid system stability analysis closed-loop dynamic model, and simplifying a network construction type admittance transfer function; based on a stability analysis closed-loop dynamic model of the new energy multi-infeed series-parallel system, simplifying the new energy multi-infeed series-parallel system into an equivalent single-infeed system, and evaluating the stability of the equivalent single-infeed system through a generalized operation short-circuit ratio and a critical generalized operation short-circuit ratio; analyzing and quantifying the influence of key parameters of the network construction type equipment on the stability of the system through a parameter sensitivity analysis method; a node participation factor is used as an evaluation index, and control parameters are optimized based on a stability optimization method of key parameters of network construction type equipment of system weak nodes. The method provided by the invention has good effectiveness and reliability, and can realize gradual improvement and refined regulation and control of the system stability.

The invention discloses an entity digitization and link framework algorithm based on a heterogeneous graph attention network, and the algorithm comprises the following steps: S1, heterogeneous information network construction: carrying out the unified modeling of all multi-source heterogeneous data into a heterogeneous information network containing various types of nodes and various types of edges, s2, meta-path definition and guidance: defining "meta-paths" connecting different types of nodes to capture a complex deep semantic relationship, S3, heterogeneous graph attention network embedding: adopting an attention mechanism to enable a model to automatically learn importance of different neighbor nodes and different meta-paths, generating a final embedding vector of each entity, and establishing a heterogeneous graph attention network model; according to the method, the information fidelity is higher, modeling is directly conducted on different types of nodes and relations on a heterogeneous graph, more abundant and heterogeneous semantic information in data can be reserved compared with a multi-view method, and the end-to-end learning ability is higher; and the complexity of manually designing a fusion strategy is reduced.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

The invention relates to the technical field of network construction type converter control, in particular to a network construction type converter fault ride-through method and system based on dynamic virtual resistance control, and the method comprises the steps: adding a control link based on virtual impedance in a network construction type converter control system; detecting the actual value of the power grid voltage, and judging whether the power grid voltage drops or not; detecting the current amplitude output by the grid-forming converter in real time, and judging whether the current amplitude exceeds a set value or not; if one of the two conditions is met, a virtual impedance control link is started: stage-type current limiting control is performed on the virtual resistor, and the size of the virtual resistor provided by the network-forming converter to the power system is adjusted in real time along with the amplitude of the fault current; and when the power grid voltage recovers and the current amplitude is smaller than the set value, the virtual resistor staged current-limiting control exits. According to the invention, the amplitude limiting of the fault current in the fault ride-through of the network-constructed converter can be realized, so that the fault ride-through capability and transient stability of the network-constructed converter are improved.

The invention relates to the technical field of ship design optimization, and discloses a hull shape optimization method based on neural network modeling. In the data acquisition stage of the method, initial appearance parameters and hydrodynamic performance data of a ship body are obtained, the appearance parameters comprise geometric dimensions and shape features, and the performance data comprise resistance coefficients and wave-making resistance values. In the neural network construction stage, a neural network model with a multi-layer perceptron structure is trained by using collected data, weights are updated through a back propagation algorithm, and a nonlinear mapping relation between appearance parameters and hydrodynamic performance indexes is established. In the shape optimization stage, the trained neural network model is used for carrying out iterative adjustment on the shape of the ship body, fluid dynamic performance indexes are recalculated through the model after each adjustment until preset convergence conditions are met, and finally optimized ship body shape data are output. According to the method, partial complex calculation is replaced by the neural network, and intelligent optimization of the hull appearance is realized.

The invention provides a fault ride-through control method for a network construction type converter, and belongs to the technical field of energy storage converters and the like. The method comprises the following steps: firstly, measuring PCC voltage and power grid side current, calculating amplitudes of the PCC voltage and the power grid side current, and then judging whether the PCC voltage drops below a set threshold value or not; if not, keeping the active power reference and the voltage amplitude reference unchanged; and if so, performing active power reference modification: judging whether a current limiting strategy is triggered, if not, locking the PCC voltage reference as a measured value, and if so, gradually decreasing the PCC voltage reference by an increment value based on the measured value. According to the invention, accurate evaluation of the existence of the stable equilibrium point is realized; meanwhile, an improved fault ride-through control method is provided, active power reference and voltage amplitude reference are dynamically modified, so that the grid-forming converter is transited to a new stable balance point during a fault period, and current limiting, transient synchronization stability improvement, PCC voltage source characteristic maintenance and maximum fault current output can be taken into account.

The invention discloses an intelligent inspection and fault prediction system based on computing power service, and belongs to the technical field of power grid inspection. The instruction analysis module analyzes the power grid dispatching instruction and determines target equipment and an expected state path; the topology network construction module constructs an equipment operation intention topology network and marks key nodes; the task screening module generates a sensing set containing equipment state recognition and robot movement control tasks according to the environment data, and screens out tasks outputting key node state parameters as a core task set; the computing power distribution module calculates the minimum computing power of the core task according to the weight of the key node and the environmental interference coefficient, and triggers non-core task degradation when the minimum computing power is insufficient; the fault prediction module inputs equipment state parameters output by the core task into a prediction network to generate an abnormal coefficient and a maintenance sequence; the real-time performance of key tasks is guaranteed by dynamically allocating computing power resources, and the operation and maintenance efficiency and reliability of the power grid are improved.

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.

The invention discloses a system safety assessment method and system for an energy storage power station containing a network-building type energy storage converter, and the method comprises the following steps: S1, collecting converter control parameters, power grid dynamic data and lithium battery mechanism data through multi-source heterogeneous data fusion, and carrying out the preprocessing through noise elimination and a normalization algorithm; s2, constructing a network construction characteristic quantitative evaluation system including indexes such as dynamic stability and voltage supporting capability; s3, establishing a three-layer evaluation system of an equipment layer, a control layer and a system layer, and calculating a total risk value of the system by using a weight distribution algorithm; s4, verifying system adaptability and optimizing parameters through time domain simulation in scenes such as a strong power grid and a weak power grid; and S5, generating an evaluation report containing parameter optimization suggestions, triggering graded early warning based on an index threshold value, and automatically generating a closed-loop control instruction. According to the invention, multi-dimensional safety assessment of the energy storage power station is realized, and systematic technical support is provided for safe operation of the network-forming type energy storage power station.

The invention relates to the technical field of power grid control, in particular to a control method of a network following / constructing new energy LCC-HVDC grid-connected system, electronic equipment and a storage medium. System optimization control is realized through six key steps: firstly, obtaining basic parameters such as a network construction new energy capacity ratio and a system short-circuit ratio; secondly, calculating reactive vacancy of the system; then determining a system working mode; establishing a new energy mathematical model; a cooperative control strategy is obtained; and finally formulating a high-low penetration control strategy. Therefore, a complete control closed loop is established, and a systematic solution is formed from parameter acquisition to strategy execution. And the advantages of the following network type new energy and the constructed network type new energy are integrated, so that the high efficiency of the following network type new energy is reserved, and the power grid supporting capability of the constructed network type new energy is also realized.

The invention discloses a target detection method for cross-resolution distillation, and relates to the field of computer vision and artificial intelligence. The method comprises the following steps: (1) introducing a double-branch network to construct a cross-resolution distillation framework, in a training process, performing down-sampling on a high-quality image to form a high-low resolution image pair which is respectively used for training a teacher model and a student model, and introducing additional convolution to change the step length of a teacher network so as to enable features in the network to have the same size, so that the features in the network have the same size; then cross-resolution knowledge distillation is carried out; (2) replacing the path aggregation feature pyramid structure in the frame in the step (1) with a backbone enhancement feature pyramid structure; and (3) introducing a space channel attention module in the process of constructing the feature pyramid by the teacher network of the framework in the step (1). According to the method, the low-resolution image feature extraction capability of the detection network is effectively improved, the low-resolution image target detection precision is improved, and the method has a wide application prospect in the fields of image information analysis and the like.

The invention discloses an air-space integrated safety monitoring method for distribution network construction based on an unmanned aerial vehicle, and the method comprises the following steps: A, constructing an air-space integrated cooperative monitoring network, and collecting the multi-source data of a distribution network construction region through the air-space integrated cooperative monitoring network; c, constructing a construction safety analysis model; the construction safety analysis model comprises a data acquisition and preprocessing module, a multi-source data fusion analysis module, a risk diagnosis and evaluation module, an intelligent prediction and early warning module, an early warning decision and disposal module and a visualization and feedback module; and D, based on the trained construction safety analysis model, collecting multi-source data by using an air-space integrated cooperative monitoring network, and carrying out online monitoring, early warning and disposal on the construction safety of the distribution network in combination with a meteorological database. Unmanned aerial vehicle near-earth monitoring and satellite remote sensing data can be fused, the distribution network construction process is effectively monitored, and potential safety hazards are reduced.

The invention discloses a cooperative parameter optimizing and setting method and system for a network construction type reactive power compensation device, terminal equipment and a storage medium, and belongs to the field of power system control. The method comprises the steps of obtaining operation condition data and a power flow initial value of a power grid region where a reactive power compensation device to be set is located, constructing a discretization state space model, calculating a damping ratio according to a calculated system characteristic root, and determining a dominant oscillation mode; respectively changing the installed capacity, configuration points and control parameters of the reactive power compensation device to be set in a preset step length, and generating a characteristic root moving track; the installed capacity, the configuration points and the control parameters are continuously optimized based on the feature root moving trajectory until the preset stabilization capability index is larger than 0 and the optimized feature root real part is smaller than 0, the optimal installed capacity, the optimal configuration points and the optimal control parameters of the to-be-set reactive power compensation device are obtained, the to-be-set reactive power compensation device is adjusted, and the to-be-set reactive power compensation device is adjusted. According to the invention, the problem of lack of cross-band parameter setting in the prior art is solved.

The invention discloses an advertisement putting platform based on AI dynamic creative optimization, and relates to the technical field of advertisement putting, and the platform comprises five modules: a dynamic creative basic network construction module deploys multi-modal nodes to construct a full-scene generation network; the creative reference index acquisition module acquires effect and adaptive data, and analyzes to obtain a quantitative index; the creative failure evaluation module evaluates whether the effect of the creative element is attenuated according to the index; the alternative creativity planning module analyzes available alternative elements and plans a scheme; and the creative emergency response scheme execution module executes the emergency scheme when the alternative elements are all attenuated. The effect data comprises a click conversion rate and the like, and the adaptive data comprises theme correlation degree and the like, and calculating indexes through normalization processing. Attenuation judgment refers to effect index decreasing amplitude and adaptive index fluctuation, alternative schemes are screened according to priorities, emergency schemes are rapidly generated, new creativity is tested, and the advertisement putting effect and efficiency are improved.

The invention relates to a multi-modal oscillation suppression method and system for a network construction type power supply grid-connected system, belongs to the technical field of power system control, and solves the problem that multi-modal oscillation of a large-scale inversion type power supply grid-connected system cannot be cooperatively and rapidly suppressed. The method comprises the following steps: when a system generates broadband oscillation, collecting grid-connected point voltage and current signals of each power station to extract a dominant oscillation mode of each power station; constructing a modal coupling matrix based on the dominant oscillation mode to extract modal coupling strength; identifying a key path of oscillation propagation in the dominant oscillation mode, and extracting a path weight and a power station participation degree; calculating the comprehensive power regulation and control weight of each power generation station in multiple modes so as to determine the power regulation quantity; and distributing the power regulation quantity to an energy storage device, a static var generator (SVG) and each power generation station according to oscillation characteristics of different frequency bands so as to cooperatively suppress multi-modal oscillation. The active / reactive oscillation energy is cooperatively adjusted, so that the oscillation energy of each frequency band is effectively suppressed.

The invention discloses a light energy power station fault prediction system based on deep learning. The system comprises a data acquisition module used for reading equipment operation data from a sensor; the data preprocessing module is used for denoising, interpolating and standardizing the equipment data; the graph convolutional network construction module is used for constructing an equipment data graph structure and extracting features; the Lemap dimension reduction module is used for mapping the high-dimensional equipment features to a low-dimensional space; the time sequence modeling module is used for constructing a time sequence prediction model based on the low-dimensional features; the hyper-parameter optimization module is used for optimizing hyper-parameters of the time sequence model; the model verification module is used for evaluating the precision and response time of the fault prediction model; the model deployment module is used for deploying the prediction model to a monitoring system; the fault prediction and early warning module is used for monitoring in real time and generating fault early warning; and the continuous optimization module is used for regularly optimizing and retraining the fault prediction model. The method achieves the high efficiency of fault prediction of the light energy power station, remarkably improves the prediction precision and the reliability of equipment operation, and is widely suitable for equipment monitoring and early warning.

The invention provides a live broadcast advertisement effect real-time optimization method and system based on multi-modal AI, and relates to the technical field of advertisement optimization, and the method comprises the steps: collecting the multi-modal input information of a live broadcast scene, and extracting a feature vector after preprocessing; constructing a multi-modal-advertisement effect association graph, and integrating the multi-modal-advertisement effect association graph with the multi-modal knowledge graph to form a dynamically updated advertisement optimization knowledge network; based on real-time context awareness and a knowledge network, determining an optimal strategy through reinforcement learning evaluation sorting; the optimal strategy is converted into a specific instruction, and dynamic optimization is executed; collecting effect feedback data, and returning to a feature fusion and weight optimization link; the system comprises a multi-modal data acquisition and feature extraction module, a cross-modal feature alignment and knowledge network construction module, a reinforcement learning strategy generation and evaluation module and the like. According to the invention, the delay waiting time of the live broadcast advertisement optimization response is reduced, the real-time adaptability is improved, and the advertisement putting accuracy and conversion efficiency are improved.

The invention discloses a network construction type novel energy storage multi-objective optimization coordinated scheduling method, which comprises the following steps: S1, energy storage classification and data acquisition: (1) according to application scene function requirements, dividing energy storage types according to rated continuous discharge duration; (2) data acquisition; s2, calling an energy storage scene and combining with weight parameter setting to construct a multi-objective optimization model: (1) establishing the multi-objective optimization model with the purposes of minimizing the system operation cost, maximizing the energy storage utilization rate and improving the power grid stability; (2) setting constraint conditions; s3, a dynamic optimization algorithm: (1) adopting an improved multi-target particle swarm algorithm or a genetic algorithm, introducing an adaptive weight mechanism, and combining real-time data to dynamically adjust the weight of a target function; (2) aiming at uncertainty factors, embedding a scene analysis method or a robust optimization strategy, and judging whether the comprehensive efficiency is optimal or not; and S4, scheduling execution and monitoring.

The invention discloses an LSTM (Long Short Term Memory)-based harmonic frequency coupling model optimization method for a network construction type converter, which considers the influence of a dead zone and establishes a converter model controlled by a virtual synchronous generator based on a harmonic state space method. In the power electronic modeling process, due to approximate simplification, switch neglecting and other nonlinear relations, the model is not accurate enough, meanwhile, in the harmonic state space model modeling process, system state variable parameters need to be considered, and the precision of the model affects harmonic transmission characteristics and coupling analysis, so that the modeling efficiency is greatly improved. Errors caused by power electronic modeling are reduced through an LSTM neural network algorithm, the harmonic amplitude estimation difference is reduced, state variable parameter values in a system model are accurately estimated, the model precision is further improved, and then the harmonic coupling condition under the dead zone influence is effectively and accurately analyzed.

The invention discloses a network construction type energy storage optimization method and system based on power grid strength constraint, and belongs to the technical field of power grid energy storage optimization. Obtaining a reduced frequency domain admittance matrix based on power synchronization control and a node admittance matrix of the power system; constructing an optimization target model of the power system according to the multi-dimensional stability evaluation index and the reduced frequency domain admittance matrix; constructing a double-layer reinforcement learning model which comprises a first TD3 model and a second TD3 model, and repeating alternate iteration of the first TD3 model and the second TD3 model until an optimization target model of the power system is met; and obtaining a selection node and a capacity of the network construction type energy storage unit output by the first TD3 model when the optimization target model of the power system is satisfied, and taking the selection node and the capacity as a network construction type energy storage optimization configuration result of the power system. According to the invention, efficient site selection and constant volume of the network-forming type energy storage system can be realized, and the stability and the new energy accepting capability of a power system are improved.

The invention discloses a graph mapping method based on precise synthesis, and the method comprises the steps: constructing a precise synthesis structure library based on NPN equivalence classes, and inputting the precise synthesis structure library into a Boolean network; k-Cut cutting enumeration is carried out on the input Boolean network, a truth table is calculated, cutting is matched with a structure in a precise comprehensive structure library through Boolean matching, and rapid search is realized in combination with NPN classification; the overlay network is generated through multi-round mapping optimization, logic sharing nodes are mined in combination with structural hash, and the multi-round mapping optimization comprises delay-oriented mapping, global area topological optimization and local accurate area optimization; and a new target network is generated based on the optimized overlay network, redundant nodes are removed, and final network construction is completed. According to the method, manual intervention is not needed through full-process automatic mapping and a redundancy removal mechanism, the labor cost of a technical mapping link in chip design is remarkably reduced, and the method is particularly suitable for efficient design of complex circuits and emerging majority of logic base technologies.

The invention discloses a social group simulation method and device based on multi-agent driving. The method comprises the following steps: performing user attribute sampling processing on a real e-commerce user data set, and constructing agent basic attributes based on user attributes in combination with a large language model to obtain agent basic attribute features; performing construction processing on the agent basic behaviors based on a preset memory mechanism in combination with the agent basic attribute features; according to the agent basic attribute features and the agent basic behavior features, performing construction processing on agent interaction behaviors in combination with a large language model; performing relation network construction processing based on a small-world network model on the plurality of agents; and performing simulation processing based on execution agent behaviors on the agent social group model to obtain a multi-user behavior simulation result. By constructing a multi-agent simulation system in an e-commerce scene and combining the multi-agent simulation system with a large language model, social group simulation in a complex e-commerce interaction scene is realized, and the accuracy of social group simulation in the e-commerce scene is improved.