4685 results about "Whole systems" patented technology

The invention provides an optical storage and charging cooperative control method and system based on multi-energy complementation. The method comprises the following sub-steps: respectively establishing a photovoltaic power generation model, an energy storage system model and a charging load model; acquiring historical illumination information, charging information and electricity price information, constructing a prediction model based on a neural network, and predicting and outputting illumination intensity, charging load demand power and electricity price in a future time period; constructing a target optimization function by taking the annual net cost and the power deviation rate as optimization targets; according to the method, the cooperative optimization control of the photovoltaic power generation, energy storage and charging system is realized, the annual net cost is reduced, the power deviation is reduced, the energy storage and charging cooperative control strategy is established, the target optimization function is solved by adopting the improved whale optimization algorithm, and the charging and discharging power sequence is generated according to the optimal solution obtained by solving and is input to the system for execution. And the operation efficiency and reliability of the whole system are improved.

The invention relates to the technical field of liquid cooling, and discloses a server cabinet liquid cooling control method and system based on intelligent liquid cooling distribution, and the method comprises the steps: collecting the temperature, cooling liquid flow, pressure and environmental parameters of each node in a server cabinet in real time through a liquid cooling distribution unit; the load change of the server is predicted based on a dynamic distribution algorithm, and the pump set rotating speed and the valve opening degree of the liquid cooling distribution unit are dynamically adjusted so as to dynamically distribute the cooling liquid flow according to needs; a multi-parameter coupling optimization algorithm is adopted, and the energy efficiency ratio of the liquid cooling system is adjusted according to the temperature, the cooling liquid flow and the pressure; cooling liquid leakage is detected through a multi-mode sensor, a liquid leakage protection mechanism is triggered in a grading mode according to the severity degree of leakage, and the liquid leakage protection mechanism comprises local pipeline isolation and whole system shutdown. The system corresponds to the method. By adopting the method, the energy efficiency improvement and the reliability enhancement of the heat dissipation system of the server cabinet are realized.

The invention discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The invention relates to a synchronous line loss intelligent diagnosis and analysis system based on an electric power knowledge graph. The synchronous line loss intelligent diagnosis and analysis system comprises a data acquisition layer, a data access layer, a data storage layer, a data analysis layer and a visualization layer, the data acquisition layer is used for acquiring power grid node data; the data access layer is used for carrying out dynamic synchronization on power grid node data based on Apache Kafka; the data storage layer comprises a time sequence database and a graph database; the data analysis layer comprises a detection module and an inference engine; and the visualization layer is used for dynamically displaying a propagation path and node characteristics of an abnormal region based on a topological structure of the knowledge graph, and querying an abnormal reason of the node and the influence on the whole system by clicking the node of the device. According to the invention, intelligent detection and diagnosis of the line loss abnormal area are realized, and the problem root cause is rapidly identified.

The present invention relates to the technical field of secondary device monitoring. Disclosed are an online monitoring method for an operating state of a secondary current loop neutral line, and a system. The method comprises: by using a high-precision sensor for collection and a composite filtering algorithm for processing, obtaining a secondary current fundamental component of each neutral line of a transformer substation which is not influenced by harmonic waves and transient states, and uploading the secondary current fundamental component to a protection information master station; retrieving originally configured access protection information master station information to obtain three-phase secondary current loop information of each bay of the transformer substation, summarizing secondary current information of all monitoring ranges, and performing grouping and classification marking on said information by transformer substation, bay and winding; and using the collection information for operation analysis, automatically identifying and confirming an operating state of the line, and on the basis of the operating state, calculating the outlier deviation linearity and the continuous dynamic trend deviation degree of each winding of each bay. The present invention solves the problem that intermittent faults in secondary current loop neutral lines are concealed and are not easily found, realizes comprehensive monitoring for the whole system, and can quickly and accurately position a specific winding and issue an abnormal early warning.

The invention discloses an electric power capital construction site operation environment data monitoring and safety management method, which belongs to the field of intelligent decision technology and electric power safety management, and comprises the following steps: constructing a semantic network framework according to a construction plan; collecting and calibrating multi-source environment data to generate a trusted data set; generating a real-time risk network graph based on the semantic framework and the trusted data set; calculating a robust risk index and performing sensitivity deconstruction; generating a closed-loop intervention instruction when the risk indicator exceeds a safety threshold; and finally, collecting, feeding back, iteratively optimizing the whole system, and generating a cross-project multiplexing intelligent template library. According to the method, a comprehensive technical path of semantic modeling, causal inference and closed-loop adaptive optimization is adopted, the operation situation can be deeply analyzed, potential risks can be quantified and attributed prospectively, the optimal intervention strategy is intelligently generated, and the intelligence, precision and prospective level of safety management of the electric power capital construction site is remarkably improved.

The invention relates to the technical field of resource scheduling, in particular to a cloud computing resource management system. In the cloud computing resource management process, the execution data of the computing tasks are analyzed, the task combinations with relevance are screened, and the computing path task set is established, so that the scheduling of the computing tasks is more intelligent, and the resource utilization rate is improved. And resource allocation is performed according to the matching degree of the calculation demand and the computing power pool, the adaptability of the calculation resources is dynamically adjusted, and accurate scheduling of the calculation task is realized. And in combination with the load state of the calculation node, the CPU utilization rate, the memory occupancy rate, the I / O load and other key indexes are monitored in real time, so that the resource allocation is more flexible, the calculation pressure of the high-load node is reduced, and the stability of the whole system is ensured. The load conditions of the computing power pools are evaluated, the task allocation strategy is adjusted, the computing tasks are reasonably distributed among the multiple computing power pools, the task migration and resource expansion strategy is optimized, and the balanced utilization rate of computing resources is improved.

The invention provides an AI flood real-time simulation method for urban hydrological multi-physics field decoupling supervision, and the method comprises the steps: constructing an intelligent physical prediction model of urban flood, carrying out the layered decoupling of an urban hydrological hydrodynamic system, separating a one-dimensional drainage pipe network submodule from a two-dimensional surface flood module, designing a multi-scale physical constraint loss function for different links, and carrying out the real-time simulation of the AI flood. Microcosmic, mesoscopic and macroscopic multi-scale physical constraint fusion is realized, and residual constraints of the Saint-Venant equation are embedded in a neural network agent of a drainage system in a microcosmic level; in the mid-level, embedding the residual constraint of the shallow water equation in the surface flood routing system; in the macroscopic level, the water balance constraint of the whole system is realized; urban hydrology and hydrodynamic force are decoupled, and multi-scale physical field supervision constraint is achieved.

The invention relates to the technical field of fire-fighting management, and discloses an intelligent fire-fighting service management system, which comprises a data acquisition unit used for acquiring three basic parameters, namely environment data, working condition data and battery data, of a fire-fighting object in an office building, and the system acquires the environment, working condition and battery data of a fire extinguisher through the data acquisition unit; the fusion unit generates a dynamic composite abnormal threshold value, and the adjustment unit executes a three-stage sampling frequency adjustment mechanism according to the threshold value, the battery health degree score and the data deviation degree, which means that the sampling frequency can be reduced to reduce the battery energy consumption when the state of the fire extinguisher is normal and the risk is relatively low, and when the risk is increased or the battery health degree is reduced, the battery energy consumption is reduced. The whole system realizes accurate management and regulation of the battery according to the actual working condition of the fire extinguisher, energy waste is avoided, the service life of the battery is prolonged, the cost of frequently replacing the battery is reduced, and the sustainability of intelligent fire control management is enhanced.

The present disclosure provides a system for generating motor control commands for a humanoid robot, comprising an alpha model with over 1 billion parameters that processes visual observations and language instructions at a first frequency to generate contextual embeddings, and a beta model operating at a higher second frequency. The beta model includes an embodiment-specific state encoder projecting robot state information into a shared embedding space, a diffusion transformer module generating denoised action sequences through iterative flow-matching that cross-attends to the alpha model's contextual embeddings, and an embodiment-specific action decoder converting denoised sequences into motor control commands. The beta model generates action chunks comprising future action sequences over a predetermined time horizon in a single inference step, with the complete system having less than 5 billion parameters.

According to the intelligent grotto excavation stability analysis system based on the BIM, through deep fusion of the BIM model and multi-source monitoring data, whole-process dynamic control over the grotto excavation stability is achieved. The system adopts a lightweight model processing technology to remarkably improve the calculation efficiency, and an intelligent inverse algorithm effectively solves the subjectivity problem of traditional parameter determination. A three-dimensional numerical simulation and real-time monitoring data interactive verification mechanism greatly improves the reliability of an analysis result, and a dynamic risk assessment system realizes the accuracy of risk early warning through multi-index fusion analysis. A final support scheme optimization module combines engineering experience with an intelligent algorithm, safety is ensured, economical efficiency is taken into account, and an intelligent solution capable of being self-adaptive to engineering condition changes is formed. According to the whole system, seamless connection of all links is achieved through modular design, and full-chain technical support from data to decision making is provided for safety construction of underground engineering.

The invention relates to the technical field of power grids, and discloses a port green power virtual energy storage aggregation regulation and control method, which comprises the following steps: acquiring an aggregation scene and resource data; generating a power hard constraint mapping and pile division execution reference by calculating a multi-band energy index and identifying a system power transfer characteristic; a local rapid control configuration set is generated by solving a virtual impedance feasible region and combining power hard constraint allocation compensation budget; and fusing a sub-pile execution reference (slow channel) generated based on the power hard constraint with a fast channel execution track obtained after local online execution compensation, performing priority arbitration under the constraint of the power hard constraint, and outputting a final execution power track. According to the invention, a double-layer regulation and control framework of slow channel planning and fast channel compensation is constructed, and through a core link of power hard constraint mapping, coordinated operation of fast and slow channels and safety and stability of the whole system are ensured, and fast and reliable dynamic support capability can be provided for a power grid.

The invention discloses an AI (artificial intelligence) digital human interaction system, which comprises a dialogue management module for receiving emotional state information transmitted by an emotional recognition module and other information input by a user feedback processing module; tracking a dialogue state, selecting a proper response mode according to a dialogue strategy, and generating a corresponding system behavior; the system behavior instruction is transmitted to an action and expression generation module; the user feedback processing module is used for collecting and analyzing feedback data of the user; and the feedback module analyzes and processes the feedback data, extracts valuable information, and transmits an analysis result to the dialogue management module and the reinforcement learning module for guiding the improvement and optimization of the system. Comprehensive information acquisition is realized through the multi-modal information acquisition module, continuity and individuation can be enhanced through the dialogue management module, an interaction strategy is optimized through the reinforcement learning module, and continuous improvement and real-time feedback are ensured through the user feedback processing module; and the interaction effect of the whole system is more natural and vivid.

The invention discloses a task scheduling method and system for a dense warehousing system, and the method comprises the steps: building an elevator task distribution model, a shuttle vehicle selection model and a warehouse-in and warehouse-out task time mathematical model according to the characteristics of warehousing operation, and preliminarily determining a task distribution scheme of an elevator through employing a heuristic rule on this basis, an improved A * algorithm is used for carrying out path planning on the shuttle vehicle, the time needed by the shuttle vehicle for completing a task is calculated, it is ensured that the elevator and the shuttle vehicle start to run, the scheme is further optimized through an improved PSO-SA algorithm, and the algorithm combines particle swarm optimization and a simulated annealing algorithm, so that the algorithm is optimized. A specific scheduling result of cooperative task execution of the elevator and the shuttle vehicle can be obtained, a scheduling scheme which enables the cooperative work effect of the whole system to be optimal is finally obtained, and maximization of the work efficiency is ensured.

The invention belongs to the technical field of matching control of a network construction type system, and particularly discloses a power feedback control method suitable for matching control low-frequency oscillation suppression, which comprises the following steps: establishing a power-frequency transfer relation of a multi-matching control parallel system, analyzing the torque of the multi-matching control parallel system based on a damping torque method, and calculating the power-frequency transfer relation of the multi-matching control parallel system; determining an introduction factor of a negative damping torque component in the multi-matching control parallel system and a stability condition in the multi-matching control parallel system; and then additional damping control based on power feedback is designed through a lead-lag compensator, a negative damping torque part in the multi-matching control parallel system is compensated, and power feedback control is completed. According to the method, the problems that the dynamic response of the whole system becomes extremely complex due to the dynamic interaction among the converters when a plurality of matched control network-forming converters exist in the network-forming system, and the oscillation suppression effect under the multi-machine system is difficult to guarantee by the existing suppression strategy are solved.

A system for real-time creditworthiness assessment of small and medium-sized enterprises (SMEs) based on agent-based artificial intelligence (AI), the system includes: a large number of autonomous agents connected to each other via secure communication protocols; a data ingestion processing unit configured to capture and preprocess heterogeneous data streams from structured and unstructured sources, including APIs for financial transactions, tax databases, e-invoicing registers, inventory management systems, social sentiment feeds, and IoT-enabled devices, with preprocessing including schema harmonization, anomaly detection, and encryption-based integrity preservation; an ensemble of scoring control units configured to generate dynamic credit scores, wherein the ensemble includes machine learning models, including gradient-enhanced decision trees, recurrent neural networks, and graph neural networks, the graph neural networks being configured to assess relational dependencies between SMEs, suppliers, and customer nodes in a business ecosystem; an audit controller configured to generate regulatory-compliant justification paths for each credit score; a feedback processing unit configured to update the evaluation control unit ensemble using reinforcement learning signals derived from observed repayment behavior, payment default trends, and fraud detection markers; and a user interface configured to display timestamped credit histories, confidence intervals and explanatory justifications, with the entire system running on a containerized cloud-native infrastructure with blockchain-based ledgers for version control of each point update.

The invention relates to the technical field of unmanned aerial vehicle inspection, and provides an unmanned aerial vehicle-based forest fire prevention inspection and disposal method, device and system. When an inspection unmanned aerial vehicle equipped with a multi-modal sensor is deployed to cruise according to a preset route, a visible light camera of the inspection unmanned aerial vehicle continuously collects forest region videos and transmits the videos to edge computing equipment of an inspection airport; fire hazards are analyzed in real time through a target recognition algorithm, and when a fire source is detected, position information is reported to a background terminal immediately; the system automatically dispatches the nearest fire extinguishing airport to dispatch the fire extinguishing unmanned aerial vehicle, and secondary accurate positioning is performed on a fire source by using the airborne infrared vision module, so that accurate throwing of the fire extinguishing bomb is ensured. The whole system enhances the real-time analysis capability through the edge computing device, breaks through the single-machine computing power limitation, also realizes the full-process automation from fire behavior recognition to fire extinguishing treatment, and significantly improves the response efficiency and treatment safety of forest fire prevention.

The invention discloses a high-precision multi-modal twin model assembly method based on a large language model and a multiple docking optimization mechanism. The whole system framework is composed of three modules: a database, an interaction system and human factors. Wherein the database module integrates a model library module, a user interaction data recording module and a temporary data recording module, and is used for storing and managing various data information required for constructing a virtual scene; the interactive system module is used as a core part, integrates a model dynamic loading module, a model accurate positioning module and a model adaptive assembly module, realizes instruction analysis and model generation by relying on a large language model, and combines a multi-docking optimization mechanism of eye movement tracking, gesture recognition, automatic docking and logic rule binding to realize multi-docking. Efficient and accurate interaction between the user and the database is effectively guaranteed; the method not only provides a solid foundation for virtual experiments and virtual-real symbiosis, but also significantly improves the construction efficiency and precision of digital twin model assembly.

The invention discloses a reliable data transmission method for a narrowband satellite internet of things. The method comprises the following steps: defining a reverse data frame format; link state monitoring and quantification: continuously or periodically monitoring a quality index representing a link of the satellite Internet of Things terminal; continuously or periodically monitoring the uplink state of the satellite Internet of Things terminal by using a terminal link state evaluation module of the satellite master station baseband system; link state grading: dividing the current link state into a plurality of predefined state grades; identifying the transmission rate of the satellite Internet of Things terminal; constructing a self-adaptive confirmation window strategy: a sending end of the satellite Internet of Things terminal uses a confirmation window with a dynamic window size; the number of data packets confirmed by the transmitted ACK / NACK message, namely the window size, is dynamically adjusted according to the currently sensed link state and the identified terminal transmission rate; the performance of the whole system is improved.

The invention belongs to the field of artificial intelligence, and particularly relates to a disaster risk early warning algorithm based on multi-modal fusion and a space-time propagation model, which comprises a multi-modal data management module, a cross-modal semantic alignment module, a space-time propagation graph modeling module, a propagation constraint deduction module, a graded early warning generation module and an online closed-loop calibration module. Depth alignment of multi-modal data on a semantic level, dynamic deduction of a disaster propagation process under constraint of a physical mechanism and operable conversion of an early warning result in a business scene are realized, and the whole system takes a unified space-time framework as a base, takes a propagation graph as a link and takes closed-loop calibration as a guarantee, so that the accuracy of the system is improved. The structural defects of extensive data splicing, propagation modeling deficiency and decision chain fracture in a traditional early warning method are fundamentally overcome.

The invention discloses an equipment identity authentication and data encryption method and related equipment thereof, and the comprehensive effects of identity credibility, key security superposition and channel encryption capability enhancement are realized by introducing a mixed key system and a double digital signature mechanism into an equipment identity authentication and data encryption process. Meanwhile, a national cryptographic algorithm and a post-quantum algorithm are adopted, and the two cryptographic systems cooperate in parallel in an authentication and encryption link, so that the whole system has a robust anti-attack ability in both a traditional computing environment and a potential quantum computing environment, the accuracy of identity authentication is ensured by a dual signature mechanism, and the counterfeiting difficulty and tampering detection ability are remarkably improved. According to the method, the bidirectional verification chain is constructed in the authentication process, the safety is guaranteed by using the mixed key in the session stage, the safety robustness and the expandability of the system are improved through multi-layer protection, and the whole-process protection of the identity, the key and the data is realized, so that the safety robustness and the expandability of the system are integrally improved.

The invention discloses an artificial intelligence city emergency management system based on space-time prediction, which belongs to the technical field of city emergency management and comprises a data acquisition unit, a data preprocessing unit, a data processing unit, a model optimization unit, an emergency decision-making unit and an intelligent decision-making unit. Large-scale information from various sensors, monitoring systems and historical data is analyzed and processed by adopting a machine learning algorithm, potential risk modes and trends are identified, the information can be quickly screened and integrated by adopting a big data analysis technology, the real-time performance and accuracy of key data are ensured, and the real-time performance of the system is improved. The whole system generates early warning information by using a prediction result, and transmits the information to related departments in time through the emergency decision unit, so that rapid response and effective processing of emergencies are realized, the emergency response speed and accuracy can be improved, an emergency strategy can be flexibly adjusted according to real-time conditions, and the safety of the system is improved. And the requirements of cities for coping with complex and changeable conditions are met.

The invention relates to the technical field of data modeling, in particular to an AI personalized tutoring method based on cognitive portraits. Through implicit feedback dynamic weighting driven by multi-modal emotions, behavior deviation caused by task errors and emotional fluctuation is effectively distinguished, and the input quality and recommendation matching degree of potential factorization are remarkably improved; structured modeling is carried out on multi-dimensional behavior indexes by using a graph convolutional network, and interaction association among the indexes is deeply captured, so that the system can sensitively recognize subtle state changes such as user attention distraction or strategy adjustment; through self-adaptive fusion of a historical portrait and new input information, it is ensured that portrait updating is stable and timely in the process from tension to adaptation or from fatigue to recovery of a user; and finally, reliable dynamic signals are provided for the whole system through a portrait-strategy-feedback-portrait closed-loop mechanism and robustness and interpretability of multi-modal emotion estimation, so that personalized tutoring can be continuously optimized along with data accumulation.

According to the read transmission bridging device and method for the AXI master device and the CHI request node, the master device which does not support cache consistency can be interconnected with a CHI bus to perform read transmission and maintain the cache consistency and address allocation of the whole system under the condition that the design is not modified, and the device has good compatibility, conforms to a standard AXI4 protocol and a standard CHI protocol, and is convenient to use. And the device maintains the cache consistency in the system, ensures the data consistency when the master device accesses the shared data, and avoids errors caused by non-updating of the cache or conflicts. According to the read transmission bridging device of the AXI master device and the CHI request node, the AXI master device is connected to the request node of a CHI bus, a central processing unit (CPU) is connected to the request node of the CHI bus, and an external device and a memory are connected to a completion node of the CHI bus.

The invention discloses a PCBA (Printed Circuit Board Assembly) processing component layout optimization method and system, and relates to the technical field of electronic design automation. Data stream information of each functional module of a PCBA is collected, data stream characteristics are analyzed, and key signal paths and interaction relationships are determined; the method comprises the following steps of: dividing priorities for different data streams according to functional requirements and signal importance of a PCBA (Printed Circuit Board Assembly), and determining a core signal; by optimizing component layout, reducing roundabout and intersection of signal paths, reducing signal transmission loss and interference, reasonably dividing functional areas and compactly arranging associated components according to data stream directions and core signals, it is ensured that the signal transmission paths are clear and efficient, meanwhile, the transmission quality of high-priority signals is preferentially guaranteed, and the transmission efficiency is improved. By adopting a transmission line with strong anti-interference capability, adding a signal buffer and filter circuit and other measures, the signal integrity is obviously improved, and the signal is kept stable and reliable in the transmission process, so that the performance of the whole PCBA system is improved.

The invention discloses a power grid project-oriented information matching review system based on a large language model, and belongs to the technical field of intelligent review of electric power engineering. Analyzing the uploaded file through a project structure analysis module, loading a rule template, and generating a first review plan (basic information review) and a second review plan (special information review) which comprise required files; the text preprocessing module performs format conversion and unit standardization processing on the file and updates the file to an examination plan; the basic judgment module generates conclusions of file content matching, timeliness and engineering information matching according to the rule template; a special item extraction module generates a special item information matching conclusion; and the report generation module combines the two conclusions to form a preliminary result, and finally outputs a readable information matching examination report after expert reexamination and correction. The whole system realizes full-chain automatic processing of power grid project file intelligent analysis, rule and semantic dual-drive review, cross-file consistency verification and man-machine collaborative optimization.

The invention discloses a wheelchair humanoid robot controlled by humanoid mechanical arms. The wheelchair humanoid robot comprises a chassis, a robot trunk, the two mechanical arms and a control calculation unit. The chassis comprises four independent driving wheels; a wheelchair seat is arranged on the robot trunk; the mechanical arm is a seven-axis mechanical arm and comprises three tail end joints and an operation tail end. The control calculation unit is provided with an operation control moving mode; in the operation control movement mode, the control calculation unit senses the operation intention of a user on the operation tail end by collecting data of the joint position sensor and the force feedback sensor, generates a movement command of the chassis through mapping of the operation intention, and converts the movement command into a wheel speed control command of the independent driving wheels. The tail end joint of the mechanical arm serves as a sensing and input device, the independent driving wheel chassis serves as an execution device, and the whole system has the advantages of being high in freedom degree, natural in interaction, safe, smooth and high in space adaptability through cooperation of the two devices.

The invention provides an intelligent optimization control method for stable operation of a hydraulic system in a complex environment, which comprises the following steps of: aiming at an abnormal mode, optimizing power configuration parameters by adopting a genetic algorithm, determining a standby pump activation threshold value and a main pump switching condition, and obtaining a power redundancy scheme; according to the slow response reason, pipeline interface data are obtained, a feedback control mechanism is adopted to adjust valve group cooperation parameters, and a rapid pressure adjustment sequence is obtained; acquiring real-time feedback data according to the final optimization instruction, and processing high-temperature and high-dust influence through an iterative updating process to obtain stable operation configuration; according to the stable operation configuration, the state of the whole system is judged, and if the state meets the preset standard, a cooperative control result is output and used for maintaining continuous operation of the hydraulic system in the complex environment.

An elongated functional system configured to be advanced in the lumen of a pipe, a duct or a tube. The system has a body part having a main proximal part and a distal part, the distal part being a continuous extension of the proximal part, the distal part including one functional end terminating with a tip, and at least one active area located upstream the functional end on the distal part; and an actuator configured to transform an amount of energy to the distal part sufficient to cause a reversible and multiple curvature of the active area, thereby preventing undesired spring back of the whole system; the actuator being connectable to a source of energy.

The invention belongs to the technical field of inverter control, and discloses a self-adaptive adjustment strategy for the inertia of a network-forming inverter in a large-disturbance scene of a parallel system, and the parallel system comprises the network-forming inverter and a network-following inverter, and is used for carrying out the self-adaptive adjustment of the inertia of the network-forming inverter in the large-disturbance scene. The method specifically comprises the following steps that a phase-locked loop is used for controlling the following grid type inverter, and following grid type dynamic influence factors of the following grid type inverter in a large disturbance scene are analyzed based on a following grid type inverter control block diagram; based on the following network type dynamic influence factors, constructing a following network type dynamic control equation used for describing the large disturbance process of the following network type inverter; according to the method, the inertia of the network-building type inverters is effectively adjusted in a large disturbance scene of the multi-inverter parallel system, the transient synchronization characteristic of the network-building type inverters in the multi-inverter parallel system is improved, the parallel system has more stable voltage and frequency support, and the stability of the whole system is further improved.