1967 results about "Network construction" patented technology

The invention relates to the technical field of power system fault diagnosis, and discloses a power distribution network fault identification and positioning system based on a wide area measurement technology, and the system comprises a key measurement point optimization configuration module which determines the arrangement position of an optimal measurement point, and identifies a high-risk weak link in a power distribution network; the virtual-real fusion measurement network construction module adopts a generative adversarial network to generate blind area virtual measurement data conforming to a physical rule, and fuses actual measurement data and the virtual measurement data; the active fault excitation implementation module is used for implementing safe and controllable tiny disturbance injection and actively detecting response characteristics of weak links; the state estimation enhanced fault positioning module is used for constructing a health state evaluation model and calculating the deviation between a transfer function and a health baseline; the toughness fault positioning execution module is used for extracting key information dimensions and ensuring the reliability of fault positioning; through a virtual-real fusion measurement technology, efficient monitoring of the whole area of the power distribution network is realized on the basis of limited actual measurement equipment.

The invention provides an AI agent process automation method and system for an education scene, and relates to the technical field of AI education, and the method comprises the steps: constructing a hierarchical knowledge graph through semantic segmentation, and constructing an agent encoder model through a graph attention network and comparative learning. Based on agent operation data and a feedback mechanism, operation parameters are dynamically adjusted, task decomposition is performed, sub-tasks are processed by using a meta-learner, and a knowledge cache module is established. And semantic analysis and association network construction are performed by using the knowledge cache module, a decision model is trained, and an execution process is optimized. The workflow evolution trend is predicted according to the agent state data, sub-task configuration is optimized, closed-loop optimization is formed, and therefore the automatic processing efficiency and adaptability of the AI agent in an education scene are improved.

The invention discloses an AI-based bridge construction intelligent monitoring method, particularly relates to the field of data analysis, and comprises the steps of multi-modal sensing network construction, cross-domain characteristic parameter extraction, comprehensive state evaluation modeling and intelligent decision and regulation. According to the method, deformation, corrosion, vibration and other multi-physical field data are synchronously collected through a multi-modal sensing network, a cross-scale feature extraction algorithm is adopted to construct creep-corrosion collaborative factors and other composite indexes, the limitation of traditional single-dimensional parameter analysis is broken through, a dynamic evaluation model is fused with weighted integral and a nonlinear function, and the dynamic evaluation accuracy is improved. Precise early warning of risks such as bridge pier instability and cantilever cracking is achieved, the multi-field coupling effect quantification capacity is remarkably improved, an intelligent joint control mechanism automatically triggers instructions such as jacking reinforcement and damping tuning through a three-level emergency response system, a monitoring-analysis-regulation closed-loop system is constructed, the hysteresis of manual decision is effectively eliminated, and the safety and reliability of the system are improved. And all-weather self-adaptive safety guarantee is provided for bridge construction.

The invention belongs to the technical field of rock freeze-thaw damage prediction, and relates to a deep learning rock freeze-thaw damage prediction method based on physical constraints, which is used for improving the physical consistency and prediction precision of a rock freeze-thaw damage prediction model. According to the method, a physical constraint formula is introduced into a data-driven deep learning model, and organic unification of data fitting and physical rationality is realized by using an adaptive weight strategy. The prediction method comprises the steps of data acquisition, data set generation, network construction, physical constraint mechanism formula design, rock freezing and thawing experiment observation or theoretical formula derivation, design of a formula reflecting evolution of various physical properties, model training and prediction output. The deep learning rock freeze-thaw damage prediction method based on the physical constraint has the advantages that generalization ability is remarkably improved, physical continuity and consistency are higher, multi-parameter and multi-source data are comprehensively applied, a staged self-adaptive training strategy is adopted, cost and resources are saved, operation is easy and convenient, and application and popularization are convenient.

The invention discloses an urban traffic intelligent regulation and control system and method based on digital twinning. The method comprises the following steps: S1, collecting and fusing multi-source data in real time; s2, constructing a digital twinborn model; s3, traffic flow prediction and optimization; s4, multi-system cooperative control is carried out; the system comprises the following modules: a multi-source data real-time acquisition and fusion module which comprises a video data acquisition and processing unit, a geomagnetic sensor network construction unit and a floating car data integration unit; the digital twinborn model building module comprises a three-dimensional road network model building unit and a data fusion and calibration unit; the traffic flow prediction and optimization module comprises an intelligent prediction model unit and a real-time optimization control unit; and the multi-system cooperative control module comprises an emergency priority passing system unit, a reversible lane dynamic adjustment unit and a parking lot induction unit. The traffic efficiency can be remarkably improved, the cooperative response speed is greatly increased, and carbon emission is effectively reduced.

The invention relates to the technical field of intelligent temperature control, in particular to a solid state disk intelligent temperature control method and system based on edge computing. The method comprises the following steps: acquiring solid state disk array data; performing array topology analysis on the solid state disk array data to generate solid state disk array topology data; performing initial self-organizing heat dissipation network construction on the solid state disk array data based on the solid state disk array topological data to obtain an initial self-organizing heat dissipation network; edge computing node deployment is carried out on the initial self-organizing heat dissipation network, and temperature data of the solid state disk are collected according to edge computing nodes; and network heat conduction path optimization is carried out on the initial self-organizing heat dissipation network through the temperature data, and a self-organizing heat dissipation optimization network is generated. According to the invention, through self-organization of the heat dissipation network, edge calculation optimization, thermal-electric mode conversion and long-term prediction, SSD array-level intelligent temperature control is realized, and the heat dissipation efficiency, the response speed and the long-term stability are improved.

The invention relates to the technical field of slope protection, in particular to a design method of a slope protection monitoring and early warning system. According to the technical scheme, the method comprises the steps of multi-dimensional sensing network construction, spatial-temporal feature analysis and multi-physical field coupling modeling, dynamic risk assessment and intelligent early warning strategy, hierarchical response mechanism and system verification. The data complementarity and analysis reliability are improved through the multi-dimensional sensing network, the problem of missed sampling is solved in combination with a dynamic sampling measure, the misjudgment rate is reduced through improved Moran's I index analysis, in addition, the water-ion-heat interaction effect is quantified through the seepage-pressure-temperature coupling model, the system monitoring and early warning accuracy is further improved, and the real-time performance of the system is improved. And finally, an early warning threshold value is automatically adjusted through a dynamic threshold value mechanism, so that the problem that a fixed threshold value adopted by a traditional system cannot be dynamically adjusted along with the change of the environment is solved, and the false alarm rate and the missing report rate are reduced.

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 a video stream processing method for dynamic Gaussian compression and adaptive code rate regulation, which is suitable for scenes such as virtual reality, augmented reality and three-dimensional video, and comprises the following steps: S1, Gaussian attribute modeling and initialization; s2, constructing a binary hash grid; s3, constructing a deformation prediction network; s4, designing a mask pruning mechanism; s5, entropy modeling and arithmetic coding and decoding module design; s6, model training; and S7, video stream transmission under multiple code rates. According to the method, a unified scheme combining Gaussian volume cloud coding and adaptive video transmission is proposed for the first time, the video data storage and transmission cost is remarkably reduced, and the comprehensive performance superior to that of an existing method is obtained on multiple real and synthetic data sets.

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 relates to the technical field of power distribution cabinet detection, and discloses an intelligent detection system and method for a high-low voltage power distribution cabinet, and the system comprises a data collection module, a feature extraction module, a network construction and updating module, a fault positioning module, a positioning map generation module, and a self-healing control module. The method comprises the following steps: generating a multi-physical field coupling data set by synchronously collecting electromagnetic radiation spectrum and cabinet vibration waveform, performing time-frequency domain conjoint analysis to extract fault feature vectors, constructing and dynamically updating a fault propagation network, identifying key fault sources by combining betweenness centrality and historical case matching, and generating a fault positioning map; accurate self-healing control is realized by applying reverse electromagnetic pulse and adjusting contact pressure based on the atlas; according to the invention, the problems of low positioning precision and mechanical self-healing control means during detection of the high-low voltage power distribution cabinet are solved, and the fault detection efficiency and reliability are significantly improved.

The invention relates to the technical field of logistics supply, and discloses a multi-source data fusion-based e-commerce supply chain anti-break decision-making system, which comprises a heterogeneous graph network construction and risk prediction module, a multi-source data fusion-based e-commerce supply chain anti-break decision-making module, a multi-source data fusion-based e-commerce supply chain anti-break decision-making module and a multi-source data fusion-based e-commerce supply chain anti-break decision-making module, a time sequence sensitive graph attention network is used to predict a goods shortage risk propagation path; the elastic recovery path generation module is used for modeling supply chain recovery into a sequence decision problem on a graph based on the constructed heterogeneous graph network, and generating a plurality of elastic recovery paths through a variational inference method; according to the method, the heterogeneous graph network is constructed and the time-sequence-sensitive graph attention network is realized, so that the system can accurately capture the cascade effect and the risk propagation path in the complex supply network, the prediction accuracy of the cargo shortage risk propagation path is improved, and false report and missing report are greatly reduced.

The invention discloses a medium and long term weather prediction system based on a physical law correction deep learning technology, and the system comprises a high-resolution data input module, a multi-resolution network construction module, a sub-graph processing module based on a graph neural network, and a linear attention processing module. The system comprises an advanced attention processing module used for reducing secondary calculation complexity to linear complexity, a feature fusion and adaptive fusion module, a multi-level decoding and physical corresponding area optimization correction model, a rolling forecasting module and a multi-step training fine tuning module based on DORA. The medium and long term weather prediction system based on the physical law correction deep learning technology can process multi-resolution input data, and realizes high-precision and long-time weather prediction. Furthermore, global weather forecast with the resolution ratio of 16 days and 13 kilometers can be achieved, and meanwhile refined forecast with the higher resolution ratio is achieved in China.

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 discloses a multi-scene cooperative control method, device and equipment for a network construction type energy storage converter, and a storage medium, relates to the technical field of converter control, and discloses the multi-scene cooperative control method for the network construction type energy storage converter, and the method comprises the steps: obtaining the state data of a power grid, determining grid-connected point power, power grid frequency, power grid voltage and power grid harmonic frequency spectrum according to the power grid state data; establishing a multi-objective optimization function, an energy storage system charge state constraint and a converter output current constraint based on the grid-connected point power, the power grid frequency, the power grid voltage and the power grid harmonic spectrum; and performing multi-objective optimization solution based on the multi-objective optimization function, the charge state constraint of the energy storage system and the output current constraint of the converter to obtain a fundamental current reference value and a harmonic current limit value so as to control the output current of the grid-forming type energy storage converter. According to the scheme of the invention, the capacity conflict between network construction type control and harmonic suppression can be solved.

The invention discloses a bridge safety assessment method based on multi-intelligent algorithm fusion, and belongs to the technical field of bridge engineering and intelligent algorithms. Aiming at the problems of insufficient multi-source data processing, lack of physical constraints and the like of a traditional evaluation technology, the method integrates multi-dimensional indexes through eight steps of multi-source data fusion preprocessing, physical information neural network construction, adaptive deep forest feature learning and the like, and generates a structural vulnerability index (SVI) to guide a maintenance decision. Innovation points comprise a five-algorithm deep coupling architecture, a physically embedded digital twinborn body, a dynamic weight proof theory and the like. The method has the advantages that high-precision recognition, interpretable prediction and visual decision making of bridge damage are achieved, evaluation precision and operation and maintenance efficiency are remarkably improved, and the method is suitable for full-life-cycle management of various bridges.

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 discloses an underground pipe network construction drawing generation and verification method and system based on image recognition, relates to the technical field of construction drawing verification analysis, and solves the technical problem that in the verification process, detection of pipe network element integrity and space conflicts is not comprehensive and efficient enough. According to the method, extracted pipe network element data is compared with original design data element by element, verification is carried out from element types, number, attributes and other dimensions, whether a construction drawing is complete or not can be rapidly and accurately judged, missing or wrong elements can be found in time, and a minimum rectangular bounding box and a three-dimensional projection overlapping judgment algorithm are utilized to judge whether the construction drawing is complete or not. According to the method, the space conflicts among the pipe network elements can be efficiently detected, the conflict detection efficiency and accuracy are improved, the overlapped elements are classified according to the elastic modulus of the element material, the hard and non-hard overlapped elements are subjected to targeted analysis, whether design conflicts exist or not can be judged, and a more scientific basis is provided for design optimization.

The invention relates to the technical field of silicon waveguide and optical fiber coupling, and particularly provides an AI-based silicon waveguide-optical fiber mode field self-alignment coupling method and system, and the method comprises the steps: monitoring the strain distribution of a chip in real time through an integrated distributed fiber bragg grating array, and obtaining the light field intensity distribution of a coupling region through cooperation with a near-field optical probe array; constructing a deep space-time convolutional network, and generating a deformation compensation vector and a waveguide reconstruction parameter by an output layer; a nanofluid channel is integrated on the side wall of the waveguide, and the refractive index of liquid is adjusted through an electrowetting effect to realize dynamic broadening of a mode field; and the electrostatic driving micro-beam embedded below the waveguide adjusts the spatial pose of the waveguide according to an instruction of an output layer of the depth space-time convolutional network. The system comprises a light field intensity acquisition module, a convolutional network construction module and a spatial pose adjustment module. According to the method, real-time analysis and prediction are performed, so that system parameters are actively adjusted, and alignment precision and stability are improved.

The invention relates to the technical field of laser surface modification, and discloses a water turbine laser cladding process parameter intelligent optimization system and method, and the system comprises a data preparation and preprocessing module which is used for obtaining and preprocessing water turbine laser cladding process parameters and corresponding cladding layer quality index data; a physical information guided multi-task prediction network construction and training module; a causal inference analysis module; and a causal perception multi-objective optimization algorithm module. By constructing a multi-task prediction network guided by physical information, the internal physical law of the laser cladding process is fused into a deep learning model, and the accuracy and reliability of predicting a plurality of key quality indexes of the laser cladding layer of the water turbine are remarkably improved. Compared with a traditional pure data driving model, the method can generate a prediction result more conforming to an actual cladding mechanism by means of guidance of physical constraints even under the condition of limited experimental data, and lays a solid data foundation for subsequent process optimization.

The invention provides a network construction type photovoltaic energy storage control system and method for improving the supporting capacity of a power grid. The system comprises a data sampling module, a reference power generation module, a VSG module, a voltage feed-forward compensation and virtual impedance control module, a voltage and current closed-loop control module and a PWM generation module. According to the invention, modeling is carried out on a network construction type photovoltaic energy storage control model, then virtual impedance is introduced into VSG reactive voltage control, and a power grid voltage feed-forward compensation model is provided according to a vector diagram, so that the value of the virtual impedance is determined to weaken the influence of voltage drop when a power grid fails and suppress overcurrent. The problem that a photovoltaic energy storage face is insufficient in low-voltage ride-through capacity is solved.

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 relates to the technical field of wind energy control systems, and discloses a wind power energy-saving control method and system based on big data analysis, and the system comprises a data collection and transmission layer, a data storage and management layer, a data analysis and prediction layer, an intelligent control layer, and an equipment full life cycle management layer. The data acquisition and transmission layer further comprises multi-source data acquisition system construction, edge computing data preprocessing and data transmission network construction. According to the intelligent control strategy, by means of a distributed cooperative control algorithm and an intelligent variable-pitch variable-speed control algorithm, the fan blade angle, the rotating speed and the power output are dynamically adjusted according to the real-time meteorological data, the power grid load requirement and the fan running state, wind power output is smoother through accurate regulation and control, and the wind power output efficiency is improved. The impact on the power grid caused by power fluctuation is effectively reduced, the wind power acceptance capability of the power grid is remarkably improved, and the stability and reliability of the operation of the power grid are guaranteed.

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 belongs to the technical field of safety monitoring, and discloses a gas pipe network construction site safety monitoring method and related equipment, and the method comprises the steps: collecting the original inclination angle data of different positions of a construction region in real time through distributed monitoring nodes, and carrying out the filtering processing of the original inclination angle data through a moving average filtering algorithm, the differential settlement value is calculated according to the filtered inclination angle data of the adjacent monitoring nodes, the differential settlement value and the dynamic settlement difference threshold value are compared to determine the settlement risk and give an early warning, real-time monitoring of gas pipe network construction site ground settlement is achieved, and the settlement monitoring accuracy is improved.

The invention discloses a multi-target cooperative fault ride-through method suitable for a network-forming converter, and the method comprises the steps: carrying out the precise amplitude limiting of a fault current through an annular current limiter, and adjusting a power angle to a lower boundary of a transient stability domain during a fault period; and multi-target collaborative optimization of transient synchronous stability, power grid reactive power support and fault current suppression in the fault ride-through process is realized. An optimal power angle set meeting fault ride-through multi-target cooperation in a multi-element fault scene is constructed by performing off-line beforehand rehearsal training on a system physical model; when the power grid breaks down, the network construction type converter carries out online table look-up search based on the trained set, an active power instruction meeting the transient stability and the optimal reactive power support in the current fault scene is calculated, and the supporting capacity of the network construction type converter to the power grid is mined to the maximum extent while the network construction type converter operates safely and stably. According to the method provided by the invention, the network construction operation characteristics of the converter can be maintained under the power grid fault, and the multi-target collaborative fault ride-through capability of equipment is enhanced.

The invention discloses a photovoltaic power station operation data monitoring method and system, and relates to the technical field of photovoltaic power station equipment monitoring, and the method comprises the steps: collecting equipment operation parameters to form an original data set; constructing an equipment association network by taking the equipment as a node; based on the data set and the association network, time sequence collaborative change and correlation analysis are carried out on the equipment parameters, and a collaborative change model is established; judging that equipment is abnormal through a model, and calling same-type parameters of directly connected nodes to form a to-be-verified set; in combination with the network connection edge weight, calculating the influence degree of the associated parameters on the abnormal equipment by applying a dynamic weight evaluation algorithm, and generating a priority list; and performing iterative optimization on the collaborative change model according to the priority list, and outputting a monitoring report. The system comprises a data acquisition module, a network construction module, an analysis modeling module, an anomaly judgment module, a weight evaluation module, a model optimization module, a data storage module and the like to realize abnormal associated factor identification and monitoring logic optimization.

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.